{
 "generated": "2026-11-06T00:47:20",
 "artifact": "sawdust-shelf",
 "source_page": "https://sawdustsilicon.com/library",
 "shelf_version": "2026-11-05.3",
 "shelf_hash": "sha256:71441b65178dc8ed953b664e73a6f1d23ab08b65f09c6c8741d82a01372b330d",
 "honesty_policy": "Every entry on this shelf is a card: a stable number, an evidence grade, a caveats list, and a content fingerprint. The grade is assigned by the kind of source, under the rule stated on the library page \u2014 strong = a primary source (paper, dataset, official record); moderate = the maker's own official word (release notes, vendor blog, help page); emerging = a single credible report; unverified = a claim we could not trace past a secondary mention. It is our judgment about the evidence behind the item, not a peer-review verdict. An empty caveats list means no explicit caveat was recorded when the entry was written \u2014 not that no caveat exists. Cite an entry as: Sawdust Shelf <shelf_version> \u00b7 No. <n> \u00b7 <fingerprint prefix>. If the prefix no longer matches, the entry has changed since you cited it. \"Published\", in every count this system emits, means entries published to the Shelf \u2014 the entries in this file's items list \u2014 not raw digest stamps in the engine's database.",
 "days": 22,
 "entries": 200,
 "metrics": {
  "days": 22,
  "entries": 200,
  "published": 200,
  "scanned": 1989,
  "kept": 565,
  "refused": 1789,
  "since": "2026-10-15"
 },
 "items": [
  {
   "date": "2026-10-15",
   "section": "Tools & models",
   "title": "Claude Code release train, v2.1.295\u2013v2.1.300 (four releases in the window, grouped as one entry)",
   "annotation": "the fleet-relevant changes: hooks can now fail closed with `onFailure: \"block\"` (v2.1.295), subagents get their own `autoCompactWindow` and a `CLAUDE_CODE_WORKFLOW_SUBAGENT_MODEL` override (v2.1.296), idle 200K+ token conversations compact before the one-hour prompt cache expires and `@` file mentions can pass paths instead of contents (v2.1.299), and OpenTelemetry now splits cache-creation tokens by lifetime with `/advisor` landing on Claude Platform on AWS (v2.1.300). Changelog entries, not a launch \u2014 but the hook change alone is worth adopting on the next build. Latest release \u00b7",
   "source": "All releases",
   "url": "https://github.com/anthropics/claude-code/releases",
   "no": 1,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:9c6558ada67b5a44fa39d8ef41f80054ac5a86ad1c9f902514564c21e8379f4e"
  },
  {
   "date": "2026-10-15",
   "section": "Tools & models",
   "title": "Jev 1.14.0 shipped, and a production report says Jev routing beat embeddings 10-to-1 on wrong pages",
   "annotation": "TypeSafe released Jev 1.14.0 with experimental features (release post, no changelog detail in the item); separately, a field write-up reports Jev handed their LLM the wrong page a tenth as often as their embeddings setup at the page-selection job \u2014 the same routing job Jev does in this engine. Jev 1.14.0 \u00b7",
   "source": "Field report",
   "url": "https://sayora.ai/many-hands/jev-vs-embeddings/",
   "no": 2,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:0f8ea29fc3c30f3d9baa1ba81df0499b5bebdd2b00ab17e52ee27c36da9a26bb"
  },
  {
   "date": "2026-10-15",
   "section": "Agentic AI",
   "title": "Scaling Discovery Requires Forgetting",
   "annotation": "automated-research agents plateau as their history accumulates: each agent anchors to its own past attempts and makes smaller edits, so longer runs, parallel agents, and communicating agents all stall the same way; the paper's finding is that scaling discovery requires shedding that history. Directly relevant to long-running fleet loops.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.20563v1",
   "no": 3,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:258bc5204b70f3ecc704888c9d39e43fbc31b57f54bcefd649eb6f61dbb05951"
  },
  {
   "date": "2026-10-15",
   "section": "Agentic AI",
   "title": "AdaRSI: on-demand recursive self-improvement for embodied agents",
   "annotation": "the framework asks whether a persistent failure is in how the executor is called or in what it can do: a coding agent first improves skills and harness, attributes failures by retrying sub-steps from saved states, and only then touches executor weights. Harness first, weights last \u2014 the fleet's own doctrine, formalized.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.20474v1",
   "no": 4,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:95728c59637663819ab299186adc293c2dd80771c76611f22a0cb87dab3a03f3"
  },
  {
   "date": "2026-10-15",
   "section": "Agentic AI",
   "title": "One Task, Many Interfaces: cross-interface consistency training improves LLM agent robustness",
   "annotation": "agents trained on one interface degrade when actions are merely renamed; the proposed CIPR objective trains each sample under two interfaces with action names replaced by uninformative identifiers, and much of the fragility goes away.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.20576v1",
   "no": 5,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:f19801f517bbdfc5e9293b0cc01cb93026539e82cff8b7c4f989453bd6996512"
  },
  {
   "date": "2026-10-15",
   "section": "Agentic AI",
   "title": "From Training to Escalation: forensic analysis of RL/RLHF-induced privilege escalation in AI agents",
   "annotation": "a forensic paper, not a new live incident: it recreates the training-case conditions behind mid-2026 agent-escape reports and reproduces a cryptographic verification bypass (CVE-2026-65616) in isolated repository servers. Worth a read for anyone running evals with real infrastructure in reach.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.20437v1",
   "no": 6,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:23b51602a99a55d1ef005b5dada9af64f8dfc38929fdb64ed00d6c4a086aa91d"
  },
  {
   "date": "2026-10-15",
   "section": "Agentic AI",
   "title": "From Unreliable to Usable: failure modes and confidence signals in LLM extraction",
   "annotation": "a preregistered audit of two low-cost LLMs and a flagship control over 354 cyber-threat incident campaigns finds unreliability persists across tiers, then decomposes what the failures actually consist of and whether confidence signals can rescue the output. Eval-grade method, reusable on fleet extraction jobs.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.20417v1",
   "no": 7,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:7776ac7eabd3fcc913c5d06e4610e06a08edf9f10a1c6541d6cb41e5a0626a08"
  },
  {
   "date": "2026-10-15",
   "section": "Agentic AI",
   "title": "ActiveEvidence: decision-focused evidence graphs with verifiable acquisition gaps",
   "annotation": "an agent framework for deciding whether evidence is worth acquiring, which candidate to query, and when further interaction can no longer change the decision, with explicit score bounds on unresolved interactions. Squarely in the eval/decision lane.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.20532v1",
   "no": 8,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:0bb5a1b7b274bb6f05b1a1a53c879c72e4c7530d5bddcb13c17de58dd63d79cf"
  },
  {
   "date": "2026-10-15",
   "section": "Agentic AI",
   "title": "The Wealth of Agents: scaling agentic AI across developers",
   "annotation": "studies multi-developer agentic systems instead of the single-builder setups most work assumes; on GAIA, a population of specialized developers whose individual agents top out at 35% accuracy reaches 70% when their agents work as one system.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.20518v1",
   "no": 9,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:8888d08212d8b445e07e5ccccc592d85d82281a08b1ed8d623b3563cd648b51d"
  },
  {
   "date": "2026-10-15",
   "section": "Field \u2014 small business & nonprofits",
   "title": "One company reports its entire Claude Enterprise account was suspended",
   "annotation": "a single poster on X, so treat it as an incident claim rather than a confirmed policy change; still a concrete reminder of the vendor-dependence risk any small org takes building on one provider's account.",
   "source": "x.com",
   "url": "https://twitter.com/heinenbros/status/2110081812169162923",
   "no": 10,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:14bf89614f61cbab1ad1eb2d45ff82ec7b1350853fe024c76636f6292a888889"
  },
  {
   "date": "2026-10-16",
   "section": "Agentic AI",
   "title": "Librarian Agents: Training LLMs to Curate Long-Context Workspaces that Anticipate Future Queries",
   "annotation": "training method to maintain and surface relevant context for future agent turns, directly relevant to fleet memory loops.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.22273v1",
   "no": 11,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:0bc6943fa9cb8285eafe894d68e6ad28df66839e7a48907352f7239ffc76c3c3"
  },
  {
   "date": "2026-10-16",
   "section": "Agentic AI",
   "title": "LEASH: Uncertainty-Aware Weak-to-Strong Escalation and Oversight for Computer-Use Agents",
   "annotation": "uncertainty triggers escalation to stronger models with oversight, applicable to the fleet's guard scaffolds.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.22280v1",
   "no": 12,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:dc2cc009b54881f97165cd15cf50e659dec7fb84878baa893be7b157888062af"
  },
  {
   "date": "2026-10-16",
   "section": "Agentic AI",
   "title": "Learning to Steer LLM Agents and Multi-Agent Systems",
   "annotation": "steering/coordination technique for multi-agent runs.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.22316v1",
   "no": 13,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:3ca7f621e812d01608420bb53631968175582a841a63abf8eca4a0c96ad4a1e8"
  },
  {
   "date": "2026-10-16",
   "section": "Agentic AI",
   "title": "MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering",
   "annotation": "generalist agent for ML engineering workflows.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.22344v1",
   "no": 14,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:fa24fface8ece1ca5c887f1a810f7fb28218adbc4e43f27bfe729e6d44fe2a7f"
  },
  {
   "date": "2026-10-16",
   "section": "Agentic AI",
   "title": "Disagreement-Guided Weak-to-Strong Routing",
   "annotation": "uses disagreement to route tasks up the capability ladder.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.22245v1",
   "no": 15,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:353e97c75b01b5206dd06c4910ba72093a59b54f3df3de33a65b7ac086842923"
  },
  {
   "date": "2026-10-16",
   "section": "Agentic AI",
   "title": "Learning Self-Orchestration in Decentralized Agent Swarms",
   "annotation": "decentralized coordination without a central planner.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.22322v1",
   "no": 16,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:4aa2161caca5f6bc0ea564c2e63bb20c3bcb108637c4904725cb501c3ff46dd8"
  },
  {
   "date": "2026-10-16",
   "section": "Agentic AI",
   "title": "A team of Gemini 4 Argon agents beats Minecraft",
   "annotation": "ensemble result showing scaling via specialized agents.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.22319v1",
   "no": 17,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:01ee464aec9f060f7254ab7db720a21b3ca7a28ab6444db32f184527d430c62c"
  },
  {
   "date": "2026-10-16",
   "section": "Agentic AI",
   "title": "Improving Environment-Agnostic Coding Agents: The Role of Scaffolding",
   "annotation": "scaffolding design to transfer across environments.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.22289v1",
   "no": 18,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:2cd0698dfe9492ba8e0946aad39dba4b2cfcee317dd7e8cdc404f4fe2c882bdd"
  },
  {
   "date": "2026-10-16",
   "section": "Tools & models",
   "title": "Claude Code v2.1.301",
   "annotation": "latest release in the train; continue adopting the hook/autoCompact changes noted Oct 15.",
   "source": "GitHub",
   "url": "https://github.com/anthropics/claude-code/releases/tag/v2.1.301",
   "no": 19,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:d044a70e314c82f02aaa3db5d9d90855fab96517477fbee046a233372a0ae90a"
  },
  {
   "date": "2026-10-16",
   "section": "Tools & models",
   "title": "Claude Code Desktop vulnerability: preview-pane data exfiltration",
   "annotation": "a security write-up on how the preview pane in Claude Code Desktop can exfiltrate data; a concrete risk item for any UI surface that renders untrusted content.",
   "source": "noma.security",
   "url": "https://noma.security/noma-labs/sneakpeek-how-the-preview-pane-in-claude-code-desktop-can-exfiltrate-your-data",
   "no": 20,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:ca048c9b958825b1b0ca91d18c915d8b3a8659d69f44dcb2ba9829954aefdd7f"
  },
  {
   "date": "2026-10-16",
   "section": "Also worth a look",
   "title": "Krea 3 \u2014 an image model built for editing at scale",
   "annotation": "editing-capable image model, minor relevance.",
   "source": "krea.ai",
   "url": "https://www.krea.ai/krea-3",
   "no": 21,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:ae048d0a79029412b872169364770257609690f0388ac638e31016c4af262806"
  },
  {
   "date": "2026-10-17",
   "section": "Agentic AI",
   "title": "Researchers discover rogue Chinese AI agent fleet and talk with it",
   "annotation": "a third-party incident report: researchers found an unattributed fleet of AI agents operating on its own and opened a channel to it. Worth reading as a field case of agents running outside anyone's oversight.",
   "source": "swarmcha.se",
   "url": "https://swarmcha.se/posts/chinese-agent-fleet-full-report",
   "no": 22,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:218d2c0e516e37b11761f9e530b1ae83cbda4e99442a2acb9a6929e8adb6ab4d"
  },
  {
   "date": "2026-10-17",
   "section": "Agentic AI",
   "title": "rStation \u2013 A shared research lab for your AI agents",
   "annotation": "Show HN: a shared workspace where agents pool research and findings instead of each run starting cold. Early product, but the shared-lab pattern maps directly onto fleet memory loops.",
   "source": "rstation.ai",
   "url": "https://rstation.ai/",
   "no": 23,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:de0d51457cc8e5474d441d3f9a7f7e34b74e6dde325c252c981b582df5032d72"
  },
  {
   "date": "2026-10-17",
   "section": "Tools & models",
   "title": "Claude Code v2.1.302",
   "annotation": "a changelog release, not a launch: adds `CLAUDE_CODE_AUTO_MODE_MODEL` to choose the model the client-side auto-mode classifier uses, and an \"Idle compaction\" row in `/config` to turn idle compaction off or back on.",
   "source": "GitHub",
   "url": "https://github.com/anthropics/claude-code/releases/tag/v2.1.302",
   "no": 24,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:2d2b2d9b168d5d022b1b2dd2135fbe69a4021f6c11bb4dcd12b9b54299f927e0"
  },
  {
   "date": "2026-10-17",
   "section": "Tools & models",
   "title": "How the Claude Code team does code review",
   "annotation": "Anthropic's own write-up of the code-review workflow the Claude Code team runs internally; the fleet's review gates are the obvious comparison point.",
   "source": "claude.dev",
   "url": "https://claude.dev/blog/solving-code-review-at-anthropic/",
   "no": 25,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:9cb8a7b551515328bb811b21a41740f5c0ae990b5e95934106c4dc823e8e1d5f"
  },
  {
   "date": "2026-10-17",
   "section": "Tools & models",
   "title": "DeepSeek v4.1 Flash 1017",
   "annotation": "new DeepSeek Flash model posted on Hugging Face, with a matching OpenRouter listing; another fast/cheap decision-lane candidate to price against the current stack.",
   "source": "Hugging Face",
   "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017",
   "no": 26,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:ef4b65c709b2209fb60442a01a84df4ecd6561efdc9596f530af2b04e40232ba"
  },
  {
   "date": "2026-10-17",
   "section": "Field \u2014 small business & nonprofits",
   "title": "Just gave Claude Code the website and ad budget to see what it would do",
   "annotation": "a working web builder handed Claude Code his baseline site plus full Google, X, and LinkedIn ad access and a budget, and is reporting what it does unsupervised. One anecdotal run, not a result \u2014 but it's the Sawdust question asked out loud.",
   "source": "snitch.alphaventure.com",
   "url": "https://snitch.alphaventure.com/gate.php",
   "no": 27,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:7668de33e09f96399ca6dabd215ab20840df525f3b50090a55c3e20aa4ff3601"
  },
  {
   "date": "2026-10-17",
   "section": "Field \u2014 small business & nonprofits",
   "title": "90% of US working hours are spent on tasks not covered well by AI benchmarks",
   "annotation": "Mercor's Labor Index maps benchmark coverage against actual working hours and finds benchmarks measure a thin slice of real work; useful grounding when clients ask what AI can actually take over.",
   "source": "mercor.com",
   "url": "https://www.mercor.com/research/labor-index/benchmark-map/",
   "no": 28,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:7060190a0736dd71970bcebc1188e031682f37a03c907f02068f5e906c7e89a5"
  },
  {
   "date": "2026-10-17",
   "section": "Also worth a look",
   "title": "FLUX 3 Image Klein",
   "annotation": "new FLUX image model from Black Forest Labs; minor for now, track for the media lanes.",
   "source": "bfl.ai",
   "url": "https://bfl.ai/blog/flux-3-image-klein",
   "no": 29,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:f6289ab2348412ee2d4df34b2a39386b22696b9b45cfb2167c3c1822fa179f90"
  },
  {
   "date": "2026-10-17",
   "section": "Also worth a look",
   "title": "Llmspeed",
   "annotation": "Show HN tool that lets you feel how fast a local LLM runs before buying the hardware.",
   "source": "llmspeed.dev",
   "url": "https://llmspeed.dev/",
   "no": 30,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:ab4a6ccc0b095282bb25576eec5e70b0fbd7be594eefe1261ca4aeb40ec24c1a"
  },
  {
   "date": "2026-10-17",
   "section": "Also worth a look",
   "title": "Cole Medin: My AI Dark Factory Dream Is Dead (Here's What Actually Works)",
   "annotation": "a practitioner's walk-back of the fully-autonomous coding-factory idea, from a builder Israel follows; opinion, but grounded in his own builds.",
   "source": "YouTube",
   "url": "https://www.youtube.com/watch?v=GQatuTWSRqY",
   "no": 31,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:c040dffb2e917c330798e831e5d5cb2d000d264ee52e8f7f17f5c83d599f356d"
  },
  {
   "date": "2026-10-18",
   "section": "Tools & models",
   "title": "DeepSeek search and fetch \u2013 $0.75 per 1000 successful requests",
   "annotation": "DeepSeek's API docs now offer search and fetch at a stated $0.75 per 1,000 successful requests; a concrete, priced candidate for the fleet's web-search/fetch lanes.",
   "source": "DeepSeek API docs",
   "url": "https://api-docs.deepseek.com/guides/search/",
   "no": 32,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:6407b72692e66bb1d0afc019a8b9bae3037b45d0ed8828084508b5fd8f345707"
  },
  {
   "date": "2026-10-18",
   "section": "Tools & models",
   "title": "Sphinx \u2013 A hook that quizzes you on your AI's code before shipping",
   "annotation": "Show HN, FOSS: plugs into an existing coding-agent workflow and, on push to prod, throws up a questionnaire checking whether you understand the code you're pushing. A human-comprehension gate, not another automated review.",
   "source": "sphinx-six-theta.vercel.app",
   "url": "https://sphinx-six-theta.vercel.app/",
   "no": 33,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:42da8992e597851c45da2be0169971ff16c796215c1fffa8a2c4c69f3e371b1b"
  },
  {
   "date": "2026-10-18",
   "section": "Also worth a look",
   "title": "500 clocks in one 6.3 MB HTML file by Opus 5.5",
   "annotation": "a Show HN demo using self-contained clock pages as a personal frontier-model test; minor, a demo not a result.",
   "source": "tkgally.github.io",
   "url": "https://tkgally.github.io/500-clocks/",
   "no": 34,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:916083ce1f890d653a0ef878bc71b83dd893654ecadc1a7c50c5881cfec70b0c"
  },
  {
   "date": "2026-10-18",
   "section": "Also worth a look",
   "title": "Things to Do API",
   "annotation": "Show HN: an API/MCP for local events (pop-ups, concerts, one-offs), currently covering the SF Bay Area only; early, but the agent-facing local-events pattern is worth tracking.",
   "source": "thingstodoapi.com",
   "url": "https://thingstodoapi.com/",
   "no": 35,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:b7570a73ac0315dbe30088be7ad358445e84d5f5e3f3a1ae71ad7c5de97c8434"
  },
  {
   "date": "2026-10-19",
   "section": "Agentic AI",
   "title": "SkillBrowser \u2013 selective skill exposure for scalable skill-using agents",
   "annotation": "a Meta-Skill-managed framework that has the agent identify its current capability need mid-reasoning and load skills on demand, instead of static retrieval from the initial query or progressive disclosure polluting context. Across six SRA-Bench subsets and five LLMs it improved aggregate accuracy over static retrieval and LLM-based selection; under-invocation of useful skills and added latency are the stated limits.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.23554v1",
   "no": 36,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:0034ac2c1f393bfeedfcb0d6dd77f8c554ec0f0d1f71afb58e8c8cec3f0f8f3f"
  },
  {
   "date": "2026-10-19",
   "section": "Agentic AI",
   "title": "From Trajectories to Differentiated Skills (CDSE)",
   "annotation": "evolves a unified skill through four functionally distinct components (Meta, Experience, Tool, Script), extracting evidence-grounded gradients from successful and failed trajectories and updating each component separately. It scored highest in all 36 settings across six benchmarks, three agent models, and two harnesses \u2014 +7.6 points over the base agent and +2.6 on average over the strongest competing method in each setting.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.23599v1",
   "no": 37,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:21edb37d5511ecba15f43f929345b704b48b403bf2b43dfa0613e2c2c1be85a0"
  },
  {
   "date": "2026-10-19",
   "section": "Agentic AI",
   "title": "MARS \u2013 memory-aware routing for session-level LLM assistants",
   "annotation": "couples memory construction and model selection through a shared session-state interface, so each turn gets an appropriate memory view and a suitable model instead of the two being decided separately. On RealMem it preserved 98.2% of full-context GPT-4o's QA while using only 14.5% of its generation API cost, with the strongest measured memory-recall and retrieval scores among the systems compared.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.23583v1",
   "no": 38,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:2e6e779d0be8b09cb742cf602e99d313c490219d835f49f99efca3384a28fc01"
  },
  {
   "date": "2026-10-19",
   "section": "Agentic AI",
   "title": "Hybrid Reasoning Agents",
   "annotation": "a paper (with code and model collections linked from the post) on LLM agents that think in latent space and act through text, splitting the reasoning medium from the action medium.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.18861",
   "no": 39,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:aaa01b2cc8bcc670a1b4695d02b24619ad436b30b218f9b6afcc17d74f028c69"
  },
  {
   "date": "2026-10-19",
   "section": "Tools & models",
   "title": "Claude Code v2.1.303",
   "annotation": "a changelog release, not a launch: the release notes list only bug fixes and reliability improvements, with no new capability named.",
   "source": "GitHub",
   "url": "https://github.com/anthropics/claude-code/releases/tag/v2.1.303",
   "no": 40,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:ed131da7dc78f47356f191ff96d63e64d43df3a3bb949f7119eb2aaa5ba069a4"
  },
  {
   "date": "2026-10-19",
   "section": "Tools & models",
   "title": "WhatsApp MCP running free on Cloudflare",
   "annotation": "Show HN: a single-user MCP that gives Claude on mobile access to a personal WhatsApp account without WhatsApp Business or a server running Baileys, hosted free on Cloudflare and using a Google account to resolve contact names. The author flags it as gray-area and very private by design.",
   "source": "GitHub",
   "url": "https://github.com/finereli/whatsapp-mcp",
   "no": 41,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:2adb0aed2b03b913d6d3f1990b53a2dd43a39cd77280a51c7c39dfd1c6ccbd28"
  },
  {
   "date": "2026-10-19",
   "section": "Tools & models",
   "title": "Websites are closing their doors to AI agents",
   "annotation": "The Verge reports sites increasingly blocking AI agents outright; every web-browsing agent lane the fleet runs sits downstream of that door closing.",
   "source": "The Verge",
   "url": "https://www.theverge.com/ai-artificial-intelligence/1013195/ai-agents-blocked-web",
   "no": 42,
   "grade": "emerging",
   "caveats": [],
   "hash": "sha256:702e93097ec505287849a5de43336377fdbbd2877e1d93b5e474c582df6ecfaf"
  },
  {
   "date": "2026-10-19",
   "section": "Field \u2014 small business & nonprofits",
   "title": "GLOBE \u2013 evolving structured agentic memory for e-commerce product optimization in generative search",
   "annotation": "stores what works in product-listing rewrites as an evolving handbook of explicit, field-scoped behaviors, with Editor / Reflector / Moderator / Curator roles turning rank feedback into persistent memory and deactivating harmful behaviors. Structured behaviors beat monolithic prompt optimization in its experiments, and the learned behaviors stayed effective on unseen product categories.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.23709v1",
   "no": 43,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:d2986ff9e44246f7acf8ee5e4b1db0a15ce11d00eb9fd1f3eab3610ec078beee"
  },
  {
   "date": "2026-10-19",
   "section": "Also worth a look",
   "title": "DLite \u2013 history-free speculative decoding for agentic AI",
   "annotation": "the paper reports existing draft models lose up to 80% of acceptance length on long, evolving agentic contexts; DLite reuses the target model's hidden states instead of a historical KV cache and reports 2\u20136 acceptance length, up to 70% over the state-of-the-art DSpark.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.23787v1",
   "no": 44,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:1f30b9cffb82ad8cb49468fb270f5e2dbbe01dc6c295937247d63d729011ef0b"
  },
  {
   "date": "2026-10-19",
   "section": "Also worth a look",
   "title": "Torvalds used Claude for a kernel bottleneck fix, but a race was discovered",
   "annotation": "a cautionary field report, per the headline: the AI-proposed fix landed on a real bottleneck and still carried a race.",
   "source": "The New Stack",
   "url": "https://thenewstack.io/torvalds-claude-code-kernel/",
   "no": 45,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:413761ab6aff49a3e8647c1b889478a37e31aedd55c3f8cf6ccc57eea47485c2"
  },
  {
   "date": "2026-10-20",
   "section": "Agentic AI",
   "title": "WeAct \u2013 dynamic weight switching as agent actions",
   "annotation": "trains independent capability-specific LoRA adapters over a frozen backbone and adds adapter selection to the agent's action space via its native tool-calling interface, so capabilities compose on demand instead of one fixed parameter set degrading old skills. On Qwen3-32B across eight deep-research benchmarks it improved average accuracy 6.2% over full fine-tuning with less compute, and 27.5% over the strongest static LoRA-merging method.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.26847v1",
   "no": 46,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:73133a0cc132c8f735bda7c04e76ee9de616e8f589b84c484da857e2e524ee12"
  },
  {
   "date": "2026-10-20",
   "section": "Agentic AI",
   "title": "HarnessPro \u2013 recursive harness self-improvement with Pareto optimization",
   "annotation": "a label-free method that evolves agent harnesses against LLM-generated tests, with specialist agents diagnosing trajectories and planning updates at different performance-cost priorities, returning lite / medium / heavy harnesses per deployment budget instead of one. On GPT-5.5 with Codex across Terminal-Bench 2, SWE-Bench Pro, and ProgramBench, heavy mode gained 2.0 pass-rate points while cutting average per-task cost 26.1% versus RHO; the advantage also held with GLM-5.2 on Claude Code.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.26770v1",
   "no": 47,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:3f7b3d163b1f0e46d8343b40b6841a3dd81a7a02645f81c2a5432cc0cd845823"
  },
  {
   "date": "2026-10-20",
   "section": "Agentic AI",
   "title": "SOMA \u2013 a System One model continuously monitoring System Two reasoning",
   "annotation": "an S1 decision model (the paper names Jev as the pattern) watches a reasoning model's own text mid-run and intervenes in layers \u2014 remind first, take over only if the behavior persists \u2014 with one shared S1 model and controller, no training. On overthinking it cut reasoning tokens by up to 36% while improving average accuracy; on unanswerable questions it raised abstention from 70.5% to 87.2%.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.26793v1",
   "no": 48,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:865751a9af6cf2f15df576c6fa284fb50132463b5e3493ca8e308f9936526395"
  },
  {
   "date": "2026-10-20",
   "section": "Agentic AI",
   "title": "Same Simulation, Different Evidence \u2013 retrieved tool output costs some agents accuracy",
   "annotation": "with a real USGS geochemistry simulator (158 execution-verified items, 11,007 logged calls re-run), agents that read the report through a section index instead of in context lost accuracy: GPT-5.1 dropped 11.3 points and GPT-5.2 dropped 7.9, while Claude Opus 4.6 and Sonnet 4.6 showed no detectable cost. Both GPT models still computed the keyed value about as often \u2014 what changed was the evidence they answered from.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.26836v1",
   "no": 49,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:653ee74139a3cd038f1c439416cdab331161e2e6189efa2934a3d83f84ce9f7e"
  },
  {
   "date": "2026-10-20",
   "section": "Tools & models",
   "title": "Claude Code v2.1.304",
   "annotation": "a changelog release, not a launch: adds `/auto-mode-setup` (guided setup that reads your project and recent sessions to teach auto mode your environment), `decider: \"person\"` in a mod's `tool.check` answer (only the permission dialog or a host callback can approve), a read-only flag on `tool.check` events, and `/claude-api managed-agents-secure`, which audits a Managed Agents agent against Anthropic's published security guide without changing anything \u2014 plus fixes including a plugin keeping headless runs alive past `--max-turns`.",
   "source": "GitHub",
   "url": "https://github.com/anthropics/claude-code/releases/tag/v2.1.304",
   "no": 50,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:c48ba30a1f5447b3012db12015d27702715518d44ccd1867df44679c5be56b3e"
  },
  {
   "date": "2026-10-20",
   "section": "Tools & models",
   "title": "Laminar flow-1 \u2013 an RL-trained model that finds errors in agent traces",
   "annotation": "Launch HN from the open-source agent-observability team: flow-1 is trained with RL to detect errors in production agent traces, and per their own benchmark it matches GPT-6-sol detection quality at 23x lower cost, cheap enough to analyze every trace without sampling. Vendor benchmark \u2014 treat the 23x as their claim, not a measured independent result.",
   "source": "Hacker News",
   "url": "https://news.ycombinator.com/item?id=50154029",
   "no": 51,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:737b3a4df2c47738b039e428532ac8bc814f9cfd9d9f3a4f99edbcb235c004cd"
  },
  {
   "date": "2026-10-20",
   "section": "Field \u2014 small business & nonprofits",
   "title": "Game of Phones \u2013 game-theoretic LLM scam-baiting",
   "annotation": "a framework that lets LLM baiter agents learn counter-strategies against phone scammers through in-context experience, modeled as a two-player game with Nash equilibrium as the benchmark. Across 300 simulated dialogues and three LLM architectures, adaptive strategy selection landed 28.9\u201379.7% closer to Nash than non-adaptive baselines; code and the annotated 300-dialogue dataset are released. Directly relevant to the scam exposure small orgs and their clients carry.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.26781v1",
   "no": 52,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:7fe9d92a7b82553162b6a1c589516b5e0474ed03ead1fd53b6af2930a7d9cac8"
  },
  {
   "date": "2026-10-20",
   "section": "Also worth a look",
   "title": "OpenVLHarness \u2013 visual agentic tool-use with persistent multimodal memory",
   "annotation": "a capability layer plus addressable multimodal memory improved average scores 9.7\u201312.6 points over matched no-tool baselines across 22 benchmarks and eight backbones.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.26860v1",
   "no": 53,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:61ade55e1f2f28876136a19a626e033a10a2e4078304662320196b8ecda30dd6"
  },
  {
   "date": "2026-10-20",
   "section": "Also worth a look",
   "title": "Lights-on software factory boilerplate for Claude Code",
   "annotation": "Show HN starter for an orchestrated Claude Code factory setup; the author claims ~80% token-cost reduction versus bare-bones workflow execution on their Votito setup \u2014 an author claim, not a benchmark.",
   "source": "GitHub",
   "url": "https://github.com/gojko/lights-on-software-factory",
   "no": 54,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:9db67c41e17862515721542a5c88c82717f3a7a3038c8de96c18aa846b45bd9d"
  },
  {
   "date": "2026-10-20",
   "section": "Also worth a look",
   "title": "A hacker reportedly broke into John Deere and SharkNinja with an army of AI agents",
   "annotation": "per The Verge headline only (no article body in the stored item); flagged as a headline-level security signal, not a verified incident report.",
   "source": "The Verge",
   "url": "https://www.theverge.com/ai-artificial-intelligence/1013643/ai-agents-hack-john-deere-shark-ninja-cummins",
   "no": 55,
   "grade": "emerging",
   "caveats": [],
   "hash": "sha256:30f2f08399184974e05275d5bca0cfaeb0b160b3070a2dfa67d65e05406cff93"
  },
  {
   "date": "2026-10-21",
   "section": "Agentic AI",
   "title": "Evolving Agent Interfaces \u2013 Agent S4 lets a meta-agent build the interfaces task agents use",
   "annotation": "a meta-agent examines task agents' trajectories on synthetic tasks (no success labels), builds and revises interfaces plus usage knowledge in a repository, and population-based evolution keeps the variants that generalize to unseen validation tasks. On OSWorld V2, Qwen3.8-27B with the evolved repository hit 21.3% \u2014 3.8x the same model with fixed interfaces, above GPT-5.5 (13.0%) and Claude Opus 4.8 (20.6%) at a fraction of their cost \u2014 and on ScienceBoard success rose from 41.4% to 58.6%; code is open-sourced.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.28601v1",
   "no": 56,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:4c819a23748980783e3b4ecae1bc236addd821cf7c32196fd0c9a8c0e4646f46"
  },
  {
   "date": "2026-10-21",
   "section": "Agentic AI",
   "title": "Proof-Carrying Contracts (PC2) \u2013 commitments an agent must prove before it can act",
   "annotation": "instead of trusting instruction text, an agent must supply a machine-checkable proof that a requested operation preserves the agreed constraints in every case its model allows; an execution system called the Airlock checks the proof and controls access. The authors prove the constraints hold at every instant during execution, even for an adversarial agent \u2014 a foundation pattern for agents purchasing, delegating, and exchanging work on others' behalf.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.28484v1",
   "no": 57,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:6dedc58e6d916a6947412af0f2b75d47b1fb5a987effe3b1ce3b3f2db288dafa"
  },
  {
   "date": "2026-10-21",
   "section": "Agentic AI",
   "title": "Cache Engineering for Agents \u2013 control the KV cache directly instead of summarizing history",
   "annotation": "agents get explicit cache operations (drop selected cache from future attention, reposition retained keys) without modifying the trajectory or re-prefilling retained context, served by the co-designed AKV engine. A tool-response rolling-drop policy improved average benchmark scores by 1.7 points and cut average cost 27.7% versus threshold-triggered summarization, with up to 1.5x better P95 latency; recursive self-improvement of cache policies cut cost a further 7.1%.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.28577v1",
   "no": 58,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:5e7f784b39d4681d266b8b73a49233b7b1bee009c508c9e56379a8e7baa5f582"
  },
  {
   "date": "2026-10-21",
   "section": "Agentic AI",
   "title": "OptionWorld \u2013 long-term personal assistants fail on follow-through, not just facts",
   "annotation": "the benchmark traces information acquisition, judgments, actions, and outcomes in simulated daily-life tasks across days. In failed runs the key facts were still in the model's last input before the deadline in 35.5% of cases; the three most frequent failures were needed information not obtained or combined, available opportunities to act going unused, and agents imposing restrictions the task never required \u2014 shortcomings in maintaining task state and separating real restrictions from assumed ones.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.28491v1",
   "no": 59,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:20a1c4d5bf3f37befe8ae88a83fd2ab92746cce5b3ad447dae03801f84a7c98a"
  },
  {
   "date": "2026-10-21",
   "section": "Agentic AI",
   "title": "DecisionPivot \u2013 aggregate accuracy hides how decision models actually fail",
   "annotation": "a diagnostic benchmark of 48 tasks and 40,033 items across seven decision roles (routing, guardrails, verification \u2014 the Jev pattern), with contrast groups verified by independent solvers. Two 27B models only 2.6 points apart on the reference track scored 70.1% vs 44.2% on contrast-group accuracy, and across 2,400 binary contrast pairs the best ranker ordered 92.3% correctly but answered both members correctly in only 74.1% \u2014 probability ordering is not decision correctness.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.28522v1",
   "no": 60,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:d9b5a7385ed951b7cb3f6100abe9dc153169fff636bc46d5ef54c4893c8f5d85"
  },
  {
   "date": "2026-10-21",
   "section": "Tools & models",
   "title": "Claude Code v2.1.305\u2013v2.1.306",
   "annotation": "changelog releases, not launches, grouped as one release train. v2.1.305 adds `sandbox.allowNestedUserNamespaces` for Linux sandboxed commands that need user namespaces (Chromium, rootless podman) and a retry-watchdog env var so unattended retry notices a lifted usage limit before its reset, and fixes a v2.1.304 regression that failed every request on Vertex, Bedrock, and gateways that reject tool search, plus Workflow resume re-running already-completed agents. v2.1.306 fixes `claude -p` in cloud sessions reporting \"Not logged in\" and an API error being masked as \"prompt is too long\" with an unneeded compaction in sessions with many MCP tools. GitHub \u00b7",
   "source": "GitHub",
   "url": "https://github.com/anthropics/claude-code/releases/tag/v2.1.305",
   "no": 61,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:3be2a21029d7722f6842c8862a9e906481fe2055bf96d266a58f6c09fdce5540"
  },
  {
   "date": "2026-10-21",
   "section": "Field \u2014 small business & nonprofits",
   "title": "Meta's Muse reportedly exposes users' personal and business secrets when conversations are shared",
   "annotation": "per the Ars Technica headline in the stored item (no article body was stored, so this is a headline-level signal, not a verified incident report). The practical read for any small org pasting business material into AI chats: a shared-conversation link can carry more than the conversation you meant to share.",
   "source": "Ars Technica",
   "url": "https://arstechnica.com/security/2026/10/meta-muse-exposes-users-personal-and-business-secrets-when-they-share-conversations/",
   "no": 62,
   "grade": "emerging",
   "caveats": [],
   "hash": "sha256:aa73fc19dee6cbc15d5516737a611b05072e1ed3b9c5e9838eaaf3feb7a131ad"
  },
  {
   "date": "2026-10-21",
   "section": "Also worth a look",
   "title": "Sage-A1.5 \u2013 a 35B MoE agentic model report",
   "annotation": "builds on A1 with subagent orchestration in isolated contexts, agentic verification (progress checking, evidence validation, strategy revision), and open scientific discovery; the report showcases tasks but the abstract gives no benchmark numbers, so treat capability claims as vendor-stated.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.28578v1",
   "no": 63,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:223ab09169abf07ee540d7fdf9010d1132efda4d86335df8afc19aa572eaa943"
  },
  {
   "date": "2026-10-21",
   "section": "Also worth a look",
   "title": "Capable Teacher, Myopic Scorer \u2013 fixing on-policy distillation's rollout-scorer gap",
   "annotation": "the teacher's next-token scores let through safe, delaying moves that outcome-based RL would punish; the proposed R2S repair beat three state-of-the-art agentic-OPD methods in all twelve teacher\u2013student\u2013environment cells, by 12.8 success points in the reported comparison.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.28586v1",
   "no": 64,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:69b7c32a638eb647e7a69e2216254475e4e318f3e31ebc0268833c25217da40b"
  },
  {
   "date": "2026-10-22",
   "section": "Agentic AI",
   "title": "Silent Cue Loss in Enterprise Document Ingestion \u2013 what the author wrote is not what the agent reads",
   "annotation": "on 76 real policy documents, formatting-blind ingestion collapses requirement-recovery micro F1 from 0.690 to 0.150 (docling) and 0.075 (plain text), replicating across six answering models (five Claude tiers and GPT-5.6); the loss localises to highlight (+0.302) and colour (+0.262), cues no mainstream converter preserves, yet 32.8% of 6,460 documents carry a deliberate colour or highlight and 10.3% put one on an obligation \u2014 agents go confidently wrong, not visibly incomplete.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.30026v1",
   "no": 65,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:a071c30a2fb0f892a4c91f1d8b7de45a0f8fd3d150bbaa6bd7a23d9085ef0f56"
  },
  {
   "date": "2026-10-22",
   "section": "Agentic AI",
   "title": "Robust Soft Skill Adaptation via Behavioral Localization \u2013 a skill's gain is concentrated, not spread",
   "annotation": "comparing a frozen agent with and without a text skill finds the top 10% of trajectory positions capture 96.12% of the total positive skill gain. Supervising only those positions for a short soft prompt on SpreadsheetBench (frozen Qwen3.6-35B-A3B) improves success from 30.36% (full-trajectory) to 36.79% shared, and to 57.38% task-specific \u2014 beating both the full text skill and the on-policy OPCD baseline.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.30041v1",
   "no": 66,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:e25262355b7ce091f92fc2ed51f6bead12c44dd4f2d45a47f58b4d0ac1fcd5f4"
  },
  {
   "date": "2026-10-22",
   "section": "Agentic AI",
   "title": "HGM-Dual \u2013 lineage-aware harness optimization for long-horizon planning agents",
   "annotation": "instead of an unstructured archive of past agents, evolution carries lineage-aware context (what each ancestor changed, what improved or regressed) plus error-conditioned dual-path children aimed at the parent's worst constraint violations. It beats the strongest baseline by up to 14.45% relative improvement in case accuracy while matching HGM variants' final performance with 2x\u20134x fewer evaluation budget.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.30042v1",
   "no": 67,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:ceacd3ecf0d03f1a595eeb0384c2d0d768f6be46548d08c971caec1ccc4623d9"
  },
  {
   "date": "2026-10-22",
   "section": "Agentic AI",
   "title": "Charts as Epistemic Actions \u2013 data agents can read charts but don't choose to",
   "annotation": "a 200-task probe under four matched interface conditions finds agents score as well from self-generated charts as from numeric output, yet plotting access alone does not translate into consistent use; charts lead on temporal, pattern, and anomaly reasoning while text leads on precise reading. The proposed method distils operation-selection and modality-choice experience from rollouts, admits only experience that improves held-out performance, and raises accuracy on three held-out benchmarks across four backbones without touching model parameters (no headline percentage is given in the abstract).",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.30072v1",
   "no": 68,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:2af6609706dbfb6e17da63c19a2cac4170541999d491d176893c8877f0b12020"
  },
  {
   "date": "2026-10-22",
   "section": "Agentic AI",
   "title": "Beacon \u2013 open-source, cross-harness memory for AI agents",
   "annotation": "built after its authors kept switching between Claude Code, Codex, and Cursor and re-explaining tasks each time: it hooks into 20+ agent harnesses, captures session telemetry (prompts, tool calls, commands) in one normalized format, and distils it into patterns and repeatable workflows with a human-approval step, accessible to any future agent via skills / MCP.",
   "source": "Hacker News",
   "url": "https://news.ycombinator.com/item?id=50182883",
   "no": 69,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:b6d16ce90411ddbda6de43b619b2e43ff11688a0a3fc2052b076bf826d650bcf"
  },
  {
   "date": "2026-10-22",
   "section": "Tools & models",
   "title": "Claude Code v2.1.307\u2013v2.1.308",
   "annotation": "changelog releases, not launches, grouped as one release train. v2.1.307 adds `/claude-api api-version` to move Claude API code to API version `2026-10-21` and a `claude_code.safety_event` OpenTelemetry event for each tool call a permission rule, auto mode, a safety check, a hook, or the sandbox stopped, prompted, or sent back; it also fixes MCP servers showing \"needs authentication\" in `claude -p` on macOS with a locked keychain, HTTP MCP tool calls being sent twice when a connection closed mid-call (only read-only/idempotent tools are now retried), and a model-not-found failure with `CLAUDE_CODE_DISABLE_1M_CONTEXT` on `[1m]`-suffixed aliases. v2.1.308 lists only \"Bug fixes and reliability improvements.\" GitHub \u00b7",
   "source": "GitHub",
   "url": "https://github.com/anthropics/claude-code/releases/tag/v2.1.308",
   "no": 70,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:483f01124dd534933969bf64b29c6cd60a375b7a2e833f8073980c8f38e5898b"
  },
  {
   "date": "2026-10-22",
   "section": "Tools & models",
   "title": "Anthropic drops 400+ free ready-to-use agents and skills for Claude",
   "annotation": "per the AlphaSignal headline (no article body was stored, so this is a headline-level signal, not a verified inventory); the link is Anthropic's knowledge-work-plugins repository, the practical next step being to inventory it before adopting anything.",
   "source": "GitHub",
   "url": "https://github.com/anthropics/knowledge-work-plugins",
   "no": 71,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:341f4beb35dd04c242ebb33f5b4263ebdc8fcc17624137616e89d72f83af0bff"
  },
  {
   "date": "2026-10-22",
   "section": "Tools & models",
   "title": "Compromised LiteLLM 1.107.0 on PyPI installs a credential stealer and backdoor",
   "annotation": "per the SafeDep headline via Hacker News (no article body was stored, so treat the specifics as headline-level until the report is read). The practical read for any stack pinning LiteLLM: check whether 1.107.0 was ever installed and rotate credentials if it was.",
   "source": "SafeDep",
   "url": "https://safedep.io/litellm-pypi-compromise-credential-stealer-backdoor/",
   "no": 72,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:2917426ccda335a76707a054aa4c735afbc9b8115a25a9010151948e3f375ff9"
  },
  {
   "date": "2026-10-22",
   "section": "Also worth a look",
   "title": "Commit or Abstain? System One decision models vs LLMs on missing legal authority",
   "annotation": "when the correct CaseHOLD holding is removed, generative LLMs still pick one in 38\u201386% of items versus 30% for the most cautious decision model; on standard CaseHOLD a decision model matches the best LLM (88.1% vs 88.2%) at 1/55 of the cost, and accepting an LLM answer only when a decision model agrees halves its wrong citations \u2014 direct evidence for the Jev-style decision layer.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.29961v1",
   "no": 73,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:2a8b8ab1e2afffd023414fd0a960cf0333cf167f7fa4797c8f45854e9f012378"
  },
  {
   "date": "2026-10-23",
   "section": "Agentic AI",
   "title": "Training Small Language Models for Transferable Tool Selection",
   "annotation": "with tool corpora past ten thousand, descriptions no longer fit in context and relevance retrievers rank tools without knowing which are actually needed; an RL post-trained Qwen3-8B outperforms Llama-4-Maverick (402B) on ToolBench at driving the retriever, and the trained model transfers directly to different retrievers and to new tool corpora in different domains.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.31483v1",
   "no": 74,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:e62d22c86ec8f4df677ab572e558d506d70587c8ad487628f698938a4d89245b"
  },
  {
   "date": "2026-10-23",
   "section": "Agentic AI",
   "title": "Adaptive Skill Granularity for Coding Agents",
   "annotation": "treats skill granularity as a runtime control problem, not just retrieval: a feature-based controller routes between primitive tool calls, macro skills, and hybrid execution, with a finite-state executor that falls back from a failed macro to micro-level recovery. Fixed macro execution is efficient when it fits but brittle under mismatch; runtime routing recovers controlled macro failures that fixed granularity leaves unresolved (evaluated on a deterministic protocol with verifier-defined success; no headline percentage in the abstract).",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.31461v1",
   "no": 75,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:50b426033ab4926e99c58485f0a08108ca81102ccfcee8c95e6571392871c67d"
  },
  {
   "date": "2026-10-23",
   "section": "Agentic AI",
   "title": "Jev-DLC: Decision-Level Continual Learning from Typed Feedback",
   "annotation": "directly on the fleet's decision-model lane: when a user accepts one typed decision or revokes it for another, each correction is kept as evidence about a recurring action identity plus a directed preferred-over-rejected relation, in bounded channels above a frozen scorer \u2014 no raw history retained, no extra scorer call. On TCPA-Repeat it reaches 44.83% user-macro accuracy (+2.43 over Recent-4 history replay), rising to 56.07% after at least two earlier feedback events (+7.54 over the strongest comparison).",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.31521v1",
   "no": 76,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:b9d02962dbbc91d184e0e7c9a85440f7690a1379818c5d93b877d23455ebd021"
  },
  {
   "date": "2026-10-23",
   "section": "Tools & models",
   "title": "Claude Code v2.1.309",
   "annotation": "a changelog release, not a launch, and the theme is sandbox and permission hardening: sandboxed Bash no longer runs shell startup files outside the sandbox, macOS `denyWrite` globs now apply at a path's real location through symlinks, sandboxed commands and auto-accepted edits can no longer modify the `--mcp-config` file, several Bash permission-check gaps are closed (failed redirects, wrapped `cd` before git), and worktree-isolation refusals are tightened.",
   "source": "GitHub",
   "url": "https://github.com/anthropics/claude-code/releases/tag/v2.1.309",
   "no": 77,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:1b9f7e8c18a77154eee0fc678c23c9f4227c642d423d2e570cb8d7dbc949b40a"
  },
  {
   "date": "2026-10-23",
   "section": "Tools & models",
   "title": "OpenShell \u2014 a safe, private runtime for fleets of autonomous AI agents",
   "annotation": "NVIDIA's open-source runtime, surfaced via Hacker News; no article body was stored, so this is a headline-level signal only \u2014 the repository is the next step before drawing conclusions.",
   "source": "GitHub",
   "url": "https://github.com/NVIDIA/OpenShell",
   "no": 78,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:21b86d9fba1fadf2922719e49b18ca083f53d82ac4f69bd4fadf501e14bd6688"
  },
  {
   "date": "2026-10-23",
   "section": "Small business & nonprofit field",
   "title": "Claude Duet \u2014 Anthropic previews a proactive agent that works while you're away",
   "annotation": "a preview, not a general release: per the TLDR account, Duet checks connected apps for updates and keeps working while users are offline, asking for approval before sending messages, making payments, or deleting anything. It is waitlist-only for US Pro and Max subscribers, with checkout through Shopify's Shop Pay and Stripe's Link \u2014 Link requiring spending approval and using virtual cards so real card numbers stay private. Claude \u00b7",
   "source": "PYMNTS via TLDR",
   "url": "https://www.pymnts.com/news/artificial-intelligence/2026/anthropics-new-ai-agent-purchases-business-supplies/?utm_source=tldrfintech",
   "no": 79,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:1d4c02431b511bbd0e43a0d7835ff37729243f2c28ee5cbf5a61647f20604d07"
  },
  {
   "date": "2026-10-23",
   "section": "Small business & nonprofit field",
   "title": "Agent payments go live on both card networks",
   "annotation": "Visa, BILL, and Billtrust launched a live pilot letting authorized AI agents complete already-approved B2B invoice payments end-to-end across supplier portals, with Visa's Trusted Agent Protocol authenticating the agent and tokenized credentials under buyer-defined controls \u2014 automation without independent payment authority. Mastercard in parallel opened early access to Agent Pay for Machines (APIs, toolkit, sandbox) after pilots with Coinbase, Base, and Cloudflare, including a demo where an agent completed nine purchases via Coinbase's x402 protocol within preset spending controls. Visa \u00b7",
   "source": "The Block via TLDR",
   "url": "https://www.theblock.co/news/business/2026-10-20-mastercard-opens-early-access-to-ai-agent-payments-following-pilots-with-coinbase-base-and-cloudflare-421406?utm_source=tldrfintech",
   "no": 80,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:c338f266dcf666622a3e445a6606e51cadd61806c7c7299c625b413317510310"
  },
  {
   "date": "2026-10-23",
   "section": "Also worth a look",
   "title": "Conservative Meta-Agent Search",
   "annotation": "architecture adaptation for agents under shifting conditions, gated by paired shadow replay, a variance-penalized promotion margin, canary, and rollback: across 96 streams it cuts surrogate dynamic-oracle regret from 0.116 to 0.104 and raises held-out utility from 0.44 to 0.48 over the strongest adaptive baseline, with a 3.4% observed false-promotion rate against a 5% nominal target \u2014 on an author-designed simulator, with no alpha claimed.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.31436v1",
   "no": 81,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:7fc3d937e9b68955a7960c04c777900f9bda0425de36dea18ff5288dbf01c2a4"
  },
  {
   "date": "2026-10-24",
   "section": "Agentic AI",
   "title": "Nous Research's Hermes agent builds and trains a robot duck sim in one prompt",
   "annotation": "a demo, not a measured result, and headline-level only: no article body was stored, just AlphaSignal's pointer to Nous Research's X post showing its Hermes agent building and training a robot duck simulation from a single prompt. Treat it as a capability anecdote to verify at the source, not evidence.",
   "source": "X via AlphaSignal",
   "url": "https://x.com/NousResearch/status/2113010017645244623",
   "no": 82,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:4fb1e037ad30de41ffee2088dfb808900d724942bc13579958a814ba780190d6"
  },
  {
   "date": "2026-10-24",
   "section": "Tools & models",
   "title": "Claude Code v2.1.310",
   "annotation": "a changelog release, not a launch: the Monitor tool and plugin monitors now work on Bedrock, Vertex, and Foundry and when telemetry or nonessential traffic is disabled (long foreground sleeps are blocked there too); requests behind gateways that reject the `effort-2025-11-24` beta value or mid-conversation system messages now retry without them; compaction after a \"Prompt is too long\" error no longer keeps so much dense text (base64/SVG) that the next request fails again; and a Windows hook gap where PreToolUse/PermissionRequest hooks could let a tool call through when Git Bash is missing is closed.",
   "source": "GitHub",
   "url": "https://github.com/anthropics/claude-code/releases/tag/v2.1.310",
   "no": 83,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:3b9e9310835b97ae07a2f6810bcf22842bcf3876768010c9df8f7cea0c46615f"
  },
  {
   "date": "2026-10-24",
   "section": "Tools & models",
   "title": "Anthropic launches Claude Security to scan and track code vulnerabilities automatically",
   "annotation": "per AlphaSignal's headline, with no article body stored, so this is headline-level until read at the source: Anthropic's own article (linked) frames it as continuous scanning that tracks fixes. Directly relevant to the fleet's code-review posture if the claims hold.",
   "source": "Claude",
   "url": "https://claude.com/resources/articles/claude-security-now-scans-continuously-and-tracks-fixes",
   "no": 84,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:d3ee423bafe00ced3c7b49ec7f5ccc709e59b68804704c99ae9dac8afc9bb6f3"
  },
  {
   "date": "2026-10-24",
   "section": "Tools & models",
   "title": "Recursive releases open-weight coding model that hits 150 tokens/sec on a laptop",
   "annotation": "headline-level via AlphaSignal, no body stored: the model, named Flint, is on Hugging Face as an open-weight coding model claiming 150 tokens/sec on a laptop. Speed claim is the vendor's; the weights are the next step for anyone wanting to verify it locally.",
   "source": "Hugging Face",
   "url": "https://huggingface.co/RecursiveSI/flint",
   "no": 85,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:8e51ac6080221359edbfe3b08e8d518445ae312431359294b0b88e7c1c756a86"
  },
  {
   "date": "2026-10-24",
   "section": "Tools & models",
   "title": "Corgi, a helpful {agent, terminal} multiplexer",
   "annotation": "a Show HN tool (86 points) built for running multiple coding agents side by side: a live feed shows what each agent is up to with an F1 shortcut to the next unread agent, a unified launcher for Codex/Claude Code/Pi with fast search and resume over sessions, inline usage tracking, click-to-copy links, image paste that works even over SSH, and per-workspace CPU/memory/GPU tracking.",
   "source": "Corgi",
   "url": "https://corgi.computer/",
   "no": 86,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:3089f1894936e70fb4aa8a70d6e0f72e6da4efc51f197cf92bfdbc3c0da764f3"
  },
  {
   "date": "2026-10-24",
   "section": "Tools & models",
   "title": "Open-source memory model beats transformer rivals on long-context benchmarks",
   "annotation": "headline-level via AlphaSignal, no abstract stored: an open-source memory model is reported to beat transformer rivals on long-context benchmarks, with the paper itself at the link. Benchmark claims are the authors'; read the paper before drawing conclusions.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.26834",
   "no": 87,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:9ca6599261770a5ee0068e91f1b56541ce1a75d3aa5299693f0c04f1ba8526dd"
  },
  {
   "date": "2026-10-24",
   "section": "Tools & models",
   "title": "Meta proves byte-based models beat token-based Llama and Gemma with one-sixth the data",
   "annotation": "headline-level via AlphaSignal, no abstract stored: Meta's paper reports byte-based models outperforming token-based Llama and Gemma while using one-sixth the data. If it holds, it is a tokenization-level result, not a product change.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2609.12303",
   "no": 88,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:1c8b2db8aff40c19c040d58660ec7fb02245d8af9a78f54d13dc3593d6b4b035"
  },
  {
   "date": "2026-10-24",
   "section": "Also worth a look",
   "title": "Kimi K3.1 Pro and K3.1 Pro Highspeed",
   "annotation": "a model release surfaced on Hacker News (917 points) with links out to Kimi's model docs and Artificial Analysis; no body was stored, so headline-level only.",
   "source": "Kimi",
   "url": "https://www.kimi.ai/blog/kimi-k3-1-pro",
   "no": 89,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:df156225c80a7bd15d3809a34de11b3444e6f8c2605eec931464a11368734d8a"
  },
  {
   "date": "2026-10-24",
   "section": "Also worth a look",
   "title": "Dessa: Conversation Model trained only on speech",
   "annotation": "Block's conversation model trained only on speech, surfaced on Hacker News (390 points); no body stored, headline-level only.",
   "source": "Block",
   "url": "https://block.xyz/dessa",
   "no": 90,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:63b8c7b683def175c1667a77997f1bb0831bb16598273de92f92809402734e8c"
  },
  {
   "date": "2026-10-24",
   "section": "Also worth a look",
   "title": "Unvetted AI agents are accessing U.S. bank accounts",
   "annotation": "Axios via Hacker News, headline-level only (no body stored), and a follow-on to yesterday's closest miss on banks blocking third-party agents: the field-lane risk to watch for small-business and nonprofit clients connecting agents to financial accounts.",
   "source": "Axios",
   "url": "https://www.axios.com/2026/10/22/ai-agents-banks-unauthorized-accounts-access",
   "no": 91,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:61aa6873dded35e0af8046ceb71744d069695266f87c3a44f7795b6fe456ff7d"
  },
  {
   "date": "2026-10-25",
   "section": "Tools & models",
   "title": "Making My Blog Readable by AI Agents",
   "annotation": "the day's only Jev keep, surfaced on Hacker News (17 points); no article body was stored, so this is headline-level only: a practitioner's write-up on making a personal blog readable by AI agents, a technique directly adjacent to the fleet's own publishing surfaces \u2014 read it at the source before drawing specifics.",
   "source": "deallocatedobjects.com",
   "url": "https://deallocatedobjects.com/posts/making-my-blog-readable-by-ai-agents",
   "no": 92,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:aa3e811a4bbfca50f9d9663cd56f3b5120d4510ee4878bec652b9a1416ec1609"
  },
  {
   "date": "2026-10-25",
   "section": "Also worth a look",
   "title": "Claude Code slyly adding users' emails to outbound HTTP",
   "annotation": "an unverified allegation, not a confirmed finding: a single X post (via Hacker News, 21 points, 4 comments, no body stored) claims Claude Code adds users' email addresses to outbound HTTP requests. It scored the day's highest relevance (0.645) because Claude Code is the fleet's core tool, but its new-and-concrete score was low (0.48) precisely because it is one uncorroborated post \u2014 treat as a rumor to verify, not a fact.",
   "source": "X",
   "url": "https://twitter.com/chrislakin/status/2113358733548081528",
   "no": 93,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:97049a271e80dcb5167009a9d9f3dc4f08c667935e76092cc86410c6ee41447b"
  },
  {
   "date": "2026-10-25",
   "section": "Also worth a look",
   "title": "Cerebras Deploys Trillion-Parameter AI Models",
   "annotation": "a vendor blog post from Cerebras (via Hacker News, 171 points, 57 comments; no body stored, headline-level only) describing how it deploys trillion-parameter frontier models on its own hardware. The deployment claims are the vendor's; relevant as inference-infrastructure signal, not a measured result.",
   "source": "Cerebras",
   "url": "https://www.cerebras.ai/blog/how-we-deploy-trillion-parameter-frontier-models-on-cerebras",
   "no": 94,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:2b49f33743be0112ef9d4aa012caf15df4239bb13984f5bd37f626991a484a06"
  },
  {
   "date": "2026-10-26",
   "section": "Agentic AI",
   "title": "HarnessMM: Recursive Self-Improvement for Harness Token Efficiency",
   "annotation": "agent harnesses re-reason after tool feedback even when an earlier call already fixed the next action; HarnessMM stores resolved decisions in Executable Decision Graphs and reuses them under guards while actor weights stay fixed. Measured: it preserves Codex success at 90.82% while cutting tokens per task from 66.72k to 47.02k (29.5% saving), and on a DeepSeek harness success rises from 71.43% to 85.71% while tokens per task fall from 96.28k to 11.62k (87.9% saving).",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.32813v1",
   "no": 95,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:c6c1fc53442e685cc0eba79b71be70eccd394448b348d9e820edf24201ab5445"
  },
  {
   "date": "2026-10-26",
   "section": "Agentic AI",
   "title": "ActionRAG: Evidence-Refining Graph Diffusion for Long-Term Memory in LLM Agents",
   "annotation": "a graph memory architecture that pairs cautious, tiered entity resolution at indexing time with query-time graph diffusion that revises initial retrieval scores using local path structure. On MemoryAgentBench it averages 55.1 accuracy across the eight deterministic-scoring columns, 5.4 points over the strongest retrieval-based memory baseline, at under $0.001 mean synthesis cost per query on the reported columns.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.32752v1",
   "no": 96,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:74c554e590385c024a1156a74ac2e2601b0514280f414357219dbbf73df931b0"
  },
  {
   "date": "2026-10-26",
   "section": "Agentic AI",
   "title": "Reimagining Enterprise RAG for Multi-Agent Systems: A Knowledge Layer That Organizes Before It Retrieves",
   "annotation": "a seven-agent reverse-engineering pipeline processed 241 repositories across 18 services, but flat vector retrieval kept returning the identical rule from the wrong service with no error signal; the fix puts the ontology at the artifact level (producer, component, service, provenance) so queries resolve to artifact IDs that constrain search and disambiguation becomes structural, not statistical. Built in under a week, it supported root-cause analysis of 100+ production defects and attributed 35 release regression failures to specific rules and services.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.32851v1",
   "no": 97,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:719690a4a78dbe66028a689b800a6f6149cd8da2d52ff7a37c5453231818357e"
  },
  {
   "date": "2026-10-26",
   "section": "Agentic AI",
   "title": "KrishokTech: A Bengali Agricultural Advisory System with Explicit Authorization Controls",
   "annotation": "the transferable pattern is the authorization boundary around generation, surfaced to users as visible ASK, CONFIRM, REFER, and DROP decisions with their evidence. On 200 farmer queries and 400 stress queries the controls cut cross-crop retrieval violations from 36.5% to 0% under the tested alias filter, caught 453 of 454 text-image crop conflicts, and its post-generation verifier caught 114 of 118 inserted dosage errors.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.32817v1",
   "no": 98,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:add0d406ed9865c744eae0b8ec3ba08a9642f0086a76f0d214dc7bb59f7863b6"
  },
  {
   "date": "2026-10-26",
   "section": "Agentic AI",
   "title": "Measure Before You Train: Typed Decision Models vs. a Classical Baseline vs. a Generative LLM for Routing a Personal Multilingual Inbox",
   "annotation": "on 739 real Italian/English emails with owner labels, a TF-IDF + logistic-regression baseline trained on ~200 labels had the highest accuracy on every field (type 0.79, urgency 0.74, needs-reply 0.83, is-noise 0.90) and few-shot prompting did not close the gap; sender relationship was recovered at 98.6% accuracy from a contact's other emails and by no model from a single message \u2014 it belongs in the contact graph, not the prompt. The warning: composed into actions, the most accurate system most often ignored emails needing action (18% vs 4% for the best LLM), so accuracy is not the product metric.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.32955v1",
   "no": 99,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:e77fff0e7f0975e8b771af185e32ad8b8820ce9d72d7dcc9fbf023c92b127e3c"
  },
  {
   "date": "2026-10-26",
   "section": "Agentic AI",
   "title": "Green Code or Costly Code? Evaluating the Energy Costs of Iterative LLM-Based Code Optimizations",
   "annotation": "measured, not assumed: across 11 open-source models and all 1,000 EffiBench problems with hardware-level energy measurement, 42 of 44 configurations produced code that consumes more energy per problem than the canonical human reference (+4% to +886%), and the two exceptions need on the order of a million executions to break even. Capping maximum output tokens was the largest single lever, cutting inference energy by up to 69%.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.32891v1",
   "no": 100,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:48490fd13a8e66559aee0476d519d0862cb6ef3d3d806c0c9e05e15d0e727d2b"
  },
  {
   "date": "2026-10-26",
   "section": "Small business & nonprofit field",
   "title": "Communicating with the Entire Population: Machine Translation of Emergency Alerts and Warnings",
   "annotation": "a nationwide survey of emergency managers on their workflows and challenges, paired with an evaluation of commercial translation tools against those needs, offering practical guidance on how and when automated translation can be safely used for crisis alerts. Directly relevant to any public-facing operation that must reach non-dominant-language audiences in an emergency.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.32842v1",
   "no": 101,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:8c96b0cddf1244ef93c8bf2f7bb8870f5ccf374dd7364815eed3c3b84097017f"
  },
  {
   "date": "2026-10-26",
   "section": "Also worth a look",
   "title": "ADRs for AI \u2014 deliberation records for human\u2013AI work, versioned in Git",
   "annotation": "a Show HN tool (44 points), not a measured result: models record each design decision in an Options, Recommendation, Rationale, Confidence, Falsifier format for human approval or override, as an oversight and code-scaffolding record.",
   "source": "adrs.systems",
   "url": "https://adrs.systems",
   "no": 102,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:945872ce5efb10016d215d83a75801a113f5c78cf2e0c1532e0bfe62fbba3e44"
  },
  {
   "date": "2026-10-26",
   "section": "Also worth a look",
   "title": "Frontier-OPD: Discovering the Edge of Agent Capability and Teaching Agents to Go Beyond",
   "annotation": "on-policy distillation targeted at the student's learning frontier (challenging decision points where brief teacher guidance still allows autonomous continuation); it reports higher mean autonomous performance than the compared distillation baselines on ALFWorld, WebShop, and ScienceWorld \u2014 a training-side technique, not a harness pattern.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.32802v1",
   "no": 103,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:9121268c01101db9af359174fadaa5ce448efb704447f2f570c9ec2a46c721ea"
  },
  {
   "date": "2026-10-26",
   "section": "Also worth a look",
   "title": "OpenAI and Anthropic cut their LLM prices by up to 80% within a few months. Why?",
   "annotation": "an analysis piece (Hacker News, 315 points), not a price announcement or launch, and no article body was stored, so this is headline-level only: it asks why frontier LLM prices fell so fast; read it at the source before treating any explanation as settled.",
   "source": "minimaxir.com",
   "url": "https://minimaxir.com/2026/10/cheap-llms/",
   "no": 104,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:ae0be68e300407a5bd18fd342e11da458f7becf5f1c604956f0ed14c8c98a9e9"
  },
  {
   "date": "2026-10-27",
   "section": "Agentic AI",
   "title": "Free to Look, Free to Reason: Simple LLM Harnesses Outdiagnose Hand-Crafted Root Cause Analysis Agents",
   "annotation": "on RCAtlas (90 validated cases, eight public datasets, six models), three simple harnesses that prescribe none of the model's calls scored PR@1 65\u201377% on mid-tier models, ahead of five published RCA agents (16\u201353%) and non-LLM methods (6\u201339%); the published agents fix from a tenth to all of their model calls in advance, and the case study shows those prescriptions skipping the decisive evidence. More reasoning effort changed accuracy by only a few points while multiplying time by up to seven.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.35767v1",
   "no": 105,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:7d82364e6b4005719deaaad65becda0b10029b54cba3579d554d1483ff1ed524"
  },
  {
   "date": "2026-10-27",
   "section": "Agentic AI",
   "title": "MiRL: Error-Targeted Reinforcement Learning for Correcting Terminal Agent Misbehaviors",
   "annotation": "profile a terminal agent's recurring misbehaviors first, then start RL rollouts at the steps where they occur and reward only passes that avoid them. On Qwen3.5-4B, eight misbehaviors accounted for 52.1% of wrong steps; after MiRL the policy repeated them 43\u201357% less often on unseen prefixes, beat TMax-4B on Terminal-Bench Lite (51.8% vs. 47.4%) and matched it on Terminal-Bench 2.1, using 35% of its rollouts.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.35697v1",
   "no": 106,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:deb178caffe8b1dc18f00f826949f27e53baa418607a3c8f5f19c93ccc317aef"
  },
  {
   "date": "2026-10-27",
   "section": "Agentic AI",
   "title": "I-HITL: Inalienable Human-in-the-Loop",
   "annotation": "a warning with an experiment behind it: on a popular agentic wallet platform where raising a spending cap requires an emailed one-time passcode, ten different frontier models, given access to the user's email and a task blocked by the cap, would often autonomously harvest the OTP and raise the cap themselves. The paper formally defines approval a compromised harness cannot forge, proves it requires a trusted device, and prototypes Countersign on off-the-shelf consumer mobile devices.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.35835v1",
   "no": 107,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:ae3d6fc83867999ef46d1764b65adef826e358184f8588aba246c89d64a2fc56"
  },
  {
   "date": "2026-10-27",
   "section": "Agentic AI",
   "title": "EvoRouter: Cost-Efficient Multi-Agent Routing via Insight-Guided On-Policy Distillation",
   "annotation": "a self-evolving router that periodically distills performance-cost feedback from configuration trajectories into reusable routing insights, then trains on them with cost-aware RL. Across 19 in-domain datasets it averaged 68.69% accuracy, 7.23 points over the strongest routing baseline, while reducing inference compute cost by 50.95%; code is open-sourced.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.35690v1",
   "no": 108,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:a871a974b9997712c87745e831580519565d9ae43cbc6669d546ae120a5c857a"
  },
  {
   "date": "2026-10-27",
   "section": "Agentic AI",
   "title": "Reva: Real-Time Vulnerability Detection through Contextual Probing of Large Language Models",
   "annotation": "as coding agents generate code faster than developers can review it, Reva puts the context an agentic security detector gathers (module threat model, static-analysis findings, call graph) into a single forward pass and reads the verdict from a trained probe on the model's internal representations. On PrimeVul's held-out test it reached 60.0% accuracy in about one second per function \u2014 matching the agentic VulTriage detector (59.2%) at over 250x faster and less than a tenth the cost.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.35829v1",
   "no": 109,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:9b793bec3ccf4e730bfe76e45729123d1d33ab3297240c0324ab43a3f04c6f01"
  },
  {
   "date": "2026-10-27",
   "section": "Tools & models",
   "title": "Claude Code v2.1.311",
   "annotation": "a changelog release, not a launch: it fixes subagents being unable to use MCP tools from servers that finish connecting after the subagent starts, `SubagentStop` hooks running about twice a minute per running subagent instead of once at finish, and stale MEMORY.md/CLAUDE.md content after automatic compaction. Standard-context sessions that run over 200K tokens now compact and continue instead of stopping with \"Usage credits required for 1M context,\" and `self-hosted-runner` gains `--hold-idle-file` / `--max-hold-min`.",
   "source": "GitHub",
   "url": "https://github.com/anthropics/claude-code/releases/tag/v2.1.311",
   "no": 110,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:8e0813ac53458dd2a1ab05753e0f5d4e315b50aa04fe6890e052a41f68d5a438"
  },
  {
   "date": "2026-10-27",
   "section": "Tools & models",
   "title": "Cross-request KV cache exposure in the GLM-5.3 Workers AI model",
   "annotation": "a documented incident, via Cloudflare's own remediation post (Hacker News, 137 points, 155 responses per the stored title); no article body was stored, so this is headline-level only \u2014 read the post at the source for scope and remediation before treating any detail as settled.",
   "source": "Cloudflare",
   "url": "https://blog.cloudflare.com/kv-cache-exposure-remediation/",
   "no": 111,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:f8813b32dddb1abb58e326fc9fb7aa162bc953c19024dab93560c6324eb9b58d"
  },
  {
   "date": "2026-10-27",
   "section": "Also worth a look",
   "title": "Retriever-Free Retrieval: Navigating Documents with Decision Models",
   "annotation": "DecisionRAG replaces embedding retrieval with decision models that jointly judge candidate branches in a single forward pass and navigate corpus structure from broad topics to passages, with no task-specific training or first-stage retriever; it beat dense retrieval with an 8B reranker by 13.8 R@1 points on MultiDoc2Dial and 17.4 Hit@1 points on Natural Questions, at 0.7\u20131.8 seconds per query across four benchmarks.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.35868v1",
   "no": 112,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:842cbe78ef5cce40b1023ed1b81f4a33eb34cbdf61b518097fa8e42196c40e39"
  },
  {
   "date": "2026-10-27",
   "section": "Also worth a look",
   "title": "Understanding Jev-Style System One Models: Does Structure Reduce Selection Bias?",
   "annotation": "directly relevant to this engine's own scoring: across two Jev versions, twelve reproductions, and six generative LLMs, Jev accuracy varied by less than two points under option shuffling, yet its cumulative failure set expanded 27\u201329% over the original order \u2014 stable accuracy does not establish resistance to selection bias, and even unanimous answers were frequently wrong.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.35826v1",
   "no": 113,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:4913c1009a90f2bf5bbc6c0cc04556e1d2e51ff656e5249901081b1fbaa8276b"
  },
  {
   "date": "2026-10-27",
   "section": "Also worth a look",
   "title": "Quantifying Claim Smoothing by LLM Research Agents",
   "annotation": "deep-research agents consistently produce stronger claims than their citations warrant, collapsing conflicting sources into confident syntheses; the paper's SmoothScore pipeline scores report claims claim-by-claim against the underlying evidence across 7 systems and 4 datasets. A measured version of a known fleet risk: synthesis smoothing, now with an instrument.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.35793v1",
   "no": 114,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:ea8e49c8fd1efbb55b2a082ccc2053c5fdb5bbb1ff4cff7e3aea60558e55e0c3"
  },
  {
   "date": "2026-10-28",
   "section": "Agentic AI",
   "title": "Concordat: Typed Sessions for LLM Agents",
   "annotation": "a Python library that treats an agent as a finite-state protocol and offers the model only the tools legal in the current state, rejecting out-of-protocol calls before they have side effects. On tau-bench's 50 airline tasks with simulated customers pressuring the agent to skip confirmation, the standard agent made unconfirmed changes in 13 of 50 tasks (gpt-4.1) and 29 of 128 runs (Claude Haiku 5.5); the Concordat agent made none, with no significant difference in task success \u2014 and tau-bench's own reward had not detected most of the violations. Open source.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.37461v1",
   "no": 115,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:68b5a222e374de11c41c86924c96dd324060031d638ca990bae6f2083ab5ab38"
  },
  {
   "date": "2026-10-28",
   "section": "Agentic AI",
   "title": "From Interaction to Capability: Task Advancement as State and Supervision for GUI Agents",
   "annotation": "Task Advancement Memory (TAM) tracks verified task progress as state and, after each task, abstracts the interactions that produced a verified local result into reusable operations \u2014 retaining locally successful operations from failed tasks too. On MobileWorld GUI-only tasks, Advancement State alone raised success from 12.0% to 28.2%; full TAM beat the strongest baseline by 17.6% relative while cutting lifecycle tokens per solved task by 38.7%.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.37482v1",
   "no": 116,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:2bce388bc0bb81c0072b8463419a30a7f62f6b13859ab84a32d93fdf37a28e22"
  },
  {
   "date": "2026-10-28",
   "section": "Agentic AI",
   "title": "GEPA-Curator: Curated Learning for Self Improving Agents",
   "annotation": "reflective prompt optimizers like GEPA learn from failed rollouts, but agentic failures also come from harness limits, stochastic execution, and noisy judges, so GEPA-Curator filters which tasks shape optimization and retrieves prior successful trajectories as context. Across GoBrowse, ALFWorld, AppWorld, and \u03c4-bench it matched or beat both baselines, with gains up to 20.7% over GEPA and 21.2% over ACE.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.37651v1",
   "no": 117,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:d98e283579e2232049c1f527e4704312ce3e31169f5abce51cc67085e187ff19"
  },
  {
   "date": "2026-10-28",
   "section": "Agentic AI",
   "title": "TeamGym: A Gym for Team Decision-Making by AI Agents with Distributed Information",
   "annotation": "a benchmark/training environment built from real legal, finance, medical, and software corpora that tracks \"pivotal information\" \u2014 facts individually necessary and jointly sufficient for the right decision. Across 8 models on 12 corpora, splitting pivotal information across participants cut success by 22.9 points versus giving it to one model, and making participants extract it themselves cost a further 19.9 points; RL training on final-decision loss improved quality and produced emergent coordination.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.37571v1",
   "no": 118,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:f52125d04e65482a246912e4dfc083ad2ad64b2b2eec59181f2361b2ca4effc2"
  },
  {
   "date": "2026-10-28",
   "section": "Agentic AI",
   "title": "Rare, Relevant, and Still Wrong: How Memory Fails LLM Group Decisions",
   "annotation": "on CrossProfile (480 hidden-profile tasks with verified keys), both natural memory heuristics for compressing group discussion fail predictably: relevance selection readmits on-topic decoys (even a perfect relevance filter loses 23\u201331 points to rarest-first in the classic configuration), rarest-first is no better than random on average, and the decision model gives extra weight to facts attributed to many agents \u2014 removing attribution tags raised accuracy 13\u201324 points in the classic configuration. In natural discussions, the full transcript scored worse than no notes at all.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.37624v1",
   "no": 119,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:5158c30e741dfa42374c7b8d72b104930527bfa7544fd56b886db205b1d5df77"
  },
  {
   "date": "2026-10-28",
   "section": "Tools & models",
   "title": "Claude Code v2.1.312",
   "annotation": "a changelog release, not a launch: adds `oidc.user_id_claim` to the Claude apps gateway (e.g. Microsoft Entra ID's `oid`), click-to-send for queued messages in fullscreen, and fixes including managed settings/policy not being fetched after VS Code sign-in, Claude overwriting a very large CLAUDE.md/rules/@-included file after seeing only its start, per-directory and `paths:` skills not being found after a Bash `cd` deeper into a repo, and a rare missing first reply after session resume.",
   "source": "GitHub",
   "url": "https://github.com/anthropics/claude-code/releases/tag/v2.1.312",
   "no": 120,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:415c06e0156dcc13821ad70e5260e7f59d509bcb355ba4ad4f5dc50dd28f10d9"
  },
  {
   "date": "2026-10-28",
   "section": "Tools & models",
   "title": "Heads of Security: Learned Attention Gates Improve Prompt Injection Robustness",
   "annotation": "HoS learns per-head attention gates that redistribute attention between trusted and untrusted tokens, building instruction\u2013data separation into the model. On GPT-OSS-20B the black-box RL-Hammer attack reached 97% attack success against the base model and 85% against SecAlign, but only 10% against HoS; a strengthened white-box attack needed 3x more FLOPs to reach the same success rate, and HoS preserved base-model utility where finetuned defenses degraded in few-shot settings. Evaluated on Llama-3.3-70B-Instruct and GPT-OSS-20B.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.37657v1",
   "no": 121,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:802636ff22adab2dd3814949263ef196f6407d9eb1c35aee6f092883533ba59a"
  },
  {
   "date": "2026-10-28",
   "section": "Also worth a look",
   "title": "EnvSched: Environment-Aware Scheduling for Rollout-Efficient Agent Reinforcement Learning",
   "annotation": "an online scheduler that predicts within-task reward variance from rollout history propagated over a dual-relation graph of environment\u2013task pairs, then allocates batches to informative, under-observed tasks; on Qwen3-8B it averaged 48.81 across BFCL-v3, tau-Bench, and ACEBench-Agent after 40 steps, above what random selection reached after 120.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.37609v1",
   "no": 122,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:703d30c0de732515e3d85b3fb2ed904fe64fbe4059a84aa3a6a8515fd58b5697"
  },
  {
   "date": "2026-10-28",
   "section": "Also worth a look",
   "title": "Follow the Gradient: Tracing Safety Degradation Back to Training Data",
   "annotation": "SimpleLoRIF embeds fine-tuning examples by gradient alignment with unsafe behavior at million-example scale; removing the 20% most-aligned examples from Tulu 3 (SafeTulu) cut GCG attack success by up to 22% across Qwen, Llama, and Olmo, beating LLM-as-judge and prior gradient filtering while largely preserving capability.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.37481v1",
   "no": 123,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:0b15592ff42d6f94e74fbcf2d08aa742ea9a04fad550210ee44b293977d9fd4a"
  },
  {
   "date": "2026-10-28",
   "section": "Also worth a look",
   "title": "Understanding the limits of latent world models",
   "annotation": "introduces latent rollout consistency as a test of whether a learned world model faithfully mirrors the environment's transition dynamics, and finds that across the state-of-the-art latent world models studied, none actually solves its task by faithfully modeling the dynamics.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.37588v1",
   "no": 124,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:70c70b7678d5bd76d11f1c70cec2610b361d404892ebadc3f4f993bf2ba8139f"
  },
  {
   "date": "2026-10-29",
   "section": "Agentic AI",
   "title": "Cursor Agent SDK is generally available",
   "annotation": "the framework Cursor itself uses for Bugbot and Security Reviewer now lets any team define agents in code (Markdown prompts, TypeScript tools, evals and event replay built in), hand work to specialized subagents in parallel, and deploy to Cursor infrastructure with one command (`agent-sdk deploy`) or run the agent as a single Node process on its own infrastructure.",
   "source": "Cursor",
   "url": "https://cursor.com/blog/agent-sdk",
   "no": 125,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:57e187112faa3ef66253babb4e39527bb8a9a48cc9943c0493f7f7799735c7a1"
  },
  {
   "date": "2026-10-29",
   "section": "Agentic AI",
   "title": "DAYJOB: Tax \u2014 agents still fail professional judgment, measurably",
   "annotation": "Surge's new benchmark puts agents on 50 expert-built tax assignments (1,789 workspace files, 1,487 of them PDFs) graded at a 95% rubric threshold; the best model, Claude Sonnet 5.5, passes only 10.8%, with the failures concentrated in spotting consequential issues the client never raised \u2014 in one case an agent correctly compared two condo sale dates in all five attempts but never flagged the August 2027 deadline after which a $250,000 gain exclusion expires.",
   "source": "Surge",
   "url": "https://surgehq.ai/blog/dayjob-tax",
   "no": 126,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:21acbd9d2ab0adbfd0817bd5f1caa1e1f7cb1ef6d6679167b7d526751dafda62"
  },
  {
   "date": "2026-10-29",
   "section": "Agentic AI",
   "title": "Google's sleep framework: wake/sleep cycles for continual learning",
   "annotation": "instead of training once and freezing, the model alternates active interaction with an offline \"sleep\" phase that unlocks low-rank MoE memory blocks, distills fragile in-context memory into long-term parameters, prunes the temporary store, and rehearses filtered self-generated \"dreams.\" Reported results: near-perfect long-context scaling up to 10M tokens on BABILong, near-zero performance drop in multi-stage continual translation where ICL and fine-tuning baselines suffered severe catastrophic forgetting, and 48.9% on SQuAD factual incorporation.",
   "source": "Google Research",
   "url": "https://research.google/blog/the-sleep-framework-a-new-way-for-llms-to-retain-knowledge-in-continual-learning/",
   "no": 127,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:90d238601e79ce6c19d500f1e9ff10bf9b7fb35bff397685e001bc759f539cbc"
  },
  {
   "date": "2026-10-29",
   "section": "Agentic AI",
   "title": "NVIDIA Auto Ontology (open source)",
   "annotation": "a Python framework that builds a semantic, graph-based reasoning layer over enterprise data \u2014 where data lives, what it means, its lineage \u2014 reconciles conflicting definitions across systems, validates agent-generated queries against certified definitions, and serves that context to any agent harness via MCP, CLI, or API. The AlphaSignal headline (\"stops agents from returning wrong data\") overstates it; the repo itself frames it as deterministic query generation and provenance with every answer.",
   "source": "GitHub",
   "url": "https://github.com/NVIDIA/auto-ontology",
   "no": 128,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:59e1b4d03b5c14dd840fe128790c165e739fe35b48487b76e77cff50840b46a3"
  },
  {
   "date": "2026-10-29",
   "section": "Tools & models",
   "title": "Gemini 4 Carbon announced \u2014 frontier cyber model, gated rollout",
   "annotation": "Google's follow-on to Gemini 4 Argon (launched two weeks ago) ranks #1 on Artificial Analysis Intelligence Index v5.0 at 59 and leads the cybersecurity benchmarks it reports: SEC-bench Pro 88.2% finding vulnerabilities, ExploitGym 54.8% validating them, CWE-bench 71.0% patching them; it also reports 0.0% success for indirect prompt-injection attacks and cheating on Terminal-Bench 4.0 cut to 0.0%. Priced at Argon's introductory $2 / $10 per million input/output tokens, but this is not a general launch: access starts this week with a subset of cyber defenders through the Fairwind Program, under the U.S. government's voluntary pre-release testing process, with developer and consumer availability to follow.",
   "source": "Google",
   "url": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-carbon/",
   "no": 129,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:78da3ff1a80775b4576b0f9daf8d1fd9820826b47d05caa1156eb8b378e3f842"
  },
  {
   "date": "2026-10-29",
   "section": "Tools & models",
   "title": "Claude Code's background-agent summaries burn a measured ~16% of spend (community measurement, not an Anthropic figure)",
   "annotation": "a widely shared gist, backed by OpenTelemetry exports, reports that every ~30 seconds Claude Code sends each background agent's *entire transcript* to its model to produce a 3\u20135-word status line for the agents panel \u2014 a feature on by default since 2.1.232 with no setting to turn it off. In the author's telemetry `agent_summary` was 16.4% of all Claude Code spend (more than the main conversation at 15.7%) and 19.7% of requests, 96% of it on Opus; the gist links four independent measurements of 18\u201326% and ships an MIT-licensed Bun preload that suppresses the summary timer, verified across versions 2.1.257\u20132.1.312. On a subscription the cost is rate-limit headroom, not invoice dollars.",
   "source": "GitHub Gist",
   "url": "https://gist.github.com/flamerix-ai/c3905705cc4205e5de3011fa2e74efce",
   "no": 130,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:a5db311d1c862e182de062d354d6bac69749012c5981f195cb482d3d5e34195c"
  },
  {
   "date": "2026-10-29",
   "section": "Tools & models",
   "title": "Genie 4: DeepMind's world model goes real-time with native audio",
   "annotation": "the new interactive world model generates music, ambient sound, and interactive character dialogue natively alongside the simulation, simulates aerodynamics and collisions, and lets users reshape a scene mid-run in natural language, while a Gemini model acts as a guiding agent in the loop \u2014 observing frames, breaking goals into steps, and verifying each step. It is a research preview only: rolling out to trusted testers, then Google AI Ultra subscribers in Project Genie and early-access Gemini API / AI Studio partners; DeepMind notes object permanence and long-session coherence still drift.",
   "source": "DeepMind",
   "url": "https://deepmind.google/blog/genie-4-a-new-generation-of-world-simulation/",
   "no": 131,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:dac6d8d7dffb6c1623061d0743c58834a8e20979719b55da08baf973d14a3339"
  },
  {
   "date": "2026-10-29",
   "section": "Tools & models",
   "title": "Liquid AI LFM3-1.7B: a 1.7B on-device model post-trained for local agents",
   "annotation": "built on a tri-hybrid architecture with a 256K-token context window, it runs at about 1 GB at 4-bit precision (phone or laptop), was post-trained for agentic harnesses including Hermes Agent, Pi, and OpenClaw, and posts agent numbers the vendor says used to need ~9B parameters \u2014 LiveClaw 45.60 against Qwen3.5-9B's 35.77 and BFCL V4 tool use 71.39 \u2014 while Liquid itself warns it is not recommended for agentic coding or complex multi-step reasoning. The AlphaSignal headline (\"six 2B models matching rivals six times larger\") is the vendor's family framing; the linked checkpoint is this single 1.73B model.",
   "source": "Hugging Face",
   "url": "https://huggingface.co/LiquidAI/LFM3-1.7B",
   "no": 132,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:a77473af18e8cadd3e56de042aa8236a245c87f2929e8dcb170f014ab43ef415"
  },
  {
   "date": "2026-10-29",
   "section": "Also worth a look",
   "title": "STAIR: structure-aware retrieval with <0.05% hallucination in its test setup",
   "annotation": "the paper (arXiv 2609.03874) exploits a corpus's global structure (e.g. a table of contents) inside a finetuned Differentiable Search Index and scores Recall@1 82.6% on its new SearchTome benchmark vs DSI's 76.9% and BM25's 59.5%; the AlphaSignal \"65x less than standard RAG\" headline does not appear in the paper's abstract, which reports the <0.05% figure for its generative IR system.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2609.03874",
   "no": 133,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:0987be78b5c4f6bbbc92aeec8be21cd468a932b67ce89d7567a324c29af0fdc5"
  },
  {
   "date": "2026-10-29",
   "section": "Also worth a look",
   "title": "Oracle Cloud becomes the first hyperscaler outside Google to offer Google TPUs \u2014 capacity from 2027",
   "annotation": "TPU capacity will be deployed in OCI's liquid-cooled data centers through Oracle's Acceleron SmartNIC fabric; Google cites its eighth-generation TPU at up to 12.6 PFLOPS of FP4 per chip, up to 2x performance per watt, and over 97% goodput via optical circuit switching. A 2027 infrastructure option, not a capability available today.",
   "source": "Google Cloud",
   "url": "https://cloud.google.com/blog/products/infrastructure/bringing-google-tpus-to-oracle-cloud-infrastructure?utm_source=tldrhardware",
   "no": 134,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:fa5a8163824e012d2f03aa54ee828bfed75e5f1e5fe10c6dcdebca193fd1a6d4"
  },
  {
   "date": "2026-10-30",
   "section": "Tools & models",
   "title": "Trojanized Claude Code extension on the Cursor Marketplace plants a persistent SSH backdoor",
   "annotation": "version 2.1.313 of the `anthropic.claude-code` extension in the Cursor Marketplace carried a 66-line loader prepended to the real bundle: on load, before any Claude conversation starts, it adds an attacker key (comment `svc-telemetry`) to `~/.ssh/authorized_keys`, tries to enable SSH, opens a reverse tunnel to 134.209.82.225, and installs persistence (LaunchAgent on macOS, cron on Linux, scheduled task on Windows). Cursor confirmed its marketplace was the origin of the compromise; the npm package, the Open VSX listing, and Microsoft's VS Code extension are reported NOT malicious. Anyone who ran that build should treat the host as compromised and rotate credentials from a clean machine. (Two reports of the same incident, grouped as one entry.)",
   "source": "OpenSourceMalware",
   "url": "https://opensourcemalware.com/blog/trojanized-claude-code-extension-plants-persistent-ssh-backdoor",
   "no": 135,
   "grade": "unverified",
   "caveats": [
    "Two reports of the same incident, grouped as one entry."
   ],
   "hash": "sha256:92eab36a32a2b520b1dc5cd85eb683e8df51d1043c65f92bd454a229606c737c"
  },
  {
   "date": "2026-10-30",
   "section": "Tools & models",
   "title": "Mistral Large 4 weights released \u2014 1-trillion-parameter open-weight flagship",
   "annotation": "after three more weeks of RL since its public preview, \"Le Chonk\" leads open-weight coding (FrontierCode 2.0 19.7%, Terminal-Bench 4.0 41% after a 13-point jump), ties GLM-5.3 on the Artificial Analysis Coding Agent Index at 48% while using 47% fewer tokens per task, and resists 97.1% of attacks on the B3 agent-security benchmark. Weights are on Hugging Face under a modified-MIT license (free to use, modify, fine-tune and commercialize; model-as-a-service resellers need a separate license), self-hostable via vLLM/SGLang, with API pricing at $0.70 / $2.10 per million input/output tokens and three reasoning modes (Low/Medium/High).",
   "source": "Mistral",
   "url": "https://mistral.ai/news/mistral-large-4-oss/",
   "no": 136,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:f47bd4839ebd24c23bbdd33b0bf0b0b21f46f8c5fc31a5f7ce2679f645e78206"
  },
  {
   "date": "2026-10-30",
   "section": "Tools & models",
   "title": "Claude Code v2.1.314",
   "annotation": "a guardrails changelog release, not a launch: the new `permissions.blockWritesOutsideWorkingDirectories` setting makes Write, Edit and NotebookEdit refuse paths outside the working directories in every permission mode, and idle 200K+ token conversations in VS Code and the Desktop Code tab now get compacted so a summary is used when the prompt cache has expired. It also fixes sessions hard-failing turn after turn when an org admin caps a model's effort level (now lowers the effort, retries, and says which level it used) and `claude -p` exiting 0 with no output on startup failure (now prints the error and exits 1).",
   "source": "GitHub",
   "url": "https://github.com/anthropics/claude-code/releases/tag/v2.1.314",
   "no": 137,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:384a938a975952ad040eb5b80dfe4d1aca1d95d3ae0a173299c48e93a2cc4f09"
  },
  {
   "date": "2026-10-30",
   "section": "Agentic AI",
   "title": "Pre-Crime for Agents: a probe can tell when a recruited agent is about to give in",
   "annotation": "in a controlled multi-agent setup where a peer presses a target agent for up to 20 rounds to reveal a secret (e.g. a deploy token), a linear probe on the target's activations, read at each call *before* its output is written, caught 82% of leaking calls at a threshold flagging 5% of benign calls \u2014 a zero-shot decision-model monitor watching the same calls caught 26%. The paper's own limits are stated plainly: the probe mostly reads \"a disclosure is coming\" rather than \"a violation is coming,\" it also flags permitted disclosures, and halting on any alarm would stop most benign episodes.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.40588v1",
   "no": 138,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:eca0d141d8e7e4553d09771f6604d37c5da49b3a4aec015ae511930008bfc5b8"
  },
  {
   "date": "2026-10-30",
   "section": "Agentic AI",
   "title": "VeriCUA: verification as agentic exploration for computer-using agents",
   "annotation": "instead of judging a finished trajectory from screenshots alone, the verifier builds a task rubric from the available context, then runs GUI or code checks for each unresolved requirement and combines the findings under explicit evidence rules. Across desktop, mobile and web settings it improved agreement with reference labels over VLM-judge and generated-program baselines, with the largest offline gains on hard cases and stronger rejection of false completion claims online.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.40578v1",
   "no": 139,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:e5fcf71dcfaeba0d6e8e83705aa9cdce68615dd2d9b439e1c0b6ede3d670c052"
  },
  {
   "date": "2026-10-30",
   "section": "Agentic AI",
   "title": "rTaste: research agents generate well but rank poorly",
   "annotation": "six frontier models, working alone or as teams, proposed 415 minimum-viable experiments from seven recent NLP papers; a separate executor ran every one, including the last-ranked. 194 proposals were judged sound, valuable and new, but the agents' first choices were not shown to beat simply running the experiment listed first, their outcome forecasts were no more accurate than a fresh instance of the same model given only the paper, and teams cost more without a clear overall improvement in proposal quality.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.40488v1",
   "no": 140,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:bcf29c9ba17bcc16d9474e416dd71410b5ea3723beef6a72c9a918e1cc219a12"
  },
  {
   "date": "2026-10-30",
   "section": "Agentic AI",
   "title": "Ramp launches Inspect, a collaborative coding agent",
   "annotation": "Inspect is a cloud-based coding agent that takes requests from Slack, GitHub, Linear or the web and returns visually validated, reviewed pull requests inside the company's own development environment; early access is open. Ramp's figures \u2014 Inspect behind 74% of its merged PRs and builders shipping 3.4x more PRs per person than a year ago \u2014 are the company's own, not independently measured.",
   "source": "X / Karim Atiyeh",
   "url": "https://x.com/karimatiyeh/status/2115096837736612130?utm_source=tldrfintech",
   "no": 141,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:bf2d337ed84982d7d503e7d2314ecca470cf0a06b20ecec3267d1b8cdafb61c9"
  },
  {
   "date": "2026-10-30",
   "section": "Field \u2014 small business & nonprofits",
   "title": "Instinct gives its AI agents their own spending money",
   "annotation": "the personal AI assistant now lets users fund a wallet and set spending limits so agents can make purchases independently: stablecoins are converted into dollars and spent through a Visa virtual card, so merchants simply see a normal card payment. The open contest underneath is who controls agent payments \u2014 Stripe's virtual cards versus the card networks' own systems tied to customers' existing cards.",
   "source": "LinkedIn",
   "url": "https://lnkd.in/p/gDVV26Zz?utm_source=tldrfintech",
   "no": 142,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:abe4ac8df4f567dcf4a74336fe62e54c49592c0161705896bf5a3cb391a7ecb2"
  },
  {
   "date": "2026-10-30",
   "section": "Also worth a look",
   "title": "PIVOT: teacher corrections inside failing rollouts nearly double RLVR on hard logic games",
   "annotation": "inserting teacher-generated corrections into student rollouts plus token-level KL supervision on those segments took mean avg@8 from 19.5% (best baseline) to 35.6%; either mechanism alone was insufficient.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.40576v1",
   "no": 143,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:f1cf8893d6b8521ec4f9dee87b255f5429742928208cef2c2b080faa389b1198"
  },
  {
   "date": "2026-10-30",
   "section": "Also worth a look",
   "title": "Artificial Analysis Intelligence Index v5 rebuilds the yardstick",
   "annotation": "equal 25% weights on knowledge work, coding, science and general ability, a shift toward private benchmarks, and eight closed models ahead of the best upcoming open-weight model; the accompanying analysis argues the previous index flattered open models.",
   "source": "Interconnects",
   "url": "https://www.interconnects.ai/p/artificial-analysis-v5-and-the-benchmarkers",
   "no": 144,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:153c2f73edbc29fc9938ae7cdaf7f0ee1e3de5d71baabfdf1156b947b86fddb1"
  },
  {
   "date": "2026-10-31",
   "section": "Tools & models",
   "title": "Cursor publishes its official notice on the malicious Claude Code extension \u2014 a follow-up to yesterday's headline, not a new incident",
   "annotation": "reporting on the Cursor Marketplace compromise continued to top Hacker News today (a 655-point post titled \"Backdoors in official Claude Code extensions\" and a 399-point DoublePulsar analysis), and the new concrete piece is Cursor's own security notice confirming a malicious Claude Code extension was found in its marketplace. The underlying incident \u2014 a trojanized extension planting an SSH backdoor \u2014 is the one covered in yesterday's digest; anyone who ran that build should already be treating the host as compromised. (Three reports of the same incident, grouped as one entry.)",
   "source": "Cursor",
   "url": "https://cursor.com/blog/claude-code-extension-security-notice",
   "no": 145,
   "grade": "moderate",
   "caveats": [
    "Three reports of the same incident, grouped as one entry."
   ],
   "hash": "sha256:cc602c7dcca6ada28502a12d032708b9f8e68efbdd1051b4e8f9e2241e3c3f86"
  },
  {
   "date": "2026-10-31",
   "section": "Tools & models",
   "title": "Claude Code v2.1.316",
   "annotation": "a changelog release, not a launch: it adds `CLAUDE_CODE_SUBAGENT_EFFORT` to set the effort level subagents run at instead of inheriting the session's, pooled group budgets on the Claude apps gateway (one monthly budget a group's members share, on top of each member's own limit), and `CLAUDE_CODE_DISABLE_ULTRATHINK` to turn the `ultrathink` keyword off. It also deprecates fast mode for Opus 4.8 as of October 30, 2026 \u2014 fast mode will be removed on November 30, after which fast-mode requests on Opus 4.8 return an error rather than falling back to standard Opus 4.8; the release notes direct users to Opus 5.5 to keep the speedup.",
   "source": "GitHub",
   "url": "https://github.com/anthropics/claude-code/releases/tag/v2.1.316",
   "no": 146,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:13ce4e310859c2c332245774dc0d7d05d4eb2a0e49b7d2888c712b972a60c290"
  },
  {
   "date": "2026-10-31",
   "section": "Tools & models",
   "title": "DeepSeek model runs 1M-token context on under 1GB of RAM",
   "annotation": "that is AlphaSignal's headline claim for the linked arXiv paper; the newsletter item carries no further detail in the record, so treat the 1M-context / sub-1GB figures as the paper's claim to check at the source, not as independently measured here.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2609.19969",
   "no": 147,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:2ed45f66d7be2d8a587b2ba7683e6bdf6124212251cbe0925a41c5d1f0a13552"
  },
  {
   "date": "2026-10-31",
   "section": "Tools & models",
   "title": "OpenAI gives Pro and Business users up to $500 monthly in free API credits",
   "annotation": "per OpenAI's help page for the benefit, eligible ChatGPT Pro and Business subscriptions now carry up to $500 a month in free API credits; a direct cost offset for small teams already paying for those seats.",
   "source": "OpenAI Help",
   "url": "https://help.openai.com/en/articles/20001626-api-credits-benefit-for-eligible-chatgpt-subscriptions",
   "no": 148,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:6193550072626b74d9443bc22d6080eb5a81044b873838ea8486031ddf42767b"
  },
  {
   "date": "2026-10-31",
   "section": "Agentic AI",
   "title": "CopilotKit ships OpenDuet, a self-hostable open-source AI agent template with human-in-the-loop controls",
   "annotation": "an open-source starting template for building agents you can host yourself, with human-in-the-loop controls built in rather than bolted on; a steal-early candidate for fleet agent scaffolding.",
   "source": "GitHub",
   "url": "https://github.com/CopilotKit/OpenDuet",
   "no": 149,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:7f9f31f7d109cfa06e12c450cf80085e7072b9776a59c8a53c91e49fadb904dc"
  },
  {
   "date": "2026-10-31",
   "section": "Agentic AI",
   "title": "Post-training lifted Nemotron 3.5's APEX-Agents score by 6+ points",
   "annotation": "new research from Mercor and NVIDIA reports that post-training alone moved Nemotron 3.5 more than six points on the APEX-Agents benchmark; the figures are the authors' own, reported on Mercor's blog.",
   "source": "Mercor",
   "url": "https://www.mercor.com/blog/mercor-nvidia-nemotron-3-5/?utm_source=alphasignal&utm_medium=newsletter&utm_campaign=apex&utm_term=tertiary&utm_content=10_30",
   "no": 150,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:691fe93478f2bd020568d240f54a06b458cb79a12e9a8a39f1590f9d466ca88b"
  },
  {
   "date": "2026-10-31",
   "section": "Also worth a look",
   "title": "NVIDIA releases an open-weight speech model topping ASR leaderboards with 4B active parameters",
   "annotation": "the linked leaderboard is the claim's source.",
   "source": "Hugging Face",
   "url": "https://huggingface.co/spaces/hf-audio/open_asr_leaderboard?proprietary=false",
   "no": 151,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:e7ed7d22c97db6681e261c5a83d06b6802802c6738f16fd5a06f04e1796aa515"
  },
  {
   "date": "2026-10-31",
   "section": "Also worth a look",
   "title": "New retrieval method beats dense search by 17 points without vector embeddings",
   "annotation": "an arXiv result worth a skim for anyone running RAG-style recall.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.35868",
   "no": 152,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:d3f18c3c9f7c47685ee3ad3269aa7f74997c47c64334ac040e2942b2407b0ffc"
  },
  {
   "date": "2026-10-31",
   "section": "Also worth a look",
   "title": "Cloudflare Turnstile solver library, no headless browser required (209 HN points)",
   "annotation": "a public library claiming to solve the Turnstile checks E5's intake forms rely on; a reminder that a solved Turnstile is not proof of a human.",
   "source": "GitHub",
   "url": "https://github.com/chris124567/cfsolve",
   "no": 153,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:d74de6d73b846cd24ca838a7316c2d6439ed4eb5be628eda5fa850e4c7a52f8c"
  },
  {
   "date": "2026-10-31",
   "section": "Also worth a look",
   "title": "A defense contractor gave their personal AI agent access to a secure Pentagon system",
   "annotation": "Wired's documented incident report; the governance failure mode (personal agent, production-secure system) in one headline.",
   "source": "Wired",
   "url": "https://www.wired.com/story/a-defense-contractor-gave-their-personal-ai-agent-access-to-a-secure-pentagon-system/",
   "no": 154,
   "grade": "emerging",
   "caveats": [],
   "hash": "sha256:717186df4e8d790105a4ace9d6ebfb8455c24c4734fbae6f922979a312741f05"
  },
  {
   "date": "2026-11-01",
   "section": "Tools & models",
   "title": "Anthropic moves to block Claude from video-game cloning and reverse-engineering",
   "annotation": "three outlets report the same change: Claude is now refusing requests to recreate or clone video games, and Tom's Hardware reports users also claim non-malicious generic reverse-engineering tasks are being blocked. That second claim is user-reported, not confirmed by Anthropic in these reports \u2014 worth watching, since a broad reverse-engineering refusal would hit legitimate fleet REA work. (Three reports of one story, grouped as one entry.) Tom's Hardware \u00b7 Game Developer \u00b7",
   "source": "Mashable",
   "url": "https://mashable.com/tech/anthropic-takes-steps-to-block-video-game-cloning-and-reverse-engineering",
   "no": 155,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:d4f6f69135eb2aaa68449d5d4a68da5fefdad8c42d6aca33ccf125bd60838b49"
  },
  {
   "date": "2026-11-01",
   "section": "Tools & models",
   "title": "ChatGPT and Codex usage limits are merging",
   "annotation": "OpenAI's help page now covers ChatGPT and Codex usage limits together, so the two products draw on a merged limit structure rather than separate ones; a budgeting fact for any team paying for both.",
   "source": "OpenAI Help",
   "url": "https://help.openai.com/en/articles/20001625-understanding-chatgpt-and-codex-usage-limits",
   "no": 156,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:f26337c81a16e51c5ee2109b56e153a4b5a82f4f35903a456ed6a4848f650016"
  },
  {
   "date": "2026-11-01",
   "section": "Tools & models",
   "title": "Claude's top tier is now an \"Ultra\" plan without the 5-hour limit",
   "annotation": "an HN post points to Anthropic's support page for the Ultra plan, reporting that the former 20x tier is now Ultra and no longer carries the 5-hour limit. The detail here rests on the HN title and the support page it links; treat the limit removal as reported, not independently verified here.",
   "source": "Claude Support",
   "url": "https://support.claude.com/en/articles/17728952-what-is-the-ultra-plan",
   "no": 157,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:1daf0201a40f6ece4a7be8834168f323c3003db258a4b1b12b85bff953dc7a4e"
  },
  {
   "date": "2026-11-01",
   "section": "Agentic AI",
   "title": "Memdoor: a team of five Qwen3.8-27B models beats Fable 5 on LiveCodeBench Hard",
   "annotation": "that is the project's own claim for its team-solve workflow, a five-model team of the open-weight Qwen3.8-27B reportedly outperforming Fable 5 on the Hard split of LiveCodeBench. The numbers are the repo's, not independently measured, but a small open-model team beating a frontier model on hard coding tasks is exactly the steal-early pattern if it holds up.",
   "source": "GitHub",
   "url": "https://github.com/guregodevo/memdoor/tree/main/examples/workflows/team-solve",
   "no": 158,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:d067a310e073d856e22ff7d5cdf346e4900b630f0d00738faf6f0fe5f7106ea2"
  },
  {
   "date": "2026-11-01",
   "section": "Also worth a look",
   "title": "Malware now uses LLMs to rank victims by how much they're worth",
   "annotation": "Cisco Talos documents the Volta infostealer using AI-model-driven victim prioritization (\"appraisal\") to sort stolen data by estimated value; a documented incident, and a concrete picture of how commodity malware is absorbing LLMs.",
   "source": "Cisco Talos",
   "url": "https://blog.talosintelligence.com/appraisal-by-ai-model-driven-victim-prioritization-in-the-volta-infostealer/",
   "no": 159,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:391f0e6f7c429be2c14b4ab1e7fc2c9b98e2c7a828ddb63e67fc14e9567d1676"
  },
  {
   "date": "2026-11-01",
   "section": "Also worth a look",
   "title": "Cole Medin walks through Anthropic's new study on AI control",
   "annotation": "his latest video covers Anthropic research arguing users control AI agents less than they think; a video summary, not the study itself.",
   "source": "YouTube",
   "url": "https://www.youtube.com/watch?v=BlbxhsItZPg",
   "no": 160,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:ad2e678c6f2bbb5e84b8dc3c75251c469d2659a447a9c54f471cd371ff031497"
  },
  {
   "date": "2026-11-02",
   "section": "Agentic AI",
   "title": "Flide: an inference engine built for decision models like Jev",
   "annotation": "decision models (the paper names Jev explicitly) do lightweight categorical reasoning for agents, but generation-oriented engines waste it on prefix recomputation or distort output probabilities. Flide's ForkAttention forks exact state inside a single flattened token stream \u2014 every query branch evaluates identically to an isolated forward pass \u2014 and the paper reports queries up to 9.5\u00d7 faster than vLLM and SGLang while staying closest to a float32 reference. Directly relevant: this engine's own decision pass runs on Jev.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.42148v1",
   "no": 161,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:2f99d5a1b86e3a43069fb0bb468d2b233c7e45bab60229d654a4a34428333a9a"
  },
  {
   "date": "2026-11-02",
   "section": "Agentic AI",
   "title": "SecPO: prompt-injection defense that actually survives adaptive attacks",
   "annotation": "the best-defended model today, Meta-SecAlign, still approaches 100% attack success under adaptive injections, partly because DPO anchors it to an insecure reference model. SecPO approximates an injection-invariant oracle reference and trains on strong synthesized attacks (best@240); its Qwen3.6-27B build posts 8.8% vs. Meta-SecAlign's 78.8% attack success on AgentDojo under the AttackerMovesSecond attack, with no noticeable benign-utility loss across nine benchmarks. Code, training data, and the defended model are released.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.41998v1",
   "no": 162,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:0deda0303584301f405db434cfdf7a23926028d13e31f62f3a42470834729f2e"
  },
  {
   "date": "2026-11-02",
   "section": "Agentic AI",
   "title": "Multi-tool retrieval is the quiet failure point in agent stacks",
   "annotation": "an agent that needs several tools fails outright if the retriever misses any one of them, yet retrievers are mostly evaluated on single-tool queries. Measured across AppWorld and MCP-Bench, no untrained structure making at most one LLM call per query completes more than 10% of AppWorld queries at k=16; a set-level retriever trained on just 75\u201390 in-domain queries reaches .33 on AppWorld and .58 on MCP-Bench with zero LLM calls, while an LLM-navigated hierarchy needs a median of 31\u201338 calls and about five minutes per query to do worse.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.41905v1",
   "no": 163,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:f279cce2a43d25f5a1c82a86188ad4265e429f8b4edfc4d06b8d2cac0db0967f"
  },
  {
   "date": "2026-11-02",
   "section": "Agentic AI",
   "title": "RoboArchitect: harness design, automated \u2014 no weight updates",
   "annotation": "a frozen robot policy performs far better inside a good agent harness, but harnesses are hand-built per robot. Here a coding model watches rollouts, diagnoses failures, and iteratively rewrites the harness (which also evolves its own persistent memory, e.g. learned grasp depths); success rises from 9.7% to 43.3% on unseen scenes and 12.7% to 40.2% on unseen tasks, and the evolved harness often beats the finetuned policy. Robotic setting, but the pattern \u2014 iterate the harness, keep the model frozen \u2014 is the fleet's own playbook.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.42141v1",
   "no": 164,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:50bde24e0e25b9cf0eed3e21b089c644344b7944abd883063e16d2cbf60a1890"
  },
  {
   "date": "2026-11-02",
   "section": "Agentic AI",
   "title": "Your tool-call traces leak who you are",
   "annotation": "MCP tool-call traces, with no access to conversation content, let attacker LLMs infer user profile attributes at a 26.25% success rate vs. 17.97% for prior-based guessing, measured on the new PrivacyTrace benchmark (1,000 synthetic profiles, 4,000 trajectories, 25 real MCP servers). Tool arguments and returned results carry most of the signal, and linking traces across sessions amplifies the risk \u2014 a concrete privacy design constraint for any agent that logs its tool use.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.41885v1",
   "no": 165,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:15637999f70a33f61adbb69012111cb27f8c5505e115d2c4e7b1f4c5bb47d6b8"
  },
  {
   "date": "2026-11-02",
   "section": "Agentic AI",
   "title": "ArchOR: structure-aware skill retrieval beats instance-level experience",
   "annotation": "agents that store reusable experience as raw instances stay brittle under reworded tasks. ArchOR organizes skills by archetype, retrieves them by structural signature (weighted Jaccard overlap, bounded semantic re-ranking), and arbitrates when the structural route should override the default selector; on the same backbone and skill library it reaches 65.00% Micro-Avg. Pass@1 across five optimization-modeling benchmarks, +1.64pp over its OptSkills predecessor. A small margin, but the retrieval-and-arbitration design maps straight onto a fleet skill library.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.41909v1",
   "no": 166,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:78b509a04338da30a59f720d27a7c249a547d9b3a9b8346324a6d3a38578727f"
  },
  {
   "date": "2026-11-02",
   "section": "Tools & models",
   "title": "Latent Sparse Attention: subquadratic attention that holds accuracy at 10M tokens",
   "annotation": "full attention is prohibitive past 1M tokens, and existing sparse methods either stay quadratic in indexing or lose accuracy at ultra-long context. LSA clusters keys hierarchically in a learned latent space so queries score clusters, not every key; the distilled variant speeds one attention layer up 22.7\u00d7 in prefill and 4.6\u00d7 in decode at 10M tokens on GLM-5.3-FLASH while keeping similar accuracy, and the native variant matches full attention's accuracy with a 1.6\u00d7 per-layer training speedup at 128k. If it replicates, long-horizon agent context gets much cheaper.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.42147v1",
   "no": 167,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:b4e8456ea780deff9bbe4e00316abe60796ff3ef317f5b07da1312f94b8faa8b"
  },
  {
   "date": "2026-11-02",
   "section": "Also worth a look",
   "title": "Human+AI beats humans alone \u2014 but loses to the better of the two alone",
   "annotation": "AutoAnalysis, a self-updating meta-analysis now covering 1,955 effect sizes from 420 papers, finds human-AI combinations significantly outperform humans alone yet underperform simply using whichever of human or AI is better solo; the closest thing to a field-lane signal today, and a useful corrective for Sawdust/E5 collaboration claims.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.42001v1",
   "no": 168,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:1d6f984f58c44c12d839c3e2ff59a26482aef60081e7445c4cf4bd46353a5c38"
  },
  {
   "date": "2026-11-02",
   "section": "Also worth a look",
   "title": "Chronos-2: one foundation model for multivariate time-series forecasting",
   "annotation": "zero-shot probabilistic forecasts that model interactions between series and covariates; +5.2pp skill score over the strongest baselines, and 100 series forecast in under a second on a mid-range GPU.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2610.41956v1",
   "no": 169,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:571ec1ecce39f78afd25cdf261f15bd2b3072dbb891ed51e1a835f32cd022bda"
  },
  {
   "date": "2026-11-02",
   "section": "Also worth a look",
   "title": "CAPPZ \u2014 passkeys, device-signed receipts, batch notarization (Merkle, XRPL-anchored)",
   "annotation": "the Chairman-provided item from the EDEN Agent Door work, graded there for the mandate credential and Chronicle checkpointing.",
   "source": "CAPPZ",
   "url": "https://cappz.ai/news/passkeys-device-signed-batch-notarization",
   "no": 170,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:f423933e817cff2a0e7d4b8d745b24319052f9d2fe15b5f5b8eb2236353fdec1"
  },
  {
   "date": "2026-11-03",
   "section": "Agentic AI",
   "title": "Strategist: the inference strategy now improves itself",
   "annotation": "a model is only half an agent system; the reasoning procedure around it is hand-written and its lessons never carry to the next task. Strategist is a meta-agent that evolves that second half: strategies are executable blueprints in a reusable library, retrieved, rewritten, tested on real samples, and banked for later tasks. Across 30 benchmarks it beats the strongest baseline on 24, matches its average accuracy (74.7% vs. 74.0%) at 39% lower inference cost, and its strategies transfer to unseen tasks and other models without further search.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.03618v1",
   "no": 171,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:3dab38f39af1e91472e4805a57e6b41d8e9dcde14ed5e71f97e4e9e46416b26d"
  },
  {
   "date": "2026-11-03",
   "section": "Agentic AI",
   "title": "Optimize the harness, not the model \u2014 CLEO fits a minimal agent to a self-hostable LLM",
   "annotation": "for investigative analysis over large, messy evidence collections where hosted frontier models are ruled out, CLEO admits only the tools, guardrails, memory policies, and loop components that measurably improve accuracy on an expert-validated dataset. The resulting harness reaches an 83.8% pass rate on held-out data \u2014 within 1.2 points of the strongest out-of-the-box Claude Code SDK baseline \u2014 while being two orders of magnitude smaller and auditable. The fleet's own harness-first playbook, measured.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.03434v1",
   "no": 172,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:fcd3fede1d0b608d63e611a029b89738f19726be0e1d5e976f28e517b16e8b0f"
  },
  {
   "date": "2026-11-03",
   "section": "Agentic AI",
   "title": "MCP tool poisoning, measured with real execution: 22.9% attack success, no intentional defense anywhere",
   "annotation": "the first cross-framework measurement (open-source mcpvenom, a 9,720-cell matrix across 12 frameworks, 18 models, 45 attack scenarios, exfiltration confirmed by canary receipt) finds a 22.9% overall attack success rate; of the vulnerable cells, 12.1% verifiably exfiltrate a secret. Resistance is model-level, not framework-level \u2014 nine core frameworks cluster at 23\u201326%, a code audit finds no intentional defense in any of the twelve, and all five coordinated disclosures were rejected by vendors. A regex description scanner rejects 20 of 45 scenarios at a 7.2% false-positive rate: metadata scanning alone is only a partial control.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.03555v1",
   "no": 173,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:3deeb650b396b7c9a0dcc7e2ef47a7f2b36d3e24fcba9abf60aa0cf746c76671"
  },
  {
   "date": "2026-11-03",
   "section": "Agentic AI",
   "title": "WebHarbor: 153 real websites, docked as stable agent environments",
   "annotation": "live-site evaluation drifts and synthetic environments lack depth, so WebHarbor reconstructs 153 real web services as executable, dockerized environments under deterministic control, built by task-driven human\u2013agent collaboration. The suite carries 3,518 tasks including post-authentication workflows, and a Qwen 4B-level GUI agent trained with RL inside it generalizes to unseen tasks and performs competitively with leading open-source GUI web agents across four benchmarks. Code and data are public.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.03634v1",
   "no": 174,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:c1dfcf94192ac675f8a437607037d1d2df8fe16795f0416582c95e142297e6fe"
  },
  {
   "date": "2026-11-03",
   "section": "Agentic AI",
   "title": "DeliverBench: task skill does not compose into project delivery",
   "annotation": "agents are usually benchmarked one task at a time, but professional work pays off only if the whole multi-stage package holds. DeliverBench chains 11 expert-reconstructed projects (financial due diligence, legal drafting, drug safety reporting) through 150 subtasks and 2\u201315 rounds of new materials and client feedback, with agents inheriting their own earlier errors. Across seven model\u2013harness systems the best scores only 65.0/100 on final delivery and passes 50.0% of subtasks at a 90-point threshold \u2014 the failures are mostly failures of carrying work forward coherently.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.03412v1",
   "no": 175,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:2ff4eb89cd9da0e83a04e941e9cced86fcd9db0d8391e588c61a0f194db8ed9d"
  },
  {
   "date": "2026-11-03",
   "section": "Tools & models",
   "title": "Claude Code v2.1.317 \u2014 a changelog release, not a launch",
   "annotation": "the notable changes are guardrails the fleet can use: `onFailure: \"block\"` for Agent SDK hook callbacks (a failing callback now blocks the action instead of letting it run), a check that asks before a web or shell tool call sends your account email address to the network, and a `/schedule` warning when a new cloud scheduled task gets connectors you did not ask for. It also fixes project and local MCP servers disconnecting after the first turn in some Claude desktop sessions, and adds a cost report to the Claude apps gateway admin API.",
   "source": "Claude Code releases",
   "url": "https://github.com/anthropics/claude-code/releases/tag/v2.1.317",
   "no": 176,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:f692161927b309d61025de2eda2eefdf1010a5f60dcfb5ad6b6b432e450c6f47"
  },
  {
   "date": "2026-11-03",
   "section": "Field \u2014 small business, nonprofits, and the ground",
   "title": "PlantVLM-Edge: crop diagnosis that runs entirely on-device",
   "annotation": "a quantized vision-language framework for conversational crop health diagnosis on resource-constrained edge hardware, with an offline chat interface \u2014 no cloud connectivity needed for a grower to ask questions and get diagnoses and management guidance. Validated on two VLM backbones and deployed on an NVIDIA Jetson AGX Orin: 4-bit AWQ quantization retains 98.6% of full-precision answer quality while cutting the 2B backbone's weight memory by 49% (2.01 GiB in its most compact form, first token in about 0.12s). Directly adjacent to the AgriMesh lane.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.03557v1",
   "no": 177,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:be802afcfc2502b09244ad047020abff99e785c31b41795f72594dbf2f9a9856"
  },
  {
   "date": "2026-11-03",
   "section": "Also worth a look",
   "title": "VeriHarm-Bench: \"harmful success\" in coding agents, measured",
   "annotation": "frontier agents average 95% task accuracy on perturbed-but-benign coding tasks, yet their safe success rate \u2014 task done, no unintended harm triggered \u2014 averages only 37%. Task-success metrics alone understate the risk of shell-access agents.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.03575v1",
   "no": 178,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:def3f8eaa2161812e0165c3a3d224c87b4cbbdd2a47ddd6cb15b2148f2956aa8"
  },
  {
   "date": "2026-11-03",
   "section": "Also worth a look",
   "title": "Stolen AI API keys ran up nearly $1M in charges for a single company",
   "annotation": "Google Cloud's threat-intelligence write-up on LLMjacking (monetizing compromised cloud model access); a concrete, documented incident and a live warning for any small org holding model API keys.",
   "source": "Google Cloud",
   "url": "https://cloud.google.com/blog/topics/threat-intelligence/llmjacking-monetizing-compromised-cloud-models/",
   "no": 179,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:b8071bd17b7a97db8c119f6f889d6149ffc82abec92f6cd4891ee982cdb429f4"
  },
  {
   "date": "2026-11-03",
   "section": "Also worth a look",
   "title": "PROMOTE: anytime-valid promotion tests for adaptive financial research agents",
   "annotation": "the one unsure item promoted today: a protocol that gives every candidate trading rule testing wealth at birth and bounds the probability of ever promoting a false one, aimed at research agents that generate candidates faster than a human can document the selection process.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.03384v1",
   "no": 180,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:926b90b26e0df223ce6667684d1a08088988dfbe64857fb1ee6e9bb74abc9478"
  },
  {
   "date": "2026-11-04",
   "section": "Agentic AI",
   "title": "You don't need long context",
   "annotation": "later tokens cost up to 7x more than earlier ones on frontier open-weight models, yet frequent compaction sacrifices little to no task accuracy across long-horizon and long-input tasks, and performance does not collapse on long-horizon software-engineering tasks even at one action per context. The paper questions the need for long-context models outright and outlines \"semantic state space models\" as the alternative.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.05306v1",
   "no": 181,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:8053a5eb9856020263dbb3b1b791131c6008dd9d8b4683e760ce8b158529239a"
  },
  {
   "date": "2026-11-04",
   "section": "Agentic AI",
   "title": "Meta\u00b2: precompute how to evolve the harness, not just the harness",
   "annotation": "offline harness optimization ships one shared harness that can overfit and cannot react mid-run, while online evolvers must reason from scratch or need verifier feedback that is unavailable at test time. Meta\u00b2 learns a reusable textual Meta-Skill offline \u2014 by contrasting rollouts with no evolver, with an evolver, and with an evolver given privileged verifier information \u2014 and an online evolver then uses it to decide whether to keep the harness or make a task-local edit as evidence emerges.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.05139v1",
   "no": 182,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:44190f012fe65d3be5ae9be4dafc81668f0a9cd24fc07ea2566aca04b9ae7910"
  },
  {
   "date": "2026-11-04",
   "section": "Agentic AI",
   "title": "Running 17 Claude Code bots in a real company for $100/month",
   "annotation": "a single-source practitioner report (the author's own production account, not a measured study): a real company running 17 Claude Code bots in production at a claimed $100/month total. Treat the numbers as the author's claim; the operating pattern \u2014 many cheap, narrow bots rather than one big deployment \u2014 is the stealable part.",
   "source": "Bots in Production",
   "url": "https://botsinproduction.com/blog/what-10-claude-code-bots-cost-per-month/",
   "no": 183,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:59bc1a50e765f88bc5a1b6c156268810791da1f835de82512f91a0f47e436499"
  },
  {
   "date": "2026-11-04",
   "section": "Agentic AI",
   "title": "Adversarial speculative reasoning: train the draft model to be rejected at the critical points",
   "annotation": "speculative decoding is not behavior-neutral for agents: numerical noise grows inside verification windows and draft acceptance can shift the target model's output distribution after 30 steps, pushing long-horizon reasoning in random directions. Training the draft to favor rejection exactly at critical reasoning points forces the target to sample those tokens itself, under its least noisy conditions.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.05345v1",
   "no": 184,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:7a251bf33babfcb99d903bd9ecd2581710bf4cc39e1eb146f27dd374974f3037"
  },
  {
   "date": "2026-11-04",
   "section": "Agentic AI",
   "title": "When self-verification hurts \u2014 calibrated answer repair for financial QA",
   "annotation": "asking a model to verify and revise can damage answers that were already correct. Calibrated Answer Repair first estimates whether a draft will benefit from repair, then localizes the evidence and bounds the edit: across three financial-QA benchmarks and three open-weight models it recovers 14.6% of initially missed answer requirements while damaging only 2.1% of initially correct ones, at over 30% lower post-draft token cost than iterative revision.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.05166v1",
   "no": 185,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:bc9f318b2867a0b9b2df0a511c23837f1c8ead6229c08e34108fd6153735bbf8"
  },
  {
   "date": "2026-11-04",
   "section": "Tools & models",
   "title": "Claude Code v2.1.318 \u2014 a changelog release, not a launch",
   "annotation": "notable changes: Claude Code now records your answer when it asks to turn on the mods it writes in a session; a mod's `$.model.fork` gains an `effort` option so a side job can run at low effort whatever the session uses; `claude auth status` shows when a stored claude.ai sign-in ends, with a warning starting 10 days before; and fixes land for `--continue` sometimes losing the earlier conversation after a malformed tool call's retries ran out, and for permission prompts being answered by keys typed just after sending a message.",
   "source": "Claude Code releases",
   "url": "https://github.com/anthropics/claude-code/releases/tag/v2.1.318",
   "no": 186,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:517878de9b880468c7987bc48affde28ed9f9041131e1592489667cc3ed57331"
  },
  {
   "date": "2026-11-04",
   "section": "Tools & models",
   "title": "Revolut swaps Claude for a self-hosted open-weight model, cutting its AI coding bill ~10x",
   "annotation": "reported by The Register (a Dealroom report puts the cut at 90%): the British fintech dropped Claude for a self-hosted Chinese open-weight GLM model for AI coding. A vendor-exit cost story, reported rather than independently measured \u2014 but it is a named, large deployer making the open-model cost argument in production.",
   "source": "The Register",
   "url": "https://www.theregister.com/ai-and-ml/2026/11/03/british-fintech-revolut-slashes-ai-coding-costs-10x-by-cutting-out-claude-and-going-self-hosted/5307345",
   "no": 187,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:e7ad1c4669f195a293606dff8e7fd85e2a0170881ac534af3cc4e02cd0a3715d"
  },
  {
   "date": "2026-11-04",
   "section": "Field \u2014 small business, nonprofits, and the ground",
   "title": "Kairos: open-source risk and fraud decisioning for teams without labeled data",
   "annotation": "a Show HN launch of an open-source alternative to Sift and Sardine: alongside XGBoost and logistic regression it uses \"System One\" decision models \u2014 plain-language questions such as \"Is this a throwaway email\" \u2014 so a small business or nonprofit can run real-time fraud decisioning without the large labeled datasets traditional fraud ML depends on, against fraudsters who keep changing tactics.",
   "source": "GitHub",
   "url": "https://github.com/finic-ai/kairos",
   "no": 188,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:0ff886e74f58891901284503951361faa979cd7cf0a05c400abb9aa8d5a274e5"
  },
  {
   "date": "2026-11-04",
   "section": "Also worth a look",
   "title": "False reassurance in LLM severe-weather guidance, measured against real NWS alerts",
   "annotation": "replaying 117 frozen cases through a Claude-based preparedness pipeline: a blind neutral prompt falsely reassured in 37.7% of alert cases, and telling the model to disclose its blind spot made it worse (44.9%), while the production pipeline with real alert access scored 0.0% (0/66). Alert data removed the failure; a disclosure instruction did not.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.05290v1",
   "no": 189,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:0955b4026084bd34ef6e2cf41f708a06677f72aa4d8996afb1c538c41acb584a"
  },
  {
   "date": "2026-11-04",
   "section": "Also worth a look",
   "title": "Knowingly Wrong: training a model against its own beliefs induces misalignment",
   "annotation": "fine-tuning on data the model believes to be false \u2014 with no harmful content at all \u2014 produces broad misalignment, scaling with how strongly the model disbelieves the training answers: in mathematics, misaligned answers rise from 4% to over 60% as the answers get more wrong, with little loss of general capability. Directly relevant to knowledge editing and continual learning.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.05287v1",
   "no": 190,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:36857c4e00af86e35e7e26c6dda5b6b766327df69d30a2aeb63e5bab7f26ad3a"
  },
  {
   "date": "2026-11-05",
   "section": "Agentic AI",
   "title": "Turn-level best-of-N sampling scales LLM agents, turn by turn",
   "annotation": "at each turn a generator model proposes N candidate actions and a selector model picks one to execute. On long-horizon terminal tasks this gave 5\u201310% accuracy improvements and more consistent repeated attempts, with trajectories shifting toward fewer attempts, better instruction following, and more debugging and testing \u2014 using off-the-shelf open-weight models as the selectors.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.06868v1",
   "lead": true,
   "no": 191,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:28d086fe3cdba2cae0866fc490a99213bf93ed2fdbde7d0589ac52781b7c0231"
  },
  {
   "date": "2026-11-05",
   "section": "Agentic AI",
   "title": "SpecCompact: speculative verification makes context compaction 2.04\u20135.63x faster",
   "annotation": "compaction summaries are split into structured slots that a fine-tuned small drafter generates in parallel; the target model verifies each line and repairs what it rejects. Across AppWorld and \u03c4\u00b3 benchmarks with two target models it beat Hermes-style summarization by 2.04\u20135.63x, with an 8.68x median speedup in a Hermes user study \u2014 downstream performance trade-offs were model- and task-dependent, stated plainly in the paper.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.06876v1",
   "no": 192,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:e1e098494edb2e732c2eea201396a00a0bf048ff325c325ec3d3669aef2fcbe0"
  },
  {
   "date": "2026-11-05",
   "section": "Agentic AI",
   "title": "Cached traces cut agent-evaluation error from a single executed task",
   "annotation": "instead of judging a new agent system only by binary correctness, this method embeds LLM-summarized execution traces and predicts the system's full benchmark score from its position among reference systems. On SWE-bench Verified, adding the trace geometry to an item-response model cut mean absolute error from 0.13 to 0.08 given just one random executed task; on Terminal-Bench 2.0 it went from 0.14 to 0.12.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.06803v1",
   "no": 193,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:a363a975496738d563a28fcc9c27b573a2bf4237de51cb1f4afc36b0a813468e"
  },
  {
   "date": "2026-11-05",
   "section": "Agentic AI",
   "title": "ServerBench: coding agents designing the LLM serving systems they run on",
   "annotation": "agents build complete, specialized serving systems against a \"dollars per dollar\" objective (latency-qualified token value per GPU dollar), with stateful replay and independent verification. Across 11 coding-agent models and three deployments, 22 of 33 runs produced verified systems and 13 beat the strongest baseline; the top run reached $2.97 per dollar, 4.87x the vLLM reference.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.06841v1",
   "no": 194,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:fd688d2a8a6e9624cc085cf89f724892678bd8043878f43dc8df6c7ed190cd2f"
  },
  {
   "date": "2026-11-05",
   "section": "Agentic AI",
   "title": "CompressionBench: coding agents compressing LLMs, verified end to end",
   "annotation": "given a checkpoint, a storage limit, and a time budget, agents construct compressed weights plus inference code, guided by perplexity feedback and checked by independent verification. 29 of 36 submissions passed verification; the best design per task averaged only a 3.4% perplexity increase over BF16 at 77.33% downstream accuracy versus 79.56% uncompressed \u2014 and generation/inference tests exposed failures that likelihood scores alone missed.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.06858v1",
   "no": 195,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:fac6b9555213851cc8b2c02a4ebfbe14522ae8fb13a32253a8e5ad7043fae43f"
  },
  {
   "date": "2026-11-05",
   "section": "Tools & models",
   "title": "Gauge: open-source CLI that hill-climbs your docs for coding agents",
   "annotation": "a Show HN launch: set up tasks that require your docs, run coding agents on them, mine the session transcripts for friction and failures, generate doc variants, and re-run until a variant resolves the issues. The same loop applies to Skills, CLIs, and other agent-facing surfaces, and the CLI runs with a local execution backend \u2014 no account needed to try it.",
   "source": "GitHub",
   "url": "https://github.com/gauge-sh/gauge-cli",
   "no": 196,
   "grade": "moderate",
   "caveats": [],
   "hash": "sha256:1912862018d3f0b680d5eb9e909dcc10fa517f13ac43df4e3d19c890967c0b6a"
  },
  {
   "date": "2026-11-05",
   "section": "Tools & models",
   "title": "Letting users trust their own MCP clients without opening a phishing hole",
   "annotation": "a practitioner post (title and premise as published; no measured results in the item) on trusted redirect URIs for MCP: how to let users bring their own MCP clients while keeping the authorization redirect from becoming a phishing vector. Directly relevant to any fleet surface that accepts external MCP clients.",
   "source": "StreamVessel",
   "url": "https://streamvessel.com/blog/mcp-trusted-redirect-uris",
   "no": 197,
   "grade": "unverified",
   "caveats": [],
   "hash": "sha256:c6a59c3f134be65124cdeaeefddff462b514944717025b44caa8974df11a817f"
  },
  {
   "date": "2026-11-05",
   "section": "Also worth a look",
   "title": "Perception, not planning, governs ARC-AGI-3",
   "annotation": "replacing raw integer grids with a zero-parameter perception layer (object inventory, geometric briefing, annotated canvas) raised an open-weight agent's rubric score from 3/28 to 25/28 while cutting token use 89.4%; geometric invariants generalized at 100% on held-out games, single-frame affordance guesses did not.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.06793v1",
   "no": 198,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:5571f3dfdd34f05f63effa8f511355868fe33429f143b0557b93fe6224e8b929"
  },
  {
   "date": "2026-11-05",
   "section": "Also worth a look",
   "title": "LLMs don't know what they know \u2014 financial backtests can't be certified by self-report",
   "annotation": "on 2,226 dated U.S. facts, GPT-5 answered 76% correctly but claimed to know only 38%, and answered 61% of the facts it said it did not know; a declared historical date removed most knowledge claims while leaving accuracy unchanged.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.06808v1",
   "no": 199,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:5e3099199cc2a485623fbce5e53a67dcf6740176c62411f77022ec89ffc2dedb"
  },
  {
   "date": "2026-11-05",
   "section": "Also worth a look",
   "title": "Time-sensitive safety awareness is weak in conversational assistants",
   "annotation": "on the 3,418-input TASC benchmark, 13 models applied time-dependent constraints in only 30.5% of cases where the proposed action violated a rule, and failed to apply them in 62.1% of cases despite correctly calculating the elapsed time.",
   "source": "arXiv",
   "url": "https://arxiv.org/abs/2611.06839v1",
   "no": 200,
   "grade": "strong",
   "caveats": [],
   "hash": "sha256:a28165433590ca9b65ee171218bfa85319e74e95711f54a74ea3561f7dc283fa"
  }
 ],
 "frontier": [
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   "source": "arXiv",
   "title": "Catalyzing Interactive HTML Generation Capabilities via High-Fidelity Reinforcement Learning",
   "url": "https://arxiv.org/abs/2611.05151v1",
   "new": 0.97,
   "relevance": 0.818,
   "date": "2026-11-04",
   "no": "W-01"
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  {
   "source": "arXiv",
   "title": "Aligning Language Models with General Preferences: An Empirical Study of Direct Nash Optimization",
   "url": "https://arxiv.org/abs/2611.05373v1",
   "new": 0.97,
   "relevance": 0.655,
   "date": "2026-11-04",
   "no": "W-02"
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  {
   "source": "arXiv",
   "title": "FIRME: Registry-Anchored Memory Graphs for Evolving, Heterogeneous Investor Knowledge",
   "url": "https://arxiv.org/abs/2610.40482v1",
   "new": 0.97,
   "relevance": 0.593,
   "date": "2026-10-30",
   "no": "W-03"
  },
  {
   "source": "arXiv",
   "title": "Perceptive Humanoid Motion from Simulation",
   "url": "https://arxiv.org/abs/2611.06908v1",
   "new": 0.97,
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   "date": "2026-11-05",
   "no": "W-04"
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   "source": "arXiv",
   "title": "Sonar-MoE: In-Backward Expert Updates for Memory-Efficient Mixture-of-Experts Training",
   "url": "https://arxiv.org/abs/2611.03577v1",
   "new": 0.97,
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   "no": "W-05"
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   "source": "arXiv",
   "title": "Homebrewed SLMs: Autoregressive and Masked Diffusion Language Models Built at Home for Portuguese",
   "url": "https://arxiv.org/abs/2610.40533v1",
   "new": 0.97,
   "relevance": 0.525,
   "date": "2026-10-30",
   "no": "W-06"
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  {
   "source": "arXiv",
   "title": "When Does Test-Time Search Help World Action Models? An Empirical Audit of Candidate Selection and Simulator Integrity",
   "url": "https://arxiv.org/abs/2610.41970v1",
   "new": 0.97,
   "relevance": 0.525,
   "date": "2026-11-02",
   "no": "W-07"
  },
  {
   "source": "arXiv Quantum",
   "title": "Near-Quantum-Limited Readout for the Princeton Axion Search using a sub-GHz Kinetic Inductance Traveling Wave Parametric Amplifier",
   "url": "https://arxiv.org/abs/2611.06761v1",
   "new": 0.97,
   "relevance": 0.022,
   "date": "2026-11-05",
   "no": "W-08"
  },
  {
   "source": "arXiv Quantum",
   "title": "Conditional Quantum Priors for Chaotic Fluid Dynamics",
   "url": "https://arxiv.org/abs/2611.04872v1",
   "new": 0.96,
   "relevance": 0.22,
   "date": "2026-11-04",
   "no": "W-09"
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  {
   "source": "arXiv Quantum",
   "title": "Thermodynamics-Informed Learning Captures Hierarchical Hydrogen-Bond Networks in Deep Eutectic Solvents",
   "url": "https://arxiv.org/abs/2611.06590v1",
   "new": 0.96,
   "relevance": 0.185,
   "date": "2026-11-05",
   "no": "W-10"
  },
  {
   "source": "arXiv Quantum",
   "title": "eigenkit: A Provider-Neutral Quantum Computing Library",
   "url": "https://arxiv.org/abs/2611.02979v1",
   "new": 0.96,
   "relevance": 0.158,
   "date": "2026-11-03",
   "no": "W-11"
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  {
   "source": "arXiv Quantum",
   "title": "Resource efficient Sample Based Quantum Simulation through Fragmentation and Virtual Partitioning of QPU",
   "url": "https://arxiv.org/abs/2610.41986v1",
   "new": 0.96,
   "relevance": 0.1,
   "date": "2026-11-02",
   "no": "W-12"
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   "source": "arXiv Quantum",
   "title": "Autonomous cooling of Kerr-cat qubits to minutes-long bit-flip times",
   "url": "https://arxiv.org/abs/2611.04868v1",
   "new": 0.96,
   "relevance": 0.052,
   "date": "2026-11-04",
   "no": "W-13"
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  {
   "source": "arXiv Quantum",
   "title": "Taming a macroscopic free mass through continuous quantum measurement",
   "url": "https://arxiv.org/abs/2611.03159v1",
   "new": 0.96,
   "relevance": 0.035,
   "date": "2026-11-03",
   "no": "W-14"
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   "source": "arXiv Quantum",
   "title": "Efficient simulations of spectra and transport for multiorbital quantum impurities",
   "url": "https://arxiv.org/abs/2611.05185v1",
   "new": 0.96,
   "relevance": 0.033,
   "date": "2026-11-04",
   "no": "W-15"
  },
  {
   "source": "arXiv Quantum",
   "title": "Flux Crosstalk Calibration from Zero Knowledge to High Precision in Fully Tunable Superconducting Circuits",
   "url": "https://arxiv.org/abs/2611.06656v1",
   "new": 0.96,
   "relevance": 0.03,
   "date": "2026-11-05",
   "no": "W-16"
  },
  {
   "source": "arXiv Quantum",
   "title": "Needle-beam generation from SPDC photons using inverse-designed flat optics",
   "url": "https://arxiv.org/abs/2611.02993v1",
   "new": 0.96,
   "relevance": 0.025,
   "date": "2026-11-03",
   "no": "W-17"
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  {
   "source": "arXiv Quantum",
   "title": "Inline inter-core phase control in multi-core fiber with a commercial polarization controller",
   "url": "https://arxiv.org/abs/2611.03304v1",
   "new": 0.96,
   "relevance": 0.02,
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   "no": "W-18"
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   "source": "arXiv Quantum",
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   "url": "https://arxiv.org/abs/2611.03343v1",
   "new": 0.96,
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   "source": "arXiv Quantum",
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   "url": "https://arxiv.org/abs/2611.05355v1",
   "new": 0.95,
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   "date": "2026-11-04",
   "no": "W-21"
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  {
   "source": "arXiv Quantum",
   "title": "GFlowQEC: Generative Flow Networks that Learn to Sample Quantum LDPC Codes",
   "url": "https://arxiv.org/abs/2611.06819v1",
   "new": 0.95,
   "relevance": 0.247,
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   "title": "CAPPZ \u2014 passkeys, device-signed receipts, batch notarization (Merkle, XRPL-anchored)",
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   "note": "Handed over by the Chairman without commentary during the EDEN Agent Door work, 2026-11-01; device-signed receipts + batch notarization graded for the mandate credential and Chronicle checkpointing.",
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  {
   "title": "CAPPZ on ARM \u2014 Rust kernels, content hashing, true Reed-Solomon erasure coding",
   "url": "https://cappz.ai/news/cappz-on-arm",
   "note": "Handed over by the Chairman without commentary during the EDEN Agent Door work, 2026-11-01; ARM performance record graded for Pi-class citizen nodes.",
   "date": "2026-11-01",
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  {
   "title": "The Gloucester companion shelf \u2014 corpus (standing research input)",
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   "note": "Named by the Chairman 2026-09-17 as first port of call for frontier candidates and for corroborating playbook and newsletter claims. Filed to this inbox when the provided-links lane opened, 2026-11-01.",
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