{"schema":"sb.node.v1","id":"sb:e/c35349429f5636f1e636e720","kind":"entity","label":{"text":"llm agents","locale":"en"},"identity":{"anchor":"label:en:llm agents","strength":"label"},"asOf":{"knowledgeCutoff":"2026-10-13T17:34:46.374496+00:00","snapshot":"deploy 1a346a74971ab37c5c0ff00444e83e0b5f58a0b8"},"lens":{"kind":"public"},"build":{"plane":"projected","builtFrom":["entity_graph.slim.json","entity_receipts (deploy asset)","event_graph.json","truth_records (deploy asset)","corroborated_record.json"],"rule":"node-projection.entity@v1"},"standing":{"evidence":{"supporting":86,"separatePublishers":14,"basis":"documents that mention the subject and the outlets that carried them; a mention is evidence the referent is in use, not evidence about any claim","receiptsOnTopic":{"state":"measured","share":0.721,"named":62,"of":86,"basis":"documents whose own title or summary lead names the subject, over every counted document that mentions it anywhere (the entity graph's measurement); receipts are drawn from the former only"}},"admission":{"state":"provisional","reason":"entity graph provenance 'observed'; the node carries no promotion receipt in v1","policy":"EQ-PROMOTION (receipt not carried)"},"disposition":{"state":"served","reason":"public record"},"validity":{"from":"2026-03-10","to":"2026-10-12","basis":"first and last day we saw it mentioned; not the subject's own lifetime"},"measurement":{"state":"partial","blind":["assertion coverage 0.093: about 91% of mentions carry no typed assertion"]}},"dimensions":{"domains":["auto","ai_investing"],"activeDays":37,"assertions":{}},"up":[],"upNote":"An entity is a root: it is contained by nothing. Pan out by following R.","children":{"count":12,"byKind":{"observation":12},"items":[{"id":"sb:o/248dee6658eb49a6bb850b6e","kind":"observation","label":"ArXiv cs.AI · 2026-10-06 · Agentic Trading: When LLM Agents Meet Financial Markets","url":"https://arxiv.org/abs/2605.19337"},{"id":"sb:o/3ca2cf5179bf07e34e2f3e3e","kind":"observation","label":"arXiv cs.AI · 2026-10-05 · D2K-Bench: Can LLM Agents Turn Expert Designs into Efficient GPU Kernels?","url":"https://arxiv.org/abs/2610.03226"},{"id":"sb:o/96469acfb7395769ecffcf50","kind":"observation","label":"UNKNOWN (source name missing from feed) · 2026-09-18 · How Do Agent Harnesses Create Value? Planning Information and Release Control in Stateful LLM Agents","url":"https://arxiv.org/abs/2609.20474"},{"id":"sb:o/959eed80cdf8f9e77f291770","kind":"observation","label":"UNKNOWN (source name missing from feed) · 2026-09-18 · Dual-Axis Policy Optimization for LLM Agents: Bayesian Feedback Attribution and Trajectory Mass Normalization","url":"https://arxiv.org/abs/2609.19830"},{"id":"sb:o/2ae9925ec18eed0538af396b","kind":"observation","label":"UNKNOWN (source name missing from feed) · 2026-09-18 · Closed-World Resolution Against Tool Hallucination in LLM Agents","url":"https://arxiv.org/abs/2609.19425"},{"id":"sb:o/a7faac4f833c309cbfe2356c","kind":"observation","label":"UNKNOWN (source name missing from feed) · 2026-09-18 · Characterizing Web Search by Conversational LLM Agents: From Search Decisions and Strategies to Results and Responses","url":"https://arxiv.org/abs/2609.19244"},{"id":"sb:o/210224369a5a0a7ee25fe8da","kind":"observation","label":"UNKNOWN (source name missing from feed) · 2026-09-17 · Symbolic Temporal Supervision of LLM Agents Using Contracts","url":"https://arxiv.org/abs/2609.18128"},{"id":"sb:o/c5620d714dd5fcc0305ccb54","kind":"observation","label":"UNKNOWN (source name missing from feed) · 2026-09-17 · CERA-MoA: Co-Evolving Routing Mechanisms with Continually Learning LLM Agents","url":"https://arxiv.org/abs/2609.18779"},{"id":"sb:o/aec9c2e1971b130378e7e6ad","kind":"observation","label":"UNKNOWN (source name missing from feed) · 2026-09-16 · World Model Science: Self-Organized Criticality, Weak Chaos, and Metastable Belief Dynamics in Long-Horizon LLM Agents","url":"https://arxiv.org/abs/2609.17419"},{"id":"sb:o/e8862c7c4878d4b215c21d81","kind":"observation","label":"UNKNOWN (source name missing from feed) · 2026-09-16 · Turn-level Multiscale Density Ratio Estimation for LLM Agents","url":"https://arxiv.org/abs/2609.16760"}],"cursor":"c1.eyJpZCI6InNiOmUvYzM1MzQ5NDI5ZjU2MzZmMWU2MzZlNzIwIiwibCI6ImNoaWxkcmVuIiwibyI6MTAsImsiOm51bGwsImEiOiIyMDI2LTEwLTEzVDE3OjM0OjQ2LjM3NDQ5NiswMDowMCJ9","order":"newest first; we keep the 12 newest receipts for this entity whose own title or summary lead (the entity graph's test) names it. Its other observations are counted in standing.evidence.supporting but are not addressable in v1"},"relations":{"count":9,"byKind":{"co-mentioned":9},"items":[{"kind":"relation","label":"co-mentioned with ai (65 documents)","role":"co-mentioned","relationKind":"co-mentioned","members":2,"id":"sb:r/b70b6b341f96aef5cceb0883"},{"kind":"relation","label":"co-mentioned with explorer (56 documents)","role":"co-mentioned","relationKind":"co-mentioned","members":2,"id":"sb:r/6c090bc8b5dc426cd927222e"},{"kind":"relation","label":"co-mentioned with citations (55 documents)","role":"co-mentioned","relationKind":"co-mentioned","members":2,"id":"sb:r/8e955bd34b1e2ccef2996a5b"},{"kind":"relation","label":"co-mentioned with huggingface (55 documents)","role":"co-mentioned","relationKind":"co-mentioned","members":2,"id":"sb:r/ca20823fa60fa76201c72fff"},{"kind":"relation","label":"co-mentioned with llms (19 documents)","role":"co-mentioned","relationKind":"co-mentioned","members":2,"id":"sb:r/ee0429ce83d2b2c5909496d0"},{"kind":"relation","label":"co-mentioned with anthropic (13 documents)","role":"co-mentioned","relationKind":"co-mentioned","members":2,"id":"sb:r/c501ef9a791601b413c17fbd"},{"kind":"relation","label":"co-mentioned with github (12 documents)","role":"co-mentioned","relationKind":"co-mentioned","members":2,"id":"sb:r/0bf66cea2dfd69b177439b3f"},{"kind":"relation","label":"co-mentioned with ai agents (8 documents)","role":"co-mentioned","relationKind":"co-mentioned","members":2,"id":"sb:r/e19f58df817ef6f22d0f2300"},{"kind":"relation","label":"co-mentioned with large language models (8 documents)","role":"co-mentioned","relationKind":"co-mentioned","members":2,"id":"sb:r/325a4902372d21940ca2a2ad"}],"cursor":null,"order":"situations first, then claims (the subject's own maintained record, then site-wide corroborated clusters; each label ends with its standing), then co-mentions by shared documents, descending. Co-mentions are the ten strongest the entity graph keeps per entity"},"edge":{"state":"continues"},"links":{"rest":"/api/v1/node/e/c35349429f5636f1e636e720","mcp":"traverse {\"id\":\"sb:e/c35349429f5636f1e636e720\"}","page":"/entity/llm-agents/"},"limits":{"levelsPerCall":1,"pageSize":10,"maxPageSize":50,"membersMax":50,"statement":"One level per call: depth is 1 and only 1, and there is no whole-graph dump. pageSize 1-50 (default 10); a page limit is a cursor, never an edge. Members are listed up to 50. At most 60 traverse calls a minute per caller (runaway-loop protection, not a meter). One knowledge cutoff is served: the deploy's."},"actions":[{"verb":"unfold","lit":true,"effect":"read","layer":"cantina","call":"traverse {\"id\":\"sb:e/c35349429f5636f1e636e720\",\"level\":\"children\"}"},{"verb":"fold","lit":true,"effect":"read","layer":"cantina","call":"traverse {\"id\":\"sb:e/c35349429f5636f1e636e720\"}"},{"verb":"panOut","lit":false,"effect":"read","layer":"cantina","reason":"An entity is a root: it is contained by nothing. Pan out by following R."},{"verb":"follow","lit":true,"effect":"read","layer":"cantina","call":"traverse {\"id\":\"sb:e/c35349429f5636f1e636e720\",\"level\":\"relations\"}"},{"verb":"receipts","lit":false,"effect":"read","layer":"cantina","reason":"receipts are the observations below this node; unfold to reach them"},{"verb":"edge","lit":true,"effect":"read","layer":"cantina","call":"traverse {\"id\":\"sb:e/c35349429f5636f1e636e720\"}"},{"verb":"asOf","lit":false,"effect":"read","layer":"cantina","reason":"v1 serves one knowledge cutoff, this deploy's; walking an earlier cutoff needs retained history (EQ-REPLAY), which v1 does not keep"},{"verb":"switchObserver","lit":false,"effect":"read","layer":"cantina","reason":"the public record is the same for every observer; traversal through a Watch's lens is not served in v1"},{"verb":"readOriginal","lit":false,"effect":"read","layer":"cantina","reason":"no translation is applied on this node; every label is the source's own text"},{"verb":"save","lit":false,"effect":"watch-declaration","layer":"workshop","reason":"saving applies to a configured Watch; configure one around this node first (free), then save it"},{"verb":"deliver","lit":false,"effect":"watch-declaration","layer":"workshop","reason":"delivery applies to a saved Watch"},{"verb":"format","lit":false,"effect":"watch-declaration","layer":"workshop","reason":"output format applies to a saved Watch"},{"verb":"deltaR","lit":false,"effect":"read","layer":"workshop","reason":"Delta R needs a saved Watch with a previous reading"},{"verb":"read","lit":false,"effect":"read","layer":"docking-bay","reason":"one-time Reads are parked until the Commander decides their shape (#938)"}]}