{"event":{"id":"evt-react-20221006","dedupe_key":"reasoning_action_research:2022-10-06:react-interleaves-reasoning-and-acting-in-language-models","event_type":"reasoning_action_research","title":"ReAct interleaves reasoning and acting in language models","summary":"Researchers published a method in which language models alternate reasoning traces and task-specific actions, including interaction with Wikipedia, ALFWorld and WebShop environments.","occurred_at":"2022-10-06T01:00:32.000Z","published_at":"2022-10-06T01:00:32.000Z","observed_at":"2026-10-03T14:26:11.000Z","reconstructed_at":"2026-10-03T14:26:11.000Z","ingest_type":"live_world_scan","locations":null,"status":"verified","confidence":0.98,"metadata":{"actors":["Princeton University","Google Research"],"what_happened_at_time":"A language model was prompted to reason and act in an interleaved loop, gathering external information or changing an environment before continuing reasoning.","evidence_at_time":"The October 6 arXiv preprint documents the architecture and evaluations.","affected_layers":["layer-models","layer-agents","layer-research","layer-software"],"change_kind":"bounded_agent_architecture_maturation","q3_continuity":"Q3 already contained retrieval, memory, feedback and embodied closed loops; ReAct added a compact reasoning-action pattern spanning knowledge retrieval and interactive environments.","retrospective_inference":"Strong precursor evidence for tool-using agent architectures, not persistent autonomous agents or production deployment.","uncertainty":"Benchmark success does not establish broad real-world reliability, durable memory or delegated autonomy."},"created_at":"2026-10-03T14:45:10.911Z","updated_at":"2026-10-03T14:45:10.911Z"},"artifacts":[{"id":"art-react-20221006","canonical_url":"https://arxiv.org/abs/2210.03629","artifact_type":"paper_preprint","title":"ReAct","summary":"ReAct interleaved language-model reasoning traces with task-specific actions and external environment or knowledge interactions.","creator_entities":["Princeton University","Google Research"],"released_at":"2022-10-06T01:00:32.000Z","content_hash":null,"metadata":{"historical_scope":"2022-Q4"},"created_at":"2026-10-03T14:45:10.906Z","updated_at":"2026-10-03T14:45:10.906Z","relation":"evidenced_by"}],"signals":[{"id":"sig-q4-reason-act-architecture","statement":"Bounded agent research increasingly integrated reasoning, external action and goal-directed dialogue in the same system, while remaining task- or environment-specific rather than persistent general autonomy.","signal_type":"agent_architecture_maturation","direction":"increasing","confidence":0.95,"epistemic_status":"supported_inference","mapping_mode":"retrospective","reconstructed_at":"2026-10-03T14:26:11.000Z","created_at":"2026-10-03T14:45:10.919Z","updated_at":"2026-10-03T14:45:10.919Z"}]}