{"event":{"id":"evt-cot-preprint-20220128","dedupe_key":"reasoning_method_release:2022-01-28:chain-of-thought-prompting-preprint-reports-inference-time-reasoning-gains","event_type":"reasoning_method_release","title":"Chain-of-thought prompting preprint reports inference-time reasoning gains","summary":"A Google research preprint reported that prompting large language models with intermediate reasoning examples can improve multi-step reasoning without changing model weights.","occurred_at":"2022-01-28T02:33:07.000Z","published_at":"2022-01-28T02:33:07.000Z","observed_at":"2026-10-01T17:04:00.000Z","reconstructed_at":"2026-10-01T17:34:32.000Z","ingest_type":"live_world_scan","locations":null,"status":"verified","confidence":0.91,"metadata":{"actors":["Google Research"],"what_happened_at_time":"A prompting method for eliciting intermediate reasoning was publicly documented.","evidence_at_time":"The arXiv preprint was submitted Jan 28, 2022.","retrospective_inference":"Software/prompt structure could act as a capability surface over fixed model weights, a precursor to later reasoning scaffolds.","uncertainty":"The current arXiv rendering may include later revisions; this mapping does not use later PaLM-specific significance or claim agent capability."},"created_at":"2026-10-01T18:05:36.365Z","updated_at":"2026-10-01T18:05:36.365Z"},"artifacts":[{"id":"art-google-cot-20220128","canonical_url":"https://arxiv.org/abs/2201.11903","artifact_type":"paper_preprint","title":"Chain-of-Thought Prompting Elicits Reasoning in Large Language Models","summary":"A preprint reported that providing intermediate reasoning exemplars in prompts can improve performance on multi-step reasoning tasks in sufficiently large language models.","creator_entities":["Google Research"],"released_at":"2022-01-28T00:00:00.000Z","content_hash":null,"metadata":{"historical_scope":"2022-Q1","version_caution":"Only the core prompting result is mapped to Q1."},"created_at":"2026-10-01T18:05:36.359Z","updated_at":"2026-10-01T18:05:36.359Z","relation":"documented_by"}],"signals":[{"id":"sig-models-to-software-2022q1","statement":"Large language models were becoming capable of producing nontrivial program solutions, establishing an early direct dependency path from model capability into software work.","signal_type":"cross_layer_convergence","direction":"increasing","confidence":0.94,"epistemic_status":"supported_inference","mapping_mode":"retrospective","reconstructed_at":"2026-10-01T17:34:32.000Z","created_at":"2026-10-01T18:05:36.375Z","updated_at":"2026-10-01T18:05:36.375Z"},{"id":"sig-prompting-capability-surface-2022q1","statement":"Prompt structure was beginning to function as a software-level capability surface that could elicit additional reasoning behavior from fixed model weights.","signal_type":"capability_emergence","direction":"increasing","confidence":0.86,"epistemic_status":"supported_inference","mapping_mode":"retrospective","reconstructed_at":"2026-10-01T17:34:32.000Z","created_at":"2026-10-01T18:05:36.374Z","updated_at":"2026-10-01T18:05:36.374Z"}]}