{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:GPLXUEYHARJIRUT7BJ2NP2P7D7","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"72aaa59d85c5a9a09c2ab5a032d17c21adbe3cefec5299271522072e0404dbea","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-15T06:12:28Z","title_canon_sha256":"d1bf39bc9623b4f793251570aa3bc35b3b479b96e057105e26b1be7668bf742a"},"schema_version":"1.0","source":{"id":"2211.09066","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.09066","created_at":"2026-07-05T05:16:47Z"},{"alias_kind":"arxiv_version","alias_value":"2211.09066v1","created_at":"2026-07-05T05:16:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.09066","created_at":"2026-07-05T05:16:47Z"},{"alias_kind":"pith_short_12","alias_value":"GPLXUEYHARJI","created_at":"2026-07-05T05:16:47Z"},{"alias_kind":"pith_short_16","alias_value":"GPLXUEYHARJIRUT7","created_at":"2026-07-05T05:16:47Z"},{"alias_kind":"pith_short_8","alias_value":"GPLXUEYH","created_at":"2026-07-05T05:16:47Z"}],"graph_snapshots":[{"event_id":"sha256:b12f32e87c2ad6a43afdf12100d399c864b8bb76915d22ef293251c9ece123e8","target":"graph","created_at":"2026-07-05T05:16:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2211.09066/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) have shown increasing in-context learning capabilities through scaling up model and data size. Despite this progress, LLMs are still unable to solve algorithmic reasoning problems. While providing a rationale with the final answer has led to further improvements in multi-step reasoning problems, Anil et al. 2022 showed that even simple algorithmic reasoning tasks such as parity are far from solved. In this work, we identify and study four key stages for successfully teaching algorithmic reasoning to LLMs: (1) formulating algorithms as skills, (2) teaching multiple ","authors_text":"Aaron Courville, Azade Nova, Behnam Neyshabur, Hanie Sedghi, Hattie Zhou, Hugo Larochelle","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-15T06:12:28Z","title":"Teaching Algorithmic Reasoning via In-context Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.09066","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:658493db594894d37628230d2fd1536b1f1b59f9b40180fa638f7c9dca20999b","target":"record","created_at":"2026-07-05T05:16:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"72aaa59d85c5a9a09c2ab5a032d17c21adbe3cefec5299271522072e0404dbea","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-15T06:12:28Z","title_canon_sha256":"d1bf39bc9623b4f793251570aa3bc35b3b479b96e057105e26b1be7668bf742a"},"schema_version":"1.0","source":{"id":"2211.09066","kind":"arxiv","version":1}},"canonical_sha256":"33d77a1307045288d27f0a74d7e9ff1ff0b76038e4e388275801b2eda512f425","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"33d77a1307045288d27f0a74d7e9ff1ff0b76038e4e388275801b2eda512f425","first_computed_at":"2026-07-05T05:16:47.291863Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:16:47.291863Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DEkvGUC44Kddy60B8NcMm8KQ+xSSV1Cd5USPHBdL76v8rKh2zH+r1hlCSnndTLIMRVnQQidJq7bjbs3oKvZ4BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:16:47.292375Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.09066","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:658493db594894d37628230d2fd1536b1f1b59f9b40180fa638f7c9dca20999b","sha256:b12f32e87c2ad6a43afdf12100d399c864b8bb76915d22ef293251c9ece123e8"],"state_sha256":"ba27ebf55afc01f86330bca7612b0e079a734c472f7065f5fd7216d038fa11c9"}