{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WBG3V2BLRYE6CVRXQHC2BENXGM","short_pith_number":"pith:WBG3V2BL","schema_version":"1.0","canonical_sha256":"b04dbae82b8e09e1563781c5a091b73313fa08b3d6fae8de61ad1cd564381e5b","source":{"kind":"arxiv","id":"2406.12288","version":3},"attestation_state":"computed","paper":{"title":"An Investigation of Neuron Activation as a Unified Lens to Explain Chain-of-Thought Eliciting Arithmetic Reasoning of LLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Daking Rai, Ziyu Yao","submitted_at":"2024-06-18T05:49:24Z","abstract_excerpt":"Large language models (LLMs) have shown strong arithmetic reasoning capabilities when prompted with Chain-of-Thought (CoT) prompts. However, we have only a limited understanding of how they are processed by LLMs. To demystify it, prior work has primarily focused on ablating different components in the CoT prompt and empirically observing their resulting LLM performance change. Yet, the reason why these components are important to LLM reasoning is not explored. To fill this gap, in this work, we investigate ``neuron activation'' as a lens to provide a unified explanation to observations made by"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2406.12288","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-06-18T05:49:24Z","cross_cats_sorted":[],"title_canon_sha256":"af2ff385efb00a799edad490f6afa2145ae5637b570f14e93ae9897f4e559075","abstract_canon_sha256":"91bf651cae7f46da1c6af4b5a56a5e721536bdbc99aa3c59f15bb6d70e04a925"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:02:21.476370Z","signature_b64":"W14wbUVaxykZveTZs0CYjF5CNXAUl7EIvNwxoCi/DqZ2TWtGg7wxxDx4qtFNfBmC2MxnZ7A6f3dlE7h07H/8Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b04dbae82b8e09e1563781c5a091b73313fa08b3d6fae8de61ad1cd564381e5b","last_reissued_at":"2026-07-05T09:02:21.475968Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:02:21.475968Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Investigation of Neuron Activation as a Unified Lens to Explain Chain-of-Thought Eliciting Arithmetic Reasoning of LLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Daking Rai, Ziyu Yao","submitted_at":"2024-06-18T05:49:24Z","abstract_excerpt":"Large language models (LLMs) have shown strong arithmetic reasoning capabilities when prompted with Chain-of-Thought (CoT) prompts. However, we have only a limited understanding of how they are processed by LLMs. To demystify it, prior work has primarily focused on ablating different components in the CoT prompt and empirically observing their resulting LLM performance change. Yet, the reason why these components are important to LLM reasoning is not explored. To fill this gap, in this work, we investigate ``neuron activation'' as a lens to provide a unified explanation to observations made by"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.12288","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2406.12288/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2406.12288","created_at":"2026-07-05T09:02:21.476018+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.12288v3","created_at":"2026-07-05T09:02:21.476018+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.12288","created_at":"2026-07-05T09:02:21.476018+00:00"},{"alias_kind":"pith_short_12","alias_value":"WBG3V2BLRYE6","created_at":"2026-07-05T09:02:21.476018+00:00"},{"alias_kind":"pith_short_16","alias_value":"WBG3V2BLRYE6CVRX","created_at":"2026-07-05T09:02:21.476018+00:00"},{"alias_kind":"pith_short_8","alias_value":"WBG3V2BL","created_at":"2026-07-05T09:02:21.476018+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.17697","citing_title":"Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Models","ref_index":42,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WBG3V2BLRYE6CVRXQHC2BENXGM","json":"https://pith.science/pith/WBG3V2BLRYE6CVRXQHC2BENXGM.json","graph_json":"https://pith.science/api/pith-number/WBG3V2BLRYE6CVRXQHC2BENXGM/graph.json","events_json":"https://pith.science/api/pith-number/WBG3V2BLRYE6CVRXQHC2BENXGM/events.json","paper":"https://pith.science/paper/WBG3V2BL"},"agent_actions":{"view_html":"https://pith.science/pith/WBG3V2BLRYE6CVRXQHC2BENXGM","download_json":"https://pith.science/pith/WBG3V2BLRYE6CVRXQHC2BENXGM.json","view_paper":"https://pith.science/paper/WBG3V2BL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.12288&json=true","fetch_graph":"https://pith.science/api/pith-number/WBG3V2BLRYE6CVRXQHC2BENXGM/graph.json","fetch_events":"https://pith.science/api/pith-number/WBG3V2BLRYE6CVRXQHC2BENXGM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WBG3V2BLRYE6CVRXQHC2BENXGM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WBG3V2BLRYE6CVRXQHC2BENXGM/action/storage_attestation","attest_author":"https://pith.science/pith/WBG3V2BLRYE6CVRXQHC2BENXGM/action/author_attestation","sign_citation":"https://pith.science/pith/WBG3V2BLRYE6CVRXQHC2BENXGM/action/citation_signature","submit_replication":"https://pith.science/pith/WBG3V2BLRYE6CVRXQHC2BENXGM/action/replication_record"}},"created_at":"2026-07-05T09:02:21.476018+00:00","updated_at":"2026-07-05T09:02:21.476018+00:00"}