{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4RRFB7NTVSHOSAPMZFHKOHE72Q","short_pith_number":"pith:4RRFB7NT","schema_version":"1.0","canonical_sha256":"e46250fdb3ac8ee901ecc94ea71c9fd43efff043f5e77a9dff2aa0059158b94a","source":{"kind":"arxiv","id":"2405.15302","version":3},"attestation_state":"computed","paper":{"title":"Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Hui Jin, Jiacheng Sun, Tianyang Hu, Yaoyu Zhang, Yunji Wang, Zhangchen Zhou, Zhenguo Li, Zhi-Qin John Xu, Zhiwei Wang, Zhongwang Zhang","submitted_at":"2024-05-24T07:41:26Z","abstract_excerpt":"Large language models have consistently struggled with complex reasoning tasks, such as mathematical problem-solving. Investigating the internal reasoning mechanisms of these models can help us design better model architectures and training strategies, ultimately enhancing their reasoning capability. In this study, we constructed a symbolic multi-step reasoning task to investigate the information propagation mechanisms in Transformer models when solving the task through direct answering and Chain-of-Thought (CoT) reasoning. We introduced the concept of buffer mechanism: the model stores variou"},"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":"2405.15302","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-05-24T07:41:26Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"36a3a652bf2fc146877fd68f286169ec52ff2ebb06027c60642190b689ddbc83","abstract_canon_sha256":"95232ebc3e8c09166a47fa282dac2910bdd34dcb75e6819186c228245e833937"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:59.541662Z","signature_b64":"kBCrYlPxdM0ZYRyznW/ukpmxBh3ikllKGNr3qR1tSRNwO//LZlpG7I4+uqGu5cBdMAfudoKntQQvgGsUfCFtAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e46250fdb3ac8ee901ecc94ea71c9fd43efff043f5e77a9dff2aa0059158b94a","last_reissued_at":"2026-07-05T12:06:59.541082Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:59.541082Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Hui Jin, Jiacheng Sun, Tianyang Hu, Yaoyu Zhang, Yunji Wang, Zhangchen Zhou, Zhenguo Li, Zhi-Qin John Xu, Zhiwei Wang, Zhongwang Zhang","submitted_at":"2024-05-24T07:41:26Z","abstract_excerpt":"Large language models have consistently struggled with complex reasoning tasks, such as mathematical problem-solving. Investigating the internal reasoning mechanisms of these models can help us design better model architectures and training strategies, ultimately enhancing their reasoning capability. In this study, we constructed a symbolic multi-step reasoning task to investigate the information propagation mechanisms in Transformer models when solving the task through direct answering and Chain-of-Thought (CoT) reasoning. We introduced the concept of buffer mechanism: the model stores variou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.15302","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/2405.15302/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":"2405.15302","created_at":"2026-07-05T12:06:59.541158+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.15302v3","created_at":"2026-07-05T12:06:59.541158+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.15302","created_at":"2026-07-05T12:06:59.541158+00:00"},{"alias_kind":"pith_short_12","alias_value":"4RRFB7NTVSHO","created_at":"2026-07-05T12:06:59.541158+00:00"},{"alias_kind":"pith_short_16","alias_value":"4RRFB7NTVSHOSAPM","created_at":"2026-07-05T12:06:59.541158+00:00"},{"alias_kind":"pith_short_8","alias_value":"4RRFB7NT","created_at":"2026-07-05T12:06:59.541158+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.29247","citing_title":"DenseSteer: Steering Small Language Models towards Dense Math Reasoning","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4RRFB7NTVSHOSAPMZFHKOHE72Q","json":"https://pith.science/pith/4RRFB7NTVSHOSAPMZFHKOHE72Q.json","graph_json":"https://pith.science/api/pith-number/4RRFB7NTVSHOSAPMZFHKOHE72Q/graph.json","events_json":"https://pith.science/api/pith-number/4RRFB7NTVSHOSAPMZFHKOHE72Q/events.json","paper":"https://pith.science/paper/4RRFB7NT"},"agent_actions":{"view_html":"https://pith.science/pith/4RRFB7NTVSHOSAPMZFHKOHE72Q","download_json":"https://pith.science/pith/4RRFB7NTVSHOSAPMZFHKOHE72Q.json","view_paper":"https://pith.science/paper/4RRFB7NT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.15302&json=true","fetch_graph":"https://pith.science/api/pith-number/4RRFB7NTVSHOSAPMZFHKOHE72Q/graph.json","fetch_events":"https://pith.science/api/pith-number/4RRFB7NTVSHOSAPMZFHKOHE72Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4RRFB7NTVSHOSAPMZFHKOHE72Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4RRFB7NTVSHOSAPMZFHKOHE72Q/action/storage_attestation","attest_author":"https://pith.science/pith/4RRFB7NTVSHOSAPMZFHKOHE72Q/action/author_attestation","sign_citation":"https://pith.science/pith/4RRFB7NTVSHOSAPMZFHKOHE72Q/action/citation_signature","submit_replication":"https://pith.science/pith/4RRFB7NTVSHOSAPMZFHKOHE72Q/action/replication_record"}},"created_at":"2026-07-05T12:06:59.541158+00:00","updated_at":"2026-07-05T12:06:59.541158+00:00"}