{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:54PJQEHPHEZCGIVGSXYHQ6C4PY","short_pith_number":"pith:54PJQEHP","schema_version":"1.0","canonical_sha256":"ef1e9810ef39322322a695f078785c7e2319ce8115c04dc3bdb085d137f5d181","source":{"kind":"arxiv","id":"2603.15500","version":2},"attestation_state":"computed","paper":{"title":"Understanding Reasoning in LLMs through Strategic Information Allocation under Uncertainty","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Dongsheng Li, Jeonghye Kim, Minbeom Kim, Sangmook Lee, Xufang Luo, Yuqing Yang","submitted_at":"2026-03-16T16:31:24Z","abstract_excerpt":"LLMs often exhibit Aha moments such as self-correction after tokens like \"Wait,\" yet the underlying mechanism remains unclear. Standard LLMs collapse mainly through silent divergence, where trajectories drift from the correct answer yet remain locally coherent, so no explicit error triggers reactive self-correction. We introduce an information-theoretic framework that separates reasoning into procedural advancement and epistemic verbalization, the token-level externalization of uncertainty, and prove that sporadic verbalization restores convergence toward the correct answer even without explic"},"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":"2603.15500","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-03-16T16:31:24Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"41b2fb30a15a4e30d1e80b6e3ea57108e458d6960f1d2d695c6eb50aaad4e334","abstract_canon_sha256":"79aa911dcd485e70c35ef90e83c43b5ff18f25e43cd46400844ada618585f983"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-27T02:05:13.678694Z","signature_b64":"HrZ7q8uGGsJGOJ0K89KZHqvrpxEF16HS73q+a2xt9pWrCLYcrf1/9hRURF+8DamqiGETg8YUPqdCbszd2A80DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef1e9810ef39322322a695f078785c7e2319ce8115c04dc3bdb085d137f5d181","last_reissued_at":"2026-05-27T02:05:13.678031Z","signature_status":"signed_v1","first_computed_at":"2026-05-27T02:05:13.678031Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Understanding Reasoning in LLMs through Strategic Information Allocation under Uncertainty","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Dongsheng Li, Jeonghye Kim, Minbeom Kim, Sangmook Lee, Xufang Luo, Yuqing Yang","submitted_at":"2026-03-16T16:31:24Z","abstract_excerpt":"LLMs often exhibit Aha moments such as self-correction after tokens like \"Wait,\" yet the underlying mechanism remains unclear. Standard LLMs collapse mainly through silent divergence, where trajectories drift from the correct answer yet remain locally coherent, so no explicit error triggers reactive self-correction. We introduce an information-theoretic framework that separates reasoning into procedural advancement and epistemic verbalization, the token-level externalization of uncertainty, and prove that sporadic verbalization restores convergence toward the correct answer even without explic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.15500","kind":"arxiv","version":2},"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/2603.15500/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":"2603.15500","created_at":"2026-05-27T02:05:13.678096+00:00"},{"alias_kind":"arxiv_version","alias_value":"2603.15500v2","created_at":"2026-05-27T02:05:13.678096+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.15500","created_at":"2026-05-27T02:05:13.678096+00:00"},{"alias_kind":"pith_short_12","alias_value":"54PJQEHPHEZC","created_at":"2026-05-27T02:05:13.678096+00:00"},{"alias_kind":"pith_short_16","alias_value":"54PJQEHPHEZCGIVG","created_at":"2026-05-27T02:05:13.678096+00:00"},{"alias_kind":"pith_short_8","alias_value":"54PJQEHP","created_at":"2026-05-27T02:05:13.678096+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2604.23333","citing_title":"Process Supervision of Confidence Margin for Calibrated LLM Reasoning","ref_index":35,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/54PJQEHPHEZCGIVGSXYHQ6C4PY","json":"https://pith.science/pith/54PJQEHPHEZCGIVGSXYHQ6C4PY.json","graph_json":"https://pith.science/api/pith-number/54PJQEHPHEZCGIVGSXYHQ6C4PY/graph.json","events_json":"https://pith.science/api/pith-number/54PJQEHPHEZCGIVGSXYHQ6C4PY/events.json","paper":"https://pith.science/paper/54PJQEHP"},"agent_actions":{"view_html":"https://pith.science/pith/54PJQEHPHEZCGIVGSXYHQ6C4PY","download_json":"https://pith.science/pith/54PJQEHPHEZCGIVGSXYHQ6C4PY.json","view_paper":"https://pith.science/paper/54PJQEHP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2603.15500&json=true","fetch_graph":"https://pith.science/api/pith-number/54PJQEHPHEZCGIVGSXYHQ6C4PY/graph.json","fetch_events":"https://pith.science/api/pith-number/54PJQEHPHEZCGIVGSXYHQ6C4PY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/54PJQEHPHEZCGIVGSXYHQ6C4PY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/54PJQEHPHEZCGIVGSXYHQ6C4PY/action/storage_attestation","attest_author":"https://pith.science/pith/54PJQEHPHEZCGIVGSXYHQ6C4PY/action/author_attestation","sign_citation":"https://pith.science/pith/54PJQEHPHEZCGIVGSXYHQ6C4PY/action/citation_signature","submit_replication":"https://pith.science/pith/54PJQEHPHEZCGIVGSXYHQ6C4PY/action/replication_record"}},"created_at":"2026-05-27T02:05:13.678096+00:00","updated_at":"2026-05-27T02:05:13.678096+00:00"}