{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:44MRUYVGWUUVUUQAXNRICZWTV3","short_pith_number":"pith:44MRUYVG","schema_version":"1.0","canonical_sha256":"e7191a62a6b5295a5200bb628166d3aecbfe124b7aaf3477657a8d191f16a447","source":{"kind":"arxiv","id":"2502.15335","version":2},"attestation_state":"computed","paper":{"title":"Stepwise Informativeness Search for Efficient and Effective LLM Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Enda Zhao, Siyuan Wang, Xiang Ren, Zhongyu Wei","submitted_at":"2025-02-21T09:39:27Z","abstract_excerpt":"Advances in Large Language Models (LLMs) have significantly improved multi-step reasoning through generating free-text rationales. However, recent studies show that LLMs tend to lose focus over the middle of long contexts. This raises concerns that as reasoning progresses, LLMs may overlook information in earlier steps when decoding subsequent steps, leading to generate unreliable and redundant rationales. To address this, we propose guiding LLMs to generate more accurate and concise step-by-step rationales by (1) proactively referencing information from underutilized prior steps, and (2) mini"},"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":"2502.15335","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-21T09:39:27Z","cross_cats_sorted":[],"title_canon_sha256":"29fd0c217a5ac5facb127f115aa74015e163a5fbbcf047c41434e2ebd2b2f344","abstract_canon_sha256":"7f30171aed2cb9ab1ccdc20c202d68364d4ba03ac0fb0653f025e74e552ee3af"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:47:57.362164Z","signature_b64":"Rxn3YQlvuJEoJxZym0LGzpFSZpuYFfozJdv1sgnkc6n1T+5llpfzeMA9PCIdrzaiEB9RYRS1PM/siuSy3s6xCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e7191a62a6b5295a5200bb628166d3aecbfe124b7aaf3477657a8d191f16a447","last_reissued_at":"2026-07-05T10:47:57.361620Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:47:57.361620Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Stepwise Informativeness Search for Efficient and Effective LLM Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Enda Zhao, Siyuan Wang, Xiang Ren, Zhongyu Wei","submitted_at":"2025-02-21T09:39:27Z","abstract_excerpt":"Advances in Large Language Models (LLMs) have significantly improved multi-step reasoning through generating free-text rationales. However, recent studies show that LLMs tend to lose focus over the middle of long contexts. This raises concerns that as reasoning progresses, LLMs may overlook information in earlier steps when decoding subsequent steps, leading to generate unreliable and redundant rationales. To address this, we propose guiding LLMs to generate more accurate and concise step-by-step rationales by (1) proactively referencing information from underutilized prior steps, and (2) mini"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.15335","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/2502.15335/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":"2502.15335","created_at":"2026-07-05T10:47:57.361690+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.15335v2","created_at":"2026-07-05T10:47:57.361690+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.15335","created_at":"2026-07-05T10:47:57.361690+00:00"},{"alias_kind":"pith_short_12","alias_value":"44MRUYVGWUUV","created_at":"2026-07-05T10:47:57.361690+00:00"},{"alias_kind":"pith_short_16","alias_value":"44MRUYVGWUUVUUQA","created_at":"2026-07-05T10:47:57.361690+00:00"},{"alias_kind":"pith_short_8","alias_value":"44MRUYVG","created_at":"2026-07-05T10:47:57.361690+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.17196","citing_title":"BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/44MRUYVGWUUVUUQAXNRICZWTV3","json":"https://pith.science/pith/44MRUYVGWUUVUUQAXNRICZWTV3.json","graph_json":"https://pith.science/api/pith-number/44MRUYVGWUUVUUQAXNRICZWTV3/graph.json","events_json":"https://pith.science/api/pith-number/44MRUYVGWUUVUUQAXNRICZWTV3/events.json","paper":"https://pith.science/paper/44MRUYVG"},"agent_actions":{"view_html":"https://pith.science/pith/44MRUYVGWUUVUUQAXNRICZWTV3","download_json":"https://pith.science/pith/44MRUYVGWUUVUUQAXNRICZWTV3.json","view_paper":"https://pith.science/paper/44MRUYVG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.15335&json=true","fetch_graph":"https://pith.science/api/pith-number/44MRUYVGWUUVUUQAXNRICZWTV3/graph.json","fetch_events":"https://pith.science/api/pith-number/44MRUYVGWUUVUUQAXNRICZWTV3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/44MRUYVGWUUVUUQAXNRICZWTV3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/44MRUYVGWUUVUUQAXNRICZWTV3/action/storage_attestation","attest_author":"https://pith.science/pith/44MRUYVGWUUVUUQAXNRICZWTV3/action/author_attestation","sign_citation":"https://pith.science/pith/44MRUYVGWUUVUUQAXNRICZWTV3/action/citation_signature","submit_replication":"https://pith.science/pith/44MRUYVGWUUVUUQAXNRICZWTV3/action/replication_record"}},"created_at":"2026-07-05T10:47:57.361690+00:00","updated_at":"2026-07-05T10:47:57.361690+00:00"}