{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IM5PJETN4PFLBDR6V2JBJWGBM7","short_pith_number":"pith:IM5PJETN","schema_version":"1.0","canonical_sha256":"433af4926de3cab08e3eae9214d8c167cbd97b4f8b3fbf4b192b793f454d574a","source":{"kind":"arxiv","id":"2505.07049","version":1},"attestation_state":"computed","paper":{"title":"DialogueReason: Rule-Based RL Sparks Dialogue Reasoning in LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Chen Hu, Daxin Jiang, Shuchang Zhou, Xin Wu, Yubo Shu, Zhewei Huang","submitted_at":"2025-05-11T16:39:58Z","abstract_excerpt":"We propose DialogueReason, a reasoning paradigm that uncovers the lost roles in monologue-style reasoning models, aiming to boost diversity and coherency of the reasoning process. Recent advances in RL-based large reasoning models have led to impressive long CoT capabilities and high performance on math and science benchmarks. However, these reasoning models rely mainly on monologue-style reasoning, which often limits reasoning diversity and coherency, frequently recycling fixed strategies or exhibiting unnecessary shifts in attention. Our work consists of an analysis of monologue reasoning pa"},"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":"2505.07049","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-05-11T16:39:58Z","cross_cats_sorted":[],"title_canon_sha256":"fa4888cb6df1f89bab99c8d59e3942ea059a47d4697c2aeb3ef24cec75358c16","abstract_canon_sha256":"c3292bcab7c094481819b86eb0bb0e0384dfb101a114dc43bb45701700054d4c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:01:41.210773Z","signature_b64":"XqMvjEkzKzOhhy5yH1DhOz4sW+Wn4M3WVw93bhNjCcOP3I25c0uzCyfpejj/ZfeEha0cXc0Cs1lC/YqROygiCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"433af4926de3cab08e3eae9214d8c167cbd97b4f8b3fbf4b192b793f454d574a","last_reissued_at":"2026-07-05T11:01:41.210339Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:01:41.210339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DialogueReason: Rule-Based RL Sparks Dialogue Reasoning in LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Chen Hu, Daxin Jiang, Shuchang Zhou, Xin Wu, Yubo Shu, Zhewei Huang","submitted_at":"2025-05-11T16:39:58Z","abstract_excerpt":"We propose DialogueReason, a reasoning paradigm that uncovers the lost roles in monologue-style reasoning models, aiming to boost diversity and coherency of the reasoning process. Recent advances in RL-based large reasoning models have led to impressive long CoT capabilities and high performance on math and science benchmarks. However, these reasoning models rely mainly on monologue-style reasoning, which often limits reasoning diversity and coherency, frequently recycling fixed strategies or exhibiting unnecessary shifts in attention. Our work consists of an analysis of monologue reasoning pa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.07049","kind":"arxiv","version":1},"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/2505.07049/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":"2505.07049","created_at":"2026-07-05T11:01:41.210401+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.07049v1","created_at":"2026-07-05T11:01:41.210401+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.07049","created_at":"2026-07-05T11:01:41.210401+00:00"},{"alias_kind":"pith_short_12","alias_value":"IM5PJETN4PFL","created_at":"2026-07-05T11:01:41.210401+00:00"},{"alias_kind":"pith_short_16","alias_value":"IM5PJETN4PFLBDR6","created_at":"2026-07-05T11:01:41.210401+00:00"},{"alias_kind":"pith_short_8","alias_value":"IM5PJETN","created_at":"2026-07-05T11:01:41.210401+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.05669","citing_title":"Context-Aware Multi-Turn Visual-Textual Reasoning in LVLMs via Dynamic Memory and Adaptive Visual Guidance","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IM5PJETN4PFLBDR6V2JBJWGBM7","json":"https://pith.science/pith/IM5PJETN4PFLBDR6V2JBJWGBM7.json","graph_json":"https://pith.science/api/pith-number/IM5PJETN4PFLBDR6V2JBJWGBM7/graph.json","events_json":"https://pith.science/api/pith-number/IM5PJETN4PFLBDR6V2JBJWGBM7/events.json","paper":"https://pith.science/paper/IM5PJETN"},"agent_actions":{"view_html":"https://pith.science/pith/IM5PJETN4PFLBDR6V2JBJWGBM7","download_json":"https://pith.science/pith/IM5PJETN4PFLBDR6V2JBJWGBM7.json","view_paper":"https://pith.science/paper/IM5PJETN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.07049&json=true","fetch_graph":"https://pith.science/api/pith-number/IM5PJETN4PFLBDR6V2JBJWGBM7/graph.json","fetch_events":"https://pith.science/api/pith-number/IM5PJETN4PFLBDR6V2JBJWGBM7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IM5PJETN4PFLBDR6V2JBJWGBM7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IM5PJETN4PFLBDR6V2JBJWGBM7/action/storage_attestation","attest_author":"https://pith.science/pith/IM5PJETN4PFLBDR6V2JBJWGBM7/action/author_attestation","sign_citation":"https://pith.science/pith/IM5PJETN4PFLBDR6V2JBJWGBM7/action/citation_signature","submit_replication":"https://pith.science/pith/IM5PJETN4PFLBDR6V2JBJWGBM7/action/replication_record"}},"created_at":"2026-07-05T11:01:41.210401+00:00","updated_at":"2026-07-05T11:01:41.210401+00:00"}