{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:6F3ZPQHJ27MFEHKIDBIKJHBPOH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"40ff6b549ab18d6500475428a38db835cf4a9d3a266cb2bf15aae691b9790b4b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-11T21:01:54Z","title_canon_sha256":"e7267be17bf7cdb6bd7f909add9a8aa3e627af6efa57e61d0ba81f325127c0d0"},"schema_version":"1.0","source":{"id":"2509.09852","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.09852","created_at":"2026-07-05T12:09:50Z"},{"alias_kind":"arxiv_version","alias_value":"2509.09852v1","created_at":"2026-07-05T12:09:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.09852","created_at":"2026-07-05T12:09:50Z"},{"alias_kind":"pith_short_12","alias_value":"6F3ZPQHJ27MF","created_at":"2026-07-05T12:09:50Z"},{"alias_kind":"pith_short_16","alias_value":"6F3ZPQHJ27MFEHKI","created_at":"2026-07-05T12:09:50Z"},{"alias_kind":"pith_short_8","alias_value":"6F3ZPQHJ","created_at":"2026-07-05T12:09:50Z"}],"graph_snapshots":[{"event_id":"sha256:4d8f3f1736154c13ad2eecebcb19bddc3c2bb0611ff5d5604496ed38d37066c3","target":"graph","created_at":"2026-07-05T12:09:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2509.09852/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A key challenge in Multi-Document Summarization (MDS) is effectively integrating information from multiple sources while maintaining coherence and topical relevance. While Large Language Models have shown impressive results in single-document summarization, their performance on MDS still leaves room for improvement. In this paper, we propose a topic-guided reinforcement learning approach to improve content selection in MDS. We first show that explicitly prompting models with topic labels enhances the informativeness of the generated summaries. Building on this insight, we propose a novel topic","authors_text":"Austin Xu, Chuyuan Li, Giuseppe Carenini, Shafiq Joty","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-11T21:01:54Z","title":"Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.09852","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:00fa360c50e9f678d6c231af2f9ab2c7e0b3b24159d028d3bc476390cd6e5704","target":"record","created_at":"2026-07-05T12:09:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"40ff6b549ab18d6500475428a38db835cf4a9d3a266cb2bf15aae691b9790b4b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-11T21:01:54Z","title_canon_sha256":"e7267be17bf7cdb6bd7f909add9a8aa3e627af6efa57e61d0ba81f325127c0d0"},"schema_version":"1.0","source":{"id":"2509.09852","kind":"arxiv","version":1}},"canonical_sha256":"f17797c0e9d7d8521d481850a49c2f71c747dee3244e9bd19477c90c46f8f1e1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f17797c0e9d7d8521d481850a49c2f71c747dee3244e9bd19477c90c46f8f1e1","first_computed_at":"2026-07-05T12:09:50.697107Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:09:50.697107Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GjhDwFfPNkYsKxa0Brs6EmJvp5csdn1FPRBKtEivVXMFLosssOa6Sm/rp5yXzNgzcXhNYK7OHIpMhm3pzeuPDg==","signature_status":"signed_v1","signed_at":"2026-07-05T12:09:50.697696Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.09852","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:00fa360c50e9f678d6c231af2f9ab2c7e0b3b24159d028d3bc476390cd6e5704","sha256:4d8f3f1736154c13ad2eecebcb19bddc3c2bb0611ff5d5604496ed38d37066c3"],"state_sha256":"e998848193f830b0c6d356d4499bf47bbba8f17a8310629b5ab1bb9b01c36340"}