{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:TVV6NZH2WBNCOMIMZKCMKGAOCO","short_pith_number":"pith:TVV6NZH2","schema_version":"1.0","canonical_sha256":"9d6be6e4fab05a27310cca84c5180e13aec4ece50176b95ea544a36adb2b96d1","source":{"kind":"arxiv","id":"2606.07610","version":1},"attestation_state":"computed","paper":{"title":"LEAF: Growing Trees Without Branching for Speech-Aware Large Language Model Post-Training","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Argyrios Gerogiannis, Mark Hasegawa-Johnson, Venugopal V. Veeravalli, Yekaterina Yegorova","submitted_at":"2026-05-29T15:50:50Z","abstract_excerpt":"State-of-the-art GRPO-style methods for speech-aware large language model post-training suffer from coarse credit assignment, broadcasting the same terminal-reward advantage to every token in a response. This ignores useful structure within rollout batches, where speech-conditioned completions often share prefixes before diverging at important decisions. We propose Low-rank Exploration with Adaptive Forking (LEAF), a retrospective tree-based RL method that recovers this structure without online branching or additional decoding. LEAF samples complete responses, selects high-surprisal boundaries"},"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":"2606.07610","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-29T15:50:50Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"f6de22a9fd45aaf44001b99ee4bf39260d2d3f23374fb028434763cf43b0dce7","abstract_canon_sha256":"79c6a286502a83e2f2d57db945af1da981a2e68cec99e2ef93a22082291f661c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T00:04:44.783748Z","signature_b64":"B65fn72jxybv8+7hHK1jL888nFCGRHm+8zTQ6zkKOqjSnupzM8IhwGw8LzI0syaqO7Ha7X0AeOM3AJI1hHiHDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9d6be6e4fab05a27310cca84c5180e13aec4ece50176b95ea544a36adb2b96d1","last_reissued_at":"2026-06-09T00:04:44.783213Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T00:04:44.783213Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LEAF: Growing Trees Without Branching for Speech-Aware Large Language Model Post-Training","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Argyrios Gerogiannis, Mark Hasegawa-Johnson, Venugopal V. Veeravalli, Yekaterina Yegorova","submitted_at":"2026-05-29T15:50:50Z","abstract_excerpt":"State-of-the-art GRPO-style methods for speech-aware large language model post-training suffer from coarse credit assignment, broadcasting the same terminal-reward advantage to every token in a response. This ignores useful structure within rollout batches, where speech-conditioned completions often share prefixes before diverging at important decisions. We propose Low-rank Exploration with Adaptive Forking (LEAF), a retrospective tree-based RL method that recovers this structure without online branching or additional decoding. LEAF samples complete responses, selects high-surprisal boundaries"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07610","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/2606.07610/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":"2606.07610","created_at":"2026-06-09T00:04:44.783285+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.07610v1","created_at":"2026-06-09T00:04:44.783285+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07610","created_at":"2026-06-09T00:04:44.783285+00:00"},{"alias_kind":"pith_short_12","alias_value":"TVV6NZH2WBNC","created_at":"2026-06-09T00:04:44.783285+00:00"},{"alias_kind":"pith_short_16","alias_value":"TVV6NZH2WBNCOMIM","created_at":"2026-06-09T00:04:44.783285+00:00"},{"alias_kind":"pith_short_8","alias_value":"TVV6NZH2","created_at":"2026-06-09T00:04:44.783285+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TVV6NZH2WBNCOMIMZKCMKGAOCO","json":"https://pith.science/pith/TVV6NZH2WBNCOMIMZKCMKGAOCO.json","graph_json":"https://pith.science/api/pith-number/TVV6NZH2WBNCOMIMZKCMKGAOCO/graph.json","events_json":"https://pith.science/api/pith-number/TVV6NZH2WBNCOMIMZKCMKGAOCO/events.json","paper":"https://pith.science/paper/TVV6NZH2"},"agent_actions":{"view_html":"https://pith.science/pith/TVV6NZH2WBNCOMIMZKCMKGAOCO","download_json":"https://pith.science/pith/TVV6NZH2WBNCOMIMZKCMKGAOCO.json","view_paper":"https://pith.science/paper/TVV6NZH2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.07610&json=true","fetch_graph":"https://pith.science/api/pith-number/TVV6NZH2WBNCOMIMZKCMKGAOCO/graph.json","fetch_events":"https://pith.science/api/pith-number/TVV6NZH2WBNCOMIMZKCMKGAOCO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TVV6NZH2WBNCOMIMZKCMKGAOCO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TVV6NZH2WBNCOMIMZKCMKGAOCO/action/storage_attestation","attest_author":"https://pith.science/pith/TVV6NZH2WBNCOMIMZKCMKGAOCO/action/author_attestation","sign_citation":"https://pith.science/pith/TVV6NZH2WBNCOMIMZKCMKGAOCO/action/citation_signature","submit_replication":"https://pith.science/pith/TVV6NZH2WBNCOMIMZKCMKGAOCO/action/replication_record"}},"created_at":"2026-06-09T00:04:44.783285+00:00","updated_at":"2026-06-09T00:04:44.783285+00:00"}