{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:4TKVYUQ35CIMP6EFZW2EEDAWDD","short_pith_number":"pith:4TKVYUQ3","canonical_record":{"source":{"id":"2604.17237","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-04-19T03:43:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"460dfc3d414688b56310e270bb82f31d4742ed23010bf53b87b7a06001d8e8ef","abstract_canon_sha256":"44ad9b279e33eb1e6c26b522cd6cda643171e9178f61986c6cf3972ea785d4e9"},"schema_version":"1.0"},"canonical_sha256":"e4d55c521be890c7f885cdb4420c1618eac1c35bef825b35821ce0dc4a4c10b3","source":{"kind":"arxiv","id":"2604.17237","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.17237","created_at":"2026-05-20T01:05:13Z"},{"alias_kind":"arxiv_version","alias_value":"2604.17237v2","created_at":"2026-05-20T01:05:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.17237","created_at":"2026-05-20T01:05:13Z"},{"alias_kind":"pith_short_12","alias_value":"4TKVYUQ35CIM","created_at":"2026-05-20T01:05:13Z"},{"alias_kind":"pith_short_16","alias_value":"4TKVYUQ35CIMP6EF","created_at":"2026-05-20T01:05:13Z"},{"alias_kind":"pith_short_8","alias_value":"4TKVYUQ3","created_at":"2026-05-20T01:05:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:4TKVYUQ35CIMP6EFZW2EEDAWDD","target":"record","payload":{"canonical_record":{"source":{"id":"2604.17237","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-04-19T03:43:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"460dfc3d414688b56310e270bb82f31d4742ed23010bf53b87b7a06001d8e8ef","abstract_canon_sha256":"44ad9b279e33eb1e6c26b522cd6cda643171e9178f61986c6cf3972ea785d4e9"},"schema_version":"1.0"},"canonical_sha256":"e4d55c521be890c7f885cdb4420c1618eac1c35bef825b35821ce0dc4a4c10b3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T01:05:13.894354Z","signature_b64":"q3WmNBLVeCyc89lUY0OAweY6bYWn8UqHWSkgbwrOC8N8AeZJdCkxkG57VbYj9L6YjwQKQQlIfpYfVyKbOhipDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e4d55c521be890c7f885cdb4420c1618eac1c35bef825b35821ce0dc4a4c10b3","last_reissued_at":"2026-05-20T01:05:13.893425Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T01:05:13.893425Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2604.17237","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T01:05:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cah0GzqbGzf8xZeYEbg6u7W7itWUnQRItKmSJozu7oEVZ2AmQDjSjlV8+KU6BvHLYp4l7b4wP71ljTPif1PAAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T12:57:48.880413Z"},"content_sha256":"64c4249582d7d3cc47962180366916d3b24e683b62d9125af2af593178765c0d","schema_version":"1.0","event_id":"sha256:64c4249582d7d3cc47962180366916d3b24e683b62d9125af2af593178765c0d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:4TKVYUQ35CIMP6EFZW2EEDAWDD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"HeadRank: Decoding-Free Passage Reranking via Preference-Aligned Attention Heads","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"HeadRank reranks passages by aligning LLM attention heads to preferences in continuous space without decoding.","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Aolin Li, Chenxing Wang, Dongliang Liao, Haijun Wu, Huiyun Hu, Jin Xu, Junwu Du, Juyuan Wang, Ligang Liu, Shunlin Rong, Yuchen Fang","submitted_at":"2026-04-19T03:43:42Z","abstract_excerpt":"Decoding-free reranking methods that read relevance signals directly from LLM attention weights offer significant latency advantages over autoregressive approaches, yet suffer from attention score homogenization: middle-context documents receive near-identical scores, destroying the fine-grained distinctions required for ranking. We propose HeadRank, a framework that lifts preference optimization from discrete token space into the continuous attention domain through entropy-regularized head selection, hard adjacent-level preference pairs, and a distribution regularizer that jointly sharpen dis"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"HeadRank consistently outperforms generative and decoding-free baselines with 100% formatting success. At 4B, 57.4% of relevant middle-zone documents reach the top quartile versus 14.2% for irrelevant ones -- a 43-percentage-point selectivity gap that demonstrates the effectiveness of attention-space preference alignment for listwise reranking.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That entropy-regularized head selection combined with hard adjacent-level preference pairs and a distribution regularizer can reliably overcome attention homogenization in middle context using only 211 training queries without introducing new biases or overfitting to the small training set.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"HeadRank improves decoding-free passage reranking by preference-aligning attention heads to increase discriminability in middle-context documents, outperforming baselines on 14 benchmarks with only 211 training queries.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"HeadRank reranks passages by aligning LLM attention heads to preferences in continuous space without decoding.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"6ba0c05f1268c983e42c044331acbab37d2e608a6661f2f2f869a04e2edd3fb2"},"source":{"id":"2604.17237","kind":"arxiv","version":2},"verdict":{"id":"873dd888-bab8-4e3b-8f88-e5afebace4d0","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T06:31:49.425911Z","strongest_claim":"HeadRank consistently outperforms generative and decoding-free baselines with 100% formatting success. At 4B, 57.4% of relevant middle-zone documents reach the top quartile versus 14.2% for irrelevant ones -- a 43-percentage-point selectivity gap that demonstrates the effectiveness of attention-space preference alignment for listwise reranking.","one_line_summary":"HeadRank improves decoding-free passage reranking by preference-aligning attention heads to increase discriminability in middle-context documents, outperforming baselines on 14 benchmarks with only 211 training queries.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That entropy-regularized head selection combined with hard adjacent-level preference pairs and a distribution regularizer can reliably overcome attention homogenization in middle context using only 211 training queries without introducing new biases or overfitting to the small training set.","pith_extraction_headline":"HeadRank reranks passages by aligning LLM attention heads to preferences in continuous space without decoding."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.17237/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"},"verdict_id":"873dd888-bab8-4e3b-8f88-e5afebace4d0"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T01:05:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"us221F9levYDDnF12bMlxKniyA+l/z95JnhAnz2doTtCIXZ80UG/02wBDXcnc+tFS+juKb9m2NnHiLiPPOMaCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T12:57:48.881053Z"},"content_sha256":"4e58ffe33f557d7ab7b670937422fce45011fde459f6d8e4e056e982c7f809de","schema_version":"1.0","event_id":"sha256:4e58ffe33f557d7ab7b670937422fce45011fde459f6d8e4e056e982c7f809de"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4TKVYUQ35CIMP6EFZW2EEDAWDD/bundle.json","state_url":"https://pith.science/pith/4TKVYUQ35CIMP6EFZW2EEDAWDD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4TKVYUQ35CIMP6EFZW2EEDAWDD/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-26T12:57:48Z","links":{"resolver":"https://pith.science/pith/4TKVYUQ35CIMP6EFZW2EEDAWDD","bundle":"https://pith.science/pith/4TKVYUQ35CIMP6EFZW2EEDAWDD/bundle.json","state":"https://pith.science/pith/4TKVYUQ35CIMP6EFZW2EEDAWDD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4TKVYUQ35CIMP6EFZW2EEDAWDD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:4TKVYUQ35CIMP6EFZW2EEDAWDD","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":"44ad9b279e33eb1e6c26b522cd6cda643171e9178f61986c6cf3972ea785d4e9","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-04-19T03:43:42Z","title_canon_sha256":"460dfc3d414688b56310e270bb82f31d4742ed23010bf53b87b7a06001d8e8ef"},"schema_version":"1.0","source":{"id":"2604.17237","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.17237","created_at":"2026-05-20T01:05:13Z"},{"alias_kind":"arxiv_version","alias_value":"2604.17237v2","created_at":"2026-05-20T01:05:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.17237","created_at":"2026-05-20T01:05:13Z"},{"alias_kind":"pith_short_12","alias_value":"4TKVYUQ35CIM","created_at":"2026-05-20T01:05:13Z"},{"alias_kind":"pith_short_16","alias_value":"4TKVYUQ35CIMP6EF","created_at":"2026-05-20T01:05:13Z"},{"alias_kind":"pith_short_8","alias_value":"4TKVYUQ3","created_at":"2026-05-20T01:05:13Z"}],"graph_snapshots":[{"event_id":"sha256:4e58ffe33f557d7ab7b670937422fce45011fde459f6d8e4e056e982c7f809de","target":"graph","created_at":"2026-05-20T01:05:13Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"HeadRank consistently outperforms generative and decoding-free baselines with 100% formatting success. At 4B, 57.4% of relevant middle-zone documents reach the top quartile versus 14.2% for irrelevant ones -- a 43-percentage-point selectivity gap that demonstrates the effectiveness of attention-space preference alignment for listwise reranking."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That entropy-regularized head selection combined with hard adjacent-level preference pairs and a distribution regularizer can reliably overcome attention homogenization in middle context using only 211 training queries without introducing new biases or overfitting to the small training set."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"HeadRank improves decoding-free passage reranking by preference-aligning attention heads to increase discriminability in middle-context documents, outperforming baselines on 14 benchmarks with only 211 training queries."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"HeadRank reranks passages by aligning LLM attention heads to preferences in continuous space without decoding."}],"snapshot_sha256":"6ba0c05f1268c983e42c044331acbab37d2e608a6661f2f2f869a04e2edd3fb2"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.17237/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Decoding-free reranking methods that read relevance signals directly from LLM attention weights offer significant latency advantages over autoregressive approaches, yet suffer from attention score homogenization: middle-context documents receive near-identical scores, destroying the fine-grained distinctions required for ranking. We propose HeadRank, a framework that lifts preference optimization from discrete token space into the continuous attention domain through entropy-regularized head selection, hard adjacent-level preference pairs, and a distribution regularizer that jointly sharpen dis","authors_text":"Aolin Li, Chenxing Wang, Dongliang Liao, Haijun Wu, Huiyun Hu, Jin Xu, Junwu Du, Juyuan Wang, Ligang Liu, Shunlin Rong, Yuchen Fang","cross_cats":["cs.AI"],"headline":"HeadRank reranks passages by aligning LLM attention heads to preferences in continuous space without decoding.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-04-19T03:43:42Z","title":"HeadRank: Decoding-Free Passage Reranking via Preference-Aligned Attention Heads"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.17237","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-10T06:31:49.425911Z","id":"873dd888-bab8-4e3b-8f88-e5afebace4d0","model_set":{"reader":"grok-4.3"},"one_line_summary":"HeadRank improves decoding-free passage reranking by preference-aligning attention heads to increase discriminability in middle-context documents, outperforming baselines on 14 benchmarks with only 211 training queries.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"HeadRank reranks passages by aligning LLM attention heads to preferences in continuous space without decoding.","strongest_claim":"HeadRank consistently outperforms generative and decoding-free baselines with 100% formatting success. At 4B, 57.4% of relevant middle-zone documents reach the top quartile versus 14.2% for irrelevant ones -- a 43-percentage-point selectivity gap that demonstrates the effectiveness of attention-space preference alignment for listwise reranking.","weakest_assumption":"That entropy-regularized head selection combined with hard adjacent-level preference pairs and a distribution regularizer can reliably overcome attention homogenization in middle context using only 211 training queries without introducing new biases or overfitting to the small training set."}},"verdict_id":"873dd888-bab8-4e3b-8f88-e5afebace4d0"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:64c4249582d7d3cc47962180366916d3b24e683b62d9125af2af593178765c0d","target":"record","created_at":"2026-05-20T01:05:13Z","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":"44ad9b279e33eb1e6c26b522cd6cda643171e9178f61986c6cf3972ea785d4e9","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-04-19T03:43:42Z","title_canon_sha256":"460dfc3d414688b56310e270bb82f31d4742ed23010bf53b87b7a06001d8e8ef"},"schema_version":"1.0","source":{"id":"2604.17237","kind":"arxiv","version":2}},"canonical_sha256":"e4d55c521be890c7f885cdb4420c1618eac1c35bef825b35821ce0dc4a4c10b3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e4d55c521be890c7f885cdb4420c1618eac1c35bef825b35821ce0dc4a4c10b3","first_computed_at":"2026-05-20T01:05:13.893425Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T01:05:13.893425Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"q3WmNBLVeCyc89lUY0OAweY6bYWn8UqHWSkgbwrOC8N8AeZJdCkxkG57VbYj9L6YjwQKQQlIfpYfVyKbOhipDA==","signature_status":"signed_v1","signed_at":"2026-05-20T01:05:13.894354Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.17237","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:64c4249582d7d3cc47962180366916d3b24e683b62d9125af2af593178765c0d","sha256:4e58ffe33f557d7ab7b670937422fce45011fde459f6d8e4e056e982c7f809de"],"state_sha256":"b8d405b68335b33c7dcad2ea4b5f54d42dc0121bd6f5e14d483855c5d830eed9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cGoPljEBQowOvGAGcc6Yp/jujHuDO51FRYi8P708Dv3Yp4b642FV89OgM/xgCBm1ML5BiUJIIZcJZFnU90nQAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T12:57:48.885969Z","bundle_sha256":"b4a2e3641c7a054921190aa6a93e5115078024b6ec3b464593e508cb1bfdf700"}}