{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:RJAH4S7NAEEMFFEXASUEU2QRIE","short_pith_number":"pith:RJAH4S7N","canonical_record":{"source":{"id":"2504.17220","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-24T03:18:16Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"241b51db62406b6b995f7f89fa53d95c250dca6b0e7c424e98f212e4b30d211d","abstract_canon_sha256":"8ee7199ac18eb8d4f367edd9d4775d592e1b089f089381a91eb9601960486e10"},"schema_version":"1.0"},"canonical_sha256":"8a407e4bed0108c2949704a84a6a11410882f128fdb37f369f52da8799e1da07","source":{"kind":"arxiv","id":"2504.17220","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.17220","created_at":"2026-07-05T10:53:24Z"},{"alias_kind":"arxiv_version","alias_value":"2504.17220v1","created_at":"2026-07-05T10:53:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.17220","created_at":"2026-07-05T10:53:24Z"},{"alias_kind":"pith_short_12","alias_value":"RJAH4S7NAEEM","created_at":"2026-07-05T10:53:24Z"},{"alias_kind":"pith_short_16","alias_value":"RJAH4S7NAEEMFFEX","created_at":"2026-07-05T10:53:24Z"},{"alias_kind":"pith_short_8","alias_value":"RJAH4S7N","created_at":"2026-07-05T10:53:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:RJAH4S7NAEEMFFEXASUEU2QRIE","target":"record","payload":{"canonical_record":{"source":{"id":"2504.17220","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-24T03:18:16Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"241b51db62406b6b995f7f89fa53d95c250dca6b0e7c424e98f212e4b30d211d","abstract_canon_sha256":"8ee7199ac18eb8d4f367edd9d4775d592e1b089f089381a91eb9601960486e10"},"schema_version":"1.0"},"canonical_sha256":"8a407e4bed0108c2949704a84a6a11410882f128fdb37f369f52da8799e1da07","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:53:24.536435Z","signature_b64":"OKJVY73Q2FkymD5YQBiWiW0/cHjkYz6lu5vaH42RKM1PDsqmxt5k13Cg/OLzJwJLm6ePe0J0KBkYc3Jr6oOFDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a407e4bed0108c2949704a84a6a11410882f128fdb37f369f52da8799e1da07","last_reissued_at":"2026-07-05T10:53:24.535890Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:53:24.535890Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.17220","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:53:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h+ZkNVbGCGFfXfWtDzGirs/dSk5R2EISIlUOQF3U8kFc6oE/fS2y/s+4A8gAaskF14/zClSGiUwfL7j2dZ/FDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T10:51:10.934637Z"},"content_sha256":"db102bb4807cc236b4d41d4dab39bfbbb5b80d73365469fdb90dca96f4bd8910","schema_version":"1.0","event_id":"sha256:db102bb4807cc236b4d41d4dab39bfbbb5b80d73365469fdb90dca96f4bd8910"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:RJAH4S7NAEEMFFEXASUEU2QRIE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Does Knowledge Distillation Matter for Large Language Model based Bundle Generation?","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Hui Fang, Jie Yang, Kaidong Feng, Wenyuan Liu, Xinghua Qu, Zhu Sun","submitted_at":"2025-04-24T03:18:16Z","abstract_excerpt":"LLMs are increasingly explored for bundle generation, thanks to their reasoning capabilities and knowledge. However, deploying large-scale LLMs introduces significant efficiency challenges, primarily high computational costs during fine-tuning and inference due to their massive parameterization. Knowledge distillation (KD) offers a promising solution, transferring expertise from large teacher models to compact student models. This study systematically investigates knowledge distillation approaches for bundle generation, aiming to minimize computational demands while preserving performance. We "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.17220","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/2504.17220/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":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:53:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DmkGTTLSv9spm5rAquPmWbzTjnWZ4a+w2EeHWhHu6wK1/JQPbCpJhnzsCpbZKhtHLuu5DjesDooExaGnNVaaCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T10:51:10.935020Z"},"content_sha256":"189ed54fb46e9ac4d8efedf6c896ff68055c3e0f5e41ebd34a6340c077af98b6","schema_version":"1.0","event_id":"sha256:189ed54fb46e9ac4d8efedf6c896ff68055c3e0f5e41ebd34a6340c077af98b6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RJAH4S7NAEEMFFEXASUEU2QRIE/bundle.json","state_url":"https://pith.science/pith/RJAH4S7NAEEMFFEXASUEU2QRIE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RJAH4S7NAEEMFFEXASUEU2QRIE/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":"2026-08-19T10:51:10Z","links":{"resolver":"https://pith.science/pith/RJAH4S7NAEEMFFEXASUEU2QRIE","bundle":"https://pith.science/pith/RJAH4S7NAEEMFFEXASUEU2QRIE/bundle.json","state":"https://pith.science/pith/RJAH4S7NAEEMFFEXASUEU2QRIE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RJAH4S7NAEEMFFEXASUEU2QRIE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:RJAH4S7NAEEMFFEXASUEU2QRIE","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":"8ee7199ac18eb8d4f367edd9d4775d592e1b089f089381a91eb9601960486e10","cross_cats_sorted":["cs.IR"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-24T03:18:16Z","title_canon_sha256":"241b51db62406b6b995f7f89fa53d95c250dca6b0e7c424e98f212e4b30d211d"},"schema_version":"1.0","source":{"id":"2504.17220","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.17220","created_at":"2026-07-05T10:53:24Z"},{"alias_kind":"arxiv_version","alias_value":"2504.17220v1","created_at":"2026-07-05T10:53:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.17220","created_at":"2026-07-05T10:53:24Z"},{"alias_kind":"pith_short_12","alias_value":"RJAH4S7NAEEM","created_at":"2026-07-05T10:53:24Z"},{"alias_kind":"pith_short_16","alias_value":"RJAH4S7NAEEMFFEX","created_at":"2026-07-05T10:53:24Z"},{"alias_kind":"pith_short_8","alias_value":"RJAH4S7N","created_at":"2026-07-05T10:53:24Z"}],"graph_snapshots":[{"event_id":"sha256:189ed54fb46e9ac4d8efedf6c896ff68055c3e0f5e41ebd34a6340c077af98b6","target":"graph","created_at":"2026-07-05T10:53:24Z","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/2504.17220/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"LLMs are increasingly explored for bundle generation, thanks to their reasoning capabilities and knowledge. However, deploying large-scale LLMs introduces significant efficiency challenges, primarily high computational costs during fine-tuning and inference due to their massive parameterization. Knowledge distillation (KD) offers a promising solution, transferring expertise from large teacher models to compact student models. This study systematically investigates knowledge distillation approaches for bundle generation, aiming to minimize computational demands while preserving performance. We ","authors_text":"Hui Fang, Jie Yang, Kaidong Feng, Wenyuan Liu, Xinghua Qu, Zhu Sun","cross_cats":["cs.IR"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-24T03:18:16Z","title":"Does Knowledge Distillation Matter for Large Language Model based Bundle Generation?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.17220","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:db102bb4807cc236b4d41d4dab39bfbbb5b80d73365469fdb90dca96f4bd8910","target":"record","created_at":"2026-07-05T10:53:24Z","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":"8ee7199ac18eb8d4f367edd9d4775d592e1b089f089381a91eb9601960486e10","cross_cats_sorted":["cs.IR"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-24T03:18:16Z","title_canon_sha256":"241b51db62406b6b995f7f89fa53d95c250dca6b0e7c424e98f212e4b30d211d"},"schema_version":"1.0","source":{"id":"2504.17220","kind":"arxiv","version":1}},"canonical_sha256":"8a407e4bed0108c2949704a84a6a11410882f128fdb37f369f52da8799e1da07","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8a407e4bed0108c2949704a84a6a11410882f128fdb37f369f52da8799e1da07","first_computed_at":"2026-07-05T10:53:24.535890Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:53:24.535890Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OKJVY73Q2FkymD5YQBiWiW0/cHjkYz6lu5vaH42RKM1PDsqmxt5k13Cg/OLzJwJLm6ePe0J0KBkYc3Jr6oOFDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:53:24.536435Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.17220","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:db102bb4807cc236b4d41d4dab39bfbbb5b80d73365469fdb90dca96f4bd8910","sha256:189ed54fb46e9ac4d8efedf6c896ff68055c3e0f5e41ebd34a6340c077af98b6"],"state_sha256":"b109a1a2ddb201faebbe7f917c0919c32b27d1f1b031285a41dffe4ac3f568b6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xI7XfK54Vus7yM1uo3RvGqCT08w/glieTFKNvidM4rdSiBEjfxYxhnRSdIYgaAD3IZRZyDubwWQtbXXt418SBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T10:51:10.938112Z","bundle_sha256":"edd38defb6e9d512ee36196b05ae9928fe6a14de079347485eba0f8387f1f206"}}