{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:WODWRBA223DYVJ56D74XE3CFD3","short_pith_number":"pith:WODWRBA2","canonical_record":{"source":{"id":"2402.14195","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-22T00:41:23Z","cross_cats_sorted":[],"title_canon_sha256":"745724029ee340bc365aaab9f690a2a6f73e1c7e94ab9d586eb594993b702b92","abstract_canon_sha256":"c857b45ec0b482534703951130863dcbcd73ca560812f52452291569f73e7332"},"schema_version":"1.0"},"canonical_sha256":"b38768841ad6c78aa7be1ff9726c451ee86b3f3f3a7c6f59a2e6fa33f73c5f3b","source":{"kind":"arxiv","id":"2402.14195","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.14195","created_at":"2026-07-05T07:48:06Z"},{"alias_kind":"arxiv_version","alias_value":"2402.14195v1","created_at":"2026-07-05T07:48:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.14195","created_at":"2026-07-05T07:48:06Z"},{"alias_kind":"pith_short_12","alias_value":"WODWRBA223DY","created_at":"2026-07-05T07:48:06Z"},{"alias_kind":"pith_short_16","alias_value":"WODWRBA223DYVJ56","created_at":"2026-07-05T07:48:06Z"},{"alias_kind":"pith_short_8","alias_value":"WODWRBA2","created_at":"2026-07-05T07:48:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:WODWRBA223DYVJ56D74XE3CFD3","target":"record","payload":{"canonical_record":{"source":{"id":"2402.14195","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-22T00:41:23Z","cross_cats_sorted":[],"title_canon_sha256":"745724029ee340bc365aaab9f690a2a6f73e1c7e94ab9d586eb594993b702b92","abstract_canon_sha256":"c857b45ec0b482534703951130863dcbcd73ca560812f52452291569f73e7332"},"schema_version":"1.0"},"canonical_sha256":"b38768841ad6c78aa7be1ff9726c451ee86b3f3f3a7c6f59a2e6fa33f73c5f3b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:48:06.655981Z","signature_b64":"xZmkdPbnUFLSYC8KFaJxaRnLaVp8tm6YM0BA/1hPREubeUY2YpTXlvyM5mUHotyDMda0mkFjNUMf74/5adlmCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b38768841ad6c78aa7be1ff9726c451ee86b3f3f3a7c6f59a2e6fa33f73c5f3b","last_reissued_at":"2026-07-05T07:48:06.655524Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:48:06.655524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.14195","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":"2026-07-05T07:48:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YdpZzEacmHlv/ww09K79/jHFmuhs0bRdK6gX6IdSAnzDO5Lh0AWYX8QBEmi/RJGGRLRzW3SK53CRXQyyEB3WBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T16:45:13.498931Z"},"content_sha256":"467c679f44c79b1c3e24857925a91253d53a5a4fff1a2c170afba5f0b0de95c4","schema_version":"1.0","event_id":"sha256:467c679f44c79b1c3e24857925a91253d53a5a4fff1a2c170afba5f0b0de95c4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:WODWRBA223DYVJ56D74XE3CFD3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning to Reduce: Optimal Representations of Structured Data in Prompting Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ryan A. Rossi, Sungchul Kim, Tong Yu, Xiang Chen, Younghun Lee","submitted_at":"2024-02-22T00:41:23Z","abstract_excerpt":"Large Language Models (LLMs) have been widely used as general-purpose AI agents showing comparable performance on many downstream tasks. However, existing work shows that it is challenging for LLMs to integrate structured data (e.g. KG, tables, DBs) into their prompts; LLMs need to either understand long text data or select the most relevant evidence prior to inference, and both approaches are not trivial.\n  In this paper, we propose a framework, Learning to Reduce, that fine-tunes a language model to generate a reduced version of an input context, given a task description and context input. T"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.14195","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/2402.14195/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":"2026-07-05T07:48:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ViIcJf4N452ckPUpR6l3g1DXF0i5bQhQZtLS/uOsYvgql0weRG20NhmqgOeqs2v8lWHFuphBLkNz0ihC8/PUAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T16:45:13.499471Z"},"content_sha256":"c44ac6ba3181571def10e7453a6a94096035c17cf4e9164a6b90ba7b518515ae","schema_version":"1.0","event_id":"sha256:c44ac6ba3181571def10e7453a6a94096035c17cf4e9164a6b90ba7b518515ae"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WODWRBA223DYVJ56D74XE3CFD3/bundle.json","state_url":"https://pith.science/pith/WODWRBA223DYVJ56D74XE3CFD3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WODWRBA223DYVJ56D74XE3CFD3/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":"2026-08-07T16:45:13Z","links":{"resolver":"https://pith.science/pith/WODWRBA223DYVJ56D74XE3CFD3","bundle":"https://pith.science/pith/WODWRBA223DYVJ56D74XE3CFD3/bundle.json","state":"https://pith.science/pith/WODWRBA223DYVJ56D74XE3CFD3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WODWRBA223DYVJ56D74XE3CFD3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:WODWRBA223DYVJ56D74XE3CFD3","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":"c857b45ec0b482534703951130863dcbcd73ca560812f52452291569f73e7332","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-22T00:41:23Z","title_canon_sha256":"745724029ee340bc365aaab9f690a2a6f73e1c7e94ab9d586eb594993b702b92"},"schema_version":"1.0","source":{"id":"2402.14195","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.14195","created_at":"2026-07-05T07:48:06Z"},{"alias_kind":"arxiv_version","alias_value":"2402.14195v1","created_at":"2026-07-05T07:48:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.14195","created_at":"2026-07-05T07:48:06Z"},{"alias_kind":"pith_short_12","alias_value":"WODWRBA223DY","created_at":"2026-07-05T07:48:06Z"},{"alias_kind":"pith_short_16","alias_value":"WODWRBA223DYVJ56","created_at":"2026-07-05T07:48:06Z"},{"alias_kind":"pith_short_8","alias_value":"WODWRBA2","created_at":"2026-07-05T07:48:06Z"}],"graph_snapshots":[{"event_id":"sha256:c44ac6ba3181571def10e7453a6a94096035c17cf4e9164a6b90ba7b518515ae","target":"graph","created_at":"2026-07-05T07:48:06Z","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/2402.14195/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) have been widely used as general-purpose AI agents showing comparable performance on many downstream tasks. However, existing work shows that it is challenging for LLMs to integrate structured data (e.g. KG, tables, DBs) into their prompts; LLMs need to either understand long text data or select the most relevant evidence prior to inference, and both approaches are not trivial.\n  In this paper, we propose a framework, Learning to Reduce, that fine-tunes a language model to generate a reduced version of an input context, given a task description and context input. T","authors_text":"Ryan A. Rossi, Sungchul Kim, Tong Yu, Xiang Chen, Younghun Lee","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-22T00:41:23Z","title":"Learning to Reduce: Optimal Representations of Structured Data in Prompting Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.14195","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":"sha256:467c679f44c79b1c3e24857925a91253d53a5a4fff1a2c170afba5f0b0de95c4","target":"record","created_at":"2026-07-05T07:48:06Z","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":"c857b45ec0b482534703951130863dcbcd73ca560812f52452291569f73e7332","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-22T00:41:23Z","title_canon_sha256":"745724029ee340bc365aaab9f690a2a6f73e1c7e94ab9d586eb594993b702b92"},"schema_version":"1.0","source":{"id":"2402.14195","kind":"arxiv","version":1}},"canonical_sha256":"b38768841ad6c78aa7be1ff9726c451ee86b3f3f3a7c6f59a2e6fa33f73c5f3b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b38768841ad6c78aa7be1ff9726c451ee86b3f3f3a7c6f59a2e6fa33f73c5f3b","first_computed_at":"2026-07-05T07:48:06.655524Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:48:06.655524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xZmkdPbnUFLSYC8KFaJxaRnLaVp8tm6YM0BA/1hPREubeUY2YpTXlvyM5mUHotyDMda0mkFjNUMf74/5adlmCw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:48:06.655981Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.14195","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:467c679f44c79b1c3e24857925a91253d53a5a4fff1a2c170afba5f0b0de95c4","sha256:c44ac6ba3181571def10e7453a6a94096035c17cf4e9164a6b90ba7b518515ae"],"state_sha256":"2ca2d450b287166fb44c6a8c35fce01fc0042cf48dd9cfda1f6e350ac8051b6f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q86foXxf6In1o54xUi+hrj1hf49e8D7KHAa4LCT2D5fFMZ+OAr+D7gQMKaS4dGXqwPcdxeaf7t6F+73qYJWAAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T16:45:13.504969Z","bundle_sha256":"36705b5383311b02a5f4c80276ccc8675cef9eff0f7cce0e3096ca49a4354239"}}