{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:YCROZPIJWJGB2SABPTZAMY5B62","short_pith_number":"pith:YCROZPIJ","canonical_record":{"source":{"id":"2508.09263","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-12T18:07:11Z","cross_cats_sorted":[],"title_canon_sha256":"b69a7368edec0717f956f6cbc34a28e41139644941fb5d93622644dcaa87e321","abstract_canon_sha256":"dc5662e4a0c8f92ba2d8a89045376228188188affe72cda2c9467a1fd1f06e8d"},"schema_version":"1.0"},"canonical_sha256":"c0a2ecbd09b24c1d48017cf20663a1f6b6c90ebbcf14dd9daf9b49715360d982","source":{"kind":"arxiv","id":"2508.09263","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.09263","created_at":"2026-07-05T11:53:11Z"},{"alias_kind":"arxiv_version","alias_value":"2508.09263v1","created_at":"2026-07-05T11:53:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.09263","created_at":"2026-07-05T11:53:11Z"},{"alias_kind":"pith_short_12","alias_value":"YCROZPIJWJGB","created_at":"2026-07-05T11:53:11Z"},{"alias_kind":"pith_short_16","alias_value":"YCROZPIJWJGB2SAB","created_at":"2026-07-05T11:53:11Z"},{"alias_kind":"pith_short_8","alias_value":"YCROZPIJ","created_at":"2026-07-05T11:53:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:YCROZPIJWJGB2SABPTZAMY5B62","target":"record","payload":{"canonical_record":{"source":{"id":"2508.09263","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-12T18:07:11Z","cross_cats_sorted":[],"title_canon_sha256":"b69a7368edec0717f956f6cbc34a28e41139644941fb5d93622644dcaa87e321","abstract_canon_sha256":"dc5662e4a0c8f92ba2d8a89045376228188188affe72cda2c9467a1fd1f06e8d"},"schema_version":"1.0"},"canonical_sha256":"c0a2ecbd09b24c1d48017cf20663a1f6b6c90ebbcf14dd9daf9b49715360d982","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:53:11.505645Z","signature_b64":"oLpoT5pGQF6rO1asBm5th7FNSJOBJxJNB158u9CeHKlTHZBrYgEjrAcQuI3lIWjDIFGXvbwfOro10s4Esxl0Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c0a2ecbd09b24c1d48017cf20663a1f6b6c90ebbcf14dd9daf9b49715360d982","last_reissued_at":"2026-07-05T11:53:11.505139Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:53:11.505139Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.09263","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":"2026-07-05T11:53:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sd6TLYHE2QGYVDxUEh7jWlnM1/GzxelZDZg6cj4JGIrsR+hUBrBq2iFNVrB/lGXxUHiWIKYy3XR2cTLPwYKODw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T00:34:18.875918Z"},"content_sha256":"092ce16a35d4cc43815e6d2ff810208926c8c62a4a81e06c67de9051ad89fef3","schema_version":"1.0","event_id":"sha256:092ce16a35d4cc43815e6d2ff810208926c8c62a4a81e06c67de9051ad89fef3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:YCROZPIJWJGB2SABPTZAMY5B62","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LLM Empowered Prototype Learning for Zero and Few-Shot Tasks on Tabular Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dandan Guo, Dongsheng Wang, Hangting Ye, He Zhao, Peng Wang, Yi Chang","submitted_at":"2025-08-12T18:07:11Z","abstract_excerpt":"Recent breakthroughs in large language models (LLMs) have opened the door to in-depth investigation of their potential in tabular data modeling. However, effectively utilizing advanced LLMs in few-shot and even zero-shot scenarios is still challenging. To this end, we propose a novel LLM-based prototype estimation framework for tabular learning. Our key idea is to query the LLM to generate feature values based example-free prompt, which solely relies on task and feature descriptions. With the feature values generated by LLM, we can build a zero-shot prototype in a training-free manner, which c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.09263","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/2508.09263/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":"2026-07-05T11:53:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3lJehtEMaPkZFAWhMbwe8K7fuU8Vro9Y0ClUH8iY8C4PUWEeJJh9I0q69gGTX/r5e79L2fHZL5TcljYHW39eDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T00:34:18.876403Z"},"content_sha256":"7f47bfbc75ec30fcb50e988654c8f2ce9faa0c84691212ea884a0ba84e77f867","schema_version":"1.0","event_id":"sha256:7f47bfbc75ec30fcb50e988654c8f2ce9faa0c84691212ea884a0ba84e77f867"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YCROZPIJWJGB2SABPTZAMY5B62/bundle.json","state_url":"https://pith.science/pith/YCROZPIJWJGB2SABPTZAMY5B62/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YCROZPIJWJGB2SABPTZAMY5B62/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":"2026-08-07T00:34:18Z","links":{"resolver":"https://pith.science/pith/YCROZPIJWJGB2SABPTZAMY5B62","bundle":"https://pith.science/pith/YCROZPIJWJGB2SABPTZAMY5B62/bundle.json","state":"https://pith.science/pith/YCROZPIJWJGB2SABPTZAMY5B62/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YCROZPIJWJGB2SABPTZAMY5B62/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YCROZPIJWJGB2SABPTZAMY5B62","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":"dc5662e4a0c8f92ba2d8a89045376228188188affe72cda2c9467a1fd1f06e8d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-12T18:07:11Z","title_canon_sha256":"b69a7368edec0717f956f6cbc34a28e41139644941fb5d93622644dcaa87e321"},"schema_version":"1.0","source":{"id":"2508.09263","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.09263","created_at":"2026-07-05T11:53:11Z"},{"alias_kind":"arxiv_version","alias_value":"2508.09263v1","created_at":"2026-07-05T11:53:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.09263","created_at":"2026-07-05T11:53:11Z"},{"alias_kind":"pith_short_12","alias_value":"YCROZPIJWJGB","created_at":"2026-07-05T11:53:11Z"},{"alias_kind":"pith_short_16","alias_value":"YCROZPIJWJGB2SAB","created_at":"2026-07-05T11:53:11Z"},{"alias_kind":"pith_short_8","alias_value":"YCROZPIJ","created_at":"2026-07-05T11:53:11Z"}],"graph_snapshots":[{"event_id":"sha256:7f47bfbc75ec30fcb50e988654c8f2ce9faa0c84691212ea884a0ba84e77f867","target":"graph","created_at":"2026-07-05T11:53:11Z","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/2508.09263/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent breakthroughs in large language models (LLMs) have opened the door to in-depth investigation of their potential in tabular data modeling. However, effectively utilizing advanced LLMs in few-shot and even zero-shot scenarios is still challenging. To this end, we propose a novel LLM-based prototype estimation framework for tabular learning. Our key idea is to query the LLM to generate feature values based example-free prompt, which solely relies on task and feature descriptions. With the feature values generated by LLM, we can build a zero-shot prototype in a training-free manner, which c","authors_text":"Dandan Guo, Dongsheng Wang, Hangting Ye, He Zhao, Peng Wang, Yi Chang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-12T18:07:11Z","title":"LLM Empowered Prototype Learning for Zero and Few-Shot Tasks on Tabular Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.09263","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":"sha256:092ce16a35d4cc43815e6d2ff810208926c8c62a4a81e06c67de9051ad89fef3","target":"record","created_at":"2026-07-05T11:53:11Z","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":"dc5662e4a0c8f92ba2d8a89045376228188188affe72cda2c9467a1fd1f06e8d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-12T18:07:11Z","title_canon_sha256":"b69a7368edec0717f956f6cbc34a28e41139644941fb5d93622644dcaa87e321"},"schema_version":"1.0","source":{"id":"2508.09263","kind":"arxiv","version":1}},"canonical_sha256":"c0a2ecbd09b24c1d48017cf20663a1f6b6c90ebbcf14dd9daf9b49715360d982","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c0a2ecbd09b24c1d48017cf20663a1f6b6c90ebbcf14dd9daf9b49715360d982","first_computed_at":"2026-07-05T11:53:11.505139Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:53:11.505139Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oLpoT5pGQF6rO1asBm5th7FNSJOBJxJNB158u9CeHKlTHZBrYgEjrAcQuI3lIWjDIFGXvbwfOro10s4Esxl0Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T11:53:11.505645Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.09263","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:092ce16a35d4cc43815e6d2ff810208926c8c62a4a81e06c67de9051ad89fef3","sha256:7f47bfbc75ec30fcb50e988654c8f2ce9faa0c84691212ea884a0ba84e77f867"],"state_sha256":"59931cef0496b9fabf08b4c9394bfb167268359cde581a1f1da6aa97f88732ae"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nul2QCbirGFaQZrMmlTDTSNYK0bXAFqg856Org0H8X9JDixpipg2R+SwOuuuIKepgn85TpQDElPuiX/cW49JCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T00:34:18.907019Z","bundle_sha256":"1a7ed7071f9d9a4d9230e4f18d9f64829fce72c472a419bef27648bc1735ada6"}}