{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:B4PMBXBBUTQ2FV2NOEBVO2BCUO","short_pith_number":"pith:B4PMBXBB","canonical_record":{"source":{"id":"2608.03565","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-08-04T12:29:37Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3330e1b9f53d28bea6e82ecba86ef75601e04a1e6855b54f75e45dfd3b064028","abstract_canon_sha256":"2c41b8009b9b8d5d159b86e930e8b45873e9d53e0424524aa47130d39923a551"},"schema_version":"1.0"},"canonical_sha256":"0f1ec0dc21a4e1a2d74d7103576822a3b9bef15393f7b81b2b7007bb47025931","source":{"kind":"arxiv","id":"2608.03565","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.03565","created_at":"2026-08-05T01:36:39Z"},{"alias_kind":"arxiv_version","alias_value":"2608.03565v1","created_at":"2026-08-05T01:36:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.03565","created_at":"2026-08-05T01:36:39Z"},{"alias_kind":"pith_short_12","alias_value":"B4PMBXBBUTQ2","created_at":"2026-08-05T01:36:39Z"},{"alias_kind":"pith_short_16","alias_value":"B4PMBXBBUTQ2FV2N","created_at":"2026-08-05T01:36:39Z"},{"alias_kind":"pith_short_8","alias_value":"B4PMBXBB","created_at":"2026-08-05T01:36:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:B4PMBXBBUTQ2FV2NOEBVO2BCUO","target":"record","payload":{"canonical_record":{"source":{"id":"2608.03565","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-08-04T12:29:37Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3330e1b9f53d28bea6e82ecba86ef75601e04a1e6855b54f75e45dfd3b064028","abstract_canon_sha256":"2c41b8009b9b8d5d159b86e930e8b45873e9d53e0424524aa47130d39923a551"},"schema_version":"1.0"},"canonical_sha256":"0f1ec0dc21a4e1a2d74d7103576822a3b9bef15393f7b81b2b7007bb47025931","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-05T01:36:39.418189Z","signature_b64":"0tzSxdSgQXCxsPvEvKEBoZeCOOOwIHJ3XnEZs9Ed2Sqka2PfAJeEQxi2Ib3+k6CtPWOPuZPLA8KlVhSVDH+OBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0f1ec0dc21a4e1a2d74d7103576822a3b9bef15393f7b81b2b7007bb47025931","last_reissued_at":"2026-08-05T01:36:39.416630Z","signature_status":"signed_v1","first_computed_at":"2026-08-05T01:36:39.416630Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2608.03565","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":"2026-08-05T01:36:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mju6qoWqeZMh2PVqLJG29hEiLWIrNol/xNfJEc5RBac/oAT9eMl3o2BYQbJD92HJxVVRN2IJWxP+F+4YXq0XDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:48:38.149279Z"},"content_sha256":"bd1bea795b9df9ba634ad9f97ff3458e64b8b976fa8a0edbaff601214f4cdcbd","schema_version":"1.0","event_id":"sha256:bd1bea795b9df9ba634ad9f97ff3458e64b8b976fa8a0edbaff601214f4cdcbd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:B4PMBXBBUTQ2FV2NOEBVO2BCUO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Tabular Learners with Context-Aware Semantic Embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"G\\\"unther Schindler, Johannes H\\\"ohne, Maximilian Schambach","submitted_at":"2026-08-04T12:29:37Z","abstract_excerpt":"While modern tabular learners excel at capturing statistical patterns, they frequently operate in a semantic vacuum, treating textual features as discrete symbols, ignoring the rich semantics inherent in feature names or cell entries. We propose CASE (Context-Aware Semantic Embeddings), a novel framework that bridges the gap between the semantic understanding of Large Language Models (LLMs) and the statistical capabilities of tabular learners. Unlike existing methods that embed rows in isolation, CASE utilizes a contextualization strategy: we pre-fill the KV cache of a custom-trained Gemma 3-b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.03565","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/2608.03565/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":"2026-08-05T01:36:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vEH6ic0c2TxTJF9FekoBIrjG2w7EJG7OZqByc6XiUPfPPbEhos+86mwczwBWYyvE2wUT7X1cbdsb3vDHOdvGBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:48:38.149798Z"},"content_sha256":"5a8908363e6223e87c50feaa96257a5b9e3d0bf780912e3d2facbdc43c6b6330","schema_version":"1.0","event_id":"sha256:5a8908363e6223e87c50feaa96257a5b9e3d0bf780912e3d2facbdc43c6b6330"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B4PMBXBBUTQ2FV2NOEBVO2BCUO/bundle.json","state_url":"https://pith.science/pith/B4PMBXBBUTQ2FV2NOEBVO2BCUO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B4PMBXBBUTQ2FV2NOEBVO2BCUO/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":"2026-08-07T23:48:38Z","links":{"resolver":"https://pith.science/pith/B4PMBXBBUTQ2FV2NOEBVO2BCUO","bundle":"https://pith.science/pith/B4PMBXBBUTQ2FV2NOEBVO2BCUO/bundle.json","state":"https://pith.science/pith/B4PMBXBBUTQ2FV2NOEBVO2BCUO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B4PMBXBBUTQ2FV2NOEBVO2BCUO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:B4PMBXBBUTQ2FV2NOEBVO2BCUO","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":"2c41b8009b9b8d5d159b86e930e8b45873e9d53e0424524aa47130d39923a551","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-08-04T12:29:37Z","title_canon_sha256":"3330e1b9f53d28bea6e82ecba86ef75601e04a1e6855b54f75e45dfd3b064028"},"schema_version":"1.0","source":{"id":"2608.03565","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.03565","created_at":"2026-08-05T01:36:39Z"},{"alias_kind":"arxiv_version","alias_value":"2608.03565v1","created_at":"2026-08-05T01:36:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.03565","created_at":"2026-08-05T01:36:39Z"},{"alias_kind":"pith_short_12","alias_value":"B4PMBXBBUTQ2","created_at":"2026-08-05T01:36:39Z"},{"alias_kind":"pith_short_16","alias_value":"B4PMBXBBUTQ2FV2N","created_at":"2026-08-05T01:36:39Z"},{"alias_kind":"pith_short_8","alias_value":"B4PMBXBB","created_at":"2026-08-05T01:36:39Z"}],"graph_snapshots":[{"event_id":"sha256:5a8908363e6223e87c50feaa96257a5b9e3d0bf780912e3d2facbdc43c6b6330","target":"graph","created_at":"2026-08-05T01:36:39Z","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/2608.03565/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While modern tabular learners excel at capturing statistical patterns, they frequently operate in a semantic vacuum, treating textual features as discrete symbols, ignoring the rich semantics inherent in feature names or cell entries. We propose CASE (Context-Aware Semantic Embeddings), a novel framework that bridges the gap between the semantic understanding of Large Language Models (LLMs) and the statistical capabilities of tabular learners. Unlike existing methods that embed rows in isolation, CASE utilizes a contextualization strategy: we pre-fill the KV cache of a custom-trained Gemma 3-b","authors_text":"G\\\"unther Schindler, Johannes H\\\"ohne, Maximilian Schambach","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-08-04T12:29:37Z","title":"Enhancing Tabular Learners with Context-Aware Semantic Embeddings"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.03565","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:bd1bea795b9df9ba634ad9f97ff3458e64b8b976fa8a0edbaff601214f4cdcbd","target":"record","created_at":"2026-08-05T01:36:39Z","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":"2c41b8009b9b8d5d159b86e930e8b45873e9d53e0424524aa47130d39923a551","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-08-04T12:29:37Z","title_canon_sha256":"3330e1b9f53d28bea6e82ecba86ef75601e04a1e6855b54f75e45dfd3b064028"},"schema_version":"1.0","source":{"id":"2608.03565","kind":"arxiv","version":1}},"canonical_sha256":"0f1ec0dc21a4e1a2d74d7103576822a3b9bef15393f7b81b2b7007bb47025931","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0f1ec0dc21a4e1a2d74d7103576822a3b9bef15393f7b81b2b7007bb47025931","first_computed_at":"2026-08-05T01:36:39.416630Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-08-05T01:36:39.416630Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0tzSxdSgQXCxsPvEvKEBoZeCOOOwIHJ3XnEZs9Ed2Sqka2PfAJeEQxi2Ib3+k6CtPWOPuZPLA8KlVhSVDH+OBQ==","signature_status":"signed_v1","signed_at":"2026-08-05T01:36:39.418189Z","signed_message":"canonical_sha256_bytes"},"source_id":"2608.03565","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bd1bea795b9df9ba634ad9f97ff3458e64b8b976fa8a0edbaff601214f4cdcbd","sha256:5a8908363e6223e87c50feaa96257a5b9e3d0bf780912e3d2facbdc43c6b6330"],"state_sha256":"94e576c6a6c6457a97fe1c95678182a2048ead4856f2eb91a967725364694d8b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TShtTQJGBgbfWf62mcBpYKCTj2FRItx6OyAbzfFEP90mB8FQsuly6EkCEixOJVmfht2vGHhE8FxBLJFhGrMMAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T23:48:38.153543Z","bundle_sha256":"379bdda0ce943a611fb323105852204a0042f965d889af2f901ef195ed228e05"}}