{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:7SN2QPAVBTWLE3KJHKJEAOACWZ","short_pith_number":"pith:7SN2QPAV","canonical_record":{"source":{"id":"2608.10837","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-08-11T12:03:37Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ae0f3f1b3af32eb279c7ccee48aa2e1184f36b6968c84ed18a28a071b88f3cd2","abstract_canon_sha256":"1a11c7f588ccc8d4af9ab76bec4ab895fd6388031ad1875918ead0ae621749cf"},"schema_version":"1.0"},"canonical_sha256":"fc9ba83c150cecb26d493a92403802b654d5ec87cb6e46896fb7f1f510a26504","source":{"kind":"arxiv","id":"2608.10837","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.10837","created_at":"2026-08-12T01:23:27Z"},{"alias_kind":"arxiv_version","alias_value":"2608.10837v1","created_at":"2026-08-12T01:23:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.10837","created_at":"2026-08-12T01:23:27Z"},{"alias_kind":"pith_short_12","alias_value":"7SN2QPAVBTWL","created_at":"2026-08-12T01:23:27Z"},{"alias_kind":"pith_short_16","alias_value":"7SN2QPAVBTWLE3KJ","created_at":"2026-08-12T01:23:27Z"},{"alias_kind":"pith_short_8","alias_value":"7SN2QPAV","created_at":"2026-08-12T01:23:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:7SN2QPAVBTWLE3KJHKJEAOACWZ","target":"record","payload":{"canonical_record":{"source":{"id":"2608.10837","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-08-11T12:03:37Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ae0f3f1b3af32eb279c7ccee48aa2e1184f36b6968c84ed18a28a071b88f3cd2","abstract_canon_sha256":"1a11c7f588ccc8d4af9ab76bec4ab895fd6388031ad1875918ead0ae621749cf"},"schema_version":"1.0"},"canonical_sha256":"fc9ba83c150cecb26d493a92403802b654d5ec87cb6e46896fb7f1f510a26504","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-12T01:23:27.057144Z","signature_b64":"hK99kIEQzNX6TdUMuqL4LUrhTBNXS8txe1fooA25N6lvsbZuc0iT4JFN5j1gYXP7snQt0KKDN2+ccwShjKziAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc9ba83c150cecb26d493a92403802b654d5ec87cb6e46896fb7f1f510a26504","last_reissued_at":"2026-08-12T01:23:27.054883Z","signature_status":"signed_v1","first_computed_at":"2026-08-12T01:23:27.054883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2608.10837","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":"2026-08-12T01:23:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Iz2Cp/45/ygRKWJ1B6Ji1Fxnqguk3vB0iz/DMk1LBjwT+PRAxuCw19eywHDKohesj426NfMRN4XcKZphS2XWCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T19:00:28.407469Z"},"content_sha256":"0d3d3342662914444d375b6cd5590efa10c553c9de47023e1714fb71e809b8df","schema_version":"1.0","event_id":"sha256:0d3d3342662914444d375b6cd5590efa10c553c9de47023e1714fb71e809b8df"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:7SN2QPAVBTWLE3KJHKJEAOACWZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TACTICL: Task-Aware Compression of Tabular ICL Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Katharina Eggensperger, Matthias Feurer, Mykhailo Koshil","submitted_at":"2026-08-11T12:03:37Z","abstract_excerpt":"The strong performance of foundation models for tabular tasks comes at substantial inference costs. Distilling models into task-specific architectures reduces model size and computational demands but also sacrifices in-context adaptability. Here we introduce TACTICL, an automated task-aware compression framework for tabular in-context learning models that jointly prunes transformer layers and replaces them with lightweight adapters trained on downstream tasks, thus blending in-context with in-weight learning. We study TACTICL on 47 benchmark datasets and show that we can substitute up to 85% o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.10837","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/2608.10837/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":"2026-08-12T01:23:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o6vpXhJ0NEwooIgwstl/1LigYcYJbtKsk7k5dH9ZRr+EKRZUhIrsqW3yltwBw2lcoIfqfkkvKrLD8zIu8539AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T19:00:28.408358Z"},"content_sha256":"ed9e7a8ebd2a501352813fbd43245183ab42e7e812003c01215a70eb46b8806f","schema_version":"1.0","event_id":"sha256:ed9e7a8ebd2a501352813fbd43245183ab42e7e812003c01215a70eb46b8806f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7SN2QPAVBTWLE3KJHKJEAOACWZ/bundle.json","state_url":"https://pith.science/pith/7SN2QPAVBTWLE3KJHKJEAOACWZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7SN2QPAVBTWLE3KJHKJEAOACWZ/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":"2026-08-20T19:00:28Z","links":{"resolver":"https://pith.science/pith/7SN2QPAVBTWLE3KJHKJEAOACWZ","bundle":"https://pith.science/pith/7SN2QPAVBTWLE3KJHKJEAOACWZ/bundle.json","state":"https://pith.science/pith/7SN2QPAVBTWLE3KJHKJEAOACWZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7SN2QPAVBTWLE3KJHKJEAOACWZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:7SN2QPAVBTWLE3KJHKJEAOACWZ","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":"1a11c7f588ccc8d4af9ab76bec4ab895fd6388031ad1875918ead0ae621749cf","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-08-11T12:03:37Z","title_canon_sha256":"ae0f3f1b3af32eb279c7ccee48aa2e1184f36b6968c84ed18a28a071b88f3cd2"},"schema_version":"1.0","source":{"id":"2608.10837","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.10837","created_at":"2026-08-12T01:23:27Z"},{"alias_kind":"arxiv_version","alias_value":"2608.10837v1","created_at":"2026-08-12T01:23:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.10837","created_at":"2026-08-12T01:23:27Z"},{"alias_kind":"pith_short_12","alias_value":"7SN2QPAVBTWL","created_at":"2026-08-12T01:23:27Z"},{"alias_kind":"pith_short_16","alias_value":"7SN2QPAVBTWLE3KJ","created_at":"2026-08-12T01:23:27Z"},{"alias_kind":"pith_short_8","alias_value":"7SN2QPAV","created_at":"2026-08-12T01:23:27Z"}],"graph_snapshots":[{"event_id":"sha256:ed9e7a8ebd2a501352813fbd43245183ab42e7e812003c01215a70eb46b8806f","target":"graph","created_at":"2026-08-12T01:23:27Z","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/2608.10837/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The strong performance of foundation models for tabular tasks comes at substantial inference costs. Distilling models into task-specific architectures reduces model size and computational demands but also sacrifices in-context adaptability. Here we introduce TACTICL, an automated task-aware compression framework for tabular in-context learning models that jointly prunes transformer layers and replaces them with lightweight adapters trained on downstream tasks, thus blending in-context with in-weight learning. We study TACTICL on 47 benchmark datasets and show that we can substitute up to 85% o","authors_text":"Katharina Eggensperger, Matthias Feurer, Mykhailo Koshil","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-08-11T12:03:37Z","title":"TACTICL: Task-Aware Compression of Tabular ICL Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.10837","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":"sha256:0d3d3342662914444d375b6cd5590efa10c553c9de47023e1714fb71e809b8df","target":"record","created_at":"2026-08-12T01:23:27Z","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":"1a11c7f588ccc8d4af9ab76bec4ab895fd6388031ad1875918ead0ae621749cf","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-08-11T12:03:37Z","title_canon_sha256":"ae0f3f1b3af32eb279c7ccee48aa2e1184f36b6968c84ed18a28a071b88f3cd2"},"schema_version":"1.0","source":{"id":"2608.10837","kind":"arxiv","version":1}},"canonical_sha256":"fc9ba83c150cecb26d493a92403802b654d5ec87cb6e46896fb7f1f510a26504","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fc9ba83c150cecb26d493a92403802b654d5ec87cb6e46896fb7f1f510a26504","first_computed_at":"2026-08-12T01:23:27.054883Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-08-12T01:23:27.054883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hK99kIEQzNX6TdUMuqL4LUrhTBNXS8txe1fooA25N6lvsbZuc0iT4JFN5j1gYXP7snQt0KKDN2+ccwShjKziAQ==","signature_status":"signed_v1","signed_at":"2026-08-12T01:23:27.057144Z","signed_message":"canonical_sha256_bytes"},"source_id":"2608.10837","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0d3d3342662914444d375b6cd5590efa10c553c9de47023e1714fb71e809b8df","sha256:ed9e7a8ebd2a501352813fbd43245183ab42e7e812003c01215a70eb46b8806f"],"state_sha256":"f4d73f3437d6f5b9c9fb3063f8dfd9531c18bcf4f11bcefed833340026c9d0d0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G/Q6T6/jVcB3fCguqPgqqYtq5KSNGSmecZJOyOBDLoTtET+/nPzPnghZut6ugt0iilJDjfsunjXNN8+06c+GCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T19:00:28.415231Z","bundle_sha256":"1af1d580bddb1125d0b9a2535cfbbf88cd0a713824082c6d54ea3755e704969f"}}