{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:WND7YMCY6G46OQO4XZTWGXOM3S","short_pith_number":"pith:WND7YMCY","canonical_record":{"source":{"id":"2504.01241","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-01T23:06:55Z","cross_cats_sorted":[],"title_canon_sha256":"b94a1ea1a03075df49d33372ab88216f014ace58f039b5bf3ba9b68ecddc7ef4","abstract_canon_sha256":"1c712235f7df5b9cfedf3b4435d5c021b7b1d4979504c0e80f43b7aa27211592"},"schema_version":"1.0"},"canonical_sha256":"b347fc3058f1b9e741dcbe67635dccdc8101c342c95286bb4b1af9494b1189f8","source":{"kind":"arxiv","id":"2504.01241","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.01241","created_at":"2026-07-05T10:43:11Z"},{"alias_kind":"arxiv_version","alias_value":"2504.01241v1","created_at":"2026-07-05T10:43:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.01241","created_at":"2026-07-05T10:43:11Z"},{"alias_kind":"pith_short_12","alias_value":"WND7YMCY6G46","created_at":"2026-07-05T10:43:11Z"},{"alias_kind":"pith_short_16","alias_value":"WND7YMCY6G46OQO4","created_at":"2026-07-05T10:43:11Z"},{"alias_kind":"pith_short_8","alias_value":"WND7YMCY","created_at":"2026-07-05T10:43:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:WND7YMCY6G46OQO4XZTWGXOM3S","target":"record","payload":{"canonical_record":{"source":{"id":"2504.01241","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-01T23:06:55Z","cross_cats_sorted":[],"title_canon_sha256":"b94a1ea1a03075df49d33372ab88216f014ace58f039b5bf3ba9b68ecddc7ef4","abstract_canon_sha256":"1c712235f7df5b9cfedf3b4435d5c021b7b1d4979504c0e80f43b7aa27211592"},"schema_version":"1.0"},"canonical_sha256":"b347fc3058f1b9e741dcbe67635dccdc8101c342c95286bb4b1af9494b1189f8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:43:11.492800Z","signature_b64":"uo/KF57snLjRmZfJ65qEX83FXgwuTHBYMMnB6nI/jQwB7ZPLGojxRBLCxeQ7Amm0AGnYrw62JHN/EsNlTzyQCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b347fc3058f1b9e741dcbe67635dccdc8101c342c95286bb4b1af9494b1189f8","last_reissued_at":"2026-07-05T10:43:11.492336Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:43:11.492336Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.01241","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":"2026-07-05T10:43:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ttfLPwBPz+oE/xFfj1gNgziiriKnQRRhvRp0wNBG+YDLPlie2Tebaj/3yeUyDkc5uyTHXkLHxSz+xdeNO8tGCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T20:04:54.185381Z"},"content_sha256":"5072970b0863689f46b578574b4da42ea1506558caaefc7cf8f27f63d64e421b","schema_version":"1.0","event_id":"sha256:5072970b0863689f46b578574b4da42ea1506558caaefc7cf8f27f63d64e421b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:WND7YMCY6G46OQO4XZTWGXOM3S","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Catastrophic Forgetting in LLMs: A Comparative Analysis Across Language Tasks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Naimul Haque","submitted_at":"2025-04-01T23:06:55Z","abstract_excerpt":"Large Language Models (LLMs) have significantly advanced Natural Language Processing (NLP), particularly in Natural Language Understanding (NLU) tasks. As we progress toward an agentic world where LLM-based agents autonomously handle specialized tasks, it becomes crucial for these models to adapt to new tasks without forgetting previously learned information - a challenge known as catastrophic forgetting. This study evaluates the continual fine-tuning of various open-source LLMs with different parameter sizes (specifically models under 10 billion parameters) on key NLU tasks from the GLUE benc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.01241","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/2504.01241/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":"2026-07-05T10:43:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MqWBCvq2Ys+Wx0E4/D51+Haa/tJEjVwrpcT/5+qZJsL4nN9BR/Gv7d6vIamdb0OkKGSpCtqndLPLYKzwDrO6DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T20:04:54.185874Z"},"content_sha256":"bae1aea786b4a6fa0049c046e2c8c18edcb9cd106270ea82dec95ad42c6908ca","schema_version":"1.0","event_id":"sha256:bae1aea786b4a6fa0049c046e2c8c18edcb9cd106270ea82dec95ad42c6908ca"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WND7YMCY6G46OQO4XZTWGXOM3S/bundle.json","state_url":"https://pith.science/pith/WND7YMCY6G46OQO4XZTWGXOM3S/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WND7YMCY6G46OQO4XZTWGXOM3S/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":"2026-08-21T20:04:54Z","links":{"resolver":"https://pith.science/pith/WND7YMCY6G46OQO4XZTWGXOM3S","bundle":"https://pith.science/pith/WND7YMCY6G46OQO4XZTWGXOM3S/bundle.json","state":"https://pith.science/pith/WND7YMCY6G46OQO4XZTWGXOM3S/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WND7YMCY6G46OQO4XZTWGXOM3S/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:WND7YMCY6G46OQO4XZTWGXOM3S","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":"1c712235f7df5b9cfedf3b4435d5c021b7b1d4979504c0e80f43b7aa27211592","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-01T23:06:55Z","title_canon_sha256":"b94a1ea1a03075df49d33372ab88216f014ace58f039b5bf3ba9b68ecddc7ef4"},"schema_version":"1.0","source":{"id":"2504.01241","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.01241","created_at":"2026-07-05T10:43:11Z"},{"alias_kind":"arxiv_version","alias_value":"2504.01241v1","created_at":"2026-07-05T10:43:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.01241","created_at":"2026-07-05T10:43:11Z"},{"alias_kind":"pith_short_12","alias_value":"WND7YMCY6G46","created_at":"2026-07-05T10:43:11Z"},{"alias_kind":"pith_short_16","alias_value":"WND7YMCY6G46OQO4","created_at":"2026-07-05T10:43:11Z"},{"alias_kind":"pith_short_8","alias_value":"WND7YMCY","created_at":"2026-07-05T10:43:11Z"}],"graph_snapshots":[{"event_id":"sha256:bae1aea786b4a6fa0049c046e2c8c18edcb9cd106270ea82dec95ad42c6908ca","target":"graph","created_at":"2026-07-05T10:43:11Z","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/2504.01241/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) have significantly advanced Natural Language Processing (NLP), particularly in Natural Language Understanding (NLU) tasks. As we progress toward an agentic world where LLM-based agents autonomously handle specialized tasks, it becomes crucial for these models to adapt to new tasks without forgetting previously learned information - a challenge known as catastrophic forgetting. This study evaluates the continual fine-tuning of various open-source LLMs with different parameter sizes (specifically models under 10 billion parameters) on key NLU tasks from the GLUE benc","authors_text":"Naimul Haque","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-01T23:06:55Z","title":"Catastrophic Forgetting in LLMs: A Comparative Analysis Across Language Tasks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.01241","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":"sha256:5072970b0863689f46b578574b4da42ea1506558caaefc7cf8f27f63d64e421b","target":"record","created_at":"2026-07-05T10:43:11Z","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":"1c712235f7df5b9cfedf3b4435d5c021b7b1d4979504c0e80f43b7aa27211592","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-01T23:06:55Z","title_canon_sha256":"b94a1ea1a03075df49d33372ab88216f014ace58f039b5bf3ba9b68ecddc7ef4"},"schema_version":"1.0","source":{"id":"2504.01241","kind":"arxiv","version":1}},"canonical_sha256":"b347fc3058f1b9e741dcbe67635dccdc8101c342c95286bb4b1af9494b1189f8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b347fc3058f1b9e741dcbe67635dccdc8101c342c95286bb4b1af9494b1189f8","first_computed_at":"2026-07-05T10:43:11.492336Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:43:11.492336Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uo/KF57snLjRmZfJ65qEX83FXgwuTHBYMMnB6nI/jQwB7ZPLGojxRBLCxeQ7Amm0AGnYrw62JHN/EsNlTzyQCA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:43:11.492800Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.01241","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5072970b0863689f46b578574b4da42ea1506558caaefc7cf8f27f63d64e421b","sha256:bae1aea786b4a6fa0049c046e2c8c18edcb9cd106270ea82dec95ad42c6908ca"],"state_sha256":"db8630e650c2e77cbee21547241faf867f196b910c7b998a4c075fa2ee2ebc6a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jJ9tpwRX9PUEkavv9oeKhv9nPWTfoGtJEiwxYWGx3jjeTmQnh2gHUzeSMHeDk3EcBrU78IWR+bE8azU4VD72AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T20:04:54.189922Z","bundle_sha256":"5eb64e0c29794ecf456ca4fe8fe7e067dd3ab7c11d6be611d2d6df31120f0c72"}}