{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:FCHUILD2FFNBALQJJALDS3A22V","short_pith_number":"pith:FCHUILD2","canonical_record":{"source":{"id":"2502.09690","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-13T17:05:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"57fe9f96760c7dc304da1350ef631e1f21db5c818832080f60af47c7425c2f03","abstract_canon_sha256":"0b7838fded748c2c0f436733b9f26bca1c8f9b7af4ea5b479248e5e69e428d2d"},"schema_version":"1.0"},"canonical_sha256":"288f442c7a295a102e094816396c1ad54dcf1e7bf9cd03a2331b9a41bcfb942b","source":{"kind":"arxiv","id":"2502.09690","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.09690","created_at":"2026-07-05T10:14:10Z"},{"alias_kind":"arxiv_version","alias_value":"2502.09690v1","created_at":"2026-07-05T10:14:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.09690","created_at":"2026-07-05T10:14:10Z"},{"alias_kind":"pith_short_12","alias_value":"FCHUILD2FFNB","created_at":"2026-07-05T10:14:10Z"},{"alias_kind":"pith_short_16","alias_value":"FCHUILD2FFNBALQJ","created_at":"2026-07-05T10:14:10Z"},{"alias_kind":"pith_short_8","alias_value":"FCHUILD2","created_at":"2026-07-05T10:14:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:FCHUILD2FFNBALQJJALDS3A22V","target":"record","payload":{"canonical_record":{"source":{"id":"2502.09690","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-13T17:05:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"57fe9f96760c7dc304da1350ef631e1f21db5c818832080f60af47c7425c2f03","abstract_canon_sha256":"0b7838fded748c2c0f436733b9f26bca1c8f9b7af4ea5b479248e5e69e428d2d"},"schema_version":"1.0"},"canonical_sha256":"288f442c7a295a102e094816396c1ad54dcf1e7bf9cd03a2331b9a41bcfb942b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:14:10.684215Z","signature_b64":"sMYjwKW2ucT/uTErDFBJY8Vg+G8z8OzDmL/JrbjrF1lYa5QnJaKIbFsCX3A3gSr6e2SvUZZrfnJrsdNI8i81DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"288f442c7a295a102e094816396c1ad54dcf1e7bf9cd03a2331b9a41bcfb942b","last_reissued_at":"2026-07-05T10:14:10.683625Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:14:10.683625Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.09690","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":"2026-07-05T10:14:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y5bN0x3xyP6ABEFr6uvrcuBhhMrpvKqCAbW6bYGy5hCbpX09rVvTwRR/bYCfCG0GSpeYesYkE0NU3d87Sz/wCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T07:58:00.427192Z"},"content_sha256":"5ef87893719c9ff4d526b5fb479db5998c89e12c87e69113130a34f9ca70e36d","schema_version":"1.0","event_id":"sha256:5ef87893719c9ff4d526b5fb479db5998c89e12c87e69113130a34f9ca70e36d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:FCHUILD2FFNBALQJJALDS3A22V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Trust at Your Own Peril: A Mixed Methods Exploration of the Ability of Large Language Models to Generate Expert-Like Systems Engineering Artifacts and a Characterization of Failure Modes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Max Ofsa, Mohammed Husain, Paul Wach, Taylan G. Topcu","submitted_at":"2025-02-13T17:05:18Z","abstract_excerpt":"Multi-purpose Large Language Models (LLMs), a subset of generative Artificial Intelligence (AI), have recently made significant progress. While expectations for LLMs to assist systems engineering (SE) tasks are paramount; the interdisciplinary and complex nature of systems, along with the need to synthesize deep-domain knowledge and operational context, raise questions regarding the efficacy of LLMs to generate SE artifacts, particularly given that they are trained using data that is broadly available on the internet. To that end, we present results from an empirical exploration, where a human"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.09690","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/2502.09690/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":"2026-07-05T10:14:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g5He64lBTUAvW/aJcwiD8PTKHEeDeMICKawtfyiAdpz+NGb+szB/85KzdZzY0ahZYQ7mNrnq9FSgTHv4LobqDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T07:58:00.427769Z"},"content_sha256":"006fe4e44247ad73c6ac4fa3d63dde26c672b712d1e573460fb7cbda7d8272f6","schema_version":"1.0","event_id":"sha256:006fe4e44247ad73c6ac4fa3d63dde26c672b712d1e573460fb7cbda7d8272f6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FCHUILD2FFNBALQJJALDS3A22V/bundle.json","state_url":"https://pith.science/pith/FCHUILD2FFNBALQJJALDS3A22V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FCHUILD2FFNBALQJJALDS3A22V/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":"2026-08-09T07:58:00Z","links":{"resolver":"https://pith.science/pith/FCHUILD2FFNBALQJJALDS3A22V","bundle":"https://pith.science/pith/FCHUILD2FFNBALQJJALDS3A22V/bundle.json","state":"https://pith.science/pith/FCHUILD2FFNBALQJJALDS3A22V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FCHUILD2FFNBALQJJALDS3A22V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FCHUILD2FFNBALQJJALDS3A22V","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":"0b7838fded748c2c0f436733b9f26bca1c8f9b7af4ea5b479248e5e69e428d2d","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-13T17:05:18Z","title_canon_sha256":"57fe9f96760c7dc304da1350ef631e1f21db5c818832080f60af47c7425c2f03"},"schema_version":"1.0","source":{"id":"2502.09690","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.09690","created_at":"2026-07-05T10:14:10Z"},{"alias_kind":"arxiv_version","alias_value":"2502.09690v1","created_at":"2026-07-05T10:14:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.09690","created_at":"2026-07-05T10:14:10Z"},{"alias_kind":"pith_short_12","alias_value":"FCHUILD2FFNB","created_at":"2026-07-05T10:14:10Z"},{"alias_kind":"pith_short_16","alias_value":"FCHUILD2FFNBALQJ","created_at":"2026-07-05T10:14:10Z"},{"alias_kind":"pith_short_8","alias_value":"FCHUILD2","created_at":"2026-07-05T10:14:10Z"}],"graph_snapshots":[{"event_id":"sha256:006fe4e44247ad73c6ac4fa3d63dde26c672b712d1e573460fb7cbda7d8272f6","target":"graph","created_at":"2026-07-05T10:14:10Z","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/2502.09690/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-purpose Large Language Models (LLMs), a subset of generative Artificial Intelligence (AI), have recently made significant progress. While expectations for LLMs to assist systems engineering (SE) tasks are paramount; the interdisciplinary and complex nature of systems, along with the need to synthesize deep-domain knowledge and operational context, raise questions regarding the efficacy of LLMs to generate SE artifacts, particularly given that they are trained using data that is broadly available on the internet. To that end, we present results from an empirical exploration, where a human","authors_text":"Max Ofsa, Mohammed Husain, Paul Wach, Taylan G. Topcu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-13T17:05:18Z","title":"Trust at Your Own Peril: A Mixed Methods Exploration of the Ability of Large Language Models to Generate Expert-Like Systems Engineering Artifacts and a Characterization of Failure Modes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.09690","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":"sha256:5ef87893719c9ff4d526b5fb479db5998c89e12c87e69113130a34f9ca70e36d","target":"record","created_at":"2026-07-05T10:14:10Z","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":"0b7838fded748c2c0f436733b9f26bca1c8f9b7af4ea5b479248e5e69e428d2d","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-13T17:05:18Z","title_canon_sha256":"57fe9f96760c7dc304da1350ef631e1f21db5c818832080f60af47c7425c2f03"},"schema_version":"1.0","source":{"id":"2502.09690","kind":"arxiv","version":1}},"canonical_sha256":"288f442c7a295a102e094816396c1ad54dcf1e7bf9cd03a2331b9a41bcfb942b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"288f442c7a295a102e094816396c1ad54dcf1e7bf9cd03a2331b9a41bcfb942b","first_computed_at":"2026-07-05T10:14:10.683625Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:14:10.683625Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sMYjwKW2ucT/uTErDFBJY8Vg+G8z8OzDmL/JrbjrF1lYa5QnJaKIbFsCX3A3gSr6e2SvUZZrfnJrsdNI8i81DA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:14:10.684215Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.09690","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5ef87893719c9ff4d526b5fb479db5998c89e12c87e69113130a34f9ca70e36d","sha256:006fe4e44247ad73c6ac4fa3d63dde26c672b712d1e573460fb7cbda7d8272f6"],"state_sha256":"fee5a923ca2b0f3f4cd0ef933122f09db07f47fa9d3461228e43fc5a5a1637b1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aNuQLyOOXRwXz0QskHin3JlJqsXz0SVntvGGg0eaQDm6Qpq5NCQT7D4YxunBD3nLGW90tvtLJh17Z37gkr0zCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T07:58:00.432913Z","bundle_sha256":"5e1ba90925c9f1d0f7fe06a5399087b894a60e6a3e2813d1d9f6557332278f80"}}