{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:FKKG27IZXYXSAGE6Y6BJK3LIE4","short_pith_number":"pith:FKKG27IZ","canonical_record":{"source":{"id":"2508.12555","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-18T01:17:11Z","cross_cats_sorted":[],"title_canon_sha256":"e972e2a0fb3685cdba04dda79f6dbacecb68d08263fb6e578f513e66ee0f4e00","abstract_canon_sha256":"a11b3f7b6c5df1a1c6ce4ac21c24cd89b8ffde0d70f79daab5f03ea3accf27f9"},"schema_version":"1.0"},"canonical_sha256":"2a946d7d19be2f20189ec782956d68272f763464eaeb96f923a2c7e9c8fe7c79","source":{"kind":"arxiv","id":"2508.12555","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.12555","created_at":"2026-07-05T11:55:22Z"},{"alias_kind":"arxiv_version","alias_value":"2508.12555v1","created_at":"2026-07-05T11:55:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.12555","created_at":"2026-07-05T11:55:22Z"},{"alias_kind":"pith_short_12","alias_value":"FKKG27IZXYXS","created_at":"2026-07-05T11:55:22Z"},{"alias_kind":"pith_short_16","alias_value":"FKKG27IZXYXSAGE6","created_at":"2026-07-05T11:55:22Z"},{"alias_kind":"pith_short_8","alias_value":"FKKG27IZ","created_at":"2026-07-05T11:55:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:FKKG27IZXYXSAGE6Y6BJK3LIE4","target":"record","payload":{"canonical_record":{"source":{"id":"2508.12555","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-18T01:17:11Z","cross_cats_sorted":[],"title_canon_sha256":"e972e2a0fb3685cdba04dda79f6dbacecb68d08263fb6e578f513e66ee0f4e00","abstract_canon_sha256":"a11b3f7b6c5df1a1c6ce4ac21c24cd89b8ffde0d70f79daab5f03ea3accf27f9"},"schema_version":"1.0"},"canonical_sha256":"2a946d7d19be2f20189ec782956d68272f763464eaeb96f923a2c7e9c8fe7c79","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:55:22.943968Z","signature_b64":"62KLmOMYS0BRi/+69dalcnsx3zGCmiAQXFi/Ta7pISoJ7EPmo0t06uQ6PW4iIhU/ueEj8K7WisNlroQBiFmNCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2a946d7d19be2f20189ec782956d68272f763464eaeb96f923a2c7e9c8fe7c79","last_reissued_at":"2026-07-05T11:55:22.943341Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:55:22.943341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.12555","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":"2026-07-05T11:55:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9MNGNarnq9aQJGrqtofYOo5eCqNGEQAsFj5JBIPauDWWN9DovySSCG7delD2YrySfu8qJ56U4lmQwxk+rWlLAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T06:20:12.682781Z"},"content_sha256":"060414225aca3f352a325f8a94df4b0557055c883ffdbce11d1c20ad2736abcb","schema_version":"1.0","event_id":"sha256:060414225aca3f352a325f8a94df4b0557055c883ffdbce11d1c20ad2736abcb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:FKKG27IZXYXSAGE6Y6BJK3LIE4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Illuminating LLM Coding Agents: Visual Analytics for Deeper Understanding and Enhancement","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chin-Chia Michael Yeh, Junpeng Wang, Mahashweta Das, Menghai Pan, Yuzhong Chen","submitted_at":"2025-08-18T01:17:11Z","abstract_excerpt":"Coding agents powered by large language models (LLMs) have gained traction for automating code generation through iterative problem-solving with minimal human involvement. Despite the emergence of various frameworks, e.g., LangChain, AutoML, and AIDE, ML scientists still struggle to effectively review and adjust the agents' coding process. The current approach of manually inspecting individual outputs is inefficient, making it difficult to track code evolution, compare coding iterations, and identify improvement opportunities. To address this challenge, we introduce a visual analytics system d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.12555","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/2508.12555/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":"2026-07-05T11:55:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pSm9y7xkEy5331lIo9Lk4gB+AG5GnDEf+JvBBKhGvXbkW7vILJQve0ednovzgf0OT7j7edBgI49bFWkKwlRVDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T06:20:12.683395Z"},"content_sha256":"267553f966f0838cbbf468a771fed2ad8434678ecd5017b222ee1158a27d48d6","schema_version":"1.0","event_id":"sha256:267553f966f0838cbbf468a771fed2ad8434678ecd5017b222ee1158a27d48d6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FKKG27IZXYXSAGE6Y6BJK3LIE4/bundle.json","state_url":"https://pith.science/pith/FKKG27IZXYXSAGE6Y6BJK3LIE4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FKKG27IZXYXSAGE6Y6BJK3LIE4/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":"2026-08-15T06:20:12Z","links":{"resolver":"https://pith.science/pith/FKKG27IZXYXSAGE6Y6BJK3LIE4","bundle":"https://pith.science/pith/FKKG27IZXYXSAGE6Y6BJK3LIE4/bundle.json","state":"https://pith.science/pith/FKKG27IZXYXSAGE6Y6BJK3LIE4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FKKG27IZXYXSAGE6Y6BJK3LIE4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FKKG27IZXYXSAGE6Y6BJK3LIE4","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":"a11b3f7b6c5df1a1c6ce4ac21c24cd89b8ffde0d70f79daab5f03ea3accf27f9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-18T01:17:11Z","title_canon_sha256":"e972e2a0fb3685cdba04dda79f6dbacecb68d08263fb6e578f513e66ee0f4e00"},"schema_version":"1.0","source":{"id":"2508.12555","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.12555","created_at":"2026-07-05T11:55:22Z"},{"alias_kind":"arxiv_version","alias_value":"2508.12555v1","created_at":"2026-07-05T11:55:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.12555","created_at":"2026-07-05T11:55:22Z"},{"alias_kind":"pith_short_12","alias_value":"FKKG27IZXYXS","created_at":"2026-07-05T11:55:22Z"},{"alias_kind":"pith_short_16","alias_value":"FKKG27IZXYXSAGE6","created_at":"2026-07-05T11:55:22Z"},{"alias_kind":"pith_short_8","alias_value":"FKKG27IZ","created_at":"2026-07-05T11:55:22Z"}],"graph_snapshots":[{"event_id":"sha256:267553f966f0838cbbf468a771fed2ad8434678ecd5017b222ee1158a27d48d6","target":"graph","created_at":"2026-07-05T11:55:22Z","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/2508.12555/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Coding agents powered by large language models (LLMs) have gained traction for automating code generation through iterative problem-solving with minimal human involvement. Despite the emergence of various frameworks, e.g., LangChain, AutoML, and AIDE, ML scientists still struggle to effectively review and adjust the agents' coding process. The current approach of manually inspecting individual outputs is inefficient, making it difficult to track code evolution, compare coding iterations, and identify improvement opportunities. To address this challenge, we introduce a visual analytics system d","authors_text":"Chin-Chia Michael Yeh, Junpeng Wang, Mahashweta Das, Menghai Pan, Yuzhong Chen","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-18T01:17:11Z","title":"Illuminating LLM Coding Agents: Visual Analytics for Deeper Understanding and Enhancement"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.12555","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":"sha256:060414225aca3f352a325f8a94df4b0557055c883ffdbce11d1c20ad2736abcb","target":"record","created_at":"2026-07-05T11:55:22Z","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":"a11b3f7b6c5df1a1c6ce4ac21c24cd89b8ffde0d70f79daab5f03ea3accf27f9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-18T01:17:11Z","title_canon_sha256":"e972e2a0fb3685cdba04dda79f6dbacecb68d08263fb6e578f513e66ee0f4e00"},"schema_version":"1.0","source":{"id":"2508.12555","kind":"arxiv","version":1}},"canonical_sha256":"2a946d7d19be2f20189ec782956d68272f763464eaeb96f923a2c7e9c8fe7c79","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2a946d7d19be2f20189ec782956d68272f763464eaeb96f923a2c7e9c8fe7c79","first_computed_at":"2026-07-05T11:55:22.943341Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:55:22.943341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"62KLmOMYS0BRi/+69dalcnsx3zGCmiAQXFi/Ta7pISoJ7EPmo0t06uQ6PW4iIhU/ueEj8K7WisNlroQBiFmNCA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:55:22.943968Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.12555","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:060414225aca3f352a325f8a94df4b0557055c883ffdbce11d1c20ad2736abcb","sha256:267553f966f0838cbbf468a771fed2ad8434678ecd5017b222ee1158a27d48d6"],"state_sha256":"ba12dadfd49fb0f2d78128c5d12186db79af0ec5c15195ad4f4bb455f0861966"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H61H9VuoDm2q7cO7ZOAFN9/wV8WruoYBfsnXKTt1sHjVzZDbCsHBQqNTqyct8s1VdKt8kZZ5ZAWjdcmkpvU1Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T06:20:12.686632Z","bundle_sha256":"593b524e13cb5dc4b3c03b1db9c128b2ca4639f729c565a9a733c8bdac3bf19d"}}