{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:T2YNBZM6G63HVXZR4BMRLGJNKS","short_pith_number":"pith:T2YNBZM6","canonical_record":{"source":{"id":"2605.16909","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-16T09:49:25Z","cross_cats_sorted":[],"title_canon_sha256":"0e94e30a6ceaafe893039407fa64982ab22b7f9f0eaf06eb2cb03e742c3a6581","abstract_canon_sha256":"b192cbfb18685ada9aacd4aa72068a361a4b9a656f3f16fa2fdd249eef0bec17"},"schema_version":"1.0"},"canonical_sha256":"9eb0d0e59e37b67adf31e05915992d54bd7035b4e0d6a506695297e14e951b55","source":{"kind":"arxiv","id":"2605.16909","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16909","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16909v1","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16909","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"pith_short_12","alias_value":"T2YNBZM6G63H","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"pith_short_16","alias_value":"T2YNBZM6G63HVXZR","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"pith_short_8","alias_value":"T2YNBZM6","created_at":"2026-05-20T00:03:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:T2YNBZM6G63HVXZR4BMRLGJNKS","target":"record","payload":{"canonical_record":{"source":{"id":"2605.16909","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-16T09:49:25Z","cross_cats_sorted":[],"title_canon_sha256":"0e94e30a6ceaafe893039407fa64982ab22b7f9f0eaf06eb2cb03e742c3a6581","abstract_canon_sha256":"b192cbfb18685ada9aacd4aa72068a361a4b9a656f3f16fa2fdd249eef0bec17"},"schema_version":"1.0"},"canonical_sha256":"9eb0d0e59e37b67adf31e05915992d54bd7035b4e0d6a506695297e14e951b55","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:29.678399Z","signature_b64":"/4ygHccYDYkQKzcurYefOZoALLCiMtexstY0ecWGV94RslgJa14Q0iNmcjd4JFMwgHfg1eI3QLX5M+uF9zVXCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9eb0d0e59e37b67adf31e05915992d54bd7035b4e0d6a506695297e14e951b55","last_reissued_at":"2026-05-20T00:03:29.677333Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:29.677333Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.16909","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":"2026-05-20T00:03:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lMdZizKAGt6Bw8vugIJnpdlzgf+v5+eGbrs/s4XNDcYN8WvRnjGErtW7t70xYNvFtnfCRktn06/lcjq1L19nDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T01:08:33.089515Z"},"content_sha256":"9746c6f9b70e05a2765532736c7900a1c6b3f79a79377cc35cec66ad5ceef36e","schema_version":"1.0","event_id":"sha256:9746c6f9b70e05a2765532736c7900a1c6b3f79a79377cc35cec66ad5ceef36e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:T2YNBZM6G63HVXZR4BMRLGJNKS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TOBench: A Task-Oriented Omni-Modal Benchmark for Real-World Tool-Using Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Current AI agents succeed on only 32 percent of realistic omni-modal tool tasks while humans reach 94 percent.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Chenyang Si, Haochen Yin, Wenhui Dong, Yilang Tan, Yuwen Qu, Zhiqiang Liu","submitted_at":"2026-05-16T09:49:25Z","abstract_excerpt":"Tool-using agents are increasingly expected to operate across realistic professional workflows, where they must interpret multimodal inputs, coordinate external tools, inspect intermediate artifacts, and revise their actions before producing a final result. Existing benchmarks, however, often evaluate tool use, computer use, and multimodal reasoning in isolation, leaving a gap between benchmark settings and end-to-end omni-modal tool use in the real world. To address this gap, we introduce MM-ToolBench, a benchmark and evaluation harness for task-oriented omni-modal tool use. MM-ToolBench cont"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Claude Opus 4.6 achieves only 32.0% task success on MM-ToolBench, far below the 94.0% human benchmark, demonstrating that current models remain highly challenged by closed-loop omni-modal tool use.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The 100 tasks and their grounded evaluators accurately capture the essential requirements of real-world professional omni-modal tool use without introducing artificial simplifications or biases in scenario selection.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MM-ToolBench introduces 100 closed-loop multimodal tasks across two domains with 27 MCP servers and 324 tools, where agents must execute, inspect artifacts, and revise before final output.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Current AI agents succeed on only 32 percent of realistic omni-modal tool tasks while humans reach 94 percent.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"9e58f697628c14eb4f749eed1678b03d88ffb46e2d1acea0b4aef2d0397e72b9"},"source":{"id":"2605.16909","kind":"arxiv","version":1},"verdict":{"id":"02ff2674-b347-498f-b63a-5f70e3549118","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T20:39:03.648521Z","strongest_claim":"Claude Opus 4.6 achieves only 32.0% task success on MM-ToolBench, far below the 94.0% human benchmark, demonstrating that current models remain highly challenged by closed-loop omni-modal tool use.","one_line_summary":"MM-ToolBench introduces 100 closed-loop multimodal tasks across two domains with 27 MCP servers and 324 tools, where agents must execute, inspect artifacts, and revise before final output.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The 100 tasks and their grounded evaluators accurately capture the essential requirements of real-world professional omni-modal tool use without introducing artificial simplifications or biases in scenario selection.","pith_extraction_headline":"Current AI agents succeed on only 32 percent of realistic omni-modal tool tasks while humans reach 94 percent."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16909/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T21:01:19.175129Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"cited_work_retraction","ran_at":"2026-05-19T20:52:03.375217Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T20:51:10.914645Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.270679Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.350458Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"687dbccfed29a51642e36e4681b8b923c2b40a512d7e63cc5dd6a71ced00063a"},"references":{"count":101,"sample":[{"doi":"","year":2023,"title":"GPT-4 Technical Report","work_id":"b928e041-6991-4c08-8c81-0359e4097c7b","ref_index":1,"cited_arxiv_id":"2303.08774","is_internal_anchor":true},{"doi":"","year":2024,"title":"GPT-4o System Card","work_id":"f37bf1c7-4964-4e56-9762-d20da8d9009f","ref_index":2,"cited_arxiv_id":"2410.21276","is_internal_anchor":true},{"doi":"","year":2024,"title":"$\\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains","work_id":"6a8d8dc4-0cc0-4052-8109-abbcdcd4a962","ref_index":3,"cited_arxiv_id":"2406.12045","is_internal_anchor":true},{"doi":"","year":2023,"title":"Toolllm: Facilitating large language models to master 16000+ real-world apis","work_id":"aec240ca-e2df-4ba3-b9d1-980be81c76a3","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"The berkeley function calling leaderboard (bfcl): From tool use to agentic evaluation of large language models","work_id":"c6761b98-dc2b-49f4-849a-3ad1bc3aa106","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":101,"snapshot_sha256":"1e3d1a055b43e64d465a2190190ca741305efe892721119b24929d242a31a1b1","internal_anchors":7},"formal_canon":{"evidence_count":1,"snapshot_sha256":"3009f9ad72421404b7f765dd9cb85328744f65d646a8129d2ae98c1125a90f30"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"02ff2674-b347-498f-b63a-5f70e3549118"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+3P91sT45dxxWJ85Dtr/BiNXknbRp3+lGoiYmbdrxZxMWDU55ktzR2iu8UvsCuyXzTeHXcorv4TQNERzPdEIAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T01:08:33.090725Z"},"content_sha256":"9c6b5901e8314fd52fddd02879b41af3b4b097f6892bf83a2f008c94ac563ba7","schema_version":"1.0","event_id":"sha256:9c6b5901e8314fd52fddd02879b41af3b4b097f6892bf83a2f008c94ac563ba7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/T2YNBZM6G63HVXZR4BMRLGJNKS/bundle.json","state_url":"https://pith.science/pith/T2YNBZM6G63HVXZR4BMRLGJNKS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/T2YNBZM6G63HVXZR4BMRLGJNKS/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":"2026-06-05T01:08:33Z","links":{"resolver":"https://pith.science/pith/T2YNBZM6G63HVXZR4BMRLGJNKS","bundle":"https://pith.science/pith/T2YNBZM6G63HVXZR4BMRLGJNKS/bundle.json","state":"https://pith.science/pith/T2YNBZM6G63HVXZR4BMRLGJNKS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/T2YNBZM6G63HVXZR4BMRLGJNKS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:T2YNBZM6G63HVXZR4BMRLGJNKS","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":"b192cbfb18685ada9aacd4aa72068a361a4b9a656f3f16fa2fdd249eef0bec17","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-16T09:49:25Z","title_canon_sha256":"0e94e30a6ceaafe893039407fa64982ab22b7f9f0eaf06eb2cb03e742c3a6581"},"schema_version":"1.0","source":{"id":"2605.16909","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16909","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16909v1","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16909","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"pith_short_12","alias_value":"T2YNBZM6G63H","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"pith_short_16","alias_value":"T2YNBZM6G63HVXZR","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"pith_short_8","alias_value":"T2YNBZM6","created_at":"2026-05-20T00:03:29Z"}],"graph_snapshots":[{"event_id":"sha256:9c6b5901e8314fd52fddd02879b41af3b4b097f6892bf83a2f008c94ac563ba7","target":"graph","created_at":"2026-05-20T00:03:29Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Claude Opus 4.6 achieves only 32.0% task success on MM-ToolBench, far below the 94.0% human benchmark, demonstrating that current models remain highly challenged by closed-loop omni-modal tool use."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The 100 tasks and their grounded evaluators accurately capture the essential requirements of real-world professional omni-modal tool use without introducing artificial simplifications or biases in scenario selection."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"MM-ToolBench introduces 100 closed-loop multimodal tasks across two domains with 27 MCP servers and 324 tools, where agents must execute, inspect artifacts, and revise before final output."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Current AI agents succeed on only 32 percent of realistic omni-modal tool tasks while humans reach 94 percent."}],"snapshot_sha256":"9e58f697628c14eb4f749eed1678b03d88ffb46e2d1acea0b4aef2d0397e72b9"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"3009f9ad72421404b7f765dd9cb85328744f65d646a8129d2ae98c1125a90f30"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T21:01:19.175129Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"cited_work_retraction","ran_at":"2026-05-19T20:52:03.375217Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T20:51:10.914645Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.270679Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.350458Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.16909/integrity.json","findings":[],"snapshot_sha256":"687dbccfed29a51642e36e4681b8b923c2b40a512d7e63cc5dd6a71ced00063a","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Tool-using agents are increasingly expected to operate across realistic professional workflows, where they must interpret multimodal inputs, coordinate external tools, inspect intermediate artifacts, and revise their actions before producing a final result. Existing benchmarks, however, often evaluate tool use, computer use, and multimodal reasoning in isolation, leaving a gap between benchmark settings and end-to-end omni-modal tool use in the real world. To address this gap, we introduce MM-ToolBench, a benchmark and evaluation harness for task-oriented omni-modal tool use. MM-ToolBench cont","authors_text":"Chenyang Si, Haochen Yin, Wenhui Dong, Yilang Tan, Yuwen Qu, Zhiqiang Liu","cross_cats":[],"headline":"Current AI agents succeed on only 32 percent of realistic omni-modal tool tasks while humans reach 94 percent.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-16T09:49:25Z","title":"TOBench: A Task-Oriented Omni-Modal Benchmark for Real-World Tool-Using Agents"},"references":{"count":101,"internal_anchors":7,"resolved_work":101,"sample":[{"cited_arxiv_id":"2303.08774","doi":"","is_internal_anchor":true,"ref_index":1,"title":"GPT-4 Technical Report","work_id":"b928e041-6991-4c08-8c81-0359e4097c7b","year":2023},{"cited_arxiv_id":"2410.21276","doi":"","is_internal_anchor":true,"ref_index":2,"title":"GPT-4o System Card","work_id":"f37bf1c7-4964-4e56-9762-d20da8d9009f","year":2024},{"cited_arxiv_id":"2406.12045","doi":"","is_internal_anchor":true,"ref_index":3,"title":"$\\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains","work_id":"6a8d8dc4-0cc0-4052-8109-abbcdcd4a962","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Toolllm: Facilitating large language models to master 16000+ real-world apis","work_id":"aec240ca-e2df-4ba3-b9d1-980be81c76a3","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"The berkeley function calling leaderboard (bfcl): From tool use to agentic evaluation of large language models","work_id":"c6761b98-dc2b-49f4-849a-3ad1bc3aa106","year":2025}],"snapshot_sha256":"1e3d1a055b43e64d465a2190190ca741305efe892721119b24929d242a31a1b1"},"source":{"id":"2605.16909","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T20:39:03.648521Z","id":"02ff2674-b347-498f-b63a-5f70e3549118","model_set":{"reader":"grok-4.3"},"one_line_summary":"MM-ToolBench introduces 100 closed-loop multimodal tasks across two domains with 27 MCP servers and 324 tools, where agents must execute, inspect artifacts, and revise before final output.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Current AI agents succeed on only 32 percent of realistic omni-modal tool tasks while humans reach 94 percent.","strongest_claim":"Claude Opus 4.6 achieves only 32.0% task success on MM-ToolBench, far below the 94.0% human benchmark, demonstrating that current models remain highly challenged by closed-loop omni-modal tool use.","weakest_assumption":"The 100 tasks and their grounded evaluators accurately capture the essential requirements of real-world professional omni-modal tool use without introducing artificial simplifications or biases in scenario selection."}},"verdict_id":"02ff2674-b347-498f-b63a-5f70e3549118"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9746c6f9b70e05a2765532736c7900a1c6b3f79a79377cc35cec66ad5ceef36e","target":"record","created_at":"2026-05-20T00:03:29Z","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":"b192cbfb18685ada9aacd4aa72068a361a4b9a656f3f16fa2fdd249eef0bec17","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-16T09:49:25Z","title_canon_sha256":"0e94e30a6ceaafe893039407fa64982ab22b7f9f0eaf06eb2cb03e742c3a6581"},"schema_version":"1.0","source":{"id":"2605.16909","kind":"arxiv","version":1}},"canonical_sha256":"9eb0d0e59e37b67adf31e05915992d54bd7035b4e0d6a506695297e14e951b55","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9eb0d0e59e37b67adf31e05915992d54bd7035b4e0d6a506695297e14e951b55","first_computed_at":"2026-05-20T00:03:29.677333Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:29.677333Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/4ygHccYDYkQKzcurYefOZoALLCiMtexstY0ecWGV94RslgJa14Q0iNmcjd4JFMwgHfg1eI3QLX5M+uF9zVXCg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:29.678399Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16909","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9746c6f9b70e05a2765532736c7900a1c6b3f79a79377cc35cec66ad5ceef36e","sha256:9c6b5901e8314fd52fddd02879b41af3b4b097f6892bf83a2f008c94ac563ba7"],"state_sha256":"df8bb13fb84d4bb32a6bc076ee02097cd787e2b80fa52c80922c1e4b75f85848"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xllmBCnH222oT/lp1Yc947THmt6wvScAlmknT6e4ZC9NJ8Ge/uMK0ar7lYYFeMTRXVH7zK/KMVquaLCR971lAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T01:08:33.096288Z","bundle_sha256":"84174717a61bd1148e62f478a3102ea38b01ebcc71025657ca72a4fd0ad58646"}}