{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:L2IHZOSO7UFWKVGIHMD34SRIUY","short_pith_number":"pith:L2IHZOSO","canonical_record":{"source":{"id":"2308.09658","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-08-18T16:21:40Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"74526f4f86411d5f30d8b30460b0c842ba129350783e4dd921d39488aa577d30","abstract_canon_sha256":"e2cea421c2f2c7d4c48713b3dbeca81f62890aacbbae03f238a9329373c59d89"},"schema_version":"1.0"},"canonical_sha256":"5e907cba4efd0b6554c83b07be4a28a6041ef3d81bd15398e3d12370d805e910","source":{"kind":"arxiv","id":"2308.09658","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.09658","created_at":"2026-07-05T06:43:06Z"},{"alias_kind":"arxiv_version","alias_value":"2308.09658v2","created_at":"2026-07-05T06:43:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.09658","created_at":"2026-07-05T06:43:06Z"},{"alias_kind":"pith_short_12","alias_value":"L2IHZOSO7UFW","created_at":"2026-07-05T06:43:06Z"},{"alias_kind":"pith_short_16","alias_value":"L2IHZOSO7UFWKVGI","created_at":"2026-07-05T06:43:06Z"},{"alias_kind":"pith_short_8","alias_value":"L2IHZOSO","created_at":"2026-07-05T06:43:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:L2IHZOSO7UFWKVGIHMD34SRIUY","target":"record","payload":{"canonical_record":{"source":{"id":"2308.09658","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-08-18T16:21:40Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"74526f4f86411d5f30d8b30460b0c842ba129350783e4dd921d39488aa577d30","abstract_canon_sha256":"e2cea421c2f2c7d4c48713b3dbeca81f62890aacbbae03f238a9329373c59d89"},"schema_version":"1.0"},"canonical_sha256":"5e907cba4efd0b6554c83b07be4a28a6041ef3d81bd15398e3d12370d805e910","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:43:06.297156Z","signature_b64":"JR6FLy0URIRen8bQcDb54EoGZffkhYow2KPE6VZkwXYSYGTScTtuwI3OoCyigpLG5lENJsfbRq+147tNJNgADA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e907cba4efd0b6554c83b07be4a28a6041ef3d81bd15398e3d12370d805e910","last_reissued_at":"2026-07-05T06:43:06.296655Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:43:06.296655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2308.09658","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:43:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a9llX4b1V7SYdHspsDlIszK+KIIkY3aWKMb3Rvy8z4qY4PLCFDRPXelr5pXShszw1VLVi2JRvzx0ADyvCxRVAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T14:22:21.550023Z"},"content_sha256":"ea60fa363b5492e7d549fac02f416cad1e36190dd6f491308b90266c74a301fb","schema_version":"1.0","event_id":"sha256:ea60fa363b5492e7d549fac02f416cad1e36190dd6f491308b90266c74a301fb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:L2IHZOSO7UFWKVGIHMD34SRIUY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.CL","authors_text":"Bing Quan, Hong Li, Ji Qi, Pengbo Hu, Ruiyu Wang, Xingyu Li, Xinqi Wang, Yi Zhou","submitted_at":"2023-08-18T16:21:40Z","abstract_excerpt":"There emerges a promising trend of using large language models (LLMs) to generate code-like plans for complex inference tasks such as visual reasoning. This paradigm, known as LLM-based planning, provides flexibility in problem solving and endows better interpretability. However, current research is mostly limited to basic scenarios of simple questions that can be straightforward answered in a few inference steps. Planning for the more challenging multi-hop visual reasoning tasks remains under-explored. Specifically, under multi-hop reasoning situations, the trade-off between accuracy and the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.09658","kind":"arxiv","version":2},"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/2308.09658/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":"2026-07-05T06:43:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DbHqlmB2BclN/QgiNWMkKzsO5CfUXKADMtqPTcovI9sDezkucwtopoLVYC50M3lPFpGgUicP+3UunYe55NfqBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T14:22:21.550676Z"},"content_sha256":"11836d271d081b72457625a9f69af980c78167c49a1c60859ec5cfb151db8e52","schema_version":"1.0","event_id":"sha256:11836d271d081b72457625a9f69af980c78167c49a1c60859ec5cfb151db8e52"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L2IHZOSO7UFWKVGIHMD34SRIUY/bundle.json","state_url":"https://pith.science/pith/L2IHZOSO7UFWKVGIHMD34SRIUY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L2IHZOSO7UFWKVGIHMD34SRIUY/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":"2026-08-15T14:22:21Z","links":{"resolver":"https://pith.science/pith/L2IHZOSO7UFWKVGIHMD34SRIUY","bundle":"https://pith.science/pith/L2IHZOSO7UFWKVGIHMD34SRIUY/bundle.json","state":"https://pith.science/pith/L2IHZOSO7UFWKVGIHMD34SRIUY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L2IHZOSO7UFWKVGIHMD34SRIUY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:L2IHZOSO7UFWKVGIHMD34SRIUY","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":"e2cea421c2f2c7d4c48713b3dbeca81f62890aacbbae03f238a9329373c59d89","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-08-18T16:21:40Z","title_canon_sha256":"74526f4f86411d5f30d8b30460b0c842ba129350783e4dd921d39488aa577d30"},"schema_version":"1.0","source":{"id":"2308.09658","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.09658","created_at":"2026-07-05T06:43:06Z"},{"alias_kind":"arxiv_version","alias_value":"2308.09658v2","created_at":"2026-07-05T06:43:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.09658","created_at":"2026-07-05T06:43:06Z"},{"alias_kind":"pith_short_12","alias_value":"L2IHZOSO7UFW","created_at":"2026-07-05T06:43:06Z"},{"alias_kind":"pith_short_16","alias_value":"L2IHZOSO7UFWKVGI","created_at":"2026-07-05T06:43:06Z"},{"alias_kind":"pith_short_8","alias_value":"L2IHZOSO","created_at":"2026-07-05T06:43:06Z"}],"graph_snapshots":[{"event_id":"sha256:11836d271d081b72457625a9f69af980c78167c49a1c60859ec5cfb151db8e52","target":"graph","created_at":"2026-07-05T06:43:06Z","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/2308.09658/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"There emerges a promising trend of using large language models (LLMs) to generate code-like plans for complex inference tasks such as visual reasoning. This paradigm, known as LLM-based planning, provides flexibility in problem solving and endows better interpretability. However, current research is mostly limited to basic scenarios of simple questions that can be straightforward answered in a few inference steps. Planning for the more challenging multi-hop visual reasoning tasks remains under-explored. Specifically, under multi-hop reasoning situations, the trade-off between accuracy and the ","authors_text":"Bing Quan, Hong Li, Ji Qi, Pengbo Hu, Ruiyu Wang, Xingyu Li, Xinqi Wang, Yi Zhou","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-08-18T16:21:40Z","title":"Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.09658","kind":"arxiv","version":2},"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:ea60fa363b5492e7d549fac02f416cad1e36190dd6f491308b90266c74a301fb","target":"record","created_at":"2026-07-05T06:43:06Z","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":"e2cea421c2f2c7d4c48713b3dbeca81f62890aacbbae03f238a9329373c59d89","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-08-18T16:21:40Z","title_canon_sha256":"74526f4f86411d5f30d8b30460b0c842ba129350783e4dd921d39488aa577d30"},"schema_version":"1.0","source":{"id":"2308.09658","kind":"arxiv","version":2}},"canonical_sha256":"5e907cba4efd0b6554c83b07be4a28a6041ef3d81bd15398e3d12370d805e910","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5e907cba4efd0b6554c83b07be4a28a6041ef3d81bd15398e3d12370d805e910","first_computed_at":"2026-07-05T06:43:06.296655Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:43:06.296655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JR6FLy0URIRen8bQcDb54EoGZffkhYow2KPE6VZkwXYSYGTScTtuwI3OoCyigpLG5lENJsfbRq+147tNJNgADA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:43:06.297156Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.09658","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ea60fa363b5492e7d549fac02f416cad1e36190dd6f491308b90266c74a301fb","sha256:11836d271d081b72457625a9f69af980c78167c49a1c60859ec5cfb151db8e52"],"state_sha256":"665bb7ba1cfa9472c8e82c382b39986da3dd247ba3aeaff3bc25fe3b9c550d79"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9LQho5Qk7bJsX9/b1hQzHX2sV733dbTiaw7jCWfvC7kDkv5kOyug61I4808MYPM0xVhUdXRhhEcL2FQ7P/p6Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T14:22:21.557257Z","bundle_sha256":"b29292ca6dbc23982c02edf0bca5cb6591e69c2c39ed0502ad86dd528c95ffe2"}}