{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:4AZXZF4L4TDULFMIFWYGQZBY7M","short_pith_number":"pith:4AZXZF4L","canonical_record":{"source":{"id":"2506.13705","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-16T17:12:26Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a0d9e2520065de50f900df505456646e3115b54f3d4e7e41dafcae3d8fc3625c","abstract_canon_sha256":"fa6ce0770b4d3d0c053b815ac85155f4bd5c40df7bdceae18c20dcfe060ebee3"},"schema_version":"1.0"},"canonical_sha256":"e0337c978be4c74595882db0686438fb2102c50965628f24a1dd32e0f2c57cf9","source":{"kind":"arxiv","id":"2506.13705","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.13705","created_at":"2026-07-05T11:22:25Z"},{"alias_kind":"arxiv_version","alias_value":"2506.13705v1","created_at":"2026-07-05T11:22:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.13705","created_at":"2026-07-05T11:22:25Z"},{"alias_kind":"pith_short_12","alias_value":"4AZXZF4L4TDU","created_at":"2026-07-05T11:22:25Z"},{"alias_kind":"pith_short_16","alias_value":"4AZXZF4L4TDULFMI","created_at":"2026-07-05T11:22:25Z"},{"alias_kind":"pith_short_8","alias_value":"4AZXZF4L","created_at":"2026-07-05T11:22:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:4AZXZF4L4TDULFMIFWYGQZBY7M","target":"record","payload":{"canonical_record":{"source":{"id":"2506.13705","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-16T17:12:26Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a0d9e2520065de50f900df505456646e3115b54f3d4e7e41dafcae3d8fc3625c","abstract_canon_sha256":"fa6ce0770b4d3d0c053b815ac85155f4bd5c40df7bdceae18c20dcfe060ebee3"},"schema_version":"1.0"},"canonical_sha256":"e0337c978be4c74595882db0686438fb2102c50965628f24a1dd32e0f2c57cf9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:25.269561Z","signature_b64":"JmWEoa0Q/jN9ulXywWHWZZbxn93Y/3T1uxvIdbEVq+qvosGE6SXtn0wyVTm5w0h55DofXPGM5cdMIVCZel0TBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e0337c978be4c74595882db0686438fb2102c50965628f24a1dd32e0f2c57cf9","last_reissued_at":"2026-07-05T11:22:25.269011Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:25.269011Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.13705","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":"2026-07-05T11:22:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zpe1O0Xpj+gfevIyqneMmf05ndqhJuajSPhMBKwi3aMqGMDP54BeATTgMheyyKu+kQtDIIMkc76AO0osCmmZDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T18:50:44.605411Z"},"content_sha256":"5513de3b25b6562e1255759a6eb97ceb4cab62748909d366def15ae170322842","schema_version":"1.0","event_id":"sha256:5513de3b25b6562e1255759a6eb97ceb4cab62748909d366def15ae170322842"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:4AZXZF4L4TDULFMIFWYGQZBY7M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Duanqing Xu, Junru Zhang, Lang Feng, Xu Guo, Yabo Dong, Yuhan Wu","submitted_at":"2025-06-16T17:12:26Z","abstract_excerpt":"Time-series reasoning remains a significant challenge in multimodal large language models (MLLMs) due to the dynamic temporal patterns, ambiguous semantics, and lack of temporal priors. In this work, we introduce TimeMaster, a reinforcement learning (RL)-based method that enables time-series MLLMs to perform structured, interpretable reasoning directly over visualized time-series inputs and task prompts. TimeMaster adopts a three-part structured output format, reasoning, classification, and domain-specific extension, and is optimized via a composite reward function that aligns format adherence"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.13705","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/2506.13705/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":"2026-07-05T11:22:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f5klVDI2qrQ+LVfF3+H/XcXfK4wqn9iiiZTC44Yq0D0zewkkk9BShl6G9TXjTVMl32bQF++BPtodPmRXZEDpAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T18:50:44.605995Z"},"content_sha256":"37449c659de4115f78d80209a2b39027baf9f66cf6921a28e5b6e0696c67b475","schema_version":"1.0","event_id":"sha256:37449c659de4115f78d80209a2b39027baf9f66cf6921a28e5b6e0696c67b475"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4AZXZF4L4TDULFMIFWYGQZBY7M/bundle.json","state_url":"https://pith.science/pith/4AZXZF4L4TDULFMIFWYGQZBY7M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4AZXZF4L4TDULFMIFWYGQZBY7M/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":"2026-08-09T18:50:44Z","links":{"resolver":"https://pith.science/pith/4AZXZF4L4TDULFMIFWYGQZBY7M","bundle":"https://pith.science/pith/4AZXZF4L4TDULFMIFWYGQZBY7M/bundle.json","state":"https://pith.science/pith/4AZXZF4L4TDULFMIFWYGQZBY7M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4AZXZF4L4TDULFMIFWYGQZBY7M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:4AZXZF4L4TDULFMIFWYGQZBY7M","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":"fa6ce0770b4d3d0c053b815ac85155f4bd5c40df7bdceae18c20dcfe060ebee3","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-16T17:12:26Z","title_canon_sha256":"a0d9e2520065de50f900df505456646e3115b54f3d4e7e41dafcae3d8fc3625c"},"schema_version":"1.0","source":{"id":"2506.13705","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.13705","created_at":"2026-07-05T11:22:25Z"},{"alias_kind":"arxiv_version","alias_value":"2506.13705v1","created_at":"2026-07-05T11:22:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.13705","created_at":"2026-07-05T11:22:25Z"},{"alias_kind":"pith_short_12","alias_value":"4AZXZF4L4TDU","created_at":"2026-07-05T11:22:25Z"},{"alias_kind":"pith_short_16","alias_value":"4AZXZF4L4TDULFMI","created_at":"2026-07-05T11:22:25Z"},{"alias_kind":"pith_short_8","alias_value":"4AZXZF4L","created_at":"2026-07-05T11:22:25Z"}],"graph_snapshots":[{"event_id":"sha256:37449c659de4115f78d80209a2b39027baf9f66cf6921a28e5b6e0696c67b475","target":"graph","created_at":"2026-07-05T11:22:25Z","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/2506.13705/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time-series reasoning remains a significant challenge in multimodal large language models (MLLMs) due to the dynamic temporal patterns, ambiguous semantics, and lack of temporal priors. In this work, we introduce TimeMaster, a reinforcement learning (RL)-based method that enables time-series MLLMs to perform structured, interpretable reasoning directly over visualized time-series inputs and task prompts. TimeMaster adopts a three-part structured output format, reasoning, classification, and domain-specific extension, and is optimized via a composite reward function that aligns format adherence","authors_text":"Duanqing Xu, Junru Zhang, Lang Feng, Xu Guo, Yabo Dong, Yuhan Wu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-16T17:12:26Z","title":"TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.13705","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":"sha256:5513de3b25b6562e1255759a6eb97ceb4cab62748909d366def15ae170322842","target":"record","created_at":"2026-07-05T11:22:25Z","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":"fa6ce0770b4d3d0c053b815ac85155f4bd5c40df7bdceae18c20dcfe060ebee3","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-16T17:12:26Z","title_canon_sha256":"a0d9e2520065de50f900df505456646e3115b54f3d4e7e41dafcae3d8fc3625c"},"schema_version":"1.0","source":{"id":"2506.13705","kind":"arxiv","version":1}},"canonical_sha256":"e0337c978be4c74595882db0686438fb2102c50965628f24a1dd32e0f2c57cf9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e0337c978be4c74595882db0686438fb2102c50965628f24a1dd32e0f2c57cf9","first_computed_at":"2026-07-05T11:22:25.269011Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:22:25.269011Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JmWEoa0Q/jN9ulXywWHWZZbxn93Y/3T1uxvIdbEVq+qvosGE6SXtn0wyVTm5w0h55DofXPGM5cdMIVCZel0TBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:22:25.269561Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.13705","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5513de3b25b6562e1255759a6eb97ceb4cab62748909d366def15ae170322842","sha256:37449c659de4115f78d80209a2b39027baf9f66cf6921a28e5b6e0696c67b475"],"state_sha256":"5997348d34508053eded476d0bb985ed0aa4d501b53c12bcefcdc98e22f877c9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xxdjHdT4ao6Seroc3SSW0d33QwWobLAIc33LemCTp3mnQTB8fMYQtzLOp3naP06qS+9cOyaLHCtUGiRV2XJYBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T18:50:44.610733Z","bundle_sha256":"149d74e224fb3e0053430d3887de3c6b53670153ba7acd7f28c1896bad8862c8"}}