{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:FXKNWDE35YQYYXVR36EMDNI2W4","short_pith_number":"pith:FXKNWDE3","canonical_record":{"source":{"id":"2607.08940","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-09T21:09:05Z","cross_cats_sorted":[],"title_canon_sha256":"e5ea3463b7af1066e6a00b0850b3776e28064c7e09e6528eb8cdeaf95f5c3ba4","abstract_canon_sha256":"e03c98595b14a1002e5e0e931e1738a649fbe5c808ab82af0a86f4e14960f73c"},"schema_version":"1.0"},"canonical_sha256":"2dd4db0c9bee218c5eb1df88c1b51ab7265f0fc10ae6ff7dc077d74261a0fb74","source":{"kind":"arxiv","id":"2607.08940","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.08940","created_at":"2026-07-13T00:17:28Z"},{"alias_kind":"arxiv_version","alias_value":"2607.08940v1","created_at":"2026-07-13T00:17:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.08940","created_at":"2026-07-13T00:17:28Z"},{"alias_kind":"pith_short_12","alias_value":"FXKNWDE35YQY","created_at":"2026-07-13T00:17:28Z"},{"alias_kind":"pith_short_16","alias_value":"FXKNWDE35YQYYXVR","created_at":"2026-07-13T00:17:28Z"},{"alias_kind":"pith_short_8","alias_value":"FXKNWDE3","created_at":"2026-07-13T00:17:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:FXKNWDE35YQYYXVR36EMDNI2W4","target":"record","payload":{"canonical_record":{"source":{"id":"2607.08940","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-09T21:09:05Z","cross_cats_sorted":[],"title_canon_sha256":"e5ea3463b7af1066e6a00b0850b3776e28064c7e09e6528eb8cdeaf95f5c3ba4","abstract_canon_sha256":"e03c98595b14a1002e5e0e931e1738a649fbe5c808ab82af0a86f4e14960f73c"},"schema_version":"1.0"},"canonical_sha256":"2dd4db0c9bee218c5eb1df88c1b51ab7265f0fc10ae6ff7dc077d74261a0fb74","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-13T00:17:28.593433Z","signature_b64":"8beaOSKnLVnsOh6oi3fLCz5KAQIjaX0MUDSBLKIJl0VW6LD7nQx505LNjxAldKvNTP+5HKMO86uw97kULJGrDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2dd4db0c9bee218c5eb1df88c1b51ab7265f0fc10ae6ff7dc077d74261a0fb74","last_reissued_at":"2026-07-13T00:17:28.592375Z","signature_status":"signed_v1","first_computed_at":"2026-07-13T00:17:28.592375Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.08940","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":"2026-07-13T00:17:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E++DnQhY5FCrNDqgx8ZffAj9Mdx9Uzu3lmDu16fIwwcL+hC7qWKZz5t46jvqCbHsXWiJQpq7dJxLi6avmnR5CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T22:37:37.744808Z"},"content_sha256":"08ec90948a308342944e44550b7c686cbf93ce1067789c177bfc063cd4fd005b","schema_version":"1.0","event_id":"sha256:08ec90948a308342944e44550b7c686cbf93ce1067789c177bfc063cd4fd005b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:FXKNWDE35YQYYXVR36EMDNI2W4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dehai Min, Fangxu Yu, Ge Liu, Lu Cheng, Tao Feng, Tianyi Zhou","submitted_at":"2026-07-09T21:09:05Z","abstract_excerpt":"Time series reasoning is essential for real-world problem-solving. While both Large Language Models (LLMs) and Vision-Language Models (VLMs) can reason about time-series data, their capabilities are complementary: LLMs process time series as text sequences and thus preserve exact numerical understanding, but struggle with global patterns, whereas VLMs efficiently capture these patterns by visualizing time series but may lose fine-grained details. Moreover, models vary significantly in task-specific expertise and inference costs. Dynamically selecting the most suitable modality and model for ea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.08940","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/2607.08940/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":"2026-07-13T00:17:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I/HSabWxJsZKTS46tnVjlQBGw7AX1JJcCS/184ShabFYtrjo1cgRs4rL4NS3UGU/fnfIrcVEr5BuuHyg+uOUDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T22:37:37.745323Z"},"content_sha256":"413f9b34c25da804967b33cb9f83e75b9ed14782f5ed6ea10d9ec6bcf6b5c125","schema_version":"1.0","event_id":"sha256:413f9b34c25da804967b33cb9f83e75b9ed14782f5ed6ea10d9ec6bcf6b5c125"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FXKNWDE35YQYYXVR36EMDNI2W4/bundle.json","state_url":"https://pith.science/pith/FXKNWDE35YQYYXVR36EMDNI2W4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FXKNWDE35YQYYXVR36EMDNI2W4/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":"2026-07-31T22:37:37Z","links":{"resolver":"https://pith.science/pith/FXKNWDE35YQYYXVR36EMDNI2W4","bundle":"https://pith.science/pith/FXKNWDE35YQYYXVR36EMDNI2W4/bundle.json","state":"https://pith.science/pith/FXKNWDE35YQYYXVR36EMDNI2W4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FXKNWDE35YQYYXVR36EMDNI2W4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:FXKNWDE35YQYYXVR36EMDNI2W4","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":"e03c98595b14a1002e5e0e931e1738a649fbe5c808ab82af0a86f4e14960f73c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-09T21:09:05Z","title_canon_sha256":"e5ea3463b7af1066e6a00b0850b3776e28064c7e09e6528eb8cdeaf95f5c3ba4"},"schema_version":"1.0","source":{"id":"2607.08940","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.08940","created_at":"2026-07-13T00:17:28Z"},{"alias_kind":"arxiv_version","alias_value":"2607.08940v1","created_at":"2026-07-13T00:17:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.08940","created_at":"2026-07-13T00:17:28Z"},{"alias_kind":"pith_short_12","alias_value":"FXKNWDE35YQY","created_at":"2026-07-13T00:17:28Z"},{"alias_kind":"pith_short_16","alias_value":"FXKNWDE35YQYYXVR","created_at":"2026-07-13T00:17:28Z"},{"alias_kind":"pith_short_8","alias_value":"FXKNWDE3","created_at":"2026-07-13T00:17:28Z"}],"graph_snapshots":[{"event_id":"sha256:413f9b34c25da804967b33cb9f83e75b9ed14782f5ed6ea10d9ec6bcf6b5c125","target":"graph","created_at":"2026-07-13T00:17:28Z","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/2607.08940/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time series reasoning is essential for real-world problem-solving. While both Large Language Models (LLMs) and Vision-Language Models (VLMs) can reason about time-series data, their capabilities are complementary: LLMs process time series as text sequences and thus preserve exact numerical understanding, but struggle with global patterns, whereas VLMs efficiently capture these patterns by visualizing time series but may lose fine-grained details. Moreover, models vary significantly in task-specific expertise and inference costs. Dynamically selecting the most suitable modality and model for ea","authors_text":"Dehai Min, Fangxu Yu, Ge Liu, Lu Cheng, Tao Feng, Tianyi Zhou","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-09T21:09:05Z","title":"TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.08940","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":"sha256:08ec90948a308342944e44550b7c686cbf93ce1067789c177bfc063cd4fd005b","target":"record","created_at":"2026-07-13T00:17:28Z","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":"e03c98595b14a1002e5e0e931e1738a649fbe5c808ab82af0a86f4e14960f73c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-09T21:09:05Z","title_canon_sha256":"e5ea3463b7af1066e6a00b0850b3776e28064c7e09e6528eb8cdeaf95f5c3ba4"},"schema_version":"1.0","source":{"id":"2607.08940","kind":"arxiv","version":1}},"canonical_sha256":"2dd4db0c9bee218c5eb1df88c1b51ab7265f0fc10ae6ff7dc077d74261a0fb74","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2dd4db0c9bee218c5eb1df88c1b51ab7265f0fc10ae6ff7dc077d74261a0fb74","first_computed_at":"2026-07-13T00:17:28.592375Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-13T00:17:28.592375Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8beaOSKnLVnsOh6oi3fLCz5KAQIjaX0MUDSBLKIJl0VW6LD7nQx505LNjxAldKvNTP+5HKMO86uw97kULJGrDg==","signature_status":"signed_v1","signed_at":"2026-07-13T00:17:28.593433Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.08940","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:08ec90948a308342944e44550b7c686cbf93ce1067789c177bfc063cd4fd005b","sha256:413f9b34c25da804967b33cb9f83e75b9ed14782f5ed6ea10d9ec6bcf6b5c125"],"state_sha256":"ab3833634b2553583d377f73782af542dea3b95e6bf3b370249726fea20d0a04"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jlKO5MMjOY/9V4EXyEedACWvPj7UmXti4arf6QMUVsv/OUu8C+Edf1BAecNh15VZBIuc9JK79AjHa6jiDrjRDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-31T22:37:37.752828Z","bundle_sha256":"2aa3ffcddec66d5e953fccb2677f2c4684468500691d9f942de49d083bbbb492"}}