{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:2OPE4BKS6OG6E4JTND75GETPJ5","short_pith_number":"pith:2OPE4BKS","canonical_record":{"source":{"id":"2605.30898","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-29T06:31:21Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"813db50c0256115f7aa397045f49a96db8a239dde472f29b8628e6eb9d96db69","abstract_canon_sha256":"9ce4ccf549e6e6bbcd35addff948814c448ea58eb66b2de1c7b53e73d8f0b7cc"},"schema_version":"1.0"},"canonical_sha256":"d39e4e0552f38de2713368ffd3126f4f550711b4e2f6d9269391da12253ede5a","source":{"kind":"arxiv","id":"2605.30898","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.30898","created_at":"2026-06-01T01:03:24Z"},{"alias_kind":"arxiv_version","alias_value":"2605.30898v1","created_at":"2026-06-01T01:03:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30898","created_at":"2026-06-01T01:03:24Z"},{"alias_kind":"pith_short_12","alias_value":"2OPE4BKS6OG6","created_at":"2026-06-01T01:03:24Z"},{"alias_kind":"pith_short_16","alias_value":"2OPE4BKS6OG6E4JT","created_at":"2026-06-01T01:03:24Z"},{"alias_kind":"pith_short_8","alias_value":"2OPE4BKS","created_at":"2026-06-01T01:03:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:2OPE4BKS6OG6E4JTND75GETPJ5","target":"record","payload":{"canonical_record":{"source":{"id":"2605.30898","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-29T06:31:21Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"813db50c0256115f7aa397045f49a96db8a239dde472f29b8628e6eb9d96db69","abstract_canon_sha256":"9ce4ccf549e6e6bbcd35addff948814c448ea58eb66b2de1c7b53e73d8f0b7cc"},"schema_version":"1.0"},"canonical_sha256":"d39e4e0552f38de2713368ffd3126f4f550711b4e2f6d9269391da12253ede5a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:03:24.021163Z","signature_b64":"irsyTsTIru7ZpCORaSlylRAfYZG5zzM9wmwFMhVX/JxraGnlfKQv2qWcQDFPsx+AkBgwK74WkAbxo3E0FtTrDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d39e4e0552f38de2713368ffd3126f4f550711b4e2f6d9269391da12253ede5a","last_reissued_at":"2026-06-01T01:03:24.020309Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:03:24.020309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.30898","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":"2026-06-01T01:03:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lfyaiB7GDBK/cspv3wL9K7twkagbHj+2haGFUZQJd0rolzlGfZ1NCuXM4RhqDPBqz9nHuGJBD/2gTdgFiPXsCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T04:41:41.071288Z"},"content_sha256":"b0c0a5acd166cb174518b953ba0b79f70624ffcd07e72d0554cd0756627ec346","schema_version":"1.0","event_id":"sha256:b0c0a5acd166cb174518b953ba0b79f70624ffcd07e72d0554cd0756627ec346"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:2OPE4BKS6OG6E4JTND75GETPJ5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"UniScale: Adaptive Unified Inference Scaling via Online Joint Optimization of Model Routing and Test-Time Scaling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Hong Xu, Kaiyu Huang, Minchen Yu, Mingze Kong, Qingjiang Shi, Xingyu Wang, Yuqian Hou, Zhongxiang Dai, Zhubo Shi","submitted_at":"2026-05-29T06:31:21Z","abstract_excerpt":"In real-world deployments of large language models (LLMs), balancing inference quality and computational cost has become a central challenge. Existing approaches tackle this trade-off along two largely independent dimensions: model routing, which switches among models of different scales to match request complexity, and test-time scaling (TTS), which adjusts inference-time compute within a fixed model for fine-grained control. However, this decoupled design introduces inherent limitations. Model routing yields coarse-grained, discrete performance changes due to the sparse set of model scales, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30898","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/2605.30898/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":"2026-06-01T01:03:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PgDjHo7ANYA2Lr/HoG1F8cwxVPV3F395hSr6uiwl9VQoGj61S7Y+tR6s/ETIIqconlSD0HUv1YhFTeVd8PtyCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T04:41:41.071691Z"},"content_sha256":"cb81351946057483dc8667a27817986ad3457ca345c83a52da98a7bb64579b0d","schema_version":"1.0","event_id":"sha256:cb81351946057483dc8667a27817986ad3457ca345c83a52da98a7bb64579b0d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2OPE4BKS6OG6E4JTND75GETPJ5/bundle.json","state_url":"https://pith.science/pith/2OPE4BKS6OG6E4JTND75GETPJ5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2OPE4BKS6OG6E4JTND75GETPJ5/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":"2026-06-03T04:41:41Z","links":{"resolver":"https://pith.science/pith/2OPE4BKS6OG6E4JTND75GETPJ5","bundle":"https://pith.science/pith/2OPE4BKS6OG6E4JTND75GETPJ5/bundle.json","state":"https://pith.science/pith/2OPE4BKS6OG6E4JTND75GETPJ5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2OPE4BKS6OG6E4JTND75GETPJ5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:2OPE4BKS6OG6E4JTND75GETPJ5","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":"9ce4ccf549e6e6bbcd35addff948814c448ea58eb66b2de1c7b53e73d8f0b7cc","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-29T06:31:21Z","title_canon_sha256":"813db50c0256115f7aa397045f49a96db8a239dde472f29b8628e6eb9d96db69"},"schema_version":"1.0","source":{"id":"2605.30898","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.30898","created_at":"2026-06-01T01:03:24Z"},{"alias_kind":"arxiv_version","alias_value":"2605.30898v1","created_at":"2026-06-01T01:03:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30898","created_at":"2026-06-01T01:03:24Z"},{"alias_kind":"pith_short_12","alias_value":"2OPE4BKS6OG6","created_at":"2026-06-01T01:03:24Z"},{"alias_kind":"pith_short_16","alias_value":"2OPE4BKS6OG6E4JT","created_at":"2026-06-01T01:03:24Z"},{"alias_kind":"pith_short_8","alias_value":"2OPE4BKS","created_at":"2026-06-01T01:03:24Z"}],"graph_snapshots":[{"event_id":"sha256:cb81351946057483dc8667a27817986ad3457ca345c83a52da98a7bb64579b0d","target":"graph","created_at":"2026-06-01T01:03:24Z","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/2605.30898/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In real-world deployments of large language models (LLMs), balancing inference quality and computational cost has become a central challenge. Existing approaches tackle this trade-off along two largely independent dimensions: model routing, which switches among models of different scales to match request complexity, and test-time scaling (TTS), which adjusts inference-time compute within a fixed model for fine-grained control. However, this decoupled design introduces inherent limitations. Model routing yields coarse-grained, discrete performance changes due to the sparse set of model scales, ","authors_text":"Hong Xu, Kaiyu Huang, Minchen Yu, Mingze Kong, Qingjiang Shi, Xingyu Wang, Yuqian Hou, Zhongxiang Dai, Zhubo Shi","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-29T06:31:21Z","title":"UniScale: Adaptive Unified Inference Scaling via Online Joint Optimization of Model Routing and Test-Time Scaling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30898","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:b0c0a5acd166cb174518b953ba0b79f70624ffcd07e72d0554cd0756627ec346","target":"record","created_at":"2026-06-01T01:03:24Z","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":"9ce4ccf549e6e6bbcd35addff948814c448ea58eb66b2de1c7b53e73d8f0b7cc","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-29T06:31:21Z","title_canon_sha256":"813db50c0256115f7aa397045f49a96db8a239dde472f29b8628e6eb9d96db69"},"schema_version":"1.0","source":{"id":"2605.30898","kind":"arxiv","version":1}},"canonical_sha256":"d39e4e0552f38de2713368ffd3126f4f550711b4e2f6d9269391da12253ede5a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d39e4e0552f38de2713368ffd3126f4f550711b4e2f6d9269391da12253ede5a","first_computed_at":"2026-06-01T01:03:24.020309Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-01T01:03:24.020309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"irsyTsTIru7ZpCORaSlylRAfYZG5zzM9wmwFMhVX/JxraGnlfKQv2qWcQDFPsx+AkBgwK74WkAbxo3E0FtTrDA==","signature_status":"signed_v1","signed_at":"2026-06-01T01:03:24.021163Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.30898","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b0c0a5acd166cb174518b953ba0b79f70624ffcd07e72d0554cd0756627ec346","sha256:cb81351946057483dc8667a27817986ad3457ca345c83a52da98a7bb64579b0d"],"state_sha256":"6da103621c61fa1ad7d9413543c5c040dfdf017bf030190f2559c4df298c8fbe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LZe2BxtLdChfbzaIZY+UAgZxZZX7ROw2rP0t8XQeBaBe0ESzNX4DIIMU/WkjeH0cAjvz+WuR1Npees31j9gtCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T04:41:41.073794Z","bundle_sha256":"c38684a87ae6550c8aab9d35f150138d0f803afa16748c3bb71efa725fa4df32"}}