{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:DHNEOZB3LA6N2OKUVASG6XIODB","short_pith_number":"pith:DHNEOZB3","canonical_record":{"source":{"id":"2512.24776","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-12-31T10:51:32Z","cross_cats_sorted":[],"title_canon_sha256":"56bee45b78260d238dc8047d6dabc0251ad1e8ec15ce90b17ddfbaa0a36442d1","abstract_canon_sha256":"4a0f7f3ceda271e007eccde154dc039492e81615e9a6f6874009f19c9dda237c"},"schema_version":"1.0"},"canonical_sha256":"19da47643b583cdd3954a8246f5d0e1877e77dbd503167a1809b217b43d391bc","source":{"kind":"arxiv","id":"2512.24776","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2512.24776","created_at":"2026-06-23T18:17:13Z"},{"alias_kind":"arxiv_version","alias_value":"2512.24776v1","created_at":"2026-06-23T18:17:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.24776","created_at":"2026-06-23T18:17:13Z"},{"alias_kind":"pith_short_12","alias_value":"DHNEOZB3LA6N","created_at":"2026-06-23T18:17:13Z"},{"alias_kind":"pith_short_16","alias_value":"DHNEOZB3LA6N2OKU","created_at":"2026-06-23T18:17:13Z"},{"alias_kind":"pith_short_8","alias_value":"DHNEOZB3","created_at":"2026-06-23T18:17:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:DHNEOZB3LA6N2OKUVASG6XIODB","target":"record","payload":{"canonical_record":{"source":{"id":"2512.24776","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-12-31T10:51:32Z","cross_cats_sorted":[],"title_canon_sha256":"56bee45b78260d238dc8047d6dabc0251ad1e8ec15ce90b17ddfbaa0a36442d1","abstract_canon_sha256":"4a0f7f3ceda271e007eccde154dc039492e81615e9a6f6874009f19c9dda237c"},"schema_version":"1.0"},"canonical_sha256":"19da47643b583cdd3954a8246f5d0e1877e77dbd503167a1809b217b43d391bc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T18:17:13.713762Z","signature_b64":"8343MyzUBeW+U0R346jBUYSbO7tlTvWUtDxZLYjX8LfE+rU5uMPrLcxcV0CumNUwkDn/NA4Ka0sAZbe2NQVVBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"19da47643b583cdd3954a8246f5d0e1877e77dbd503167a1809b217b43d391bc","last_reissued_at":"2026-06-23T18:17:13.712151Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T18:17:13.712151Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2512.24776","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":"2026-06-23T18:17:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L7rrnhwsQJ+9avySDx7F/99ltHBi78DVFPOFAN7CpHnuiIvx8dKRN1edmFEN19+ztby8WcP65wJ1tucIiqOIBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T18:02:15.759121Z"},"content_sha256":"5dcbabe30ccf4c17e18b951ca99e4ba1e400988152e2426b8f75780e206a49d5","schema_version":"1.0","event_id":"sha256:5dcbabe30ccf4c17e18b951ca99e4ba1e400988152e2426b8f75780e206a49d5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:DHNEOZB3LA6N2OKUVASG6XIODB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Compute-Accuracy Pareto Frontiers for Open-Source Reasoning Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"\\'Akos Prucs, M\\'ark Marosi, M\\'aty\\'as Antal, Nara Csutora","submitted_at":"2025-12-31T10:51:32Z","abstract_excerpt":"Large Language Models (LLMs) are demonstrating rapid improvements on complex reasoning benchmarks, particularly when allowed to utilize intermediate reasoning steps before converging on a final solution. However, current literature often overlooks the significant computational burden associated with generating long reasoning sequences. For industrial applications, model selection depends not only on raw accuracy but also on resource constraints and inference costs. In this work, we conduct a test-time-compute aware evaluation of both contemporary and older open-source LLMs, mapping their Paret"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.24776","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/2512.24776/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":"2026-06-23T18:17:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6MFGAUB4hAoKDOOtT6kap3inlaRU1BbohZofRKZdC1biEE3ZXpXJTY8tSMGfMqSO+3TeFFNY4+LbUYC+hn7ACA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T18:02:15.759652Z"},"content_sha256":"197fab381c5abd90eec232c27e7685030a3fa3007ba376c0e249f4b977de39a8","schema_version":"1.0","event_id":"sha256:197fab381c5abd90eec232c27e7685030a3fa3007ba376c0e249f4b977de39a8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DHNEOZB3LA6N2OKUVASG6XIODB/bundle.json","state_url":"https://pith.science/pith/DHNEOZB3LA6N2OKUVASG6XIODB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DHNEOZB3LA6N2OKUVASG6XIODB/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":"2026-08-05T18:02:15Z","links":{"resolver":"https://pith.science/pith/DHNEOZB3LA6N2OKUVASG6XIODB","bundle":"https://pith.science/pith/DHNEOZB3LA6N2OKUVASG6XIODB/bundle.json","state":"https://pith.science/pith/DHNEOZB3LA6N2OKUVASG6XIODB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DHNEOZB3LA6N2OKUVASG6XIODB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:DHNEOZB3LA6N2OKUVASG6XIODB","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":"4a0f7f3ceda271e007eccde154dc039492e81615e9a6f6874009f19c9dda237c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-12-31T10:51:32Z","title_canon_sha256":"56bee45b78260d238dc8047d6dabc0251ad1e8ec15ce90b17ddfbaa0a36442d1"},"schema_version":"1.0","source":{"id":"2512.24776","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2512.24776","created_at":"2026-06-23T18:17:13Z"},{"alias_kind":"arxiv_version","alias_value":"2512.24776v1","created_at":"2026-06-23T18:17:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.24776","created_at":"2026-06-23T18:17:13Z"},{"alias_kind":"pith_short_12","alias_value":"DHNEOZB3LA6N","created_at":"2026-06-23T18:17:13Z"},{"alias_kind":"pith_short_16","alias_value":"DHNEOZB3LA6N2OKU","created_at":"2026-06-23T18:17:13Z"},{"alias_kind":"pith_short_8","alias_value":"DHNEOZB3","created_at":"2026-06-23T18:17:13Z"}],"graph_snapshots":[{"event_id":"sha256:197fab381c5abd90eec232c27e7685030a3fa3007ba376c0e249f4b977de39a8","target":"graph","created_at":"2026-06-23T18:17:13Z","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/2512.24776/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) are demonstrating rapid improvements on complex reasoning benchmarks, particularly when allowed to utilize intermediate reasoning steps before converging on a final solution. However, current literature often overlooks the significant computational burden associated with generating long reasoning sequences. For industrial applications, model selection depends not only on raw accuracy but also on resource constraints and inference costs. In this work, we conduct a test-time-compute aware evaluation of both contemporary and older open-source LLMs, mapping their Paret","authors_text":"\\'Akos Prucs, M\\'ark Marosi, M\\'aty\\'as Antal, Nara Csutora","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-12-31T10:51:32Z","title":"Compute-Accuracy Pareto Frontiers for Open-Source Reasoning Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.24776","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":"sha256:5dcbabe30ccf4c17e18b951ca99e4ba1e400988152e2426b8f75780e206a49d5","target":"record","created_at":"2026-06-23T18:17:13Z","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":"4a0f7f3ceda271e007eccde154dc039492e81615e9a6f6874009f19c9dda237c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-12-31T10:51:32Z","title_canon_sha256":"56bee45b78260d238dc8047d6dabc0251ad1e8ec15ce90b17ddfbaa0a36442d1"},"schema_version":"1.0","source":{"id":"2512.24776","kind":"arxiv","version":1}},"canonical_sha256":"19da47643b583cdd3954a8246f5d0e1877e77dbd503167a1809b217b43d391bc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"19da47643b583cdd3954a8246f5d0e1877e77dbd503167a1809b217b43d391bc","first_computed_at":"2026-06-23T18:17:13.712151Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-23T18:17:13.712151Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8343MyzUBeW+U0R346jBUYSbO7tlTvWUtDxZLYjX8LfE+rU5uMPrLcxcV0CumNUwkDn/NA4Ka0sAZbe2NQVVBA==","signature_status":"signed_v1","signed_at":"2026-06-23T18:17:13.713762Z","signed_message":"canonical_sha256_bytes"},"source_id":"2512.24776","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5dcbabe30ccf4c17e18b951ca99e4ba1e400988152e2426b8f75780e206a49d5","sha256:197fab381c5abd90eec232c27e7685030a3fa3007ba376c0e249f4b977de39a8"],"state_sha256":"a250c127b8a7f7ea89810169defcb14da51b0d96b94e2899d89c9b5b35314b6a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ITZDC3EzXK5hXpwoFkqn9GQZqXdnPseMnnP0u5lx+jG15U4hKMMZ0ygT5ZDwuxxzCpNsqOyWvEHU+ArRdhtGDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T18:02:15.763478Z","bundle_sha256":"7d8a3518dcc06ca05560fcaf7521a537d4f7dd3e0b0ff906c29ac1920094d83c"}}