{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:GCH6GTNW55NJPDHRPSN7HLQ4X2","short_pith_number":"pith:GCH6GTNW","canonical_record":{"source":{"id":"2508.19613","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-27T06:46:15Z","cross_cats_sorted":[],"title_canon_sha256":"106edb39d472c628f721d897e1bfb9ce4c36f583e7896bd33e940f7e0401e6f7","abstract_canon_sha256":"25b438bd5e8807adfefdc93738c5f23cdf258f61019102ccad1b8a0b8eec1734"},"schema_version":"1.0"},"canonical_sha256":"308fe34db6ef5a978cf17c9bf3ae1cbe9b7561e80a7107378eac4cdb2440f2a2","source":{"kind":"arxiv","id":"2508.19613","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.19613","created_at":"2026-07-05T12:00:15Z"},{"alias_kind":"arxiv_version","alias_value":"2508.19613v1","created_at":"2026-07-05T12:00:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.19613","created_at":"2026-07-05T12:00:15Z"},{"alias_kind":"pith_short_12","alias_value":"GCH6GTNW55NJ","created_at":"2026-07-05T12:00:15Z"},{"alias_kind":"pith_short_16","alias_value":"GCH6GTNW55NJPDHR","created_at":"2026-07-05T12:00:15Z"},{"alias_kind":"pith_short_8","alias_value":"GCH6GTNW","created_at":"2026-07-05T12:00:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:GCH6GTNW55NJPDHRPSN7HLQ4X2","target":"record","payload":{"canonical_record":{"source":{"id":"2508.19613","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-27T06:46:15Z","cross_cats_sorted":[],"title_canon_sha256":"106edb39d472c628f721d897e1bfb9ce4c36f583e7896bd33e940f7e0401e6f7","abstract_canon_sha256":"25b438bd5e8807adfefdc93738c5f23cdf258f61019102ccad1b8a0b8eec1734"},"schema_version":"1.0"},"canonical_sha256":"308fe34db6ef5a978cf17c9bf3ae1cbe9b7561e80a7107378eac4cdb2440f2a2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:00:15.060223Z","signature_b64":"9X79EDcwJSZZ+XQ0rr3S3pzbvKurMUfiLFUfR6wAhBNXPRB1DtHDvQhOrd95XYGNEiHD92a52vkIL4T2qQwFAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"308fe34db6ef5a978cf17c9bf3ae1cbe9b7561e80a7107378eac4cdb2440f2a2","last_reissued_at":"2026-07-05T12:00:15.059734Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:00:15.059734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.19613","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":"2026-07-05T12:00:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vmskXbbKauU67M5WyWwyzkGni33Idsv3AwFEPloi7EDId2BvmNzDGVo0AzCWnjJRvzX8aIc7czPhKwGPep22Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T04:52:24.325520Z"},"content_sha256":"5d542e85a6f5e6527abdfd40900cf8d48b63599f45f294ba96eb2eda714bc3da","schema_version":"1.0","event_id":"sha256:5d542e85a6f5e6527abdfd40900cf8d48b63599f45f294ba96eb2eda714bc3da"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:GCH6GTNW55NJPDHRPSN7HLQ4X2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ALSA: Anchors in Logit Space for Out-of-Distribution Accuracy Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chenzhi Liu, Mahsa Baktashmotlagh, Ruihong Qiu, Yanran Tang, Zi Huang","submitted_at":"2025-08-27T06:46:15Z","abstract_excerpt":"Estimating model accuracy on unseen, unlabeled datasets is crucial for real-world machine learning applications, especially under distribution shifts that can degrade performance. Existing methods often rely on predicted class probabilities (softmax scores) or data similarity metrics. While softmax-based approaches benefit from representing predictions on the standard simplex, compressing logits into probabilities leads to information loss. Meanwhile, similarity-based methods can be computationally expensive and domain-specific, limiting their broader applicability. In this paper, we introduce"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.19613","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/2508.19613/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":"2026-07-05T12:00:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nPWZY1hDpYgdRExYm5WOW0F3M+7pfrWmThu5/HJp4HI6Oe6Kggp3zwfz5Yv4WBUtF9ltfDqqxZvGVc2A2KQqBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T04:52:24.326041Z"},"content_sha256":"9a76aba32e227f44bcafa5cf1aefc00397bc46a1ec98be743e8f09e0ea5e822d","schema_version":"1.0","event_id":"sha256:9a76aba32e227f44bcafa5cf1aefc00397bc46a1ec98be743e8f09e0ea5e822d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GCH6GTNW55NJPDHRPSN7HLQ4X2/bundle.json","state_url":"https://pith.science/pith/GCH6GTNW55NJPDHRPSN7HLQ4X2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GCH6GTNW55NJPDHRPSN7HLQ4X2/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":"2026-08-09T04:52:24Z","links":{"resolver":"https://pith.science/pith/GCH6GTNW55NJPDHRPSN7HLQ4X2","bundle":"https://pith.science/pith/GCH6GTNW55NJPDHRPSN7HLQ4X2/bundle.json","state":"https://pith.science/pith/GCH6GTNW55NJPDHRPSN7HLQ4X2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GCH6GTNW55NJPDHRPSN7HLQ4X2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:GCH6GTNW55NJPDHRPSN7HLQ4X2","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":"25b438bd5e8807adfefdc93738c5f23cdf258f61019102ccad1b8a0b8eec1734","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-27T06:46:15Z","title_canon_sha256":"106edb39d472c628f721d897e1bfb9ce4c36f583e7896bd33e940f7e0401e6f7"},"schema_version":"1.0","source":{"id":"2508.19613","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.19613","created_at":"2026-07-05T12:00:15Z"},{"alias_kind":"arxiv_version","alias_value":"2508.19613v1","created_at":"2026-07-05T12:00:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.19613","created_at":"2026-07-05T12:00:15Z"},{"alias_kind":"pith_short_12","alias_value":"GCH6GTNW55NJ","created_at":"2026-07-05T12:00:15Z"},{"alias_kind":"pith_short_16","alias_value":"GCH6GTNW55NJPDHR","created_at":"2026-07-05T12:00:15Z"},{"alias_kind":"pith_short_8","alias_value":"GCH6GTNW","created_at":"2026-07-05T12:00:15Z"}],"graph_snapshots":[{"event_id":"sha256:9a76aba32e227f44bcafa5cf1aefc00397bc46a1ec98be743e8f09e0ea5e822d","target":"graph","created_at":"2026-07-05T12:00:15Z","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/2508.19613/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Estimating model accuracy on unseen, unlabeled datasets is crucial for real-world machine learning applications, especially under distribution shifts that can degrade performance. Existing methods often rely on predicted class probabilities (softmax scores) or data similarity metrics. While softmax-based approaches benefit from representing predictions on the standard simplex, compressing logits into probabilities leads to information loss. Meanwhile, similarity-based methods can be computationally expensive and domain-specific, limiting their broader applicability. In this paper, we introduce","authors_text":"Chenzhi Liu, Mahsa Baktashmotlagh, Ruihong Qiu, Yanran Tang, Zi Huang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-27T06:46:15Z","title":"ALSA: Anchors in Logit Space for Out-of-Distribution Accuracy Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.19613","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":"sha256:5d542e85a6f5e6527abdfd40900cf8d48b63599f45f294ba96eb2eda714bc3da","target":"record","created_at":"2026-07-05T12:00:15Z","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":"25b438bd5e8807adfefdc93738c5f23cdf258f61019102ccad1b8a0b8eec1734","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-27T06:46:15Z","title_canon_sha256":"106edb39d472c628f721d897e1bfb9ce4c36f583e7896bd33e940f7e0401e6f7"},"schema_version":"1.0","source":{"id":"2508.19613","kind":"arxiv","version":1}},"canonical_sha256":"308fe34db6ef5a978cf17c9bf3ae1cbe9b7561e80a7107378eac4cdb2440f2a2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"308fe34db6ef5a978cf17c9bf3ae1cbe9b7561e80a7107378eac4cdb2440f2a2","first_computed_at":"2026-07-05T12:00:15.059734Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:00:15.059734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9X79EDcwJSZZ+XQ0rr3S3pzbvKurMUfiLFUfR6wAhBNXPRB1DtHDvQhOrd95XYGNEiHD92a52vkIL4T2qQwFAw==","signature_status":"signed_v1","signed_at":"2026-07-05T12:00:15.060223Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.19613","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5d542e85a6f5e6527abdfd40900cf8d48b63599f45f294ba96eb2eda714bc3da","sha256:9a76aba32e227f44bcafa5cf1aefc00397bc46a1ec98be743e8f09e0ea5e822d"],"state_sha256":"b73c667a5189ad4a89704a8a08d0e9165bcb56acd6940696d59187834c7bb96a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9nScNmN4ValVJLKtZKJfQG3f6u7Ee9r8kl02D4jipqNBAPTFOydQIOaJ9uTOdl0gzdeNSor18MEDKGru2/pHAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T04:52:24.331785Z","bundle_sha256":"01fe427b6a615183227526863417bb1bcf43f16c08f4d5ca7019571b827e5e2a"}}