{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:7QIDXOVBURNMVFTAWSIHH55EAU","short_pith_number":"pith:7QIDXOVB","canonical_record":{"source":{"id":"2311.16102","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-27T18:59:53Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"f28104283ad11e65a80dbe774bd4621683484d72170da34f0a42274857889731","abstract_canon_sha256":"4b758c5f3095b7738f88536e46572b0946e9dce832f56ab6660d30f58fe616ab"},"schema_version":"1.0"},"canonical_sha256":"fc103bbaa1a45aca9660b49073f7a405264aa3d966b7eb3330d9a5f301da14cd","source":{"kind":"arxiv","id":"2311.16102","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.16102","created_at":"2026-07-05T07:18:32Z"},{"alias_kind":"arxiv_version","alias_value":"2311.16102v2","created_at":"2026-07-05T07:18:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.16102","created_at":"2026-07-05T07:18:32Z"},{"alias_kind":"pith_short_12","alias_value":"7QIDXOVBURNM","created_at":"2026-07-05T07:18:32Z"},{"alias_kind":"pith_short_16","alias_value":"7QIDXOVBURNMVFTA","created_at":"2026-07-05T07:18:32Z"},{"alias_kind":"pith_short_8","alias_value":"7QIDXOVB","created_at":"2026-07-05T07:18:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:7QIDXOVBURNMVFTAWSIHH55EAU","target":"record","payload":{"canonical_record":{"source":{"id":"2311.16102","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-27T18:59:53Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"f28104283ad11e65a80dbe774bd4621683484d72170da34f0a42274857889731","abstract_canon_sha256":"4b758c5f3095b7738f88536e46572b0946e9dce832f56ab6660d30f58fe616ab"},"schema_version":"1.0"},"canonical_sha256":"fc103bbaa1a45aca9660b49073f7a405264aa3d966b7eb3330d9a5f301da14cd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:18:32.784111Z","signature_b64":"QIcqOc+6kv7IXYClF5cTxWvCa4I362ovlzHo4sAVM6GQlnKmHoZxnizfT9M+aMivGqN9AKCtOoWYwTyCnCx5CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc103bbaa1a45aca9660b49073f7a405264aa3d966b7eb3330d9a5f301da14cd","last_reissued_at":"2026-07-05T07:18:32.783575Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:18:32.783575Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.16102","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:18:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M0Nm7vruB6dTdWg69yXoBDGqNeyKF+Bv7MxaVVqC1Ctg9HXhVrzRUAJvolBfDJjlgz8nwf2Rsj4A1U4FYKSpAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T03:47:48.537029Z"},"content_sha256":"72739dc2d911819b953541f2c76d89830c5b3d16fcba9cafac4b94d224ff8343","schema_version":"1.0","event_id":"sha256:72739dc2d911819b953541f2c76d89830c5b3d16fcba9cafac4b94d224ff8343"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:7QIDXOVBURNMVFTAWSIHH55EAU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Diffusion-TTA: Test-time Adaptation of Discriminative Models via Generative Feedback","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Alexander C. Li, Deepak Pathak, Katerina Fragkiadaki, Mihir Prabhudesai, Tsung-Wei Ke","submitted_at":"2023-11-27T18:59:53Z","abstract_excerpt":"The advancements in generative modeling, particularly the advent of diffusion models, have sparked a fundamental question: how can these models be effectively used for discriminative tasks? In this work, we find that generative models can be great test-time adapters for discriminative models. Our method, Diffusion-TTA, adapts pre-trained discriminative models such as image classifiers, segmenters and depth predictors, to each unlabelled example in the test set using generative feedback from a diffusion model. We achieve this by modulating the conditioning of the diffusion model using the outpu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.16102","kind":"arxiv","version":2},"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/2311.16102/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":"2026-07-05T07:18:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T0TFdPKkXGQssdddeBhyXvr7sE1LmeahajNFwjRtk+v+X+3WTy0MfU7l5UNWDsWYWS+7L5HH8NtD16ZIUOhFAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T03:47:48.537547Z"},"content_sha256":"e5e9c751bbe14a46c83d6413e77d83421fd6db439819b9aab544613f4be6ae6b","schema_version":"1.0","event_id":"sha256:e5e9c751bbe14a46c83d6413e77d83421fd6db439819b9aab544613f4be6ae6b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7QIDXOVBURNMVFTAWSIHH55EAU/bundle.json","state_url":"https://pith.science/pith/7QIDXOVBURNMVFTAWSIHH55EAU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7QIDXOVBURNMVFTAWSIHH55EAU/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":"2026-08-07T03:47:48Z","links":{"resolver":"https://pith.science/pith/7QIDXOVBURNMVFTAWSIHH55EAU","bundle":"https://pith.science/pith/7QIDXOVBURNMVFTAWSIHH55EAU/bundle.json","state":"https://pith.science/pith/7QIDXOVBURNMVFTAWSIHH55EAU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7QIDXOVBURNMVFTAWSIHH55EAU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:7QIDXOVBURNMVFTAWSIHH55EAU","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":"4b758c5f3095b7738f88536e46572b0946e9dce832f56ab6660d30f58fe616ab","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-27T18:59:53Z","title_canon_sha256":"f28104283ad11e65a80dbe774bd4621683484d72170da34f0a42274857889731"},"schema_version":"1.0","source":{"id":"2311.16102","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.16102","created_at":"2026-07-05T07:18:32Z"},{"alias_kind":"arxiv_version","alias_value":"2311.16102v2","created_at":"2026-07-05T07:18:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.16102","created_at":"2026-07-05T07:18:32Z"},{"alias_kind":"pith_short_12","alias_value":"7QIDXOVBURNM","created_at":"2026-07-05T07:18:32Z"},{"alias_kind":"pith_short_16","alias_value":"7QIDXOVBURNMVFTA","created_at":"2026-07-05T07:18:32Z"},{"alias_kind":"pith_short_8","alias_value":"7QIDXOVB","created_at":"2026-07-05T07:18:32Z"}],"graph_snapshots":[{"event_id":"sha256:e5e9c751bbe14a46c83d6413e77d83421fd6db439819b9aab544613f4be6ae6b","target":"graph","created_at":"2026-07-05T07:18:32Z","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/2311.16102/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The advancements in generative modeling, particularly the advent of diffusion models, have sparked a fundamental question: how can these models be effectively used for discriminative tasks? In this work, we find that generative models can be great test-time adapters for discriminative models. Our method, Diffusion-TTA, adapts pre-trained discriminative models such as image classifiers, segmenters and depth predictors, to each unlabelled example in the test set using generative feedback from a diffusion model. We achieve this by modulating the conditioning of the diffusion model using the outpu","authors_text":"Alexander C. Li, Deepak Pathak, Katerina Fragkiadaki, Mihir Prabhudesai, Tsung-Wei Ke","cross_cats":["cs.AI","cs.LG","cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-27T18:59:53Z","title":"Diffusion-TTA: Test-time Adaptation of Discriminative Models via Generative Feedback"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.16102","kind":"arxiv","version":2},"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":"sha256:72739dc2d911819b953541f2c76d89830c5b3d16fcba9cafac4b94d224ff8343","target":"record","created_at":"2026-07-05T07:18:32Z","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":"4b758c5f3095b7738f88536e46572b0946e9dce832f56ab6660d30f58fe616ab","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-27T18:59:53Z","title_canon_sha256":"f28104283ad11e65a80dbe774bd4621683484d72170da34f0a42274857889731"},"schema_version":"1.0","source":{"id":"2311.16102","kind":"arxiv","version":2}},"canonical_sha256":"fc103bbaa1a45aca9660b49073f7a405264aa3d966b7eb3330d9a5f301da14cd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fc103bbaa1a45aca9660b49073f7a405264aa3d966b7eb3330d9a5f301da14cd","first_computed_at":"2026-07-05T07:18:32.783575Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:18:32.783575Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QIcqOc+6kv7IXYClF5cTxWvCa4I362ovlzHo4sAVM6GQlnKmHoZxnizfT9M+aMivGqN9AKCtOoWYwTyCnCx5CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:18:32.784111Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.16102","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:72739dc2d911819b953541f2c76d89830c5b3d16fcba9cafac4b94d224ff8343","sha256:e5e9c751bbe14a46c83d6413e77d83421fd6db439819b9aab544613f4be6ae6b"],"state_sha256":"c97a57c289a69d7a07c8baf0e09c28122a9c360fd11ee56c6032558479f0c18b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BqwLXNJ4pZDJ6c9kXpAwyIOGzFajxuDAxEkOjkN27IzIkxzl7Hk/y686/bfL7TiZQ9ERa5H12duwF5wIO7m/Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T03:47:48.543349Z","bundle_sha256":"5ad71e6f01d877166b1a1e212f2bc858327c1ec10be80901fe8437c1722868b4"}}