{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:WQTQSYHMRW3OZVJ2IEOBWG7Q7Q","short_pith_number":"pith:WQTQSYHM","canonical_record":{"source":{"id":"2605.15921","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T13:03:42Z","cross_cats_sorted":[],"title_canon_sha256":"1dfec604bb67d122da9a9bb2b42ab5c16086b7669b6038b29d1637db1176f47f","abstract_canon_sha256":"d3d903cc40fd873bb7b8f586e1eded60f0b82be8a17b4500358a478012e389ce"},"schema_version":"1.0"},"canonical_sha256":"b4270960ec8db6ecd53a411c1b1bf0fc2a1f070b4bff514190db0fb33c8b1b0e","source":{"kind":"arxiv","id":"2605.15921","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15921","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15921v1","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15921","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"pith_short_12","alias_value":"WQTQSYHMRW3O","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"pith_short_16","alias_value":"WQTQSYHMRW3OZVJ2","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"pith_short_8","alias_value":"WQTQSYHM","created_at":"2026-05-20T00:01:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:WQTQSYHMRW3OZVJ2IEOBWG7Q7Q","target":"record","payload":{"canonical_record":{"source":{"id":"2605.15921","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T13:03:42Z","cross_cats_sorted":[],"title_canon_sha256":"1dfec604bb67d122da9a9bb2b42ab5c16086b7669b6038b29d1637db1176f47f","abstract_canon_sha256":"d3d903cc40fd873bb7b8f586e1eded60f0b82be8a17b4500358a478012e389ce"},"schema_version":"1.0"},"canonical_sha256":"b4270960ec8db6ecd53a411c1b1bf0fc2a1f070b4bff514190db0fb33c8b1b0e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:01:45.245406Z","signature_b64":"tK3aAiIx+HlCuOeULFZFp1/qSh2NC4RmnSMUu1+tvCdiSDsyKbi5Sq3hZm9VcEpCwfKF5RAi56QZgB/AD20IDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b4270960ec8db6ecd53a411c1b1bf0fc2a1f070b4bff514190db0fb33c8b1b0e","last_reissued_at":"2026-05-20T00:01:45.244787Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:01:45.244787Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.15921","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":"2026-05-20T00:01:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZhKMkawkiG34Bp0qxa0toInKod+MC0Jue9XXXlDBsqZu9wnLpDc0+nE+yMqy5MwC3g+c3dz839e6ClatR4ZyCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T01:07:05.400132Z"},"content_sha256":"d96c4f1c91a368ef0fb8213823c090ac0a9193e5d7334e15bace9d2e77669b99","schema_version":"1.0","event_id":"sha256:d96c4f1c91a368ef0fb8213823c090ac0a9193e5d7334e15bace9d2e77669b99"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:WQTQSYHMRW3OZVJ2IEOBWG7Q7Q","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AdaEraser: Training-Free Object Removal via Adaptive Attention Suppression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dingming Liu","submitted_at":"2026-05-15T13:03:42Z","abstract_excerpt":"Object removal aims to eliminate specified objects from images while plausibly inpainting the affected regions with background content. Current training-free methods typically block attention to object regions within self-attention layers during the image generation process, leveraging surrounding background information to restore the image. However, indiscriminate suppression of self-attention in the vacated areas can degrade generation quality, as the model must simultaneously reconstruct background content in these regions. To solve this conflict, we propose AdaEraser, an adaptive framework"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.15921","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/2605.15921/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:46.551938Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T17:01:55.751449Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"44303db177f5ae6dca2eb7e00ea93db5f0f7489b55a1cfc292585f48ba9c7992"},"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":"2026-05-20T00:01:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1GRmjMW2Rx+PHVG/HwunuzXw5eAhl5YJXaNtxAwNfVK3olYdkBWW2KQxtVLxaeFccPUM/v2vVQbYZcyUWJRqBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T01:07:05.400590Z"},"content_sha256":"3e689d81944a12cab7e0149801b1df55d86ae95183b4a025b4821523839bef38","schema_version":"1.0","event_id":"sha256:3e689d81944a12cab7e0149801b1df55d86ae95183b4a025b4821523839bef38"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WQTQSYHMRW3OZVJ2IEOBWG7Q7Q/bundle.json","state_url":"https://pith.science/pith/WQTQSYHMRW3OZVJ2IEOBWG7Q7Q/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WQTQSYHMRW3OZVJ2IEOBWG7Q7Q/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":"2026-06-12T01:07:05Z","links":{"resolver":"https://pith.science/pith/WQTQSYHMRW3OZVJ2IEOBWG7Q7Q","bundle":"https://pith.science/pith/WQTQSYHMRW3OZVJ2IEOBWG7Q7Q/bundle.json","state":"https://pith.science/pith/WQTQSYHMRW3OZVJ2IEOBWG7Q7Q/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WQTQSYHMRW3OZVJ2IEOBWG7Q7Q/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:WQTQSYHMRW3OZVJ2IEOBWG7Q7Q","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":"d3d903cc40fd873bb7b8f586e1eded60f0b82be8a17b4500358a478012e389ce","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T13:03:42Z","title_canon_sha256":"1dfec604bb67d122da9a9bb2b42ab5c16086b7669b6038b29d1637db1176f47f"},"schema_version":"1.0","source":{"id":"2605.15921","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15921","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15921v1","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15921","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"pith_short_12","alias_value":"WQTQSYHMRW3O","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"pith_short_16","alias_value":"WQTQSYHMRW3OZVJ2","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"pith_short_8","alias_value":"WQTQSYHM","created_at":"2026-05-20T00:01:45Z"}],"graph_snapshots":[{"event_id":"sha256:3e689d81944a12cab7e0149801b1df55d86ae95183b4a025b4821523839bef38","target":"graph","created_at":"2026-05-20T00:01:45Z","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":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:46.551938Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T17:01:55.751449Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.15921/integrity.json","findings":[],"snapshot_sha256":"44303db177f5ae6dca2eb7e00ea93db5f0f7489b55a1cfc292585f48ba9c7992","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Object removal aims to eliminate specified objects from images while plausibly inpainting the affected regions with background content. Current training-free methods typically block attention to object regions within self-attention layers during the image generation process, leveraging surrounding background information to restore the image. However, indiscriminate suppression of self-attention in the vacated areas can degrade generation quality, as the model must simultaneously reconstruct background content in these regions. To solve this conflict, we propose AdaEraser, an adaptive framework","authors_text":"Dingming Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T13:03:42Z","title":"AdaEraser: Training-Free Object Removal via Adaptive Attention Suppression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.15921","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:d96c4f1c91a368ef0fb8213823c090ac0a9193e5d7334e15bace9d2e77669b99","target":"record","created_at":"2026-05-20T00:01:45Z","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":"d3d903cc40fd873bb7b8f586e1eded60f0b82be8a17b4500358a478012e389ce","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T13:03:42Z","title_canon_sha256":"1dfec604bb67d122da9a9bb2b42ab5c16086b7669b6038b29d1637db1176f47f"},"schema_version":"1.0","source":{"id":"2605.15921","kind":"arxiv","version":1}},"canonical_sha256":"b4270960ec8db6ecd53a411c1b1bf0fc2a1f070b4bff514190db0fb33c8b1b0e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b4270960ec8db6ecd53a411c1b1bf0fc2a1f070b4bff514190db0fb33c8b1b0e","first_computed_at":"2026-05-20T00:01:45.244787Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:45.244787Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tK3aAiIx+HlCuOeULFZFp1/qSh2NC4RmnSMUu1+tvCdiSDsyKbi5Sq3hZm9VcEpCwfKF5RAi56QZgB/AD20IDw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:45.245406Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15921","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d96c4f1c91a368ef0fb8213823c090ac0a9193e5d7334e15bace9d2e77669b99","sha256:3e689d81944a12cab7e0149801b1df55d86ae95183b4a025b4821523839bef38"],"state_sha256":"af7d35aa4b08ff4afc1ec5d9756fc2852d0f1520e979f2c73732fdc49b5e8436"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2ZrWEIHDlvYQm2SD2eJHM8ZrlpEZmEmaUl6PaQoTGnUKzSteeu4mJ02/fCCLXTzmLuqAAVobHJthXGwtkUXgDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-12T01:07:05.404055Z","bundle_sha256":"9535ea5df4801d41663b8c40557d1c6665385c45acf77748b33752181b096ccb"}}