{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:5RQPEQ4YRRAPKO4DPG2WAM2AE4","short_pith_number":"pith:5RQPEQ4Y","schema_version":"1.0","canonical_sha256":"ec60f243988c40f53b8379b5603340273dc872d908d3376e33749057162b4bf9","source":{"kind":"arxiv","id":"2604.06052","version":2},"attestation_state":"computed","paper":{"title":"Attention, May I Have Your Decision? Localizing Generative Choices in Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Self-attention layers localize the implicit decisions that resolve ambiguous prompts in diffusion models.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kamil Deja, Katarzyna Zaleska, {\\L}ukasz Popek, Monika Wysocza\\'nska","submitted_at":"2026-03-29T22:06:21Z","abstract_excerpt":"Text-to-image diffusion models exhibit remarkable generative capabilities, yet their internal operations remain opaque, particularly when handling prompts that are not fully descriptive. In such scenarios, models must make implicit decisions to generate details not explicitly specified in the text. This work investigates the hypothesis that this decision-making process is not diffuse but is computationally localized within the model's architecture. While existing localization techniques focus on prompt-related interventions, we notice that such explicit conditioning may differ from implicit de"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":true},"canonical_record":{"source":{"id":"2604.06052","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-03-29T22:06:21Z","cross_cats_sorted":[],"title_canon_sha256":"df043576d93be4780d53b4f3ae5416df288b394f08de0a42415e1880183ac668","abstract_canon_sha256":"cd6b0df70a0771bf666601e8fb4cedb3f624f89de1366fdad7f0521fe86875a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:50.272384Z","signature_b64":"TpP83cwN5G7u5rD4tFzsth2QpTG0JTaqhcMNPjAXz0gkJ0I0aGNkP0VxEwbqd4ByUBvti92Dir+m8Ir+CtTfCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ec60f243988c40f53b8379b5603340273dc872d908d3376e33749057162b4bf9","last_reissued_at":"2026-06-03T01:05:50.271915Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:50.271915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Attention, May I Have Your Decision? Localizing Generative Choices in Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Self-attention layers localize the implicit decisions that resolve ambiguous prompts in diffusion models.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kamil Deja, Katarzyna Zaleska, {\\L}ukasz Popek, Monika Wysocza\\'nska","submitted_at":"2026-03-29T22:06:21Z","abstract_excerpt":"Text-to-image diffusion models exhibit remarkable generative capabilities, yet their internal operations remain opaque, particularly when handling prompts that are not fully descriptive. In such scenarios, models must make implicit decisions to generate details not explicitly specified in the text. This work investigates the hypothesis that this decision-making process is not diffuse but is computationally localized within the model's architecture. While existing localization techniques focus on prompt-related interventions, we notice that such explicit conditioning may differ from implicit de"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our findings indicate that the resolution of ambiguous concepts is governed principally by self-attention layers, identifying them as the most effective point for intervention.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the probing-based localization technique identifies layers responsible for implicit decisions separately from explicit prompt conditioning, and that interventions on these layers causally affect the generative choices without major unintended effects.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Implicit generative choices in diffusion models for ambiguous prompts are localized principally in self-attention layers, enabling a targeted ICM steering method that outperforms prior debiasing approaches.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Self-attention layers localize the implicit decisions that resolve ambiguous prompts in diffusion models.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"3af14be6314ee74ead316aa030199d6cc84a4c55d3958e71e4460c1bbf5cb33d"},"source":{"id":"2604.06052","kind":"arxiv","version":2},"verdict":{"id":"e326035d-b388-486d-986e-361dab716a50","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T21:06:28.499707Z","strongest_claim":"Our findings indicate that the resolution of ambiguous concepts is governed principally by self-attention layers, identifying them as the most effective point for intervention.","one_line_summary":"Implicit generative choices in diffusion models for ambiguous prompts are localized principally in self-attention layers, enabling a targeted ICM steering method that outperforms prior debiasing approaches.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the probing-based localization technique identifies layers responsible for implicit decisions separately from explicit prompt conditioning, and that interventions on these layers causally affect the generative choices without major unintended effects.","pith_extraction_headline":"Self-attention layers localize the implicit decisions that resolve ambiguous prompts in diffusion models."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.06052/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":1,"snapshot_sha256":"a7e0c12f831c3028a794bf35715242397e9e36c4a740eeacea4e2b80bbc9380e"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2604.06052","created_at":"2026-06-03T01:05:50.271973+00:00"},{"alias_kind":"arxiv_version","alias_value":"2604.06052v2","created_at":"2026-06-03T01:05:50.271973+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.06052","created_at":"2026-06-03T01:05:50.271973+00:00"},{"alias_kind":"pith_short_12","alias_value":"5RQPEQ4YRRAP","created_at":"2026-06-03T01:05:50.271973+00:00"},{"alias_kind":"pith_short_16","alias_value":"5RQPEQ4YRRAPKO4D","created_at":"2026-06-03T01:05:50.271973+00:00"},{"alias_kind":"pith_short_8","alias_value":"5RQPEQ4Y","created_at":"2026-06-03T01:05:50.271973+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":1,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5RQPEQ4YRRAPKO4DPG2WAM2AE4","json":"https://pith.science/pith/5RQPEQ4YRRAPKO4DPG2WAM2AE4.json","graph_json":"https://pith.science/api/pith-number/5RQPEQ4YRRAPKO4DPG2WAM2AE4/graph.json","events_json":"https://pith.science/api/pith-number/5RQPEQ4YRRAPKO4DPG2WAM2AE4/events.json","paper":"https://pith.science/paper/5RQPEQ4Y"},"agent_actions":{"view_html":"https://pith.science/pith/5RQPEQ4YRRAPKO4DPG2WAM2AE4","download_json":"https://pith.science/pith/5RQPEQ4YRRAPKO4DPG2WAM2AE4.json","view_paper":"https://pith.science/paper/5RQPEQ4Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2604.06052&json=true","fetch_graph":"https://pith.science/api/pith-number/5RQPEQ4YRRAPKO4DPG2WAM2AE4/graph.json","fetch_events":"https://pith.science/api/pith-number/5RQPEQ4YRRAPKO4DPG2WAM2AE4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5RQPEQ4YRRAPKO4DPG2WAM2AE4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5RQPEQ4YRRAPKO4DPG2WAM2AE4/action/storage_attestation","attest_author":"https://pith.science/pith/5RQPEQ4YRRAPKO4DPG2WAM2AE4/action/author_attestation","sign_citation":"https://pith.science/pith/5RQPEQ4YRRAPKO4DPG2WAM2AE4/action/citation_signature","submit_replication":"https://pith.science/pith/5RQPEQ4YRRAPKO4DPG2WAM2AE4/action/replication_record"}},"created_at":"2026-06-03T01:05:50.271973+00:00","updated_at":"2026-06-03T01:05:50.271973+00:00"}