{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:3BXTBKB44AX7DEQK47X3NOPNBL","short_pith_number":"pith:3BXTBKB4","schema_version":"1.0","canonical_sha256":"d86f30a83ce02ff1920ae7efb6b9ed0af23774391aac7ca393f8ffb719d4fb88","source":{"kind":"arxiv","id":"2505.23729","version":2},"attestation_state":"computed","paper":{"title":"Bounded Rationality for LLMs: Satisficing Alignment at Inference-Time","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Amrit Singh Bedi, Avinash Reddy, Dinesh Manocha, Hao Zhu, Mohamad Chehade, Soumya Suvra Ghosal, Souradip Chakraborty","submitted_at":"2025-05-29T17:56:05Z","abstract_excerpt":"Aligning large language models with humans is challenging due to the inherently multifaceted nature of preference feedback. While existing approaches typically frame this as a multi-objective optimization problem, they often overlook how humans actually make decisions. Research on bounded rationality suggests that human decision making follows satisficing strategies-optimizing primary objectives while ensuring others meet acceptable thresholds. To bridge this gap and operationalize the notion of satisficing alignment, we propose SITAlign: an inference time framework that addresses the multifac"},"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":false},"canonical_record":{"source":{"id":"2505.23729","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-29T17:56:05Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"009bd8b3c9541f0c076052b14dddbcb5380913e65ee91a25b08d7da26e56af1c","abstract_canon_sha256":"afc4c222047255fc638d3414f1ce90dd0b0a047ce0600885f158247b61d77389"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:27.913605Z","signature_b64":"V1JRJF2hjVU4jujP5r+Xt0cxygW3FCsS6krZSZUDGP6R5uiyG6F9W9dKxuTp7qn/uFxu2q0pUR68XsSUE3J+Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d86f30a83ce02ff1920ae7efb6b9ed0af23774391aac7ca393f8ffb719d4fb88","last_reissued_at":"2026-07-05T11:13:27.913143Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:27.913143Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bounded Rationality for LLMs: Satisficing Alignment at Inference-Time","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Amrit Singh Bedi, Avinash Reddy, Dinesh Manocha, Hao Zhu, Mohamad Chehade, Soumya Suvra Ghosal, Souradip Chakraborty","submitted_at":"2025-05-29T17:56:05Z","abstract_excerpt":"Aligning large language models with humans is challenging due to the inherently multifaceted nature of preference feedback. While existing approaches typically frame this as a multi-objective optimization problem, they often overlook how humans actually make decisions. Research on bounded rationality suggests that human decision making follows satisficing strategies-optimizing primary objectives while ensuring others meet acceptable thresholds. To bridge this gap and operationalize the notion of satisficing alignment, we propose SITAlign: an inference time framework that addresses the multifac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.23729","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/2505.23729/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2505.23729","created_at":"2026-07-05T11:13:27.913199+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.23729v2","created_at":"2026-07-05T11:13:27.913199+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.23729","created_at":"2026-07-05T11:13:27.913199+00:00"},{"alias_kind":"pith_short_12","alias_value":"3BXTBKB44AX7","created_at":"2026-07-05T11:13:27.913199+00:00"},{"alias_kind":"pith_short_16","alias_value":"3BXTBKB44AX7DEQK","created_at":"2026-07-05T11:13:27.913199+00:00"},{"alias_kind":"pith_short_8","alias_value":"3BXTBKB4","created_at":"2026-07-05T11:13:27.913199+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.25088","citing_title":"Cooperate to Compete: Strategic Coordination in Multi-Agent Conquest","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3BXTBKB44AX7DEQK47X3NOPNBL","json":"https://pith.science/pith/3BXTBKB44AX7DEQK47X3NOPNBL.json","graph_json":"https://pith.science/api/pith-number/3BXTBKB44AX7DEQK47X3NOPNBL/graph.json","events_json":"https://pith.science/api/pith-number/3BXTBKB44AX7DEQK47X3NOPNBL/events.json","paper":"https://pith.science/paper/3BXTBKB4"},"agent_actions":{"view_html":"https://pith.science/pith/3BXTBKB44AX7DEQK47X3NOPNBL","download_json":"https://pith.science/pith/3BXTBKB44AX7DEQK47X3NOPNBL.json","view_paper":"https://pith.science/paper/3BXTBKB4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.23729&json=true","fetch_graph":"https://pith.science/api/pith-number/3BXTBKB44AX7DEQK47X3NOPNBL/graph.json","fetch_events":"https://pith.science/api/pith-number/3BXTBKB44AX7DEQK47X3NOPNBL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3BXTBKB44AX7DEQK47X3NOPNBL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3BXTBKB44AX7DEQK47X3NOPNBL/action/storage_attestation","attest_author":"https://pith.science/pith/3BXTBKB44AX7DEQK47X3NOPNBL/action/author_attestation","sign_citation":"https://pith.science/pith/3BXTBKB44AX7DEQK47X3NOPNBL/action/citation_signature","submit_replication":"https://pith.science/pith/3BXTBKB44AX7DEQK47X3NOPNBL/action/replication_record"}},"created_at":"2026-07-05T11:13:27.913199+00:00","updated_at":"2026-07-05T11:13:27.913199+00:00"}