{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:3FVYZ57RV7G22DYWBAK6DFMAGM","short_pith_number":"pith:3FVYZ57R","canonical_record":{"source":{"id":"2602.03429","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-02-03T11:51:46Z","cross_cats_sorted":["cs.CL","cs.HC","cs.LG"],"title_canon_sha256":"f4faf62c6ba7142f7d7a5b0ccd3496b19dc31968042f03e6124738a030d1fb3f","abstract_canon_sha256":"f8a269b51a77612a0ff8ffb2fc51d215c6040df5440f782d95dfcf60d72ccdbd"},"schema_version":"1.0"},"canonical_sha256":"d96b8cf7f1afcdad0f160815e195803326acb3598b65a881fea40a5985aaf87d","source":{"kind":"arxiv","id":"2602.03429","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.03429","created_at":"2026-05-18T02:44:31Z"},{"alias_kind":"arxiv_version","alias_value":"2602.03429v2","created_at":"2026-05-18T02:44:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.03429","created_at":"2026-05-18T02:44:31Z"},{"alias_kind":"pith_short_12","alias_value":"3FVYZ57RV7G2","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"3FVYZ57RV7G22DYW","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"3FVYZ57R","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:3FVYZ57RV7G22DYWBAK6DFMAGM","target":"record","payload":{"canonical_record":{"source":{"id":"2602.03429","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-02-03T11:51:46Z","cross_cats_sorted":["cs.CL","cs.HC","cs.LG"],"title_canon_sha256":"f4faf62c6ba7142f7d7a5b0ccd3496b19dc31968042f03e6124738a030d1fb3f","abstract_canon_sha256":"f8a269b51a77612a0ff8ffb2fc51d215c6040df5440f782d95dfcf60d72ccdbd"},"schema_version":"1.0"},"canonical_sha256":"d96b8cf7f1afcdad0f160815e195803326acb3598b65a881fea40a5985aaf87d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:44:31.660132Z","signature_b64":"Blrzm+yD2S+N8xH5OM3CqypncltWZi3TWpJb25361x9FsTFBEqnX4xlSDyPawr2ho2PH6hZ6uLnl/rz3re3cAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d96b8cf7f1afcdad0f160815e195803326acb3598b65a881fea40a5985aaf87d","last_reissued_at":"2026-05-18T02:44:31.659498Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:44:31.659498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2602.03429","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":"2026-05-18T02:44:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jlw93JqG2NAqGyCfsP9Wm9jNvsFE/duwYbmJwHkhAjZx85y3l2jxslbjQaj1pgvI1k7HTRmVrRb4ECN3Ee77Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T17:48:34.464446Z"},"content_sha256":"a459d2ff104e44c6a1f3df89dc496a2e1d97b767611deed31c7bc2dbb6352253","schema_version":"1.0","event_id":"sha256:a459d2ff104e44c6a1f3df89dc496a2e1d97b767611deed31c7bc2dbb6352253"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:3FVYZ57RV7G22DYWBAK6DFMAGM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DiscoverLLM: From Executing Intents to Discovering Them","license":"http://creativecommons.org/licenses/by/4.0/","headline":"DiscoverLLM trains LLMs to help users form and discover intents they have not yet formed by modeling how those intents concretize during interaction.","cross_cats":["cs.CL","cs.HC","cs.LG"],"primary_cat":"cs.AI","authors_text":"Jaesang Yu, John Joon Young Chung, Juho Kim, Tae Soo Kim, Yoonjoo Lee","submitted_at":"2026-02-03T11:51:46Z","abstract_excerpt":"To handle ambiguous and open-ended requests, Large Language Models (LLMs) are increasingly trained to interact with users to surface intents they have not yet expressed (e.g., ask clarification questions). However, users are often ambiguous because they have not yet formed their intents: they must observe and explore outcomes to discover what they want. Simply asking \"what kind of tone do you want?\" fails when users themselves do not know. We introduce DiscoverLLM, a novel and generalizable framework that trains LLMs to help users form and discover their intents. Central to our approach is a n"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Across proposed interactive benchmarks in creative writing, technical writing, and SVG drawing, DiscoverLLM achieves over 10% higher task performance while reducing conversation length by up to 40%. In a user study with 75 human participants, DiscoverLLM improved conversation satisfaction and efficiency compared to baselines.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The user simulator accurately models human cognitive state via a hierarchy of intents whose degree of concretization provides a reliable reward signal for training.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"DiscoverLLM introduces a hierarchical intent simulator that rewards LLMs for helping users concretize vague requests, yielding over 10% better task performance and up to 40% shorter conversations in creative writing, technical writing, and SVG drawing benchmarks plus higher satisfaction in a 75-part","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"DiscoverLLM trains LLMs to help users form and discover intents they have not yet formed by modeling how those intents concretize during interaction.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"84c9070f317528019b725f79f2e3749ce45852e13a939de1234e310408aee632"},"source":{"id":"2602.03429","kind":"arxiv","version":2},"verdict":{"id":"6eb266c7-f7ba-4e3e-8461-084fe6c20460","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T07:59:13.500390Z","strongest_claim":"Across proposed interactive benchmarks in creative writing, technical writing, and SVG drawing, DiscoverLLM achieves over 10% higher task performance while reducing conversation length by up to 40%. In a user study with 75 human participants, DiscoverLLM improved conversation satisfaction and efficiency compared to baselines.","one_line_summary":"DiscoverLLM introduces a hierarchical intent simulator that rewards LLMs for helping users concretize vague requests, yielding over 10% better task performance and up to 40% shorter conversations in creative writing, technical writing, and SVG drawing benchmarks plus higher satisfaction in a 75-part","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The user simulator accurately models human cognitive state via a hierarchy of intents whose degree of concretization provides a reliable reward signal for training.","pith_extraction_headline":"DiscoverLLM trains LLMs to help users form and discover intents they have not yet formed by modeling how those intents concretize during interaction."},"references":{"count":58,"sample":[{"doi":"","year":null,"title":"Establishes m yst er y ar ound t he band name's origin","work_id":"c9e5646c-6f85-47db-908e-7b58f2356835","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"1 Establishes pr olonged m yst er y ar ound t he band name's origin","work_id":"07a16b8e-9a37-4df1-af92-be453f138dbd","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"includes a cat","work_id":"4be9785e-5d52-45ed-b971-5f64226658c9","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"I want it to be about a cat, not a dog","work_id":"091b3c74-80aa-4654-aec0-e54673fbf034","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"maybe a smaller, more domestic animal?","work_id":"23351dfd-40d9-460d-bb77-067b18f5db30","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":58,"snapshot_sha256":"87f95d96e27beadc32031be52a4f2c8cb6e3bfa3422d09bbccece62faf81d7a4","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":"6eb266c7-f7ba-4e3e-8461-084fe6c20460"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:44:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RN4nqLZF77cU8Yj0mqjT9gj7W0oZgmMP8b+3HXT0oBysJfG64BecfnSo3A0wxvBd45N2tDSFfCpFzpbJqyqeCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T17:48:34.464993Z"},"content_sha256":"b1da12e72a53c98284cb8cb7b9270b2c0aada812a2f228d64bd2e0a2eb5f5df3","schema_version":"1.0","event_id":"sha256:b1da12e72a53c98284cb8cb7b9270b2c0aada812a2f228d64bd2e0a2eb5f5df3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3FVYZ57RV7G22DYWBAK6DFMAGM/bundle.json","state_url":"https://pith.science/pith/3FVYZ57RV7G22DYWBAK6DFMAGM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3FVYZ57RV7G22DYWBAK6DFMAGM/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":"2026-06-03T17:48:34Z","links":{"resolver":"https://pith.science/pith/3FVYZ57RV7G22DYWBAK6DFMAGM","bundle":"https://pith.science/pith/3FVYZ57RV7G22DYWBAK6DFMAGM/bundle.json","state":"https://pith.science/pith/3FVYZ57RV7G22DYWBAK6DFMAGM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3FVYZ57RV7G22DYWBAK6DFMAGM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:3FVYZ57RV7G22DYWBAK6DFMAGM","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":"f8a269b51a77612a0ff8ffb2fc51d215c6040df5440f782d95dfcf60d72ccdbd","cross_cats_sorted":["cs.CL","cs.HC","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-02-03T11:51:46Z","title_canon_sha256":"f4faf62c6ba7142f7d7a5b0ccd3496b19dc31968042f03e6124738a030d1fb3f"},"schema_version":"1.0","source":{"id":"2602.03429","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.03429","created_at":"2026-05-18T02:44:31Z"},{"alias_kind":"arxiv_version","alias_value":"2602.03429v2","created_at":"2026-05-18T02:44:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.03429","created_at":"2026-05-18T02:44:31Z"},{"alias_kind":"pith_short_12","alias_value":"3FVYZ57RV7G2","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"3FVYZ57RV7G22DYW","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"3FVYZ57R","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:b1da12e72a53c98284cb8cb7b9270b2c0aada812a2f228d64bd2e0a2eb5f5df3","target":"graph","created_at":"2026-05-18T02:44:31Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Across proposed interactive benchmarks in creative writing, technical writing, and SVG drawing, DiscoverLLM achieves over 10% higher task performance while reducing conversation length by up to 40%. In a user study with 75 human participants, DiscoverLLM improved conversation satisfaction and efficiency compared to baselines."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The user simulator accurately models human cognitive state via a hierarchy of intents whose degree of concretization provides a reliable reward signal for training."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"DiscoverLLM introduces a hierarchical intent simulator that rewards LLMs for helping users concretize vague requests, yielding over 10% better task performance and up to 40% shorter conversations in creative writing, technical writing, and SVG drawing benchmarks plus higher satisfaction in a 75-part"},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"DiscoverLLM trains LLMs to help users form and discover intents they have not yet formed by modeling how those intents concretize during interaction."}],"snapshot_sha256":"84c9070f317528019b725f79f2e3749ce45852e13a939de1234e310408aee632"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"To handle ambiguous and open-ended requests, Large Language Models (LLMs) are increasingly trained to interact with users to surface intents they have not yet expressed (e.g., ask clarification questions). However, users are often ambiguous because they have not yet formed their intents: they must observe and explore outcomes to discover what they want. Simply asking \"what kind of tone do you want?\" fails when users themselves do not know. We introduce DiscoverLLM, a novel and generalizable framework that trains LLMs to help users form and discover their intents. Central to our approach is a n","authors_text":"Jaesang Yu, John Joon Young Chung, Juho Kim, Tae Soo Kim, Yoonjoo Lee","cross_cats":["cs.CL","cs.HC","cs.LG"],"headline":"DiscoverLLM trains LLMs to help users form and discover intents they have not yet formed by modeling how those intents concretize during interaction.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-02-03T11:51:46Z","title":"DiscoverLLM: From Executing Intents to Discovering Them"},"references":{"count":58,"internal_anchors":0,"resolved_work":58,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Establishes m yst er y ar ound t he band name's origin","work_id":"c9e5646c-6f85-47db-908e-7b58f2356835","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"1 Establishes pr olonged m yst er y ar ound t he band name's origin","work_id":"07a16b8e-9a37-4df1-af92-be453f138dbd","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"includes a cat","work_id":"4be9785e-5d52-45ed-b971-5f64226658c9","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"I want it to be about a cat, not a dog","work_id":"091b3c74-80aa-4654-aec0-e54673fbf034","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"maybe a smaller, more domestic animal?","work_id":"23351dfd-40d9-460d-bb77-067b18f5db30","year":null}],"snapshot_sha256":"87f95d96e27beadc32031be52a4f2c8cb6e3bfa3422d09bbccece62faf81d7a4"},"source":{"id":"2602.03429","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-16T07:59:13.500390Z","id":"6eb266c7-f7ba-4e3e-8461-084fe6c20460","model_set":{"reader":"grok-4.3"},"one_line_summary":"DiscoverLLM introduces a hierarchical intent simulator that rewards LLMs for helping users concretize vague requests, yielding over 10% better task performance and up to 40% shorter conversations in creative writing, technical writing, and SVG drawing benchmarks plus higher satisfaction in a 75-part","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"DiscoverLLM trains LLMs to help users form and discover intents they have not yet formed by modeling how those intents concretize during interaction.","strongest_claim":"Across proposed interactive benchmarks in creative writing, technical writing, and SVG drawing, DiscoverLLM achieves over 10% higher task performance while reducing conversation length by up to 40%. In a user study with 75 human participants, DiscoverLLM improved conversation satisfaction and efficiency compared to baselines.","weakest_assumption":"The user simulator accurately models human cognitive state via a hierarchy of intents whose degree of concretization provides a reliable reward signal for training."}},"verdict_id":"6eb266c7-f7ba-4e3e-8461-084fe6c20460"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a459d2ff104e44c6a1f3df89dc496a2e1d97b767611deed31c7bc2dbb6352253","target":"record","created_at":"2026-05-18T02:44:31Z","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":"f8a269b51a77612a0ff8ffb2fc51d215c6040df5440f782d95dfcf60d72ccdbd","cross_cats_sorted":["cs.CL","cs.HC","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-02-03T11:51:46Z","title_canon_sha256":"f4faf62c6ba7142f7d7a5b0ccd3496b19dc31968042f03e6124738a030d1fb3f"},"schema_version":"1.0","source":{"id":"2602.03429","kind":"arxiv","version":2}},"canonical_sha256":"d96b8cf7f1afcdad0f160815e195803326acb3598b65a881fea40a5985aaf87d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d96b8cf7f1afcdad0f160815e195803326acb3598b65a881fea40a5985aaf87d","first_computed_at":"2026-05-18T02:44:31.659498Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:44:31.659498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Blrzm+yD2S+N8xH5OM3CqypncltWZi3TWpJb25361x9FsTFBEqnX4xlSDyPawr2ho2PH6hZ6uLnl/rz3re3cAA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:44:31.660132Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.03429","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a459d2ff104e44c6a1f3df89dc496a2e1d97b767611deed31c7bc2dbb6352253","sha256:b1da12e72a53c98284cb8cb7b9270b2c0aada812a2f228d64bd2e0a2eb5f5df3"],"state_sha256":"217c5f708fb78ba99528485bf78feb59d914c26175e35e6eb1281920b5180f81"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"is7g1Ud7+tXB9fT4/a5A/ctU7u5QMnlTbZuXtN7oL3BDBNfAXy1FMUxvkJ0Nv7DuNInsBBJXd6bHGQbtzEU6Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T17:48:34.468254Z","bundle_sha256":"29aa95058c4b662c4c325358f73dd8ba359e6a2539ca2855c68b284bac8ac166"}}