{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VF5TMBUHJJY2JBQMELA4VLQE47","short_pith_number":"pith:VF5TMBUH","schema_version":"1.0","canonical_sha256":"a97b3606874a71a4860c22c1caae04e7d98e7715bfd3dd7a8cc96532157e5ec8","source":{"kind":"arxiv","id":"2503.13975","version":2},"attestation_state":"computed","paper":{"title":"Navigating Rifts in Human-LLM Grounding: Study and Benchmark","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.CL","authors_text":"Adam Fourney, Eric Horvitz, Gagan Bansal, Hussein Mozannar, Omar Shaikh","submitted_at":"2025-03-18T07:24:05Z","abstract_excerpt":"Language models excel at following instructions but often struggle with the collaborative aspects of conversation that humans naturally employ. This limitation in grounding -- the process by which conversation participants establish mutual understanding -- can lead to outcomes ranging from frustrated users to serious consequences in high-stakes scenarios. To systematically study grounding challenges in human-LLM interactions, we analyze logs from three human-assistant datasets: WildChat, MultiWOZ, and Bing Chat. We develop a taxonomy of grounding acts and build models to annotate and forecast "},"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":"2503.13975","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-18T07:24:05Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"aa3ec5f083ad2d96af1693aa2886d5d058a02eb884d641b9b06c106674900427","abstract_canon_sha256":"93cb0ed92d99ef12fc65309a2139aebbd745753decbac87aa8c21bacd4d79b7a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:22.729339Z","signature_b64":"EROnZqTG6+pVK/A+Qub8bnKeK7PapJmfF+f6yaL24QAm/l0prQVTMOF2CWSAwihcT/WbYb8Cw4yFSXus6V1zAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a97b3606874a71a4860c22c1caae04e7d98e7715bfd3dd7a8cc96532157e5ec8","last_reissued_at":"2026-07-05T11:13:22.728816Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:22.728816Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Navigating Rifts in Human-LLM Grounding: Study and Benchmark","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.CL","authors_text":"Adam Fourney, Eric Horvitz, Gagan Bansal, Hussein Mozannar, Omar Shaikh","submitted_at":"2025-03-18T07:24:05Z","abstract_excerpt":"Language models excel at following instructions but often struggle with the collaborative aspects of conversation that humans naturally employ. This limitation in grounding -- the process by which conversation participants establish mutual understanding -- can lead to outcomes ranging from frustrated users to serious consequences in high-stakes scenarios. To systematically study grounding challenges in human-LLM interactions, we analyze logs from three human-assistant datasets: WildChat, MultiWOZ, and Bing Chat. We develop a taxonomy of grounding acts and build models to annotate and forecast "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.13975","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/2503.13975/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":"2503.13975","created_at":"2026-07-05T11:13:22.728873+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.13975v2","created_at":"2026-07-05T11:13:22.728873+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.13975","created_at":"2026-07-05T11:13:22.728873+00:00"},{"alias_kind":"pith_short_12","alias_value":"VF5TMBUHJJY2","created_at":"2026-07-05T11:13:22.728873+00:00"},{"alias_kind":"pith_short_16","alias_value":"VF5TMBUHJJY2JBQM","created_at":"2026-07-05T11:13:22.728873+00:00"},{"alias_kind":"pith_short_8","alias_value":"VF5TMBUH","created_at":"2026-07-05T11:13:22.728873+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2509.11295","citing_title":"The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences","ref_index":109,"is_internal_anchor":false},{"citing_arxiv_id":"2509.11295","citing_title":"The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences","ref_index":110,"is_internal_anchor":false},{"citing_arxiv_id":"2505.06120","citing_title":"LLMs Get Lost In Multi-Turn Conversation","ref_index":75,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21827","citing_title":"Alignment has a Fantasia Problem","ref_index":55,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10587","citing_title":"CogInstrument: Modeling Cognitive Processes for Bidirectional Human-LLM Alignment in Planning Tasks","ref_index":49,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VF5TMBUHJJY2JBQMELA4VLQE47","json":"https://pith.science/pith/VF5TMBUHJJY2JBQMELA4VLQE47.json","graph_json":"https://pith.science/api/pith-number/VF5TMBUHJJY2JBQMELA4VLQE47/graph.json","events_json":"https://pith.science/api/pith-number/VF5TMBUHJJY2JBQMELA4VLQE47/events.json","paper":"https://pith.science/paper/VF5TMBUH"},"agent_actions":{"view_html":"https://pith.science/pith/VF5TMBUHJJY2JBQMELA4VLQE47","download_json":"https://pith.science/pith/VF5TMBUHJJY2JBQMELA4VLQE47.json","view_paper":"https://pith.science/paper/VF5TMBUH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.13975&json=true","fetch_graph":"https://pith.science/api/pith-number/VF5TMBUHJJY2JBQMELA4VLQE47/graph.json","fetch_events":"https://pith.science/api/pith-number/VF5TMBUHJJY2JBQMELA4VLQE47/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VF5TMBUHJJY2JBQMELA4VLQE47/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VF5TMBUHJJY2JBQMELA4VLQE47/action/storage_attestation","attest_author":"https://pith.science/pith/VF5TMBUHJJY2JBQMELA4VLQE47/action/author_attestation","sign_citation":"https://pith.science/pith/VF5TMBUHJJY2JBQMELA4VLQE47/action/citation_signature","submit_replication":"https://pith.science/pith/VF5TMBUHJJY2JBQMELA4VLQE47/action/replication_record"}},"created_at":"2026-07-05T11:13:22.728873+00:00","updated_at":"2026-07-05T11:13:22.728873+00:00"}