{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CZ77KGGACFJ435R532ZXZLFTGJ","short_pith_number":"pith:CZ77KGGA","schema_version":"1.0","canonical_sha256":"167ff518c01153cdf63ddeb37cacb3325ab827e90ffe3b18c410050d30bbc6ad","source":{"kind":"arxiv","id":"2404.01644","version":2},"attestation_state":"computed","paper":{"title":"InsightLens: Augmenting LLM-Powered Data Analysis with Interactive Insight Management and Navigation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Danqing Huang, Haozhe Feng, Junyu Lu, Luoxuan Weng, Wei Chen, Xingbo Wang, Yihan Liu, Yingchaojie Feng","submitted_at":"2024-04-02T05:20:12Z","abstract_excerpt":"The proliferation of large language models (LLMs) has revolutionized the capabilities of natural language interfaces (NLIs) for data analysis. LLMs can perform multi-step and complex reasoning to generate data insights based on users' analytic intents. However, these insights often entangle with an abundance of contexts in analytic conversations such as code, visualizations, and natural language explanations. This hinders efficient recording, organization, and navigation of insights within the current chat-based LLM interfaces. In this paper, we first conduct a formative study with eight data "},"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":"2404.01644","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2024-04-02T05:20:12Z","cross_cats_sorted":[],"title_canon_sha256":"b54a18fb9c90aa04d141624ec4999feb014c0574947c730700d61319542d0ea3","abstract_canon_sha256":"66d44a625bf13ab19240ae36bebf53e92e33dbd9e2cc31f4780dc33ede8ffb1d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:52:37.912665Z","signature_b64":"uzpnSfob6Swls+Ca7ei6KicEUvBJbE1qkp7pcZznZP2Dxm7IpeU6s/vLI7wJNBPAuXMbtm3zqUBukBMyrKzuBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"167ff518c01153cdf63ddeb37cacb3325ab827e90ffe3b18c410050d30bbc6ad","last_reissued_at":"2026-07-05T09:52:37.912107Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:52:37.912107Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"InsightLens: Augmenting LLM-Powered Data Analysis with Interactive Insight Management and Navigation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Danqing Huang, Haozhe Feng, Junyu Lu, Luoxuan Weng, Wei Chen, Xingbo Wang, Yihan Liu, Yingchaojie Feng","submitted_at":"2024-04-02T05:20:12Z","abstract_excerpt":"The proliferation of large language models (LLMs) has revolutionized the capabilities of natural language interfaces (NLIs) for data analysis. LLMs can perform multi-step and complex reasoning to generate data insights based on users' analytic intents. However, these insights often entangle with an abundance of contexts in analytic conversations such as code, visualizations, and natural language explanations. This hinders efficient recording, organization, and navigation of insights within the current chat-based LLM interfaces. In this paper, we first conduct a formative study with eight data "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.01644","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/2404.01644/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":"2404.01644","created_at":"2026-07-05T09:52:37.912187+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.01644v2","created_at":"2026-07-05T09:52:37.912187+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.01644","created_at":"2026-07-05T09:52:37.912187+00:00"},{"alias_kind":"pith_short_12","alias_value":"CZ77KGGACFJ4","created_at":"2026-07-05T09:52:37.912187+00:00"},{"alias_kind":"pith_short_16","alias_value":"CZ77KGGACFJ435R5","created_at":"2026-07-05T09:52:37.912187+00:00"},{"alias_kind":"pith_short_8","alias_value":"CZ77KGGA","created_at":"2026-07-05T09:52:37.912187+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.14668","citing_title":"Beyond Chat and Clicks: GUI Agents for In-Situ Assistance via Live Interface Transformation","ref_index":49,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CZ77KGGACFJ435R532ZXZLFTGJ","json":"https://pith.science/pith/CZ77KGGACFJ435R532ZXZLFTGJ.json","graph_json":"https://pith.science/api/pith-number/CZ77KGGACFJ435R532ZXZLFTGJ/graph.json","events_json":"https://pith.science/api/pith-number/CZ77KGGACFJ435R532ZXZLFTGJ/events.json","paper":"https://pith.science/paper/CZ77KGGA"},"agent_actions":{"view_html":"https://pith.science/pith/CZ77KGGACFJ435R532ZXZLFTGJ","download_json":"https://pith.science/pith/CZ77KGGACFJ435R532ZXZLFTGJ.json","view_paper":"https://pith.science/paper/CZ77KGGA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.01644&json=true","fetch_graph":"https://pith.science/api/pith-number/CZ77KGGACFJ435R532ZXZLFTGJ/graph.json","fetch_events":"https://pith.science/api/pith-number/CZ77KGGACFJ435R532ZXZLFTGJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CZ77KGGACFJ435R532ZXZLFTGJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CZ77KGGACFJ435R532ZXZLFTGJ/action/storage_attestation","attest_author":"https://pith.science/pith/CZ77KGGACFJ435R532ZXZLFTGJ/action/author_attestation","sign_citation":"https://pith.science/pith/CZ77KGGACFJ435R532ZXZLFTGJ/action/citation_signature","submit_replication":"https://pith.science/pith/CZ77KGGACFJ435R532ZXZLFTGJ/action/replication_record"}},"created_at":"2026-07-05T09:52:37.912187+00:00","updated_at":"2026-07-05T09:52:37.912187+00:00"}