{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UFPEEZYCRVHG6MITNL6TLEH7T3","short_pith_number":"pith:UFPEEZYC","schema_version":"1.0","canonical_sha256":"a15e4267028d4e6f31136afd3590ff9ef422de48752f8fd5e2c9657a9cd56a1d","source":{"kind":"arxiv","id":"2412.17836","version":1},"attestation_state":"computed","paper":{"title":"Look Ahead Text Understanding and LLM Stitching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"CA, China), City University of Hong Kong, Hong Kong, Junlin Julian Jiang (Piedmont High School, Piedmont, USA), Xin Li (College of Business","submitted_at":"2024-12-16T03:32:32Z","abstract_excerpt":"This paper proposes a look ahead text understanding problem with look ahead section identification (LASI) as an example. This problem may appear in generative AI as well as human interactions, where we want to understand the direction of a developing text or conversation. We tackle the problem using transformer-based LLMs. We show that LASI is more challenging than classic section identification (SI). We argue that both bidirectional contextual information (e.g., BERT) and unidirectional predictive ability (e.g., GPT) will benefit the task. We propose two approaches to stitch together BERT and"},"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":"2412.17836","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-16T03:32:32Z","cross_cats_sorted":[],"title_canon_sha256":"d8e51661bdb39a0d601a7bc8765f119ee64b2f6d55a97f2ff66b87cac00f87e7","abstract_canon_sha256":"378b86ac0e4d519c209ed445996a3cdeece6070ae954abf38ef8b417b568500a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:53:29.844058Z","signature_b64":"7qQ313AfysRLydNYgbg30bqN8v9HBWVHhLb1Gxxx04MeU6PKnzw0ydG6ryo59zGXiQ7C59SqAL4HCiZjLKuqDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a15e4267028d4e6f31136afd3590ff9ef422de48752f8fd5e2c9657a9cd56a1d","last_reissued_at":"2026-07-05T09:53:29.843589Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:53:29.843589Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Look Ahead Text Understanding and LLM Stitching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"CA, China), City University of Hong Kong, Hong Kong, Junlin Julian Jiang (Piedmont High School, Piedmont, USA), Xin Li (College of Business","submitted_at":"2024-12-16T03:32:32Z","abstract_excerpt":"This paper proposes a look ahead text understanding problem with look ahead section identification (LASI) as an example. This problem may appear in generative AI as well as human interactions, where we want to understand the direction of a developing text or conversation. We tackle the problem using transformer-based LLMs. We show that LASI is more challenging than classic section identification (SI). We argue that both bidirectional contextual information (e.g., BERT) and unidirectional predictive ability (e.g., GPT) will benefit the task. We propose two approaches to stitch together BERT and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.17836","kind":"arxiv","version":1},"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/2412.17836/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":"2412.17836","created_at":"2026-07-05T09:53:29.843646+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.17836v1","created_at":"2026-07-05T09:53:29.843646+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.17836","created_at":"2026-07-05T09:53:29.843646+00:00"},{"alias_kind":"pith_short_12","alias_value":"UFPEEZYCRVHG","created_at":"2026-07-05T09:53:29.843646+00:00"},{"alias_kind":"pith_short_16","alias_value":"UFPEEZYCRVHG6MIT","created_at":"2026-07-05T09:53:29.843646+00:00"},{"alias_kind":"pith_short_8","alias_value":"UFPEEZYC","created_at":"2026-07-05T09:53:29.843646+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UFPEEZYCRVHG6MITNL6TLEH7T3","json":"https://pith.science/pith/UFPEEZYCRVHG6MITNL6TLEH7T3.json","graph_json":"https://pith.science/api/pith-number/UFPEEZYCRVHG6MITNL6TLEH7T3/graph.json","events_json":"https://pith.science/api/pith-number/UFPEEZYCRVHG6MITNL6TLEH7T3/events.json","paper":"https://pith.science/paper/UFPEEZYC"},"agent_actions":{"view_html":"https://pith.science/pith/UFPEEZYCRVHG6MITNL6TLEH7T3","download_json":"https://pith.science/pith/UFPEEZYCRVHG6MITNL6TLEH7T3.json","view_paper":"https://pith.science/paper/UFPEEZYC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.17836&json=true","fetch_graph":"https://pith.science/api/pith-number/UFPEEZYCRVHG6MITNL6TLEH7T3/graph.json","fetch_events":"https://pith.science/api/pith-number/UFPEEZYCRVHG6MITNL6TLEH7T3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UFPEEZYCRVHG6MITNL6TLEH7T3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UFPEEZYCRVHG6MITNL6TLEH7T3/action/storage_attestation","attest_author":"https://pith.science/pith/UFPEEZYCRVHG6MITNL6TLEH7T3/action/author_attestation","sign_citation":"https://pith.science/pith/UFPEEZYCRVHG6MITNL6TLEH7T3/action/citation_signature","submit_replication":"https://pith.science/pith/UFPEEZYCRVHG6MITNL6TLEH7T3/action/replication_record"}},"created_at":"2026-07-05T09:53:29.843646+00:00","updated_at":"2026-07-05T09:53:29.843646+00:00"}