{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:KKLOUNELGGGYZ4W4TDJ6NDVZZQ","short_pith_number":"pith:KKLOUNEL","canonical_record":{"source":{"id":"2411.12307","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-19T07:48:35Z","cross_cats_sorted":["cs.AI","cs.IR"],"title_canon_sha256":"8c2fd415123702c0f33414357a2d9288f21fb4640dc63c772c66db196dd7c429","abstract_canon_sha256":"a193854e8d5b15951d8f50bd9d4c50d0f6e0bb7a78e1f25b5b6f5855e52089c4"},"schema_version":"1.0"},"canonical_sha256":"5296ea348b318d8cf2dc98d3e68eb9cc205ee97a73259c5484fa45da91bc1e8f","source":{"kind":"arxiv","id":"2411.12307","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.12307","created_at":"2026-07-05T09:37:19Z"},{"alias_kind":"arxiv_version","alias_value":"2411.12307v1","created_at":"2026-07-05T09:37:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.12307","created_at":"2026-07-05T09:37:19Z"},{"alias_kind":"pith_short_12","alias_value":"KKLOUNELGGGY","created_at":"2026-07-05T09:37:19Z"},{"alias_kind":"pith_short_16","alias_value":"KKLOUNELGGGYZ4W4","created_at":"2026-07-05T09:37:19Z"},{"alias_kind":"pith_short_8","alias_value":"KKLOUNEL","created_at":"2026-07-05T09:37:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:KKLOUNELGGGYZ4W4TDJ6NDVZZQ","target":"record","payload":{"canonical_record":{"source":{"id":"2411.12307","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-19T07:48:35Z","cross_cats_sorted":["cs.AI","cs.IR"],"title_canon_sha256":"8c2fd415123702c0f33414357a2d9288f21fb4640dc63c772c66db196dd7c429","abstract_canon_sha256":"a193854e8d5b15951d8f50bd9d4c50d0f6e0bb7a78e1f25b5b6f5855e52089c4"},"schema_version":"1.0"},"canonical_sha256":"5296ea348b318d8cf2dc98d3e68eb9cc205ee97a73259c5484fa45da91bc1e8f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:37:19.305922Z","signature_b64":"JQ2bXdZ2C/Pk5Nz6ZTdpFfXenbz22T7WMRasds7yOJTW3MZqkZQGETzA40g3dhVjjL7V37eauNnEf6J5D8mVDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5296ea348b318d8cf2dc98d3e68eb9cc205ee97a73259c5484fa45da91bc1e8f","last_reissued_at":"2026-07-05T09:37:19.305507Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:37:19.305507Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.12307","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:37:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z8auHoleMQZks7J3qsVtDz+rq0QEhIdKJqyEGkzDpsgNTq3m/Ayw03yaaS4gwEMmHNXwTsMWY12wKaeevVL0Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T00:29:47.834564Z"},"content_sha256":"5ed12ca851019e0b9a52ee1a63f1ce31bcd61cb00dca93cef0e25a175971c741","schema_version":"1.0","event_id":"sha256:5ed12ca851019e0b9a52ee1a63f1ce31bcd61cb00dca93cef0e25a175971c741"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:KKLOUNELGGGYZ4W4TDJ6NDVZZQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Balancing Accuracy and Efficiency in Multi-Turn Intent Classification for LLM-Powered Dialog Systems in Production","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IR"],"primary_cat":"cs.CL","authors_text":"Bin Fu, Junhua Liu, Kwan Hui Lim, Yong Keat Tan","submitted_at":"2024-11-19T07:48:35Z","abstract_excerpt":"Accurate multi-turn intent classification is essential for advancing conversational AI systems. However, challenges such as the scarcity of comprehensive datasets and the complexity of contextual dependencies across dialogue turns hinder progress. This paper presents two novel approaches leveraging Large Language Models (LLMs) to enhance scalability and reduce latency in production dialogue systems. First, we introduce Symbol Tuning, which simplifies intent labels to reduce task complexity and improve performance in multi-turn dialogues. Second, we propose C-LARA (Consistency-aware, Linguistic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.12307","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/2411.12307/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:37:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N+IJZRJ/pJZ8QZK04kAgPko1po5GF3WeYTsN9r81FOZ3rMCFfwccajLdwUL4G2XC5nENexMkxn7KaPkIqCqsBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T00:29:47.834948Z"},"content_sha256":"d7cbbd3b5836daaeba3663c77f788b11ea0f4001065fae08c0473d1962e48f81","schema_version":"1.0","event_id":"sha256:d7cbbd3b5836daaeba3663c77f788b11ea0f4001065fae08c0473d1962e48f81"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KKLOUNELGGGYZ4W4TDJ6NDVZZQ/bundle.json","state_url":"https://pith.science/pith/KKLOUNELGGGYZ4W4TDJ6NDVZZQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KKLOUNELGGGYZ4W4TDJ6NDVZZQ/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":"2026-08-18T00:29:47Z","links":{"resolver":"https://pith.science/pith/KKLOUNELGGGYZ4W4TDJ6NDVZZQ","bundle":"https://pith.science/pith/KKLOUNELGGGYZ4W4TDJ6NDVZZQ/bundle.json","state":"https://pith.science/pith/KKLOUNELGGGYZ4W4TDJ6NDVZZQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KKLOUNELGGGYZ4W4TDJ6NDVZZQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:KKLOUNELGGGYZ4W4TDJ6NDVZZQ","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":"a193854e8d5b15951d8f50bd9d4c50d0f6e0bb7a78e1f25b5b6f5855e52089c4","cross_cats_sorted":["cs.AI","cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-19T07:48:35Z","title_canon_sha256":"8c2fd415123702c0f33414357a2d9288f21fb4640dc63c772c66db196dd7c429"},"schema_version":"1.0","source":{"id":"2411.12307","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.12307","created_at":"2026-07-05T09:37:19Z"},{"alias_kind":"arxiv_version","alias_value":"2411.12307v1","created_at":"2026-07-05T09:37:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.12307","created_at":"2026-07-05T09:37:19Z"},{"alias_kind":"pith_short_12","alias_value":"KKLOUNELGGGY","created_at":"2026-07-05T09:37:19Z"},{"alias_kind":"pith_short_16","alias_value":"KKLOUNELGGGYZ4W4","created_at":"2026-07-05T09:37:19Z"},{"alias_kind":"pith_short_8","alias_value":"KKLOUNEL","created_at":"2026-07-05T09:37:19Z"}],"graph_snapshots":[{"event_id":"sha256:d7cbbd3b5836daaeba3663c77f788b11ea0f4001065fae08c0473d1962e48f81","target":"graph","created_at":"2026-07-05T09:37:19Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2411.12307/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate multi-turn intent classification is essential for advancing conversational AI systems. However, challenges such as the scarcity of comprehensive datasets and the complexity of contextual dependencies across dialogue turns hinder progress. This paper presents two novel approaches leveraging Large Language Models (LLMs) to enhance scalability and reduce latency in production dialogue systems. First, we introduce Symbol Tuning, which simplifies intent labels to reduce task complexity and improve performance in multi-turn dialogues. Second, we propose C-LARA (Consistency-aware, Linguistic","authors_text":"Bin Fu, Junhua Liu, Kwan Hui Lim, Yong Keat Tan","cross_cats":["cs.AI","cs.IR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-19T07:48:35Z","title":"Balancing Accuracy and Efficiency in Multi-Turn Intent Classification for LLM-Powered Dialog Systems in Production"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.12307","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5ed12ca851019e0b9a52ee1a63f1ce31bcd61cb00dca93cef0e25a175971c741","target":"record","created_at":"2026-07-05T09:37:19Z","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":"a193854e8d5b15951d8f50bd9d4c50d0f6e0bb7a78e1f25b5b6f5855e52089c4","cross_cats_sorted":["cs.AI","cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-19T07:48:35Z","title_canon_sha256":"8c2fd415123702c0f33414357a2d9288f21fb4640dc63c772c66db196dd7c429"},"schema_version":"1.0","source":{"id":"2411.12307","kind":"arxiv","version":1}},"canonical_sha256":"5296ea348b318d8cf2dc98d3e68eb9cc205ee97a73259c5484fa45da91bc1e8f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5296ea348b318d8cf2dc98d3e68eb9cc205ee97a73259c5484fa45da91bc1e8f","first_computed_at":"2026-07-05T09:37:19.305507Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:37:19.305507Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JQ2bXdZ2C/Pk5Nz6ZTdpFfXenbz22T7WMRasds7yOJTW3MZqkZQGETzA40g3dhVjjL7V37eauNnEf6J5D8mVDw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:37:19.305922Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.12307","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5ed12ca851019e0b9a52ee1a63f1ce31bcd61cb00dca93cef0e25a175971c741","sha256:d7cbbd3b5836daaeba3663c77f788b11ea0f4001065fae08c0473d1962e48f81"],"state_sha256":"423cbdef3e1f5b6ed477805425a3498a3d5d89430aedcd53b3b4d84ce3b83f4f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iI1jd7rklNyDjfdvn4RMcgvDfbzmEdOhGSk0e7A+yNQbADuu5uV0ab04ctHYmwa56XlmA00m+yfwrS1VbhFfBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T00:29:47.837436Z","bundle_sha256":"441597b5b0d31db8d2b842680b667bde0138f676f507fa68e31072c162fc0331"}}