{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:GCKVGZD5LFSF33KTIY7KNJMT6H","short_pith_number":"pith:GCKVGZD5","canonical_record":{"source":{"id":"2401.02921","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-05T17:58:10Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"52087a0269e1abd8d08e339fc83999ae737405ccaeec1f8339c2a707902d3101","abstract_canon_sha256":"d4efdbaaeb5fc590ed5657d7e6df5163af68af364b89cab8f1d187cbb134dfdc"},"schema_version":"1.0"},"canonical_sha256":"309553647d59645ded53463ea6a593f1e4d29a08f86f28498da50bddd5c49ccf","source":{"kind":"arxiv","id":"2401.02921","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.02921","created_at":"2026-07-05T07:30:40Z"},{"alias_kind":"arxiv_version","alias_value":"2401.02921v1","created_at":"2026-07-05T07:30:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.02921","created_at":"2026-07-05T07:30:40Z"},{"alias_kind":"pith_short_12","alias_value":"GCKVGZD5LFSF","created_at":"2026-07-05T07:30:40Z"},{"alias_kind":"pith_short_16","alias_value":"GCKVGZD5LFSF33KT","created_at":"2026-07-05T07:30:40Z"},{"alias_kind":"pith_short_8","alias_value":"GCKVGZD5","created_at":"2026-07-05T07:30:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:GCKVGZD5LFSF33KTIY7KNJMT6H","target":"record","payload":{"canonical_record":{"source":{"id":"2401.02921","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-05T17:58:10Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"52087a0269e1abd8d08e339fc83999ae737405ccaeec1f8339c2a707902d3101","abstract_canon_sha256":"d4efdbaaeb5fc590ed5657d7e6df5163af68af364b89cab8f1d187cbb134dfdc"},"schema_version":"1.0"},"canonical_sha256":"309553647d59645ded53463ea6a593f1e4d29a08f86f28498da50bddd5c49ccf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:30:40.236212Z","signature_b64":"jXWmuW5+fWyamrI5yKcbHkxWtwOQ+sP2nJbTj+gt9pk/KYK807LxwaDl2igdDgBt/mq4ERqEhiWGw/JVDEqtCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"309553647d59645ded53463ea6a593f1e4d29a08f86f28498da50bddd5c49ccf","last_reissued_at":"2026-07-05T07:30:40.235787Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:30:40.235787Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.02921","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":"2026-07-05T07:30:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V6nH1JzSapyL7nZrtsCPd55nu6ghQhQvocobxgsPm5s6pwNz9RocJE/swJMOl23npTFkWRYeJ9efZyF0ztZUCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T04:14:19.780894Z"},"content_sha256":"4433da555b0d1c19df37edaa8cefc186df34882914a4061078c0e40b222e9011","schema_version":"1.0","event_id":"sha256:4433da555b0d1c19df37edaa8cefc186df34882914a4061078c0e40b222e9011"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:GCKVGZD5LFSF33KTIY7KNJMT6H","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.CL","authors_text":"Andreas Stolcke, Ankur Gandhe, Ariya Rastrow, Guan-Ting Lin, Huck Yang, Hung-yi Lee, Ivan Bulyko, Jari Kolehmainen, Kevin Everson, Prashanth Gurunath Shivakumar, Shalini Ghosh, Wael Hamza, Yile Gu","submitted_at":"2024-01-05T17:58:10Z","abstract_excerpt":"In the realm of spoken language understanding (SLU), numerous natural language understanding (NLU) methodologies have been adapted by supplying large language models (LLMs) with transcribed speech instead of conventional written text. In real-world scenarios, prior to input into an LLM, an automated speech recognition (ASR) system generates an output transcript hypothesis, where inherent errors can degrade subsequent SLU tasks. Here we introduce a method that utilizes the ASR system's lattice output instead of relying solely on the top hypothesis, aiming to encapsulate speech ambiguities and e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.02921","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/2401.02921/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":"2026-07-05T07:30:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3+zhOEYwCbNv1XeD9e54A3HcqDS+/cBxAruEmeyozn8kYDbgRHBSsTJx6s/z27vN2yBQL2VSu7J3Tl2WNgBeAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T04:14:19.781422Z"},"content_sha256":"b4506611b3c9885a9bb59dbb23d264719777f7194e915c0d8dc9ad8b0f4ee277","schema_version":"1.0","event_id":"sha256:b4506611b3c9885a9bb59dbb23d264719777f7194e915c0d8dc9ad8b0f4ee277"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GCKVGZD5LFSF33KTIY7KNJMT6H/bundle.json","state_url":"https://pith.science/pith/GCKVGZD5LFSF33KTIY7KNJMT6H/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GCKVGZD5LFSF33KTIY7KNJMT6H/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":"2026-08-23T04:14:19Z","links":{"resolver":"https://pith.science/pith/GCKVGZD5LFSF33KTIY7KNJMT6H","bundle":"https://pith.science/pith/GCKVGZD5LFSF33KTIY7KNJMT6H/bundle.json","state":"https://pith.science/pith/GCKVGZD5LFSF33KTIY7KNJMT6H/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GCKVGZD5LFSF33KTIY7KNJMT6H/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:GCKVGZD5LFSF33KTIY7KNJMT6H","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":"d4efdbaaeb5fc590ed5657d7e6df5163af68af364b89cab8f1d187cbb134dfdc","cross_cats_sorted":["eess.AS"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-05T17:58:10Z","title_canon_sha256":"52087a0269e1abd8d08e339fc83999ae737405ccaeec1f8339c2a707902d3101"},"schema_version":"1.0","source":{"id":"2401.02921","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.02921","created_at":"2026-07-05T07:30:40Z"},{"alias_kind":"arxiv_version","alias_value":"2401.02921v1","created_at":"2026-07-05T07:30:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.02921","created_at":"2026-07-05T07:30:40Z"},{"alias_kind":"pith_short_12","alias_value":"GCKVGZD5LFSF","created_at":"2026-07-05T07:30:40Z"},{"alias_kind":"pith_short_16","alias_value":"GCKVGZD5LFSF33KT","created_at":"2026-07-05T07:30:40Z"},{"alias_kind":"pith_short_8","alias_value":"GCKVGZD5","created_at":"2026-07-05T07:30:40Z"}],"graph_snapshots":[{"event_id":"sha256:b4506611b3c9885a9bb59dbb23d264719777f7194e915c0d8dc9ad8b0f4ee277","target":"graph","created_at":"2026-07-05T07:30:40Z","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/2401.02921/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the realm of spoken language understanding (SLU), numerous natural language understanding (NLU) methodologies have been adapted by supplying large language models (LLMs) with transcribed speech instead of conventional written text. In real-world scenarios, prior to input into an LLM, an automated speech recognition (ASR) system generates an output transcript hypothesis, where inherent errors can degrade subsequent SLU tasks. Here we introduce a method that utilizes the ASR system's lattice output instead of relying solely on the top hypothesis, aiming to encapsulate speech ambiguities and e","authors_text":"Andreas Stolcke, Ankur Gandhe, Ariya Rastrow, Guan-Ting Lin, Huck Yang, Hung-yi Lee, Ivan Bulyko, Jari Kolehmainen, Kevin Everson, Prashanth Gurunath Shivakumar, Shalini Ghosh, Wael Hamza, Yile Gu","cross_cats":["eess.AS"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-05T17:58:10Z","title":"Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.02921","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":"sha256:4433da555b0d1c19df37edaa8cefc186df34882914a4061078c0e40b222e9011","target":"record","created_at":"2026-07-05T07:30:40Z","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":"d4efdbaaeb5fc590ed5657d7e6df5163af68af364b89cab8f1d187cbb134dfdc","cross_cats_sorted":["eess.AS"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-05T17:58:10Z","title_canon_sha256":"52087a0269e1abd8d08e339fc83999ae737405ccaeec1f8339c2a707902d3101"},"schema_version":"1.0","source":{"id":"2401.02921","kind":"arxiv","version":1}},"canonical_sha256":"309553647d59645ded53463ea6a593f1e4d29a08f86f28498da50bddd5c49ccf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"309553647d59645ded53463ea6a593f1e4d29a08f86f28498da50bddd5c49ccf","first_computed_at":"2026-07-05T07:30:40.235787Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:30:40.235787Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jXWmuW5+fWyamrI5yKcbHkxWtwOQ+sP2nJbTj+gt9pk/KYK807LxwaDl2igdDgBt/mq4ERqEhiWGw/JVDEqtCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:30:40.236212Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.02921","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4433da555b0d1c19df37edaa8cefc186df34882914a4061078c0e40b222e9011","sha256:b4506611b3c9885a9bb59dbb23d264719777f7194e915c0d8dc9ad8b0f4ee277"],"state_sha256":"6209cef4d8d49df2eaa3bf584f22e030d265ffe36b685d4082ae24fe7b3d038f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iSy9y6Nq39YoN6+KGfQJF5OOmbEVFsxQ5auuyQx1lamSkQAIJTdWGBZiXXV9Nnw/cehm4FJecnHGl+7Mf+2ABA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T04:14:19.786506Z","bundle_sha256":"31d25a24c68e5cdcf8ff07f8e949d2f30c7fe5a3d260480eed25194c3950c70c"}}