{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:FAJGYN2FFVBINKQGXCQIMS64LE","short_pith_number":"pith:FAJGYN2F","canonical_record":{"source":{"id":"2509.04473","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-29T22:38:16Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"95da922c13073d115f9a279719dd7002c6ae34184b466428164187672c389678","abstract_canon_sha256":"e6400c755c03ec067a6de42bd161232186b220ef0f1c667858d0549d3989a424"},"schema_version":"1.0"},"canonical_sha256":"28126c37452d4286aa06b8a0864bdc590e4c7de7cdfeac52361deaf76d7e00b7","source":{"kind":"arxiv","id":"2509.04473","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.04473","created_at":"2026-07-05T12:05:23Z"},{"alias_kind":"arxiv_version","alias_value":"2509.04473v1","created_at":"2026-07-05T12:05:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.04473","created_at":"2026-07-05T12:05:23Z"},{"alias_kind":"pith_short_12","alias_value":"FAJGYN2FFVBI","created_at":"2026-07-05T12:05:23Z"},{"alias_kind":"pith_short_16","alias_value":"FAJGYN2FFVBINKQG","created_at":"2026-07-05T12:05:23Z"},{"alias_kind":"pith_short_8","alias_value":"FAJGYN2F","created_at":"2026-07-05T12:05:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:FAJGYN2FFVBINKQGXCQIMS64LE","target":"record","payload":{"canonical_record":{"source":{"id":"2509.04473","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-29T22:38:16Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"95da922c13073d115f9a279719dd7002c6ae34184b466428164187672c389678","abstract_canon_sha256":"e6400c755c03ec067a6de42bd161232186b220ef0f1c667858d0549d3989a424"},"schema_version":"1.0"},"canonical_sha256":"28126c37452d4286aa06b8a0864bdc590e4c7de7cdfeac52361deaf76d7e00b7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:05:23.655305Z","signature_b64":"ieo90SvqarnwZ5v65BKaocbLKiKNSKvbwaJZsnrpe9QkMKaC2CdnudYLX5gGzPUB4/38koeyGqrqmK3uwKFqDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"28126c37452d4286aa06b8a0864bdc590e4c7de7cdfeac52361deaf76d7e00b7","last_reissued_at":"2026-07-05T12:05:23.654783Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:05:23.654783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2509.04473","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":"2026-07-05T12:05:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aq43AJ98MGSX2sQ4s6kvtpvMa/yT+AhV1JywlazEHx7C3DLE7JcqbqGWdooqVpB0MHDhlqj117rAsDO/r08sAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T06:15:32.635974Z"},"content_sha256":"008deaa989ffd3b94a0d0ca20079c0ff37252a2cc4731265f4a00c2b57f66243","schema_version":"1.0","event_id":"sha256:008deaa989ffd3b94a0d0ca20079c0ff37252a2cc4731265f4a00c2b57f66243"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:FAJGYN2FFVBINKQGXCQIMS64LE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SpeechLLM: Unified Speech and Language Model for Enhanced Multi-Task Understanding in Low Resource Settings","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Abenezer Girma, Chandra Dhir, Divya Tadimeti, Jaekwon Yoo, Kunal Chandiramani","submitted_at":"2025-08-29T22:38:16Z","abstract_excerpt":"While integrating speech encoder with LLM requires substantial data and resources, use cases face limitations due to insufficient availability. To address this, we propose a solution with a parameter-efficient adapter that converts speech embeddings into LLM-compatible tokens, focusing on end-to-end automatic speech recognition (ASR), named entity recognition (NER), and sentiment analysis (SA). To reduce labeling costs, we employ an LLM-based synthetic dataset annotation technique. The proposed adapter, using 7x fewer trainable parameters, achieves significant performance gains: a 26% relative"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.04473","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/2509.04473/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":"2026-07-05T12:05:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sNllEoV6h9AubAXUFy6xH3+E3mdteRaSfnSLR6g4dSEKUv9WskpDb85gb4zNDt6kO/GTtMg6UpZjjfR4E1OFCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T06:15:32.636465Z"},"content_sha256":"9e5b2bad4a1482fa7210435c5d3bda8da0dfc233c646936388b4f1e9bdec0bfb","schema_version":"1.0","event_id":"sha256:9e5b2bad4a1482fa7210435c5d3bda8da0dfc233c646936388b4f1e9bdec0bfb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FAJGYN2FFVBINKQGXCQIMS64LE/bundle.json","state_url":"https://pith.science/pith/FAJGYN2FFVBINKQGXCQIMS64LE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FAJGYN2FFVBINKQGXCQIMS64LE/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":"2026-08-06T06:15:32Z","links":{"resolver":"https://pith.science/pith/FAJGYN2FFVBINKQGXCQIMS64LE","bundle":"https://pith.science/pith/FAJGYN2FFVBINKQGXCQIMS64LE/bundle.json","state":"https://pith.science/pith/FAJGYN2FFVBINKQGXCQIMS64LE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FAJGYN2FFVBINKQGXCQIMS64LE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FAJGYN2FFVBINKQGXCQIMS64LE","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":"e6400c755c03ec067a6de42bd161232186b220ef0f1c667858d0549d3989a424","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-29T22:38:16Z","title_canon_sha256":"95da922c13073d115f9a279719dd7002c6ae34184b466428164187672c389678"},"schema_version":"1.0","source":{"id":"2509.04473","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.04473","created_at":"2026-07-05T12:05:23Z"},{"alias_kind":"arxiv_version","alias_value":"2509.04473v1","created_at":"2026-07-05T12:05:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.04473","created_at":"2026-07-05T12:05:23Z"},{"alias_kind":"pith_short_12","alias_value":"FAJGYN2FFVBI","created_at":"2026-07-05T12:05:23Z"},{"alias_kind":"pith_short_16","alias_value":"FAJGYN2FFVBINKQG","created_at":"2026-07-05T12:05:23Z"},{"alias_kind":"pith_short_8","alias_value":"FAJGYN2F","created_at":"2026-07-05T12:05:23Z"}],"graph_snapshots":[{"event_id":"sha256:9e5b2bad4a1482fa7210435c5d3bda8da0dfc233c646936388b4f1e9bdec0bfb","target":"graph","created_at":"2026-07-05T12:05:23Z","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/2509.04473/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While integrating speech encoder with LLM requires substantial data and resources, use cases face limitations due to insufficient availability. To address this, we propose a solution with a parameter-efficient adapter that converts speech embeddings into LLM-compatible tokens, focusing on end-to-end automatic speech recognition (ASR), named entity recognition (NER), and sentiment analysis (SA). To reduce labeling costs, we employ an LLM-based synthetic dataset annotation technique. The proposed adapter, using 7x fewer trainable parameters, achieves significant performance gains: a 26% relative","authors_text":"Abenezer Girma, Chandra Dhir, Divya Tadimeti, Jaekwon Yoo, Kunal Chandiramani","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-29T22:38:16Z","title":"SpeechLLM: Unified Speech and Language Model for Enhanced Multi-Task Understanding in Low Resource Settings"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.04473","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":"sha256:008deaa989ffd3b94a0d0ca20079c0ff37252a2cc4731265f4a00c2b57f66243","target":"record","created_at":"2026-07-05T12:05:23Z","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":"e6400c755c03ec067a6de42bd161232186b220ef0f1c667858d0549d3989a424","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-29T22:38:16Z","title_canon_sha256":"95da922c13073d115f9a279719dd7002c6ae34184b466428164187672c389678"},"schema_version":"1.0","source":{"id":"2509.04473","kind":"arxiv","version":1}},"canonical_sha256":"28126c37452d4286aa06b8a0864bdc590e4c7de7cdfeac52361deaf76d7e00b7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"28126c37452d4286aa06b8a0864bdc590e4c7de7cdfeac52361deaf76d7e00b7","first_computed_at":"2026-07-05T12:05:23.654783Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:05:23.654783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ieo90SvqarnwZ5v65BKaocbLKiKNSKvbwaJZsnrpe9QkMKaC2CdnudYLX5gGzPUB4/38koeyGqrqmK3uwKFqDg==","signature_status":"signed_v1","signed_at":"2026-07-05T12:05:23.655305Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.04473","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:008deaa989ffd3b94a0d0ca20079c0ff37252a2cc4731265f4a00c2b57f66243","sha256:9e5b2bad4a1482fa7210435c5d3bda8da0dfc233c646936388b4f1e9bdec0bfb"],"state_sha256":"062cf01fe4271b4bdf0168673de7fd224439e9d47c4e3bce797762adb8375625"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3MNUTaYfq/Vt+x1/D1/Yj4eZN+4EbzSf/48vscwFd6cimcPDqoX2aPuRwNOLneYYeiFXBliqvX0+5irp3o/ACw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T06:15:32.640100Z","bundle_sha256":"ae3e60c9e31928b4a9a8339fab584e19b94c45e01cd0f99cacaf196bdbb22ccc"}}