{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2OJSO4IQTNVIPVG7CVNWICDPWN","short_pith_number":"pith:2OJSO4IQ","schema_version":"1.0","canonical_sha256":"d3932771109b6a87d4df155b64086fb3597a4ce1561e4d03384f2911cd896907","source":{"kind":"arxiv","id":"2508.02209","version":1},"attestation_state":"computed","paper":{"title":"Balancing Information Accuracy and Response Timeliness in Networked LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.IT","cs.NI","math.IT"],"primary_cat":"cs.LG","authors_text":"Baturalp Buyukates, Melih Bastopcu, Yigit Turkmen","submitted_at":"2025-08-04T09:00:01Z","abstract_excerpt":"Recent advancements in Large Language Models (LLMs) have transformed many fields including scientific discovery, content generation, biomedical text mining, and educational technology. However, the substantial requirements for training data, computational resources, and energy consumption pose significant challenges for their practical deployment. A promising alternative is to leverage smaller, specialized language models and aggregate their outputs to improve overall response quality. In this work, we investigate a networked LLM system composed of multiple users, a central task processor, 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":"2508.02209","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-04T09:00:01Z","cross_cats_sorted":["cs.AI","cs.IT","cs.NI","math.IT"],"title_canon_sha256":"8521fb5cc6c665fe08fcb35e8134f498cdbce3d1f88393d9111131170887e414","abstract_canon_sha256":"316a4c54f4ba1e7ff897c1e69abe435b97bf7fbec4ea296b4b0ac24480f7817a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:48:10.256667Z","signature_b64":"X5CMhQKKJJeEUrRx6Qxvyra5bc2bOUBhAl+nWX7JyLxGct0/Axfo1MaYdD2Mu0xvb8fsNMWRAIL9TqD04eGyAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d3932771109b6a87d4df155b64086fb3597a4ce1561e4d03384f2911cd896907","last_reissued_at":"2026-07-05T11:48:10.256173Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:48:10.256173Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Balancing Information Accuracy and Response Timeliness in Networked LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.IT","cs.NI","math.IT"],"primary_cat":"cs.LG","authors_text":"Baturalp Buyukates, Melih Bastopcu, Yigit Turkmen","submitted_at":"2025-08-04T09:00:01Z","abstract_excerpt":"Recent advancements in Large Language Models (LLMs) have transformed many fields including scientific discovery, content generation, biomedical text mining, and educational technology. However, the substantial requirements for training data, computational resources, and energy consumption pose significant challenges for their practical deployment. A promising alternative is to leverage smaller, specialized language models and aggregate their outputs to improve overall response quality. In this work, we investigate a networked LLM system composed of multiple users, a central task processor, and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.02209","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/2508.02209/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":"2508.02209","created_at":"2026-07-05T11:48:10.256232+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.02209v1","created_at":"2026-07-05T11:48:10.256232+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.02209","created_at":"2026-07-05T11:48:10.256232+00:00"},{"alias_kind":"pith_short_12","alias_value":"2OJSO4IQTNVI","created_at":"2026-07-05T11:48:10.256232+00:00"},{"alias_kind":"pith_short_16","alias_value":"2OJSO4IQTNVIPVG7","created_at":"2026-07-05T11:48:10.256232+00:00"},{"alias_kind":"pith_short_8","alias_value":"2OJSO4IQ","created_at":"2026-07-05T11:48:10.256232+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/2OJSO4IQTNVIPVG7CVNWICDPWN","json":"https://pith.science/pith/2OJSO4IQTNVIPVG7CVNWICDPWN.json","graph_json":"https://pith.science/api/pith-number/2OJSO4IQTNVIPVG7CVNWICDPWN/graph.json","events_json":"https://pith.science/api/pith-number/2OJSO4IQTNVIPVG7CVNWICDPWN/events.json","paper":"https://pith.science/paper/2OJSO4IQ"},"agent_actions":{"view_html":"https://pith.science/pith/2OJSO4IQTNVIPVG7CVNWICDPWN","download_json":"https://pith.science/pith/2OJSO4IQTNVIPVG7CVNWICDPWN.json","view_paper":"https://pith.science/paper/2OJSO4IQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.02209&json=true","fetch_graph":"https://pith.science/api/pith-number/2OJSO4IQTNVIPVG7CVNWICDPWN/graph.json","fetch_events":"https://pith.science/api/pith-number/2OJSO4IQTNVIPVG7CVNWICDPWN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2OJSO4IQTNVIPVG7CVNWICDPWN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2OJSO4IQTNVIPVG7CVNWICDPWN/action/storage_attestation","attest_author":"https://pith.science/pith/2OJSO4IQTNVIPVG7CVNWICDPWN/action/author_attestation","sign_citation":"https://pith.science/pith/2OJSO4IQTNVIPVG7CVNWICDPWN/action/citation_signature","submit_replication":"https://pith.science/pith/2OJSO4IQTNVIPVG7CVNWICDPWN/action/replication_record"}},"created_at":"2026-07-05T11:48:10.256232+00:00","updated_at":"2026-07-05T11:48:10.256232+00:00"}