{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:3E4QNBUHJTPMKC6EMRZGNSGJP2","short_pith_number":"pith:3E4QNBUH","canonical_record":{"source":{"id":"2407.19825","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-29T09:21:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"908591bcb8e44ca0e45a040c1de0270cc5d460beb086543adb555c32c87ac341","abstract_canon_sha256":"2de376c12e3a82a89c63168fe3bb8b6de5cc859f76df61636ff1c6dce09ab112"},"schema_version":"1.0"},"canonical_sha256":"d9390686874cdec50bc4647266c8c97e95829f573624cc449d54477692fbdd49","source":{"kind":"arxiv","id":"2407.19825","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.19825","created_at":"2026-07-05T10:04:15Z"},{"alias_kind":"arxiv_version","alias_value":"2407.19825v2","created_at":"2026-07-05T10:04:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.19825","created_at":"2026-07-05T10:04:15Z"},{"alias_kind":"pith_short_12","alias_value":"3E4QNBUHJTPM","created_at":"2026-07-05T10:04:15Z"},{"alias_kind":"pith_short_16","alias_value":"3E4QNBUHJTPMKC6E","created_at":"2026-07-05T10:04:15Z"},{"alias_kind":"pith_short_8","alias_value":"3E4QNBUH","created_at":"2026-07-05T10:04:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:3E4QNBUHJTPMKC6EMRZGNSGJP2","target":"record","payload":{"canonical_record":{"source":{"id":"2407.19825","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-29T09:21:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"908591bcb8e44ca0e45a040c1de0270cc5d460beb086543adb555c32c87ac341","abstract_canon_sha256":"2de376c12e3a82a89c63168fe3bb8b6de5cc859f76df61636ff1c6dce09ab112"},"schema_version":"1.0"},"canonical_sha256":"d9390686874cdec50bc4647266c8c97e95829f573624cc449d54477692fbdd49","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:04:15.122354Z","signature_b64":"aAgTaICzuIBz0gIdXaLyujt1eTWNsd1go1ZU0wL5njRMDC8T+o6OTrvg8LS8DcnMi84njLc8x7otryIEJ4poCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d9390686874cdec50bc4647266c8c97e95829f573624cc449d54477692fbdd49","last_reissued_at":"2026-07-05T10:04:15.121929Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:04:15.121929Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.19825","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:04:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ta09EXn+PayGr0x3W3CnX6dWiXYsZnrwjWIKW+mDYRvRoeGzUpKrAeFoiKWsM39B8v8AFsfs7qjegVON8YZqCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T23:37:10.183322Z"},"content_sha256":"6ba874398d11da1a8e835ae50129f7bbfa96030509691f1cbe578238d322d906","schema_version":"1.0","event_id":"sha256:6ba874398d11da1a8e835ae50129f7bbfa96030509691f1cbe578238d322d906"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:3E4QNBUHJTPMKC6EMRZGNSGJP2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Andrea Saracino, Fabrizio Giacomelli, Giorgio Buttazzo, Giulio Rossolini, Marco Simoni, Nicolamaria Manes, Sania Nayab","submitted_at":"2024-07-29T09:21:52Z","abstract_excerpt":"Today's large language models (LLMs) can solve challenging question-answering tasks, and prompt engineering techniques, such as chain-of-thought (CoT), have gained attention for enhancing the explanation and correctness of outputs. However, many models and techniques tend to produce excessively verbose and lengthy answers, leading to issues with both conciseness and generation time. To address this, this paper analyzes the impact of output lengths on LLM inference pipelines by introducing and proposing novel metrics to evaluate the \\textit{correct conciseness} of a model and related prompting "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.19825","kind":"arxiv","version":2},"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/2407.19825/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":"2026-07-05T10:04:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+VXeG72D3VUQ4d9Rd9IezlTWmbg85oiSMgJJYhS4XJnvyKDyeN4IKZ5YBI+uEeuXspzal5f/YpctOGRD24FJAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T23:37:10.184138Z"},"content_sha256":"0068e90149acfb6aba9a48dbf37055e4af2eb84a0694a91f348cb6f49a58a015","schema_version":"1.0","event_id":"sha256:0068e90149acfb6aba9a48dbf37055e4af2eb84a0694a91f348cb6f49a58a015"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3E4QNBUHJTPMKC6EMRZGNSGJP2/bundle.json","state_url":"https://pith.science/pith/3E4QNBUHJTPMKC6EMRZGNSGJP2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3E4QNBUHJTPMKC6EMRZGNSGJP2/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":"2026-08-04T23:37:10Z","links":{"resolver":"https://pith.science/pith/3E4QNBUHJTPMKC6EMRZGNSGJP2","bundle":"https://pith.science/pith/3E4QNBUHJTPMKC6EMRZGNSGJP2/bundle.json","state":"https://pith.science/pith/3E4QNBUHJTPMKC6EMRZGNSGJP2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3E4QNBUHJTPMKC6EMRZGNSGJP2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:3E4QNBUHJTPMKC6EMRZGNSGJP2","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":"2de376c12e3a82a89c63168fe3bb8b6de5cc859f76df61636ff1c6dce09ab112","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-29T09:21:52Z","title_canon_sha256":"908591bcb8e44ca0e45a040c1de0270cc5d460beb086543adb555c32c87ac341"},"schema_version":"1.0","source":{"id":"2407.19825","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.19825","created_at":"2026-07-05T10:04:15Z"},{"alias_kind":"arxiv_version","alias_value":"2407.19825v2","created_at":"2026-07-05T10:04:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.19825","created_at":"2026-07-05T10:04:15Z"},{"alias_kind":"pith_short_12","alias_value":"3E4QNBUHJTPM","created_at":"2026-07-05T10:04:15Z"},{"alias_kind":"pith_short_16","alias_value":"3E4QNBUHJTPMKC6E","created_at":"2026-07-05T10:04:15Z"},{"alias_kind":"pith_short_8","alias_value":"3E4QNBUH","created_at":"2026-07-05T10:04:15Z"}],"graph_snapshots":[{"event_id":"sha256:0068e90149acfb6aba9a48dbf37055e4af2eb84a0694a91f348cb6f49a58a015","target":"graph","created_at":"2026-07-05T10:04:15Z","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/2407.19825/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Today's large language models (LLMs) can solve challenging question-answering tasks, and prompt engineering techniques, such as chain-of-thought (CoT), have gained attention for enhancing the explanation and correctness of outputs. However, many models and techniques tend to produce excessively verbose and lengthy answers, leading to issues with both conciseness and generation time. To address this, this paper analyzes the impact of output lengths on LLM inference pipelines by introducing and proposing novel metrics to evaluate the \\textit{correct conciseness} of a model and related prompting ","authors_text":"Andrea Saracino, Fabrizio Giacomelli, Giorgio Buttazzo, Giulio Rossolini, Marco Simoni, Nicolamaria Manes, Sania Nayab","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-29T09:21:52Z","title":"Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.19825","kind":"arxiv","version":2},"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":"sha256:6ba874398d11da1a8e835ae50129f7bbfa96030509691f1cbe578238d322d906","target":"record","created_at":"2026-07-05T10:04:15Z","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":"2de376c12e3a82a89c63168fe3bb8b6de5cc859f76df61636ff1c6dce09ab112","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-29T09:21:52Z","title_canon_sha256":"908591bcb8e44ca0e45a040c1de0270cc5d460beb086543adb555c32c87ac341"},"schema_version":"1.0","source":{"id":"2407.19825","kind":"arxiv","version":2}},"canonical_sha256":"d9390686874cdec50bc4647266c8c97e95829f573624cc449d54477692fbdd49","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d9390686874cdec50bc4647266c8c97e95829f573624cc449d54477692fbdd49","first_computed_at":"2026-07-05T10:04:15.121929Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:04:15.121929Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aAgTaICzuIBz0gIdXaLyujt1eTWNsd1go1ZU0wL5njRMDC8T+o6OTrvg8LS8DcnMi84njLc8x7otryIEJ4poCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:04:15.122354Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.19825","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6ba874398d11da1a8e835ae50129f7bbfa96030509691f1cbe578238d322d906","sha256:0068e90149acfb6aba9a48dbf37055e4af2eb84a0694a91f348cb6f49a58a015"],"state_sha256":"ba124fbe6ad9da3755d1ca22d0b858800ac7a1d7c3179779287fc34172fca80e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SV4EI11WzCR4KShOxJov5MpmoGP93Z0zn4VeMsZLnx8O32N9Ft1TsaP+mggIB4LWoeszLDOH5Liu7G5wD68yCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T23:37:10.242457Z","bundle_sha256":"712991640010583214558333f8ae6cb724b91069adb027bfaa19abe0acf27048"}}