{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:T77B2G6R5UK3E5LPBRHC2IPCV4","short_pith_number":"pith:T77B2G6R","schema_version":"1.0","canonical_sha256":"9ffe1d1bd1ed15b2756f0c4e2d21e2af37d16e38396dd8a24ebe9cb801feae98","source":{"kind":"arxiv","id":"2607.28707","version":1},"attestation_state":"computed","paper":{"title":"Demystifying Entropy-based Selection for Chain-of-Thought Compression in Large Reasoning Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daniel Scalena, Elisabetta Fersini, Gabriele Sarti, Luca Bortolussi, Malvina Nissim, Sara Candussio","submitted_at":"2026-07-30T15:59:51Z","abstract_excerpt":"Entropy-based pruning has been proposed as an effective method for compressing Chain-of-Thought (CoT) reasoning with negligible accuracy loss. We test the robustness of low- and high-entropy CoT step selection methods across various models and reasoning tasks, showing that entropy offers no advantage over random pruning in any evaluated setting. Moving from sentences to tokens, we then show that retaining low-entropy tokens seems effective only on mathematical benchmarks. We find this is due to the inherently low-entropy nature of numeric tokens, which also convey semantic content in such prob"},"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":"2607.28707","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-30T15:59:51Z","cross_cats_sorted":[],"title_canon_sha256":"024c140cdf7e71c92617329d7b7ef368ee949ff08d683a12b43edfb390826884","abstract_canon_sha256":"1b633ccedea7b11c823aae4d8e6180a55070cf2596b39011c82c6d8f6295156b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-03T00:12:13.862150Z","signature_b64":"OjmvcuLZs8EuZEB8eXnDk880QfJUYSIr4gf6lpzBNUApu8g1Oxweckk6BMpr7IUwFf84gpM0kplCMIzgNCVlBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ffe1d1bd1ed15b2756f0c4e2d21e2af37d16e38396dd8a24ebe9cb801feae98","last_reissued_at":"2026-08-03T00:12:13.859985Z","signature_status":"signed_v1","first_computed_at":"2026-08-03T00:12:13.859985Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Demystifying Entropy-based Selection for Chain-of-Thought Compression in Large Reasoning Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daniel Scalena, Elisabetta Fersini, Gabriele Sarti, Luca Bortolussi, Malvina Nissim, Sara Candussio","submitted_at":"2026-07-30T15:59:51Z","abstract_excerpt":"Entropy-based pruning has been proposed as an effective method for compressing Chain-of-Thought (CoT) reasoning with negligible accuracy loss. We test the robustness of low- and high-entropy CoT step selection methods across various models and reasoning tasks, showing that entropy offers no advantage over random pruning in any evaluated setting. Moving from sentences to tokens, we then show that retaining low-entropy tokens seems effective only on mathematical benchmarks. We find this is due to the inherently low-entropy nature of numeric tokens, which also convey semantic content in such prob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.28707","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/2607.28707/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":"2607.28707","created_at":"2026-08-03T00:12:13.861474+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.28707v1","created_at":"2026-08-03T00:12:13.861474+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.28707","created_at":"2026-08-03T00:12:13.861474+00:00"},{"alias_kind":"pith_short_12","alias_value":"T77B2G6R5UK3","created_at":"2026-08-03T00:12:13.861474+00:00"},{"alias_kind":"pith_short_16","alias_value":"T77B2G6R5UK3E5LP","created_at":"2026-08-03T00:12:13.861474+00:00"},{"alias_kind":"pith_short_8","alias_value":"T77B2G6R","created_at":"2026-08-03T00:12:13.861474+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/T77B2G6R5UK3E5LPBRHC2IPCV4","json":"https://pith.science/pith/T77B2G6R5UK3E5LPBRHC2IPCV4.json","graph_json":"https://pith.science/api/pith-number/T77B2G6R5UK3E5LPBRHC2IPCV4/graph.json","events_json":"https://pith.science/api/pith-number/T77B2G6R5UK3E5LPBRHC2IPCV4/events.json","paper":"https://pith.science/paper/T77B2G6R"},"agent_actions":{"view_html":"https://pith.science/pith/T77B2G6R5UK3E5LPBRHC2IPCV4","download_json":"https://pith.science/pith/T77B2G6R5UK3E5LPBRHC2IPCV4.json","view_paper":"https://pith.science/paper/T77B2G6R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.28707&json=true","fetch_graph":"https://pith.science/api/pith-number/T77B2G6R5UK3E5LPBRHC2IPCV4/graph.json","fetch_events":"https://pith.science/api/pith-number/T77B2G6R5UK3E5LPBRHC2IPCV4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T77B2G6R5UK3E5LPBRHC2IPCV4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T77B2G6R5UK3E5LPBRHC2IPCV4/action/storage_attestation","attest_author":"https://pith.science/pith/T77B2G6R5UK3E5LPBRHC2IPCV4/action/author_attestation","sign_citation":"https://pith.science/pith/T77B2G6R5UK3E5LPBRHC2IPCV4/action/citation_signature","submit_replication":"https://pith.science/pith/T77B2G6R5UK3E5LPBRHC2IPCV4/action/replication_record"}},"created_at":"2026-08-03T00:12:13.861474+00:00","updated_at":"2026-08-03T00:12:13.861474+00:00"}