{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:T4R7ZNS5PVJAT7FLTNTGQJ2CZU","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":"f9556a572c0a528ff768530f033ba4e698f1c200637f5a1430a09cfe743e6508","cross_cats_sorted":["cs.CL","cs.SE"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-28T05:03:24Z","title_canon_sha256":"f7fe06bfb4aa62eb0547967cf498430ea53bc81cdfb78c93ee707b32ca52fd37"},"schema_version":"1.0","source":{"id":"2601.20255","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.20255","created_at":"2026-05-29T01:05:02Z"},{"alias_kind":"arxiv_version","alias_value":"2601.20255v3","created_at":"2026-05-29T01:05:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.20255","created_at":"2026-05-29T01:05:02Z"},{"alias_kind":"pith_short_12","alias_value":"T4R7ZNS5PVJA","created_at":"2026-05-29T01:05:02Z"},{"alias_kind":"pith_short_16","alias_value":"T4R7ZNS5PVJAT7FL","created_at":"2026-05-29T01:05:02Z"},{"alias_kind":"pith_short_8","alias_value":"T4R7ZNS5","created_at":"2026-05-29T01:05:02Z"}],"graph_snapshots":[{"event_id":"sha256:8b64f6d8254a43da298eb76ac2dd85f8e1736444525d7b3bc3ecd506a6184e5c","target":"graph","created_at":"2026-05-29T01:05:02Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"We propose the Entropy Compression Hypothesis, redefining intelligence not by scalar Top-1 compression, but by the capacity to structure uncertainty into Entropy-Compressed States of low orders (reasonable hesitation). Grounded in this fine-grained entropy analysis, we formulate a novel metric, HE-SNR."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the Entropy Compression Hypothesis is valid and that HE-SNR exhibits stronger correlation with downstream SWE-bench performance than perplexity while avoiding the long-context tax, without post-hoc fitting or selection effects."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"HE-SNR is a high-entropy signal-to-noise ratio metric derived from the Entropy Compression Hypothesis to better guide LLM mid-training on complex software engineering benchmarks."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"HE-SNR measures how models structure uncertainty into low-order states to guide mid-training better than perplexity for software engineering tasks."}],"snapshot_sha256":"300ce2cc0c6106d4b752a0175ba2f9ab40d43c423b5eff4a492300c0bd9dec63"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"71535000f829d58d5419211554c3c034a8fa97a301135cabf1e0b3385d56dd56"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2601.20255/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"SWE-bench has emerged as the premier benchmark for evaluating Large Language Models on complex software engineering tasks. While these capabilities are fundamentally acquired during the mid-training phase and subsequently elicited during Supervised Fine-Tuning (SFT), there remains a critical deficit in metrics capable of guiding mid-training effectively. Standard metrics such as Perplexity (PPL) are compromised by the \"Long-Context Tax\" and exhibit weak correlation with downstream SWE performance. In this paper, we bridge this gap by first introducing a rigorous data filtering strategy. Crucia","authors_text":"Baolong Bi, Jiawei Fu, Xiaoqing Liu, Xili Wang, Yueyang Wang","cross_cats":["cs.CL","cs.SE"],"headline":"HE-SNR measures how models structure uncertainty into low-order states to guide mid-training better than perplexity for software engineering tasks.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-28T05:03:24Z","title":"HE-SNR: Uncovering Latent Logic via Entropy for Guiding Mid-Training on SWE-bench"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.20255","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-16T10:59:28.388616Z","id":"34c9e9df-1f2b-4ee4-94d2-72899a88b740","model_set":{"reader":"grok-4.3"},"one_line_summary":"HE-SNR is a high-entropy signal-to-noise ratio metric derived from the Entropy Compression Hypothesis to better guide LLM mid-training on complex software engineering benchmarks.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"HE-SNR measures how models structure uncertainty into low-order states to guide mid-training better than perplexity for software engineering tasks.","strongest_claim":"We propose the Entropy Compression Hypothesis, redefining intelligence not by scalar Top-1 compression, but by the capacity to structure uncertainty into Entropy-Compressed States of low orders (reasonable hesitation). Grounded in this fine-grained entropy analysis, we formulate a novel metric, HE-SNR.","weakest_assumption":"That the Entropy Compression Hypothesis is valid and that HE-SNR exhibits stronger correlation with downstream SWE-bench performance than perplexity while avoiding the long-context tax, without post-hoc fitting or selection effects."}},"verdict_id":"34c9e9df-1f2b-4ee4-94d2-72899a88b740"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:04176f78e0740a0bbcf755326c2fd7b1e342fb869d7892f9af43f640cafde0a5","target":"record","created_at":"2026-05-29T01:05:02Z","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":"f9556a572c0a528ff768530f033ba4e698f1c200637f5a1430a09cfe743e6508","cross_cats_sorted":["cs.CL","cs.SE"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-28T05:03:24Z","title_canon_sha256":"f7fe06bfb4aa62eb0547967cf498430ea53bc81cdfb78c93ee707b32ca52fd37"},"schema_version":"1.0","source":{"id":"2601.20255","kind":"arxiv","version":3}},"canonical_sha256":"9f23fcb65d7d5209fcab9b66682742cd191d3790044decfe376f936c0124c7ea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9f23fcb65d7d5209fcab9b66682742cd191d3790044decfe376f936c0124c7ea","first_computed_at":"2026-05-29T01:05:02.397777Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-29T01:05:02.397777Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"d9DVqz8xO3RwVekQxv14Syq3nPksUc/xwiWkIYMLqHwdCrIXrhwTcGRqKtHjsT9zA7QUOUUBBaRZS/J4U7ctAA==","signature_status":"signed_v1","signed_at":"2026-05-29T01:05:02.398804Z","signed_message":"canonical_sha256_bytes"},"source_id":"2601.20255","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:04176f78e0740a0bbcf755326c2fd7b1e342fb869d7892f9af43f640cafde0a5","sha256:8b64f6d8254a43da298eb76ac2dd85f8e1736444525d7b3bc3ecd506a6184e5c"],"state_sha256":"db95d78d70349d152cdce7f7488ea6bf8f6396f0d933c72696f52076eed07ebc"}