Pith Number

pith:T4R7ZNS5

pith:2026:T4R7ZNS5PVJAT7FLTNTGQJ2CZU

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HE-SNR: Uncovering Latent Logic via Entropy for Guiding Mid-Training on SWE-bench

Baolong Bi, Jiawei Fu, Xiaoqing Liu, Xili Wang, Yueyang Wang

HE-SNR measures how models structure uncertainty into low-order states to guide mid-training better than perplexity for software engineering tasks.

arxiv:2601.20255 v3 · 2026-01-28 · cs.LG · cs.CL · cs.SE

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\pithnumber{T4R7ZNS5PVJAT7FLTNTGQJ2CZU}

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest 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.

C2weakest 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.

C3one 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.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-29T01:05:02.397777Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9f23fcb65d7d5209fcab9b66682742cd191d3790044decfe376f936c0124c7ea

Aliases

arxiv: 2601.20255 · arxiv_version: 2601.20255v3 · doi: 10.48550/arxiv.2601.20255 · pith_short_12: T4R7ZNS5PVJA · pith_short_16: T4R7ZNS5PVJAT7FL · pith_short_8: T4R7ZNS5

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/T4R7ZNS5PVJAT7FLTNTGQJ2CZU \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 9f23fcb65d7d5209fcab9b66682742cd191d3790044decfe376f936c0124c7ea

Canonical record JSON

{
  "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
  }
}