Pith Number

pith:2B5PEQVX

pith:2026:2B5PEQVXTKYX3R2TVN3PC6H5HL

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LLMs Know When They Know, but Do Not Act on It: A Metacognitive Harness for Test-time Scaling

Peijia Qin, Pengtao Xie, Qi Cao, Shuhao Zhang, Yufan Wang

Large language models can use their own pre- and post-solution self-assessments to control inference and raise accuracy on reasoning tasks without any training or fine-tuning.

arxiv:2605.14186 v1 · 2026-05-13 · cs.LG

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\usepackage{pith}
\pithnumber{2B5PEQVXTKYX3R2TVN3PC6H5HL}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

Across text, code, and multimodal reasoning benchmarks, our harness substantially improves a fixed Claude Sonnet-4.6 base model without parameter updates or benchmark-specific fine-tuning. On the evaluated public benchmark snapshots, it raises pooled accuracy from 48.3 to 56.9 and exceeds the strongest listed leaderboard entries on the three primary evaluation settings: HLE-Verified, LiveCodeBench v6, and R-Bench-V.

C2weakest assumption

That the pre-solve feeling-of-knowing and post-solve judgment-of-learning signals elicited from the LLM are sufficiently reliable, consistent, and actionable to serve as effective control inputs for trust/retry/aggregate decisions without introducing systematic bias or new failure modes.

C3one line summary

A metacognitive harness uses LLMs' pre- and post-solution self-monitoring signals to control test-time reasoning, raising pooled accuracy from 48.3% to 56.9% on text, code, and multimodal benchmarks.

References

61 extracted · 61 resolved · 11 Pith anchors

[1] Harness engineering: Leveraging codex in an agent-first world 2026

[2] GPT-4 Technical Report 2023 · arXiv:2303.08774

[3] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V . Le, and Denny Zhou. Chain-of-thought prompting elicits reasoning in large language models. InAdvances 2022

[4] Scaling llm test-time compute optimally can be more effective than scaling model parameters for reasoning 2025

[5] Code Llama: Open Foundation Models for Code 2023 · arXiv:2308.12950

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:11.197499Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d07af242b79ab17dc753ab76f178fd3afb2892ecd26659597bfd6e4bacd2043a

Aliases

arxiv: 2605.14186 · arxiv_version: 2605.14186v1 · doi: 10.48550/arxiv.2605.14186 · pith_short_12: 2B5PEQVXTKYX · pith_short_16: 2B5PEQVXTKYX3R2T · pith_short_8: 2B5PEQVX

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/2B5PEQVXTKYX3R2TVN3PC6H5HL \
  | 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: d07af242b79ab17dc753ab76f178fd3afb2892ecd26659597bfd6e4bacd2043a

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "37c3c24371f40bd0284ed88b67efea9857f9c52d33f4326886c00c20cf00248b",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T23:09:25Z",
    "title_canon_sha256": "6ce10d1245f7e0387396c35dd485891841c0774ad727828724568f537aedb970"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14186",
    "kind": "arxiv",
    "version": 1
  }
}