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ARXIV:2604.11996 · LLM EVALUATION · SUBMITTED 15 APR · 20:33 UTC · FRESHNESS STALE
ARXIV:2604.11996LLM EVALUATIONSUBMITTED 15 APR · 20:33 UTCFRESHNESS STALEManas Pathak · Xingyao Chen · Shuozhe Li · Amy Zhang · Liu Leqi · arXiv
A new evaluation metric for LLMs that assesses reasoning quality beyond simple accuracy, with open-source code available.
Opportunity summary
Pain A new evaluation metric for LLMs that assesses reasoning quality beyond simple accuracy, with open-source code available.
Evidence 0 refs | 4 sources | 83% coverage
Blocker Evidence partial
A new evaluation metric for LLMs that assesses reasoning quality beyond simple accuracy, with open-source code available. LLMs achieve high accuracy on reasoning benchmarks, but correctness alone does not reveal the quality of the…
Should we trust Large Language Models (LLMs) with high accuracy? LLMs achieve high accuracy on reasoning benchmarks, but correctness alone does not reveal the quality of the reasoning used to produce it.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. LLMs achieve high accuracy on reasoning benchmarks, but correctness alone does not reveal the quality of the reasoning used to produce it. A public…
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A new evaluation metric for LLMs that assesses reasoning quality beyond simple accuracy, with open-source code available.
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10.48550/arXiv.2604.11996A new evaluation metric for LLMs that assesses reasoning quality beyond simple accuracy, with open-source code available.
Abstract
Should we trust Large Language Models (LLMs) with high accuracy? LLMs achieve high accuracy on reasoning benchmarks, but correctness alone does not reveal the quality of the reasoning used to produce it. This highlights a fundamental limitation of outcome-based evaluation: models may arrive at correct answers through flawed reasoning, and models with substantially different reasoning capabilities can nevertheless exhibit similar benchmark accuracy, for example due to memorization or over-optimization. In this paper, we ask: given existing benchmarks, can we move beyond outcome-based evaluation to assess the quality of reasoning itself? We seek metrics that (1) differentiate models with similar accuracy and (2) are robust to variations in input prompts and generation configurations. To this end, we propose a reasoning score that evaluates reasoning traces along dimensions such as faithfulness, coherence, utility, and factuality. A remaining question is how to aggregate this score across multiple sampled traces. Naively averaging them is undesirable, particularly in long-horizon settings, where the number of possible trajectories grows rapidly, and low-confidence correct traces are more likely to be coincidental. To address this, we introduce the Filtered Reasoning Score (FRS), which computes reasoning quality using only the top-K% most confident traces. Evaluating with FRS, models that are indistinguishable under standard accuracy exhibit significant differences in reasoning quality. Moreover, models with higher FRS on one benchmark tend to perform better on other reasoning benchmarks, in both accuracy and reasoning quality. Together, these findings suggest that FRS complements accuracy by capturing a model's transferable reasoning capabilities. We open source our evaluation codebase: https://github.com/Manas2006/benchmark_reproducibility.
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Proof status
partial0 refs; 4 sources; 83% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
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Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
A new evaluation metric for LLMs that assesses reasoning quality beyond simple accuracy, with open-source code available. LLMs achieve high accuracy on reasoning benchmarks, but correctness alone does not reveal the quality of the reasoning used to produce it.
METHOD
Should we trust Large Language Models (LLMs) with high accuracy? LLMs achieve high accuracy on reasoning benchmarks, but correctness alone does not reveal the quality of the reasoning used to produce it.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. LLMs achieve high accuracy on reasoning benchmarks, but correctness alone does not reveal the quality of the reasoning used to produce it. A public repository is linked, so build verification can inspect...
WHY NOW
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A new evaluation metric for LLMs that assesses reasoning quality beyond simple accuracy, with open-source code available. LLMs achieve high accuracy on reasoning benchmarks, but correctness alone does not reveal the quality of the reasoning used to produce it.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Should we trust Large Language Models (LLMs) with high accuracy? LLMs achieve high accuracy on reasoning benchmarks, but correctness alone does not reveal the quality of the reasoning used to produce it.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. LLMs achieve high accuracy on reasoning benchmarks, but correctness alone does not reveal the quality of the reasoning used to produce it. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A new evaluation metric for LLMs that assesses reasoning quality beyond simple accuracy, with open-source code available.
Segment
LLM Evaluation
Adoption evidence
Public code linked for build inspection
Commercial read
7.0/10 public viability
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Commercially relevant
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2/3 checks · 67%
Build Passport
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missing
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proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 4 sources / 83% coverage
stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
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Evidence
0 references, 4 sources, 83% evidence coverage.
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Buyer clarity
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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People
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Operator workflow not sourced.
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People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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