Opportunity summary
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ARXIV:2603.10960 · RANKING ALGORITHMS FOR LLMS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.10960RANKING ALGORITHMS FOR LLMSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Scorio is an open-source library for ranking reasoning LLMs under test-time scaling using advanced statistical methods.
Opportunity summary
Pain Scorio is an open-source library for ranking reasoning LLMs under test-time scaling using advanced statistical methods.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Scorio is an open-source library for ranking reasoning LLMs under test-time scaling using advanced statistical methods. We formalize dense benchmark ranking under test-time scaling and introduce Scorio, a library that implements statistical ranking methods…
Test-time scaling evaluates reasoning LLMs by sampling multiple outputs per prompt, but ranking models in this regime remains underexplored. We formalize dense benchmark ranking under test-time scaling and introduce Scorio, a library that implements…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. These results identify reliable ranking methods for both high- and low-budget test-time scaling.
Ranking Algorithms for LLMs moved forward this cycle; last verified April 2026. Public score 8.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score8.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
Scorio is an open-source library for ranking reasoning LLMs under test-time scaling using advanced statistical methods.
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Paper Pack
10.48550/arXiv.2603.10960Scorio is an open-source library for ranking reasoning LLMs under test-time scaling using advanced statistical methods.
Abstract
Test-time scaling evaluates reasoning LLMs by sampling multiple outputs per prompt, but ranking models in this regime remains underexplored. We formalize dense benchmark ranking under test-time scaling and introduce Scorio, a library that implements statistical ranking methods such as paired-comparison models, item response theory (IRT) models, voting rules, and graph- and spectral-based methods. Across $20$ reasoning models on four Olympiad-style math benchmarks (AIME'24, AIME'25, HMMT'25, and BrUMO'25; up to $N=80$ trials), most full-trial rankings agree closely with the Bayesian gold standard $\mathrm{Bayes}_{\mathcal{U}}@80$ (mean Kendall's $τ_b = 0.93$--$0.95$), and $19$--$34$ methods recover exactly the same ordering. In the single-trial regime, the best methods reach $τ_b \approx 0.86$. Using greedy decoding as an empirical prior ($\mathrm{Bayes}_{\mathbf{R}_0}@N$) reduces variance at $N=1$ by $16$--$52\%$, but can bias rankings when greedy and stochastic sampling disagree. These results identify reliable ranking methods for both high- and low-budget test-time scaling. We release Scorio as an open-source library at https://github.com/mohsenhariri/scorio.
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Extraction status
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Proof status
unverified0 refs; 0 sources; 17% 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 8.0
PROBLEM
Scorio is an open-source library for ranking reasoning LLMs under test-time scaling using advanced statistical methods. We formalize dense benchmark ranking under test-time scaling and introduce Scorio, a library that implements statistical ranking methods such as paired-compari...
METHOD
Test-time scaling evaluates reasoning LLMs by sampling multiple outputs per prompt, but ranking models in this regime remains underexplored. We formalize dense benchmark ranking under test-time scaling and introduce Scorio, a library that implements statistical ranking methods s...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. These results identify reliable ranking methods for both high- and low-budget test-time scaling.
WHY NOW
Ranking Algorithms for LLMs moved forward this cycle; last verified April 2026. Public score 8.0/10.
We formalize dense benchmark ranking under test-time scaling and introduce Scorio, a library that implements statistical ranking methods such as paired-comparison models, item response theory (IRT) models, voting rules, and graph- and spectral-based methods.
Directly stated in abstract with specific method names and purpose.
partial
Across $20$ reasoning models on four Olympiad-style math benchmarks (AIME'24, AIME'25, HMMT'25, and BrUMO'25; up to $N=80$ trials), most full-trial rankings agree closely with the Bayesian gold standard $\mathrm{Bayes}_{\mathcal{U}}@80$ (mean Kendall's $τ_b = 0.93$--$0.95$)
Direct numeric evidence provided in abstract with specific statistical measure.
partial
In the single-trial regime, the best methods reach $τ_b \approx 0.86$.
Direct numeric evidence provided in abstract.
partial
Using greedy decoding as an empirical prior ($\mathrm{Bayes}_{\mathbf{R}_0}@N$) reduces variance at $N=1$ by $16$--$52\%$
Direct numeric evidence with specific percentage range provided.
partial
but can bias rankings when greedy and stochastic sampling disagree.
Directly stated limitation with clear causal relationship.
partial
and $19$--$34$ methods recover exactly the same ordering.
Direct numeric evidence with specific range provided.
partial
These results identify reliable ranking methods for both high- and low-budget test-time scaling.
Direct statement of contribution in abstract, though 'reliable' requires some interpretation.
partial
We release Scorio as an open-source library at https://github.com/mohsenhariri/scorio.
Direct statement with specific URL provided.
partial
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Concepts
Methods
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Scorio is an open-source library for ranking reasoning LLMs under test-time scaling using advanced statistical methods.
Segment
Ranking Algorithms for LLMs
Adoption evidence
No public code link in the paper record yet
Commercial read
8.0/10 public viability
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Build Passport
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status
missing
reason
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proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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stale
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Build readiness
BuildPassport EvidenceState
passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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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
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 17% 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.
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missing
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Gaps
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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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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
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No buyer or workflow interview attached.
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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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TIMELINE
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