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ARXIV:2604.12191 · LLM EVALUATION · SUBMITTED 15 APR · 17:00 UTC · FRESHNESS STALE
ARXIV:2604.12191LLM EVALUATIONSUBMITTED 15 APR · 17:00 UTCFRESHNESS STALEXu Zhang · Xudong Gong · Jiacheng Qin · Qiang Wang · JiaQi Liao · Zhe Wang · +2 at arXiv
A cognitive diagnostic framework for LLMs that moves beyond single scores to provide fine-grained ability assessments across multiple scientific domains, enabling targeted improvement and selection.
Opportunity summary
Pain A cognitive diagnostic framework for LLMs that moves beyond single scores to provide fine-grained ability assessments across multiple scientific domains, enabling targeted improvement and selection.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A cognitive diagnostic framework for LLMs that moves beyond single scores to provide fine-grained ability assessments across multiple scientific domains, enabling targeted improvement and selection. This obscures fine-grained ability variation, limiting targeted model improvement…
Current evaluations of large language models aggregate performance across diverse tasks into single scores. This obscures fine-grained ability variation, limiting targeted model improvement and ability-guided selection for specific tasks.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The framework employs multidimensional Item Response Theory with an item-ability association matrix to estimate fine-grained ability levels, which in turn enable prediction of performance…
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A cognitive diagnostic framework for LLMs that moves beyond single scores to provide fine-grained ability assessments across multiple scientific domains, enabling targeted improvement and selection.
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Paper Pack
10.48550/arXiv.2604.12191A cognitive diagnostic framework for LLMs that moves beyond single scores to provide fine-grained ability assessments across multiple scientific domains, enabling targeted improvement and selection.
Abstract
Current evaluations of large language models aggregate performance across diverse tasks into single scores. This obscures fine-grained ability variation, limiting targeted model improvement and ability-guided selection for specific tasks. Motivated by this gap, we propose a cognitive diagnostic framework that estimates model abilities across multiple fine-grained dimensions. For mathematics, we construct a 35-dimensional ability taxonomy grounded in cognitive theory and domain knowledge. The framework employs multidimensional Item Response Theory with an item-ability association matrix to estimate fine-grained ability levels, which in turn enable prediction of performance on unseen items (questions of benchmark). Evaluated on 41 models, our approach demonstrates strong criterion validity, consistent ability estimates across benchmarks, and accurate prediction of unseen items with AUC ranging from 0.80 to 0.89 within benchmarks and from 0.77 to 0.86 across benchmarks, substantially exceeding trivial baselines. The framework generalizes across scientific domains, producing consistent diagnostic performance in physics (27 dimensions), chemistry (58 dimensions), and computer science (12 dimensions). This work establishes a principled framework for fine-grained assessment of abilities, with potential applications in targeted training, ability-guided model selection, and ability-aware benchmark design.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
A cognitive diagnostic framework for LLMs that moves beyond single scores to provide fine-grained ability assessments across multiple scientific domains, enabling targeted improvement and selection. This obscures fine-grained ability variation, limiting targeted model improvemen...
METHOD
Current evaluations of large language models aggregate performance across diverse tasks into single scores. This obscures fine-grained ability variation, limiting targeted model improvement and ability-guided selection for specific tasks.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The framework employs multidimensional Item Response Theory with an item-ability association matrix to estimate fine-grained ability levels, which in turn enable prediction of performance on unseen items...
WHY NOW
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A cognitive diagnostic framework for LLMs that moves beyond single scores to provide fine-grained ability assessments across multiple scientific domains, enabling targeted improvement and selection. This obscures fine-grained ability variation, limiting targeted model improvement and ability-guided selection for specific tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Current evaluations of large language models aggregate performance across diverse tasks into single scores. This obscures fine-grained ability variation, limiting targeted model improvement and ability-guided selection for specific tasks.
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. The framework employs multidimensional Item Response Theory with an item-ability association matrix to estimate fine-grained ability levels, which in turn enable prediction of performance on unseen items (questions of benchmark). Code availability is flagged in the production record; the public repository link still needs proof alignment.
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. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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A cognitive diagnostic framework for LLMs that moves beyond single scores to provide fine-grained ability assessments across multiple scientific domains, enabling targeted improvement and selection.
Segment
LLM Evaluation
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
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CITED BY
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
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No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
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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 / 3 sources / 50% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
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
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
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Evidence
0 references, 3 sources, 50% evidence coverage.
Gaps
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Buyer clarity
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Current read
No budget owner is verified for this paper.
Evidence
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Defensibility
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Defensibility signals are missing.
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Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Run cost passport or mark the cost field not applicable.
Regulatory load
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Evidence
Build Passport ledger does not include regulatory flags.
Gaps
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Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
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.
People
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Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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Gaps
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People
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Regulatory need unclassified.
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People
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Gaps
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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TIMELINE
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BUZZ
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