Opportunity summary
Score4.0PainA new framework to systematically evaluate LLMs on complex, real-world problem-solving tasks, revealing critical execution gaps.
Evidence0 refs | 0 sources | 0% coverage
Blockerno shell-level blocker reported
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ARXIV:2604.04791 · LLM EVALUATION · SUBMITTED 07 APR · 20:13 UTC · FRESHNESS UNKNOWN
ARXIV:2604.04791LLM EVALUATIONSUBMITTED 07 APR · 20:13 UTCFRESHNESS UNKNOWNYuhang Liu · Heyan Huang · Yizhe Yang · Hongyan Zhao · Zhizhuo Zeng · Yang Gao · arXiv
A new framework to systematically evaluate LLMs on complex, real-world problem-solving tasks, revealing critical execution gaps.
Opportunity summary
Pain A new framework to systematically evaluate LLMs on complex, real-world problem-solving tasks, revealing critical execution gaps.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A new framework to systematically evaluate LLMs on complex, real-world problem-solving tasks, revealing critical execution gaps. Mathematical modeling competitions provide a stringent testbed for evaluating such end-to-end problem-solving capability.
Large language models (LLMs) have achieved strong performance on reasoning benchmarks, yet their ability to solve real-world problems requiring end-to-end workflows remains unclear. Mathematical modeling competitions provide a stringent testbed for evaluating such end-to-end…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Using this framework, we reveal a comprehension-execution gap in state-of-the-art LLMs: while they perform well in early stages such as problem identification and formulation,…
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score4.0Analysis summary
A new framework to systematically evaluate LLMs on complex, real-world problem-solving tasks, revealing critical execution gaps.
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Paper Pack
10.48550/arXiv.2604.04791A new framework to systematically evaluate LLMs on complex, real-world problem-solving tasks, revealing critical execution gaps.
Abstract
Large language models (LLMs) have achieved strong performance on reasoning benchmarks, yet their ability to solve real-world problems requiring end-to-end workflows remains unclear. Mathematical modeling competitions provide a stringent testbed for evaluating such end-to-end problem-solving capability. We propose a problem-oriented, stage-wise evaluation framework that assesses LLM performance across modeling stages using expert-verified criteria. We validate the framework's reliability by comparing automatic scores with independent human expert judgments on problems from the China Postgraduate Mathematical Contest in Modeling, demonstrating substantially stronger alignment than existing evaluation schemes. Using this framework, we reveal a comprehension-execution gap in state-of-the-art LLMs: while they perform well in early stages such as problem identification and formulation, they exhibit persistent deficiencies in execution-oriented stages including model solving, code implementation, and result analysis. These gaps persist even with increased model scale. We further trace these failures to insufficient specification, missing verification, and lack of validation, with errors propagating across stages without correction. Our findings suggest that bridging this gap requires approaches beyond model scaling, offering insights for applying LLMs to complex real-world problem solving.
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What was readable
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Dimensions overall score 4.0
PROBLEM
A new framework to systematically evaluate LLMs on complex, real-world problem-solving tasks, revealing critical execution gaps. Mathematical modeling competitions provide a stringent testbed for evaluating such end-to-end problem-solving capability.
METHOD
Large language models (LLMs) have achieved strong performance on reasoning benchmarks, yet their ability to solve real-world problems requiring end-to-end workflows remains unclear. Mathematical modeling competitions provide a stringent testbed for evaluating such end-to-end pro...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Using this framework, we reveal a comprehension-execution gap in state-of-the-art LLMs: while they perform well in early stages such as problem identification and formulation, they exhibit persistent defi...
WHY NOW
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A new framework to systematically evaluate LLMs on complex, real-world problem-solving tasks, revealing critical execution gaps. Mathematical modeling competitions provide a stringent testbed for evaluating such end-to-end problem-solving capability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large language models (LLMs) have achieved strong performance on reasoning benchmarks, yet their ability to solve real-world problems requiring end-to-end workflows remains unclear. Mathematical modeling competitions provide a stringent testbed for evaluating such end-to-end problem-solving capability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Using this framework, we reveal a comprehension-execution gap in state-of-the-art LLMs: while they perform well in early stages such as problem identification and formulation, they exhibit persistent deficiencies in execution-oriented stages including model solving, code implementation, and result analysis. 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 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A new framework to systematically evaluate LLMs on complex, real-world problem-solving tasks, revealing critical execution gaps.
Segment
LLM Evaluation
Adoption evidence
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Commercial read
4.0/10 public viability
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reason
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proof status
unverified
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No verified cost estimate
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next verification path
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Source missing: Build Passport payload.
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Build readiness
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unknown
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Artifact maturity
GitHub and Hugging Face maturity payloads
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unknown
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
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Integration burden
missing
Current read
No public implementation surface observed.
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No GitHub or Hugging Face payload attached.
Gaps
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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People
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Prototype owner missing.
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Operator workflow not sourced.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
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