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ARXIV:2603.02540 · LLM EVALUATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.02540LLM EVALUATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Introduce NeuroCognition benchmark for evaluating LLM cognitive abilities distinct from existing benchmarks.
Opportunity summary
Pain Introduce NeuroCognition benchmark for evaluating LLM cognitive abilities distinct from existing benchmarks.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Introduce NeuroCognition benchmark for evaluating LLM cognitive abilities distinct from existing benchmarks. This is because current benchmarks focus on task completion, failing to probe the foundational cognitive abilities that highlight these behaviors.
Large language models (LLMs) exhibit a unified "general factor" of capability across 10 benchmarks, a finding confirmed by our factor analysis of 156 models, yet they still struggle with simple, trivial tasks for humans.…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Overall, NeuroCognition emphasizes where current LLMs align with human-like intelligence and where they lack core adaptive cognition, showing the potential to serve as a…
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 5.0/10.
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Introduce NeuroCognition benchmark for evaluating LLM cognitive abilities distinct from existing benchmarks.
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Paper Pack
10.48550/arXiv.2603.02540Introduce NeuroCognition benchmark for evaluating LLM cognitive abilities distinct from existing benchmarks.
Abstract
Large language models (LLMs) exhibit a unified "general factor" of capability across 10 benchmarks, a finding confirmed by our factor analysis of 156 models, yet they still struggle with simple, trivial tasks for humans. This is because current benchmarks focus on task completion, failing to probe the foundational cognitive abilities that highlight these behaviors. We address this by introducing the NeuroCognition benchmark, grounded in three adapted neuropsychological tests: Raven's Progressive Matrices (abstract relational reasoning), Spatial Working Memory (maintenance and systematic search), and the Wisconsin Card Sorting Test (cognitive flexibility). Our evaluation reveals that while models perform strongly on text, their performance degrades for images and with increased complexity. Furthermore, we observe that complex reasoning is not universally beneficial, whereas simple, human-like strategies yield partial gains. We also find that NeuroCognition correlates positively with standard general-capability benchmarks, while still measuring distinct cognitive abilities beyond them. Overall, NeuroCognition emphasizes where current LLMs align with human-like intelligence and where they lack core adaptive cognition, showing the potential to serve as a verifiable, scalable source for improving LLMs.
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unverified0 refs; 0 sources; 17% coverage.
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PROBLEM
Introduce NeuroCognition benchmark for evaluating LLM cognitive abilities distinct from existing benchmarks. This is because current benchmarks focus on task completion, failing to probe the foundational cognitive abilities that highlight these behaviors.
METHOD
Large language models (LLMs) exhibit a unified "general factor" of capability across 10 benchmarks, a finding confirmed by our factor analysis of 156 models, yet they still struggle with simple, trivial tasks for humans. This is because current benchmarks focus on task completio...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Overall, NeuroCognition emphasizes where current LLMs align with human-like intelligence and where they lack core adaptive cognition, showing the potential to serve as a verifiable, scalable source for im...
WHY NOW
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Introduce NeuroCognition benchmark for evaluating LLM cognitive abilities distinct from existing benchmarks. This is because current benchmarks focus on task completion, failing to probe the foundational cognitive abilities that highlight these behaviors.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large language models (LLMs) exhibit a unified "general factor" of capability across 10 benchmarks, a finding confirmed by our factor analysis of 156 models, yet they still struggle with simple, trivial tasks for humans. This is because current benchmarks focus on task completion, failing to probe the foundational cognitive abilities that highlight these behaviors.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Overall, NeuroCognition emphasizes where current LLMs align with human-like intelligence and where they lack core adaptive cognition, showing the potential to serve as a verifiable, scalable source for improving LLMs.
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 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Introduce NeuroCognition benchmark for evaluating LLM cognitive abilities distinct from existing benchmarks.
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LLM Evaluation
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5.0/10 public viability
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