The Complexity Ceiling Benchmark demonstrates geometric per-step decay in LLM sequential reasoning with domain-specific performance ceilings and introduces a trace metric showing incorrect intermediate steps in some correct final answers.
Clutrr: A diagnostic benchmark for inductive reasoning from text
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HQRE entropy regularization makes multi-agent LLM coordination well-posed, yielding unique equilibria, linear mirror convergence, bounded Bayesian regret, and DICE gains of 4.3–8.5 pp on reasoning/planning tasks.
Transformers show limited adaptive depth use on relational reasoning, with clearer evidence after finetuning on the task.
A procedurally generated benchmark decomposes spatial-reasoning tests into four compositional axes and shows LLMs degrade sharply with reasoning depth.
ChatGPT outperforms zero-shot LLMs on most tasks and improves with interaction but scores only 63.41 percent on reasoning categories and generates extrinsic hallucinations from its training data.
citing papers explorer
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The Complexity Ceiling Benchmark: A Multi-Domain Evaluation of Sequential Reasoning Under Depth Scaling
The Complexity Ceiling Benchmark demonstrates geometric per-step decay in LLM sequential reasoning with domain-specific performance ceilings and introduces a trace metric showing incorrect intermediate steps in some correct final answers.
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DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination
HQRE entropy regularization makes multi-agent LLM coordination well-posed, yielding unique equilibria, linear mirror convergence, bounded Bayesian regret, and DICE gains of 4.3–8.5 pp on reasoning/planning tasks.
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Do Transformers Use their Depth Adaptively? Evidence from a Relational Reasoning Task
Transformers show limited adaptive depth use on relational reasoning, with clearer evidence after finetuning on the task.
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DecompSR: A dataset for decomposed analyses of compositional multihop spatial reasoning
A procedurally generated benchmark decomposes spatial-reasoning tests into four compositional axes and shows LLMs degrade sharply with reasoning depth.
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A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity
ChatGPT outperforms zero-shot LLMs on most tasks and improves with interaction but scores only 63.41 percent on reasoning categories and generates extrinsic hallucinations from its training data.