arXiv AI September 17, 2026 By Tyler McDonald, Ali Emami

WordPolo: Evaluating Language Models Through Iterative Semantic Feedback

WordPolo is a word‑finding task that evaluates language models by having them guess an unknown target word and receive semantic similarity feedback. Participants start with no knowledge, make iterative guesses, and receive distance scores that guide them through semantic space. The study tests recent LLMs, LRMs, humans, and a heuristic on 1,500 puzzles, revealing that while solve rates vary widely, many models make meaningful progress and exhibit human‑like strategies, highlighting the importance of assessing reasoning processes, not just final accuracy.

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arXiv AI

Aug 12

From Reasoning Depth to Reasoning Breadth: Evaluating Multi-Point Associative Reasoning in Large Language Models

arXiv:2608. 10444v1 Announce Type: cross Abstract: Large language models (LLMs) have made substantial progress on reasoning tasks that require increasingly long and complex inferential chains.

By Si'an Xie (Beijing University of Posts and Telecommunications), Jiaxun Liu (Peking University), Biao Yang (Kuaishou Technology), Wei Yuan (Kuaishou Technology), Fan Yang (Kuaishou Technology), Tingting Gao (Kuaishou Technology), Ming Wu (Beijing University of Posts and Telecommunications)

llms rag agents benchmarks

arXiv AI

Aug 17

The Metacognitive Bottleneck: Japanese Riddles Reveal Fundamental Limits of Machine Insight and Self-Evaluation in Reasoning AI

arXiv:2509. 14704v3 Announce Type: replace Abstract: Benchmark saturation and training-data contamination increasingly obscure whether reported gains in large language models (LLMs) reflect genuine advances in reasoning or familiarity with recurring patterns in benchmark problems.

By Masaharu Mizumoto, Dat Nguyen, Zhiheng Han, Xingfu Li, Yo Nakawake, Le Minh Nguyen

llms benchmarks

arXiv AI

Aug 28

Don't Overthink, Don't Underthink: Toward Adaptive Reasoning in Agentic AI

The paper argues that large language models need adaptive reasoning rather than fixed reasoning budgets. It shows that over‑reasoning leads to high computational cost without accuracy gains, while under‑reasoning results in incorrect or incomplete solutions. The authors evaluate these failure modes on MATH‑500 and the GAIA benchmark, highlighting the need for dynamic reasoning allocation in agentic AI systems.

By Md Jueal Mia, M. Hadi Amini

llms agents benchmarks

arXiv Machine Learning

Aug 11

Rethinking Reasoning with MDLMs: Early Exits, Post-hoc Reasoning, and Beyond

arXiv:2510. 19990v2 Announce Type: replace Abstract: The reasoning paradigm, where language models reason before answering, has enabled breakthroughs on tasks such as mathematical problem-solving.

By Zachary Horvitz, Raghav Singhal, Hao Zou, Carles Domingo-Enrich, Zhou Yu, Rajesh Ranganath, Kathleen McKeown

llms diffusion reinforcement-learning fine-tuning

arXiv AI

Jun 2

Verifying Meta-Awareness via Predictive Rewards in Reasoning Models

arXiv:2510. 03259v2 Announce Type: replace-cross Abstract: Recent research on reasoning models explores the meta-awareness of language models, including their ability to determine optimal thinking duration, recognize knowledge boundaries, and structure concept-level thinking.

By Yoonjeon Kim, Doohyuk Jang, Eunho Yang

llms reinforcement-learning benchmarks

arXiv AI

Jul 17

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation

arXiv:2607. 14109v1 Announce Type: cross Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central challenges in natural language understanding.

By Inder Preet, Shuxin Lin, Dhaval Patel

llms nlp benchmarks

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Related stories

From Reasoning Depth to Reasoning Breadth: Evaluating Multi-Point Associative Reasoning in Large Language Models

The Metacognitive Bottleneck: Japanese Riddles Reveal Fundamental Limits of Machine Insight and Self-Evaluation in Reasoning AI

Don't Overthink, Don't Underthink: Toward Adaptive Reasoning in Agentic AI

Rethinking Reasoning with MDLMs: Early Exits, Post-hoc Reasoning, and Beyond

Verifying Meta-Awareness via Predictive Rewards in Reasoning Models

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation