Reasoning

LLMs are great at spitting out answers—but are they any good at thinking through problems? A new benchmark, AdvGameBench, introduces a process-based evaluation approach that places LLMs into three rule-based strategic games to measure not outcomes, but the quality of reasoning. Developed by Yuan et al., this framework focuses on how LLMs plan, revise, and make resource-limited decisions in dynamic settings. Three Games, Three Cognitive Demands 1. Tower Defense tests spatial planning and rule-following. Models place defenders on a battlefield to block enemies—positioning, cooldowns, and cost management are key. ...

In the quest for truly intelligent systems, reasoning has always stood as the ultimate benchmark. But a new paper titled “Towards a Deeper Understanding of Reasoning Capabilities in Large Language Models” by Annie Wong et al. delivers a sobering message: even the most advanced LLMs still stumble in dynamic, high-stakes environments when asked to reason, plan, and act with stability. Beyond the Benchmark Mirage Static benchmarks like math word problems or QA datasets have long given the illusion of emergent intelligence. Yet this paper dives into SmartPlay, a suite of interactive environments, to show that LLMs exhibit brittle reasoning when faced with real-time adaptation. SmartPlay is a collection of dynamic decision-making tasks designed to test planning, adaptation, and coordination under uncertainty. The team evaluates open-source models such as LLAMA3-8B, DEEPSEEK-R1-14B, and LLAMA3.3-70B on tasks involving spatial coordination, opponent modeling, and planning. The result? Larger models perform better—but only to a point. Strategic prompting can help smaller models, but also introduces volatility. ...

An open-source reasoning model achieving state-of-the-art performance in math, code, and logic tasks.

A compact, high-quality small language model from Microsoft designed for strong reasoning, on-device inference, and low-latency applications.