Tool Use

In early 2025, Apple’s now-infamous “thinking-illusion” benchmark delivered a sobering verdict: large reasoning models (LRMs)—those step-by-step thinkers like DeepSeek-R1 and Qwen 3 Thinking—failed to show meaningful advantages over simpler LLMs. Their verbose, reflective outputs didn’t help on easy problems, nor did they scale on hard ones. In some cases, they even underperformed. But what if we were judging thinking models under unfair conditions? A new study titled “Thinking Isn’t an Illusion” argues that the problem isn’t with reasoning itself—it’s with reasoning in a vacuum. When these models are augmented with tools like Python interpreters and structured scratchpads, their performance transforms dramatically. In fact, they begin to consistently outperform their non-reasoning counterparts across a diverse set of logic puzzles. ...

As LLM-powered agents become the backbone of many automation systems, their ability to reliably invoke external tools is now under the spotlight. Despite impressive multi-step reasoning, many such agents crumble in practice—not because they can’t plan, but because they can’t parse. One wrong parameter, one mismatched data type, and the whole chain collapses. A new paper titled “Butterfly Effects in Toolchains” offers the first systematic taxonomy of these failures, exposing how parameter-filling errors propagate through tool-invoking agents. The findings aren’t just technical quirks—they speak to deep flaws in how current LLM systems are evaluated, built, and safeguarded. ...

If ReAct was a spark, Pre-Act is a blueprint. In the paper Pre-Act: Multi-Step Planning and Reasoning Improves Acting in LLM Agents, Mrinal Rawat et al. challenge the single-step cognitive paradigm of ReAct, offering instead a roadmap for how agents should plan, reason, and act—especially when tool use and workflow coherence matter. What Is ReAct? A Quick Primer The ReAct framework—short for Reasoning and Acting—is a prompting strategy that allows an LLM to alternate between thinking and doing in a loop. Each iteration follows this pattern: ...