Therefore I am. I Think

2026-04-01 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new study investigates whether large language reasoning models make decisions before or after initiating their chain-of-thought processes. Researchers present evidence that early-encoded decisions significantly influence subsequent reasoning. They demonstrate that a linear probe can accurately decode tool-calling decisions from pre-generation activations, often before any reasoning tokens are produced. Causal evidence from activation steering further supports this, showing that perturbing the decision direction leads to increased deliberation and alters model behavior in 7% to 79% of examples, depending on the model and benchmark. Behavioral analysis reveals that when steering changes a decision, the model's chain-of-thought frequently rationalizes the altered choice rather than resisting it, indicating that reasoning models can encode action choices prior to textual deliberation.

Key takeaway

For research scientists investigating LLM decision-making, this work suggests that you should focus on pre-generation activations to understand and potentially influence model behavior. Your interventions at this early stage could significantly alter subsequent reasoning paths, offering a new avenue for controlling model outputs and preventing undesirable rationalizations.

Key insights

LLMs can encode action choices before textual deliberation, shaping subsequent chain-of-thought reasoning.

Principles

• Early decisions shape reasoning.
• Decisions are decodable pre-generation.

Method

A linear probe decodes tool-calling decisions from pre-generation activations. Activation steering causally perturbs decision directions to observe effects on deliberation and behavior.

In practice

• Probe pre-generation activations.
• Steer model decisions causally.

Topics

• Large Language Models
• Chain-of-Thought Reasoning
• Decision Encoding
• Activation Steering
• Tool Calling

Best for: Research Scientist, AI Scientist, NLP Engineer

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• Extracting Search Trees from LLM Reasoning Traces Reveals Myopic Planning — LLMs generate deep reasoning traces but their decisions are driven by shallow, myopic planning, unlike humans.
• Why We Think — Allocating more test-time compute via methods like Chain-of-Thought significantly boosts LLM reasoning and problem-solving capabilities.
• Are we getting better at explaining things because of our interaction with LLMs? — Crafting clear LLM prompts may enhance human explanatory and communication abilities.
• What do Language Models Learn and When? The Implicit Curriculum Hypothesis — LLM pretraining follows a consistent, compositional skill emergence order, predictable from internal representations.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.