Implicit Reasoning Steering via Concept Chaining

Jul 17, 2026

Researchers introduce Concept Chaining, a method that uses natural-language connection paragraphs to subtly bias large language models (LLMs) toward specific answers without explicit cues or instructions. By continuing pretraining on these connection paragraphs, they demonstrate that model predictions can be systematically steered in a covert manner. This approach exploits the reasoning brittleness of LLMs, showing that ordinary-looking text can be used to influence model decisions.

Why it matters: The work highlights a practical vulnerability in LLMs, showing that their decisions can be covertly manipulated through indirect, natural-language text, raising concerns about their security and reliability.

Full story at: arXiv Computation and Language ↗