Reading an AI's "chain of thought" to predict its behavior? Turns out that's not very reliable 🔮 The fresh idea: make behavior prediction itself a learning task. Title: Forecasting Future Behavior as a Learning Task URL: 🔮 Overview A method to predict how large reasoning models (LRMs) will behave on new inputs. Instead of relying on explicit explanations, it introduces trainable "Behavior Forecasters" that analyze a single reasoning trajectory to predict outputs. ❓ Challenges Solved We want to understand and predict LRM behavior, but prior approaches have limits. ・Existing explanation methods don't scale to long reasoning trajectories ・Read as natural language, those trajectories are often unreliable A model's written "thoughts" don't necessarily reflect its actual behavior. 💡 Methodology & Proposed Approach ・It treats behavior prediction itself as a learnable task ・Training data comes directly from querying LRMs — no human annotation needed ・It runs in a single forward pass at inference ・Instantiated on two tasks: estimating answer consistency across reruns, and predicting how input modifications affect outputs ・End-to-end fine-tuning of the backbone, and initializing from the target LRM's weights, proved essential 📊 Experimental Results ・Behavior Forecasters outperform GPT-5.4 and Claude Opus-4.6 as "naive readers" ・And they achieve higher accuracy at a small fraction of the inference cost #LLMInterpretability# #ReasoningModels#