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Directly verbalizing sparse autoencoder features from LLM representations transforms how we interpret model behavior, making explanations more efficient and insightful.
TrajDebug uncovers the root causes of failures in long-horizon agent trajectories, enabling targeted improvements that could significantly boost agent performance.
Steering signals are most effective when derived from execution-boundary states, not just from the presence of desired behaviors in the source text.
Environment engineering, not just agent workflows, is the key to unlocking the full potential of autonomous scientific discovery, as demonstrated by EurekAgent's record-breaking results.
LLMs can be taught to reason more comprehensively over long contexts by rewarding not just the final answer, but also the quality of the reasoning steps taken to arrive at that answer.
Educators can now create interactive STEM courseware without coding, and see a ~10-point improvement in student STEM outcomes.
