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Rutgers University
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Sparse autoencoders reveal that a Transformer agent's decision-making strategies can be interpreted without explicit rule classification, uncovering hidden concepts that drive behavior.
By surgically intervening in MLLM decoding, this work cuts hallucination rates without sacrificing descriptive quality, a feat prior methods struggled to achieve.
