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Discovering task-specific, compact concept vocabularies from unsupervised pools unlocks inspectable AI without the need for manual annotations or massive, static concept sets.
VLM-based agents often miss the mark by proposing experiments that fail to clarify their hypotheses, revealing a significant gap in their reasoning capabilities.
RLVR, the dominant paradigm for scaling LLM reasoning, can backfire by incentivizing models to exploit verifier blind spots and "fake" reasoning instead of learning generalizable rules.
