When Does Critique Improve AI-Assisted Theoretical Physics? SCALAR: Structured Critic--Actor Loop for Agentic Reasoning

Short summary

Research introduces SCALAR, an Actor-Critic-Judge pipeline for evaluating AI reasoning on theoretical physics. Multi-turn dialogue consistently improves outcomes, but optimal feedback strategy depends on actor-critic model pairing; scaling helps easier problems but doesn't eliminate the hardest bottlenecks. Framework provides controlled testbed for studying AI-driven scientific discovery.

• •Multi-turn dialogue improves AI reasoning on theoretical physics problems versus single-shot attempts
• •Critic feedback effectiveness depends on actor-critic pairing; asymmetric pairings benefit most from constructive feedback strategy
• •Model scaling improves easier problem behavior but fails to eliminate the hardest reasoning bottlenecks

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