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This paper introduces critique refinement and DISH to enhance the realism of alignment evaluations by mitigating evaluation awareness, demonstrating that combining both techniques yields significant improvements over using either alone.
Introduces 'Machine Studying' as a new formulation of continual learning where AI systems autonomously develop expertise from a corpus, and presents StudyBench for evaluation.
Introduces 'Machine Studying' as a problem where AI agents must autonomously develop expertise from a corpus, beyond RAG or long-context, and presents the StudyBench benchmark for evaluation.
This paper systematically studies how inference-time compute (token budgets, context compaction, repeated submissions) affects frontier LLM performance on challenging benchmarks, demonstrating that scores are protocol-dependent and advocating for evaluations that report capability as a function of inference compute.
TMAS introduces a multi-agent framework that enhances large language model reasoning by scaling test-time compute through structured collaboration and hierarchical memory systems. The approach uses specialized agents, cross-trajectory information flow, and hybrid reward reinforcement learning to improve iterative scaling and stability on challenging reasoning benchmarks.