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3 results for “MMLU-Pro”

1. Research · Agents

   Progressive pruning lets language-model debates beat equal-cost baselines

   Beyond Solo and Consistency: Vindicating Multi-Agent Debate via Conditional Progressive Pruning

   Conditional Progressive Pruning uses intermediate debate results and learned agent behavior to allocate computation across rounds more effectively.

   MMLU-Pro: 66.33% accuracy (as reported)

   5 Oct 2026Rutgers University, New Brunswick, University of Cambridge, Tsinghua University

2. Research · Agents

   Revision-aware agent graphs reuse valid work while avoiding stale answers

   Revision-Aware Independent Agent Graphs for Dynamic Reasoning

   RIAG combines deterministic version selection, version-keyed caching and bounded independent reasoning to handle tasks revised over time.

   Full collection (6 benchmarks): 18 calls/query calls per query (as reported)

   2 Oct 2026Harvard AI and Robotics Lab, Harvard University

3. Research · Agents

   Joint harness search balances model accuracy, safety, and token use

   MoMHa: Multi-Objective Optimization of LLM Harnesses over Accuracy, Safety, and Tokens

   Across 17 domains and 12 models, jointly optimizing the code around language models outperformed sequential and accuracy-only approaches.

   3D hypervolume: 0.36 hypervolume indicator (as reported)

   28 Sept 2026Adobe Research