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Benchmark
Reported in 25 papers on Zotpaper. The latest, each as the paper states it:
9 results for “MMLU-Pro”
Research · Evaluation & benchmarks
Cheap to Draw, Expensive to Trust: Certifying Test-Time Scaling Curves
A variance-aware audit provides simultaneous confidence bands across test-time budgets while reducing the number of generated answers.
MMLU-Pro: 192000 generated answers (as reported)
PMCC AI Lab, Peter Munk Cardiac Centre, University Health Network
Research · Agents
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)
Rutgers University, New Brunswick, University of Cambridge, Tsinghua University
Research · Reinforcement learning
Failure Is the Mother of Success: Recovering Supervision with Grounding Hindsight
RSHD generates corrective notes from failures, verifies them via replays, and distills successful behaviors into the policy without extra inference cost.
MMLU-Pro: 40.4% accuracy (as reported)
University of California, San Diego, Adobe Research, University of Virginia
Research · Language models
Query-Adaptive Neuron Steering for LLMs
Q-NeuS uses each query’s internal activation pattern to select and scale neuron interventions without updating the underlying model.
MMLU-Pro: 38.37% accuracy (as reported)
Northeastern University, Washington University in St. Louis, Nanjing University of Aeronautics and Astronautics
Research · Evaluation & benchmarks
Predictable performance is suboptimal, and what it means for model routing
Zhang and Hardt connect prediction gains beyond a model’s own confidence to its room for improvement, then test the relationship across 16 language models.
MMLU-Pro: 0.65 Pearson correlation between empirical suboptimality and (as reported)
Research · Language models
EMO: A Closed-Loop Test-Time Computation Framework via Alternating Context Optimization and Logits Steering
EMO closes the loop between feedback-driven context revision and decoding-time distribution correction, improving zero-shot performance across eight models and six benchmarks.
MMLU-Pro: 70.9% accuracy (as reported)
Peking University, Samsung Research
Research · Reinforcement learning
Rethinking Probability-Based Reinforcement Learning From Posterior Concentration
The authors identify a length-dependent failure mode they call Posterior Concentration Phenomenon, and propose a framework to mitigate it.
MMLU-Pro: 61.3% Avg@2 (as reported)
National University of Singapore, The Hong Kong University of Science and Technology, University of Science and Technology of China
Research · Agents
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)
Harvard AI and Robotics Lab, Harvard University
Research · Agents
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: 0.48 hypervolume indicator (as reported)
Adobe Research