MLS-Bench
A holistic and rigorous assessment of AI systems on building better AI.
Methods that stood the test of time and scale.
Modern AI progress is built on a small set of reusable ideas — convolutions, residual connections, attention, normalization — that generalize across architectures and survive every order-of-magnitude jump in scale.
Weight-shared receptive fields that scaled vision models.
Distributed embeddings transferable across NLP tasks.
Adversarial generator–discriminator game for sample synthesis.
Adaptive moment estimation that became the default optimizer.
Encoder–decoder with skip links — vision and diffusion staple.
Clipped policy ratio that made deep RL stable to scale.
Mean-free normalization, faster and surprisingly sufficient.
Rotary position encoding that scales with context length.
IO-aware exact attention that scaled context length.
Weight-shared receptive fields that scaled vision models.
Distributed embeddings transferable across NLP tasks.
Adversarial generator–discriminator game for sample synthesis.
Adaptive moment estimation that became the default optimizer.
Encoder–decoder with skip links — vision and diffusion staple.
Clipped policy ratio that made deep RL stable to scale.
Mean-free normalization, faster and surprisingly sufficient.
Rotary position encoding that scales with context length.
IO-aware exact attention that scaled context length.
Gated recurrence enabling long-range sequence learning.
Random unit masking that became the standard regularizer.
Normalizing activations across the batch to stabilize training.
Residual connections enabling 100+ layer training.
Self-attention as the universal sequence operator.
Input–label interpolation that improved generalization.
Denoising diffusion: learn to invert a noise process.
Low-rank adapters for parameter-efficient finetuning.
Gated recurrence enabling long-range sequence learning.
Random unit masking that became the standard regularizer.
Normalizing activations across the batch to stabilize training.
Residual connections enabling 100+ layer training.
Self-attention as the universal sequence operator.
Input–label interpolation that improved generalization.
Denoising diffusion: learn to invert a noise process.
Low-rank adapters for parameter-efficient finetuning.
MLS-Bench tests whether AI agents can invent the next ones.
Each task isolates a well-defined research question and asks the agent to propose a single modular improvement — a new loss, an attention variant, a sampler, a routing rule — then measures whether the change transfers across models, datasets, and seeds.
Quantization-Aware Language-Model Training
Attention Cache Structural Reduction
Trajectory Optimization for Model-Based Planning
3D Scene Densification Strategy
Diffusion-Prior Inverse Solver
Value-Based Visual Control
Gradient Compression for Distributed Training
Homophily-Heterophily Graph Filter
Score-Based Black-Box Linf AttackQuantization-Aware Language-Model TrainingAttention Cache Structural ReductionTrajectory Optimization for Model-Based Planning3D Scene Densification StrategyDiffusion-Prior Inverse SolverValue-Based Visual ControlGradient Compression for Distributed TrainingHomophily-Heterophily Graph FilterScore-Based Black-Box Linf Attack
Autoregressive Embedding Strategy
Efficient Diffusion Sampling for Robot Actions
Fixed-Budget Diffusion Sampler Updates
Atmospheric Column Emulator Architecture
Backbone-to-Sequence Inverse Folding
Constraint Handling for Safe RL
Evolutionary Operators for Continuous Black-Box Optimization
Discrete Causal Graph Discovery
Fused Causal Attention KernelAutoregressive Embedding StrategyEfficient Diffusion Sampling for Robot ActionsFixed-Budget Diffusion Sampler UpdatesAtmospheric Column Emulator ArchitectureBackbone-to-Sequence Inverse FoldingConstraint Handling for Safe RLEvolutionary Operators for Continuous Black-Box OptimizationDiscrete Causal Graph DiscoveryFused Causal Attention Kernel140 executable tasks across 12 domains, each built around a targeted ML component, a controlled edit surface, and multi-setting evidence for transfer.
Model Performance by Category
Each model's bar shows Vanilla as the darker lower portion and Agent as the lighter overlay, against a translucent grey Human SOTA reference computed from the reproduced human baselines. Scores use the paper's normalized task metric.
MLS-Bench-Lite Intelligence
Average normalized score on the 30-task Lite subset.
Lite is only a subset. We recommend evaluating your harness and model on the full 140-task benchmark. For our in-depth analysis of the 5 main models on all 140 tasks, see our blog or the arXiv paper.
Task Categories
140 tasks across 12 flat categories. Open a category to browse its tasks.