Event Tensor is a new compiler abstraction for dynamic megakernels that enables high-performance persistent GPU kernels with state-of-the-art LLM serving latency and reduced warmup overhead.
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SEVerA uses Formally Guarded Generative Models and a three-stage Search-Verification-Learning process to synthesize self-evolving agents that satisfy hard formal constraints while improving task performance.
MultiMat shows multimodal large models plus constrained search produce higher-quality procedural material graphs than text-only baselines on a new production dataset.
A generative model writes programs in a relational constraint DSL and uses bootstrapping to learn object placement distributions that align more closely with human annotations than data-driven or LLM baselines.
SWE-RL uses RL on software evolution data to train LLMs achieving 41% on SWE-bench Verified with generalization to other reasoning tasks.
citing papers explorer
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Event Tensor: A Unified Abstraction for Compiling Dynamic Megakernel
Event Tensor is a new compiler abstraction for dynamic megakernels that enables high-performance persistent GPU kernels with state-of-the-art LLM serving latency and reduced warmup overhead.
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SEVerA: Verified Synthesis of Self-Evolving Agents
SEVerA uses Formally Guarded Generative Models and a three-stage Search-Verification-Learning process to synthesize self-evolving agents that satisfy hard formal constraints while improving task performance.
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MultiMat: Multimodal Program Synthesis for Procedural Materials using Large Multimodal Models
MultiMat shows multimodal large models plus constrained search produce higher-quality procedural material graphs than text-only baselines on a new production dataset.
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Learning to Place Objects with Programs and Iterative Self Training
A generative model writes programs in a relational constraint DSL and uses bootstrapping to learn object placement distributions that align more closely with human annotations than data-driven or LLM baselines.
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SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution
SWE-RL uses RL on software evolution data to train LLMs achieving 41% on SWE-bench Verified with generalization to other reasoning tasks.