First empirical study of correctness bugs in torch.compile characterizes their patterns and proposes AlignGuard, which found 23 confirmed new bugs via LLM-guided test mutation.
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LLM4Drive: A Survey of Large Language Models for Au- tonomous Driving
11 Pith papers cite this work. Polarity classification is still indexing.
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An LLM framework using Object-Process Methodology for scene understanding and intent-aware interaction outperforms baselines in simulator tests for safety, comfort, and efficiency in mixed-traffic autonomous driving.
PALM improves long-horizon robotic manipulation success by distilling affordance representations for object interaction and predicting within-subtask progress in a VLA model.
ReSim is a controllable video world model trained on heterogeneous real and simulated driving data that achieves higher fidelity and controllability for both expert and non-expert actions, plus a Video2Reward module for estimating action quality from simulated futures.
LiloDriver uses LLMs and memory-augmented planning in a four-stage pipeline to outperform rule-based and learning-based methods on both common and rare scenarios in the nuPlan benchmark.
DriveMoE applies scene-specialized Vision MoE and skill-specialized Action MoE to a VLA baseline to achieve SOTA closed-loop performance on Bench2Drive.
DriveVLM adds vision-language models with scene description, analysis, and hierarchical planning modules to autonomous driving, paired with a hybrid DriveVLM-Dual system tested on nuScenes and SUP-AD datasets and deployed on a production vehicle.
On-policy GKD trains 5x smaller student LLMs to nearly match large teacher performance in AV motion planning on nuScenes while beating a dense-feedback RL baseline.
Moral alignment in LLMs improves with model size according to the power law D ∝ S^{-0.10} (R²=0.50).
Introduces structured NuScenes-S dataset and 0.9B FastDrive VLM claiming 20% higher decision accuracy and over 10x inference speedup versus larger unstructured VLMs.
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.
citing papers explorer
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Demystifying the Silence of Correctness Bugs in PyTorch Compiler
First empirical study of correctness bugs in torch.compile characterizes their patterns and proposes AlignGuard, which found 23 confirmed new bugs via LLM-guided test mutation.
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Large Language Model based Interactive Decision-Making for Autonomous Driving
An LLM framework using Object-Process Methodology for scene understanding and intent-aware interaction outperforms baselines in simulator tests for safety, comfort, and efficiency in mixed-traffic autonomous driving.
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PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation
PALM improves long-horizon robotic manipulation success by distilling affordance representations for object interaction and predicting within-subtask progress in a VLA model.
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ReSim: Reliable World Simulation for Autonomous Driving
ReSim is a controllable video world model trained on heterogeneous real and simulated driving data that achieves higher fidelity and controllability for both expert and non-expert actions, plus a Video2Reward module for estimating action quality from simulated futures.
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LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios
LiloDriver uses LLMs and memory-augmented planning in a four-stage pipeline to outperform rule-based and learning-based methods on both common and rare scenarios in the nuPlan benchmark.
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DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving
DriveMoE applies scene-specialized Vision MoE and skill-specialized Action MoE to a VLA baseline to achieve SOTA closed-loop performance on Bench2Drive.
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DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models
DriveVLM adds vision-language models with scene description, analysis, and hierarchical planning modules to autonomous driving, paired with a hybrid DriveVLM-Dual system tested on nuScenes and SUP-AD datasets and deployed on a production vehicle.
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On-Policy Distillation of Language Models for Autonomous Vehicle Motion Planning
On-policy GKD trains 5x smaller student LLMs to nearly match large teacher performance in AV motion planning on nuScenes while beating a dense-feedback RL baseline.
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Scaling Laws for Moral Machine Judgment in Large Language Models
Moral alignment in LLMs improves with model size according to the power law D ∝ S^{-0.10} (R²=0.50).
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Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving
Introduces structured NuScenes-S dataset and 0.9B FastDrive VLM claiming 20% higher decision accuracy and over 10x inference speedup versus larger unstructured VLMs.
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A Survey on the Memory Mechanism of Large Language Model based Agents
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.