NeurIPS 2025放榜了！自动驾驶之心着手汇总了中稿的相关工作，目前涉及自动驾驶、视觉感知推理、大模型训练、具身智能、强化学习、视频理解、代码生成等等方向！后续的论文更新也会第一时间上传至『自动驾驶之心知识星球』~

自动驾驶

FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving

• paper：https://arxiv.org/abs/2505.17685

• code：https://miv-xjtu.github.io/FSDrive.github.io/

• 单位：阿里、西交

AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning

• paper：https://arxiv.org/abs/2506.13757

• code：https://github.com/ucla-mobility/AutoVLA

• 单位：UCLA

自动驾驶前沿信息一手获取！

Unifying Appearance Codes and Bilateral Grids for Driving Scene Gaussian Splatting

• paper：https://arxiv.org/abs/2506.05280

• code：https://github.com/BigCiLeng/bilateral-driving

• 单位：清华AIR、北航等

SURDS: Benchmarking Spatial Understanding and Reasoning in Driving Scenarios with Vision Language Models

• paper：https://arxiv.org/abs/2411.13112

• code：https://github.com/XiandaGuo/Drive-MLLM

• 单位：中科院等

YOLOv12: Attention-Centric Real-Time Object Detectors

• paper：https://arxiv.org/abs/2502.12524

• code：https://github.com/sunsmarterjie/yolov12

• 单位：水牛城大学、中科院等

视觉感知推理

OmniSegmentor: A Flexible Multi-Modal Learning Framework for Semantic Segmentation

• paper: https://arxiv.org/pdf/2509.15096

• code：https://github.com/VCIP-RGBD/DFormer

• 单位：南开@程明明

PixFoundation 2.0: Do Video Multi-Modal LLMs Use Motion in Visual Grounding?

• paper: https://arxiv.org/pdf/2509.02807

• code: https://github.com/MSiam/PixFoundation-2.0.git.

ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning

• paper: https://arxiv.org/pdf/2507.16815

• code: https://jasper0314-huang.github.io/thinkact-vla/

• 单位：英伟达、台湾大学

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding

• paper: https://arxiv.org/pdf/2506.10084

Are Vision Language Models Ready for Clinical Diagnosis? A 3D Medical Benchmark for Tumor-centric Visual Question Answering

• paper: https://arxiv.org/pdf/2505.18915

• code: https://github.com/Schuture/DeepTumorVQA.

视频理解

PixFoundation 2.0: Do Video Multi-Modal LLMs Use Motion in Visual Grounding?

• paper: https://arxiv.org/pdf/2509.02807

• code: https://github.com/MSiam/PixFoundation-2.0.git.

图像/视频生成与编辑

Fast and Fluent Diffusion Language Models via Convolutional Decoding and Rejective Fine-tuning

• paper: https://arxiv.org/pdf/2509.15188

• code：https://github.com/ybseo-ac/Conv

AutoEdit: Automatic Hyperparameter Tuning for Image Editing

• paper: https://arxiv.org/pdf/2509.15031

OmniSync: Towards Universal Lip Synchronization via Diffusion Transformers

• paper: https://arxiv.org/pdf/2505.21448

• code：https://ziqiaopeng.github.io/OmniSync/

数据集/评估

Are Vision Language Models Ready for Clinical Diagnosis? A 3D Medical Benchmark for Tumor-centric Visual Question Answering

• paper: https://arxiv.org/pdf/2505.18915

• code: https://github.com/Schuture/DeepTumorVQA.

3D视觉

Are Vision Language Models Ready for Clinical Diagnosis? A 3D Medical Benchmark for Tumor-centric Visual Question Answering

• paper: https://arxiv.org/pdf/2505.18915

• code: https://github.com/Schuture/DeepTumorVQA.

大模型训练

Scaling Offline RL via Efficient and Expressive Shortcut Models

• paper: https://arxiv.org/pdf/2505.22866

• 单位：康奈尔大学

强化学习/偏好优化

LLM world models are mental: Output layer evidence of brittle world model use in LLM mechanical reasoning

• paper: https://arxiv.org/pdf/2507.15521

Scaling Offline RL via Efficient and Expressive Shortcut Models

• paper: https://arxiv.org/pdf/2505.22866

大模型微调

Fast and Fluent Diffusion Language Models via Convolutional Decoding and Rejective Fine-tuning

• paper: https://arxiv.org/pdf/2509.15188

Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning

• paper: https://arxiv.org/pdf/2509.15087

Differentially Private Federated Low Rank Adaptation Beyond Fixed-Matrix

• paper: https://arxiv.org/pdf/2507.09990

具身智能

Self-Improving Embodied Foundation Models

• paper: https://arxiv.org/pdf/2509.15155

• code: https://self-improving-efms.github.io

• 单位：DeepMind

ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation

• paper: https://arxiv.org/pdf/2505.22159

• code: https://sites.google.com/view/forcevla2025

• 单位：复旦、上交等

持续学习/模型幻觉

Improving Multimodal Large Language Models Using Continual Learning

• paper: https://arxiv.org/pdf/2410.19925

• code: https://shikhar-srivastava.github.io/cl-for-improving-mllms

人体

Real-Time Intuitive AI Drawing System for Collaboration: Enhancing Human Creativity through Formal and Contextual Intent Integration

• paper: https://arxiv.org/pdf/2508.19254

大模型安全

Safely Learning Controlled Stochastic Dynamics

• paper: https://arxiv.org/pdf/2506.02754

可解释性

Concept-Level Explainability for Auditing & Steering LLM Responses

• paper: https://arxiv.org/pdf/2505.07610

文档理解

STEM-POM: Evaluating Language Models Math-Symbol Reasoning in Document Parsing

• paper: https://arxiv.org/pdf/2411.00387

• code: https://github.com/jiaruzouu/STEM-PoM.

医学

Are Vision Language Models Ready for Clinical Diagnosis? A 3D Medical Benchmark for Tumor-centric Visual Question Answering

• paper: https://arxiv.org/pdf/2505.18915

• code: https://github.com/Schuture/DeepTumorVQA.

Agent

AgentMisalignment: Measuring the Propensity for Misaligned Behaviour in LLM-Based Agents

• paper: https://arxiv.org/pdf/2506.04018

混合专家模型

Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning

• paper: https://arxiv.org/pdf/2509.15087

ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation

• paper: https://arxiv.org/pdf/2505.22159

• code: https://sites.google.com/view/forcevla2025.

代码生成

Fast and Fluent Diffusion Language Models via Convolutional Decoding and Rejective Fine-tuning

• paper: https://arxiv.org/pdf/2509.15188

SBSC: Step-By-Step Coding for Improving Mathematical Olympiad Performance

• paper: https://arxiv.org/pdf/2502.16666

大模型推理优化

ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning

• paper: https://arxiv.org/pdf/2507.16815

• code: https://jasper0314-huang.github.io/thinkact-vla/

LLM world models are mental: Output layer evidence of brittle world model use in LLM mechanical reasoning

• paper: https://arxiv.org/pdf/2507.15521

STEM-POM: Evaluating Language Models Math-Symbol Reasoning in Document Parsing

• paper: https://arxiv.org/pdf/2411.00387

• code: https://github.com/jiaruzouu/STEM-PoM.

扩散模型

Fast and Fluent Diffusion Language Models via Convolutional Decoding and Rejective Fine-tuning

• paper: https://arxiv.org/pdf/2509.15188

OmniSync: Towards Universal Lip Synchronization via Diffusion Transformers

• paper: https://arxiv.org/pdf/2505.21448

其他领域

Out-of-distribution generalisation is hard: evidence from ARC-like tasks

• paper: https://arxiv.org/pdf/2505.09716

Fair Summarization: Bridging Quality and Diversity in Extractive Summaries

• paper: https://arxiv.org/pdf/2411.07521