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Yuling Shi, Chaoxiang Xie, Zhensu Sun +7 authors
Multimodal large language models can effectively understand source code when represented as compressed images, achieving significant token reduction while maintaining or improving performance on code comprehension tasks.
Xin Xu, Clive Bai, Kai Yang +7 authors
Composition-RL improves reasoning capabilities by automatically composing multiple problems into new verifiable questions for reinforcement learning training.
Jianhao Ruan, Zhihao Xu, Yiran Peng +8 authors
AOrchestra is a framework-agnostic agentic system that uses a tuple-based abstraction to dynamically create specialized task executors, achieving improved performance on complex benchmarks through automated agent creation and resource management.
Johannes Kirmayr, Lukas Stappen, Elisabeth André
Current LLM agent benchmarks fail to evaluate reliability in real-world scenarios with uncertain user inputs, prompting the creation of CAR-bench to test consistency, uncertainty management, and capability awareness in in-car assistant applications.
Shiyang Feng, Runmin Ma, Xiangchao Yan +54 authors
InternAgent-1.5 is a unified system for autonomous scientific discovery that integrates computational modeling and experimental research through coordinated subsystems for generation, verification, and evolution.
Jane Luo, Chengyu Yin, Xin Zhang +10 authors
RPG-Encoder framework transforms repository comprehension and generation into a unified cycle by encoding code into high-fidelity Repository Planning Graph representations that improve understanding and reconstruction accuracy.
Dianyi Wang, Ruihang Li, Feng Han +17 authors
DeepGen 1.0 is a lightweight 5B unified multimodal model that achieves superior performance in image generation and editing through advanced alignment techniques and progressive training strategies.
Alisia Lupidi, Bhavul Gauri, Thomas Simon Foster +34 authors
AIRS-Bench presents a comprehensive benchmark suite for evaluating LLM agents across diverse scientific domains, demonstrating current limitations while providing open-source resources for advancement.
Jinyuan Li, Chengsong Huang, Langlin Huang +4 authors
Training multimodal process reward models efficiently through balanced-information scoring that prioritizes label mixture and reliability while achieving full-data performance with only 10% of training data.
Dianyi Wang, Chaofan Ma, Feng Han +8 authors
UniReason integrates text-to-image generation and image editing through a dual reasoning paradigm that enhances planning with world knowledge and uses editing for visual refinement, achieving superior performance on reasoning-intensive benchmarks.
Taiwei Shi, Sihao Chen, Bowen Jiang +3 authors
Experiential Reinforcement Learning introduces an explicit experience-reflection-consolidation loop that improves learning efficiency and performance in sparse-reward environments by enabling structured behavioral revision without additional inference costs.
Peng Xia, Jianwen Chen, Hanyang Wang +10 authors
SkillRL enables LLM agents to improve through hierarchical skill discovery and recursive policy evolution, achieving superior performance on complex tasks while reducing computational overhead.
Daniil Plyusov, Alexey Gorbatovski, Boris Shaposhnikov +3 authors
RLVR methods using group sampling suffer from bias toward likely trajectories and missed rare-correct ones; a difficulty-aware advantage scaling technique improves performance on benchmarks without increasing computational cost.
Tianyi Jiang, Arctanx An, Hengyi Feng +12 authors
A novel training-free framework called Chain of Mindset enables step-level adaptive mindset orchestration for large language models by integrating spatial, convergent, divergent, and algorithmic reasoning approaches.
Liyan Xu, Mo Yu, Fandong Meng +1 authors
Research investigates latent planning dynamics in large language models through a probing method called Tele-Lens, revealing limited global planning and enabling improved uncertainty estimation and CoT bypass recognition.
Tiwei Bie, Maosong Cao, Xiang Cao +47 authors
LLaDA2.1 introduces a novel token-to-token editing approach with speed and quality modes, enhanced through reinforcement learning for improved reasoning and instruction following in large language diffusion models.
Yalcin Tur, Jalal Naghiyev, Haoquan Fang +4 authors
RD-VLA introduces a recurrent architecture for vision-language-action models that adapts computational depth through latent iterative refinement, achieving constant memory usage and improved task success rates.
Wenkai Yang, Weijie Liu, Ruobing Xie +3 authors
On-policy distillation is extended through a generalized framework that introduces flexible reference models and reward scaling factors, demonstrating improved performance through reward extrapolation and reward correction techniques.
Zhenxiong Yu, Zhi Yang, Zhiheng Jin +19 authors
Spider-Sense framework provides intrinsic and selective agent security through event-driven defense with intrinsic risk sensing, achieving low attack success and false positive rates with minimal latency overhead.
Baorong Shi, Bo Cui, Boyuan Jiang +17 authors
MedXIAOHE is a medical vision-language foundation model that enhances clinical understanding through entity-aware continual pretraining, reinforcement learning, and tool-augmented agentic training for reliable diagnostic reasoning.
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