11 papers
Rethinking Continual Experience Internalization for Self-Evolving LLM Agents
Jingwen Chen, Wenkai Yang, Shengda Fan +7
Experience internalization converts contextual experience from past interactions into reusable parametric capability, offering a promising path toward continual learning in large l…
WebTestBench: Evaluating Computer-Use Agents towards End-to-End Automated Web Testing
Fanheng Kong, Jingyuan Zhang, Yang Yue +10
The emergence of Large Language Models (LLMs) has catalyzed a paradigm shift in programming, giving rise to "vibe coding", where users can build complete projects and even control…
Structurally Aligned Subtask-Level Memory for Software Engineering Agents
Kangning Shen, Jingyuan Zhang, Chenxi Sun +2
Large Language Models (LLMs) have demonstrated significant potential as autonomous software engineering (SWE) agents. Recent work has further explored augmenting these agents with…
Efficient Training of Diffusion Mixture-of-Experts Models: A Practical Recipe
Yahui Liu, Yang Yue, Jingyuan Zhang +5
Recent efforts on Diffusion Mixture-of-Experts (MoE) models have primarily focused on developing more sophisticated routing mechanisms. However, we observe that the underlying arch…
Klear-AgentForge: Forging Agentic Intelligence through Posttraining Scaling
Qi Wang, Hongzhi Zhang, Jia Fu +12
Despite the proliferation of powerful agentic models, the lack of critical post-training details hinders the development of strong counterparts in the open-source community. In thi…
Leanabell-Prover-V2: Verifier-integrated Reasoning for Formal Theorem Proving via Reinforcement Learning
Xingguang Ji, Yahui Liu, Qi Wang +7
We introduce our Leanabell-Prover-V2, a 7B large language models (LLMs) that can produce formal theorem proofs in Lean 4, with verifier-integrated Long Chain-of-Thoughts (CoT). Fol…
Evaluating Multimodal Large Language Models on Video Captioning via Monte Carlo Tree Search
Linhao Yu, Xinguang Ji, Yahui Liu +7
Video captioning can be used to assess the video understanding capabilities of Multimodal Large Language Models (MLLMs). However, existing benchmarks and evaluation protocols suffe…
Data Metabolism: An Efficient Data Design Schema For Vision Language Model
Jingyuan Zhang, Hongzhi Zhang, Zhou Haonan +7
Data curation plays a crucial role in training powerful Visual Language Models (VLMs). In this work, we introduce the concept of Data Metabolism and present our data-centric framew…
Capybara-OMNI: An Efficient Paradigm for Building Omni-Modal Language Models
Xingguang Ji, Jiakang Wang, Hongzhi Zhang +6
With the development of Multimodal Large Language Models (MLLMs), numerous outstanding accomplishments have emerged within the open-source community. Due to the complexity of creat…
Routing to the Right Expertise: A Trustworthy Judge for Instruction-based Image Editing
Chenxi Sun, Hongzhi Zhang, Qi Wang +1
Instruction-based Image Editing (IIE) models have made significantly improvement due to the progress of multimodal large language models (MLLMs) and diffusion models, which can und…
Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMs
Chenxi Sun, Hongzhi Zhang, Zijia Lin +8
Large language models have demonstrated exceptional capability in natural language understanding and generation. However, their generation speed is limited by the inherently sequen…