1 citations · 1 across the 10 of their papers we have counts for
10 papers
LongR: Unleashing Long-Context Reasoning via Reinforcement Learning with Dense Utility Rewards
Qize Yu, Bowen Ping, Zijun Chen +6
Reinforcement Learning (RL) has emerged as a key driver for LLM reasoning. This capability is equally pivotal in long-context scenarios---such as long-dialogue understanding and st…
Live-Evo: Online Evolution of Agentic Memory from Continuous Feedback
Yaolun Zhang, Yiran Wu, Yijiong Yu +2
Large language model (LLM) agents are increasingly equipped with memory, which are stored experience and reusable guidance that can improve task-solving performance. Recent \emph{s…
Learning Adaptive Parallel Execution for Efficient Code Localization
Ke Xu, Siyang Xiao, Ming Liang +6
Code localization constitutes a key bottleneck in automated software development pipelines. While concurrent tool execution can enhance discovery speed, current agents demonstrate…
DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal
Peixuan Han, Yingjie Yu, Jingjun Xu +1
Despite the growing adoption of large language models (LLMs) in scientific research workflows, automated support for academic rebuttal, a crucial step in academic communication and…
Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents
Yi Yu, Liuyi Yao, Yuexiang Xie +4
Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows, making effective memory management critical. Existing method…
MemVerse: Multimodal Memory for Lifelong Learning Agents
Junming Liu, Yifei Sun, Weihua Cheng +11
Despite rapid progress in large-scale language and vision models, AI agents still suffer from a fundamental limitation: they cannot remember. Without reliable memory, agents catast…
Comparative Explanations via Counterfactual Reasoning in Recommendations
Yi Yu, Zhenxing Hu
Explainable recommendation through counterfactual reasoning seeks to identify the influential aspects of items in recommendations, which can then be used as explanations. However,…
Reallocating Attention Across Layers to Reduce Multimodal Hallucination
Haolang Lu, Bolun Chu, WeiYe Fu +7
Multimodal large reasoning models (MLRMs) often suffer from hallucinations that stem not only from insufficient visual grounding but also from imbalanced allocation between percept…
AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications
Dawei Gao, Zitao Li, Yuexiang Xie +20
Driven by rapid advancements of Large Language Models (LLMs), agents are empowered to combine intrinsic knowledge with dynamic tool use, greatly enhancing their capacity to address…
One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs
Yinghui Li, Jiayi Kuang, Haojing Huang +10
Leveraging mathematical Large Language Models (LLMs) for proof generation is a fundamental topic in LLMs research. We argue that the ability of current LLMs to prove statements lar…