1 citations · 1 across the 10 of their papers we have counts for
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Contagious Misinterpretations: Social Learning with Shared Interpretations
Pol Rodríguez Farrés, Jesus Cerquides
When people discuss which of several hypotheses is true, they share not only their beliefs but also their interpretation of each one, that is, the distribution of signals each hypo…
UniOPSD: Unifying Outcome and Hindsight Feedback for Agentic Reinforcement Learning
Zenghuang Fu, Zhaoyang Li, Qiuyuan Ai +8
Reinforcement learning has become an effective approach to training language model agents, but sparse and delayed outcome rewards provide limited guidance for credit assignment acr…
SIPO: Selective-Inference Policy Optimization for Tree-Structured Agentic RL
Zenghuang Fu, Ningqi Chen, Mingda Jia +10
Tree-structured reinforcement learning trains search agents by comparing alternative continuations and propagating terminal rewards to intermediate decisions. Adaptive expansion, h…
VisionHOPE: Visual Backbones as Self-Modifying Learning Systems
Siran Peng, Tianshuo Zhang, Tianyu Fu +8
Visual backbones have evolved from Convolutional Neural Networks (CNNs) with local aggregation to Vision Transformers (ViTs) with global interactions, State-Space Models (SSMs) wit…
AR-WAM: A Visual-Conditioned Agent-Ready World Action Model for Robotic Manipulation
Yicheng Jiang, Zesen Gan, Xiaobo Wang +8
As AI agents become increasingly capable, agent-driven robotic control is emerging as a compelling paradigm. However, prevailing vision-language-action (VLA) models and world actio…
LiLa-WAM: Lightweight Latent Reasoning World-Action Model for Robotic Manipulation
Fan Yang, Yuting Su, Xiaobo Wang +7
World-action modeling has emerged as a promising paradigm for robotic control, as it empowers models to go beyond reacting to observations and anticipate how a scene will evolve. H…
CoEvoKG: Co-Evolving Knowledge Graphs with Self-Evolving Search Agents
Zhaoyang Li, Zenghuang Fu, Qiuyuan Ai +6
Large language models can improve with reinforcement learning for search agents, yet existing self play agents repeatedly generate tasks while discarding the knowledge gained durin…
Self-Play Meets Skill Evolution: Self-Evolving Search Agents that Pose, Solve, and Remember
Zenghuang Fu, Zhaoyang Li, Qiuyuan Ai +6
Self-play agents can generate training problems without questions from target benchmarks, but their curricula lack persistent state: failures affect gradients yet do not explicitly…
Self-Supervised Skill Optimization
Siran Peng, Cuiyu Yang, Tianyu Fu +9
Agent skills provide frozen large language model (LLM) agents with reusable procedural guidance, and recent work shows that such skills can be optimized with ground-truth (GT) feed…
Dive Into the Implicit Biases of Low-rank Vision-language Alignment
Mingjia Shi, Shuo Wang, Xiaobo Wang +7
Vision-language alignment, the stage that bridges pretrained vision encoders and large language models, is widely treated as a form of pretraining requiring full-parameter updates.…
WebRetriever: A Large-Scale Comprehensive Benchmark for Efficient Web Agent Evaluation
Wei Dong, Tianyu Fu, Zhe Yu +9
As web agents increasingly demonstrate capabilities in automated task execution, the development of robust evaluation frameworks for assessing their navigation and task completion…
UPA: Unsupervised Prompt Agent via Tree-Based Search and Selection
Siran Peng, Weisong Zhao, Tianyu Fu +6
Prompt agents have recently emerged as a promising paradigm for automated prompt optimization, framing prompt discovery as a sequential decision-making problem over a structured pr…