1 citations · 1 across the 8 of their papers we have counts for
13 papers
When Is a Memory Safe Enough to Act On? Action-Conditioned Verification for Long-Lived Agents
Yiqi Wang, Yifei Wang, Jiaqi Zhang +4
Long-lived agents act on memory carried across sessions, and many of their actions are irreversible, so a stale memory can turn directly into harm. Existing work improves the memor…
Learning What to Ask for Tomorrow: Active Preference Acquisition for Long-Term Personalized Web Agents
Yiqi Wang, Zizhi Zeng, Jiaqi Zhang +3
Web agents increasingly serve the same user across repeated online tasks such as booking travel, shopping, and scheduling, so their long-term usefulness depends on how well they kn…
PACMI: Provenance-Aware Cascading Memory Invalidation for Long-Term LLM Agents
Yiqi Wang, Jiaqi Liu, Jiaqi Zhang +4
LLM agents rely on long-term memory to retain and reuse information when performing tasks over long horizons. Existing methods provide limited support for handling memories that be…
Self-Confirming Superposition Traps in Reinforcement Learning
Dai Shi, Andi Han, Feng Chen +3
Reinforcement learning (RL) trains representations on data selected by the agent's policy, which then uses the resulting returns to guide its next choices. We show that this loop c…
A Benchmark and Diagnostic Study of Epistemic Admission in Shared Agent Memory
Xiaoyang Li, Yiqi Wang, Chencheng Zhu +6
Evaluating claim admission in shared agent memory is challenging because repeated claims may be mistaken for independent evidence. An agent may copy or paraphrase a retrieved belie…
When Does Execution Provenance Help Agent Memory Retrieval?
Yiqi Wang, Jinqian Ju, Jiaqi Zhang +4
A language agent's execution history can exceed its context window, requiring its memory system to retrieve complete supporting evidence under a hard token budget. Evidence may spa…
IndustrialVLA-Bench: A Traceable Multi-Axis Evaluation of Open Robot Policy Models
Yiqi Wang, Zhifeng Rao, Jiaqi Zhang +7
Open robot policies increasingly follow two paradigms: vision-language-action models (VLAs) directly map observations and instructions to actions, whereas world-action models (WAMs…
From Faulty Memories to Corrected Actions: Dependency-Guided Rollback Repair for Memory-Augmented Agents
Caili Yu, Yiqi Wang, Jiaqi Zhang +5
Persistent memory lets language model agents reuse information across sessions, but it also makes errors durable: a poisoned, stale, or misattributed record can alter reasoning, to…
ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles
Yitian Yang, Yiqun Duan, Linghan Huang +4
Large language model (LLM)-based multi-agent simulation offers a powerful testbed for studying social opinion dynamics. Yet current approaches often adopt two contrasting methods:…
CogniFold: Always-On Proactive Memory via Cognitive Folding
Suli Wang, Yiqun Duan, Yu Deng +6
Existing agent memory remains predominantly reactive and retrieval-based, lacking the capacity to autonomously organize experience into persistent cognitive structure. Toward genui…
ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning
Jingwei Song, Meng Chen, Jie Xiao +15
Reinforcement learning (RL) is a critical stage in post-training large language models (LLMs), involving repeated interaction between rollout generation, reward evaluation, and cen…
NeuroTTT: Bridging Pretraining-Downstream Task Misalignment in EEG Foundation Models via Test-Time Training
Suli Wang, Yangshen Deng, Zhenghua Bao +2
Large-scale foundation models for EEG signals offer a promising path to generalizable brain-computer interface (BCI) applications, but they often suffer from misalignment between p…
SEDM: Selective Self-Evolving Distributed Memory for Agents
Yichen Liu, Haoran Xu, Jiacong Hu +20
Agents in a society can reuse each other's experience, but indiscriminate memory use increases processing cost and can reduce task success. We present SEDM (Selective Self-Evolving…