most citedMemory in the Age of AI Agents

1 citations · 4 across the 5 of their papers we have counts for

collaborators

9 papers

cs.AI2026

PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory

Zhifei Xie, Zongzheng Hu, Fangda Ye +10

Proactivity is a core expectation for AGI. Prior work remains largely confined to laboratory settings, leaving a clear gap in real-world proactive agent: depth, complexity, ambigui…

cs.LG20261 cited

Slow-Fast Inference: Training-Free Inference Acceleration via Within-Sentence Support Stability

Xingyu Xie, Zhaochen Yu, Yue Liao +3

Long-context autoregressive decoding remains expensive because each decoding step must repeatedly process a growing history. We observe a consistent pattern during decoding: within…

cs.CL20261 cited

Memory in the Age of AI Agents

Yuyang Hu, Shichun Liu, Yanwei Yue +44

Memory has emerged, and will continue to remain, a core capability of foundation model-based agents. As research on agent memory rapidly expands and attracts unprecedented attentio…

cs.CV2025

SAIL-RL: Guiding MLLMs in When and How to Think via Dual-Reward RL Tuning

Fangxun Shu, Yongjie Ye, Yue Liao +6

We introduce SAIL-RL, a reinforcement learning (RL) post-training framework that enhances the reasoning capabilities of multimodal large language models (MLLMs) by teaching them wh…

cs.CL20251 cited

Mini-Omni-Reasoner: Token-Level Thinking-in-Speaking in Large Speech Models

Zhifei Xie, Ziyang Ma, Zihang Liu +7

Reasoning is essential for effective communication and decision-making. While recent advances in LLMs and MLLMs have shown that incorporating explicit reasoning significantly impro…

cs.CV20251 cited

SAIL-VL2 Technical Report

Weijie Yin, Yongjie Ye, Fangxun Shu +11

We introduce SAIL-VL2, an open-suite vision-language foundation model (LVM) for comprehensive multimodal understanding and reasoning. As the successor to SAIL-VL, SAIL-VL2 achieves…