most citedSwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems

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

collaborators

5 papers

cs.IR2026

Very Efficient Listwise Multimodal Reranking for Long Documents

Yiqun Sun, Pengfei Wei, Lawrence B. Hsieh

Listwise reranking is a key yet computationally expensive component in vision-centric retrieval and multimodal retrieval-augmented generation (M-RAG) over long documents. While rec…

cs.AI2026

MedMemoryBench: Benchmarking Agent Memory in Personalized Healthcare

Yihao Wang, Haoran Xu, Renjie Gu +10

The large-scale deployment of personalized healthcare agents demands memory mechanisms that are exceptionally precise, safe, and capable of long-term clinical tracking. However, ex…

cs.LG2026

Rethinking Random Transformers as Adaptive Sequence Smoothers for Sleep Staging

Guisong Liu, Xin Gao, Martin Dresler +2

Automatic sleep staging commonly adopts Transformers under the assumption that they learn complex long-range dependencies. We challenge this view by revealing a neglected property…

cs.AI2026

OrchMAS: Orchestrated Reasoning with Multi Collaborative Heterogeneous Scientific Expert Structured Agents

Yichao Feng, Haoran Luo, Zhenghong Lin +4

Multi-agent large language model frameworks are promising for complex multi step reasoning, yet existing systems remain weak for scientific and knowledge intensive domains due to s…

cs.LG2024★ 1 cited

SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems

Kailash Gogineni, Sai Santosh Dayapule, Juan Gómez-Luna +6

Reinforcement Learning (RL) trains agents to learn optimal behavior by maximizing reward signals from experience datasets. However, RL training often faces memory limitations, lead…