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20242026
most citedTowards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions

1 citations · 2 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2026

MMDeepResearch-Bench: A Benchmark for Multimodal Deep Research Agents

Peizhou Huang, Zixuan Zhong, Zhongwei Wan +12

Deep Research Agents (DRAs) generate citation-rich reports via multi-step search and synthesis, yet existing benchmarks mainly target text-only settings or short-form multimodal QA…

cs.CV2025

Trustworthy and Fair SkinGPT-R1 for Democratizing Dermatological Reasoning across Diverse Ethnicities

Yuhao Shen, Zhangtianyi Chen, Yuanhao He +14

The clinical translation of dermatological AI is hindered by opaque reasoning and systematic performance disparities across skin tones. Here we present SkinGPT-R1, a multimodal lar…

cs.DC2025

DySTop

Yizhou Shi, Qianpiao Ma, Yan Xu +4

Federated Learning (FL) has emerged as a potential distributed learning paradigm that enables model training on edge devices (i.e., workers) while preserving data privacy. However,…

cs.LG2025

FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Data

Yushan Zhao, Jinyuan He, Donglai Chen +5

Federated learning (FL) is a decentralized collaborative machine learning (ML) technique. It provides a solution to the issues of isolated data islands and data privacy leakage in…

cs.LG2025★ 1 cited

CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs

Lejla Skelic, Yan Xu, Matthew Cox +3

The role of Large Language Models (LLMs) has not been extensively explored in analog circuit design, which could benefit from a reasoning-based approach that transcends traditional…

cs.CL2024★ 1 cited

Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions

Angana Borah, Rada Mihalcea

As Large Language Models (LLMs) continue to evolve, they are increasingly being employed in numerous studies to simulate societies and execute diverse social tasks. However, LLMs a…