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Home/Authors/Feng Gao

Feng Gao

10 indexed papers

Recent (6 mo)
10
With code
0
Influential cites
0
Benchmarked
0

Publications per year

10
26

Top categories

AI×7Vision×4NLP×3ML×2Crypto×2Signal Processing×1Comp. Eng.×1Multiagent×1

Frequent co-authors

Baolin Peng2×
Qianhui Wu2×
Hao Cheng2×
Wenlin Yao2×
Jianfeng Gao2×
Ye Sun2×

Research Timeline

2026
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses

This survey provides a comprehensive, structured review of safety research in Embodied AI, analyzing attacks and defenses across the entire embodied pipeline to guide the development of safe, robust, and reliable real-world agents.

DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models

DarkLLM introduces a novel framework that uses a Large Language Model (LLM) to translate natural language instructions into flexible, latent adversarial attack vectors, demonstrating a systemic vulnerability across diverse foundation models.

LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning

LegalGraphRAG introduces a multi-agent, hierarchical graph retrieval-augmented generation framework to overcome the limitations of traditional RAG in legal domains, achieving state-of-the-art reliable legal reasoning.

NICE: A Theory-Grounded Diagnostic Benchmark for Social Intelligence of LLMs

The paper introduces NICE, a novel, theory-grounded diagnostic benchmark for assessing the social intelligence of LLMs, which reveals that current frontier models consistently struggle with specific facets of communication.

PTCG-Bench: Can LLM Agents Master Pokémon Trading Card Game?

The paper introduces PTCG-Bench, a new benchmark using the Pokémon TCG to evaluate LLM agents' strategic decision-making and ability to self-evolve, finding that sustained self-evolution remains challenging.

CamGeo: Sparse Camera-Conditioned Image-to-Video Generation with 3D Geometry Priors

CamGeo is a novel framework that improves sparse camera-conditioned image-to-video generation by distilling rich 3D geometric priors into the diffusion backbone, resulting in geometrically consistent motion.

OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents

The paper introduces OpenWebRL, an open framework that enables training visual web agents using online multi-turn Reinforcement Learning directly on live websites, achieving state-of-the-art performance on challenging web benchmarks.

OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators

This paper proposes OPSD-V, an on-policy self-distillation method for reducing long-horizon degradation in few-step autoregressive video diffusion models by introducing real long-video data as temporal context during training.

OpenForgeRL: Train Harness-native Agents in Any Environment

OpenForgeRL is an open-source framework for training harness-based AI agents end-to-end in various environments using a lightweight proxy and Kubernetes orchestrator.

A Foundation Model for Cross-Band CSI Reconstruction

This paper proposes a model for cross-band CSI reconstruction in multi-band low-altitude wireless systems using radio-frequency metadata and pilot-guided cross-attention.

Highlighted terms show continued research focus across papers

Papers

eess.SPEmpiricalRecentJul 24, 2026

A Foundation Model for Cross-Band CSI Reconstruction

Hongpu Zhang, Shu Sun, Ruifeng Gao, Tongjia Zhang +1 more

This paper proposes a model for cross-band CSI reconstruction in multi-band low-altitude wireless systems using radio-frequency metadata and pilot-guided cross-attention.

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cs.AIcs.CLEmpiricalRecent
Jul 23, 2026

OpenForgeRL: Train Harness-native Agents in Any Environment

Xiao Yu, Baolin Peng, Ruize Xu, Hao Zou +6 more

OpenForgeRL is an open-source framework for training harness-based AI agents end-to-end in various environments using a lightweight proxy and Kubernetes orchestrator.

View →
cs.CVEmpiricalRecentJul 9, 2026

OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators

Hongyu Liu, Chun Wang, Feng Gao, Xuanhua He +5 more

This paper proposes OPSD-V, an on-policy self-distillation method for reducing long-horizon degradation in few-step autoregressive video diffusion models by introducing real long-video data as tempora…

View →
cs.LGcs.AIcs.CLRecentJun 1, 2026

OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents

Rui Yang, Qianhui Wu, Yuxi Chen, Hao Bai +6 more

The paper introduces OpenWebRL, an open framework that enables training visual web agents using online multi-turn Reinforcement Learning directly on live websites, achieving state-of-the-art performan…

View →
cs.CERecentMay 29, 2026

CamGeo: Sparse Camera-Conditioned Image-to-Video Generation with 3D Geometry Priors

Xuanyi Liu, Deyi Ji, Liqun Liu, Lanyun Zhu +7 more

CamGeo is a novel framework that improves sparse camera-conditioned image-to-video generation by distilling rich 3D geometric priors into the diffusion backbone, resulting in geometrically consistent…

View →
cs.AIRecentMay 28, 2026

NICE: A Theory-Grounded Diagnostic Benchmark for Social Intelligence of LLMs

Yunjin Qi, Zhaojun Jiang, Xuan Wu, Hanxi Pan +9 more

The paper introduces NICE, a novel, theory-grounded diagnostic benchmark for assessing the social intelligence of LLMs, which reveals that current frontier models consistently struggle with specific f…

View →
cs.AIRecentMay 28, 2026

PTCG-Bench: Can LLM Agents Master Pokémon Trading Card Game?

Dongdong Hua, Yifei Sun, Renhong Huang, Feng Gao +2 more

The paper introduces PTCG-Bench, a new benchmark using the Pokémon TCG to evaluate LLM agents' strategic decision-making and ability to self-evolve, finding that sustained self-evolution remains chall…

View →
cs.CLcs.AIcs.MARecentMay 27, 2026

LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning

Zerui Chen, Qinggang Zhang, Zhishang Xiang, Zhimin Wei +4 more

LegalGraphRAG introduces a multi-agent, hierarchical graph retrieval-augmented generation framework to overcome the limitations of traditional RAG in legal domains, achieving state-of-the-art reliable…

View →
cs.CRcs.AIcs.CVRecentMay 15, 2026

DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models

Ye Sun, Xin Wang, Jiaming Zhang, Yifeng Gao +6 more

DarkLLM introduces a novel framework that uses a Large Language Model (LLM) to translate natural language instructions into flexible, latent adversarial attack vectors, demonstrating a systemic vulner…

View →
cs.CRcs.AIcs.CVRecentMar 28, 2026

Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses

Xiao Li, Xiang Zheng, Yifeng Gao, Xinyu Xia +34 more

This survey provides a comprehensive, structured review of safety research in Embodied AI, analyzing attacks and defenses across the entire embodied pipeline to guide the development of safe, robust,…

View →