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Home/Authors/Zhou Liu

Zhou Liu

4 indexed papers

Recent (6 mo)
4
With code
0
Influential cites
0
Benchmarked
0

Publications per year

4
26

Top categories

AI×4Signal Processing×1Robotics×1ML×1Systems and Control×1NLP×1

Frequent co-authors

Wentao Zhang2×
Lizhou Liu1×
Xiaohui Chen1×
Zihan Tang1×
Mengyao Ma1×
Wenyi Zhang1×

Research Timeline

2026
TRACER: Turn-level Regret Matching with Inner Reinforcement Credit for Cooperative Multi-LLM Reasoning

TRACER introduces a novel turn-level reinforcement framework that enables cooperative multi-LLM reasoning by separating decision-making into a regret-matching controller and a generation-credit layer.

Source-Grounded Semantic Reinforcement Learning for Low-Resource Target-Language Generation

The paper introduces Source-Grounded Semantic Reinforcement Learning (SG-SRL), a framework that leverages abundant source-language monolingual data to improve target-language generation in low-resource settings by providing cross-lingual semantic supervision.

FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning

This paper presents a data-driven method to estimate external joint torques without dedicated force sensors, enabling force-feedback teleoperation on low-cost arms.

Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completion

This paper proposes a unified RF map construction framework using physics-informed neural networks and graph neural networks, achieving high-fidelity RF map construction under sparse observations.

Highlighted terms show continued research focus across papers

Papers

eess.SPcs.AIEmpiricalRecentJul 2, 2026

Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completion

Lizhou Liu, Xiaohui Chen, Zihan Tang, Mengyao Ma +1 more

This paper proposes a unified RF map construction framework using physics-informed neural networks and graph neural networks, achieving high-fidelity RF map construction under sparse observations.

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cs.ROcs.AIcs.LGEmpirical
Recent
Jun 10, 2026

FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning

Steven Oh, Jason Jingzhou Liu, Tony Tao, Philip Han +4 more

This paper presents a data-driven method to estimate external joint torques without dedicated force sensors, enabling force-feedback teleoperation on low-cost arms.

View →
cs.CLcs.AIRecentMay 28, 2026

Source-Grounded Semantic Reinforcement Learning for Low-Resource Target-Language Generation

Zeli Su, Ziyin Zhang, Zewei Pan, Zhou Liu +7 more

The paper introduces Source-Grounded Semantic Reinforcement Learning (SG-SRL), a framework that leverages abundant source-language monolingual data to improve target-language generation in low-resourc…

View →
cs.AIRecentMay 27, 2026

TRACER: Turn-level Regret Matching with Inner Reinforcement Credit for Cooperative Multi-LLM Reasoning

Chusen Li, Zhou Liu, Shuigeng Zhou, Wentao Zhang

TRACER introduces a novel turn-level reinforcement framework that enables cooperative multi-LLM reasoning by separating decision-making into a regret-matching controller and a generation-credit layer.

View →