collaborators

6 papers

cs.CV2026

Visual-OPSD: Cross-Modal On-Policy Self-Distillation for Efficient Unified Multimodal Reasoning

Pengyu Li, Zhitao Gao, Lingling Zhang +4

Unified multimodal models (UMMs) interleave generated ''visual thoughts'' (VTs) with text reasoning to improve spatial tasks. This incurs roughly an order-of-magnitude inference co…

cs.LG2026

Finding Kissing Numbers with Game-theoretic Reinforcement Learning

Chengdong Ma, Théo Tao Zhaowei, Pengyu Li +7

Since Isaac Newton first studied the Kissing Number Problem in 1694, determining the maximal number of non-overlapping spheres around a central sphere has remained a defining chall…

cs.LG2026

PathFinder: Advancing Path Loss Prediction for Single-to-Multi-Transmitter Scenario

Zhijie Zhong, Zhiwen Yu, Pengyu Li +3

Radio path loss prediction (RPP) is critical for optimizing 5G networks and enabling IoT, smart city, and similar applications. However, current deep learning-based RPP methods lac…

cs.AI2026

LogicGraph : Benchmarking Multi-Path Logical Reasoning via Neuro-Symbolic Generation and Verification

Yanrui Wu, Lingling Zhang, Xinyu Zhang +5

Evaluations of large language models (LLMs) primarily emphasize convergent logical reasoning, where success is defined by producing a single correct proof. However, many real-world…

cs.CL2026

AERO: Autonomous Evolutionary Reasoning Optimization via Endogenous Dual-Loop Feedback

Zhitao Gao, Jie Ma, Xuhong Li +5

Large Language Models (LLMs) have achieved significant success in complex reasoning but remain bottlenecked by reliance on expert-annotated data and external verifiers. While exist…

cs.LG2026

$\textbf{AGT$^{AO}$}$: Robust and Stabilized LLM Unlearning via Adversarial Gating Training with Adaptive Orthogonality

Pengyu Li, Lingling Zhang, Zhitao Gao +5

While Large Language Models (LLMs) have achieved remarkable capabilities, they unintentionally memorize sensitive data, posing critical privacy and security risks. Machine unlearni…