activity
20242026
most citedAPEER: Automatic Prompt Engineering Enhances Large Language Model Reranking

14 citations · 14 across the 15 of their papers we have counts for

collaborators

16 papers

cs.LG2026

Unlocking Lossless Speedups in LLMs via Discrete Diffusion

Subham Sekhar Sahoo, Lingjie Chen, Khiem Pham +14

Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressive (AR) structure requires slow, sequential token generation. To overco…

cs.CV2026

HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models

Feng He, Zhenting Wang, Qifan Wang +4

Hallucinations in vision language models (VLMs) are commonly treated as semantic errors, yet they often arise from partial or ambiguous visual evidence. Prior work mainly focuses o…

cs.CL2026

SLIM-RL: Risk-Budgeted Random-Masking RL for Diffusion LLMs Without Trajectory Slicing

Ruikang Zhao, Zhenting Wang, Han Gao +1

Reinforcement learning for diffusion large language models (dLLMs) has largely moved to trajectory-aware methods. The current state of the art, TraceRL, holds that random masking i…

cs.AI2026

Evidence Over Plans: Online Trajectory Verification for Skill Distillation

Yang Zhou, Zihan Dong, Zhenting Wang +7

Agent skills can remarkably improve task success rates by using human-written procedural documents, but their quality is difficult to assess without environment-grounded verificati…

cs.CV2026

UniVL: Unified Vision-Language Embedding for Spatially Grounded Contextual Image Generation

Jiayun Wang, Yu Wang, Weijie Gan +2

We introduce spatially grounded contextual image generation, a controllable image generation task that reframes the conditioning paradigm. Instead of supplying a reference image an…

cs.CL2026

MemGym: a Long-Horizon Memory Environment for LLM Agents

Wujiang Xu, Yu Wang, Kai Mei +8

Memory is a central capability for LLM agents operating across long-horizon tasks. Existing memory benchmarks predominantly evaluate retention of personalized information in multi-…

cs.CL2026

A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models

Zeru Shi, Zhenting Wang, Fan Yang +2

We investigate the origins of massive activations in large language models (LLMs) and identify a specific layer named the \textbf{Massive Emergence Layer (ME Layer)}, that is consi…

cs.AI2026

Reasoning over Precedents Alongside Statutes: Case-Augmented Deliberative Alignment for LLM Safety

Can Jin, Rui Wu, Tong Che +10

Ensuring that Large Language Models (LLMs) adhere to safety principles without refusing benign requests remains a significant challenge. While OpenAI introduces deliberative alignm…

cs.AI2025

M^3-Bench: Multi-Modal, Multi-Hop, Multi-Threaded Tool-Using MLLM Agent Benchmark

Yang Zhou, Mingyu Zhao, Zhenting Wang +6

We present M^3-Bench, the first benchmark for evaluating multimodal tool use under the Model Context Protocol. The benchmark targets realistic, multi-hop and multi-threaded workflo…

cs.CV2025

LED: LLM Enhanced Open-Vocabulary Object Detection without Human Curated Data Generation

Yang Zhou, Shiyu Zhao, Yuxiao Chen +3

Large foundation models trained on large-scale vision-language data can boost Open-Vocabulary Object Detection (OVD) via synthetic training data, yet the hand-crafted pipelines oft…

cs.CV2025

Can Large Vision-Language Models Detect Images Copyright Infringement from GenAI?

Qipan Xu, Zhenting Wang, Xiaoxiao He +2

Generative AI models, renowned for their ability to synthesize high-quality content, have sparked growing concerns over the improper generation of copyright-protected material. Whi…

cs.CV2025

The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models via Visual Information Steering

Zhuowei Li, Haizhou Shi, Yunhe Gao +7

Large Vision-Language Models (LVLMs) can reason effectively over both textual and visual inputs, but they tend to hallucinate syntactically coherent yet visually ungrounded content…

cs.CV2025

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation

Can Jin, Ying Li, Mingyu Zhao +6

Visual prompting has gained popularity as a method for adapting pre-trained models to specific tasks, particularly in the realm of parameter-efficient tuning. However, existing vis…

cs.CV2025

MLLM-as-a-Judge for Image Safety without Human Labeling

Zhenting Wang, Shuming Hu, Shiyu Zhao +12

Image content safety has become a significant challenge with the rise of visual media on online platforms. Meanwhile, in the age of AI-generated content (AIGC), many image generati…

cs.CV2024

Accelerating Multimodal Large Language Models by Searching Optimal Vision Token Reduction

Shiyu Zhao, Zhenting Wang, Felix Juefei-Xu +7

Prevailing Multimodal Large Language Models (MLLMs) encode the input image(s) as vision tokens and feed them into the language backbone, similar to how Large Language Models (LLMs)…

cs.AI2024★ 14 cited

APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking

Can Jin, Hongwu Peng, Shiyu Zhao +7

Large Language Models (LLMs) have significantly enhanced Information Retrieval (IR) across various modules, such as reranking. Despite impressive performance, current zero-shot rel…