6 citations · 19 across the 34 of their papers we have counts for
11 papers · 1 filter
Context as a Tool: Context Management for Long-Horizon SWE-Agents
Shukai Liu, Jian Yang, Bo Jiang +4
Agents based on large language models have recently shown strong potential on real-world software engineering (SWE) tasks that require long-horizon interaction with repository-scal…
SLMQuant:Benchmarking Small Language Model Quantization for Practical Deployment
Jiacheng Wang, Yejun Zeng, Jinyang Guo +3
Despite the growing interest in Small Language Models (SLMs) as resource-efficient alternatives to Large Language Models (LLMs), their deployment on edge devices remains challengin…
MoDES: Accelerating Mixture-of-Experts Multimodal Large Language Models via Dynamic Expert Skipping
Yushi Huang, Zining Wang, Zhihang Yuan +5
Mixture-of-Experts (MoE) Multimodal large language models (MLLMs) excel at vision-language tasks, but they suffer from high computational inefficiency. To reduce inference overhead…
MAS: Self-Generative, Self-Configuring, Self-Rectifying Multi-Agent Systems
Kun Wang, Guibin Zhang, ManKit Ye +6
The past two years have witnessed the meteoric rise of Large Language Model (LLM)-powered multi-agent systems (MAS), which harness collective intelligence and exhibit a remarkable…
LLMC+: Benchmarking Vision-Language Model Compression with a Plug-and-play Toolkit
Chengtao Lv, Bilang Zhang, Yang Yong +7
Large Vision-Language Models (VLMs) exhibit impressive multi-modal capabilities but suffer from prohibitive computational and memory demands, due to their long visual token sequenc…
First-Order Error Matters: Accurate Compensation for Quantized Large Language Models
Xingyu Zheng, Haotong Qin, Yuye Li +5
Post-training quantization (PTQ) offers an efficient approach to compressing large language models (LLMs), significantly reducing memory access and computational costs. Existing co…
SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning
Hui Xie, Yuhe Liu, Shaoqi Yang +6
While deep spiking neural networks (SNNs) demonstrate superior performance, their deployment on resource-constrained neuromorphic hardware still remains challenging. Network prunin…
AGENTSAFE: Benchmarking the Safety of Embodied Agents on Hazardous Instructions
Zonghao Ying, Le Wang, Yisong Xiao +7
The integration of vision-language models (VLMs) is driving a new generation of embodied agents capable of operating in human-centered environments. However, as deployment expands,…
An Empirical Study of Qwen3 Quantization
Xingyu Zheng, Yuye Li, Haoran Chu +7
The Qwen series has emerged as a leading family of open-source Large Language Models (LLMs), demonstrating remarkable capabilities in natural language understanding tasks. With the…
Efficient Token Compression for Vision Transformer with Spatial Information Preserved
Junzhu Mao, Yang Shen, Jinyang Guo +2
Token compression is essential for reducing the computational and memory requirements of transformer models, enabling their deployment in resource-constrained environments. In this…
Dynamic Parallel Tree Search for Efficient LLM Reasoning
Yifu Ding, Wentao Jiang, Shunyu Liu +9
Tree of Thoughts (ToT) enhances Large Language Model (LLM) reasoning by structuring problem-solving as a spanning tree. However, recent methods focus on search accuracy while overl…