4 citations · 9 across the 17 of their papers we have counts for
8 papers · 1 filter
ARC-KV: Amortizing Anchor Search for Reconstruction-Based KV Cache Compaction
Zheyu Shen, Guanhua Wang, Dezhan Tu +5
Long-context large language model inference is bottlenecked by KV caches that grow linearly with sequence length. This burden is especially severe for long, reusable context prefix…
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
Ang Li, Ben Liu, Bin Han +215
Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve,…
Multi-Token Residual Prediction
Yufeng Xu, Zishuo Bao, Qian Wang +6
Diffusion Language Models (DLMs) generate text by iteratively denoising masked token sequences, offering a tradeoff between parallelism and quality compared to autoregressive model…
HiMed: Incentivizing Hindi Reasoning in Medical LLMs
Dingfeng Jiang, Han Yan, Chenze Ma +12
Medical large language models hold promise for reducing healthcare disparities, yet Hindi remains severely underrepresented. While medical LLMs excel in high-resource languages, th…
Spectral-Sphere-Constrained Hyper-Connections
Zhaoyi Liu, Haichuan Zhang, Ang Li
Hyper-Connections (HC) extend residual connections into multiple streams, employing residual matrices for cross-stream mixing to enrich model expressivity. However, unconstrained m…
MoEless: Efficient MoE LLM Serving with Serverless Experts
Hanfei Yu, Bei Ouyang, Shwai He +2
Large Language Models (LLMs) increasingly adopt Mixture-of-Experts (MoE) architectures to scale efficiently under stringent resource constraints. However, MoE's sparse activation c…
Demystifying When Pruning Works via Representation Hierarchies
Shwai He, Guoheng Sun, Haichao Zhang +2
Network pruning, which removes less important parameters or architectures, is often expected to improve efficiency while preserving performance. However, this expectation does not…
ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model
Haichao Zhang, Yijiang Li, Shwai He +5
Recent progress in latent world models (e.g., V-JEPA2) has shown promising capability in forecasting future world states from video observations. Nevertheless, dense prediction fro…