11 papers
Experience Funnel: A State-Policy Alternating Loop for Self-Evolving Agents
Wenbo Gao, Zhaomou Song, Zhiyuan Ji +7
Autonomous agents powered by large language models (LLMs) continuously accumulate experience through interaction, creating an opportunity to improve future behavior through self-ev…
PSD: Pushing the Pareto Frontier of Diffusion LLMs via Parallel Speculative Decoding
Shengyin Sun, Yiming Li, Renxi Liu +7
Diffusion large language models (dLLMs) generate text by iteratively denoising masked token sequences. Although dLLMs can predict all masked positions in parallel within each step,…
MemDLM: Memory-Enhanced DLM Training
Zehua Pei, Hui-Ling Zhen, Weizhe Lin +4
Diffusion Language Models (DLMs) offer attractive advantages over Auto-Regressive (AR) models, such as full-attention parallel decoding and flexible generation. However, standard D…
AgentCollab: A Self-Evaluation-Driven Collaboration Paradigm for Efficient LLM Agents
Wenbo Gao, Renxi Liu, Xian Wang +8
Autonomous agents powered by large language models (LLMs) perform complex tasks through long-horizon reasoning and tool interaction, where a fundamental trade-off arises between ex…
DLLM Agent: See Farther, Run Faster
Huiling Zhen, Weizhe Lin, Renxi Liu +15
Diffusion large language models (DLLMs) have emerged as an alternative to autoregressive (AR) decoding with appealing efficiency and modeling properties, yet their implications for…
Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats
Pengxiang Zhao, Hui-Ling Zhen, Xing Li +10
As LLMs scale, low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. In this work, we evaluate HiFloat (HiF8 and HiF4), a family…
JuDi: Revisiting Judge Decoding from First Principles via Training-Free Distributional Divergence
Shengyin Sun, Yiming Li, Renxi Liu +5
Judge Decoding accelerates LLM inference by relaxing the strict verification of Speculative Decoding, yet it typically relies on expensive and noisy supervision. In this work, we r…
EPD-Serve: A Flexible Multimodal EPD Disaggregation Inference Serving System On Ascend
Fan Bai, Pai Peng, Zhengzhi Tang +8
With the widespread adoption of large multimodal models, efficient inference across text, image, audio, and video modalities has become critical. However, existing multimodal infer…
Towards Efficient Agents: A Co-Design of Inference Architecture and System
Weizhe Lin, Hui-Ling Zhen, Shuai Yang +14
The rapid development of large language model (LLM)-based agents has unlocked new possibilities for autonomous multi-turn reasoning and tool-augmented decision-making. However, the…
Scaling Up, Speeding Up: A Benchmark of Speculative Decoding for Efficient LLM Test-Time Scaling
Shengyin Sun, Yiming Li, Xing Li +8
Test-time scaling has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs) by allocating additional computational resources durin…
TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling
Weizhe Lin, Xing Li, Zhiyuan Yang +7
Large Reasoning Models (LRMs) demonstrate exceptional capability in tackling complex mathematical, logical, and coding tasks by leveraging extended Chain-of-Thought (CoT) reasoning…