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Showing papers from Huawei Noah's Ark Lab Show all papers

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NeurIPS 2026U Science and Technology of ChinHuawei Technologies Ltd.TianjinHuawei Noah's Ark LabRL for LLMs

Efficient Gradient-Aware Asynchronous Reinforcement Learning for LLM Post-Training

Songhan Yang, Jie Wang, Yinqi Bai, Tong Xialiang and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Renmin University of ChinaHuawei Noah's Ark LabHuawei Technologies Ltd.LLM evaluation & benchmarks

How Does Personalized Memory Shape LLM Behavior? Benchmarking Rational Preference Utilization in Personalized Assistants

Xueyang Feng, Weinan Gan, Xu Chen, Quanyu Dai and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Shanghai JiaotongXiamenDalian University of TechnologyNankaiHarbin EngineeringAgent benchmarks & environments

PCBInnoBench: Benchmarking LLM Agents on Real-World PCB Design

Fuyuan Xia, Jiaxin Hu, Kewei Yu, Zihan Zhang and 12 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Renmin University of ChinaHuawei Technologies Ltd.Huawei Noah's Ark LabWeb & GUI agents

From Local Skills to Long-Horizon Tasks: Progressive Skill Exploration for LLM Web Agents

Xueyang Feng, Xiaohe Bo, Xu Chen, Quanyu Dai and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026PekingCity University of Hong KongHuawei Technologies Ltd.Huawei Noah's Ark LabRL for LLMs

LADDERS: Length-Aware Data Distribution and Existing-Response Speculation for Fast RL Rollout Generation

Shengpeng Yin, Hui-Ling Zhen, Xing Li, Mingxuan Yuan and 2 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Science and Technology of ChinHuawei Noah's Ark LabSynthetic data

Opt-Arena: Evaluating, Selecting, and Generating Optimization Modeling Data via Tripartite Graphs

Yian Xu, Xiongwei Han, Jie Wang, Haoyang Liu and 3 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026HKUST/HuaweiHuawei Technologies Ltd.Huawei Tech. Investment Co. Ltd.Hong Kong University of Science Huawei Noah's Ark LabMechanistic interpretability

GEM: Interpretable Language Models via Geometric Embedding Alignment

Dimitrios Tsaras, Yankun Hong, Lei Chen, Zhiyao Xie and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026City University of Hong KongHuawei Noah's Ark LabHuawei Technologies Ltd.Southern University of Science aLLM reasoning & chain of thought

Insight-Driven Search: A Framework for Multi-objective Automated Heuristic Design with Large Language Models

Shunyu Yao, Fei Liu, Ji Cheng, Mingxuan Yuan and 3 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Huawei Technologies Ltd.HuaweiHuawei Noah's Ark LabSelf-employedNoah's ark labQuantization

HiFloat4 Format for Language Model Pre-training on Ascend NPUs

HiFloat4 enables stable FP4 LLM pretraining without stabilization stacks, achieving 1.55% relative loss versus 1.79% for MXFP4 and 2.00% for NVFP4 on Ascend NPUs.

Mehran Taghian Jazi, Yunke Peng, Xing Huang, Yao Wang and 21 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 3/5
76%Highly rated
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NeurIPS 2026Huawei Technologies Ltd.City University of Hong KongNoah Ark LabU the Chinese Academy of ScienceHuawei Noah's Ark LabTest-time compute

PSD: Pushing the Pareto Frontier of Diffusion LLMs via Parallel Speculative Decoding

PSD accelerates diffusion LLM inference via adaptive parallel unmasking and multi-depth speculative drafts with hierarchical verification, achieving up to 5.5x tokens per pass with near-greedy accuracy.

Shengyin Sun, Yiming Li, Renxi Liu, Xinqi Li and 6 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 4/10
strict 2/5
76%Highly rated
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NeurIPS 2026Department of Computer Science aCity University of Hong KongHuawei Technologies Ltd.The Chinese University of Hong KTokenRhythm TechnologiesLong-context modeling

MemDLM: Memory-Enhanced DLM Training

MemDLM augments diffusion language model training via bi-level optimization with parametric memory, improving convergence, long-context representations, and needle retrieval.

Zehua Pei, Hui-Ling Zhen, Weizhe Lin, Sinno Pan and 3 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 3 on Hugging Face · Code ★ 11

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lenient 4/5
medium 6/10
strict 0/5
86%Must read
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NeurIPS 2026Institute of Artificial IntelligHuawei Noah's Ark LabBeijing University of AeronauticWasedaU Science and Technology BeijingEmbodied agents

AlloSpatial: Agentic Harness Framework for Spatial Reasoning in Foundation Models

AlloSpatial is an agentic framework that converts egocentric observations into allocentric spatial priors via cognitive mapping and reasoning harnesses, improving spatial reasoning by 5%-18% and outperforming larger general-purpose models.

Shouwei Ruan, Bin Wang, Zhenyu Wu, Qihui Zhu and 4 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 4 on Hugging Face · Code ★ 21

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AI panel: 14 of 20 reviewers recommend it
lenient 4/5
medium 10/10
strict 0/5
86%Must read
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NeurIPS 2026City University of Hong KongU SydneySoutheastHuawei Noah's Ark LabCity University of Hong KongMultimodal hallucination

Revealing Epistemic Uncertainty in MLLMs via Causal-Invariant Masking

Causal-Invariant Masking decomposes MLLM uncertainty via semantic divergence to capture epistemic limitations, and Expected Embedding Drift accelerates quantification by nearly 50%.

Haoyang Luo, Linwei Tao, Jie Gui, Xinghao Chen and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 0/5
91%Must read
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NeurIPS 2026McGillHuawei Noah's Ark LabLLM reasoning & chain of thought

Pause and Reflect: Conformal Aggregation for Chain-of-Thought Reasoning

A conformal procedure for chain-of-thought reasoning replaces majority voting with calibrated weighted aggregation to provide finite-sample confident-error guarantees and improves selective accuracy without retraining.

Yu Gu, Zijun Yu, Vahid Partovi Nia, Masoud Asgharian

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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18/20 AI panelreviewers recommend it

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AI panel: 18 of 20 reviewers recommend it
lenient 5/5
medium 10/10
strict 3/5