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Showing papers from Arizona State University Show all papers

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arXivU VirginiaArizona StateIn-context learning & prompting

MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning

MAPLE uses influence-based pseudo-labeling to adaptively select many-shot ICL demonstrations, boosting LLM performance without extensive labeling costs.

Zihan Chen, Song Wang, Zhen Tan, Jundong Li and 1 more

Published May 22, 2025 · 0 citations

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AAAI 2025U VirginiaArizona StateU North Carolina at Chapel HillFlorida StateGraph neural networks

BrainMAP: Learning Multiple Activation Pathways in Brain Networks

BrainMAP learns multiple brain network activation pathways via sequential models and Mixture-of-Experts, improving fMRI analysis and interpretability.

Song Wang, Zhenyu Lei, Zhen Tan, Jiaqi Ding and 7 more

Published Apr 11, 2025 · 2 citations

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medium 1/10
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arXivArizona StateHallucination & factuality

Tuning-Free Accountable Intervention for LLM Deployment -- A Metacognitive Approach

CLEAR enables LLMs to self-identify and correct errors via concept-specific sparse subnetworks without tuning, improving deployment accountability.

Zhen Wah Tan, Jie Peng, Tianlong Chen, Huan Liu

Published Mar 8, 2024 · 3 citations

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lenient 4/5
medium 1/10
strict 0/5
69%Highly rated
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NeurIPS 2026RiceArizona StateU North Carolina at Chapel HillDepartment of Computer Science, Shanghai Artificial IntelligenceMultimodal reasoning

Do More Modalities Always Help? A Geometric Perspective on Missing-Modality Robustness

Songyuan Sui, Zhen Tan, Mohan Zhang, Rana M Khan and 2 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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lenient 1/5
medium 1/10
strict 1/5
67%Highly rated
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NeurIPS 2026Arizona StateAmazonEmbodied agents

AndroidReality: How Far Are Mobile Agents from the Real World?

Xiaoou Liu, Longchao Da, Hanyang Chen, Yuan Ling and 1 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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45%Niche pick
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NeurIPS 2026PrincetonArizona StateMicrosoftAgent benchmarks & environments

Synthetic Tasks for Training AutoResearch

Ziyang Cai, Seyyedamirhossein Saeidi, Harkirat Singh Behl

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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69%Highly rated
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NeurIPS 2026U North Carolina at Chapel HillArizona State University of North Carolina atPrivacy

Guarding the Life Code: Preserving Membership Privacy in Genomic Foundation Models

Xinyu Zhao, Jinhao Duan, Zhen Tan, Tianlong Chen

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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lenient 2/5
medium 1/10
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NeurIPS 2026U Maryland, Baltimore CountyArizona StateVrije Universiteit AmsterdamRL for LLMs

Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation

Yuxuan Jiang, Runchao Li, Shubhashis Roy Dipta, Dawei Li and 1 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Arizona StateU Central FloridaMulti-agent RL

CAFE: Causally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learning

Arun Vignesh Malarkkan, Wangyang Ying, Hongyu Cao, Dongjie Wang and 1 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Arizona StateU Texas at AustinLink prediction

From Link Prediction to Linear Community Detection: A Finite-Sample Guarantee for Graph Pretraining

Ying Chen, Zhangyang "Atlas" Wang, Yixuan He

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

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NeurIPS 2026U SydneyAustralian NationalArizona StateU North Carolina at Chapel HillMohamed bin Zayed University of LLM reasoning & chain of thought

Chain-of-Thought Oversight Should Not Treat Faithfulness as Monitorability

Sichao Li, Sai Ma, Xiyang Hu, Chudi Zhong and 1 more

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

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lenient 1/5
medium 0/10
strict 1/5
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NeurIPS 2026AppleVecML Inc.Arizona StateGraph generation

Robust Graph Diffusion Model

Yancheng Wang, Ping Li, Dongfang Sun, Yingzhen Yang

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Arizona StateAmazonAlignment & preference optimization

Steering Vectors as a Training Signal in LLM Post-Training

Tiejin Chen, Maunil R Vyas, Huaiyuan Yao, Hua Wei

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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57%Worth a look
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NeurIPS 2026SolverXKorea Advanced Institute of ScieSeoul NationalTelepixArizona StatePhysics-informed ML & PDEs

PDE-PFN: Prior-Data Fitted Neural PDE Solver

Jaehyeon Park, Mingu Kang, Dongseok Lee, Woojin Cho and 4 more

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

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medium 0/10
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57%Worth a look
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NeurIPS 2026Arizona StateUnited States Military AcademyModel-based RL

Near-Optimal Sample Complexity of Robust Reinforcement Learning with KL Uncertainty Set

Yudan Wang, Zilong Deng, Nathaniel D Bastian, Shaofeng Zou

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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lenient 1/5
medium 0/10
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83%Must read
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NeurIPS 2026Arizona StateGeorgia Institute of TechnologyGeorgia Tech Research CorporatioObject detection

Flow Mismatching: Unsupervised Anomaly Detection via Velocity Discrepancies in Flow Matching Models

Flow Mismatching detects anomalies via velocity discrepancies between normal flow dynamics and geometric paths to test images, yielding pixel heatmaps and image scores without test-time optimization and achieving state-of-the-art results.

Shengzhe Chen, Mehrdad Moradi, Kamran Paynabar, Hao Yan

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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lenient 4/5
medium 8/10
strict 1/5
88%Must read
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NeurIPS 2026Taylor GeospatialMicrosoft AI for Good Research LTechnical University of MunichMila & NASA HarvestCarletonRemote sensing

No One Knows the State-of-the-Art in Geospatial Foundation Models

Geospatial foundation model literature lacks standardized evaluation protocols, causing widespread cross-paper scoring discrepancies and unreleased weights, so six concrete community standards are proposed.

Isaac Corley, Caleb Robinson, Nils Lehmann, Gabriel Tseng and 5 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 5 on Hugging Face · Code ★ 27

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AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 3/5
71%Highly rated
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NeurIPS 2026Arizona StateLearning theory

Even Sharper Bounds for Transductive Learning and Its Applications

STLC improves transductive local complexity bounds via modified log-Sobolev and entropy closure, matching inductive rates without extra logarithmic factors and yielding sharper kernel learning bounds.

Yingzhen Yang

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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lenient 1/5
medium 4/10
strict 1/5
89%Must read
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NeurIPS 2026U California, DavisUC DavisArizona StateMorgan StanleyRecommender systems

ModelLens: Finding the Best for Your Task from Myriads of Models

ModelLens learns a latent space over model-dataset-metric tuples from noisy leaderboard data to rank unseen models on unseen datasets without target evaluation, improving routing by up to 81%.

Rui Cai, Wenjie Mo, Xiaofei Wen, Qiyao Ma and 4 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 14 on Hugging Face · Code ★ 130

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AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 2/5
88%Must read
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NeurIPS 2026Arizona StateAppleLLM agents & planning

From History to State: Constant-Context Skill Learning for LLM Agents

Constant-context skill learning embeds recurring agent workflows into lightweight modules via step-level SFT and online RL, cutting prompt tokens 2-7x while matching state-of-the-art success on ALFWorld, WebShop, and SciWorld.

Haoyang Xie, Xinyuan Wang, Yancheng Wang, Puda Zhao and 1 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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lenient 5/5
medium 8/10
strict 2/5
83%Must read
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NeurIPS 2026U Central FloridaArizona StateDARPA; University of Colorado BoRL for LLMs

Robust Nash Alignment under Preference Uncertainty

Robust Nash Alignment optimizes policies via a four-player game against uncertain preference kernels, yielding certified worst-case bounds and near-optimal convergence.

Shihab Ahmed, Debamita Ghosh, David Tang, Yudan Wang and 2 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 3/5
86%Must read
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NeurIPS 2026Arizona StateHarvardETHZ - ETH ZurichWatermarking & AI-text detection

ArcMark: Distortion-Free Multi-Byte LLM Watermark via Optimal Transport

ArcMark embeds multiple bytes into LLM text without distorting next-token distributions by formulating distortion-free watermarking as channel coding and deriving its information-theoretic capacity. It reliably encodes several bytes into a few hundred tokens and outperforms competing multi-bit water

Atefeh Gilani, Sajani Vithana, Carol X Long, Oliver Kosut and 2 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 1/5
86%Must read
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NeurIPS 2026U Southern CaliforniaNorthwesternU Illinois at ChicagoArizona StateJailbreaks & red teaming

Cat-DPO: Category-Adaptive Safety Alignment

Cat-DPO applies per-category adaptive safety margins to direct preference optimization, improving aggregate safety and reducing worst-category harm gaps across models.

Tiankai Yang, Yi Nian, Xinyuan Li, Ruiyao Xu and 5 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 1/5
76%Highly rated
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NeurIPS 2026ClemsonRochester Institute of TechnologAccentureArizona StateNortheasternRL for LLMs

StaRPO: Stability-Augmented Reinforcement Policy Optimization

StaRPO improves reasoning via stability-augmented RL using autocorrelation and path-efficiency rewards, boosting accuracy and logical consistency.

Jinghan Zhang, Fengran Mo, Tharindu Cyril Weerasooriya, Ruimin Dai and 4 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 1/5