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Showing papers from The University of Texas at Austin Show all papers

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arXivThe University of Texas at AustiZhejiangHong Kong PolytechnicU Science and Technology of ChinBeijing University of Posts and LLM reasoning & chain of thought

On the Geometry of On-Policy Distillation

On-policy distillation updates occupy a sparse, low-dimensional parameter subspace that is functionally sufficient and geometrically distinct from supervised fine-tuning and reinforcement learning.

Zhennan Shen, Yanshu Li, Qingyu Yin, Chak Tou Leong and 5 more

Published Jun 5, 2026 · 0 citations · ▲ 75 on Hugging Face

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NeurIPS 2025WorkshopU EdinburghThe University of Texas at AustiMulti-agent RL

HyperMARL: Adaptive Hypernetworks for Multi-Agent RL

HyperMARL uses agent-conditioned hypernetworks to generate agent-specific parameters that decouple gradients, reducing variance and preserving behavioral diversity across multi-agent benchmarks without added complexity.

Kale-ab Tessera, Arrasy Rahman, Amos Storkey, Stefano Albrecht

Published 2025 · 0 citations

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arXivU WashingtonThe University of Texas at AustiAllen Institute for Artificial IAlignment & preference optimization

Varying Shades of Wrong: Aligning LLMs with Wrong Answers Only

LLMs distinguish degrees of wrongness among incorrect answers, and alignment with such preferences yields less wrong answers and better calibration.

Jihan Yao, Wenxuan Ding, Shangbin Feng, Lucy Lu Wang and 1 more

Published Oct 14, 2024 · 0 citations · Code ★ 10

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71%Highly rated
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EMNLP 2024U WashingtonThe University of Texas at AustiMicrosoftHallucination & factuality

Teaching LLMs to Abstain across Languages via Multilingual Feedback

Multilingual feedback teaches LLMs to abstain from answering in low-resource languages and improves cross-lingual abstention without degrading performance.

Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding and 5 more

Published 2024 · 4 citations

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45%Niche pick
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NeurIPS 2026U CambridgeU Texas at AustinThe University of Texas at AustiUT AustinBayesian & probabilistic methods

One-Shot Private Confidence Regions via Resampling

Po-Ling Loh, Debepsita Mukherjee, Shourya Pandey, Purnamrita Sarkar

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U Texas at AustinThe University of Texas at AustiDistribution shift & OOD detection

Robust-by-Design Distributional Learning from Contaminated Samples

Nevena Gligić, Arya Farahi

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026ETHZ - ETH ZurichUT AustinU Texas at AustinEPFL - EPF LausanneETH ZurichFeature attribution

Parasite Features: Causal Abstraction Through Spurious Pathways

Denis Sutter, Qing Yao, Sasha Boguraev, Julian Minder and 4 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026U Texas at AustinThe University of Texas at AustiMaterials & chemistry

MaterialsSaddles: 34 Million Transition States and a Flow-Matching Saddle-Point Predictor for Materials

Ilgar Baghishov, Sung Hoon Jung, Graeme Henkelman

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

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NeurIPS 2026The University of Texas at AustiCarletonUBC and Vector InstituteU British ColumbiaUBC / VectorVideo understanding

LookWhen? Fast Video Recognition by Learning When, Where, and What to Compute

Ali Salamatian, Anthony Fuller, Pritam Sarkar, James Green 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 2026The University of Texas at AustiSapienza university of RomeGoogle DeepMindU Texas, AustinSapienza University of RomeProteins

Anchored Protein Engineering

Chi Zhang, Maria Rosaria Briglia, Litu Rout, Jeffrey Ouyang-Zhang and 4 more

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

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NeurIPS 2026U Texas at AustinU Texas, AustinThe University of Texas at AustiTogether AI/UT AustinPruning & sparsity

LIPAR: Latent Inter-Frame Pruning with Attention Recovery

Dennis Y Menn, Yuedong Yang, Bokun Wang, Xiwen Wei and 5 more

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

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NeurIPS 2026The University of Texas at AustiUT AustinU Texas at AustinThe University of Texas, AustinTime series foundation models

TSFM Meets LLM: Context as Covariate

Hemanth Ram Govindarajan Kirubaharan, Sai Shankar Narasimhan, Shubhankar Agarwal, Sandeep Chinchali

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

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NeurIPS 2026The University of Texas at AustiU Texas at AustinVideo-language models

AdaAlloc: Adaptive Visual Token Allocation for Long-Video Question Answering

Joungbin An, Kristen Grauman

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

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NeurIPS 2026The University of Texas at AustiNortheasternMATS ProgramU OxfordColumbiaAlignment & preference optimization

Persona Vectors: Monitoring and Controlling Character Traits in Language Models

Runjin Chen, Andy Arditi, Henry Sleight, Owain Evans 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 2026UT AustinThe University of Texas at AustiU Texas at AustinUT Austin / NVIDIADeep RL

Meta-Reinforcement Learning with Zero-Shot Reinforcement Learning

Jake Grigsby, Siddhant Agarwal, Yu Lei, Leonidas Varveropoulos and 1 more

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

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NeurIPS 2026The University of Texas at AustiUT-AustinTest-time compute

Enabling approximate joint sampling in diffusion LMs

A lightweight sampler layer on frozen diffusion LMs approximates joint token sampling, yielding MAUVE 0.87 versus 0.31 when unmasking four tokens per step.

Parikshit Bansal, Sujay Sanghavi

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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lenient 4/5
medium 7/10
strict 2/5
78%Highly rated
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NeurIPS 2026UT Austin/UC BerkeleyColumbiaThe University of Texas at AustiUnified multimodal models

Fine-tuning language encoding models on slow fMRI improves prediction for fast ECoG

Fine-tuning language models on slow fMRI improves fast ECoG encoding predictions despite lower temporal resolution, with performance scaling with fMRI data volume.

Aditya Vaidya, Richard Antonello, Alexander Huth

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

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lenient 5/5
medium 4/10
strict 2/5
72%Highly rated
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NeurIPS 2026Hanoi University of Science and PurdueU Massachusetts at AmherstThe University of Texas at AustiU Texas at AustinDeep learning theory

A Statistical Theory of Gated Attention through the Lens of Hierarchical Mixture of Experts

Gated attention represents attention matrices as hierarchical mixtures of experts and achieves polynomial sample complexity versus exponential for multi-head self-attention.

Viet Nguyen, Thinh Cao, Tuan M Pham, Tan Dinh and 3 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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lenient 2/5
medium 4/10
strict 2/5
76%Highly rated
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NeurIPS 2026The University of Texas at AustiUT-AustinTest-time compute

Token Time Continuous Diffusion for Language Modeling

Token time continuous diffusion deterministically maps continuous noise to tokens with per-token times, outperforming discrete diffusion models at high speedups.

Parikshit Bansal, Sujay Sanghavi

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

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lenient 4/5
medium 6/10
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80%Must read
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NeurIPS 2026SpotlightUT AustinU Texas, AustinThe University of Texas at AustiU Texas at AustinU.S. DEVCOM Army Research LaboraQuantization

CoreQ: Learning-Free Mismatch Correction and Successive Rounding for Quantization

CoreQ proposes a learning-free post-training quantization framework using a geometric closed-form layer-adaptive mismatch correction coefficient and successive rounding to improve LLM quantization accuracy without hyperparameter tuning.

Seohyeon Cha, Huancheng Chen, Dongjun Kim, Haoran Zhang and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 2 on Hugging Face

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 0/5
74%Highly rated
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NeurIPS 2026KoreaNew YorkThe University of Texas at AustiBostonMachine unlearning

Consistency-Preserving Concept Erasure via Unsafe–Safe Pairing and Directional Fisher-weighted Adaptation

PAIR reframes diffusion model concept erasure via unsafe-safe pairs, using paired semantic realignment and directional Fisher-weighted adaptation to remove targeted concepts while preserving structural and semantic consistency.

Yongwoo Kim, Sungmin Cha, Hyunsoo Kim, Jaewon Lee 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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lenient 5/5
medium 4/10
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78%Highly rated
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NeurIPS 2026SpotlightUT Austin | Google DeepMindThe University of Texas at AustiU Texas at AustinGoogleNanyang TechnologicalMulti-agent RL

ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork

ROTATE reformulates ad hoc teamwork as open-ended adversarial training between an agent and teammate generator to probe collaboration deficiencies, substantially improving generalization to unseen partners.

Caroline Wang, Muhammad Arrasy Rahman, Benjamin Nativi, Johnny Liu and 3 more

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

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lenient 4/5
medium 6/10
strict 1/5
76%Highly rated
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NeurIPS 2026U Tübingen / Boehringer IngelheiThe University of Texas at AustiAIDOS Lab, Department of ComputeEberhard-Karls-Universität TübinProbing & representation analysis

From Topology to Retrieval: Decoding Embedding Spaces with Unified Signatures

Topological and geometric embedding measures are redundant, so Unified Topological Signatures holistically characterize spaces to predict model properties and retrieval performance.

Florian Rottach, William Rudman, Bastian Rieck, Harrisen Scells and 1 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026The University of Texas at AustiAmazonU Texas at AustinTool use & function calling

VESTA: Visual Exploration with Statistical Tool Agents

VESTA equips vision-language models with dynamically growing statistical toolkits for data exploration and model refinement, outperforming baselines on complex domain-specific modeling tasks via reusable diagnostic tools.

William Rudman, Abhishek Divekar, Kanishk Jain, Sebastian Joseph and 5 more

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

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AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 1/5