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Showing papers from UC San Diego Show all papers

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NeurIPS 2026U California, San DiegoUC San DiegoDeep RL

Safe Score Matching: Diffusion Policies with Hamilton-Jacobi Reachability for Online Safe Reinforcement Learning

Boyang Li, Matthew Kim, Sylvia Herbert

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

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NeurIPS 2026U California San DiegoUC San DiegoPruning & sparsity

Compressible Representations: Functional Spines in Deep Neural Networks

Niranjan Rajesh, Meenakshi Khosla

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

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NeurIPS 2026Simon FraserBeijing ForestryNECNEC Labs AmericaNEC Laboratories, AmericaVideo generation

HorizonComposer: Spatiotemporally Consistent Driving Video Editing with Enriched Traffic Semantics

Mauricio Soroco, Yuqiu Liu, Zaid Tasneem, Francesco Pittaluga and 4 more

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

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NeurIPS 2026U California, San DiegoUC San DiegoLLM pretraining & scaling laws

Geometrically Disentangling Concept Learning from the Language Modeling Loss

Yupei Wang, Neil R Mallinar, Misha Belkin, Alex Warstadt

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

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NeurIPS 2026Kyung HeeUC San DiegoUCSDVideo understanding

TRACE: Trajectory-Aware Conceps for Explainable Video Understanding

Wooil Lee, Junpyo Hong, Jongseo Lee, Tuomas Oikarinen and 2 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 California, San DiegoUC San DiegoMulti-agent LLM systems

Mental-Models for Multi-Agent Systems

Hanan Gani, Lulu Shao, Manmohan Chandraker

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

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NeurIPS 2026NortheasternU California, San DiegoU Maryland, College ParkUC San DiegoModel-based RL

Generalizing Action-Conditioned Latent World Models with Video Model Rewards

Haichao Zhang, Yijiang Li, Shwai He, Van T Le and 2 more

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

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NeurIPS 2026MBZUAI Institute of Foundation MMohamed bin Zayed University of Brigham YoungUC San DiegoU California, San DiegoOptimization

Concise Reasoning Through the Lens of Lagrangian Optimization

Chengqian Gao, Haonan Li, Taylor Killian, Jianshu She and 5 more

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

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NeurIPS 2026ETH ZürichMassachusetts Institute of TechnKU LeuvenMITTechnion - Israel Institute of TTime series foundation models

PhyTS: A Benchmark for Scientific Time Series

Benedict Armstrong, Jeroen Audenaert, Hannah P Binney, Alice Cheng and 22 more

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

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NeurIPS 2026U California, San DiegoUC San DiegoLLM agents & planning

Who Wrote This Paper? Autonomous Scientific Discovery for 3DGS Research

Seemandhar Jain, Manmohan Chandraker

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

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NeurIPS 2026U California, BerkeleyUC San DiegoRL for LLMs

Correct, Route, Calibrate: Efficient Preference Optimization from Noisy, Heterogeneous Human Feedback

Zhongming Xie, Xinwei Ma, Jingshen Wang

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

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NeurIPS 2026HarvardMassachusetts Institute of Technthe Kempner Institute for the StUC San DiegoU Cambridge3D vision & reconstruction

Unified Generative-Predictive Modeling for 4D Scene Understanding

Amani Kiruga, Zhiyi Li, Ruojin Cai, Hansen Lillemark and 3 more

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

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NeurIPS 2026UCSDAWS AI Labs UC San DiegoU California, San DiegoTool use & function calling

LLM Agents Already Know When to Call Tools - Even Without Reasoning

When2Tool finds LLMs linearly encode tool necessity in hidden states, and Probe&Prefill uses this to cut unnecessary tool calls by 48% with minimal accuracy loss.

Chung-En Sun, Linbo Liu, Ge Yan, Zimo Wang and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 2 on Hugging Face · Code ★ 16

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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 9/10
strict 4/5
86%Must read
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NeurIPS 2026OralUC San DiegoU TorontoNational University of SingaporeSichuanU VirginiaSynthetic data

DD-Ranking: Rethinking the Evaluation of Dataset Distillation

DD-Ranking reveals dataset distillation gains come from extra evaluation techniques rather than image quality, proposing fair metrics to assess true synthetic dataset value.

Zekai Li, Xinhao Zhong, Samir Khaki, Zhiyuan Liang and 36 more

Atlanta Poster Session 1, Wed, Dec 9, 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
88%Must read
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NeurIPS 2026U California, San DiegoU California San DiegoUC San DiegoUCSDDiffusion models

Distance Marching for Generative Modeling

Distance Marching improves time-unconditional generative models via distance-focused losses and inference, surpassing flow matching FID with fewer steps and aiding OOD detection.

Zimo Wang, Ishit Mehta, Haolin Lu, Xunpeng Huang and 4 more

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

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 3/5
medium 9/10
strict 3/5
72%Highly rated
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NeurIPS 2026UC San DiegoU California, San DiegoUC BerkeleyGoogle Deepmind / HarvardUC San Diego, AmazonVideo generation

Equilibrium Forcing: Adaptive Video Generation Without Noise Conditioning

Equilibrium Forcing removes noise conditioning from video diffusion to enable adaptive closed-loop inference that improves generation quality and consistency.

Hansen Lillemark, Alex Rojas, Zachary Novack, Runqian (Ray) Wang 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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AI panel: 8 of 20 reviewers recommend it
lenient 2/5
medium 5/10
strict 1/5
78%Highly rated
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NeurIPS 2026U California, San DiegoUC San DiegoU North Carolina at Chapel HillBytedance Seed; The University oUCSDEfficient inference & serving

SimSD: Simple Speculative Decoding in Diffusion Language Models

SimSD proposes a plug-and-play masking strategy that enables token-level speculative decoding in diffusion language models, achieving up to 7.46x faster throughput without training.

Junxia Cui, Haotian Ye, Runchu Tian, Hongcan Guo and 8 more

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

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AI panel: 11 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 1/5
88%Must read
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NeurIPS 2026SpotlightUC San DiegoShanghai JiaotongU Science and Technology of ChinJD.comZhejiangVideo-language models

AdaCodec: A Predictive Visual Code for Video MLLMs

AdaCodec uses predictive visual codes to send full reference frames only when unpredictable, cutting video MLLM tokens by 7x while improving long-video benchmark scores and reducing latency.

Haowen Hou, Zhen Huang, Zheming Liang, Qingyi Si and 7 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 7 on Hugging Face

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AI panel: 15 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 2/5
83%Must read
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NeurIPS 2026U California, San DiegoMassachusetts Institute of TechnUC San DiegoNorthwesternMila; University of Montreal; MIMusic

Live Music Diffusion Models: Efficient Fine-Tuning and Post-Training of Interactive Diffusion Music Generators

Live Music Diffusion Models modify diffusion inference with block-wise KV caching to surpass discrete autoregressive efficiency, enabling stable alignment via ARC-Forcing and real-time interactive generation on consumer hardware.

Zachary Novack, Stephen Brade, Haven Kim, Hugo Flores and 5 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 3 on Hugging Face · Code ★ 50

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AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 2/5
83%Must read
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NeurIPS 2026UC San DiegoU California, San DiegoTime series foundation models

Handwriting decoding as a challenging motor task for EEG Foundation Models

Handwriting decoding evaluates EEG foundation models, revealing confounded datasets and inferior performance versus smaller specialist models despite scaling.

Srinivas Ravishankar, Ishayu Ghosh, Nora Zajzon, Teng (Simon) Fei and 1 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 5/5
medium 7/10
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