Ax Pengrun Huang, Kamalika Chaudhuri, Yu-Xiang Wang 5/11/2026

Dataset Watermarking for Closed LLMs with Provable Detection

Dataset watermarking technique for closed LLMs enabling provable detection of proprietary or benchmark data usage.

Ax Fred Zhangzhi Peng, Alexis Fox, Anru R. Zhang, Alexander Tong 5/11/2026

Don't Retrain, Align: Adapting Autoregressive LMs to Diffusion LMs via Representation Alignment

Method to adapt autoregressive LMs to diffusion LMs via representation alignment without retraining.

Ax Shang-Ling Hsu, Mark Tenzer, Cyrus Shahabi, Khurram Shafique 5/11/2026

TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond

TraXion proposes new pre-training framework for mobility data reflecting structural properties of human trajectories rather than importing language modeling objectives.

Ax Ziming Li, Jiatan Huang, Xiaoguang Guo, Guilin Wang, Chuxu Zhang 5/11/2026

Same Signal, Opposite Meaning: Direction-Informed Adaptive Learning for LLM Agents

Direction-informed adaptive test-time compute for LLM agents uses directional signal interpretation to decide when additional computation improves performance.

Ax Chacha Chen, Matthew J\"orke, Adam Goli\'nski, Masha Fedzechkina, Guillermo Sapiro, Sinead Williamson, Nicholas Foti 5/11/2026

LLMs are not (consistently) Bayesian: Quantifying internal (in)consistencies of LLMs' probabilistic beliefs

Studies internal inconsistencies in LLM probabilistic reasoning, analyzing whether LLMs update beliefs consistently with Bayesian principles as evidence changes.

Ax Fan Xu, Yuan Gao, Kun Wang, Rui Su, Fenghua Ling, Hao Wu, Wanli Ouyang 5/11/2026

Tyche: One Step Flow for Efficient Probabilistic Weather Forecasting

Tyche efficient probabilistic weather forecasting using one-step flow models, reducing inference cost compared to diffusion-based ensemble methods.

Ax Eshed Gal, Uri Ascher, Eldad Haber 5/11/2026

Target-Aware Data Augmentation for SAT Prediction

Target-aware data augmentation for SAT prediction reduces labeling costs by improving learning-based solvers for NP-hard Boolean satisfiability problems.

Ax Sepideh Avizeh, Tushin Mallick, Alina Oprea, Cristina Nita-Rotaru, Reihaneh Safavi-Naini 5/11/2026

MAGIQ: A Post-Quantum Multi-Agentic AI Governance System with Provable Security

MAGIQ proposes multi-agentic AI governance system with post-quantum cryptography for secure agent communication and accountability.

Ax Giansalvo Cirrincione, Adriano Fagiolini 5/11/2026

Learned Lyapunov Shielding for Adaptive Control

Learned Lyapunov shielding augments adaptive control with learned quadratic Lyapunov functions and physics-informed neural networks for safety filtering.

Ax Teo Susnjak 5/11/2026

A Reproducible Optimisation Protocol for Calibrating Prompt-Based Large Language Model Workflows in Evidence Synthesis

Reproducible calibration workflow for prompt-based LLMs in evidence synthesis tasks, separating task rules from mutable prompt harness with explicit metrics.

Ax James Fiedler 5/11/2026

Bias and Uncertainty in LLM-as-a-Judge Estimation

Analyzes systematic bias in LLM-as-a-Judge evaluation, proposing estimators to correct bias and improve calibration stability for model comparisons.

Ax Shivaram Subramanian, Zhengliang Xue, Markus Ettl, Yingdong Lu, Jayant Kalagnanam 5/11/2026

Causal-Aware Foundation-Model for Bilevel Optimization in Discrete Choice Settings

C3PO network applies causal-aware foundation models to bilevel optimization for dynamic pricing and assortment selection in discrete choice settings.

Ax Steven Song, Sahil Sethi, Brett Beaulieu-Jones, Robert L. Grossman 5/11/2026

ProtoSSL: Interpretable Prototype Learning from Unlabeled Time-Series Data

ProtoSSL enables interpretable time-series prediction through prototype learning from unlabeled data, providing case-based explanations.

Ax Chenhui Xu, Ziyue Bai, Fuxun Yu, Heng Huang, Jinjun Xiong 5/11/2026

Rollback-Free Stable Brick Structures Generation

RL-based approach for generating physically stable brick structures without external simulators, using learned constraint satisfaction during generation.

Ax Victor Livernoche, Jie Zan, Reihaneh Rabbany 5/11/2026

Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection

Kurtosis-guided denoising score matching method for detecting anomalies in tabular data by learning score functions from noise-corrupted samples.

Ax Di Wu, Chengshuai Shi, Jing Yang, Cong Shen 5/11/2026

$f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses

Unified theoretical analysis of f-divergence regularization in RLHF for LLM post-training, exploring alternatives to reverse KL divergence.

Ax Jonathn Chang, Arya Datla, Ziv Goldfeld 5/11/2026

PLOT: Progressive Localization via Optimal Transport in Neural Causal Abstraction

PLOT advances causal abstraction for neural network interpretability using optimal transport for efficient localization of relevant neural sites.

Ax Ahmed K. Kadhim, Lei Jiao, Rishad Shafik, Ole-Christoffer Granmo, Mayur Kishor Shende 5/11/2026

FastOmniTMAE: Parallel Clause Learning for Scalable and Hardware-Efficient Tsetlin Embeddings

FastOmniTMAE proposes parallel clause learning for efficient Tsetlin Machine embeddings in NLP, offering interpretable alternative to BERT and Word2Vec.

Ax Federico Echenique, Alireza Fallah, Baihe Huang, Michael I. Jordan 5/11/2026

Response Time Enhances Alignment with Heterogeneous Preferences

Framework using response time alongside binary choice data to align LLMs with heterogeneous user preferences without pooling feedback.

Ax Yonatan Slutzky, Yotam Alexander, Tomer Slor, Yoav Nagel, Nadav Cohen 5/11/2026

Why Does Agentic Safety Fail to Generalize Across Tasks?

Analysis of why safety measures in multi-task AI agents fail to generalize despite successful task execution generalization.

Ax Anupama Sridhar, Alexander Johansen 5/11/2026

Echo: KV-Cache-Free Associative Recall with Spectral Koopman Operators

Echo: KV-cache-free method using spectral Koopman operators for associative recall in long-context reasoning and tool-calling.

Ax Tianxin Zhou, Xiang Li, Haibing Lu 5/11/2026

Inductive Power Grid Cascading Failure Analysis with GRU-Gated Graph Attention

GRU-gated Graph Attention Network for identifying vulnerable transmission lines and predicting cascading failures in power grids.

Ax Da Long, Lingyi Fu, Diya Michelle Rao, Jasmine Ruales Carrera, Yang Bai, Shandian Zhe 5/11/2026

Dual-Agent Co-Training for Health Coaching via Implicit Adversarial Preference Optimization

Dual-agent framework using implicit adversarial preference optimization to improve AI health coaches based on motivational interviewing.

Ax Mahdi Erfanian, Nelson Daniel Troncoso, Aashna Garg, Amabel Gale, Xiaoyu Liu, Pareesa Ameneh Golnari, Shengyu Fu 5/11/2026

Delulu: A Verified Multi-Lingual Benchmark for Code Hallucination Detection in Fill-in-the-Middle Tasks

Delulu: Verified multi-lingual benchmark of 1,951 code hallucination samples in fill-in-the-middle tasks across 7 languages.

Ax Montaser Mohammedalamen, Kevin Roice, Reginald McLean, Alyssa Lefaivre \v{S}kopac 5/11/2026

A Systematic Investigation of The RL-Jailbreaker in LLMs

Mechanistic study of how RL-based adversarial attacks successfully jailbreak LLMs through multi-step sequential optimization.

Ax Aditya Sai Ellendula, Yi Wang, Chandrajit Bajaj 5/11/2026

Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation

Port-Hamiltonian approach to risk-aware navigation policies that adapt evasive maneuvers based on local scene context.

Ax Minghao Yan, Bo Peng, Benjamin Coleman, Ziqi Chen, Zhouhang Xie, Shuo Chen, Zhankui He, Noveen Sachdeva, Weili Wang, Ed H. Chi, Shivaram Venkataraman, Wang-Cheng Kang, Derek Zhiyuan Cheng, Beidou Wang 5/11/2026

PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents

PACEevolve++: Reinforcement learning framework enabling test-time policy adaptation for LLM-driven evolutionary search agents.

Ax Yi He, Xingyu Zhou 5/11/2026

Towards Differentially Private Reinforcement Learning with General Function Approximation

Theoretical framework for differentially private reinforcement learning with general function approximation beyond tabular/linear settings.

Ax Jiamin Xu, Jacqueline Maasch, Kyra Gan 5/11/2026

Integrating Causal DAGs in Deep RL: Activating Minimal Markovian States with Multi-Order Exposure

Method for discovering minimal Markovian states from causal DAGs in reinforcement learning without assuming states are pre-provided.

Ax Pingbang Hu, Xueshen Liu, Z. Morley Mao, Jiaqi W. Ma 5/11/2026

Dr. Post-Training: A Data Regularization Perspective on LLM Post-Training

Dr. Post-Training framework reconceptualizes data selection in LLM fine-tuning as regularization to prevent overfitting on scarce target data.

Ax Rui Cai, Weijie Jacky Mo, Xiaofei Wen, Qiyao Ma, Wenhui Zhu, Xiwen Chen, Muhao Chen, Zhe Zhao 5/11/2026

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

Framework for selecting best pretrained model for new tasks from thousands of open-source models using transferability estimation without expensive evaluation.

Ax Zekun Wang, Anant Gupta, Tianyi Zhu, Christopher J. MacLellan 5/11/2026

Test-Time Compositional Generalization in Diffusion Models via Concept Discovery

Approach for discovering and composing learned concepts from diffusion model score functions at test time for compositional generation.

Ax Majid Molaei, Gabor Paczolay, Matteo Papini, Alberto Maria Metelli, Marcello Restelli 5/11/2026

Actor-Critic with Active Importance Sampling

Actor-critic algorithm optimizing behavior policy via importance sampling to reduce variance in policy gradient estimation.

Ax Hayden Helm, Ben Johnson, Carey Priebe 5/11/2026

Query-efficient model evaluation using cached responses

Method for efficient model evaluation using cached responses from previously-evaluated models to reduce queries needed for benchmark assessment.

Ax Lucas Monteiro Paes, Natalie Mackraz, Barry-John Theobald, Federico Danieli 5/11/2026

Theoretical Limits of Language Model Alignment

Theoretical analysis of fundamental limits on reward improvement for LLM alignment via RL and best-of-N selection methods.

Ax Hao Chen, Chendi Qian, Christopher Morris, Andrea Lodi, Can Li 5/11/2026

Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks

Graph neural network approach for solving max-cut combinatorial optimization within branch-and-bound using learned semidefinite relaxations.

Ax Tao Wang, Shuo Li, Yan Sun, Dongsheng Ding, Edgar Dobriban 5/11/2026

Where to Spend Rollouts: Hit-Utility Optimal Rollout Allocation for Group-Based RLVR

Optimal rollout allocation strategy for group-based RLVR improving LLM reasoning by dynamically distributing compute based on prompt saturation.

Ax Minseok Kim, Yeongjong Kim, Namkyeong Cho, Yeoneung Kim 5/11/2026

Stabilized neural Hamilton--Jacobi--Bellman solvers: Error analysis and applications in model-based reinforcement learning

Error analysis and applications of neural solvers for Hamilton-Jacobi-Bellman equations in continuous-time model-based reinforcement learning.

Ax Zixuan Xie, Xinyu Liu, Rohan Chandra, Shangtong Zhang 5/11/2026

Convergence and Emergence of In-Context Reinforcement Learning with Chain of Thought

Theoretical analysis of in-context reinforcement learning with chain-of-thought, explaining convergence and emergence of adaptation capabilities at inference time.

Ax Zhanqi Zhang, Hua-Dong Xiong, Robert C. Wilson, Mikio Aoi, Marcelo G. Mattar, Li Ji-An 5/11/2026

The Position Curse: LLMs Struggle to Locate the Last Few Items in a List

Empirical study revealing LLMs fail at retrieving last items in short lists despite strong few-shot performance, characterizing the 'Position Curse' failure mode.

Ax Yash Ingle, Jaival Chauhan, Ankit Yadav, Sudhakar Mishra 5/11/2026

Adaptive Negative Reinforcement for LLM Reasoning:Dynamically Balancing Correction and Diversity in RLVR

Adaptive negative reinforcement method for improving LLM reasoning by dynamically balancing correction of errors and diversity in sampled trajectories.

Ax Nikhilesh Prabhakar, Varun Balaji, Athresh Karanam, Kristian Kersting, Sriraam Natarajan 5/11/2026

Neurosymbolic Imitation Learning with Human Guidance: A Privileged Information Approach

Neurosymbolic imitation learning combining neural networks with symbolic reasoning using privileged human guidance information.

Ax Guankai Li, Jiabin Chen, Yi Xu, Xichen Zhang, Yuan Lu 5/11/2026

HyperEyes: Dual-Grained Efficiency-Aware Reinforcement Learning for Parallel Multimodal Search Agents

Parallel multimodal search agent using reinforcement learning to dispatch concurrent queries with efficiency awareness.

Ax Ali Taghibakhshi, Ruisi Cai, Saurav Muralidharan, Sharath Turuvekere Sreenivas, Aditya Vavre, Ameya Sunil Mahabaleshwarkar, Bilal Kartal, Sheldon Liang, Marcin Chochowski, Zijia Chen, Akhiad Bercovich, Ran Zilberstein, Ran El-Yaniv, Yonatan Geifman, Daniel Korzekwa, Yoshi Suhara, Oluwatobi Olabiyi, Ashwath Aithal, Nima Tajbakhsh, Pavlo Molchanov 5/11/2026

Star Elastic: Many-in-One Reasoning LLMs with Efficient Budget Control

Post-training method for creating multiple nested reasoning LLM variants efficiently with computational budget control.

Ax Fred Zhangzhi Peng, Avishek Joey Bose, Anru R. Zhang, Alexander Tong 5/11/2026

Coupling Models for One-Step Discrete Generation

One-step generative model for discrete sequences using coupling between discrete structures and Gaussian latent variables.

Ax Ryan Thompson, He Zhao, Daniel M. Steinberg, Edwin V. Bonilla 5/11/2026

Arrow: A Foundation Model for Causal Discovery

Foundation model for zero-shot causal discovery on tabular data using transformer architecture with structured graph inference.

Ax Jianrong Ding, Jianyuan Zhong, Zhengyan Shi, Qiang Xu 5/11/2026

FAME: Forecasting Academic Impact via Continuous-Time Manifold Evolution

Framework for forecasting academic research impact using LLMs, evaluating frontier models on prospective manuscript evaluation.

Ax Hongyu Cheng, Yinghao Zheng, Marco Molinaro, Amitabh Basu 5/11/2026

Sample Complexity of Stochastic Optimization with Integer Variables

Sample complexity analysis for stochastic optimization with integer variables, establishing when integer optimization requires more or fewer samples than continuous counterparts.

Ax Xiaoze Liu, Dhananjay Ram, Yuting Zhang, Zhaoyang Zhang, Wei Xia, Stefano Soatto 5/11/2026

Experience Sharing in Mutual Reinforcement Learning for Heterogeneous Language Models

Mutual Reinforcement Learning framework enables concurrent RL post-training of heterogeneous LLMs with shared experience exchange and tokenizer alignment across incompatible vocabularies.

Ax Youngsik Yoon, Siwei Wang, Wei Chen, Jungseul Ok 5/11/2026

When Are Experts Misrouted? Counterfactual Routing Analysis in Mixture-of-Experts Language Models

Counterfactual routing analysis evaluates mixture-of-experts routing decisions in MoE language models by comparing standard routes against equal-compute alternatives on token prediction.