Ax Qipeng Zhan, Zhuoping Zhou, Li Shen 4/9/2026

Towards Accurate and Calibrated Classification: Regularizing Cross-Entropy From A Generative Perspective

Method for improving classification calibration using generative perspective to regularize cross-entropy loss in deep networks.

Ax Qipeng Zhan, Zhuoping Zhou, Zexuan Wang, Qi Long, Li Shen 4/9/2026

Bi-Lipschitz Autoencoder With Injectivity Guarantee

Bi-Lipschitz autoencoder with injectivity guarantee for dimensionality reduction while preserving manifold geometry.

Ax Shengchao Chen, Guodong Long, Dikai Liu, Jing Jiang 4/9/2026

Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach

Federated learning approach for training time series foundation models using bi-level heterogeneous learning to address gradient conflicts.

Ax Fumito Kimura, Jun Ohkubo 4/9/2026

Extraction of linearized models from pre-trained networks via knowledge distillation

Framework for extracting linearized neural network models via knowledge distillation for photonic hardware compatibility.

Ax Zinaid Kapi\'c, Vladimir Ja\'cimovi\'c 4/9/2026

Busemann energy-based attention for emotion analysis in Poincar\'e discs

EmBolic: hyperbolic deep learning architecture for emotion analysis from text using Busemann energy-based attention.

Ax Marshall Brett 4/9/2026

Geometric Properties of the Voronoi Tessellation in Latent Semantic Manifolds of Large Language Models

Empirical study of Voronoi tessellations in LLM latent spaces, validating scaling laws of expressibility gaps.

Ax Andrea Pollastro, Andrea Apicella, Francesco Isgr\`o, Roberto Prevete 4/9/2026

Instance-Adaptive Parametrization for Amortized Variational Inference

Instance-adaptive variational autoencoder addressing amortization gap in latent variable models through per-instance parametrization.

Ax Zhixiong Zhao, Zukang Xu, Zhixuan Chen, Dawei Yang 4/9/2026

MoBiE: Efficient Inference of Mixture of Binary Experts under Post-Training Quantization

MoBiE: Binarization framework for efficient inference of Mixture-of-Experts LLMs with post-training quantization techniques.

Ax Dihong Jiang, Ruoqi Cao, Zhiyuan Dang, Li Huang, Qingsong Zhang, Zhiyu Wang, Shihao Piao, Shenggao Zhu, Jianlong Chang, Zhouchen Lin, Qi Tian 4/9/2026

OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale

OmniTabBench: Large-scale benchmark comparing GBDTs, neural networks, and foundation models on tabular data with 100+ datasets.

Ax Minglu Liu, Cunchen Hu, Liangliang Xu, Fengming Tang, Ruijia Wang, Fu Yu 4/9/2026

STQuant: Spatio-Temporal Adaptive Framework for Optimizer Quantization in Large Multimodal Model Training

STQuant framework for adaptive quantization of optimizer states during large multimodal model training to reduce memory costs.

Ax Charbel Bou Chaaya, Mehdi Bennis 4/9/2026

Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systems

Decentralized multi-agent RL approach for vehicle-to-infrastructure systems using equivariant neural networks.

Ax Yitong Li, Junsong Chen, Shuchen Xue, Pengcuo Zeren, Siyuan Fu, Dinghao Yang, Yangyang Tang, Junjie Bai, Ping Luo, Song Han, Enze Xie 4/9/2026

FP4 Explore, BF16 Train: Diffusion Reinforcement Learning via Efficient Rollout Scaling

Efficient scaling technique for diffusion RL post-training using low-precision exploration and higher-precision training.

Ax Andoni Irazusta Garmendia 4/9/2026

A First Guess is Rarely the Final Answer: Learning to Search in the Travelling Salesperson Problem

Neural method for learning search policies in Traveling Salesperson Problem, training models to iteratively improve solutions.

Ax Ruben Vereecken, Luke Dickens, Alessandra Russo 4/9/2026

Predictive Representations for Skill Transfer in Reinforcement Learning

Transfer learning formalism using Outcome-Predictive State Representations for knowledge generalization across RL tasks.

Ax Jimmy Gammell, Bishal Thapaliya, Yoon Jung, Riyasat Ohib, Bilel Fehri, Deepayan Chakrabarti 4/9/2026

Learning to Query History: Nonstationary Classification via Learned Retrieval

Nonstationary classification approach using learned retrieval to condition classifiers on historical examples beyond training cutoff.

Ax Tobias Falke, Nicolas Anastassacos, Samson Tan, Chankrisna Richy Meas, Chandana Satya Prakash, Nitesh Sekhar, M Saiful Bari, Krishna Kompella, Gamaleldin F. Elsayed 4/9/2026

MoE Routing Testbed: Studying Expert Specialization and Routing Behavior at Small Scale

Study of expert specialization and routing behavior in sparse Mixture-of-Experts architectures for large language models at small scale.

Ax Abhilash Reddy Chenreddy, Erick Delage 4/9/2026

Epistemic Robust Offline Reinforcement Learning

Offline reinforcement learning approach addressing epistemic uncertainty through ensemble-based conservative value estimation.

Ax Ryyan Akhtar 4/9/2026

Selective Neuron Amplification for Training-Free Task Enhancement

Method to enhance LLM task performance by amplifying task-relevant neurons at inference time without parameter modification.

Ax Radu Negulescu 4/9/2026

Information as Structural Alignment: A Dynamical Theory of Continual Learning

Theoretical framework addressing catastrophic forgetting in continual learning through informational structural alignment rather than external mechanisms.

Ax Ning Yang, Chuangxin Cheng, Haijun Zhang 4/9/2026

Multi-Turn Reasoning LLMs for Task Offloading in Mobile Edge Computing

Research on using multi-turn reasoning LLMs with deep reinforcement learning for task offloading decisions in mobile edge computing systems.

Ax Tom A. Lamb, Desi R. Ivanova, Philip H. S. Torr, Tim G. J. Rudner 4/9/2026

Improving Semantic Uncertainty Quantification in Language Model Question-Answering via Token-Level Temperature Scaling

Research on calibrating uncertainty quantification in LLMs for question-answering through token-level temperature scaling, addressing gaps in existing confidence measures.

Ax Yushi Hirose, Akito Narahara, Takafumi Kanamori 4/9/2026

Mixture Proportion Estimation and Weakly-supervised Kernel Test for Conditional Independence

Mixture proportion estimation from unlabeled data using conditional independence assumptions. Application to PU learning, label noise, and domain adaptation.

Ax Lance Fortnow 4/9/2026

How Does Machine Learning Manage Complexity?

Computational complexity analysis of ML model expressiveness for complex systems. Studies how ML manages complexity through probability on sampleable distributions.

Ax Xiaoyu Li, Andi Han, Jiaojiao Jiang, Junbin Gao 4/9/2026

On the Price of Privacy for Language Identification and Generation

Theoretical study of differential privacy cost for language identification and generation. Establishes algorithms and lower bounds quantifying privacy-utility tradeoff.

Ax Vincent Abbott, Gioele Zardini 4/9/2026

Weaves, Wires, and Morphisms: Formalizing and Implementing the Algebra of Deep Learning

Categorical framework formalizing deep learning model architectures using array broadcasting and morphisms. Mathematical notation for neural network composition.

Ax Justin Lin, Julia Fukuyama 4/9/2026

A comparative analysis of machine learning models in SHAP analysis

Comparative analysis of SHAP explainability method applied to different ML models. Reviews interpretability for black-box model predictions.

Ax Guo Gan, Yuxuan Ding, Cong Chen, Yuwei Ren, Yin Huang, Hong Zhou 4/9/2026

Android Coach: Improve Online Agentic Training Efficiency with Single State Multiple Actions

Training method for Android UI agents improving RL efficiency using single state multiple actions paradigm to reduce sample inefficiency and emulator latency.

Ax Zehang Lin, Miao Yang, Haihan Zhu, Zheng Lin, Jianhao Huang, Jing Yang, Guangjin Pan, Dianxin Luan, Zihan Fang, Shunzhi Zhu, Wei Ni, John Thompson 4/9/2026

SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression

Split learning framework with frequency-aware compression reducing communication overhead in distributed neural network training on resource-constrained edge devices.

Ax Sam Gunn 4/9/2026

How to sketch a learning algorithm

Data deletion scheme predicting model behavior after training data exclusion. Fast approximation for understanding data influence on learned models.

Ax Ashmal Vayani, Parth Parag Kulkarni, Joseph Fioresi, Song Wang, Mubarak Shah 4/9/2026

MedRoute: RL-Based Dynamic Specialist Routing in Multi-Agent Medical Diagnosis

Multi-agent system using RL for dynamic specialist routing in medical diagnosis. LMM agents route diagnostic queries to appropriate specialists for precision diagnosis.

Ax Mar Gonz\`alez I Catal\`a, Haitz S\'aez de Oc\'ariz Borde, George D. Monta\~nez, Pietro Li\`o 4/9/2026

The Stepwise Informativeness Assumption: Why are Entropy Dynamics and Reasoning Correlated in LLMs?

Theoretical analysis explaining why entropy dynamics in LLM internal representations correlate with reasoning correctness. Proposes stepwise informativeness assumption.

Ax Jaehyeok Lee, Xiaoyuan Yi, Jing Yao, Hyunjin Hwang, Roy Ka-Wei Lee, Xing Xie, JinYeong Bak 4/9/2026

Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook

DOVE benchmark evaluates LLM cultural value alignment through open-ended generation. Addresses limitations of discriminative multiple-choice formats and subcultural heterogeneity.

Ax Jiayuan Liu, Barry Wang, Jiarui Gan, Tonghan Wang, Leon Xie, Mingyu Guo, Vincent Conitzer 4/9/2026

Incentive-Aware Multi-Fidelity Optimization for Generative Advertising in Large Language Models

Multi-fidelity optimization framework combining VCG incentive mechanisms with efficient sampling to optimize LLM advertising while managing advertiser strategic behavior.

Ax Shoaib Sadiq Salehmohamed, Jinal Prashant Thakkar, Hansika Aredla, Shaik Mohammed Omar, Shalmali Ayachit 4/9/2026

Weakly Supervised Distillation of Hallucination Signals into Transformer Representations

Framework to distill hallucination detection signals into transformer representations during training, enabling inference-time hallucination detection without external verification systems.

Ax Syed Irfan Ali Meerza, Feiyi Wang, Jian Liu 4/9/2026

FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs

FedSpy-LLM demonstrates data reconstruction attacks on LLMs in federated learning, highlighting privacy risks in gradient sharing.

Ax Guruprasad Viswanathan Ramesh, Asmit Nayak, Basieem Siddique, Kassem Fawaz 4/9/2026

WebSP-Eval: Evaluating Web Agents on Website Security and Privacy Tasks

WebSP-Eval benchmarks web agents on website security and privacy task execution, filling gap in agent evaluation frameworks.

Ax Shao Wang, Rui Ren, Lin Gui 4/9/2026

ForkKV: Scaling Multi-LoRA Agent Serving via Copy-on-Write Disaggregated KV Cache

ForkKV is a system for efficient multi-LoRA agent serving using copy-on-write KV cache disaggregation to reduce memory overhead.

Ax Michael Rizvi-Martel, Guillaume Rabusseau, Marius Mosbach 4/9/2026

The Illusion of Superposition? A Principled Analysis of Latent Thinking in Language Models

Research analyzing whether latent chain-of-thought reasoning in LLMs actually enables superposition of multiple solutions.

Ax Kranthi Kommuru, Kunal Khanvilkar, Gaurav Parekh 4/9/2026

ProofSketcher: Hybrid LLM + Lightweight Proof Checker for Reliable Math/Logic Reasoning

ProofSketcher combines LLMs with formal proof verification to improve mathematical and logical reasoning accuracy and reliability.

Ax Yasamin Fayyaz, Li Yang, Khalil El-Khatib 4/9/2026

Towards Resilient Intrusion Detection in CubeSats: Challenges, TinyML Solutions, and Future Directions

Proposes TinyML-based intrusion detection for CubeSats addressing cybersecurity vulnerabilities from COTS components and open-source software.

Ax Rebecca M. M. Hicke, Sil Hamilton, David Mimno, Ross Deans Kristensen-McLachlan 4/9/2026

Attention Flows: Tracing LLM Conceptual Engagement via Story Summaries

Evaluates LLM ability to integrate long-form text information through novel summarization task, comparing human and model-authored novel summaries.

Ax Youssef Abdou 4/9/2026

The Unreasonable Effectiveness of Data for Recommender Systems

Studies how offline recommendation system performance scales with training dataset size and identifies saturation points in data effectiveness.

Ax Shukai Cai, Sourav Dutta, Mark Loveland, Eirik Valseth, Peter Rivera-Casillas, Corey Trahan, Clint Dawson 4/9/2026

Operator Learning for Surrogate Modeling of Wave-Induced Forces from Sea Surface Waves

Operator learning surrogate model for wave-induced forces as alternative to expensive numerical wave models in storm surge prediction.

Ax Harrison Katz 4/9/2026

Learning Debt and Cost-Sensitive Bayesian Retraining: A Forecasting Operations Framework

Defines learning debt and actionable staleness metrics, derives Bayes retraining rule for optimal forecasting model retraining schedules.

Ax Yihua Zhang, Hongkang Li, Yuguang Yao, Aochuan Chen, Shuai Zhang, Pin-Yu Chen, Meng Wang, Sijia Liu 4/9/2026

Visual prompting reimagined: The power of the Activation Prompts

Activation Prompts improve visual prompting for vision model adaptation, closing performance gap between prompting and conventional fine-tuning.

Ax Smita Deb, Zheng-Meng Zhai, Mulugeta Haile, Ying-Cheng Lai 4/9/2026

Anticipating tipping in spatiotemporal systems with machine learning

Applies reservoir computing to anticipate critical tipping points in complex spatiotemporal dynamical systems via machine learning.

Ax Basil Kyriacou, Mo Kordzanganeh, Maniraman Periyasamy, Alexey Melnikov 4/9/2026

Soft-Quantum Algorithms

Soft-quantum algorithms combining quantum operations with classical simulation for variational quantum circuits on few-qubit problems.

Ax Emre Gurkanli, Michael Spannowsky 4/9/2026

Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach

Tensor-network autoencoder using multiscale MERA architecture for reconstruction-based anomaly detection in particle physics collider jets.

Ax Xiangming Gu, Soham De, Michalis Titsias, Larisa Markeeva, Petar Veli\v{c}kovi\'c, Razvan Pascanu 4/9/2026

The Illusion of Stochasticity in LLMs

Demonstrates LLMs fail at reliable stochastic sampling required for agentic systems, identifying critical failure point in distribution sampling from inferred data.

Ax Anna\"elle Baiget, Jaron Maene, Seongmin Lee, Benjie Wang, Guy Van den Broeck, Miryung Kim 4/9/2026

ExplainFuzz: Explainable and Constraint-Conditioned Test Generation with Probabilistic Circuits

ExplainFuzz generates test inputs using probabilistic circuits, improving on grammar-based fuzzers and LLM approaches for constraint-conditioned software testing.