Isolater - Feed

Ax Zongfang Liu, Shengkun Tang, Yifan Shen, Huan Wang, Xin Yuan 3/20/2026

AIMER: Calibration-Free Task-Agnostic MoE Pruning

Calibration-free pruning method for Mixture-of-Experts language models to reduce memory and serving overhead.

Ax Yinan Xia, Haotian Zhang, Huiming Wang 3/20/2026

Balancing the Reasoning Load: Difficulty-Differentiated Policy Optimization with Length Redistribution for Efficient and Robust Reinforcement Learning

Policy optimization approach addressing overthinking in large reasoning models through difficulty-differentiated training.

Ax Konwoo Kim, Suhas Kotha, Yejin Choi, Tatsunori Hashimoto, Nick Haber, Percy Liang 3/20/2026

Data-efficient pre-training by scaling synthetic megadocs

Study on synthetic data augmentation for efficient pre-training with better loss scaling using synthetic megadocs.

Ax Sheng Pan, Niansheng Tang 3/20/2026

Beyond Passive Aggregation: Active Auditing and Topology-Aware Defense in Decentralized Federated Learning

Research on active auditing framework against backdoor attacks in decentralized federated learning systems.

Ax Zheng Lin, Ons Aouedi, Wei Ni, Symeon Chatzinotas, Xianhao Chen 3/20/2026

GAPSL: A Gradient-Aligned Parallel Split Learning on Heterogeneous Data

GAPSL: gradient-aligned parallel split learning for federated learning on heterogeneous data, reducing client computational load.

Ax Khushiyant 3/20/2026

HEP Statistical Inference for UAV Fault Detection: CLs, LRT, and SBI Applied to Blade Damage

Transfers statistical methods from particle physics for UAV propeller fault detection using spectral features and neural inference.

Ax Amanda A. Howard, Nicholas Zolman, Bruno Jacob, Steven L. Brunton, Panos Stinis 3/20/2026

SINDy-KANs: Sparse identification of non-linear dynamics through Kolmogorov-Arnold networks

SINDy-KANs combines Kolmogorov-Arnold networks with sparse identification to learn interpretable equations for nonlinear dynamical systems.

Ax Hoang T. H. Cao, Hai D. V. Trinh, Tho Quan, Lan V. Truong 3/20/2026

Transformers Learn Robust In-Context Regression under Distributional Uncertainty

Shows Transformers learn robust in-context regression under distributional uncertainty without restrictive assumptions on data and noise.

Ax Shenggui Li, Chao Wang, Yikai Zhu, Yubo Wang, Fan Yin, Shuai Shi, Yefei Chen, Xiaomin Dong, Qiaoling Chen, Jin Pan, Ji Li, Laixin Xie, Yineng Zhang, Lei Yu, Yonggang Wen, Ivor Tsang, Tianwei Zhang 3/20/2026

SpecForge: A Flexible and Efficient Open-Source Training Framework for Speculative Decoding

SpecForge: open-source training framework for speculative decoding draft models, improving LLM inference latency through token batching.

Ax Jiahao Zhang, Yilong Wang, Suhang Wang 3/20/2026

Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks

Demonstrates adversarial attacks on GNNs exploitable through unlearning mechanisms designed for GDPR compliance in graph learning systems.

Ax Xuan Liu, Xiaobin Chang 3/20/2026

Elastic Weight Consolidation Done Right for Continual Learning

Systematic analysis of Elastic Weight Consolidation for continual learning, identifying issues with importance estimation and weight regularization methods.

Ax Kevin Song 3/20/2026

Evaluating Model-Free Policy Optimization in Masked-Action Environments via an Exact Blackjack Oracle

Evaluates model-free policy optimization algorithms using exact blackjack oracle with ground-truth benchmarks for discrete stochastic control.

Ax Anh-Tuan Dao, Driss Matrouf, Mickael Rouvier, Nicholas Evans 3/20/2026

Enhancing Multi-Corpus Training in SSL-Based Anti-Spoofing Models: Domain-Invariant Feature Extraction

Investigates multi-corpus training in speech spoofing detection using self-supervised learning, finding domain-specific biases harm generalization.

Ax Yige Liu, Dexuan Xu, Zimai Guo, Yongzhi Cao, Hanpin Wang 3/20/2026

Revisiting Label Inference Attacks in Vertical Federated Learning: Why They Are Vulnerable and How to Defend

Studies label inference attacks in vertical federated learning, analyzing vulnerabilities when passive parties infer active party's labels and proposing defenses.

Ax Zhicong Lu, Zichuan Lin, Wei Jia, Changyuan Tian, Deheng Ye, Peiguang Li, Li Jin, Nayu Liu, Guangluan Xu, Wei Feng 3/20/2026

HISR: Hindsight Information Modulated Segmental Process Rewards For Multi-turn Agentic Reinforcement Learning

HISR proposes segmental process rewards for multi-turn RL in LLM agents, addressing sparse reward propagation and credit assignment in long-horizon decision-making tasks.

Ax Chen Zhang, Liwei Liu, Jun Tao, Xiaoyu Yang, Xuenan Xu, Kai Chen, Bowen Zhou, Wen Wu, Chao Zhang 3/20/2026

STEP: Scientific Time-Series Encoder Pretraining via Cross-Domain Distillation

Investigates transfer learning from audio and time-series foundation models to scientific time-series via cross-domain distillation.

Ax Chen Sun, Beilin Xu, Boheng Tan, Jiacheng Wang, Yuefeng Sun, Rite Bo, Ying He, Yaqiang Zang, Pinghua Gong 3/20/2026

OCP: Orthogonal Constrained Projection for Sparse Scaling in Industrial Commodity Recommendation

Proposes OCP method for improving item embeddings in large-scale commodity recommendation systems.

Ax Koichi Tanaka, Ren Kishimoto, Bushun Kawagishi, Yusuke Narita, Yasuo Yamamoto, Nobuyuki Shimizu, Yuta Saito 3/20/2026

Off-Policy Learning with Limited Supply

Studies off-policy learning in contextual bandits with supply constraints for recommendation and advertising systems.

Ax Hao Wang, Licheng Pan, Zhichao Chen, Chunyuan Zheng, Zhixuan Chu, Xiaoxi Li, Yuan Lu, Xinggao Liu, Haoxuan Li, Zhouchen Lin 3/20/2026

CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks

Causal-theoretic approach for reward modeling using observational user feedback instead of expensive annotated data for RLHF alignment.

Ax Gabriele Carrino, Andrea Sassella, Nicolo Brunello, Federico Toschi, Mark James Carman 3/20/2026

Are complicated loss functions necessary for teaching LLMs to reason?

Ablation study examining necessity of components in Group Relative Policy Optimization for teaching LLMs reasoning and mathematical ability.

Ax Marcio Augusto Sampaio, Paulo Henrique Ranazzi, Martin Julian Blunt 3/20/2026

Enhancing the Parameterization of Reservoir Properties for Data Assimilation Using Deep VAE-GAN

Deep VAE-GAN approach improving reservoir parameterization for data assimilation in petroleum reservoir simulation.

Ax Channe Chwa, Xinle Wu, Yao Lu 3/20/2026

Automatic Configuration of LLM Post-Training Pipelines

AutoPipe framework for automatically configuring LLM post-training pipelines combining supervised fine-tuning and reinforcement learning under budget constraints.

Ax Hisham Husain, Valentin De Bortoli, Richard Nock 3/20/2026

Seasoning Generative Models for a Generalization Aftertaste

Study on using discriminators to enhance generative model training across GANs, weak learner frameworks, and diffusion models.

Ax Yizhou Han, Di Wu, Blesson Varghese 3/20/2026

DriftGuard: Mitigating Asynchronous Data Drift in Federated Learning

Method mitigating asynchronous data drift in federated learning where different devices experience different distribution shifts.

Ax Marcela Palejova 3/20/2026

Authority-Level Priors: An Under-Specified Constraint in Hierarchical Predictive Processing

Neuroscience framework introducing authority-level priors to hierarchical predictive processing for understanding autonomic regulation.

Ax Steffen Dereich, Thang Do, Arnulf Jentzen 3/20/2026

Uniform a priori bounds and error analysis for the Adam stochastic gradient descent optimization method

Theoretical error analysis of Adam optimizer for training deep neural networks and beyond, addressing open research gaps.

Ax Riccardo Saporiti, Fabio Nobile 3/20/2026

Neural Galerkin Normalizing Flow for Transition Probability Density Functions of Diffusion Models

Framework using normalizing flows to approximate diffusion process transition probability densities by solving Fokker-Planck equations.

Ax Ezekiel Nii Noye Nortey, Jones Asante-Koranteng, Marcellin Atemkeng, Theophilus Ansah-Narh, David Mensah, Rebecca Davis, Ravenhill Adjetey Laryea 3/20/2026

An Optimised Greedy-Weighted Ensemble Framework for Financial Loan Default Prediction

Ensemble framework for loan default prediction handling nonlinear relationships and class imbalance in financial datasets.

Ax Saaket Agashe, Jayanth Srinivasa, Gaowen Liu, Ramana Kompella, Xin Eric Wang 3/20/2026

Context Bootstrapped Reinforcement Learning

Method augmenting reinforcement learning from verifiable rewards with context bootstrapping to improve exploration and reasoning pattern acquisition.

Ax Corneille Niyonkuru, Marcellin Atemkeng, Gabin Maxime Nguegnang, Arnaud Nguembang Fadja 3/20/2026

Balancing Performance and Fairness in Explainable AI for Anomaly Detection in Distributed Power Plants Monitoring

ML framework for anomaly detection in power plant monitoring systems balancing performance and fairness across regions.

Ax Sijian Fan, Liyan Xiong, Dayuan Wang, Guoshuai Cai, Ray Bai 3/20/2026

BVSIMC: Bayesian Variable Selection-Guided Inductive Matrix Completion for Improved and Interpretable Drug Discovery

Bayesian model for drug discovery incorporating variable selection and side information through inductive matrix completion.

Ax Adrien Bolland, Gaspard Lambrechts, Damien Ernst 3/20/2026

Maximum-Entropy Exploration with Future State-Action Visitation Measures

Intrinsic reward method for reinforcement learning agents maximizing entropy of future state-action visitation distributions.

Ax Christian Di Maio, Tommaso Guidi, Luigi Quarantiello, Jack Bell, Marco Gori, Stefano Melacci, Vincenzo Lomonaco 3/20/2026

Book your room in the Turing Hotel! A symmetric and distributed Turing Test with multiple AIs and humans

Novel symmetric Turing Test variant where groups of LLMs and humans interact, judge, and respond in time-bounded discussions.

Ax An Luo, Jin Du, Xun Xian, Robert Specht, Fangqiao Tian, Ganghua Wang, Xuan Bi, Charles Fleming, Ashish Kundu, Jayanth Srinivasa, Mingyi Hong, Rui Zhang, Tianxi Li, Galin Jones, Jie Ding 3/20/2026

AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science

Benchmarking study comparing AI agents' performance to human experts on domain-specific data science tasks, evaluating LLM-based automation of data science workflows.

Ax Chen Yaoling, Liang Hao, Tu Xiaotong 3/20/2026

When Differential Privacy Meets Wireless Federated Learning: An Improved Analysis for Privacy and Convergence

Analysis of differential privacy guarantees and convergence in wireless federated learning without restrictive convexity assumptions.

Ax Mohamed Badi, Chaouki Ben Issaid, Mehdi Bennis 3/20/2026

Communication-Efficient and Robust Multi-Modal Federated Learning via Latent-Space Consensus

CoMFed framework for communication-efficient federated learning with heterogeneous multimodal clients and privacy preservation.

Ax Qin Jiang, Chengjia Wang, Michael Lones, Dongdong Chen, Wei Pang 3/20/2026

Position: Spectral GNNs Are Neither Spectral Nor Superior for Node Classification

Theoretical analysis questioning foundations of Spectral Graph Neural Networks for node classification tasks.

Ax Aravind Krishnan, Karolina Sta\'nczak, Dietrich Klakow 3/20/2026

On Optimizing Multimodal Jailbreaks for Spoken Language Models

Study of multimodal jailbreak attacks on Spoken Language Models using gradient-based optimization across text and audio modalities.

Ax Zhuofan Li (Celine), Hongkun Yang (Celine), Zhenyang Chen (Celine), Yangxuan Chen (Celine), Yingyan (Celine), Lin, Chaojian Li 3/20/2026

From Inference Efficiency to Embodied Efficiency: Revisiting Efficiency Metrics for Vision-Language-Action Models

Research on efficiency metrics for Vision-Language-Action embodied agents, showing that parameter/FLOP counts don't reflect real robotic platform performance.

Ax Mohammad Al Ridhawi, Mahtab Haj Ali, Hussein Al Osman 3/20/2026

Adaptive Regime-Aware Stock Price Prediction Using Autoencoder-Gated Dual Node Transformers with Reinforcement Learning Control

Stock prediction framework using autoencoders and transformers with reinforcement learning for adaptive market regime detection.

Ax Ines Aitsahalia, Kiyohito Iigaya 3/20/2026

Hierarchical Latent Structure Learning through Online Inference

Hierarchical Bayesian model for online latent-cause inference balancing generalization and discrimination in learning.

Ax Mingxing Zhang, Nicola Rossberg, Simone Innocente, Katarzyna Komolibus, Rekha Gautam, Barry O'Sullivan, Luca Longo, Andrea Visentin 3/20/2026

SHAPCA: Consistent and Interpretable Explanations for Machine Learning Models on Spectroscopy Data

SHAPCA: interpretability framework combining SHAP and PCA for explainable ML on high-dimensional spectroscopy data.

Ax Ruilin Li, Heming Zou, Xiufeng Yan, Zheming Liang, Jie Yang, Chenliang Li, Xue Yang 3/20/2026

Enhancing Pretrained Model-based Continual Representation Learning via Guided Random Projection

Continual learning method using random projection layers with pretrained models for improved representation learning.

Ax Yuegui Huang, Zhiyuan Fang, Weiqi Luo, Ruoyu Wu, Wuhui Chen, Zibin Zheng 3/20/2026

DyMoE: Dynamic Expert Orchestration with Mixed-Precision Quantization for Efficient MoE Inference on Edge

DyMoE: dynamic expert selection with mixed-precision quantization for efficient MoE model inference on edge devices.

Ax Edward Lin, Sahil Modi, Siva Kumar Sastry Hari, Qijing Huang, Zhifan Ye, Nestor Qin, Fengzhe Zhou, Yuan Zhang, Jingquan Wang, Sana Damani, Dheeraj Peri, Ouye Xie, Aditya Kane, Moshe Maor, Michael Behar, Triston Cao, Rishabh Mehta, Vartika Singh, Vikram Sharma Mailthody, Terry Chen, Zihao Ye, Hanfeng Chen, Tianqi Chen, Vinod Grover, Wei Chen, Wei Liu, Eric Chung, Luis Ceze, Roger Bringmann, Cyril Zeller, Michael Lightstone, Christos Kozyrakis, Humphrey Shi 3/20/2026