Ax Andrey Podivilov, Vadim Lomshakov, Sergey Savin, Matvei Startsev, Roman Pozharskiy, Maksim Parshin, Sergey Nikolenko 7/9/2026

AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation

AgentLens benchmark evaluates coding agents on full trajectory quality including instruction-following, tool-use, and error recovery, not just pass/fail.

Ax Yu Cheng, Siyue Yao, Zhongang Qi, Shanyan Guan, Wei Li, Fajie Yuan 7/9/2026

Dynamic-in-Few-Step: Unifying Dynamic Computation and Few-Step Distillation for Efficient Video Generation

Dynamic-in-Few-Step combines dynamic computation with distillation to accelerate video diffusion model inference.

Ax Amin Haeri, Mahdi Ghelichi 7/9/2026

Specification Grounding Drives Test Effectiveness for LLM Code

Analysis of how specification-grounded tests improve LLM-generated code quality on edge cases compared to model self-testing.

Ax Felix Feldman, Joshua Harris, Timothy Laurence, Leo Loman, Ollie Higgins, Fan Grayson, Poonam Soma, Bethany Pace-Bonello, Michael Borowitz, Toby Nonnenmacher 7/9/2026

Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering

Retrieval-Augmented Generation applied to medical QA reduces LLM hallucinations and grounds answers in current public health data.

Ax Issam Seddik, Sami Souihi, Mohamed Tamaazousti, Sara Tucci Piergiovanni 7/9/2026

The Power of Backdoor Absorption in Community Training

Method to detect and mitigate backdoor attacks in decentralized model training without full recomputation overhead.

Ax Zhangheng LI, Jianing Zhu, Junyuan Hong, Sungmin Eum, Shuowen Hu, Suya You, Zhangyang Wang 7/9/2026

POPS: Recovering Unlearned Multi-Modality Knowledge in MLLMs with Prompt-Optimized Parameter Shaking

POPS method recovers knowledge in multimodal LLMs after machine unlearning, addressing privacy-utility tradeoffs in MLLMs trained on sensitive data.

Ax Zeyuan Ding, Wenhai Liu, Yang Xu, Jiayu Hu, Yinda Chen, Yi Zhang, Yong Dai, Jian Tang, Xiaozhu Ju 7/9/2026

Pelican-VLA 0.5: Attending Before Acting Benefits Generalization

Vision-language-action model integrating perception, future prediction, and action planning with attention-based generalization without task-specific tuning.

Ax Inkyu Sa, Chanoh Park, Hea-Min Lee, Donghee Noh, Ho Seok Ahn 7/9/2026

Vision Language Action (VLA) Models for Unmanned Aerial Robotics and Bimanual Manipulation: A Review

Review of Vision Language Action models for embodied AI in robotics and manipulation tasks from camera images.

Ax Zavier Li 7/9/2026

Optimization Geometrodynamics: A Framework for Dynamic Geometric Optimization

Theoretical framework unifying gradient-based optimization as coupled evolution of parameters, particles, and time-varying Riemannian metrics.

Ax Mayur Kurup, Hyunjae Suh, Swathi Vaidyanathan, Pranesh Vyas, Srinidhi Madabhushi, Yegor Silyutin 7/9/2026

Deployment Risk Assessment Using Diff-Aware Features: A Case Study at Prime Video

Diff-aware ML model for predicting deployment risk at Prime Video to reduce unnecessary code freezes.

Ax Yashal Shakti Kanungo, Sumit Negi, Aruna Rajan 7/9/2026

Ad Headline Generation using Self-Critical Masked Language Model

Reinforcement learning application using policy gradient methods on masked language models for e-commerce ad headline generation.

Ax Aristotelis Papatheodorou, Pranav Vaidhyanathan, Natalia Ares, Ioannis Havoutis, Gerard J. Milburn 7/9/2026

CaLiSym: Learning Symplectic Dynamics of Real-World Systems through Structured Canonical Lifts

Framework extending symplectic learning to robotic systems with actuation and dissipation for stable dynamics prediction.

Ax Albert Zeyer, Ralf Schl\"uter, Hermann Ney 7/9/2026

Gradient-Based Speech-to-Text Alignment for Any ASR Model: From CTC to Speech LLMs

Gradient-based method for speech-to-text alignment compatible with CTC, transducer, and speech LLM models.

Ax Robert Gruhlke, Julius Berner, David Sommer, Lorenz Richter 7/9/2026

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling

Low-rank tensor train approach for high-dimensional sampling in diffusion models using score-based methods.

Ax Michael King, Aravindh Mahendran, Matthew Koichi Grimes, Fedor Kitashov, Adham Elarabawy, Pedro Velez, Maks Ovsjanikov, Viorica P\u{a}tr\u{a}ucean 7/9/2026

Gen4U: Unifying Video Generation and Understanding via Diffusion

Analysis of video diffusion model representations revealing structured latent spaces across network depth and noise levels.

Ax Jie Wang 7/9/2026

Computing with Stochastic Oracles in AI-Augmented Computation

Theoretical framework modeling AI-augmented computation as probabilistic Turing machines interacting with stochastic oracles.

Ax Sojung An, Junha Lee, Sujeong You, Nam Ik Cho, Donghyun Kim 7/9/2026

LoCA: Spatially-Aware Low-Rank Convolutional Adaptation of Vision Foundation Models

Parameter-efficient fine-tuning method extending LoRA to vision foundation models with spatial awareness for downstream tasks.

Ax Denis Belomestny, Alexander Gasnikov, Egor Gladin, Alexey Naumov, Artemy Rubtsov, Yuri Sapronov, Daniil Tiapkin, Nikita Yudin 7/9/2026

Mathematical methods of reinforcement learning

Survey of mathematical foundations in reinforcement learning, covering MDPs, Bellman operators, and convergence guarantees for value/policy iteration algorithms.

Ax Seulbin Hwang, Kiyoung Om, Daejung Kim, Jinhan Lee 7/9/2026

Flow-ERD: Agent-type Aware Flow Matching with Entropy-Regularized Distillation for Diverse Traffic Simulation

Flow-ERD combines flow matching with entropy-regularized distillation for diverse and realistic multi-agent traffic simulation in autonomous driving.

Ax Ruilin Tong, Dong Gong 7/9/2026

MILES: Modular Instruction Memory with Learnable Selection for Self-Improving LLM Reasoning

MILES proposes modular instruction memory with learnable selection enabling LLMs to accumulate and reuse reasoning experience across sequential problems.

Ax Hao Kong, Di Liu, Shuo Huai, Xiangzhong Luo, Ravi Subramaniam, Christian Makaya, Qian Lin, Weichen Liu 7/9/2026

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI

EdgeCompress framework combining dynamic image cropping and model compression techniques to reduce CNN computational overhead for resource-constrained embedded devices.

Ax Roxana Barrios, Ioannis Sgouralis 7/9/2026

Tensorized algorithms and scalable filtering methods for hidden Markov and factorial hidden Markov models

Tensorized algorithms and scalable filtering methods for hidden Markov models and factorial HMMs to efficiently represent multi-factor time-series systems.

Ax Jiabin Shen, Guang Chen, Chengjun Mao 7/9/2026

Behavior Leverage Imbalance in Multi-Teacher On-Policy Distillation

Analysis of behavior shift in multi-teacher on-policy distillation for LLM agents learning tool use, showing invisible distribution shifts in aggregate losses.

Ax Beomgu Kang, Hyunseok Seo 7/9/2026

Prior-aware and Context-guided Group Sampling for Active Probabilistic Subsampling

Active deep probabilistic subsampling approach with prior-aware context-guided group sampling for reducing measurement overhead and data transfer.

Ax Miguel Lopez-Duran, Elena Marrero, Julian Fierrez, Marta Robledo-Moreno, Ruben Vera-Rodriguez, Daniel DeAlcala, Aythami Morales, Ruben Tolosana, Oscar Delgado, Alvaro Ortigosa, Javier Ortega-Garcia 7/9/2026

Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering

Comparative evaluation of 8 open-source VLMs on document visual question answering, assessing robustness and transferability across document domains.

Ax T-H. Hubert Chan, Elaine Shi, Mengshi Zhao, Mingxun Zhou 7/9/2026

Continual Learning With Participation Privacy: An Auditable Buffering-Aggregation Recipe

Privacy-preserving continual learning framework with auditable buffering-aggregation recipe for federated and streaming systems under adaptive interaction.

Ax Sergio Rozada, Yiming Qin, Manuel Madeira, Pascal Frossard, Alejandro Ribeiro 7/9/2026

DiPhon: Diffusion on Graphons for Scalable Graph Generation

DiPhon applies diffusion models to large-scale graph generation using graphon theory to study structural statistics across different node scales.

Ax Sergei Zorkaltsev, Maciej Haranczyk, Christina Schenk 7/9/2026

Bayesian Optimization of Genetic Algorithm Hyperparameters in a Multi-Fidelity Framework for Efficient Lattice Material Design

Multi-fidelity Bayesian optimization framework for tuning genetic algorithm hyperparameters using FFT, CNN surrogates, and Gaussian processes for lattice material design.

Ax Diab W. Abueidda, Bilal Ahmed, Panos Pantidis, Mostafa E. Mobasher 7/9/2026

Physics-Audited Agentic Discovery in Scientific Machine Learning

PA-SciML introduces physics-audited verification workflow for LLM agents discovering scientific surrogate models, ensuring predictions satisfy physical constraints beyond error metrics.

Ax Yutang Ma, Kecheng Huang, Xikun Jiang, Zili Shao 7/9/2026

TF-Engram: A Train-Free Engram with SSD-Backed Memory for Large Language Models

TF-Engram proposes SSD-backed memory architecture for LLMs to enable efficient knowledge expansion without retraining, using engram-style hidden-state injection.

Ax Tanay Sodha, Aditya Sharma, Ramya Hebbalaguppe, Vinti Agarwal, Pranav Murthy Yeluripaty 7/9/2026

When Prompts Ignore Structure: Graph-Based Attribute Reasoning for Calibrated VLMs

ARGTCA improves confidence calibration in vision-language models by using graph-based attribute reasoning to represent class relationships, addressing overconfidence from prompt tuning.

Ax Jieying Wang, Shuyuan Fan, Mingkai Zheng, Zhao Zhang 7/9/2026

GIFT: Geometry-Informed Low-precision Gradient Communication for LLM Pretraining

GIFT: geometry-informed low-precision gradient quantization for distributed LLM pretraining reducing communication bottlenecks.

Ax Yazdan Jamshidi, Alexey Shvets 7/9/2026

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning

PALS: layer-aware sparsity pruning for LLMs using percentile-based activation thresholds, improving perplexity over uniform pruning methods.

Ax Grzegorz Brzezinka 7/9/2026

Does Bielik Know What It Doesn't Know? Activation Dispersion Separates Entity Familiarity from Factual Reliability Across Model Scale

Studies activation patterns in Polish Bielik LLMs to identify entity familiarity and factual reliability signals before answer generation across model scales.

Ax Hyunjae Kim, Dain Kim, Pan Xiao, Serina S. Applebaum, Younjoon Chung, Xuguang Ai, Yu Yin, Roy Jiang, Yuexi Du, Yawen Wei, Yiming Kong, Tuo Guo, Zhiyuan Cao, Mengmeng Du, Yuelei Fu, Yan Hu, Rui Shi, Gui Yang, Kevin W. Jin, Yuntian Liu, Yuxuan Tian, Jonathan Marquez, Zhen Chen, Sheng Zhang, Hoifung Poon, Hua Xu, Jaewoo Kang, Qingyu Chen 7/9/2026

MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models

MedPMC framework scales high-fidelity medical multimodal data from PubMed Central for foundation model development with clinical validation.

Ax Eitan Levin, Venkat Chandrasekaran 7/9/2026

Any-Dimensional Learning by Sampling

Studies generalization of variable-size input models (point clouds, sequences, graphs) from small training sizes to unseen larger sizes.

Ax Yair Feldman, Linxi Zhao, Nathan Godey, Dongyoung Go, Yilun Hua, Kilian Q. Weinberger, Jennifer J. Sun, Yoav Artzi 7/9/2026

Co-LMLM: Continuous-Query Limited Memory Language Models

Co-LMLM externalizes factual knowledge to continuous-query knowledge bases during LLM generation, enabling knowledge control beyond conventional weight-based approaches.

Ax Jiancong Xiao, Yanbo Fan, Ruoyu Sun, Zhi-Quan Luo 7/9/2026

Adversarial Rademacher Complexity of Deep Neural Networks

Analyzes adversarial Rademacher complexity of DNNs to provide generalization guarantees for models robust to adversarial perturbations.

Ax Yilei Chen, Vittorio Giammarino, James Queeney, Ioannis Ch. Paschalidis 7/9/2026

Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees

Studies off-policy adversarial imitation learning convergence and sample complexity, showing sample reuse improves efficiency without importance sampling correction.

Ax Yuelin Xu, Xiao Zhang 7/9/2026

How Learning Dynamics Drive Adversarially Robust Generalization?

Analyzes robust overfitting in adversarial training using dynamical systems and PAC-Bayesian theory, providing mechanistic interpretation of generalization in robust models.

Ax Jinho Chang, Changsun Lee, Hyungjin Chung, Jong Chul Ye 7/9/2026

ContrastiveCFG: Guiding Diffusion Sampling by Contrasting Positive and Negative Concepts

ContrastiveCFG improves diffusion model sampling by contrasting positive and negative concepts, addressing limitations of naive negative prompting in classifier-free guidance.

Ax Yuchen Lin, Yong Zhang, Sihan Feng, Hong Zhao 7/9/2026

Network Dynamics-Based Framework for Understanding Deep Neural Networks

Proposes theoretical framework analyzing deep neural network learning dynamics through dynamical systems theory, introducing order-preserving transformations at neuron level.

Ax Songqiao Hu, Zeyi Liu, Lufeng Hao, Yinzhong Cheng, Xiao He 7/9/2026

PB-OEL: A Performance-Bounded Online Ensemble Learning Framework With Mixed Feedback for Real-Time Safety Assessment

PB-OEL framework for real-time safety assessment using online ensemble learning with mixed feedback and performance guarantees under concept drift.

Ax Zhiqiang He, Zhi Liu 7/9/2026

Silent Neuron Theory and Plasticity Preservation for Deep Reinforcement Learning in Adaptive Video Streaming

Studies silent neurons and plasticity in deep reinforcement learning for adaptive video streaming to improve generalization across heterogeneous network conditions.

Ax Sameera Ramasinghe, Thalaiyasingam Ajanthan, Gil Avraham, Yan Zuo, Alexander Long 7/9/2026

Protocol Models: Scaling Decentralized Training with Communication-Efficient Model Parallelism

Protocol Models proposes communication-efficient model parallelism for decentralized training by compressing activations and activation gradients in distributed deep learning setups.

Ax Boris van Breugel, Yelysei Bondarenko, Paul Whatmough, Markus Nagel 7/9/2026

FPTQuant: Function-Preserving Transforms for LLM Quantization

FPTQuant introduces function-preserving transforms to enable efficient quantization of LLMs by handling magnitude outliers, improving inference efficiency without significant performance degradation.

Ax Changcun Huang 7/9/2026

Understanding Two-Layer Neural Networks with Smooth Activation Functions

Theoretical analysis of two-layer neural networks with smooth activation functions using Taylor series and spline methods.

Ax Luis Roque, Vitor Cerqueira, Carlos Soares, Luis Torgo 7/9/2026

L-GTA: Latent Generative Modeling for Time Series Augmentation

L-GTA: VAE-based generative model for time series augmentation using Bi-LSTM and temporal self-attention.

Ax Seunghyeon Kim, Taesun Yeom, Jinho Kim, Wonpyo Park, Kyuyeun Kim, Jaeho Lee 7/9/2026

Activation Quantization of Vision Encoders Needs Prefixing Registers

Quantization technique for vision encoders using register prefixing to handle outliers and reduce inference cost.

Ax Zhiwei Han, Stefan Matthes, Hao Shen 7/9/2026

Provable Affine Identifiability of Nonlinear CCA under Latent Distributional Priors

Theoretical analysis proving affine identifiability in nonlinear CCA under latent distributional priors.