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Measuring Cross-Modal Synergy: A Benchmark for VLM Explainability

arXiv:2605.22168v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) map complex visual inputs to semantic spaces, but interpreting the cross-modal reasoning of VLMs currently relies on pos

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Memory-Efficient LLM Pretraining via Minimalist Optimizer Design

DGX agent

arXiv:2506.16659v3 Announce Type: replace Abstract: Training large language models (LLMs) relies on adaptive optimizers such as Adam, which introduce extra operations and require significantly more me

model-releasesarxiv-cs-lg

23 May 2026

Memory-R2: Fair Credit Assignment for Long-Horizon Memory-Augmented LLM Agents

arXiv:2605.21768v1 Announce Type: new Abstract: Memory-augmented LLM agents enable interactions that extend beyond finite context windows by storing, updating, and reusing information across sessions.

MemReward: Graph-Based Experience Memory for LLM Reward Prediction with Limited Labels

arXiv:2603.19310v3 Announce Type: replace Abstract: Reinforcement learning has emerged as a powerful paradigm for improving large language model (LLM) reasoning, where rollouts are sampled from the po

MetaDNS: Enhancing Exploration in Discrete Neural Samplers via Well-Tempered Metadynamics

arXiv:2605.21722v1 Announce Type: cross Abstract: Sampling from discrete distributions with multiple modes and energy barriers is fundamental to machine learning and computational physics. Recent disc

Local Ai

Minimum Description Length based Granular-Ball Tree Regularization for Spectral Clustering

arXiv:2605.22410v1 Announce Type: new Abstract: Spectral clustering largely depends on the affinity graph, yet constructing a graph that preserves reliable local connectivity while adapting to heterog

local-aiarxiv-cs-lg

MMD-Balls as Credal Sets: A PAC-Bayesian Framework for Epistemic Uncertainty in Test-Time Adaptation

arXiv:2605.21783v1 Announce Type: new Abstract: Test-time adaptation (TTA) methods improve model performance under distribution shift but lack formal guarantees connecting shift magnitude to predictio

Models Can Model, But Can't Bind: Structured Grounding in Text-to-Optimization

arXiv:2605.21751v1 Announce Type: new Abstract: Text-to-optimization requires two separable capabilities: modeling -- choosing the right optimization structure -- and binding -- grounding every coeffi

MoralityGym: A Benchmark for Evaluating Hierarchical Moral Alignment in Sequential Decision-Making Agents

arXiv:2602.13372v2 Announce Type: replace-cross Abstract: Evaluating moral alignment in agents navigating conflicting, hierarchically structured human norms is a critical challenge at the intersection

Agents

MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems

arXiv:2605.22794v1 Announce Type: cross Abstract: Autonomous agentic systems are largely static after deployment: they do not learn from user interactions, and recurring failures persist until the nex

agentsarxiv-cs-lg

Multi-Modal Machine Learning for Population- and Subject-Specific lncRNA-Type 2 Diabetes Association Analysis

arXiv:2605.20747v1 Announce Type: cross Abstract: Long non-coding RNAs (lncRNAs) are emerging regulatory molecules implicated in chronic disease pathogenesis, including Type 2 Diabetes Mellitus (T2D).

Multiple Neural Operators Achieve Near-Optimal Rates for Multi-Task Learning

arXiv:2605.22724v1 Announce Type: new Abstract: We study the approximation and statistical complexity of learning collections of operators in a shared multi-task setting, with a focus on the Multiple

Near-Optimal Convergence of Accelerated Gradient Methods under Generalized and (L_0, L_1)-Smoothness

arXiv:2508.06884v2 Announce Type: replace-cross Abstract: We study first-order methods for convex optimization problems with functions f satisfying the recently proposed ell-smoothness condition ||nab

Neural Acceleration for Graph Partitioning

arXiv:2605.21519v1 Announce Type: cross Abstract: Graph Partitioning is a critical problem in numerous scientific and engineering domains including social network analysis, VLSI design, and many more.

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approcimations

arXiv:2605.22557v1 Announce Type: new Abstract: We introduce an abstract neural flow framework for neural networks and neural operators. The framework contains two continuous-depth models, namely neur

Neuro-Symbolic AI for Analytical Solutions of Differential Equations

arXiv:2502.01476v4 Announce Type: replace Abstract: Analytical solutions to differential equations offer exact, interpretable insight but are rarely available because discovering them requires expert

No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation

arXiv:2605.22248v1 Announce Type: new Abstract: Climate emulation is an out-of-distribution (OOD) projection task. This is precisely the challenge where modern Machine Learning (ML) methods are most p

Noise Schedule Design for Diffusion Models: An Optimal Control Perspective

arXiv:2605.21911v1 Announce Type: new Abstract: We develop a principled framework for analyzing and designing noise schedules in diffusion models. We show that one can recast this design problem as an

Objective-Induced Bias and Search Dynamics in Multiobjective Unsupervised Feature Selection

arXiv:2605.21561v1 Announce Type: new Abstract: Unsupervised feature selection is commonly formulated as a multiobjective optimisation problem that jointly optimises subset quality and subset size. Ye

On-Policy Consistency Training Improves LLM Safety with Minimal Capability Degradation

arXiv:2605.21834v1 Announce Type: new Abstract: Aligned models can misbehave in several ways: they are often sycophantic, fall victim to jailbreaks, or fail to include appropriate safety warnings. Con

On Robustness and Chain-of-Thought Consistency of RL-Finetuned VLMs

arXiv:2602.12506v3 Announce Type: replace Abstract: Reinforcement learning (RL) finetuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivatin

On Statistical Estimation of Edge-Reinforced Random Walks

arXiv:2503.06115v2 Announce Type: replace-cross Abstract: Reinforced random walks (RRWs), including vertex-reinforced random walks (VRRWs) and edge-reinforced random walks (ERRWs), model random walks

On the Sample Complexity of Discounted Reinforcement Learning with Optimized Certainty Equivalents

arXiv:2605.21763v1 Announce Type: new Abstract: We study risk-sensitive reinforcement learning in finite discounted MDPs, where a generative model of the MDP is assumed to be available. We consider a

One LR Doesn't Fit All: Heavy-Tail Guided Layerwise Learning Rates for LLMs

arXiv:2605.22297v1 Announce Type: new Abstract: Learning rate configuration is a fundamental aspect of modern deep learning. The prevailing practice of applying a uniform learning rate across all laye

One-Way Policy Optimization for Self-Evolving LLMs

arXiv:2605.22156v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a promising paradigm for scaling reasoning capabilities of Large Language Models (LLMs)

Local Ai

OPPO: Bayesian Value Recursion for Token-Level Credit Assignment in LLM Reasoning

arXiv:2605.21851v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has become the standard recipe for improving LLM reasoning, but the dominant algorithm GRPO assigns a sin

local-aiarxiv-cs-lg

Optimal Guarantees for Auditing Renyi Differentially Private Machine Learning

arXiv:2605.21938v1 Announce Type: new Abstract: We study black-box auditing for machine learning algorithms that claim R 'enyi differential privacy (RDP) guarantees. We introduce an auditing framework

Optimization over the intersection of manifolds

arXiv:2605.22736v1 Announce Type: cross Abstract: Optimization over the intersection of two manifolds arises in a broad range of applications, but is hindered by the coupled geometry of the feasible r

Partial Fusion of Neural Networks: Efficient Tradeoffs Between Ensembles and Weight Aggregation

arXiv:2605.22350v1 Announce Type: new Abstract: Ensembles of neural networks typically outperform individual networks but incur large computational costs, whereas weight aggregation produces less cost

PeakFocus: Bridging Peak Localization and Intensity Regression via a Unified Multi-Scale Framework for Electricity Load Forecasting

arXiv:2605.21550v1 Announce Type: new Abstract: Electricity load peak forecasting (ELPF), simultaneously predicting peak timing and intensity, is a prerequisite for effective grid scheduling and risk

PEARL: Unbiased Percentile Estimation via Contrastive Learning for Industrial-Scale Livestream Recommendation

arXiv:2605.21752v1 Announce Type: new Abstract: Recommender systems trained on user interaction data are susceptible to behavioral intensity imbalance--a systematic distortion arising from heterogeneo

Tutorials

PhylaFlow: Hybrid Flow Matching in Billera-Holmes-Vogtmann Tree Space for Phylogenetic Inference

arXiv:2605.21859v1 Announce Type: cross Abstract: Phylogenetic trees are hybrid objects: branch lengths vary continuously, while topologies change discretely through edge contractions and expansions.

tutorialsarxiv-cs-lg

Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction

arXiv:2605.22338v1 Announce Type: new Abstract: Reconstructing continuous physical fields from sparse measurements is a central inverse problem, but data-driven generative models can produce states th

Physics Priors Offer Useful Accuracy-Carbon Trade-Offs in Spatio-Temporal Forecasting

arXiv:2509.24517v2 Announce Type: replace Abstract: Development of modern deep learning methods has been driven primarily by the push for improving model efficacy (accuracy metrics). This sole focus o

Plug-in Losses for Evidential Deep Learning: A Simplified Framework for Uncertainty Estimation that Includes the Softmax Classifier

arXiv:2605.22746v1 Announce Type: new Abstract: Real-world sensor-based learning systems require uncertainty estimation that is both reliable and computationally efficient. Evidential Deep Learning (E

Tutorials

Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning

arXiv:2605.21765v1 Announce Type: new Abstract: The practical adoption of sampling-based inference (SAI) in Bayesian neural networks (BNNs) remains limited, partly due to persistent misconceptions abo

tutorialsarxiv-cs-lg

Post-Training is About States, Not Tokens: A State Distribution View of SFT, RL, and On-Policy Distillation

arXiv:2605.22731v1 Announce Type: new Abstract: Large language model post-training methods such as supervised fine-tuning (SFT), reinforcement learning (RL), and distillation are often analyzed throug

Posterior Collapse as Automatic Spectral Pruning

arXiv:2605.22691v1 Announce Type: new Abstract: We show that posterior collapse in eta-VAEs implements automatic spectral pruning. A latent mode collapses if its contribution to reconstruction is belo

Predicting Performance of Symbolic and Prompt Programs with Examples

arXiv:2605.21515v1 Announce Type: new Abstract: LLM prompting is widely used for naturally stated tasks, yet it is unreliable it may succeed on a few test cases but fail at deployment time. We study p

Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting

arXiv:2602.22270v2 Announce Type: replace Abstract: Spatio-temporal epidemic forecasting is critical for public health management, yet existing methods often struggle with insensitivity to weak epidem

Prior shift estimation for positive unlabeled data through the lens of kernel embedding

arXiv:2502.21194v3 Announce Type: replace-cross Abstract: We study estimation of a class prior for unlabeled target samples which possibly differs from that of source population. Moreover, it is assum

Protein Thoughts: Interpretable Reasoning with Tree of Thoughts and Embedding-Space Flow Matching for Protein-Protein Interaction Discovery

arXiv:2605.21522v1 Announce Type: cross Abstract: Protein-protein interactions (PPIs) govern nearly all cellular processes, yet computational methods for identifying binding partners typically produce

Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series

arXiv:2605.22055v1 Announce Type: new Abstract: Time Series Classification (TSC) is a long-standing research problem that has gained increasing attention in recent years with the rapid growth of large

Provable Joint Decontamination for Benchmarking Multiple Large Language Models

arXiv:2605.21543v1 Announce Type: new Abstract: Benchmark data contamination has become a central challenge in LLM evaluation: when evaluation examples appear in the training data of one or more audit

Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy

arXiv:2605.21780v1 Announce Type: new Abstract: Randomized smoothing is a powerful tool for certifying robustness to adversarial perturbations, including poisoning attacks via randomized training and

Provably Protecting Fine-Tuned LLMs from Training Data Extraction while Preserving Utility

arXiv:2602.00688v2 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) on sensitive datasets raises privacy concerns, as training data extraction (TDE) attacks can expose highly

Proxy-Based Approximation of Shapley and Banzhaf Interactions

arXiv:2605.22738v1 Announce Type: new Abstract: Shapley and Banzhaf interactions capture the complex dynamics inherent in modern machine learning applications. However, current estimators for these hi

Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices

arXiv:2605.22097v1 Announce Type: cross Abstract: Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challen