All entries

Automated regime classification in multidimensional time series data using sliced Wasserstein k-means clustering

arXiv:2310.01285v2 Announce Type: replace-cross Abstract: Recent work has proposed Wasserstein k-means (Wk-means) clustering as a powerful method to classify regimes in time series data, and one-dimen

applicationsarxiv-cs-lg

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Autoregression-Free Neural Operators for Time-Dependent PDEs

DGX agent

arXiv:2605.25413v1 Announce Type: cross Abstract: Neural operators learn mappings from function-dependent inputs to solutions, providing an effective framework for solving partial differential equatio

model-releasesarxiv-cs-ai

26 May 2026

Autoregressive Language Models are Secretly Energy-Based Models: Insights into the Lookahead Capabilities of Next-Token Prediction

arXiv:2512.15605v4 Announce Type: replace Abstract: Autoregressive models (ARMs) currently constitute the dominant paradigm for large language models (LLMs). Energy-based models (EBMs) represent anoth

safetyarxiv-cs-lg

Local Ai

AutoSG: LLM-Driven Solver Generation Solely from Task Prompts for Expensive Optimization

arXiv:2605.25658v1 Announce Type: cross Abstract: Expensive optimization tasks are ubiquitous in real-world applications, demanding highly specialized solvers. While LLM-driven automated solver genera

local-aiarxiv-cs-ai

AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery

arXiv:2604.05550v2 Announce Type: replace Abstract: Artificial intelligence research increasingly depends on prolonged cycles of reproduction, debugging, and iterative refinement to achieve State-Of-T

agentsarxiv-cs-cl

AvalancheBench: Evaluating Enterprise Data Agents Through Latent World Recovery

arXiv:2605.24183v1 Announce Type: cross Abstract: We introduce AvalancheBench, a benchmark for evaluating enterprise data agents through latent world recovery. AvalancheBench improves on existing benc

AvAtar: Learning to Align via Active Optimal Transport

arXiv:2605.24395v1 Announce Type: new Abstract: Alignment plays a fundamental role in many machine learning problems, such as multi-network analysis, multimodal learning, and point cloud registration.

safetyarxiv-cs-lg

AVBench: Human-Aligned and Automated Evaluation Benchmark for Audio-Video Generative Models

arXiv:2605.24652v1 Announce Type: new Abstract: Rapid advances in audio-video (AV) generation have enabled high-fidelity synthesis with synchronized sound, particularly for human-related scenarios inv

Axis-Aligned Semantics for ODRL: Resolving Dimensional Ambiguity in Policy Constraints

model-releasesarxiv-cs-cl

arXiv:2602.19878v3 Announce Type: replace Abstract: The Open Digital Rights Language (ODRL) represents policy constraints as triples of a left operand, an operator, and a value. Several spatial operan

Back to Parsimonious Latents: Learning Task-Centric World Models from Visual Foundations

arXiv:2605.25620v1 Announce Type: new Abstract: World models enable agents to predict future dynamics conditioned on actions, making the choice of latent representation central to planning and control

agentsarxiv-cs-ai

BackWeak: Backdooring Knowledge Distillation Simply with Weak Triggers and Fine-tuning

arXiv:2511.12046v2 Announce Type: replace-cross Abstract: Knowledge Distillation (KD) is essential for compressing large models, yet relying on pre-trained 'teacher' models downloaded from third-party

Balancing Fairness, Privacy, and Accuracy: A Multitask Adversarial Framework for Centralized Data-Driven Systems

arXiv:2605.24458v1 Announce Type: cross Abstract: The integration of fairness and privacy in centralized data-driven applications is critical, especially as these systems increasingly influence sector

safetyarxiv-cs-ai

BandVQ: Band-Wise Vector-Quantized EEG Foundation Model

arXiv:2605.24921v1 Announce Type: new Abstract: A central challenge in electroencephalography (EEG) foundation modeling is learning transferable representations across recordings with diverse tasks, m

Batch Normalization Amplifies Memorization and Privacy Risks

arXiv:2605.24420v1 Announce Type: cross Abstract: Batch Normalization (BN) is widely adopted to enable faster convergence and more stable training of deep neural networks. However, its impact on priva

Bayesian Distributional Models of Executive Functioning

model-releasesarxiv-cs-lg

arXiv:2510.00387v3 Announce Type: replace Abstract: This study uses controlled simulations with known ground-truth parameters to evaluate how Distributional Latent Variable Models (DLVM) and Bayesian

BC Protocol: Structured Dual-Expert Dialogue for Eliciting High-Quality Chain-of-Thought Post-Training Data

arXiv:2605.25549v1 Announce Type: cross Abstract: High-quality expert chain-of-thought (CoT) data is one of the core bottlenecks in large language model (LLM) post-training. Existing data production m

Behind EvoMap: Characterizing a Self-Evolving Agent-to-Agent Collaboration Network

arXiv:2605.25815v1 Announce Type: new Abstract: Agent-to-Agent (A2A) networks enable autonomous AI agents to collaborate by sharing reusable problem-solving instructions. However, how these decentrali

agentsarxiv-cs-ai

Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs

arXiv:2605.21602v2 Announce Type: replace Abstract: Many safety and alignment failures of large language models (LLMs) occur due to out-of-distribution (OOD) situations: unusual prompt or response pat

Benchmarking and Learning Real-World Customer Service Dialogue

model-releasesarxiv-cs-cl

arXiv:2510.22143v3 Announce Type: replace Abstract: Existing benchmarks and training pipelines for industrial intelligent customer service (ICS) remain misaligned with real-world dialogue requirements

Benchmarking non-conformity score functions in conformal prediction

arXiv:2605.24983v1 Announce Type: new Abstract: Conformal prediction is a useful and versatile alternative to model calibration in machine learning classification. It replaces single-class prediction

Benchmarking Patent Embeddings: A Multi-Task Evaluation of 22 Models Across Retrieval, Classification, and Clustering

arXiv:2605.24297v1 Announce Type: cross Abstract: Which fine-tuning signals improve patent embedding models, and do gains transfer across patent landscapes? We benchmark 22 embedding models, from 22M-

Benchmarking Pathology Foundation Models for Spatial Domain Understanding

arXiv:2605.25764v1 Announce Type: cross Abstract: Pathology foundation models (PFMs) have emerged as a core approach for learning transferable representations from whole slide images (WSIs), and they

Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork

arXiv:2605.24423v1 Announce Type: new Abstract: In-Context Reinforcement Learning (ICRL) has enabled foundation agents to adapt instantaneously to novel tasks, yet its efficacy in Ad-Hoc Teamwork (AHT

Better, Faster: Harnessing Self-Improvement in Large Reasoning Models

arXiv:2605.24998v1 Announce Type: new Abstract: Self-improvement training enables the large reasoning models (LRMs) to improve themselves by self-generating reasoning trajectories as training data wit

researcharxiv-cs-cl

Beyond Control-Flow: Integrating the Resource Perspective into Multi-Collaborative Process Modeling from Text

arXiv:2605.24546v1 Announce Type: new Abstract: Process modeling is a sub-domain of Business Process Management (BPM) focused on the translation of process artifacts into formal models. This task trad

Beyond Final Answers: Auditing Trajectory-Level Hallucinations in Multi-Agent Industrial Workflows

arXiv:2605.24219v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents that reason, use tools, and act over multiple steps. Yet most hallucination

Beyond Fixed Points: Superpolynomial Capacity of Asymmetric Hopfield Networks

arXiv:2605.24611v1 Announce Type: new Abstract: Classical Hopfield networks are limited to static patterns due to symmetric weights, whereas asymmetric networks can encode temporal sequences via limit

Beyond Generative Priors: Minority Sampling with JEPA-Guided Diffusion

arXiv:2605.24631v1 Announce Type: cross Abstract: Minority sampling aims to generate low-density instances on a data manifold and is of central importance in applications such as medical diagnosis, an

applicationsarxiv-cs-ai

Beyond Inference-Only Deployment: Comparing Weight-Based Consolidation Against Cascading Compaction

arXiv:2605.24657v1 Announce Type: new Abstract: Major LLM platforms deploy models in an inference-only configuration: the model serves requests but never updates per-user weights. Users must repeatedl

Beyond Killer Robots: General AI Attitudes and Public Support for Military AI in Nine Countries

arXiv:2605.25196v1 Announce Type: cross Abstract: AI-enabled military systems are a fixture of modern military conflict. Applications vary from autonomous drones for surveillance and attack to AI-supp

safetyarxiv-cs-ai

Beyond Literal Translation: Evaluating Cultural Effectiveness in Social Media UGC

model-releasesarxiv-cs-cl

arXiv:2605.25626v1 Announce Type: new Abstract: Social media platforms enable large-scale cross-lingual communication, but translating user-generated content (UGC) remains challenging due to its infor

Beyond Predefined Learning Objects: A Thinking-Learning Interaction Model for Up-to-Date Autonomous Robot Learning

arXiv:2605.23987v1 Announce Type: new Abstract: Autonomous robots operating in open and changing environments cannot always rely on predefined inputs, outputs, and action routines. Although existing l

agentsarxiv-cs-ai

Beyond Query Memorization: Large Language Model Routing with Query Decomposition and Historical Matching

arXiv:2605.25558v1 Announce Type: new Abstract: Optimizing the trade-off among predictive performance and computational cost is a central focus in the deployment of Large Language Models (LLMs). Curre

Beyond Static Uncertainty: Modeling Temporal Uncertainty Dynamics for Probabilistic Time Series Forecasting

arXiv:2603.24254v2 Announce Type: replace-cross Abstract: Real-world time series exhibit temporally structured uncertainty: volatility clusters in turbulent regimes, dissipates in stable periods, and

applicationsarxiv-cs-ai

Beyond Summaries: Structure-Aware Labeling of Code Changes with Large Language Models

arXiv:2605.26100v1 Announce Type: cross Abstract: Code review is a critical practice in software engineering, yet the growing scale and frequency of code patches in modern projects, together with the

Hardware

Beyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs

arXiv:2605.24684v1 Announce Type: cross Abstract: Multimodal Attributed Graph Learning (MAGL) integrates intrinsic node attributes with structural topology via graph aggregation. However, as pretraine

hardwarearxiv-cs-ai

Beyond the Frontier: Stochastic Backtracking for Efficient Test-Time Scaling

arXiv:2605.25143v1 Announce Type: new Abstract: Test-time scaling improves language model reasoning by spending additional compute to explore multiple solution trajectories. The key challenge is to ma

Beyond the Proxy: Trajectory-Distilled Guidance for Offline GFlowNet Training

arXiv:2505.20110v3 Announce Type: replace-cross Abstract: Generative Flow Networks (GFlowNets) excel at sampling diverse, high-reward objects. In many practical applications where active reward querie

safetyarxiv-cs-ai

Beyond the Target: From Imitation to Collaboration in Speculative Decoding

arXiv:2605.24793v1 Announce Type: new Abstract: Speculative decoding (SPD) accelerates large language model (LLM) inference by letting a smaller draft model propose multiple future tokens that are ver

safetyarxiv-cs-cl

BigMac: Breaking the Pareto Frontier of Compute and Memory in Multimodal LLM Training

arXiv:2605.25451v1 Announce Type: new Abstract: Training multimodal large language models (MLLMs) is challenged by both model and data heterogeneity. Existing systems redesign the training pipeline to

Bilevel Optimization of Synthetic Trajectories for Multi-Turn LLM Fine-Tuning

arXiv:2605.24743v1 Announce Type: cross Abstract: While LLMs excel at single-turn generation, they struggle with long-horizon, multi-turn interactions. Offline reinforcement learning (RL) offers a sca

Binding Visual Features Point by Point

arXiv:2605.25427v1 Announce Type: cross Abstract: Despite success on standard benchmarks, vision language models display persistent failures on tasks involving processing of multi-object scenes, inclu

BlitzRank: Principled Zero-shot Ranking Agents with Tournament Graphs

arXiv:2602.05448v4 Announce Type: replace Abstract: Selecting the top m from n items via expensive k-wise comparisons is central to settings ranging from LLM-based document reranking to crowdsourced e

applicationsarxiv-cs-lg

Blocked Gibbs meets Diffusion Transformers: Unsupervised Learning for Constraint Optimization

arXiv:2605.25129v1 Announce Type: new Abstract: Diffusion models have shown promise in learning to solve constraint optimization problems. However, they are mostly restricted to problems with binary v

safetyarxiv-cs-lg

BODHI: Precise OS Kernel Specification Inference

arXiv:2605.23931v1 Announce Type: new Abstract: The formal verification of operating system kernels requires precise specifications that capture the intended behavior of system calls. Writing these sp

Local Ai

Boosting Inference with Guided Reasoning: Stochastic Exploration for Recursive Models

arXiv:2605.25230v1 Announce Type: new Abstract: Recent work on recursive architectures has shown that tiny neural networks can be surprisingly powerful on structured reasoning tasks. The trick is to m

local-aiarxiv-cs-ai

Tutorials

BoxLitE: A Faithful Knowledge Base Embedding Based on Convex Optimization

arXiv:2605.23937v1 Announce Type: new Abstract: Knowledge base (KB) embeddings aim at combining the capability of classical knowledge graph embeddings to generalize the information present in facts, t

tutorialsarxiv-cs-ai

Branch Scaling Manifests as Implicit Architectural Regularization for Improving Generalization in Overparameterized ResNets

arXiv:2403.04545v3 Announce Type: replace Abstract: Scaling factors in residual branches have emerged as a prevalent method for boosting neural network performance, especially in normalization-free ar

applicationsarxiv-cs-lg