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Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation

arXiv:2605.14404v1 Announce Type: new Abstract: While LLMs are increasingly used in commercial services, they pose privacy risks such as leakage of sensitive personally identifiable information (PII).

researcharxiv-cs-cl

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Agents

Known By Their Actions: Fingerprinting LLM Browser Agents via UI Traces

DGX agent

arXiv:2605.14786v1 Announce Type: cross Abstract: As LLM-based agents increasingly browse the web on users' behalf, a natural question arises: can websites passively identify which underlying model po

agentsarxiv-cs-ai

15 May 2026

Kolmogorov-Arnold Chemical Reaction Neural Networks for learning pressure-dependent kinetic rate laws

model-releasesarxiv-cs-lg

arXiv:2511.07686v2 Announce Type: replace-cross Abstract: Chemical Reaction Neural Networks (CRNNs) have emerged as an interpretable machine learning framework for discovering reaction kinetics direct

Krause Synchronization Transformers

arXiv:2602.11534v3 Announce Type: replace-cross Abstract: Self-attention in Transformers relies on globally normalized softmax weights, causing all tokens to compete for influence at every layer. When

KVPO: ODE-Native GRPO for Autoregressive Video Alignment via KV Semantic Exploration

arXiv:2605.14278v1 Announce Type: new Abstract: Aligning streaming autoregressive (AR) video generators with human preferences is challenging. Existing reinforcement learning methods predominantly rel

safetyarxiv-cs-cv

L2R: Low-Rank and Lipschitz-Controlled Routing for Mixture-of-Experts

arXiv:2601.21349v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models scale neural networks by conditionally activating a small subset of experts, where the router plays a central

Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows

arXiv:2605.14527v1 Announce Type: new Abstract: Developing machine learning interatomic potentials (MLIPs) for complex materials systems remains challenging because it requires expertise in atomistic

agentsarxiv-cs-lg

@LangChain’s Interrupt 2026 conference was a blast!! such a pleasure to with @VictorMoreira16 about deep agents! ICYMI: we just dropped v0.6…

@LangChain’s Interrupt 2026 conference was a blast!! such a pleasure to with @VictorMoreira16 about deep agents! ICYMI: we just dropped v0.6, which is focused on performance at the model, harness, and

agentsharrison-chase--x

Language Generation as Optimal Control: Closed-Loop Diffusion in Latent Control Space

arXiv:2605.14531v1 Announce Type: new Abstract: This work reformulates language generation as a stochastic optimal control problem, providing a unified theoretical perspective to analyze autoregressiv

safetyarxiv-cs-cl

Language-Induced Priors for Domain Adaptation

arXiv:2605.14301v1 Announce Type: new Abstract: Domain adaptation faces a fundamental paradox in the cold-start regime. When target data is scarce, statistical methods fail to distinguish relevant sou

tutorialsarxiv-cs-lg

Large Dimensional Kernel Ridge Regression: Extending to Product Kernels

arXiv:2605.14524v1 Announce Type: cross Abstract: Recent studies have reported extit{saturation effects} and extit{multiple descent behavior} in large dimensional kernel ridge regression (KRR). Howeve

researcharxiv-cs-lg

Large Language Models for Web Accessibility: A Systematic Literature Review

arXiv:2605.13873v1 Announce Type: cross Abstract: Web accessibility aims to ensure that web content and services are usable by people with diverse abilities. In recent years, Large Language Models (LL

tutorialsarxiv-cs-ai

Latency-Quality Routing for Functionally Equivalent Tools in LLM Agents

model-releasesarxiv-cs-lg

arXiv:2605.14241v1 Announce Type: new Abstract: Tool-augmented LLM agents increasingly access the same tool type through multiple functionally equivalent providers, such as web-search APIs, retrievers

LATERN: Test-Time Context-Aware Explainable Video Anomaly Detection

arXiv:2605.15054v1 Announce Type: new Abstract: Vision-language models (VLMs) have recently emerged as a promising paradigm for video anomaly detection (VAD) due to their strong visual reasoning abili

safetyarxiv-cs-cv

Applications

LEAP: Local ECT-Based Learnable Positional Encodings for Graphs

arXiv:2510.00757v3 Announce Type: replace Abstract: Graph neural networks (GNNs) largely rely on the message-passing paradigm, where nodes iteratively aggregate information from their neighbors. Yet,

applicationsarxiv-cs-lg

Learning Cross-Coupled and Regime Dependent Dynamics for Aerial Manipulation

model-releasesarxiv-cs-ro

arXiv:2605.14805v1 Announce Type: new Abstract: Accurate dynamics models are critical for aerial manipulators operating under complex tasks such as payload transport. However, modeling these systems r

Learning Developmental Scaffoldings to Guide Self-Organisation

arXiv:2605.14998v1 Announce Type: new Abstract: From subcellular structures to entire organisms, many natural systems generate complex organisation through self-organisation: local interactions that c

local-aiarxiv-cs-ai

Learning Direct Control Policies with Flow Matching for Autonomous Driving

arXiv:2605.14832v1 Announce Type: cross Abstract: We present a flow-matching planner for autonomous driving that directly outputs actionable control trajectories defined by acceleration and curvature

agentsarxiv-cs-cv

Learning Dynamic Rope Manipulation Using Task-Level Iterative Learning Control

arXiv:2602.21302v2 Announce Type: replace Abstract: We introduce a Task-Level Iterative Learning Control method for dynamic manipulation of ropes. We demonstrate this method on a non-planar rope manip

researcharxiv-cs-ro

Learning from Failures: Correction-Oriented Policy Optimization with Verifiable Rewards

arXiv:2605.14539v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective paradigm for improving the reasoning capabilities of large language mo

safetyarxiv-cs-cl

Learning from Language Feedback via Variational Policy Distillation

arXiv:2605.15113v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) suffers from sparse outcome signals, creating severe exploration bottlenecks on complex reasoning

safetyarxiv-cs-lg

Learning Multimodal Embeddings for Traffic Accident Prediction and Causal Estimation

arXiv:2512.02920v3 Announce Type: replace-cross Abstract: We consider analyzing traffic accident patterns using both road network data and satellite images aligned to road graph nodes. Previous work f

researcharxiv-cs-cv

Learning Scenario Reduction for Two-Stage Robust Optimization with Discrete Uncertainty

arXiv:2605.14494v1 Announce Type: new Abstract: Two-Stage Robust Optimization (2RO) with discrete uncertainty is challenging, often rendering exact solutions prohibitive. Scenario reduction alleviates

Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting

arXiv:2601.21151v2 Announce Type: replace Abstract: Recent machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms-advection (lo

local-aiarxiv-cs-lg

Learning to Build the Environment: Self-Evolving Reasoning RL via Verifiable Environment Synthesis

arXiv:2605.14392v1 Announce Type: new Abstract: We pursue a vision for self-improving language models in which the model does not merely generate problems or traces to imitate, but constructs the envi

safetyarxiv-cs-ai

Learning with Semantic Priors: Stabilizing Point-Supervised Infrared Small Target Detection via Hierarchical Knowledge Distillation

arXiv:2605.14346v1 Announce Type: new Abstract: Single-frame Infrared Small Target Detection (ISTD) aims to localize weak targets under heavy background clutter, yet dense pixel-wise annotations are e

local-aiarxiv-cs-cv

Applications

Learning with Shallow Neural Networks on Cluster-Structured Features

arXiv:2605.14927v1 Announce Type: new Abstract: The success of deep learning in high-dimensional settings is often attributed to the presence of low-dimensional structure in real-world data. While sta

applicationsarxiv-cs-lg

LEMON: Learning Executable Multi-Agent Orchestration via Counterfactual Reinforcement Learning

arXiv:2605.14483v1 Announce Type: new Abstract: Large language models (LLMs) have become a strong foundation for multi-agent systems, but their effectiveness depends heavily on orchestration design. A

local-aiarxiv-cs-ai

Applications

Les vrais génies de l'histoire. 108 milliards d'humains ont vécu sur Terre. La liste ci-dessous en contient ~100. Soit environ 1 sur 1 milli…

Les vrais génies de l'histoire. 108 milliards d'humains ont vécu sur Terre. La liste ci-dessous en contient ~100. Soit environ 1 sur 1 milliard. C'est ça, le « autre chose » que les gens cherchent qua

applicationselon-musk--x

Let Robots Feel Your Touch: Visuo-Tactile Cortical Alignment for Embodied Mirror Resonance

arXiv:2605.14571v1 Announce Type: cross Abstract: Observing touch on another's body can elicit corresponding tactile sensations in the observer, a phenomenon termed mirror touch that supports empathy

safetyarxiv-cs-lg

Leveraging Speech to Identify Signatures of Insight and Transfer in Problem Solving

arXiv:2605.12970v2 Announce Type: replace Abstract: Many problems seem to require a flash of insight to solve. What form do these sudden insights take, and what impact do they have on how people appro

researcharxiv-cs-cl

Industry

Liftoff!

SpaceX announced a successful rocket launch, likely referring to a Starship or Falcon 9 mission based on the typical content shared by the company's official account. The post commemorates the liftoff

industryelon-musk--x

LiSA: Lifelong Safety Adaptation via Conservative Policy Induction

arXiv:2605.14454v1 Announce Type: cross Abstract: As AI agents move from chat interfaces to systems that read private data, call tools, and execute multi-step workflows, guardrails become a last line

local-aiarxiv-cs-cl

LiWi: Layering in the Wild

arXiv:2605.14552v1 Announce Type: new Abstract: Recent advances in generative models have empowered impressive layered image generation, yet their success is largely confined to graphic design domains

agentsarxiv-cs-cv

LLM-based Detection of Manipulative Political Narratives

arXiv:2605.14354v1 Announce Type: new Abstract: We present a new computational framework for detecting and structuring manipulative political narratives. A task that became more important due to the s

researcharxiv-cs-cl

LLM-Based Robustness Testing of Microservice Applications: An Empirical Study

arXiv:2605.14202v1 Announce Type: cross Abstract: Malformed, missing, or boundary-value inputs in microservice APIs can cascade across dependent services, threatening reliability. Robustness testing s

LLMs Know When They Know, but Do Not Act on It: A Metacognitive Harness for Test-time Scaling

model-releasesarxiv-cs-lg

arXiv:2605.14186v1 Announce Type: new Abstract: Large language models (LLMs) often expose useful signals of self-monitoring: before solving a problem, they can estimate whether they are likely to succ

LLMs learn scientific taste from institutional traces across the social sciences

arXiv:2603.16659v3 Announce Type: replace Abstract: Reinforcement-learned reasoning has powered recent AI leaps on verifiable tasks, including mathematics, code, and structure prediction. The harder b

tutorialsarxiv-cs-ai

LLMs Should Express Uncertainty Explicitly

arXiv:2604.05306v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often produce confident yet incorrect answers, which can lead to risky failures in real-world applications. We st

Local Spatiotemporal Convolutional Network for Robust Gait Recognition

arXiv:2605.14548v1 Announce Type: new Abstract: Gait recognition, as a promising biometric technology, identifies individuals through their unique walking patterns and offers distinctive advantages in

tutorialsarxiv-cs-cv

Logging Policy Design for Off-Policy Evaluation

arXiv:2605.15108v1 Announce Type: cross Abstract: Off-policy evaluation (OPE) estimates the value of a target treatment policy (e.g., a recommender system) using data collected by a different logging

safetyarxiv-cs-ai

LoMETab: Beyond Rank-1 Ensembles for Tabular Deep Learning

arXiv:2605.14365v1 Announce Type: cross Abstract: Recent tabular learning benchmarks increasingly show a tight performance cluster rather than a clear hierarchy among leading methods, spanning gradien

Industry

London-based edtech startup Multiverse raised 70M at a 2.1B valuation from Index and others as it pushes into AI training; it acquired StackFuel in January (Kieran Smith/Financial Times)

Kieran Smith / Financial Times: London-based edtech startup Multiverse raised 70M at a 2.1B valuation from Index and others as it pushes into AI training; it acquired StackFuel in January — Multiverse

industrytechmeme

LoRA in LoRA: Towards Parameter-Efficient Architecture Expansion for Continual Visual Instruction Tuning

arXiv:2508.06202v2 Announce Type: replace-cross Abstract: Continual Visual Instruction Tuning (CVIT) enables Multimodal Large Language Models (MLLMs) to incrementally learn new tasks over time. Howeve

LoRIF: Low-Rank Influence Functions for Scalable Training Data Attribution

arXiv:2601.21929v2 Announce Type: replace Abstract: Training data attribution (TDA) identifies which training examples most influenced a model's prediction. Influence function methods are a theoretica

researcharxiv-cs-lg

LoVeC: Reinforcement Learning for Better Verbalized Confidence in Long-Form Generations

arXiv:2505.23912v2 Announce Type: replace-cross Abstract: Hallucination remains a major challenge for the safe and trustworthy deployment of large language models (LLMs) in factual content generation.

LPH-VTON: Resolving the Structure-Texture Dilemma of Virtual Try-On via Latent Process Handover

arXiv:2605.14874v1 Announce Type: new Abstract: Virtual Try-On (VTON) aims to synthesize photorealistic images of garments precisely aligned with a person's body and pose. Current diffusion-based meth

safetyarxiv-cs-cv

M^2RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling

arXiv:2603.14360v2 Announce Type: replace-cross Abstract: Transformers are highly parallel but are limited to computations in the TC^0 complexity class, excluding tasks such as entity tracking and cod