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MLingualFC: Evaluating Jailbreak Vulnerabilities in Multilingual Vision-Language Models

arXiv:2606.07706v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated strong performance across multimodal tasks, yet their safety robustness remains an open challenge. Whi

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MMR-GRPO: Accelerating GRPO-Style Training through Diversity-Aware Reward Reweighting

DGX agent

arXiv:2601.09085v2 Announce Type: replace-cross Abstract: Group Relative Policy Optimization (GRPO) has become a standard approach for training mathematical reasoning models; however, its reliance on

model-releasesarxiv-cs-ai

9 Jun 2026

Model-Based Learning of Whittle indices

arXiv:2511.20397v2 Announce Type: replace Abstract: We present BLINQ, a new model-based algorithm that learns the Whittle indices of an indexable, communicating and unichain Markov Decision Process (M

Model Multiplicity for Adversarial Detection in Small Language Model Training on Edge Devices

arXiv:2606.07857v1 Announce Type: cross Abstract: The rise of edge-based machine learning has enabled distributed adaptation of language models across mobile and IoT devices, offering privacy preserva

MOLOT System Card: Malicious Operational Logic Observation Transformer

arXiv:2606.07792v1 Announce Type: cross Abstract: MOLOT (Malicious Operational Logic Observation Transformer) is a static malicious-code detection system designed for SAST setup where package metadata

Multi-Armed Bandits with Arriving Arms: Sequential Screening, Dynamic Regret, and Sublinear Guarantees

arXiv:2606.09002v1 Announce Type: cross Abstract: We study a stochastic multi-armed bandit problem in which the set of available arms expands over time. This setting arises in sequential experimentati

Multimodal Large Language Models as Synthetic Participants in Video-Based Studies: An Evaluation

arXiv:2606.07541v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown strong performance on objective tasks such as video understanding and reasoning. However, it remai

mythos will be bad ON PURPOSE on ai 'frontier llm research' tasks, this is very very sad for the research community also the fact that this …

model-releasesjeremy-howard--x

mythos will be bad ON PURPOSE on ai 'frontier llm research' tasks, this is very very sad for the research community also the fact that this is un purpose not visible to the user is crazy Introducing C

NEW: Anthropic introduces Claude Fable 5, a Mythos-class model for general use. Beginning of a new class of frontier models.

NEW: Anthropic introduces Claude Fable 5, a Mythos-class model for general use. Beginning of a new class of frontier models. Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for g

model-releasesdair-ai--x

NGram-MoSE: Efficient Remote Sensing Super-Resolution via N-Gram Context and Mixture-of-Experts

arXiv:2606.08535v1 Announce Type: new Abstract: Remote sensing applications for environmental monitoring and disaster management are frequently constrained by a spatial--temporal trade-off: imagery wi

Now You (Still) See Me: Detecting Evasive Steganographic Payloads in LLMs

arXiv:2606.09411v1 Announce Type: cross Abstract: Large language models can be fine-tuned to encode prompt-borne secrets into fluent, seemingly benign outputs. This creates a steganographic exfiltrati

NutriMLLM: Multimodal Large Language Models for Dietary Micronutrient Analysis

arXiv:2606.08948v1 Announce Type: cross Abstract: Comprehensive estimation of dietary micronutrients from food images could improve clinical nutrition care, but training such models requires large mul

Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark

arXiv:2606.07550v1 Announce Type: cross Abstract: Offline reinforcement learning (RL) offers a promising route for developing plasma controllers from historical tokamak data, since online trial-and-er

OmniCap-IF: Benchmarking and Improving Instruction Following Abilities for Omni-Video Captioning

arXiv:2606.08572v1 Announce Type: new Abstract: While Omni-modal Large Language Models (OLLMs) have demonstrated impressive capabilities in jointly processing audio and visual streams, their ability t

OmniFaceRig: Fully Automatic Inner-Mouth-Aware Face Rigging Across Diverse 3D Character Topologies

arXiv:2606.08043v1 Announce Type: cross Abstract: Facial rigging - creating FACS-based blendshapes together with inner-mouth geometry (teeth, gums, and tongue) - remains a major bottleneck in 3D chara

OmniGameArena: A Unified UE5 Benchmark for VLM Game Agents with Improvement Dynamics

arXiv:2606.09826v1 Announce Type: cross Abstract: Vision-language model (VLM) agents are increasingly deployed in interactive game environments. Yet game benchmarks for VLM agents typically report a s

OmniGen-AR: AutoRegressive Any-to-Image Generation

arXiv:2606.09156v1 Announce Type: new Abstract: Autoregressive (AR) models have demonstrated strong potential in visual generation, offering superior performance with simple architectures and optimiza

OmniMem: Perturbation-aware Memory Compression for Streaming Audio-Visual LLMs

arXiv:2606.07577v1 Announce Type: new Abstract: Audio-visual large language models (LLMs) hold strong promise for long-form video understanding, yet their long-video inference is fundamentally limited

OmniTryOn: Video Try-On Anything at Once!

arXiv:2606.08514v1 Announce Type: new Abstract: Although video virtual try-on (VVT) has achieved significant progress, existing methods still exhibit two fundamental limitations: first, they are restr

On Choosing the mu Parameter in Gaussian Differential Privacy

arXiv:2606.09582v1 Announce Type: new Abstract: Recent work argues for using Gaussian differential privacy (GDP) to report the privacy guarantees in privacy-preserving machine learning. We provide pri

Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits

arXiv:2606.08977v1 Announce Type: new Abstract: Motivated by the recency effect in online learning, we study algorithms for single-pass *sliding-window streaming multi-armed bandits (MABs)* in this pa

OpenAI is hosed. There is almost no reason to prefer them over Anthropic, they have lost their lead (despite every advantage in the world), …

model-releasesgary-marcus--x

OpenAI is hosed. There is almost no reason to prefer them over Anthropic, they have lost their lead (despite every advantage in the world), they have made commitments far far beyond their means, their

Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime

arXiv:2606.08654v1 Announce Type: new Abstract: In this paper, we propose a perturbation-based conformal prediction framework for uncertainty quantification in operator learning, with a focus on the 2

OptMuon: Closed-Loop Orthogonalized Momentum Methods for Stochastic Optimization with Zero-Noise Optimality

arXiv:2606.08783v1 Announce Type: cross Abstract: Orthogonalized momentum updates, as used in Muon-style optimizers, have recently shown strong empirical stability in large-scale deep learning. Howeve

PACT: Learning Diverse Diagnostic Strategies via Privileged Synthesis and Branch Consensus

arXiv:2606.08938v1 Announce Type: cross Abstract: Clinical diagnosis requires flexible use of multiple reasoning paradigms under incomplete patient information. Existing LLM-based medical agents show

Parameter Tuning with Generalization Guarantees for GPU-Accelerated Linear Programming

arXiv:2606.08638v1 Announce Type: cross Abstract: Recent research has developed practical, parallelizable first-order methods for large scale linear programming, but performance is highly dependent on

PEDRA: Evaluating the Realism of Pedestrian Dynamics in Video Generation

arXiv:2510.20182v2 Announce Type: replace Abstract: Pedestrian simulation traditionally relies on expert-tuned, hand-crafted models that limit scalability and generalization. Meanwhile, large-scale vi

PereStruct: Multimodal Semantic Assembly for Robust Historical Document Parsing

arXiv:2606.07661v1 Announce Type: new Abstract: Parsing historical documents with complex, non-standard layouts remains a fundamental bottleneck in large-scale archival digitization. Unlike modern typ

Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs

arXiv:2606.09038v1 Announce Type: new Abstract: Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users' preferences, contexts, and long-term histories. H

Phantom transitions in language model fine-tuning

arXiv:2606.07559v1 Announce Type: cross Abstract: Fine-tuning a language model on contexts whose correct completion has a near-synonym competitor often fails silently. The cross-entropy loss decreases

Pharmacogenomic Knowledge Graph Augmentation for Graph Neural Network-Based Drug-Drug Interaction Prediction

arXiv:2606.07698v1 Announce Type: cross Abstract: Graph neural networks (GNNs) applied to drug-drug interaction (DDI) prediction rely exclusively on molecular structure encoded as SMILES-derived graph

Phase transition in large language models and the criticality of natural languages

arXiv:2406.05335v3 Announce Type: replace-cross Abstract: Generation of text and speech in natural languages can be modeled as a stochastic process. This idea dates back to the seminal work of Markov

phepy: Visual benchmarks and improvements for out-of-distribution detectors

arXiv:2503.05169v2 Announce Type: replace Abstract: Applying machine learning to increasingly high-dimensional problems with sparse or biased training data increases the risk that a model is used on i

PIPE-Cypher: Automatic Enterprise Benchmark Generation for Text-to-Cypher Systems

arXiv:2606.08481v1 Announce Type: cross Abstract: Enterprise property graphs vary widely in schema structure, internal terminology, domain assumptions, governance constraints, and user interaction pat

PLAGUE: Plug-and-play framework for Lifelong Adaptive Generation of Multi-turn Exploits

arXiv:2510.17947v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are improving at an exceptional rate. With the advent of agentic workflows, multi-turn dialogue has become the de

POET-X: Memory-efficient LLM Training by Scaling Orthogonal Transformation

arXiv:2603.05500v2 Announce Type: replace-cross Abstract: Efficient and stable training of large language models (LLMs) remains a core challenge in modern machine learning systems. To address this cha

POISE: Position-Aware Undetectable Skill Injection on LLM Agents

arXiv:2606.07943v1 Announce Type: cross Abstract: Agent skills provide a lightweight mechanism for extending general-purpose agents, but their open format exposes them to skill-poisoning attacks. A pr

POTATR: A Lightweight Image-to-Graph Model for Page-Level Table Extraction

arXiv:2606.09788v1 Announce Type: new Abstract: Large-scale document processing requires contextually aware table extraction (TE) that is both accurate and efficient. Yet current approaches require bi

Powering the future of robotics in Europe

model-releasesgoogle-deepmind

Google DeepMind is launching a three-month accelerator program for early-stage robotics startups across Europe, designed to support the next generation of physical AI. Selected startups receive hands-

Pre-Intervention Prediction of Sparse Autoencoder Steering Side Effects

arXiv:2606.08365v1 Announce Type: cross Abstract: Sparse autoencoder (SAE) features are increasingly used to steer language models, but feature steering is rarely clean: the same intervention can beha

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

arXiv:2602.15327v2 Announce Type: replace-cross Abstract: Machine learning model performance improvements tend to arise from competition and application. For deployment, we consider prescriptive scali

Pretrained, Frozen, Still Leaking: Auditing Cross-Encoder Attribute Transfer in EEG Foundation Models

arXiv:2606.09189v1 Announce Type: cross Abstract: EEG foundation-model releases are usually audited one endpoint at a time: raw-reconstruction, membership inference, identity linkage, or DP-SGD on the

Principled Agent Debate: Adversarial Arbitration for Sycophancy Reduction in Large Language Models

arXiv:2606.07532v1 Announce Type: cross Abstract: RLHF-trained models are systematically biased toward agreement over accuracy, a structural property of the training process. We present Principled Age

PRISM: PRior-guided Imagination Sampling in world Models

arXiv:2606.07974v1 Announce Type: cross Abstract: A learned world model provides a powerful physical intuition for evaluating future states. But its effectiveness in continuous control also depends cr

ProbeAct: Probe-Guided Training-Free Failure Recovery in Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2606.09740v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models demonstrate strong perfor-1 mance on language-conditioned robotic manipulation within their training dis-2 tribution

Programmable Silicon Retina on Pixel Processor Array

arXiv:2606.08370v1 Announce Type: cross Abstract: Standard dynamic vision sensors approximate retinal processing by detecting temporal contrast changes, offering high speed and high dynamic range. In

Projection and Quantisation: A Unifying View of Learning to Hash, from Random Projections to the RAG Era

arXiv:2510.04127v2 Announce Type: replace-cross Abstract: Approximate nearest neighbour (ANN) search underpins large-scale retrieval, increasingly within the retrieval-augmented generation pipelines t

Quantum feature-map learning with reduced resource overhead

arXiv:2510.03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically. In quantum machine learning, success hinges on quantum f