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Linear-Time Global Visual Modeling without Explicit Attention

model-releasesarxiv-cs-cv

arXiv:2605.01711v1 Announce Type: new Abstract: Existing research largely attributes the global sequence modeling capability of Transformers to the explicit computation of attention weights, a process

Local Ai

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Linearizing Vision Transformer with Test-Time Training

DGX agent

arXiv:2605.02772v1 Announce Type: new Abstract: While linear-complexity attention mechanisms offer a promising alternative to Softmax attention for overcoming the quadratic bottleneck, training such m

local-aiarxiv-cs-cv

5 May 2026

LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery

arXiv:2508.12232v3 Announce Type: replace-cross Abstract: Issue-to-commit link recovery in software repositories is fundamental to software traceability and project management, yet it remains a challe

agentsarxiv-cs-ai

Linking spatial biology and clinical histology via Haiku

arXiv:2605.00925v1 Announce Type: cross Abstract: Integrating molecular, morphological, and clinical data is essential for basic and translational biomedical research, yet systematic frameworks for jo

safetyarxiv-cs-cv

LinMU: Multimodal Understanding Made Linear

arXiv:2601.01322v2 Announce Type: replace Abstract: Modern Vision-Language Models (VLMs) achieve impressive performance but are limited by the quadratic complexity of self-attention, which prevents th

researcharxiv-cs-cv

LITcoder: A General-Purpose Library for Building and Comparing Encoding Models

arXiv:2509.09152v2 Announce Type: replace Abstract: We introduce LITcoder, an open-source library for building and benchmarking neural encoding models. Designed as a flexible backend, LITcoder provide

LiteVLA-H: Dual-Rate Vision-Language-Action Inference for Onboard Aerial Guidance and Semantic Perception

model-releasesarxiv-cs-cv

arXiv:2605.00884v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown strong semantic grounding and task generalization in manipulation, but aerial deployment remains difficul

LittleBit-2: Maximizing the Spectral Energy Gain in Sub-1-Bit LLMs via Latent Geometry Alignment

model-releasesarxiv-cs-lg

arXiv:2603.00042v2 Announce Type: replace Abstract: We identify the Spectral Energy Gain in extreme model compression, where low-rank binary approximations outperform tiny-rank floating-point baseline

LLM-Augmented Semantic Steering of Text Embedding Projection Spaces

arXiv:2605.01957v1 Announce Type: cross Abstract: Low-dimensional projections of text embeddings support visual analysis of document collections, but their spatial organization may not reflect the rel

safetyarxiv-cs-cl

LLM-Based Agentic Negotiation for 6G: Addressing Uncertainty Neglect and Tail-Event Risk

arXiv:2511.19175v2 Announce Type: replace-cross Abstract: A critical barrier to the trustworthiness of sixth-generation (6G) agentic autonomous networks is the uncertainty neglect bias; a cognitive te

safetyarxiv-cs-ai

Tools

llm-echo 0.5a0

Release: llm-echo 0.5a0 New -o thinking 1 option to help test against LLM 0.32a0 and higher. This plugin provides a fake model called 'echo' for LLM which doesn't run an LLM at all - it's useful for w

toolssimon-willison

LLM-Foraging: Large Language Models for Decentralized Swarm Robot Foraging

model-releasesarxiv-cs-ro

arXiv:2605.01461v1 Announce Type: new Abstract: Swarm foraging algorithms, such as the central-place foraging algorithm (CPFA), typically rely on offline parameter optimization using genetic algorithm

LLM Ghostbusters: Surgical Hallucination Suppression via Adaptive Unlearning

arXiv:2605.01047v1 Announce Type: cross Abstract: Hallucinations, outputs that sound plausible but are factually incorrect, remain an open challenge for deployed LLMs. In code generation, models frequ

agentsarxiv-cs-cl

Hardware

LLM Output Detectability and Task Performance Can be Jointly Optimized

arXiv:2605.01350v1 Announce Type: new Abstract: Detecting machine-generated text is essential for transparency and accountability when deploying large language models (LLMs). Among detection approache

hardwarearxiv-cs-cl

LLM-VA: Resolving the Jailbreak-Overrefusal Trade-off via Vector Alignment

arXiv:2601.19487v2 Announce Type: replace Abstract: Safety-aligned LLMs suffer from two failure modes: jailbreak (answering harmful inputs) and over-refusal (declining benign queries). Existing vector

llms are getting expensive why we need oss models

Large language models are becoming increasingly costly to develop and operate, creating a need for open-source alternatives that reduce dependency on expensive proprietary models and make AI more acce

agentsharrison-chase--x

Local Hessian Spectral Filtering for Robust Intrinsic Dimension Estimation

arXiv:2605.01221v1 Announce Type: new Abstract: While diffusion models enable new approaches for estimating Local Intrinsic Dimension (LID), existing methods fail in high-dimensional spaces where nois

researcharxiv-cs-lg

Logit-Gap Steering: A Forward-Pass Diagnostic for Alignment Robustness

arXiv:2506.24056v2 Announce Type: replace-cross Abstract: RLHF-style alignment trains language models to refuse unsafe requests, but how much operational margin does this refusal rest on? We introduce

safetyarxiv-cs-cl

Logit-KL Flow Matching: Non-Autoregressive Text Generation via Sampling-Hybrid Inference

arXiv:2411.16821v5 Announce Type: replace Abstract: Non-autoregressive (NAR) language models offer notable efficiency in text generation by circumventing the sequential bottleneck of autoregressive de

Industry

London AI people, film night? Join @EMostaque at AI Movie Club x Blade Runner for an AI chat, then the film. 📍Kensington Central Library 📅…

Emad Mostaque organized an AI Movie Club event in London at Kensington Central Library featuring a discussion about AI followed by a screening of Blade Runner. The event invited London-based AI enthus

industryemad-mostaque--x

Lost in the Tower of Babel: The Adverse Effects of Incidental Multilingualism in LLMs

arXiv:2605.01224v1 Announce Type: new Abstract: This paper argues that contemporary multilingual NLP has converged on a fragile and misleading paradigm of incidental multilingualism. Today's LLMs appe

agentsarxiv-cs-cl

Low-Latency Embedded Driver Monitoring System with a Multi-Task Neural Network

arXiv:2605.02563v1 Announce Type: new Abstract: Road traffic accidents remain a significant global concern, with the majority attributed to human factors such as driver distraction and fatigue. This s

researcharxiv-cs-cv

Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy Versus Performance

arXiv:2410.18717v2 Announce Type: replace Abstract: Recent advancements in artificial intelligence hold ample potential for monitoring applications using surveillance cameras. However, concerns about

safetyarxiv-cs-cv

Local Ai

Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers

arXiv:2509.15113v2 Announce Type: replace Abstract: The growing demand for energy-efficient, high-performance AI systems has led to increased attention on alternative computing platforms (e.g., photon

local-aiarxiv-cs-lg

Local Ai

Luma Uni-1 just landed in ComfyUI. Most image models: prompt → pixels. Uni-1: prompt → reasoning → pixels. Autoregressive, not diffusion. De…

Luma Uni-1 just landed in ComfyUI. Most image models: prompt → pixels. Uni-1: prompt → reasoning → pixels. Autoregressive, not diffusion. Decomposes instructions, resolves constraints, plans compositi

local-aicomfyui--x

LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning

model-releasesarxiv-cs-lg

arXiv:2605.02133v1 Announce Type: new Abstract: AC optimal power flow (ACOPF) is foundational yet computationally expensive in power grid operations, driving learning-based surrogates for large-scale

LVLM-Aided Alignment of Task-Specific Vision Models

arXiv:2512.21985v2 Announce Type: replace Abstract: In high-stakes domains, small task-specific vision models are crucial due to their low computational requirements and the availability of numerous m

safetyarxiv-cs-cv

Applications

Machine Learning as Iterated Belief Change a la Darwiche and Pearl

arXiv:2506.13157v3 Announce Type: replace-cross Abstract: Artificial Neural Networks (ANNs) are powerful machine-learning models capable of capturing intricate non-linear relationships. They are widel

applicationsarxiv-cs-lg

Applications

Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction

arXiv:2605.01122v1 Announce Type: new Abstract: Iterative ptychographic reconstruction algorithms are widely used for coherent diffractive imaging but can exhibit slow convergence under realistic expe

applicationsarxiv-cs-lg

Machine Learning Enhanced Laser Spectroscopy for Multi-Species Gas Detection in Complex and Harsh Environments

arXiv:2605.01306v1 Announce Type: cross Abstract: Laser absorption spectroscopy (LAS) is a well-established technique for non-intrusive measurement of gas species in combustion and atmospheric environ

MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate

arXiv:2605.01347v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own trajectories under token-level teacher supervision, but existing methods are capped by a single

safetyarxiv-cs-cl

MAGIC: Multi-Step Advantage-Gated Causal Influence for Multi-agent Reinforcement Learning

arXiv:2605.01805v1 Announce Type: cross Abstract: A key challenge in multi-agent reinforcement learning (MARL) lies in designing learning signals that effectively promote coordination among agents. De

agentsarxiv-cs-lg

Hardware

Maistros: A Greek Large Language Model Adapted Through Knowledge Distillation From Large Reasoning Models

arXiv:2605.01870v1 Announce Type: new Abstract: Large Language Models (LLMs) have substantially advanced the field of Natural Language Processing (NLP), achieving state-of-the-art performance across a

hardwarearxiv-cs-cl

Major new class action lawsuit accuses Meta of copyright infringement around AI training. It says they trained on pirated books, and that AI…

Major new class action lawsuit accuses Meta of copyright infringement around AI training. It says they trained on pirated books, and that AI books flooding the market demonstrates market harm. These l

safetygary-marcus--x

Mamoda2.5: Enhancing Unified Multimodal Model with DiT-MoE

model-releasesarxiv-cs-cv

arXiv:2605.02641v1 Announce Type: new Abstract: We present Mamoda2.5, a unified AR-Diffusion framework that seamlessly integrates multimodal understanding and generation within a single architecture.

Manifold-Aligned Guided Integrated Gradients for Reliable Feature Attribution

arXiv:2605.02167v1 Announce Type: cross Abstract: Feature attribution is central to diagnosing and trusting deep neural networks, and Integrated Gradients (IG) is widely used due to its axiomatic prop

researcharxiv-cs-cv

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment

arXiv:2605.02183v1 Announce Type: new Abstract: Adversarial training is effective on balanced datasets, but its robustness degrades under longtailed class distributions, where tail classes suffer high

Mapping Discourse Reframing: A Multi-Layer Network Approach to Italian HPV Vaccine Discourse on X (2010-2024)

arXiv:2605.02629v1 Announce Type: new Abstract: Understanding how online narratives travel through coalitions is critical for identifying information disorder, yet computational analyses often rely on

MapRF: Weakly Supervised Online HD Map Construction via NeRF-Guided Self-Training

arXiv:2511.19527v2 Announce Type: replace Abstract: Autonomous driving systems benefit from high-definition (HD) maps that provide critical information about road infrastructure. The online constructi

agentsarxiv-cs-cv

Markets with Heterogeneous Agents: Dynamics and Survival of Bayesian vs. No-Regret Learners

arXiv:2502.08597v3 Announce Type: replace-cross Abstract: We analyze the performance of heterogeneous learning agents in asset markets with stochastic payoffs. Our main focus is on comparing Bayesian

agentsarxiv-cs-ai

Industry

Match Group reports Q1 revenue up 4% YoY to 864M, vs. 855M est., as Tinder's new user registrations grew for the first time since 2024, up 1% (Samantha Kelly/Bloomberg)

Samantha Kelly / Bloomberg: Match Group reports Q1 revenue up 4% YoY to 864M, vs. 855M est., as Tinder's new user registrations grew for the first time since 2024, up 1% — Match Group Inc. reported fi

industrytechmeme

May the 4th be with you!✨ Celebrate with us, you must. Join our Start Up Party Up this Thursday before SF's free 2nd St Fest 🎵 Calling all …

May the 4th be with you!✨ Celebrate with us, you must. Join our Start Up Party Up this Thursday before SF's free 2nd St Fest 🎵 Calling all #AI Jedis - leave your agents at home and sign up for our nex

agentsjerry-liu--x

mdok-style at SemEval-2026 Task 10: Finetuning LLMs for Conspiracy Detection

arXiv:2605.02712v1 Announce Type: new Abstract: SemEval-2026 Task 10 is focused on conspiracy detection. Specifically, the goal is to detect whether a Reddit comment expresses a conspiracy belief. Our

mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection

model-releasesarxiv-cs-cl

arXiv:2605.02695v1 Announce Type: new Abstract: SemEval-2026 Task 9 is focused on multilingual polarization detection. Specifically, it covers the identification of multilingual, multicultural and mul

Mean-Field Path-Integral Diffusion: From Samples to Interacting Agents

model-releasesarxiv-cs-ai

arXiv:2605.00007v1 Announce Type: cross Abstract: Independent sample generation is the prevailing paradigm in modern diffusion-based generative models of AI. We ask a different question: can samples c

Mean Testing under Truncation beyond Gaussian

arXiv:2605.01335v1 Announce Type: cross Abstract: We characterize the fundamental limits of high-dimensional mean testing under arbitrary truncation, where samples are drawn from the conditional distr

Tutorials

Measuring AI Reasoning: A Guide for Researchers

arXiv:2605.02442v1 Announce Type: cross Abstract: In this paper, we offer a guide for researchers on evaluating reasoning in language models, building the case that reasoning should be assessed throug

tutorialsarxiv-cs-cl

Measuring Differences between Conditional Distributions using Kernel Embeddings

arXiv:2605.02260v1 Announce Type: cross Abstract: Comparing conditional distributions is a fundamental challenge in statistics and machine learning, with applications across a wide range of domains. W

researcharxiv-cs-lg