All entries

industryallie-k--miller--x

In a conversation between Matt Berman and Google CEO Sundar Pichai, Pichai confirmed that Google is experiencing greater demand for its services than its current computational capacity can handle. Thi

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

MAVEN A Multi-Agent Framework for Multicultural Text-to-Video Generation

DGX agent

arXiv:2605.16716v1 Announce Type: cross Abstract: Text-to-video (T2V) generation has rapidly progressed in visual fidelity, yet its ability to faithfully represent multiple cultures within a single pr

model-releasesarxiv-cs-ai

19 May 2026

Tutorials

Maximum Likelihood Decoding of Quantum Error Correction Codes

arXiv:2605.17230v1 Announce Type: cross Abstract: Quantum error correction (QEC) is indispensable for realizing fault-tolerant quantum computation, yet its effectiveness hinges critically on the class

tutorialsarxiv-cs-lg

MCQ Difficulty Prediction via Modeling Learner Heterogeneity Using Data-Driven Cognitive Profiling

arXiv:2605.16290v1 Announce Type: cross Abstract: Predicting the difficulty of multiple-choice questions (MCQs) is important for effective assessment, yet current methods typically assume a unimodal s

Measuring Changes in Instructor Class Design and Student Learning After the Release of Large Language Models (LLMs)

arXiv:2605.16284v1 Announce Type: cross Abstract: Student use of Generative AI (GenAI) products in completing their classwork, with or without their professors' knowledge and/or approval, has resulted

safetyarxiv-cs-ai

Mechanism Learning: Prototype-Anchored Mechanism Inference for Scientific Forecasting

arXiv:2605.17091v1 Announce Type: new Abstract: Scientific forecasting typically relies on direct state prediction, an approach that grows brittle under data scarcity, extended horizons, non-stationar

Mechanistically Interpretable Neural Encoding Reveals Fine-Grained Functional Selectivity in Human Visual Cortex

arXiv:2605.16468v1 Announce Type: cross Abstract: A central goal in understanding human vision is to uncover the visual features that drive neuronal activity. A growing body of work has used artificia

Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution

arXiv:2603.05308v2 Announce Type: replace-cross Abstract: Assessing whether an article supports an assertion is essential for hallucination detection and claim verification. While large language model

Medical Context Distorts Decisions in Clinical Vision Language Models

arXiv:2605.17436v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly proposed for clinical decision support, yet their reliability in real-world scenarios that require inte

safetyarxiv-cs-cl

MedMIX: Modality-Internal Expert Fusion for Multimodal Medical Diagnosis

arXiv:2605.16639v1 Announce Type: new Abstract: Multimodal clinical prediction faces three challenges: multiple foundation models (FMs) with complementary strengths per modality, pervasive missing mod

Meltdown: Circuits and Bifurcations in Point-Cloud-Conditioned 3D Diffusion Transformers

arXiv:2602.11130v2 Announce Type: replace-cross Abstract: Sparse point clouds are a common input modality for 3D surface reconstruction, including in safety-critical settings such as surgical navigati

safetyarxiv-cs-cv

Membership Inference Attacks on Discrete Diffusion Language Models

arXiv:2605.16445v1 Announce Type: cross Abstract: Masked Diffusion Language Models MDLMs replace autoregressive generation with iterative demasking and their privacy properties are largely unstudied.

Local Ai

MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents

arXiv:2605.18652v1 Announce Type: new Abstract: Recent GUI agents have made substantial progress in visual grounding and action prediction, yet they remain brittle in long-horizon tasks that require m

local-aiarxiv-cs-cv

Local Ai

Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets

arXiv:2605.17758v1 Announce Type: new Abstract: Synthetic data is widely used in healthcare to create datasets that are similar to original data but without the privacy concerns. Generating and evalua

local-aiarxiv-cs-lg

MemOCR: Layout-Aware Visual Memory for Efficient Long-Horizon Reasoning

arXiv:2601.21468v5 Announce Type: replace Abstract: Long-horizon agentic reasoning necessitates effectively compressing growing interaction histories into a limited context window. Most existing memor

Memory-Augmented Query Intent Understanding for Efficient Chat-based Image Retrieval

arXiv:2605.17365v1 Announce Type: new Abstract: Different from traditional text-to-image retrieval tasks, chat-based image retrieval allows the human-interactive system to iteratively clarify and refi

researcharxiv-cs-cv

Memory-Efficient Differentially Private Training with Gradient Random Projection

arXiv:2506.15588v2 Announce Type: replace Abstract: Differential privacy (DP) protects sensitive data during neural network training, but standard methods like DP-Adam suffer from high memory overhead

Memory-Guided Tree Search with Cross-Branch Knowledge Transfer for LLM Solver Synthesis

arXiv:2605.17539v1 Announce Type: new Abstract: Combinatorial optimization (CO) underlies decision-making from logistics to chip design, where infeasible solutions are operationally unusable and small

Agents

MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repair

arXiv:2605.17444v1 Announce Type: cross Abstract: Modern software ecosystems face a rapidly growing number of disclosed vulnerabilities, increasing the need for automated repair techniques that can op

agentsarxiv-cs-ai

MentalBench: A DSM-Grounded Benchmark for Evaluating Psychiatric Diagnostic Capability of Large Language Models

arXiv:2602.12871v2 Announce Type: replace Abstract: Large language models (LLMs) have attracted growing interest as supportive tools for psychiatric assessment and clinical decision support. However,

Merlin's Whisper: Enabling Efficient Reasoning in Large Language Models via Black-box Persuasive Prompting

arXiv:2510.10528v3 Announce Type: replace Abstract: Large reasoning models (LRMs) have demonstrated remarkable proficiency in tackling complex tasks through step-by-step thinking. However, this length

Tutorials

Meta-Learning Guided Pruning for Few-Shot Plant Pathology on Edge Devices

arXiv:2601.02353v3 Announce Type: replace Abstract: Farmers in remote areas need quick and reliable methods for identifying plant diseases, yet they often lack access to laboratories or high-performan

tutorialsarxiv-cs-cv

Industry

Meta shifts 7,000 employees into four new AI units ahead of mass layoffs

Meta Platforms Inc. is reportedly reassigning about 7,000 workers into new artificial intelligence roles as part of the broader restructuring that brings layoffs of roughly 8,000 staff into effect on

industrysiliconangle

MetaCogAgent: A Metacognitive Multi-Agent LLM Framework with Self-Aware Task Delegation

arXiv:2605.17292v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems have shown promise for solving complex tasks through agent collaboration. However, existing frameworks as

MetaLab: Few-Shot Game Changer for Image Recognition

arXiv:2507.22057v2 Announce Type: replace Abstract: Difficult few-shot image recognition has significant application prospects, yet remaining the substantial technical gaps with the conventional large

researcharxiv-cs-cv

Metric-Guided Feature Fusion of Visual Foundation Models for Segmentation Tasks

arXiv:2605.16864v1 Announce Type: cross Abstract: Although large-scale visual foundation models (VFMs) achieve remarkable performance in semantic understanding, they still underperform in instance-awa

MHMamba: Multi-Head Mamba for 3D Brain Tumor Segmentation

arXiv:2605.16464v1 Announce Type: cross Abstract: Brain tumors exhibit high heterogeneity in morphology and multimodal contrast, making manual slice-by-slice de lineation time-consuming and experience

Applications

Milan-based legal AI company Lexroom raised a €42.9M Series B led by Left Lane, after raising a €16M Series A in September 2025, taking total funding to €62.7M (Rahul Raj/EU-Startups)

Rahul Raj / EU-Startups: Milan-based legal AI company Lexroom raised a €42.9M Series B led by Left Lane, after raising a €16M Series A in September 2025, taking total funding to €62.7M — Lexroom, a Mi

applicationstechmeme

Tutorials

Mind the Gap: Learning Modality-Agnostic Representations with a Cross-Modality UNet

arXiv:2605.16887v1 Announce Type: new Abstract: Cross-modality recognition has many important applications in science, law enforcement and entertainment. Popular methods to bridge the modality gap inc

tutorialsarxiv-cs-cv

MiniGPT: Rebuilding GPT from First Principles

arXiv:2605.17398v1 Announce Type: new Abstract: This paper presents MiniGPT, a compact from-scratch implementation of GPT-style autoregressive language modeling in PyTorch. The aim is to rebuild the c

Local Ai

Mining Forgery Traces from Reconstruction Error: A Weakly Supervised Framework for Multimodal Deepfake Temporal Localization

arXiv:2601.21458v2 Announce Type: replace Abstract: Modern deepfakes have evolved into localized and intermittent manipulations that require fine-grained temporal localization to mitigate severe digit

local-aiarxiv-cs-cv

Minor First, Major Last: A Depth-Induced Implicit Bias of Sharpness-Aware Minimization

arXiv:2603.08290v2 Announce Type: replace-cross Abstract: We study the implicit bias of Sharpness-Aware Minimization (SAM) when training L-layer linear diagonal networks on linearly separable binary c

safetyarxiv-cs-ai

MIRAGE: Robust multi-modal architectures translate fMRI-to-image models from vision to mental imagery

model-releasesarxiv-cs-cv

arXiv:2605.17198v1 Announce Type: cross Abstract: To be useful for downstream applications, vision decoding models that are trained to reconstruct seen images from human brain activity must be able to

Mirror Descent-Type Algorithms for the Variational Inequality Problem with Functional Constraints

arXiv:2605.16262v1 Announce Type: new Abstract: Variational inequalities play a key role in machine learning research, such as generative adversarial networks, reinforcement learning, adversarial trai

Mirror Mean-Field Langevin Dynamics

arXiv:2505.02621v2 Announce Type: replace Abstract: The mean-field Langevin dynamics (MFLD) minimizes an entropy-regularized nonlinear convex functional on the Wasserstein space over R^d, and has gain

MirrorBench: A Benchmark to Evaluate Conversational User-Proxy Agents for Human-Likeness

arXiv:2601.08118v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as human simulators, both for evaluating conversational systems and for generating fine-tuning da

Applications

Missing-Modality-Aware Graph Neural Network for Cancer Classification

arXiv:2506.22901v2 Announce Type: replace-cross Abstract: A key challenge in learning from multimodal biological data is missing modalities, where data from one or more modalities are absent for some

applicationsarxiv-cs-ai

Mistral acquires Vienna-based Emmi AI for an undisclosed sum to boost its industrial offerings in Europe; Emmi raised €15M in Austria's largest round in 2025 (Reuters)

Reuters: Mistral acquires Vienna-based Emmi AI for an undisclosed sum to boost its industrial offerings in Europe; Emmi raised €15M in Austria's largest round in 2025 — Europe's leading artificial int

model-releasestechmeme

Mitigating 3D Prostate Biparametric MRI Data Scarcity through Domain Adaptation using Locally-Trained Latent Diffusion Models for Prostate Cancer Detection

arXiv:2507.06384v2 Announce Type: replace-cross Abstract: Objective: Latent diffusion models (LDMs) could mitigate data scarcity challenges affecting machine learning development for medical image int

researcharxiv-cs-cv

Mitigating Conversational Inertia in Multi-Turn Agents

arXiv:2602.03664v3 Announce Type: replace Abstract: Large language models excel as few-shot learners when provided with appropriate demonstrations, yet this strength becomes problematic in multiturn a

safetyarxiv-cs-ai

Applications

Mixing Times of Glauber Dynamics on Masked Language Models

arXiv:2605.16378v1 Announce Type: cross Abstract: Masked language models (MLMs) define local conditional distributions over tokens but do not, in general, correspond to any consistent joint distributi

applicationsarxiv-cs-ai

MixSD: Mixed Contextual Self-Distillation for Knowledge Injection

arXiv:2605.16865v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is widely used to inject new knowledge into language models, but it often degrades pretrained capabilities such as reasonin

Mixture-of-Experts Can Surpass Dense LLMs Under Strictly Equal Resource

arXiv:2506.12119v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) language models dramatically expand model capacity and achieve remarkable performance without increasing per-token co

Mixture of Experts for Low-Resource LLMs

arXiv:2605.17598v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures enable efficient model scaling, yet expert routing behavior across underrepresented languages remains poorly unde

Mixup Barcodes: Quantifying Geometric-Topological Interactions between Point Clouds

arXiv:2402.15058v3 Announce Type: replace-cross Abstract: We combine standard persistent homology with image persistent homology to define a novel way of characterizing shapes and interactions between

ML-based Fast Simulation of FARICH Responses

arXiv:2605.17635v1 Announce Type: cross Abstract: A fast simulation of the detector response is a vital task in high-energy physics (HEP). Traditional Monte-Carlo methods form the backbone of modern p

MLReplicate: Benchmarking Autonomous Research Systems for Machine Learning Reproducibility

model-releasesarxiv-cs-lg

arXiv:2605.16616v1 Announce Type: new Abstract: Autonomous research systems capable of generating complete scientific manuscripts have advanced rapidly, yet robust and realistic evaluation frameworks

MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation

arXiv:2605.17743v1 Announce Type: new Abstract: Continual test-time adaptation adapts a source-pretrained model to non-stationary, unlabeled target streams while retaining past competence, yet texture

safetyarxiv-cs-cv