All entries

MAST: A Multi-fidelity Augmented Surrogate model via Spatial Trust-weighting

arXiv:2602.20974v2 Announce Type: replace Abstract: In engineering design and scientific computing, computational cost and predictive accuracy are intrinsically coupled. High-fidelity simulations prov

researcharxiv-cs-lg

Mathematical Reasoning via Intervention-Based Time-Series Causal Discovery Using LLMs as Concept Mastery Simulators

DGX agent

arXiv:2605.07600v1 Announce Type: cross Abstract: Recent methods for improving LLM mathematical reasoning, whether through MCTS-based test-time search or causal graph-guided knowledge injection, canno

model-releasesarxiv-cs-ai

11 May 2026

MathlibPR: Pull Request Merge-Readiness Benchmark for Formal Mathematical Libraries

arXiv:2605.07147v1 Announce Type: cross Abstract: The ecosystem of Lean and Mathlib has become the de facto standard for large language model (LLM) assisted formal reasoning with remarkable successes

mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments

arXiv:2605.07841v1 Announce Type: cross Abstract: Decentralized machine learning often relies on outsourcing computations, such as gradient evaluations, to untrusted worker nodes. Existing robust aggr

agentsarxiv-cs-ai

MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning

arXiv:2605.07850v1 Announce Type: cross Abstract: With the rise in scale for deep learning models to billions of parameters, the computational cost of fine-tuning remains a significant barrier to depl

MAVEN: Multi-Agent Verification-Elaboration Network with In-Step Epistemic Auditing

arXiv:2605.07646v1 Announce Type: cross Abstract: While explicit reasoning trajectories enhance model interpretability, existing paradigms often rely on monolithic chains that lack intermediate verifi

Industry

Maye Musk on Elon: He wants to leave the world in a better place than he found it “People often misunderstand Elon. He’s not motivated by mo…

Maye Musk on Elon: He wants to leave the world in a better place than he found it “People often misunderstand Elon. He’s not motivated by money or fame—he’s motivated by the idea of improving humanity

industryelon-musk--x

McNdroid: A Longitudinal Multimodal Benchmark for Robust Drift Detection in Android Malware

arXiv:2605.06894v1 Announce Type: cross Abstract: Machine learning (ML) in real-world systems must contend with concept drift, adversarial actors, and a spectrum of potential features with varying cos

Mean Mode Screaming: Mean--Variance Split Residuals for 1000-Layer Diffusion Transformers

arXiv:2605.06169v1 Announce Type: cross Abstract: Scaling Diffusion Transformers (DiTs) to hundreds of layers introduces a structural vulnerability: networks can enter a silent, mean-dominated collaps

researcharxiv-cs-cv

Mean-Pooled Cosine Similarity is Not Length-Invariant: Theory and Cross-Domain Evidence for a Length-Invariant Alternative

model-releasesarxiv-cs-cl

arXiv:2605.07345v1 Announce Type: new Abstract: Mean-pooled cosine similarity is the default metric for comparing neural representations across languages, modalities, and tasks. We establish that this

Measuring and Mitigating the Distributional Gap Between Real and Simulated User Behaviors

arXiv:2605.07847v1 Announce Type: new Abstract: As user simulators are increasingly used for interactive training and evaluation of AI assistants, it is essential that they represent the diverse behav

researcharxiv-cs-cl

Mechanistic Interpretability with Sparse Autoencoder Neural Operators

arXiv:2509.03738v4 Announce Type: replace-cross Abstract: We introduce sparse autoencoder neural operators (SAE-NOs), a new class of sparse autoencoders that operate in function spaces rather than fix

researcharxiv-cs-ai

MedAction: Towards Active Multi-turn Clinical Diagnostic LLMs

arXiv:2605.07305v1 Announce Type: cross Abstract: Most existing LLM diagnoses are evaluated on static, single-turn settings where complete patient information is provided upfront, an oversimplificatio

MedExAgent: Training LLM Agents to Ask, Examine, and Diagnose in Noisy Clinical Environments

arXiv:2605.07058v1 Announce Type: cross Abstract: Real-world clinical diagnosis is a complex process in which the doctor is required to obtain information from both interaction with the patient and co

agentsarxiv-cs-ai

Applications

Medical Imaging Classification with Cold-Atom Reservoir Computing using Auto-Encoders and Surrogate-Driven Training

arXiv:2605.06727v1 Announce Type: new Abstract: We introduce a hybrid quantum-classical pipeline, based on neutral-atom reservoir computing, for medical image classification, focusing on the binary cl

applicationsarxiv-cs-lg

MedVIGIL: Evaluating Trustworthy Medical VLMs Under Broken Visual Evidence

model-releasesarxiv-cs-cv

arXiv:2605.07919v1 Announce Type: new Abstract: Medical vision--language models (VLMs) are usually evaluated on intact image--question pairs, but trustworthy clinical use requires a stronger property:

Meet Replit Parallel Agents Build faster by running up to 10 agents in parallel Each agent gets its own copy of your app They work on their …

Replit's Parallel Agents feature enables users to run up to 10 agents simultaneously, with each agent operating on its own independent copy of an application to accelerate development and task complet

agentsreplit--x

Meet the latest Database Center, now with Gemini-powered fleet intelligence

model-releasesgoogle-cloud-ai

Managing a modern database fleet is both a scale and cognitive problem. As database estates grow, the effort required to monitor, troubleshoot, and optimize them often outpaces teams’ capacity, who fi

MELD: Multi-Task Equilibrated Learning Detector for AI-Generated Text

arXiv:2605.06903v1 Announce Type: cross Abstract: Large language models are now embedded in everyday writing workflows, making reliable AI-generated text detection important for academic integrity, co

Melding LLM and temporal logic for reliable human-swarm collaboration in complex scenarios

arXiv:2605.07877v1 Announce Type: new Abstract: Robot swarms promise scalable assistance in complex and hazardous environments. Task planning lies at the core of human-swarm collaboration, translating

agentsarxiv-cs-ro

MemCompiler: Compile, Don't Inject -- State-Conditioned Memory for Embodied Agents

arXiv:2605.07594v1 Announce Type: new Abstract: Existing memory systems for embodied agents typically inject retrieved memory as static context at episode start, a paradigm we term Ahead-of-time Monol

agentsarxiv-cs-ro

MEMOREPAIR: Barrier-First Cascade Repair in Agentic Memory

arXiv:2605.07242v1 Announce Type: new Abstract: Agentic memory evolves across tasks into durable derived artifacts: summaries, cached outputs, embeddings, learned skills, and executable tool procedure

agentsarxiv-cs-ai

Memory-Efficient Looped Transformer: Decoupling Compute from Memory in Looped Language Models

arXiv:2605.07721v1 Announce Type: cross Abstract: Recurrent LLM architectures have emerged as a promising approach for improving reasoning, as they enable multi-step computation in the embedding space

researcharxiv-cs-ai

Hardware

MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning

arXiv:2511.02805v2 Announce Type: replace-cross Abstract: LLM-based search agents often concatenate the full interaction history into the context, producing long and noisy inputs, and increasing compu

hardwarearxiv-cs-ai

Michael Burry urged investors to scale back exposure to surging technology stocks, saying the current market environment has reached histori…

Michael Burry urged investors to scale back exposure to surging technology stocks, saying the current market environment has reached historically dangerous extremes reminiscent of prior speculative bu

safetygary-marcus--x

MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices

model-releasesarxiv-cs-cv

arXiv:2602.06523v2 Announce Type: replace Abstract: Human Activity Recognition (HAR) on resource constrained wearables requires models that balance accuracy against strict memory and computational bud

MIND: Monge Inception Distance for Generative Models Evaluation

arXiv:2605.06797v1 Announce Type: new Abstract: We propose the Monge Inception Distance (MIND), a metric for evaluating generative models that addresses key limitations of the widely adopted Frechet I

Mind the Gap: Geometrically Accurate Generative Reconstruction from Disjoint Views

arXiv:2605.07550v1 Announce Type: new Abstract: 3D vision systems are fundamentally constrained by their reliance on visual overlap: reconstruction methods require it for geometric alignment, while ge

safetyarxiv-cs-cv

Miner:Mining Intrinsic Mastery for Data-Efficient RL in Large Reasoning Models

arXiv:2601.04731v2 Announce Type: replace Abstract: Current critic-free RL methods for large reasoning models suffer from severe inefficiency when training on positive homogeneous prompts (where all r

safetyarxiv-cs-ai

Minerva: Reinforcement Learning with Verifiable Rewards for Cyber Threat Intelligence LLMs

arXiv:2602.00513v3 Announce Type: replace Abstract: Cyber threat intelligence (CTI) analysts routinely convert noisy, unstructured security artifacts into standardized, automation-ready representation

researcharxiv-cs-lg

MiniAppBench: Evaluating the Shift from Text to Interactive HTML Responses in LLM-Powered Assistants

arXiv:2603.09652v3 Announce Type: replace Abstract: With the rapid advancement of Large Language Models (LLMs) in code generation, human-AI interaction is evolving from static text responses to dynami

Minimizing Modality Gap from the Input Side: Your Speech LLM Can Be a Prosody-Aware Text LLM

arXiv:2605.05927v2 Announce Type: replace Abstract: Speech large language models (SLMs) are typically built from text large language model (TLM) checkpoints, yet they still suffer from a substantial m

researcharxiv-cs-cl

Applications

MinMax Recurrent Neural Cascades

arXiv:2605.06384v2 Announce Type: replace-cross Abstract: We show that the MinMax algebra provides a form of recurrence that is expressively powerful, efficiently implementable, and most importantly i

applicationsarxiv-cs-ai

MIPIAD: Multilingual Indirect Prompt Injection Attack Defense with Qwen -- TF-IDF Hybrid and Meta-Ensemble Learning

model-releasesarxiv-cs-cl

arXiv:2605.07269v1 Announce Type: new Abstract: Indirect prompt injection remains a persistent weakness in retrieval-augmented and tool-using LLM systems, and the problem becomes harder to characteris

MISA: Mixture of Indexer Sparse Attention for Long-Context LLM Inference

arXiv:2605.07363v1 Announce Type: cross Abstract: DeepSeek Sparse Attention (DSA) sets the state of the art for fine-grained inference-time sparse attention by introducing a learned token-wise indexer

Applications

MIST: Multimodal Interactive Speech-based Tool-calling Conversational Assistants for Smart Homes

arXiv:2605.06897v1 Announce Type: cross Abstract: The rise of Internet of Things (IoT) devices in the physical world necessitates voice-based interfaces capable of handling complex user experiences. W

applicationsarxiv-cs-ai

MIT and IBM renew their research collaboration

MIT and IBM have renewed their long-standing research collaboration, focusing on advancing computing research and technology development. The partnership brings together expertise from both institutio

researchibm-research

Mitigating Cognitive Bias in RLHF by Altering Rationality

arXiv:2605.06895v1 Announce Type: new Abstract: How can we make models robust to even imperfect human feedback? In reinforcement learning from human feedback (RLHF), human preferences over model outpu

Mixture of Masters: Sparse Chess Language Models with Player Routing

arXiv:2602.04447v2 Announce Type: replace-cross Abstract: Modern chess language models are dense transformers trained on millions of games played by thousands of high-rated individuals. However, these

researcharxiv-cs-ai

MobileDev-Bench: A Benchmark for Issue Resolution in Mobile Application Development

arXiv:2603.24946v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown strong performance on automated software engineering tasks, yet existing benchmarks focus primarily on

MoCoTalk: Multi-Conditional Diffusion with Adaptive Router for Controllable Talking Head Generation

arXiv:2605.08050v1 Announce Type: new Abstract: Talking-head generation requires joint modeling of identity, head pose, facial expression, and mouth dynamics. Existing methods typically address only a

safetyarxiv-cs-cv

Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models

arXiv:2602.07026v2 Announce Type: replace-cross Abstract: Despite the success of multimodal contrastive learning in aligning visual and linguistic representations, a persistent geometric anomaly, the

safetyarxiv-cs-ai

Model-Driven Policy Optimization in Differentiable Simulators via Stochastic Exploration

arXiv:2605.07520v1 Announce Type: new Abstract: Differentiable planning enables gradient-based optimization of decision-making problems by leveraging differentiable models of system dynamics. However,

ModelLens: Finding the Best for Your Task from Myriads of Models

arXiv:2605.07075v1 Announce Type: new Abstract: The open-source model ecosystem now contains hundreds of thousands of pretrained models, yet picking the best model for a new dataset is increasingly in

Modular Lie Algebraic PDE Control of Multibody Flexible Manipulators

model-releasesarxiv-cs-ro

arXiv:2605.06709v1 Announce Type: new Abstract: This paper addresses PDE-based control for flexible multibody robotic systems, presenting a subsystem-based framework for serial manipulators with arbit

Local Ai

Modulated learning for private and distributed regression with just a single sample per client device

arXiv:2605.07233v1 Announce Type: new Abstract: This work focuses on the question of learning from a large number of devices with each device holding only a single sample of data. Several real-world a

local-aiarxiv-cs-lg

More Thinking, More Bias: Length-Driven Position Bias in Reasoning Models

arXiv:2605.06672v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning and reasoning-tuned models such as DeepSeek-R1 are commonly assumed to reduce shallow heuristic biases by thinking care

MORPH-U: Multi-Objective Resilient Motion Planning for V2X-Enabled Autonomous Driving in High-Uncertainty Environments via Simulation

arXiv:2605.07370v1 Announce Type: cross Abstract: V2X can warn an autonomous vehicle about hazards beyond line-of-sight, but it also brings uncertainty: messages may be delayed, dropped, or even forge

safetyarxiv-cs-ai