All entries

LLM-Guided ODE Discovery and Parameter Inference from Small-Cohort Aggregate Data

arXiv:2607.00733v1 Announce Type: cross Abstract: Mechanistic modeling via ordinary differential equations (ODEs) provides interpretable descriptions of complex dynamics and enables inference of under

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Applications

LLMs in the Real World: Evaluating 'AI' in Emergency Contexts

DGX agent

arXiv:2607.00019v1 Announce Type: cross Abstract: This paper offers a call to action. We urge our colleagues in the research community to play a greater role in the articulation of our findings to the

applicationsarxiv-cs-ai

2 Jul 2026

LLVM-Bench: Benchmarking and Advancing Large Language Models for LLVM Compiler Issue Resolution

arXiv:2607.00700v1 Announce Type: cross Abstract: LLVM is a widely used compiler infrastructure whose scale and complexity make issue resolution labor-intensive and challenging. Although large languag

Local Ai

Local Motion Matters: A Deconstruct-Recompose Paradigm for Reinforcement Learning Pre-training from Videos

arXiv:2607.00808v1 Announce Type: new Abstract: Pre-training on large-scale videos to improve reinforcement learning efficiency is promising yet remains challenging. Existing methods typically treat t

local-aiarxiv-cs-lg

Logit-Contribution Scoring Identifies Non-Literal Retrieval Heads

arXiv:2607.01002v1 Announce Type: cross Abstract: In long-context use, large language models frequently synthesize answers from the meaning of a relevant context span rather than literally copy-pastin

LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models

arXiv:2607.01086v1 Announce Type: cross Abstract: The evaluation of long-term video quality understanding remains an open challenge for large vision-language models (LVLMs). Existing video quality ben

Loss Smoothing for Stable Adaptation Under Distribution Shift

arXiv:2607.00634v1 Announce Type: cross Abstract: In settings such as fine-tuning and reinforcement learning, neural networks are often adapted under distribution shift. Standard adaptation methods ty

Lost in the Tail: Addressing Geographic Imbalance in Urban Visual Place Recognition

arXiv:2607.00090v1 Announce Type: cross Abstract: Urban-scale Visual Place Recognition (VPR) aims to identify the geographic location of a query image by matching it against a geo-tagged database. Whi

Low Perplexity is Repetition: A One-Dimensional Self-Conditioning Attractor in Continuous Diffusion LMs

arXiv:2607.00588v1 Announce Type: new Abstract: Continuous diffusion language models such as ELF report record-low generative perplexity (Gen-PPL). We find a catch: these models repeat far more than h

researcharxiv-cs-cl

LRAT-Catcher: Importing SAT Solver Certificates into Lean4 by Reflection

arXiv:2607.00815v1 Announce Type: cross Abstract: SAT solvers settle combinatorial problems beyond the reach of interactive theorem provers and produce LRAT certificates for independent verification.

LUMA: Benchmarking Segmentation via a Lightweight Universal Mask Adapter

arXiv:2607.00687v1 Announce Type: cross Abstract: Comparing transformer backbones for image segmentation is confounded: each is paired with a different decoder, recipe, and pretraining, so reported di

LuxIT: A Luxembourgish Instruction Tuning Dataset from Monolingual Seed Data

arXiv:2510.24434v3 Announce Type: replace Abstract: The effectiveness of instruction-tuned Large Language Models (LLMs) is often limited in low-resource linguistic settings due to a lack of high-quali

LV-ROVER: Multi-Stream Tesseract Voting for Maltese Paragraph OCR

arXiv:2607.00250v1 Announce Type: new Abstract: Maltese has decent text corpora and pretrained language models, but, like many languages outside the handful with large OCR benchmarks, only a single kn

Magic-MM-Embedding: Towards Visual-Token-Efficient Universal Multimodal Embedding with MLLMs

arXiv:2602.05275v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have shown immense promise in universal multimodal retrieval, which aims to find relevant items of various

Agents

Making Failure Safe: A Constrained, Verifiable Agent Framework for Open-Web Data Collection

arXiv:2607.00035v1 Announce Type: new Abstract: LLMs and agents can generate web scrapers from natural-language requirements, but direct generation remains unreliable because of dependency errors, bro

agentsarxiv-cs-ai

Local Ai

MalariAI: A Label-Resilient Decoupled Framework for Universal Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears

arXiv:2607.00385v1 Announce Type: cross Abstract: Automated malaria diagnosis from blood smear microscopy is a critical challenge in global health AI; in resource-limited settings, the scarcity of exp

local-aiarxiv-cs-ai

Managed Autonomy at Runtime: Gear-Based Safety and Governance for Single- and Multi-Agent Cyber-Physical Systems

arXiv:2607.00334v1 Announce Type: new Abstract: Autonomous agents, whether LLM-driven software agents or robotic physical agents, face a common class of failure modes when operating without continuous

safetyarxiv-cs-ai

Manifold-constrained Hamilton-Jacobi Reachability Learning for Decentralized Multi-Agent Motion Planning

arXiv:2511.03591v2 Announce Type: replace Abstract: Safe multi-agent motion planning (MAMP) under task-induced constraints is a critical challenge in robotics. Many real-world scenarios require robots

safetyarxiv-cs-ro

Mapping the Evaluation Frontier: An Empirical Survey of the Bias-Reliability Tradeoff Across Eleven Evaluator-Agent Conditions

arXiv:2607.00304v1 Announce Type: cross Abstract: The bias-reliability tradeoff conjectures that LLM evaluation systems are constrained in (gamma, H, CV) space, where evaluator coupling (gamma), strat

Measuring Dead Directions: Decomposing and Classifying Singular Structure off Canonical Alignment

arXiv:2607.00603v1 Announce Type: new Abstract: We give a descent-free, alignment-free measurement of singular structure on trained networks. At a single frozen checkpoint the read recovers the order

safetyarxiv-cs-lg

Measuring the Gap Between Human and LLM Research Ideas

arXiv:2607.01233v1 Announce Type: cross Abstract: LLMs are increasingly used to brainstorm research ideas, but existing evaluations mostly judge individual ideas by novelty, feasibility, or expert pre

MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentation

arXiv:2607.00409v1 Announce Type: new Abstract: Medical image segmentation relies on the ability of encoder-decoder architectures to translate rich feature representations into accurate pixel-level pr

safetyarxiv-cs-cv

Applications

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images

arXiv:2511.12110v5 Announce Type: replace-cross Abstract: Despite notable progress in text-guided medical image segmentation nowadays, these methods are limited to single-round dialogues and fail to s

applicationsarxiv-cs-ai

Memory-Native Non-Terrestrial Networks for Embodied Intelligence

arXiv:2607.00029v1 Announce Type: cross Abstract: Non-terrestrial networks (NTN) provide ubiquitous connectivity for embodied intelligence (EI), enabling robots in wilderness to leverage cloud resourc

MemSyco-Bench: Benchmarking Sycophancy in Agent Memory

arXiv:2607.01071v1 Announce Type: cross Abstract: Memory has emerged as a cornerstone of modern LLM-based agents, supporting their evolution from single-turn assistants to long-term collaborators. How

MEPA: Multi-Scale Representation Alignment for Visual Autoregressive Modeling with Mixture of Experts

arXiv:2607.00371v1 Announce Type: cross Abstract: Visual AutoRegressive modeling (VAR) has pioneered a coarse-to-fine multi-scale autoregressive generative paradigm, demonstrating strong capabilities

Message Passing Enables Efficient Reasoning

arXiv:2607.01077v1 Announce Type: new Abstract: While inference-time scaling has improved the reasoning abilities of large language models (LLMs), the need to generate long chains-of-thought (CoTs) is

researcharxiv-cs-cl

Meta-Transfer Learning for mmWave Beam Alignment

arXiv:2607.00860v1 Announce Type: cross Abstract: Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challengin

safetyarxiv-cs-ai

MetaHOPE: A Metaphor-Oriented Evaluation Framework for Analysing MT and LLM Translation Errors

arXiv:2607.00848v1 Announce Type: new Abstract: In this opinion paper, we propose MetaHOPE, an error severity-aware annotation framework for evaluating metaphor translations. Metaphors present challen

researcharxiv-cs-cl

Tutorials

MetaOthello: A Controlled Study of Multiple World Models in Transformers

arXiv:2602.23164v2 Announce Type: replace Abstract: Foundation models must handle multiple generative processes, yet mechanistic interpretability largely studies capabilities in isolation; it remains

tutorialsarxiv-cs-lg

MG-RWKV: Multi-Grained Context-Aware RWKV for Temporal Forgery Localization

arXiv:2607.00902v1 Announce Type: new Abstract: Driven by Artificial Intelligence-Generated Content (AIGC), the authenticity of audio-visual content is facing severe challenges. Temporal Forgery Local

MG-SpaIR: Multi-grade Sparse-guided Implicit Representation for Training-Data-Free Image Restoration

arXiv:2607.00138v1 Announce Type: new Abstract: MG-SpaIR is a training-data-free framework for restoring a clean image from a single observation corrupted by a mixture of blur, downsampling, noise, an

MindAU: EEG-Conditioned Facial Action Unit Editing via Dual-Stream Manifold Alignment

model-releasesarxiv-cs-cv

arXiv:2607.00410v1 Announce Type: new Abstract: Recent brain decoding studies have made substantial progress in reconstructing externally perceived visual content from neural signals. However, using e

MindEdit-Bench: Benchmarking Object-Level Counterfactual Spatial Reasoning in VLMs from In-the-Wild Photos

arXiv:2607.00491v1 Announce Type: cross Abstract: Benchmarks for vision-language models (VLMs) mostly test observational spatial reasoning: models describe relations already visible in the input. Exis

MineRobot: An Actuator-Centered Kinematic Modeling and Solving Framework for Underground Mining Robots

arXiv:2603.22055v2 Announce Type: replace-cross Abstract: Underground mining robots are increasingly modeled for planning, operator training, and digital-twin workflows, where reliable actuator-level

researcharxiv-cs-ro

Mirror-Fusion Attention for Reflection-Aware Self-Supervised Representation Learning

arXiv:2607.00850v1 Announce Type: new Abstract: Most self-supervised learning (SSL) methods encourage invariance across augmentations, but strict flip invariance can suppress informative left--right c

safetyarxiv-cs-cv

Mixture of Distributions Matters: Dynamic Sparse Attention for Efficient Video Diffusion Transformers

arXiv:2601.11641v3 Announce Type: replace Abstract: While Diffusion Transformers (DiTs) have achieved notable progress in video generation, this long-sequence generation task remains constrained by th

MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation

model-releasesarxiv-cs-cv

arXiv:2602.21397v2 Announce Type: replace Abstract: Prompt learning has become a dominant paradigm for adapting vision-language models (VLMs) such as CLIP to downstream tasks without modifying pretrai

Mnemosyne: Agentic Transaction Processing for Validating and Repairing AI-generated Workflows

arXiv:2607.00269v1 Announce Type: new Abstract: LLMs, solvers, and agent teams increasingly generate workflow actions, repairs, and plans, but a generated action may be syntactically valid yet stale,

safetyarxiv-cs-ai

MoHallBench: A Benchmark for Motion Hallucination in Video Large Language Models

model-releasesarxiv-cs-cv

arXiv:2607.01117v1 Announce Type: new Abstract: Video Large Language Models (VideoLLMs) have shown strong progress in video understanding, yet they still suffer from hallucinations that are inconsiste

Moire Video Authentication: A Physical Signature Against AI Video Generation

arXiv:2604.01654v2 Announce Type: replace-cross Abstract: Recent advances in video generation have made AI-synthesized content increasingly difficult to distinguish from real footage. We propose a phy

MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules

arXiv:2607.00464v1 Announce Type: cross Abstract: Current molecular generation benchmarks emphasize task complexity, molecule novelty, and property alignment; they largely overlook a critical concern:

MonoMSK: Monocular 3D Musculoskeletal Dynamics Estimation

arXiv:2511.19326v2 Announce Type: replace Abstract: Reconstructing biomechanically realistic 3D human motion - recovering both kinematics (motion) and kinetics (forces) - is a critical challenge. Whil

Hardware

MosaicKV: Serving Long-Context LLM with Dynamic Two-D KV Cache Compression

arXiv:2607.00760v1 Announce Type: new Abstract: Long-context LLM services now sustain prompts with hundreds of thousands to millions of tokens, making the key-value (KV) cache a first-order serving co

hardwarearxiv-cs-lg

MoVA: Learning Asymmetric Dual Projections for Modular Long Video-Text Alignment

arXiv:2607.00858v1 Announce Type: new Abstract: Contrastive pre-training has propelled video-text alignment, yet models often inherit the critical limitations of their image-text predecessors like CLI

safetyarxiv-cs-cv

MSQA: A Natively Sourced Multilingual and Multicultural SimpleQA Benchmark

arXiv:2607.00724v1 Announce Type: new Abstract: Multilingual fluency often invites a stronger assumption: a model that can speak a user's language must also understand the culture encoded by that lang

Multi-Embodiment Robotic Retargeting via Guided Diffusion Model

arXiv:2505.20857v2 Announce Type: replace Abstract: Motion retargeting for specific robot from existing motion datasets is one critical step in transferring motion patterns from human behaviors to and

researcharxiv-cs-ro

Multi-Hypothesis Test-Time Adaptation to Mitigate Underspecification

arXiv:2607.00259v1 Announce Type: cross Abstract: Test-Time Adaptation (TTA) seeks to improve model robustness under distribution shifts by adapting parameters using unlabeled target data. However, in