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MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution

arXiv:2603.18718v2 Announce Type: replace Abstract: Memory-augmented LLM agents maintain external memory banks to support long-horizon interaction, yet most existing systems treat construction, retrie

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Memory as transformation: LETHE, a self-referential gan-inspired architecture

DGX agent

arXiv:2609.04289v1 Announce Type: new Abstract: LETHE (Latent-parameter Evolution with Temporal Hierarchical quasi-Equilibrium) is a self-referential sonic-oblivion system implemented in SuperCollider

model-releasesarxiv-cs-cl

7 Sept 2026

MEOX: Compact Multimodal Mixture-of-Experts for Earth Observation

model-releasesarxiv-cs-cv

arXiv:2609.05351v1 Announce Type: new Abstract: Recent advances in Earth Observation representation learning accommodate heterogeneous sensors and missing observations, often through larger architectu

MePo++: Unifying Representation Refinement and Reconciliation for General Continual Learning

arXiv:2609.05075v1 Announce Type: new Abstract: General continual learning (GCL) aims to learn from evolving data streams without task identities, explicit boundaries, or repeated access to previous d

safetyarxiv-cs-ai

Methane Detection On Board Satellites from Unorthorectified Imagery

arXiv:2609.04906v1 Announce Type: cross Abstract: As a potent greenhouse gas, methane is a major driver of climate change. Its effective mitigation relies on timely detection. Conventional detection m

researcharxiv-cs-ai

Minimax Lower Bound for Estimating Diffusion-based Local Intrinsic Dimension

arXiv:2609.04822v1 Announce Type: cross Abstract: While diffusion-based methods have recently emerged as effective tools for probing the intrinsic geometry of high-dimensional data, their statistical

local-aiarxiv-cs-lg

MINT: A Unified Model for World-Space Camera and Hand Motion Estimation from Scalable Egocentric Pipeline Supervision

arXiv:2609.04958v1 Announce Type: new Abstract: Recovering camera and hand motion in world coordinates from egocentric video is a key capability for activity understanding, robot learning, and augment

researcharxiv-cs-cv

Agents

Mitigating Performance Discrepancy in Cross-Domain 3D Class-Incremental Learning

arXiv:2609.04860v1 Announce Type: cross Abstract: 3D perception plays a crucial role in real-world applications such as autonomous driving, robotics, and AR/VR. In practical scenarios, 3D perception m

agentsarxiv-cs-ai

Mitra-v2 Technical Report

model-releasesarxiv-cs-lg

arXiv:2609.04540v1 Announce Type: new Abstract: We introduce Mitra-v2, a tabular foundation model that delivers state-of-the-art performance on real-world classification and regression problems, from

MM-IFEval-Pro: A Multilingual and Attack-Resistant Benchmark for Instruction-Following in Vision-Language Models

arXiv:2609.04859v1 Announce Type: new Abstract: As vision-language models (VLMs) rapidly advance in image understanding, cross-modal reasoning, and complex instruction execution, instruction-following

MMTClinic: Multimodal, Multilingual Time Series Question Answering and Reasoning Benchmark for Clinical Domain

arXiv:2609.04842v1 Announce Type: cross Abstract: Time-series data in clinical settings is crucial for capturing dynamic changes in a patient's health over time, enabling timely diagnosis, personalize

Agents

Model Retirement Creates Reproducibility Risk in Biomedical AI Publications

arXiv:2609.04699v1 Announce Type: new Abstract: Background. Large language models (LLMs) are being adopted in biomedical research at a rapid and accelerating pace, yet commercial services that host ma

agentsarxiv-cs-ai

Tutorials

Modular Deep Recurrent Neural Network: Application to Quadrotors

arXiv:2609.04339v1 Announce Type: new Abstract: A modular deep Recurrent Neural Network (RNN) is introduced to facilitate the process of deploying various architectures of RNNs, and to automatically c

tutorialsarxiv-cs-lg

MoirfEolas and CriochScore: Developing Resources for and the Evaluation of Tokenization Alignment with Irish Morphology

arXiv:2609.05022v1 Announce Type: new Abstract: This paper presents new tokenization resources for Irish and evaluation measures of alignment with the morphological boundaries of the language. We pres

safetyarxiv-cs-cl

Molecular Deja Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

arXiv:2609.05381v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated on molecular property benchmarks, but accuracy cannot distinguish a model that predicts a proper

Applications

MomentQuant: an even more minimalist interval method with linear time complexity for time series classification

arXiv:2609.05136v1 Announce Type: new Abstract: Time series data is very common in many real-world applications and in numerous domains, with increasing interest for automated information extraction u

applicationsarxiv-cs-lg

Moral Competence Before Moral Content: Why LLM Agents Lack the Prerequisites for Coherent Alignment

arXiv:2609.05036v1 Announce Type: new Abstract: AI alignment requires AI systems to adhere to human norms, values, or intentions. Under value pluralism there is no correct target, but a shared prerequ

safetyarxiv-cs-ai

Morphology and actuation as inductive biases in robotic hand manipulation

arXiv:2609.05206v1 Announce Type: new Abstract: Robotic hands vary widely in anatomical fidelity and mechanical complexity, and these structural choices influence the coordination of joint motions and

researcharxiv-cs-ro

Motion-Omni: End-to-End Joint Speech and Full-Body Motion for Spoken Dialogue

arXiv:2609.04250v1 Announce Type: cross Abstract: An avatar that holds a conversation should decide what to say and to move while saying it, yet these abilities live in separate model families: spoken

safetyarxiv-cs-cv

Multi-Modal Time Series Prediction via Mixture of Modulated Experts

arXiv:2601.21547v2 Announce Type: replace-cross Abstract: Real-world time series exhibit complex and evolving dynamics, making accurate forecasting extremely challenging. Recent multi-modal forecastin

safetyarxiv-cs-ai

Multi-scale Image Representation Compression

model-releasesarxiv-cs-cv

arXiv:2609.04274v1 Announce Type: cross Abstract: Overfitted codecs have demonstrated promising performance for image and video compression. In particular, for image compression, the Cool-chic family

Multi-Step Tool-Calling over Korean Open Public APIs: A Benchmark and a Data-Synthesis Recipe

arXiv:2609.05395v1 Announce Type: new Abstract: Data-sovereignty regulations increasingly require public institutions to deploy open-source, on-premise LLM agents that chain multiple tool-calls across

MultiAttenGastro: Multi-Dimensional Attention Augmentation for Gastrointestinal Endoscopy Classification

model-releasesarxiv-cs-cv

arXiv:2609.05070v1 Announce Type: new Abstract: Automated gastrointestinal (GI) endoscopy classification requires models that generalize across diverse modalities and class distributions, often far fr

MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Model

arXiv:2603.18892v2 Announce Type: replace-cross Abstract: Spatial reasoning is foundational for Vision-Language Models (VLMs), particularly when deployed as Vision-Language-Action (VLA) agents in phys

Multilingual Models for Check-Worthy Social Media Posts Detection

arXiv:2408.06737v2 Announce Type: replace Abstract: This work presents an extensive study of transformer-based NLP models application for detection of social media posts that contain verifiable factua

researcharxiv-cs-cl

MURAL: Multimodal Uncertainty-aware Recommendation via Adaptive edge Learning

arXiv:2609.04574v1 Announce Type: cross Abstract: Multimodal Graph Neural Networks have become standard for recommendation by augmenting sparse interaction data with content features. Yet current arch

safetyarxiv-cs-lg

MZ-Rain: Moisture-Budget-Guided Zero-Inflated Model for Station-Level Precipitation Nowcasting

arXiv:2609.04864v1 Announce Type: new Abstract: Accurate station-level precipitation nowcasting is critical for agriculture, water resource management, and disaster prevention, which typically is form

researcharxiv-cs-ai

NavArena: Automated Construction of Goal-Oriented Navigation Benchmarks from 3D Gaussian Splatting Reconstructions

model-releasesarxiv-cs-ro

arXiv:2609.04602v1 Announce Type: new Abstract: Fixed 3D Gaussian Splatting (3DGS) reconstructions provide realistic novel views but lack the traversability constraints, valid goals, and closed-loop p

NEAT-POCKET: Pocket-Conditioned Autoregressive 3D Molecular Generation with a Neighborhood-Guided Set Transformer

arXiv:2609.05097v1 Announce Type: cross Abstract: AI-driven de novo molecular design offers a promising route to accelerate early-stage drug discovery by generating novel ligands directly within targe

researcharxiv-cs-ai

Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence

arXiv:2609.05385v1 Announce Type: new Abstract: LLM decision components that can operate within agent workflows often produce action-relevant recommendations or judgements together with explanations.

Nepali Passport Question Answering: A Low-Resource Dataset for Public Service Applications

arXiv:2603.13320v2 Announce Type: replace-cross Abstract: Nepali, a low-resource language, faces significant challenges in building an effective information retrieval system due to the unavailability

Nested Inductive Bias Framework for SPD Manifold Learning

arXiv:2609.04466v1 Announce Type: new Abstract: In Geometric Deep Learning, inductive biases serve two primary functions: enforcing manifold constraints and embedding relational priors. Currently, rep

safetyarxiv-cs-lg

NOTAI.AI: Explainable Detection of Machine-Generated Text via Curvature and Feature Attribution

arXiv:2603.05617v2 Announce Type: replace Abstract: We present NotAI.AI, an explainable AI-generated text detection system. Instead of returning only a binary label or confidence score, the system sho

local-aiarxiv-cs-cl

NS-ST-GraphRAG: Neuro-Symbolic Spatio-Temporal GraphRAG for Literary Knowledge Processing

model-releasesarxiv-cs-cl

arXiv:2609.05139v1 Announce Type: new Abstract: Long-form literary narratives pose a distinctive information-processing challenge for retrieval-augmented generation: relevant evidence is distributed a

Object Concepts Emerge from Motion

arXiv:2609.04348v1 Announce Type: new Abstract: Object-centric visual representations are important for physical-world perception, but existing visual pretraining methods often capture semantic catego

researcharxiv-cs-cv

On-board ML for Trace Gas detection in Imaging Spectroscopy data

arXiv:2609.04458v1 Announce Type: new Abstract: Data collected during aerial and spaceborne imaging spectroscopy campaigns enables the detection of transient events such as trace gas emissions. Howeve

On Epistemic Diversity in Large Language Models

arXiv:2609.04835v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used not only to retrieve information, but to answer questions, explain, teach, and support inquiry. In su

safetyarxiv-cs-cl

On the Abundance of Critical Points of the t-SNE Energy

arXiv:2609.04379v1 Announce Type: new Abstract: This paper considers the energy landscape of the t-SNE algorithm. While this algorithm has enjoyed broad adoption, the non-convexity of the associated e

One Diffusion Model, Two Roles: Guided Trajectory Planning and Safety-Critical Scenario Generation in Closed-Loop Simulation

arXiv:2609.04921v1 Announce Type: cross Abstract: Diffusion probabilistic models can capture the multi-modal, interaction-rich distribution of joint future trajectories in driving scenes. We show that

One Word, Different Action: A Real-Robot Benchmark for Language-Conditioned Embodied Reasoning

model-releasesarxiv-cs-ro

arXiv:2609.05260v1 Announce Type: new Abstract: Natural-language instruction changes can directly alter robot behavior. A reliable embodied system should preserve its action when the task is unchanged

Agents

Online Change-point Detection for Cooperative Multi-Agent Reinforcement Learning

arXiv:2609.05298v1 Announce Type: cross Abstract: Cooperative multi-agent reinforcement learning (MARL) systems rely on past experience for learning coordinated behaviour, but this experience may beco

agentsarxiv-cs-lg

Applications

Open-Set 3D Scene Graphs for Field Robotics: An Outdoor Case Study

arXiv:2609.04607v1 Announce Type: new Abstract: Three-dimensional scene graphs (3DSGs) have emerged as a promising approach for building geometrically grounded, semantically informed, hierarchical gen

applicationsarxiv-cs-ro

Optimal Rates for Agentic Networked Information Aggregation

model-releasesarxiv-cs-lg

arXiv:2609.05318v1 Announce Type: new Abstract: Building on the pioneering paper of Kearns, Roth, and Ryu (SODA'26), we study information aggregation in a networked learning model. The model captures

Optimizer Memory Schedules for Outscaling the Overtraining Axis

arXiv:2609.04577v1 Announce Type: new Abstract: We investigate how optimizers scale across the overtraining axis and show that relative optimizer performance and optimal hyperparameters change substan

Orientation-Robust Latent Motion Trajectory Learning for Annotation-free Cardiac Phase Detection in Fetal Echocardiography

arXiv:2602.06761v2 Announce Type: replace-cross Abstract: Fetal echocardiography is essential for detecting congenital heart disease (CHD), facilitating pregnancy management, optimized delivery planni

researcharxiv-cs-cv

Out-of-Distribution Semantic Occupancy Prediction

arXiv:2506.21185v3 Announce Type: replace Abstract: 3D semantic occupancy prediction is crucial for autonomous driving, providing a dense, semantically rich environmental representation. However, exis

safetyarxiv-cs-cv

Applications

PAC-Bayesian Reconstruction Guarantees for Time Series Variational Autoencoders

arXiv:2609.05212v1 Announce Type: cross Abstract: Forecasting time series accurately is critical for applications with complex data ranging from energy systems to healthcare and finance. Among current

applicationsarxiv-cs-lg

PACE: Propagation-Aware Collaborative Correction for One-Shot Personalized Federated Graph Learning

arXiv:2609.04832v1 Announce Type: new Abstract: Client heterogeneity creates both an opportunity and a risk in personalized federated graph learning. Knowledge held by other subgraphs may complement a

local-aiarxiv-cs-lg