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Multi-Hop Knowledge Composition is Bound by Pretraining Exposure

arXiv:2606.09338v2 Announce Type: replace Abstract: Large Language Models fail at implicit multi-hop reasoning: a model answers 'When was X born?' and 'Who is Y's closest friend?' correctly but fails

researcharxiv-cs-cl

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Human DGX agent

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Multi-Session Multimodal Underwater Mapping with Acoustic and Optical Imaging

DGX agent

arXiv:2609.17929v1 Announce Type: new Abstract: Accurate seafloor mapping is essential for marine science, archaeology, and environmental monitoring. However, integrating data from different sensors,

safetyarxiv-cs-ro

17 Sept 2026

Multi-View Mixture-of-Experts with Vision-Language Reranking for Cross-View Object Geo-Localization

model-releasesarxiv-cs-cv

arXiv:2609.18139v1 Announce Type: new Abstract: Cross-view object geo-localization (CVOGL) locates a target in satellite imagery using drone or street-view queries. Existing methods train separate det

Multimodal Behavior Tree Generation: A Small Vision-Language Model for Robot Task Planning

model-releasesarxiv-cs-ro

arXiv:2603.06084v2 Announce Type: replace Abstract: Large language models have been widely used for robotic task planning, often taking advantage of representations such as Behavior Trees (BTs). Visio

Multimodal Conditioning of Fine-Tuned Stable Diffusion XL for Controllable and Culturally Faithful Ulos Motif Generation

arXiv:2609.17987v1 Announce Type: new Abstract: The traditional Batak Ulos weaving industry faces growing challenges in producing diverse, innovative motifs due to limitations in conventional, manuall

Multitask Reinforcement Learning for Assisting Choice Model Specification

arXiv:2609.18441v1 Announce Type: cross Abstract: Discrete choice model specification is a time-consuming task in which modellers often specify and estimate multiple models while balancing goodness-of

safetyarxiv-cs-ai

MUSE: Benchmarking Large Vision-Language Models on Multi-Modal Understanding in Situated Education

arXiv:2609.19088v1 Announce Type: new Abstract: Large vision-language models have achieved remarkable progress in multi-modal understanding, yet their capabilities in educational settings remain insuf

Myovox: Reading Speech from the Muscles of the Face

arXiv:2609.17548v1 Announce Type: new Abstract: Myovox, from myo (muscle) and vox (voice), decodes open-vocabulary English text from 31-channel surface electromyography (sEMG) recorded from the muscle

researcharxiv-cs-cl

NanoBench: A Multi-Task Benchmark Dataset for Nano-Quadrotor System Identification, Control, and State Estimation

model-releasesarxiv-cs-ro

arXiv:2603.09908v2 Announce Type: replace Abstract: Existing aerial-robotics benchmarks target vehicles from hundreds of grams to several kilograms and typically expose only high-level state data. The

NeMo Data Designer: An Extensible Framework for Multimodal Synthetic Data Generation

arXiv:2609.17699v1 Announce Type: new Abstract: We present NeMo Data Designer (NDD), an open-source, general-purpose framework for multi-modal synthetic data generation (SDG). Designed to be intuitive

NeuroECG: ECGFounder-Based Deep ECG Representation for EEG-Free Neurological Prognostication After Cardiac Arrest

arXiv:2609.18891v1 Announce Type: cross Abstract: Neurological prognostication after cardiac arrest commonly relies on electroencephalography (EEG). However, EEG demands high clinical resources. Bedsi

researcharxiv-cs-ai

NeuroSketch: A Practical Design Recipe for Neural Decoding

arXiv:2512.09524v2 Announce Type: replace-cross Abstract: Neural decoding is fundamental to brain-computer interfaces, with growing applications in healthcare. Previous research has focused on leverag

Newer Is Not Fairer: Gender Stereotyping in Text-to-Image AI Across Model Generations

arXiv:2609.18007v1 Announce Type: cross Abstract: Text-to-image generative models are widely used in professional and creative settings, yet how they represent gender across occupations -- and whether

safetyarxiv-cs-ai

No Usable Linear 'Capitulation Direction' in Two Small LLMs: A Validation Protocol for Activation-Steering Claims, and a Cross-Family Behavioral Study of Sycophancy Under Pushback

arXiv:2609.17550v1 Announce Type: new Abstract: Language models frequently abandon correct answers when users push back. We study this in two small instruction-tuned models from different families, Qw

NObSP: Functional Decomposition of Neural Networks via Oblique Subspace Projections

model-releasesarxiv-cs-lg

arXiv:2609.17825v1 Announce Type: new Abstract: Understanding how deep neural networks make decisions remains a fundamental challenge. We present NObSP (Nonlinear Oblique Subspace Projections), a fram

NormLift: From Lifted Features To Semantic Reliability In 3D Gaussian Splatting

arXiv:2609.18898v1 Announce Type: new Abstract: Training-free weighted aggregation is widely used to lift 2D semantic features onto 3D Gaussians for open-vocabulary scene understanding, yet its theore

safetyarxiv-cs-cv

Not All Layers Need Tuning: Diagnosing and Directing Adaptation in Vision-Language-Action Models

model-releasesarxiv-cs-lg

arXiv:2609.18084v1 Announce Type: cross Abstract: Fine-tuning a Vision-Language-Action (VLA) model for a new deployment environment is expensive, yet most methods apply uniform-capacity adapters to ev

Not All Patches Are Equally Forgettable: Spatially Localized Domain Unlearning in Vision-Language Models

arXiv:2609.17790v1 Announce Type: new Abstract: Pre-trained vision-language models (VLMs) exhibit strong cross-domain recognition performance even without additional training. However, this robustness

researcharxiv-cs-cv

OBC-Prune: Outcome-Based Calibration for Large Reasoning Model Pruning

arXiv:2609.17890v1 Announce Type: new Abstract: Large reasoning models (LRMs) generate long chain-of-thought traces before answering, creating significant inference overhead. Pruning can reduce this c

Objective vs. Search: Decomposing What Makes a Good Tokeniser

arXiv:2609.19145v1 Announce Type: cross Abstract: Two dominant tokenisation algorithms are used by modern language models: byte-pair encoding (BPE) and UnigramLM. These differ along two orthogonal axe

researcharxiv-cs-ai

Occluded Gait Recognition with Mixture of Experts: An Action Detection Perspective

model-releasesarxiv-cs-cv

arXiv:2609.18432v1 Announce Type: new Abstract: Extensive occlusions in real-world scenarios pose challenges to gait recognition due to missing and noisy information, as well as body misalignment in p

Agents

OmniPlanner: Universal Exploration and Inspection Path Planning Across Robot Morphologies

arXiv:2603.04284v2 Announce Type: replace Abstract: Autonomous robotic systems are increasingly deployed for mapping, monitoring, and inspection in complex and unstructured environments. However, most

agentsarxiv-cs-ro

OmniRisk: Omnidirectional Trajectory-Risk Learning for Agile Quadrotor Dynamic Avoidance

arXiv:2609.18191v1 Announce Type: new Abstract: Agile quadrotor avoidance of fast-moving obstacles requires anticipating collisions and selecting feasible maneuvers within short reaction windows. Reli

applicationsarxiv-cs-ro

On Dominant Manifolds in Reservoir Computing Networks

arXiv:2604.05967v2 Announce Type: replace Abstract: Understanding how training shapes the geometry of recurrent network dynamics is a central problem in time-series modeling. We study the emergence of

researcharxiv-cs-lg

On-the-Fly Homographies Calibration for Multi-Camera Tracking

arXiv:2609.18582v1 Announce Type: cross Abstract: Precise multi-camera tracking traditionally relies on rigorous 3D site calibration, yet this requirement is often operationally impossible in large-sc

researcharxiv-cs-ai

On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Models

model-releasesarxiv-cs-lg

arXiv:2609.17942v1 Announce Type: new Abstract: The problem of identifiability in linear parametric models (LPMs) whose nodes follow either an ordered logit model or a regular one-parameter exponentia

Agents

One Axis, No Brake: Self-Knowledge Limits the Filtering of Harmful Peer Conformity in LLMs

arXiv:2609.18998v1 Announce Type: cross Abstract: Multi-agent LLM systems are expected to be more reliable because agents can catch each other's mistakes. But peer pressure cuts both ways: the same co

agentsarxiv-cs-cl

One Color Preprocessing Improves DSATUR

arXiv:2609.17633v1 Announce Type: new Abstract: The Graph Coloring Problem (GCP) is NP-hard and DSATUR stands as one of the fastest heuristics for it despite producing colorings that typically use mor

Tutorials

One Size Does Not Fit All! Dynamic Retriever and Generator Selection for RAG

arXiv:2609.17709v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems typically employ fixed retriever and generator configurations across queries, despite substantial differe

tutorialsarxiv-cs-ai

Online Multi-Camera 3D Tracking via ID Prediction over Recurrent Sparse Queries

arXiv:2609.18363v1 Announce Type: new Abstract: Online multi camera 3D tracking must maintain scene global identities across synchronized views, yet query-based trackers carry these identities only im

researcharxiv-cs-cv

Online Multimodal Workload Assessment in Contact-Rich Physical Human-Robot Interaction

arXiv:2609.18031v1 Announce Type: new Abstract: Contact-rich physical human--robot interaction (pHRI) imposes time-varying demands associated with physical interaction, motor regulation, and physiolog

researcharxiv-cs-ro

Online Robust Reinforcement Learning Through Monte-Carlo Planning

arXiv:2609.18599v1 Announce Type: cross Abstract: Monte Carlo Tree Search (MCTS) is a powerful framework for solving complex decision-making problems, yet it often relies on the assumption that the si

OpenDexGrasp: Open-vocabulary Task-Oriented Dexterous Grasping

arXiv:2609.18117v1 Announce Type: new Abstract: Dexterous grasp synthesis has advanced rapidly in generating stable and physically plausible hand poses, but real-world manipulation requires grasps tha

safetyarxiv-cs-ro

Tutorials

Opt-In Art: Learning Art Styles Only from Few Examples

arXiv:2412.00176v4 Announce Type: replace Abstract: We explore whether pre-training on datasets with paintings is necessary for a model to learn an artistic style with only a few examples. To investig

tutorialsarxiv-cs-cv

PACT: Can Enterprise AI Assistants Be Trusted Under Pressure?

arXiv:2609.18605v1 Announce Type: cross Abstract: As corporate AI adoption continues to grow, enterprise-grade LLM agents are being deployed into sensitive contexts such as hiring, healthcare, and fin

PACT-WAM: Predicting Actions and Visual Foresight with Compact Temporal Encoding for Robot Manipulation

arXiv:2602.15882v2 Announce Type: replace-cross Abstract: Robot manipulation uses temporal context to select actions and visual foresight to assess their consequences, yet dense representations of pas

PageRecall: Measuring Page Selection in Literature-Grounded Question Answering

arXiv:2609.18154v1 Announce Type: cross Abstract: We describe our system for LitTraceQA (GroundLM @ EMNLP 2026): given a research question, retrieve the relevant papers from a pool of 27,487, cite the

researcharxiv-cs-cl

PANORAMA: Panoptic Grounded Captioning via Mask Proposal Selection

arXiv:2609.19143v1 Announce Type: cross Abstract: Intelligent systems that act in the world require image understanding that is both comprehensive and spatially grounded. Current vision-language model

Local Ai

PASSAGE: Scaling Scene-Aligned Motion Learning for Perceptive Humanoid Traversal in Cluttered Environments

arXiv:2609.18732v1 Announce Type: new Abstract: Humanoid robots can step over, squeeze past, and duck under obstacles, but learning to select and coordinate these behaviors from onboard perception rem

local-aiarxiv-cs-ro

Patch the Distribution Mismatch: RL Rewriting Agent for Stable Off-Policy SFT

arXiv:2602.11220v2 Announce Type: replace-cross Abstract: Large language models are commonly adapted to downstream tasks through supervised fine-tuning (SFT), but substantial distribution mismatch bet

safetyarxiv-cs-cl

Pay Only for Disagreement: Certified No-Regression Verdicts for Model Updates with Matching Label-Complexity Bounds

arXiv:2609.17560v1 Announce Type: cross Abstract: Every production model is updated, by retraining, fine-tuning, quantization, or a silent vendor swap, and each update risks being worse than what it r

PBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics

arXiv:2505.23126v5 Announce Type: replace Abstract: Although many benchmarks evaluate the reasoning abilities of Large Language Models (LLMs) within domains such as mathematics, coding, or data wrangl

PDA++: Field-Aligned Planning and Scene-Adaptive Insertion in Remote Sensing

model-releasesarxiv-cs-cv

arXiv:2609.18329v2 Announce Type: new Abstract: Remote sensing recognition is often constrained by scarce observations of rare targets and costly annotations, making realistic synthetic augmentation p

Peak-Aware Short-Term Load Forecasting Across Distribution Grid Aggregation Levels

arXiv:2609.18588v1 Announce Type: new Abstract: For distribution system operators, short-term load forecasting (STLF) supports congestion management, voltage control, and asset protection. Most existi

researcharxiv-cs-lg

PentestChain: A Cost-Aware, MCP-Orchestrated Framework for Automated Penetration Testing with Free-Tier LLMs

arXiv:2609.18120v1 Announce Type: cross Abstract: AI-driven penetration testing has been demonstrated with premium frontier models such as GPT-4, but the per-engagement token cost makes continuous, au

PersonaPath: Towards Knowledge-Centric Personalized Learning Path Planning

arXiv:2609.18861v1 Announce Type: new Abstract: Adaptive learning systems commonly formulate learning path planning as Exercise-Centric (EC) recommendation, where the next step is inferred from item-l

PESTO: Formally Correct Registration of LiDAR Point Clouds with Limited Overlap

arXiv:2609.18082v1 Announce Type: cross Abstract: In this paper we tackle the problem of aligning LiDAR point clouds also known as the point cloud registration problem. We propose a new algorithm, PES

safetyarxiv-cs-ro

Physics-based prediction, uncertainty quantification and decision-making for IN718 crystallographic texture intensity across LPBF defocus regimes

arXiv:2609.18863v1 Announce Type: new Abstract: Reliable prediction of crystallographic texture in laser powder bed fusion is critical for linking process conditions with anisotropic response and for

researcharxiv-cs-lg