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arXiv:2609.24359v1 Announce Type: new Abstract: Image forensics is increasingly an open-world problem: manipulations range from fully synthetic images to localized edits, splicing and swapping, while

local-aiarxiv-cs-cv

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Research

Dissecting Hierarchical Reasoning Models: A Mechanistic Study

DGX agent

arXiv:2609.22197v1 Announce Type: new Abstract: We study Hierarchical Reasoning Model (HRM), a representative hierarchical Transformer-based latent reasoning model with many variants, on Sudoku, Maze,

researcharxiv-cs-lg

22 Sept 2026

Dissecting Training-Free Uncertainty Estimation in Multimodal Large Language Models

arXiv:2609.22206v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable performance across a wide range of multimodal tasks, yet understanding and quantifying

Distill What You Trust: Reliability-Aware Multi-Teacher On-Policy Distillation

arXiv:2609.23697v1 Announce Type: new Abstract: Multi-teacher on-policy distillation allows a student to learn from complementary specialists on its own trajectories. Domain-routed approaches, however

safetyarxiv-cs-cl

Distributed Infrastructure Sensors and Cues for Robotic Fire-Fighting and Safety System on a Lunar Base

arXiv:2609.22786v1 Announce Type: new Abstract: There are renewed efforts to build a lunar base and house a team of astronauts on the Moon for extended periods. However, important challenges remain, s

Distributed Linear Solvers and Data Heterogeneity

arXiv:2304.10640v5 Announce Type: replace-cross Abstract: We consider the problem of solving a large-scale system of linear equations in a distributed/federated setting. The taskmaster solves the syst

Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations

arXiv:1812.09632v3 Announce Type: replace Abstract: We propose a data-driven approach to quantify the uncertainty of models constructed by kernel methods. Our approach minimizes the needed distributio

Applications

Do Chess Explanations Reflect Model Decisions? Behavioral and Token-Level Tests of LLM Reasoning Faithfulness

arXiv:2609.22245v1 Announce Type: new Abstract: Large language models can produce fluent explanations for chess moves, but plausible language does not necessarily reflect the reasoning behind a decisi

applicationsarxiv-cs-cl

Do Language Models Know Their Own Constraints?

arXiv:2609.22151v1 Announce Type: new Abstract: We ask whether behavioral constraints acquired through post training remain explicitly reportable. Using constrained recipe generation as a testbed, fiv

Do LiDAR Language Models Really Understand Spatio-temporal Relationships?

arXiv:2609.24452v1 Announce Type: new Abstract: Recent 4D LiDAR language models aim to reason about objects and their evolving spatial relationships. Yet, in our evaluation, always selecting the same

Do LLMs Choose Like Humans? Using Cognitive Theory to Evaluate LLM Decision-Making

arXiv:2609.22225v1 Announce Type: new Abstract: Large language models (LLMs) exhibit a range of human-like decision-making behaviors, but whether these reflect similar underlying mechanisms or surface

Do Student LLMs Inherit OOD Robustness? Invariance-Weighted Distillation for Reliable Knowledge Transfer

arXiv:2609.22566v1 Announce Type: new Abstract: Knowledge distillation (KD) aims to compress high-performance teacher LLMs into lightweight students. However, distilled students often exhibit substant

DOA-SORT: Directional Occlusion-Aware Multi-Object Tracking with Distributional Observations

arXiv:2609.22706v1 Announce Type: new Abstract: Identity association in multi-object tracking (MOT) is vulnerable to partial occlusion, truncated detections, and fluctuating confidence scores. Existin

safetyarxiv-cs-cv

Document Retrieval-Aware Chunking (D-RAC): Universal Retrieval-Aware Ingestion of Enterprise Documents via PDF Normalization and Multimodal Markdown Conversion

arXiv:2609.24220v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems over enterprise knowledge bases must ingest heterogeneous document formats -- PDFs, Word documents, present

Does the Truthfulness Signal Survive Code-Mixing? Probing Hidden States for Hallucination Detection in Hinglish

arXiv:2609.22138v1 Announce Type: new Abstract: Hidden-state hallucination probing - training a linear classifier on an LLM's internal activations to detect whether a generated answer is faithful to t

DolphinBench: Mapping the Pareto Frontier of Agent Memory

arXiv:2609.24971v1 Announce Type: new Abstract: Agents today often take real-world actions that depend on long-term memory and context recall over time. However, most current memory benchmarks are bui

Domain-decomposed Evolutional Deep Neural Network with Random Features for Transient Pressure Diffusion with Discontinuous and High-Contrast Coefficients

model-releasesarxiv-cs-lg

arXiv:2609.22349v1 Announce Type: cross Abstract: Transient pressure diffusion in heterogeneous porous media becomes difficult to resolve efficiently when permeability is discontinuous and spans sever

Don’t be fooled by this summer of AI hype

model-releasesmit-tech-review

It’s been a busy few months for AI hype. At the end of April, Anthropic claimed that its model Claude Mythos is better at finding software vulnerabilities than most security experts. Then we had the O

DPTM-DT: Dual-Pretrained Transformer Multitask Representation Learning for Drug-Target Prediction

arXiv:2609.22184v1 Announce Type: new Abstract: Drug-target relation prediction supports candidate screening, drug repositioning, and mechanism analysis. Existing models often use incomplete drug or p

DriveReferee: Geometric Safety Verdicts Need Not Be Learned for Driving World-Action Models

arXiv:2609.22762v1 Announce Type: new Abstract: Generative world-action models (WAMs) jointly generate future video and vehicle actions, while their action branches remain primarily optimized by exper

safetyarxiv-cs-cv

Dropout Neural Network Training Viewed from a Percolation Perspective

arXiv:2512.13853v3 Announce Type: replace Abstract: In this work, we investigate the existence and effect of percolation in training deep Neural Networks (NNs) with dropout. Dropout methods are regula

DTKDP: A Dual Teacher Knowledge Distillation and Pruning Framework for Lightweight Oriented SAR Ship Detection

arXiv:2609.24872v1 Announce Type: new Abstract: Two-stage oriented detectors achieve high localization accuracy in synthetic aperture radar (SAR) ship detection, but their large backbones, feature pyr

safetyarxiv-cs-cv

DualWAM: Dual-System World Action Models for Asynchronous Global Planning and Local Refinement

arXiv:2609.24868v1 Announce Type: new Abstract: World Action Models (WAMs) jointly generate robot actions and predict future world states, transferring priors from video pretraining to robot control.

researcharxiv-cs-ro

Dynamic Quantiser - a way to make your own high quality dynamic quants

model-releasesr-localllama

Dynamic Quantiser TLDR - Makes dynamic and/or custom quants of ggufs. Doesn't need data, just gguf file + llama.cpp. Minimises cosine deviation - highly correlated with minimising KLD. Fast, much bett

Dynamic Thermal Gaussians: Multimodal 4D Gaussian Splatting

arXiv:2609.24531v1 Announce Type: new Abstract: Thermography plays a vital role in military and broader thermal analysis applications. Recent progress in 3D thermal reconstruction has extended tempera

EAVer: Long-Form Factuality Verification as an End-to-End Agentic Policy

arXiv:2609.22223v1 Announce Type: new Abstract: Long-form factuality verification is commonly implemented as a static decompose-search-verify pipeline, with separately prompted modules processing clai

ECP-Bench: Benchmarking and Learning Entertainment Content Promotion with Foundation Models

model-releasesarxiv-cs-lg

arXiv:2609.22150v1 Announce Type: cross Abstract: Content promotion spans a broad set of skills, from understanding content to forecasting its market reception. However, LLMs' ability to support such

Agents

EditWM: Event-Decomposed World Modeling with Incremental Correction for End-to-End Autonomous Driving

arXiv:2609.22317v1 Announce Type: new Abstract: World models support autonomous driving by predicting the scene evolution associated with candidate trajectories. Driving dynamics differ in predictabil

agentsarxiv-cs-ro

Efficient and Adaptive Simultaneous Speech Translation with Fully Unidirectional Architecture

arXiv:2504.11809v2 Announce Type: replace Abstract: Simultaneous speech translation (SimulST) produces translations incrementally while processing partial speech input. Although large language models

safetyarxiv-cs-cl

Efficient Concertormer for Image Deblurring and Beyond

arXiv:2404.06135v4 Announce Type: replace Abstract: The Transformer architecture has achieved remarkable success in natural language processing and high-level vision tasks over the past few years. How

researcharxiv-cs-cv

Applications

Efficient K-generalizable Learned Search

arXiv:2603.06159v2 Announce Type: replace-cross Abstract: Learned top-K search improves the accuracy-latency trade-off of graph-based vector search, but existing methods are designed for a fixed K: se

applicationsarxiv-cs-lg

Efficient LLM Distillation for Bangladesh Legal Context: A Smartphone-Compatible Retrieval-Augmented Generation Model

arXiv:2609.24177v1 Announce Type: new Abstract: Legal information in Bangladesh is inaccessible to most citizens. Statutory text is English-only, trained lawyers are concentrated in urban centres, and

Efficient Mixture-of-Experts with Speculative Decoding via Expert Coactivation

arXiv:2609.22471v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models are increasingly deployed alongside Speculative Decoding (SD) to accelerate inference, but combining the two is challe

Tutorials

Efficient Reasoning Exploration via State-Conditioned Latent Steering with Progress Guidance

arXiv:2609.24066v1 Announce Type: new Abstract: Best-of-N is a widely used inference strategy for complex reasoning, whose effectiveness depends on whether sampled candidates can cover diverse and hig

tutorialsarxiv-cs-cl

EgoWild2Dex: Learning Dexterous Robotic Manipulation from In-the-Wild Human Experience

model-releasesarxiv-cs-ro

arXiv:2609.23755v1 Announce Type: new Abstract: Egocentric human data provide a principled source of supervision for learning dexterous robot manipulation. Unlike prior approaches that often collect s

Applications

Elevator-VIGS: Separating Elevator Motion from Robot Motion in Visual-Inertial Gaussian Splatting SLAM

arXiv:2609.23491v1 Announce Type: new Abstract: We present Elevator-VIGS, a visual-inertial 3D Gaussian Splatting SLAM system that keeps tracking and mapping through elevator rides. Inside a moving el

applicationsarxiv-cs-ro

EmbeddGAN: A Novel GAN Framework Using an Embedding Network and Gini Distance Correlation

arXiv:2609.22508v1 Announce Type: new Abstract: Generative Adversarial Networks (GANs) have demonstrated strong performance in generating high-quality synthetic data. However, they are limited by no f

Embedding Physics Priors in Robot Learning: A Survey

arXiv:2609.22319v1 Announce Type: new Abstract: The rapid progress of artificial intelligence is reshaping robotics and accelerating the adoption of learning-based approaches. While purely data-driven

Embodied Snap: Octopus-Inspired Distributed Reach-and-Attach with a Speed-Limited Soft Arm

arXiv:2609.22926v1 Announce Type: new Abstract: Reach-and-attach of soft robotic arms with passive suction requires accurate targeting and sufficient contact speed, yet geared actuators can impose a s

Agents

EmbodiedDiffusion: End-to-End Traversability-Guided Visual Diffusion for Heterogeneous Robot Navigation

arXiv:2512.02851v5 Announce Type: replace Abstract: Visual traversability estimation is central to autonomous navigation, yet most approaches either rely on prompt-driven Vision-Language Model (VLM) o

agentsarxiv-cs-ro

Emergent Collusion in Long-Horizon LLM Agent Interaction

arXiv:2609.24967v1 Announce Type: cross Abstract: LLM agents are increasingly deployed in collaborative settings, yet long-term interaction may give rise to undesirable coordination. We study the emer

safetyarxiv-cs-cl

EmoPose: Vision-Language Model Guided Emotion-Aware Gesture Generation for Humanoid Robots

model-releasesarxiv-cs-ro

arXiv:2609.23414v1 Announce Type: new Abstract: Socially competent humanoid robots must communicate affect and intent through gesture as well as speech, yet open-ended interaction must become motion t

Hardware

Enabling Private High-Performance Production AI Inference with NVIDIA Confidential Computing

NVIDIA Confidential Computing (CC) secures large‑language‑model inference by providing encrypted virtual machines, GPUs, and NVLink, enabling processing of sensitive data in trusted hardware. Running

hardwarenvidia-developer

Enabling Vision and Cross-Modal Learning for Multimodal Stroke Recurrence Prediction: An Interpretable Two-Step Framework

arXiv:2609.22271v1 Announce Type: new Abstract: Multimodal stroke recurrence prediction requires effective integration of heterogeneous clinical and imaging data, yet modality imbalance often causes m

End-to-end Jordanian dialect speech-to-text self-supervised learning framework

arXiv:2609.24410v1 Announce Type: new Abstract: Speech-to-text engines are extremely needed nowadays for different applications, representing an essential enabler in human-robot interaction. Still, so

researcharxiv-cs-cl

Agents

Enforcing Narrative Reliability and Epistemic Pacing in LLM-Driven Detective Games via Structured Knowledge Trees

arXiv:2609.23043v1 Announce Type: cross Abstract: Large Language Models (LLMs) enable open-ended dialogue in interactive games, but their non-deterministic outputs make it difficult to preserve author

agentsarxiv-cs-cl

Enhancing Autonomous Driving Safety through World Model-Based Predictive Navigation and Adaptive Learning Algorithms for 5G Wireless Applications

arXiv:2411.15042v3 Announce Type: replace Abstract: Addressing the challenge of ensuring safety in ever-changing and unpredictable environments, particularly in the swiftly advancing realm of autonomo

Enhancing Shrimp Disease Detection via Deep Learning and Data Refinement for Resilient Aquaculture

arXiv:2609.23397v1 Announce Type: new Abstract: Shrimp diseases continue to cause devastating losses in the aquaculture industry, driving a critical need for robust, automated detection. This work con

researcharxiv-cs-cv