All entries

AcoustiClaim: A Numeric Claim Benchmark with Instrument Ground Truth

model-releasesarxiv-cs-cl

arXiv:2609.30483v1 Announce Type: cross Abstract: Audio language models state numbers for acoustic quantities, and neither human opinion nor a judge model says whether such a number is true of the sig

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Action Forcing: Training World Models on Unsupervised Video by Recovering Underlying Egomotion Bases

DGX agent

arXiv:2609.30595v1 Announce Type: cross Abstract: Synchronised action annotations are needed to train controllable world models and these datasets remain elusive. Existing approaches make use of instr

researcharxiv-cs-ai

28 Sept 2026

Actively Resolving Contextual Uncertainty for Underspecified Tasks in Natural Language

arXiv:2609.30428v1 Announce Type: cross Abstract: Foundation models provide robots with the ability to interpret natural language and reason about environmental context, yet most language-conditioned

safetyarxiv-cs-ai

ActKV: Efficient LLM Agents through Action-Guided KV Cache Management

arXiv:2609.31395v1 Announce Type: cross Abstract: Agentic LLM inference accumulates long KV caches across iterative observation-reasoning-action loops, imposing substantial memory overhead and limitin

Adapting for AI: How elementary teachers adjust their practices for an AI-integrated curriculum

arXiv:2609.31569v1 Announce Type: cross Abstract: Conversational AI tools are entering children's everyday experiences, and schools are interested in adopting them. However, successful classroom integ

Adapting Personalized Speech Enhancement for Low-Latency Audio-Visual Target-Speaker Extraction

arXiv:2609.30631v1 Announce Type: cross Abstract: Online audio-visual target-speaker extraction aims to remove competing voices while preserving speech quality and bounding lookahead. Existing extract

researcharxiv-cs-cv

Applications

Adapting Visualization Techniques for Time-Series Anomaly Detection: From Convolutional Neural Networks to Convolutional-Recurrent Neural Networks

arXiv:2411.04707v4 Announce Type: replace Abstract: Deep neural networks achieve strong performance on complex tasks but are often regarded as 'black boxes,' which limits their adoption in domains whe

applicationsarxiv-cs-cv

Local Ai

Adaptive Interaction Graphs for Particle Simulation

arXiv:2609.30822v1 Announce Type: new Abstract: Learned particle simulators based on graph neural networks achieve strong one-step accuracy, but errors compound over long horizons. An underexplored va

local-aiarxiv-cs-lg

Adaptive multi-resolution Gaussian processes: Scalable exact inference with naturally data-sparse covariance matrices

arXiv:2609.30348v1 Announce Type: cross Abstract: Gaussian processes constitute a cornerstone of probabilistic machine learning, yet scaling them to large datasets typically forces a trade-off between

Adaptive Multi-Value Control in LLMs via Causal Activation Steering

arXiv:2609.30405v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in settings where responses must reflect multiple, potentially interacting social norms and human

Adaptive Pilot Selection for Unified Semantic Communication and Semantic Sensing in ISAC

arXiv:2609.30891v1 Announce Type: cross Abstract: Semantic communication (SemCom) and integrated sensing and communication (ISAC) are promising technologies for future 6G wireless networks. Existing s

Adaptive Random Matrices in Gaussian Bandits: Spectral Universality and Selection-Induced Outliers

arXiv:2609.30321v1 Announce Type: cross Abstract: Adaptive arm selection changes the distribution of the observations collected by a bandit algorithm, but it need not change their limiting empirical s

Aerial Manipulation in the Wild with Onboard Perception, Policy Learning, and Whole-Body Control

arXiv:2609.30521v1 Announce Type: new Abstract: Aerial manipulation in outdoor environments remains challenging due to the simultaneous requirements of reliable state estimation, stable aerial motion,

safetyarxiv-cs-ro

AFA-Net: A Differential Attention Approach for Auditory Attention Detection

arXiv:2609.31402v1 Announce Type: cross Abstract: Auditory Attention Detection (AAD) utilizes electroencephalographic (EEG) signals to identify a target speaker in a multi-speaker environment. Despite

Agentic Limit Order Books: Phase Transitions and Market Impact

arXiv:2609.31260v1 Announce Type: cross Abstract: We investigate the systemic macroscopic dynamics emerging from Limit Order Books (LOBs) populated exclusively by autonomous reinforcement-learning age

Applications

AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side

arXiv:2609.31166v1 Announce Type: cross Abstract: Recommender systems have traditionally been developed for platforms. However, this has given rise to many phenomena that may be advantageous for platf

applicationsarxiv-cs-ai

AgentXploit: Autonomous Repository-to-Runtime Red-Teaming for AI Agents

arXiv:2609.31318v1 Announce Type: cross Abstract: AI agents combine language models with external data and tools that can modify files, call APIs, or execute code. Security failures can arise when adv

'AI is (not) the new...': A Diagnostic Analogy Framework for Generative AI's Cultural Impacts

arXiv:2609.31482v1 Announce Type: new Abstract: Generative AI is reshaping the cultural infrastructures through which knowledge is found, synthesized, and held accountable. To make sense of this shift

Air-Ground Collaborative Robots for Fire and Rescue Missions: A Survey from the Mapping and Navigation Perspective

arXiv:2412.20699v3 Announce Type: replace Abstract: Air-ground collaborative robots have shown great potential in the field of fire and rescue. Mapping and navigation, as the key foundation for air-gr

researcharxiv-cs-ro

Applications

ALF: An Active Learning Framework for Scientific Discovery

arXiv:2609.31197v1 Announce Type: new Abstract: Machine learning for scientific discovery is almost systematically data bound. Producing relevant high quality data, under budget constraints, is amongs

applicationsarxiv-cs-lg

Aligning One-Step Generative Models with Reward-Weighted Transport Distillation

arXiv:2609.30840v1 Announce Type: cross Abstract: One-step generators enable high-quality visual generation with a single network evaluation, but their post-training is difficult: general implicit gen

All In Good Time: Causality-Aware Framework for LLM-Based Simultaneous Speech-to-Speech Translation

arXiv:2609.30416v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong performance in low-resource offline translation; however, extending them to simultaneous speech-to-speech

safetyarxiv-cs-cl

AlphaEarth distinguishes cities but compresses urban variation

arXiv:2609.30356v1 Announce Type: new Abstract: Cities differ in built form, land cover and development history, complicating comparison across places and time. Satellite foundation models map Earth's

researcharxiv-cs-cv

Amortized quadrature for posterior expectations in inverse problems

arXiv:2606.15871v2 Announce Type: replace-cross Abstract: Uncertainty in the solution of an inverse problem and in the tasks performed on it is quantified by posterior expectations, each an average of

Amplify What You Gaze At: Target Saliency Boosting in Text-to-Image Generation

arXiv:2609.30733v1 Announce Type: new Abstract: Text-to-image generation has advanced in controlling what, where, and how objects appear, yet how visual attention is distributed among objects remains

An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Making

arXiv:2609.30396v1 Announce Type: cross Abstract: This paper presents an end-to-end causal machine learning (ML) pipeline designed for real-world applications with continuous treatments. The proposed

Analyzing and Mitigating Cost-Inefficient Behaviors in Coding Agents

model-releasesarxiv-cs-ai

arXiv:2609.30725v1 Announce Type: new Abstract: Although effective, coding agents often incur substantial monetary costs. Their recurring cost-inefficient behaviors remain underexplored. We conduct th

Anatomy-Aware Dexterity-Driven Design Optimization of Surgical Continuum Robots

arXiv:2609.30745v1 Announce Type: cross Abstract: Performing complex medical procedures with continuum robots requires careful selection of their geometric design parameters. The robot should have hig

Anchor to Expand: Semantic Anchoring for Personalized Text-to-Image Diffusion Models

arXiv:2511.22245v2 Announce Type: replace Abstract: Personalizing text-to-image diffusion models extends pretrained models to represent novel user-specific concepts from only a few reference images. H

Asymmetric Classifier-Free Guidance for Target-Speaker ASR

arXiv:2609.30476v1 Announce Type: cross Abstract: Target-speaker automatic speech recognition (TS-ASR) must identify and transcribe a desired speaker under varying overlap and noise conditions. These

researcharxiv-cs-cl

Local Ai

Atelier: Learning Local Self-Supervised Features for CryoEM Volumes via Hypernetworks

arXiv:2609.30569v1 Announce Type: new Abstract: CryoEM map interpretation requires features that are spatially localized, consistent across samples, and informative across spatial scales. Most deep le

local-aiarxiv-cs-ai

Atlases Are Already Inside: Recovering Population Templates from Pretrained Diffusion Models

model-releasesarxiv-cs-cv

arXiv:2609.30566v1 Announce Type: new Abstract: We present a new inference-time sampler for diffusion models that gives a pretrained model a capability it was never trained for: constructing the atlas

Local Ai

AtomWorld-Mem: Memory-Restored World States for Long-Horizon Atomistic Evolution

arXiv:2609.31133v1 Announce Type: new Abstract: High-fidelity atomistic evolution over long timescales requires more than observing the current crystal configuration. Instantaneous atomistic snapshots

local-aiarxiv-cs-ai

Attention-Based Adaptive Policies for Simultaneous Speech-to-Text Translation

arXiv:2609.30839v1 Announce Type: new Abstract: Simultaneous speech-to-text translation (Simul-S2TT) consists of generating partial translations while the incoming audio frames are processed by the sy

Attribution Bias in Large Language Models

model-releasesarxiv-cs-ai

arXiv:2604.05224v2 Announce Type: replace Abstract: As Large Language Models (LLMs) are increasingly used to support search and information retrieval, it is critical that they accurately attribute con

Audio emotion recognition for atypical hearing

arXiv:2609.31168v1 Announce Type: new Abstract: My doctoral work aims to explore Audio Emotion Recognition (AER) in the context of atypical listening. This research focuses on auditory hypersensitivit

Audio LLMs Know When They Can't Hear You

arXiv:2609.30625v1 Announce Type: new Abstract: Audio large language models allow users to interact with the model through speech. When an input recording is too degraded, the model may misinterpret t

safetyarxiv-cs-ai

Audit Before You Commit: Locating Belief Failures in Active Identification for One-Shot Manipulation

arXiv:2609.30608v1 Announce Type: new Abstract: A robot that probes a few times before one irreversible action, such as tapping a surface before inserting a peg, must decide when the evidence is enoug

researcharxiv-cs-ro

Auditing and Repairing LLM-as-Judge Failures in a Production Text-to-SQL Pipeline

model-releasesarxiv-cs-cl

arXiv:2609.30290v1 Announce Type: new Abstract: Production text-to-SQL pipelines often end with an LLM-as-judge whose agreement with human annotators has never actually been measured. When we checked

Auditing Latent-Space Monitors for Autonomous Driving

arXiv:2609.30557v1 Announce Type: cross Abstract: Runtime failure monitors can use a model's internal representations to anticipate failures. We audit this monitoring strategy across two autonomous-dr

Auditing System-1 Models on Biosecurity-Relevant Benchmarks: Calibration, Selective Prediction, and Permutation Instability in a Non-Generative Model

arXiv:2609.30454v1 Announce Type: new Abstract: Non-generative 'System-1' models return structured probabilistic decisions in a single forward pass, without autoregressive decoding, at a small fractio

Augmented Reality Interfaces for Human-Robot Collaboration: Development of a ROS 2-Based Sensor Streaming Framework and Validation via SLAM Algorithms

arXiv:2609.31396v1 Announce Type: new Abstract: In recent years, Human-Robot Collaboration (HRC) has taken on a central role in Industry 4.0 and collaborative robotics, demanding communication channel

researcharxiv-cs-ro

Tutorials

Aurora-X: Built for Extreme Time Series Forecasting

arXiv:2609.31038v1 Announce Type: new Abstract: Time series foundation models (TSFMs) enable cross-domain forecasting, but their development as general-purpose forecasters remains constrained by under

tutorialsarxiv-cs-lg

AuthGuard-R: Safety-Compliant Mission Hijacking and Dual-Gate Defense for LLM-Controlled Robots

model-releasesarxiv-cs-ro

arXiv:2609.31110v1 Announce Type: new Abstract: Large language models are increasingly used as high-level planners for mobile robots, robot manipulators, and autonomous vehicles. Recent studies show t

Hardware

AutoResearch at Production Scale: Failure Modes and a Multi-Agent Framework

arXiv:2609.30541v1 Announce Type: new Abstract: Optimizing embedding systems for production recommendation pipelines demands systematic exploration that consumes disproportionate engineering effort at

hardwarearxiv-cs-lg

AUWave: A Data-Driven Model for Reconstructing Significant Wave Heights Using Sparse Observations

arXiv:2509.19384v2 Announce Type: replace-cross Abstract: Reconstructing high-resolution regional significant wave height (SWH) fields from sparse buoy observations is a critical challenge for ocean m

AxonSynth: Domain-Randomized Synthetic Data for Zero-Shot 3D Axon Segmentation in Light-Sheet Microscopy

arXiv:2609.31431v1 Announce Type: new Abstract: Accurate segmentation of axons in 3D microscopy data is important for analyzing white-matter organization, but dense ground truth labels are expensive t

AYLA: Amplifying Gradient Sensitivity via Loss Transformation in Non-Convex Optimization

arXiv:2504.01875v3 Announce Type: replace Abstract: Stochastic Gradient Descent (SGD) and its variants, such as ADAM, are foundational to deep learning optimization, adjusting model parameters through