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arXiv:2610.20877v1 Announce Type: cross Abstract: Early detection of mild cognitive impairment remains a critical bottleneck in dementia care, demanding scalable tools capable of capturing subtle beha

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Research

Compiling Multi-Threshold Operators for Quantized Non-Monotonic and Fine-Granular Activations in Custom-Tailored DNN Accelerators

DGX agent

arXiv:2610.21825v1 Announce Type: cross Abstract: Aggressive quantization of neural networks can expose activation functions as bottlenecks for efficient hardware acceleration. Implementing quantized

researcharxiv-cs-lg

16 Oct 2026

Designing Experiments for External Decision Makers

model-releasesarxiv-cs-lg

arXiv:2610.22032v1 Announce Type: cross Abstract: Standard Bayesian experimental design (BED) methods assume the experimenter alone will act on the data, choosing the best action given their own belie

Dexterity from Supervised Play

arXiv:2610.22340v1 Announce Type: cross Abstract: How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulation? We present DSP: Dexterity from supervised play

safetyarxiv-cs-ai

Discovering state-of-the-art quantum circuits with large language models

arXiv:2610.22367v1 Announce Type: cross Abstract: Significant efforts are currently being directed toward the implementation of large-scale quantum information processors, but realizing useful quantum

researcharxiv-cs-ai

Diviner: Agentic RCA with Human-Governed Adaptation for Large-Scale Infrastructure

arXiv:2610.21281v1 Announce Type: new Abstract: Root-cause analysis (RCA) in large-scale distributed systems is challenging because a single symptom can cross service, data, configuration, compute, an

Does Peer Observation Help? Vision-Sharing Collaboration for Vision-Language Navigation

model-releasesarxiv-cs-cv

arXiv:2603.20804v2 Announce Type: replace Abstract: Vision-Language Navigation (VLN) systems are fundamentally constrained by partial observability, as an agent can only accumulate knowledge from loca

Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes

model-releasesarxiv-cs-lg

arXiv:2610.20893v1 Announce Type: cross Abstract: Estimating causal effects on right-censored time-to-event outcomes is challenging under unmeasured confounding. Proximal causal inference addresses un

From Documents to Spans: Span-Centric Learning for LLM-based Explainable Medical Coding

arXiv:2603.15270v3 Announce Type: replace-cross Abstract: Medical coding, which assigns standardized ICD codes to clinical documents, is a fundamental yet challenging task in modern healthcare. It req

safetyarxiv-cs-ai

Tutorials

Full-Duplex Interaction with Speech, Expressions and Gestures

arXiv:2610.21452v1 Announce Type: cross Abstract: Human face-to-face conversation requires coordinating speech, gestures and expressions across both speaking and listening. We present ELLSA 2, an end-

tutorialsarxiv-cs-ai

GraphCompliance: Structure-Aware Regulatory Compliance through Policy-Context Graph Alignment

arXiv:2510.26309v2 Announce Type: replace Abstract: Regulatory compliance is an important requirement in many industrial and administrative settings. Yet compliance assessment remains difficult becaus

safetyarxiv-cs-ai

Applications

IDfRA: Self-Verification for Iterative Design in Robotic Assembly

arXiv:2509.16998v2 Announce Type: replace Abstract: Design for Robotic Assembly (DfRA) remains largely dependent on manual planning and heuristic simulation, limiting scalability and robustness in com

applicationsarxiv-cs-ro

Jev-LiteMAS: Separating Decisions from Generation in Multi-Agent Workflows

arXiv:2610.21411v1 Announce Type: new Abstract: Multi-agent LLM systems rely on generative models for expert execution, workflow planning, and artifact review. Yet many coordination operations require

Learning to Forecast on Multiple Spatiotemporal Hierarchies

arXiv:2610.21414v1 Announce Type: new Abstract: Hierarchical forecasting exploits aggregation structures that expose multiple views of the same underlying spatiotemporal forecasting problem, while ens

safetyarxiv-cs-lg

LLM-as-an-Investigator: Evidence-First Agentic Reasoning for Robust Interactive Problem Diagnosis

arXiv:2606.13220v2 Announce Type: replace Abstract: Large language models (LLMs) are used for technical troubleshooting, where problem descriptions are often incomplete and multiple causes can explain

Mind the Delay: Latency-aware Query Routing for Heterogeneous LLM pools

arXiv:2607.18253v2 Announce Type: replace Abstract: Large language model (LLM) query routers typically assign each query to a suitable model that balances response accuracy and monetary cost. Subseque

researcharxiv-cs-ai

PAC-Causal Learnability: From Identification to Finite-Sample Guarantees

arXiv:2507.18903v2 Announce Type: replace-cross Abstract: Causal identification asks whether a causal target can be recovered from the observational distribution in an idealized infinite-data setting.

Pixels to Tokens: Token Space Efficient Active Learning for Low-Budget Semantic Segmentation

arXiv:2610.20881v1 Announce Type: new Abstract: While foundation models and vision transformers (ViTs) have redefined visual representations, current active learning (AL) strategies for these backbone

researcharxiv-cs-cv

Q(gamma_H): Entropy-Guided Horizon Mixing in Multi-Discount Q-Learning

arXiv:2610.21764v1 Announce Type: new Abstract: A fixed discount factor in Q-learning imposes a single planning horizon throughout training, despite the differing demands of early exploration and long

safetyarxiv-cs-lg

Quantum Versus Classical Machine Learning for Neuropsychiatric Disorders: A Feature-Matched Multi-Cohort Evaluation

arXiv:2610.21344v1 Announce Type: new Abstract: Resting-state functional connectivity is widely used in machine learning-based classification of neuropsychiatric disorders. Quantum machine learning mo

Reasoning without Gold Standards: A Proxy-Judge Theory of Autoformalization

arXiv:2606.09449v2 Announce Type: replace Abstract: Autoformalization (AF) requires both prover acceptance and fidelity to an informal source, but prover acceptance alone cannot establish fidelity, an

researcharxiv-cs-cl

ReFlow: Replay-Driven Decoupling for Efficient RL Post-Training

arXiv:2610.21640v1 Announce Type: new Abstract: Reinforcement learning (RL) has been a critical paradigm for improving large language model (LLM) reasoning and agentic capabilities, but its efficiency

safetyarxiv-cs-lg

RUSplatting: Robust 3D Gaussian Splatting for Sparse-View Underwater Scene Reconstruction

arXiv:2505.15737v3 Announce Type: replace Abstract: Reconstructing high-fidelity underwater scenes remains a challenging task due to light absorption, scattering, and limited visibility inherent in aq

researcharxiv-cs-cv

Safe and Fuel-Efficient Motion Planning for Lunar Propulsive Drones

arXiv:2610.22225v1 Announce Type: new Abstract: Propulsive drones are a promising new technology for generating high-resolution terrain models of the Moon. In future lunar missions, propulsive drones

agentsarxiv-cs-ro

Same Context, Different Agents: A Behavioral Study of Semantic Layers, Orchestration and Compaction in Data Agents

arXiv:2610.20959v1 Announce Type: cross Abstract: Answering questions over heterogeneous data means combining evidence from files, databases, documents and the web. Data agents automate this, but thei

Scene Reconstruction Using Ubiquitous Wireless Signals via Diffusion Posterior Inference Over physics-Grounded Representations

arXiv:2610.22120v1 Announce Type: new Abstract: Knowing the layout of the physical world matters for many applications, yet the sensors we rely on for it (e.g., cameras and LiDAR) can fail in darkness

SciPivot: Benchmarking Principal-Investigator Judgment at Pivotal Moments of Scientific AutoResearch Agents

arXiv:2610.21082v1 Announce Type: new Abstract: Large language model (LLM) agents can keep scientific experiments running for months with little human supervision, but reliable autonomous research als

Applications

Self-Guided LLM Reasoning for Error Diagnosis in Vehicle Routing Problems

arXiv:2610.21393v1 Announce Type: new Abstract: Vehicle Routing Problems (VRPs) are fundamental to many real-world applications. Recent advances in large language models (LLMs) have spurred progress i

applicationsarxiv-cs-ai

SITAR: A Situational Distortion Metric for Automatic Speech Recognition Evaluation

model-releasesarxiv-cs-cl

arXiv:2610.21440v1 Announce Type: new Abstract: In automatic speech recognition (ASR), transcription errors with similar lexical or semantic discrepancies can have markedly different consequences for

Sub-Millisecond Reflexes for Continuous Physical Human-Robot Interaction

arXiv:2610.21913v1 Announce Type: new Abstract: Continuous physical human-robot interaction requires robots to rapidly adapt their control behaviour without interrupting task execution. However, exist

safetyarxiv-cs-ro

Applications

Task-Centric Multimodal Learning for Cognitive Impairment Detection

arXiv:2610.20906v1 Announce Type: cross Abstract: Dementia affects millions of older adults worldwide, yet early detection remains difficult outside clinical settings. Traditional models primarily rel

applicationsarxiv-cs-cl

Temporal Periodic Image Registration with Implicit Neural Representations

arXiv:2610.21845v1 Announce Type: new Abstract: Implicit Neural Representations (INRs) have recently gained popularity for their ability to model functions using simple and lightweight networks. These

researcharxiv-cs-cv

The Self-Improvement Problem

arXiv:2610.21646v1 Announce Type: new Abstract: With the rise of large language models, agentic systems are becoming increasingly autonomous, as they write code, call tools, revise prompts, and propos

The Tilted Sampling Problem: Optimism, Posterior Sampling, and Logarithmic Regret

arXiv:2610.21908v1 Announce Type: new Abstract: Many modern learning systems are used not to select a single best action, but to sample diverse high-quality outputs. Examples include entropy-regulariz

Thinking with Images as Continuous Actions: Numerical Visual Chain-of-Thought

arXiv:2602.23959v2 Announce Type: replace Abstract: Recent MLLMs increasingly rely on visual chain-of-thought to perform region-grounded reasoning over images. However, existing approaches ground regi

safetyarxiv-cs-cv

Tool-Augmented Large Language Models for Reliable Medical Calculation from Patient Notes

arXiv:2610.21477v1 Announce Type: new Abstract: Medical calculators translate raw patient observations into clinically actionable values and scores. However, applying these calculators directly to fre

Two-Stage Score Estimation for Group-Specific Diffusion Models

arXiv:2610.20979v1 Announce Type: cross Abstract: Generative modeling across related distributions raises a question: how can shared structure support accurate generation for a target distribution wit

VesperMUSE: Multi-Modal Fusion Vision Transformer for Image Semantic Segmentation Under Diverse Light Conditions

arXiv:2610.21990v1 Announce Type: new Abstract: Semantic segmentation is critical for the reliable navigation and scene understanding of autonomous systems, yet its performance degrades severely in lo

agentsarxiv-cs-cv

Local Ai

Vision-Guided Closed-Loop Autonomous Navigation of Light-Driven Janus Microbots

arXiv:2610.21222v1 Announce Type: new Abstract: Artificial microbots capable of precise and autonomous navigation could provide versatile platforms for microscale transport, manipulation, and task exe

local-aiarxiv-cs-ro

VOiLA: Vectorized Online Planning with Learned Diffusion Models for POMDP Agents

arXiv:2606.19729v3 Announce Type: replace-cross Abstract: Planning under uncertainty is an essential capability for autonomous robots. The Partially Observable Markov Decision Process (POMDP) provides

What Should Robot Policies Learn to Predict? Rethinking Supervision Targets in Vision-Language-Action Learning

arXiv:2610.21915v1 Announce Type: new Abstract: Vision-language-action (VLA) policies are typically trained with action prediction as the primary supervision signal, since robots ultimately execute ac

safetyarxiv-cs-ro

WorkflowPerturb: Calibrated Stress Tests for Evaluating Multi-Agent Workflow Metrics

arXiv:2602.17990v4 Announce Type: replace Abstract: Multi-agent LLM systems that generate structured workflows from natural-language requests are now deployed in production across cloud automation, De

X-RIC: An Agentic RAN-Intelligent Controller Benchmark for Enhancing Reinforcement Learning

arXiv:2610.22269v1 Announce Type: new Abstract: The O-RAN ALLIANCE introduces architectural flexibility through disaggregation and AI-driven control. While the RAN Intelligent Controller (RIC) enables

Hardware

ZenFlow: Enabling Stall-Free Offloading for LLM Training

arXiv:2505.12242v4 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) often exceeds GPU memory limits, prompting systems to offload model states to CPU memory. However, ex

hardwarearxiv-cs-lg

A Global View of How LLMs Value Human Time

arXiv:2610.18515v2 Announce Type: cross Abstract: Large language models (LLMs) increasingly act as personal assistants; however, little is known about how they capture national-level preferences on a

ActiveEvidence: Decision-Focused Evidence Graphs with Verifiable Acquisition Gaps

arXiv:2610.20532v1 Announce Type: cross Abstract: A multimodal agent must decide whether visual evidence is worth acquiring, which candidate or resolution to query, and when further interaction can no

safetyarxiv-cs-cv

ADAPT: Audio-Depth Action Planning with Transition-Aware Rewards for Lights-Out Industrial Navigation

arXiv:2610.15956v1 Announce Type: new Abstract: Vision-language-action models for embodied navigation typically rely on RGB observations, limiting their applicability in lights-out industrial environm

Agent-Canary-Mas: End-to-End Evaluation of Protocol Risk in Long-Horizon Multi-Agent Systems

arXiv:2610.15529v2 Announce Type: new Abstract: Agent-to-agent protocols allow LLM agents to discover peers, exchange results, and coordinate tool use. A locally successful protocol attack, however, m