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Do Prompt Optimizers Break Requirements They Are Not Scored On? A Preregistered Study

arXiv:2610.13287v1 Announce Type: new Abstract: Prompt optimizers rewrite a prompt to raise one target metric, but prompts also state requirements the metric does not score, such as an output format,

researcharxiv-cs-cl

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Human DGX agent

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Model Releases

Does Financial Speech Need Continual Learning Regularization? A Study of EWC and mini-KFAC on Earnings Call ASR

DGX agent

arXiv:2610.13432v1 Announce Type: cross Abstract: Earnings call transcription presents a realistic continual learning scenario where Automatic Speech Recognition (ASR) systems are updated with new rec

model-releasesarxiv-cs-cl

12 Oct 2026

Agents

Does Knowledge About Perceptual Uncertainty Help an Agent in Automated Driving?

arXiv:2502.11864v2 Announce Type: replace-cross Abstract: Agents in real-world scenarios like automated driving deal with uncertainty in their environment, in particular due to perceptual uncertainty.

agentsarxiv-cs-ro

Does Learning Recurrent Networks Matter in Partially Observable RL?

arXiv:2610.13197v1 Announce Type: cross Abstract: Reinforcement learning (RL) under partial observability typically re- lies on recurrent networks to encode temporal information, with the recurrent mo

Does Location Matter? A Black-Box Causal Audit Reveals Model-Dependent Audio Grounding in LALM Hallucination

arXiv:2610.13112v1 Announce Type: new Abstract: Large audio-language models (LALMs) frequently exhibit object hallucination, affirming absent sounds. The dominant mitigation, Audio-Aware Decoding (AAD

local-aiarxiv-cs-ai

Don't Guess, Just Ask: Evaluating Interactive Requirement Clarification for Repository-Level Software Development

arXiv:2610.13022v1 Announce Type: cross Abstract: As LLMs become increasingly capable of implementing software, a critical bottleneck emerges: whether they truly understand what users want. Users ofte

Double-Take: Improving Capacity, Binding, and Selectivity In Long-Context Within Bidirectional Memory

arXiv:2610.13073v1 Announce Type: new Abstract: Processing a long sequence does not guarantee that distant information remains usable. As context grows, a fixed-size memory must retain many associatio

DP-MIND: Differentially private memory-guided Long context Inference

arXiv:2610.13132v1 Announce Type: new Abstract: LLM-as-a-Service has become a dominant paradigm for accessing large language models without requiring local deployment. However, the process raises seve

local-aiarxiv-cs-ai

Dream to Remember: Continual Steering of Generalist Robot Policies with World Models

arXiv:2610.13816v1 Announce Type: cross Abstract: Generalist robot policies are trained with imitation learning and are bounded by the quality and coverage of their demonstrations. Reinforcement learn

DRYLAB-Bench: Benchmarking the Biosecurity Risk of Agentic LLM Mutation Design

arXiv:2610.13116v1 Announce Type: new Abstract: As large language models (LLMs) acquire advanced biological design capabilities, they offer substantial research benefits but simultaneously introduce s

Dual-Stream Memory for Vision-Language-Action Policies

arXiv:2610.13262v1 Announce Type: new Abstract: Memory-dependent manipulation requires a policy to track execution state and recover visual evidence that is no longer observable. We propose dual-strea

DualQuant: Dual-scale Calibration-free Weight-and-Activation Quantization for LLMs

arXiv:2610.13767v1 Announce Type: cross Abstract: Low-bit inference on accelerators with native 4-bit support requires quantizing both weights and activations to realize the full throughput gains thes

DuoRate: Two-Rate Per-Expert Precision Allocation for Mixture-of-Experts Serving

arXiv:2610.13328v1 Announce Type: new Abstract: Serving a Mixture-of-Experts (MoE) model pays for expert precision twice: the resident footprint pays for every expert the router can reach, while each

DyssomniaNN: Multi-Scale Spiking Neural Modeling of Human Sleep Disorders and Perceived Restedness

arXiv:2610.13918v1 Announce Type: cross Abstract: Clinical sleep medicine repeatedly documents profound divergences between objective sleep duration and subjective daytime impairment, notably exemplif

EarLog: Opportunistic Ear Physiology Sensing toward Personalized Hearing Risk Awareness

arXiv:2610.12501v1 Announce Type: cross Abstract: Many headphone users risk irreversible hearing damage during high-load listening, yet current protection still relies on sound-pressure limits. Clinic

Applications

Easy-IIL: Reducing Human Operational Burden in Interactive Imitation Learning via an Assistant Expert

arXiv:2603.12769v2 Announce Type: replace Abstract: Interactive Imitation Learning (IIL) typically relies on extensive human involvement for both offline demonstration and online interaction. Prior wo

applicationsarxiv-cs-ro

EchoTab: Learning Measurement-Grounded Representations for Echocardiographic Diagnosis through Tabular Reconstruction

arXiv:2610.13599v1 Announce Type: new Abstract: Diagnostic information in multi-view medical imaging studies, like echocardiography, is distributed across views, each capturing different aspects of ca

Efficient Audio-Visual Speech Enhancement via Reinforcement Learning-Based Neural Architecture Search

arXiv:2610.12796v1 Announce Type: cross Abstract: Audio-visual speech enhancement (AVSE) improves speech intelligibility in noisy environments but often relies on computationally expensive models unsu

local-aiarxiv-cs-ai

Efficient Learning of Survival Analysis Models Under Outcome-Dependent Truncation and Censoring

arXiv:2610.12538v1 Announce Type: new Abstract: Modern machine learning models are typically trained on data assumed to be representative of the predictive tasks the model will face in the future, but

Tutorials

EgoReveal: Learning Active Vision and Object Manipulation under Partial Occlusions from Egocentric Demonstrations

arXiv:2610.12999v1 Announce Type: new Abstract: Learning from Demonstrations (LfD) enables robots to learn object manipulation directly from human demonstrations. In contrast to resource-intensive and

tutorialsarxiv-cs-ro

Electron-Density-Informed Molecular Property Prediction Using Surrogate Functionals

arXiv:2610.13649v1 Announce Type: cross Abstract: Machine learning methods are increasingly used to predict chemical properties directly from the molecular structure to bypass the often prohibitive co

Tutorials

EliteTable: Self-Improving Program Search for Executable Control Policies

arXiv:2610.12536v1 Announce Type: new Abstract: EliteTable is a self-improving program-search algorithm guided by large language models (LLMs) that uses a fixed-capacity table of high-scoring solution

tutorialsarxiv-cs-ai

EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents

arXiv:2610.13021v1 Announce Type: new Abstract: Continual learning requires models to accumulate past experience and transfer it to new tasks. Despite substantial research on continual learning, exist

EmoGavel: Grounding-Aware Reinforcement Learning with Verifiable Rewards for Multimodal Emotion Preference Learning

arXiv:2610.13353v1 Announce Type: cross Abstract: Multimodal emotion preference learning uses pairwise comparisons to identify which open-ended emotion descriptions align more closely with human judgm

EndoRSI: Physical-Evidence-Driven Recursive Improvement of Endoscopic Robot Policies

arXiv:2610.13376v1 Announce Type: new Abstract: Artificial intelligence is beginning to automate scientific discovery, yet physical robot-policy research still relies on scientists and engineers to in

Energy-Efficient Spiking Neural Networks for Sensing-Aided Beam Prediction

arXiv:2610.12507v1 Announce Type: cross Abstract: Sensing-aided beam prediction can reduce the beam-training overhead of millimeter-wave (mmWave) systems by leveraging environmental information from c

researcharxiv-cs-ai

Applications

Enhancing Legal Reasoning in Pre-trained Language Models via Knowledge Graph-Guided Multi-Task Pre-training

arXiv:2610.13722v1 Announce Type: new Abstract: Legal language understanding requires reasoning over semi-structured regulatory documents, yet standard pre-training objectives do not explicitly target

applicationsarxiv-cs-cl

Enriched Holomorphic Neural Networks for 2D and 3D Laplace Problems

arXiv:2610.12541v1 Announce Type: new Abstract: Boundary-value problems for the Laplace equation develop singular behaviour near corners, edges and junctions that smooth approximators resolve poorly.

local-aiarxiv-cs-lg

Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study

arXiv:2610.12542v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) is often proposed for scheduling directed-acyclic-graph (DAG) workflows, yet most studies never run the comparison that c

researcharxiv-cs-ai

Equality, Equity, and Causality in Fairness Research: A Commentary on Cheng (2026)

arXiv:2607.17679v2 Announce Type: replace-cross Abstract: This is an invited commentary on the Psychometrika focus article 'Fairness Issues and Evaluation in Psychometrics and AI/ML: What Can We Learn

Error Rate is Not Enough: Costs of Augmentation in Low-Resource ASR

arXiv:2610.13807v1 Announce Type: new Abstract: Data augmentation is standard practice for low-resource automatic speech recognition (ASR), but its benefits are almost always reported as aggregate err

researcharxiv-cs-cl

Evaluating LLM Creativity through Long-Tail Performance

model-releasesarxiv-cs-cl

arXiv:2610.12998v1 Announce Type: new Abstract: Creativity benchmarks for large language models (LLMs) vary widely in task and metric design. This raises the question of whether they capture a common

Evidence-Guided Counterfactual Learning for Long-Tailed Robotic Policies

arXiv:2610.12861v1 Announce Type: new Abstract: In the practical deployment of vision--language--action policies, handling long-tailed task distributions is a common challenge due to the highly imbala

Evidential Multimodal Transformers with Dempster-Shafer Fusion for Emotion Recognition

arXiv:2610.13013v1 Announce Type: new Abstract: Softmax-based multimodal emotion recognition methods often assign high confidence even when sensor data are corrupted or contradictory, for example when

Evo-RL: Iterative Robot Reinforcement Learning from Selective Experience

arXiv:2610.13345v1 Announce Type: new Abstract: Existing real-world reinforcement learning methods improve robot policies by combining human corrections with autonomous rollouts. However, accumulated

Evo-World: A Lightweight Vision-Language-Action Model with Future Motion Prediction via Optical-Flow Supervision

arXiv:2610.12936v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide a unified framework for visual understanding, language grounding, and robot control, yet anticipating how th

Evo^2: Evolving the Evolver through Weight-Harness Co-Evolution

model-releasesarxiv-cs-cl

arXiv:2610.13025v1 Announce Type: new Abstract: Self-evolving agents are increasingly used to improve language model systems without human intervention through either weight updates or harness improve

EvoBelief: Falsifiable World Models for Self-Improving LLM Agents

arXiv:2610.12789v1 Announce Type: new Abstract: Self-evolving LLM agents typically improve how they act through accumulated experience, reflection, or skills. Yet even procedurally capable agents can

Evolving Creatives with Clicks: CTR-Driven Advertising image Editing via Attribute Disentanglement and Preference Alignment

arXiv:2610.13066v1 Announce Type: cross Abstract: The optimization objective for e-commerce advertising creatives has shifted from aesthetic quality to online business metrics like click-through rate

Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy

arXiv:2610.12680v1 Announce Type: new Abstract: Background: Discontinuity in neonatal EEG is a critical marker of brain dysfunction but can be difficult to quantify visually. Inter-burst intervals (IB

Exploring Attention Sinks in Text-to-Image Transformers from Diffusion and Autoregressive Models

arXiv:2610.13337v1 Announce Type: cross Abstract: Attention sinks are tokens or positions that consistently receive disproportionate attention mass, often beyond what their semantic content would sugg

local-aiarxiv-cs-cl

Failure-Resilient Multi-Robot Coordination with k-Connectivity Constrained Submodular Maximization

arXiv:2610.13049v1 Announce Type: new Abstract: Multi-robot coordination is essential in applications such as coverage, target tracking, and information gathering, where robots must jointly select act

researcharxiv-cs-ro

Faithful and Complete LLM-Assisted Automated Construction of Hierarchical Hardware Knowledge Graphs

arXiv:2610.12762v1 Announce Type: new Abstract: Hardware datasheets contain engineering specifications essential for component selection, system integration, and hardware compatibility verification. H

Faithfulness Without Anchors: A Position Paper on the Structural Misalignments of Explainable AI Faithfulness Metrics for Feature Importance

arXiv:2610.12493v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) tools have often been presented as methods to increase trust in machine learning models by making aspects of t

researcharxiv-cs-ai

FallGown: A Soft Pneumatic Wearable Device for Fall Prevention

arXiv:2610.12706v1 Announce Type: new Abstract: Concerns about falling and potential resulting injuries prevent many older adults from participating in forms of health-improving exercise such as walki

researcharxiv-cs-ro

Hardware

Fast Gradient-Based Approximation of Continuous Dynamic Time Warping

arXiv:2610.13129v1 Announce Type: cross Abstract: Dynamic Time Warping (DTW) is a common choice for measuring the discrepancy between time series. It proceeds by finding an optimal warping between tim

hardwarearxiv-cs-lg

Fear Is Not Always Fear: Variation in Emotion Label Equivalence Across Datasets

arXiv:2610.12841v1 Announce Type: new Abstract: Emotion classifiers are routinely applied beyond their training data, on the assumption that same-named labels such as fear or surprise mean the same th

researcharxiv-cs-cl

FedPCD: Priority-Constrained Federated Aggregation

arXiv:2610.13508v1 Announce Type: new Abstract: Federated learning (FL) seeks to optimize a shared global model from decentralized client data. While effective for average performance, this process ca