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Closed-loop evaluation of LLM agents for embedded software development

arXiv:2610.11447v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as coding agents that edit files, run builds and tests, inspect execution results, and repair s

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Closing the Horizon Gap in Policy Optimization for Adversarial MDPs

DGX agent

arXiv:2610.12362v1 Announce Type: new Abstract: We consider policy optimization for online episodic tabular Markov decision processes (MDPs) with adversarial losses and bandit feedback. Policy optimiz

safetyarxiv-cs-lg

9 Oct 2026

Agents

CoCam4D: Geometry-Aware Cooperative 4D Perception for Camera-Only Autonomous Driving

arXiv:2610.11577v1 Announce Type: cross Abstract: Autonomous vehicles often suffer from limited perception due to occlusions, blind spots, limited sensor range, and the complex nature of surrounding e

agentsarxiv-cs-cv

Code Understanding is a Bottleneck for Coding Agents

arXiv:2610.10610v1 Announce Type: cross Abstract: Repository benchmarks (e.g., SWE-bench) for coding agents often assume that lines of code edited can predict task difficulty, but such datasets' poor

Applications

Coefficient Calibration as Selection Pressure in Symbolic Regression

arXiv:2610.10931v1 Announce Type: new Abstract: In memetic symbolic regression, candidate structures are compared after coefficient calibration, so the calibration protocol itself contributes to evolu

applicationsarxiv-cs-lg

Applications

Cognition-Oriented Emotion Tracing from Causes to Consequences in Real-World Social Scenes

arXiv:2610.11410v1 Announce Type: new Abstract: Affective computing has progressed from categorical emotion recognition to open-ended affective analysis with large multimodal models. Yet affective sci

applicationsarxiv-cs-ai

Cognitive Thermometers: Machine Learning and Logical Complexity

arXiv:2610.10724v1 Announce Type: new Abstract: How does the human mind represent semantic categories? Why do natural languages favor certain meanings over others? Prior explanations have relied on lo

researcharxiv-cs-cl

Collaboratively Guided Adversarial Robust Distillation with Teacher-Favorable Examples

arXiv:2610.11306v1 Announce Type: new Abstract: Adversarial distillation transfers robustness from high-capacity teachers to compact students. Existing adversarial distillation methods mainly use teac

researcharxiv-cs-cv

Tutorials

Compactness and Consistency: A Conjoint Framework for Deep Graph Clustering

arXiv:2610.11506v1 Announce Type: cross Abstract: Graph clustering is a fundamental task in data analysis, aiming at grouping nodes with similar characteristics in the graph into clusters. This proble

tutorialsarxiv-cs-ai

Comparing Object Detection Models for Electrical Substation Component Mapping

arXiv:2512.22454v2 Announce Type: replace Abstract: Electrical substations are a significant component of an electrical grid. Indeed, the assets at these substations (e.g., transformers) are vulnerabl

safetyarxiv-cs-cv

Compile the Table: Query-Calibrated Operator Compression for Tabular In-Context Learning

arXiv:2610.11784v1 Announce Type: new Abstract: Tabular in-context learning (ICL) has emerged as a training-free and accurate paradigm for tabular prediction, but current approaches to compressing its

Complexity of Grounded Semantics and Preferred Semantics in Finitary Argumentation Frameworks

arXiv:2610.12008v1 Announce Type: new Abstract: Abstract argumentation frameworks (AFs) introduced by Dung provide a formal foundation for non-monotonic reasoning in artificial intelligence. While dec

researcharxiv-cs-ai

Composite Online-to-Nonconvex Conversion with Optimal Oracle Complexity

arXiv:2610.12328v1 Announce Type: new Abstract: We consider stochastic nonsmooth nonconvex composite optimization, which includes several important problems such as constrained optimization and the re

Applications

Conditional Kernel Stein Discrepancy

arXiv:2610.11863v1 Announce Type: cross Abstract: Kernel Stein discrepancies (KSDs) provide a versatile tool for comparing distributions. One of their main applications is in quantifying the goodness-

applicationsarxiv-cs-lg

Conditional Residual Prediction: Improving Autoregressive Video Diffusion without a Bidirectional Teacher

arXiv:2610.11479v1 Announce Type: cross Abstract: Causal video diffusion models generate video autoregressively, which suits streaming, interactive, and long-video generation. Under standard training,

Conditional Transfer from Controlled Pretraining Mixtures to Code

arXiv:2610.11548v1 Announce Type: new Abstract: Synthetic tasks are increasingly used both as probes of language-model capability and as pretraining data. Both uses are often justified by loss reducti

Local Ai

Conformal Data Contamination Tests for In-distribution Data Acquisition

arXiv:2507.13835v2 Announce Type: replace-cross Abstract: The amount of quality data in many machine learning tasks is limited to what is available locally to data owners. The set of quality data can

local-aiarxiv-cs-lg

Conformal Prediction under Partial Verification

arXiv:2610.10829v1 Announce Type: cross Abstract: Conformal prediction provides prediction sets with finite-sample guarantees, but the label verification required for calibration can be expensive. We

Connected Self Forcing: Beyond Local Learning in Video Autoregression

arXiv:2610.12156v1 Announce Type: new Abstract: To stream long videos while maintaining visual quality and temporal consistency, Self Forcing mitigates exposure bias through self-rollout training on s

safetyarxiv-cs-cv

Constitutional Gating and Deterministic Recovery for Multi-Agent LLM Negotiation: Ablations Against a Stateful Adversarial Gatekeeper

arXiv:2610.11542v1 Announce Type: cross Abstract: Multi-agent LLM systems negotiating with a stateful counterpart waste model calls in three ways: polite loops that never meet the counterpart's hidden

Constrained Command-Conditioned Reinforcement Learning with Bandit Strategy Selection in Real-Time Strategy Games

arXiv:2610.11663v1 Announce Type: new Abstract: Deep reinforcement learning agents reach strong performance in real-time strategy games but can be brittle against opponents outside their training dist

safetyarxiv-cs-ai

Constrained Dynamic Gaussian Splatting

arXiv:2602.03538v2 Announce Type: replace Abstract: While Dynamic Gaussian Splatting enables high-fidelity 4D reconstruction, its deployment is severely hindered by a fundamental dilemma: unconstraine

researcharxiv-cs-cv

Constructing Structured Decision Sources for Consensus-Based Pseudo-Label Learning

arXiv:2610.11621v1 Announce Type: new Abstract: Consensus can make pseudo-label learning more reliable, but only when its predictors contribute genuinely different evidence. Multiple models that repea

ContiLNN: Mitigating Slice Sampling Discontinuity with Liquid Neural Networks for Medical Image Restoration

arXiv:2610.12337v1 Announce Type: cross Abstract: Anatomical continuity provides complementary information for medical image restoration, but its use requires accounting for local anatomy and variatio

Continuous Ground-Truth Construction and a Recovery Policy for Air--Water Robotic Tracking

arXiv:2610.11096v1 Announce Type: cross Abstract: Visual tracking across the air-water interface is challenged by splashes, bubbles, refraction, reflections, and abrupt appearance changes that can tem

safetyarxiv-cs-ai

ContourVLA: A Closed-Loop Perception-Action Contour Policy for Generalized Referring Expression Segmentation

arXiv:2610.12107v1 Announce Type: new Abstract: Generalized referring expression segmentation (GRES) requires dynamically balancing high-level semantics for identifying a variable number of language-s

safetyarxiv-cs-cv

Contrast Enhancement or Noise Reduction? On Improving Cervical Cancer Classification

arXiv:2610.11086v1 Announce Type: new Abstract: Purpose: Cervical cancer is one of the leading causes of mortality worldwide. Deep learning has shown promising performance in medical image classificat

researcharxiv-cs-cv

Control-Ready Uncertainty for Trajectory Diffusion

arXiv:2610.12431v1 Announce Type: new Abstract: Diffusion models can represent complex, multimodal trajectory distributions, but extracting uncertainty from them typically requires costly Monte Carlo

safetyarxiv-cs-ro

Tutorials

Controllable Exaggeration for Generative Motion Models via Training-Time Adaptation and Inference-Time Guidance

arXiv:2610.12316v1 Announce Type: new Abstract: Recent motion generative models have demonstrated strong capabilities in synthesizing physically plausible character motion, but often overlook establis

tutorialsarxiv-cs-cv

Controlled Acquisition and Abstention in Three-Channel Score Conflicts

arXiv:2610.10808v1 Announce Type: new Abstract: When audio, video, and text disagree, accuracy alone does not show whether to acquire another source or abstain. We study these choices in a controlled

Conversational Task Disambiguation over Tabular Data: Leakage-Aware Formulation, Benchmark Suite, and Training

arXiv:2610.10740v1 Announce Type: cross Abstract: Conversational task disambiguation over tabular data uses dialogue to resolve missing information about a user's intended task before producing a solu

Conversational Voice Aesthetic Model with Reinforcement Learning from Human Listeners

model-releasesarxiv-cs-cl

arXiv:2610.10868v1 Announce Type: cross Abstract: We introduce Conversational Voice Aesthetic Model, a speech large language model for describing the voice aesthetics of real or synthetic speech respo

CoPoE: Multimodal Fusion via Decomposable Disease-Coordinate Product-of-Experts for Missing-Modality Alzheimer's Diagnosis

model-releasesarxiv-cs-cv

arXiv:2610.11394v1 Announce Type: cross Abstract: Multimodal Alzheimer's disease (AD) diagnosis benefits from integrating heterogeneous clinical, imaging, genomic, and biomarker evidence, but clinical

Correlational Training of Morphological Neural Networks

arXiv:2610.11740v1 Announce Type: new Abstract: Neural networks are typically trained using first-order methods and back-propagation. It is unclear whether this approach is optimal for morphological l

Cost-Aware Mixture-of-Experts Coordination for Model Markets

arXiv:2610.11908v1 Announce Type: cross Abstract: Existing model marketplaces typically trade and select individual models as indivisible units, limiting their ability to exploit complementarities amo

Could LLM Watermark Detection be Public?

arXiv:2610.12106v1 Announce Type: cross Abstract: Watermarking large language models is popular for tracing chatbot and agentic outputs, yet detectors remain unreleased since exposing them could let a

Cova-PINN: Cross-Domain Conservation Physics-Informed Neural Network for Fluid-Solid Conjugate Heat Transfer in Complex Geometries

arXiv:2610.11108v1 Announce Type: new Abstract: Multi-domain physics-informed neural networks (PINNs) flexibly model medium-specific representations to solve fluid--solid conjugate heat transfer (CHT)

Coverage-Aware Reasoning with Medical Tokens for Diagnosis Prediction

arXiv:2610.10641v1 Announce Type: cross Abstract: Large language models (LLMs) offer promising potential for next-visit diagnosis prediction, owing to their ability to integrate longitudinal clinical

safetyarxiv-cs-ai

Coverage, Not Difficulty, Sets How Much Synthetic Data an Activation Probe Needs

arXiv:2610.10594v1 Announce Type: new Abstract: Activation probes that monitor deployed language models are trained on synthetic conversations, and how many a probe needs is open. We trace learning cu

CPU-Auth: Device Fingerprinting for Authentication via DVFS Side-Channel

arXiv:2610.10766v1 Announce Type: cross Abstract: Lack of effective authentication has resulted in numerous security and privacy breaches, including unauthorized access to protected information, ident

Credal Machine Learning for Risk-Averse Decision Making

arXiv:2610.12115v1 Announce Type: new Abstract: In many machine learning applications, it is necessary to guard against worst-case scenarios and predictions that could result in substantial losses. In

CRISP: Fixing Flying Pixels in Latent LiDAR Generation via Diffusion Decoding

arXiv:2610.11376v1 Announce Type: cross Abstract: Latent LiDAR pipelines suffer from flying pixels: convolutional VAEs blur sharp radial depth discontinuities, yielding edge depths that back-project t

researcharxiv-cs-ai

Cross-Embodiment Robot Foundation World Models with Latent Actions

model-releasesarxiv-cs-ro

arXiv:2610.10846v1 Announce Type: new Abstract: The diversity of robot embodiments and action spaces makes it challenging to build robot world models that generalize across different embodiments. We i

Cross-Provider Review as a Runtime Contract for Coding Agents: A Controlled Pilot and Fault-Injection Study

arXiv:2610.10961v1 Announce Type: cross Abstract: Coding agents increasingly share a workstation while drawing on separate providers and subscription allowances. A second agent can inspect a completed

Cross-species representation learning aligns mouse and human neural dynamics and tracks clinical drug efficacy

arXiv:2610.11222v1 Announce Type: cross Abstract: Preclinical models poorly predict human drug efficacy, particularly in neurological disorders. Neural activity offers a uniquely rich source of transl

CSF: Contextual Safety Filtering for Motion Generators

arXiv:2610.12467v1 Announce Type: cross Abstract: Text-conditioned motion generators produce trackable whole-body motion, but they have no notion of scene-dependent safety: the same action may target

safetyarxiv-cs-lg

Tutorials

Curating Always-Loaded Context for LLM Agents: A Capacitated Assortment Model with Censored Feedback

arXiv:2610.11007v1 Announce Type: new Abstract: At the start of every session, LLM agents load a fixed context file, such as exttt{AGENTS.md}. Each loaded token in the file is charged again in every l

tutorialsarxiv-cs-ai

D-SLR: The Disjoint Row-Sparse plus Low-Rank Decomposition

arXiv:2610.10636v1 Announce Type: new Abstract: Compressing a matrix for reconstruction still defaults to the truncated SVD, approximating the data with a single low-rank structure. It is common to re