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CausalDreamer: Learning Predictive World Models with Latent Disentanglement

arXiv:2610.12016v1 Announce Type: new Abstract: World models for control must capture which aspects of the environment respond to the agent's actions and which are relevant to reward. Generative world

safetyarxiv-cs-lg

Census-Based Population Autonomy For Distributed Robotic Teaming

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DGX agent

arXiv:2511.02147v2 Announce Type: replace Abstract: Collaborating teams of robots show promise due to their ability to complete missions more efficiently and with improved robustness, attributes that

local-aiarxiv-cs-ro

9 Oct 2026

Certified Corruption Budgets: Anytime-Valid Leaderboard Claims under Adaptive Rigging

arXiv:2610.10597v1 Announce Type: cross Abstract: Public leaderboards for AI models are read continuously, and attackers can see every published standing. Vote rigging, selective disclosure of private

Characterizing Overconfident Failure in LLM-Based Code Generation

arXiv:2610.11300v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for automated code generation, but generated programs can appear syntactically plausible while stil

local-aiarxiv-cs-ai

Characterizing Statistical Separability in TP-CRIV for Probabilistic AI Models

arXiv:2610.11163v1 Announce Type: cross Abstract: Third-party challenge-response identity verification (TP-CRIV) enables an independent verifier to assess whether a claimant possesses a model identica

researcharxiv-cs-ai

Agents

Chronos Enables Code Agents to Reason over Software Evolution

arXiv:2610.11578v1 Announce Type: cross Abstract: Historical pull requests record the design decisions, compatibility constraints, and implementation patterns behind a codebase's current state. Experi

agentsarxiv-cs-cl

Cited but Not Consulted: A Counterfactual Audit of Legal Chain-of-Thought Faithfulness

arXiv:2610.12361v1 Announce Type: new Abstract: Large language models increasingly justify legal decisions by naming the statute or precedent behind a verdict, treated as evidence that the decision fo

applicationsarxiv-cs-ai

CityDeploy-Bench: Benchmarking Physics-Grounded Spatial Set Planning for Multi-Transmitter Network Deployment

model-releasesarxiv-cs-lg

arXiv:2610.11065v1 Announce Type: new Abstract: Automating city-scale wireless deployment remains challenging under complex urban propagation and network-wide interference. We introduce extbf{CityDepl

Tutorials

Clarify, Then Focus: Statement Normalization for Conversation Analytics at Scale

arXiv:2610.10758v1 Announce Type: cross Abstract: Enterprise conversation analytics asks many questions of millions of interactions. Each question can require reconstructing what people mean and ident

tutorialsarxiv-cs-ai

Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning

arXiv:2405.15662v2 Announce Type: replace Abstract: Machine unlearning aims to remove the influence of specified training data or knowledge from a trained model without requiring full retraining. This

local-aiarxiv-cs-lg

Claude Malhuret Was Right about Trump. On Friday, at Trump's direction, the US Treasury issued a license waving Russian diesel back onto the…

model-releasesyann-lecun--x

Claude Malhuret Was Right about Trump. On Friday, at Trump's direction, the US Treasury issued a license waving Russian diesel back onto the world market, days after Russia killed dozens of Ukrainians

Clinician use of language models diverges from how the models are evaluated

model-releasesarxiv-cs-cl

arXiv:2610.11069v1 Announce Type: new Abstract: Large language model (LLM) assistants are being deployed to clinicians across health systems, and judgments about their readiness rest largely on benchm

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

Closing the Horizon Gap in Policy Optimization for Adversarial MDPs

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

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

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

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

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

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

model-releasesarxiv-cs-lg

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

CoreWeave targets AI inference bottlenecks with full-stack optimization

model-releasessiliconangle

AI inference is fast becoming the workload that decides the economics of the AI boom. Training built the first wave of GPU clouds, but serving models faster and cheaper will define the next. That shif

Correlational Training of Morphological Neural Networks

model-releasesarxiv-cs-lg

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