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Constraint-Aware Diffusion Priors for High-Fidelity and Versatile Quadruped Locomotion

arXiv:2605.08804v1 Announce Type: new Abstract: Reinforcement learning combined with imitation learning has significantly advanced biomimetic quadrupedal locomotion. However, scaling these frameworks

safetyarxiv-cs-ro

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Human DGX agent

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Constraint-Aware Reinforcement Learning via Adaptive Action Scaling

DGX agent

arXiv:2510.11491v3 Announce Type: replace-cross Abstract: Safe reinforcement learning (RL) seeks to mitigate unsafe behaviors that arise from exploration during training by reducing constraint violati

safetyarxiv-cs-lg

12 May 2026

Constructive conditional normalizing flows

arXiv:2602.08606v2 Announce Type: replace-cross Abstract: Motivated by applications in conditional sampling, given a probability measure mu and a diffeomorphism phi, we consider the problem of simulta

Containment Verification: AI Safety Guarantees Independent of Alignment

arXiv:2605.09045v1 Announce Type: new Abstract: Agentic frameworks are the software layer through which AI agents act in the world. Existing safety methods intervene on the model and therefore remain

Context-Augmented Code Generation: How Product Context Improves AI Coding Agent Decision Compliance by 49%

arXiv:2605.08112v1 Announce Type: cross Abstract: AI coding agents powered by large language models can read codebases and produce functional code, but they routinely violate team-specific product dec

Agents

Context Learning for Multi-Agent Discussion

arXiv:2602.02350v2 Announce Type: replace Abstract: Multi-Agent Discussion (MAD) has garnered increasing attention very recently, where multiple LLM instances collaboratively solve problems via struct

agentsarxiv-cs-ai

Contextual Plackett-Luce: An Efficient Neural Model for Probabilistic Sequence Selection under Ambiguity

arXiv:2605.09112v1 Announce Type: cross Abstract: Selecting a coherent sequence or subset of elements is a fundamental problem in structured prediction, arising in tasks such as detection, trajectory

Continual Harness: Online Adaptation for Self-Improving Foundation Agents

arXiv:2605.09998v1 Announce Type: cross Abstract: Coding harnesses such as Claude Code and OpenHands wrap foundation models with tools, memory, and planning, but no equivalent exists for embodied agen

Continuity Laws for Sequential Models

arXiv:2605.08539v1 Announce Type: cross Abstract: Inductive biases influence the behavior and performance of sequential models. In this work, we study an underexplored inductive bias in sequential mod

Continuous Latent Contexts Enable Efficient Online Learning in Transformers

arXiv:2605.09867v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit a strong capacity for in-context learning: Given labeled examples, they can generate good predictions without par

Applications

Continuum Robot Localization using Distributed Time-of-Flight Sensors

arXiv:2602.07209v2 Announce Type: replace Abstract: Localization and mapping of an environment are crucial tasks for any robot operating in unstructured environments. Time-of-flight (ToF) sensors (e.g

applicationsarxiv-cs-ro

Tutorials

Continuum Robot Modeling with Action Conditioned Flow Matching

arXiv:2605.09216v1 Announce Type: new Abstract: Predicting the shape of tendon driven continuum robots (TDCRs) at steady state from actuation remains challenging due to continuous deformation, complex

tutorialsarxiv-cs-ro

Contour-Native Bridge Defect Detection and Compact Digital Archiving with Frequency-Supervised Fourier Contours

arXiv:2605.08781v1 Announce Type: new Abstract: AI-assisted bridge defect inspection often produces bounding boxes with crude geometry or raster masks that are costly to store, transmit, and reuse. Th

researcharxiv-cs-cv

CONTRA: Conformal Prediction Region via Normalizing Flow Transformation

arXiv:2605.08561v1 Announce Type: cross Abstract: Density estimation and reliable prediction regions for outputs are crucial in supervised and unsupervised learning. While conformal prediction effecti

Control-Augmented Autoregressive Diffusion for Data Assimilation

arXiv:2510.06637v3 Announce Type: replace-cross Abstract: Despite advances in test-time scaling and diffusion finetuning, guidance for Auto-Regressive Diffusion Models (ARDMs) remains underexplored. W

Control Your View: High-Resolution Global Semantic Manipulation in Learned Image Compression

arXiv:2605.08727v1 Announce Type: cross Abstract: Learned image compression (LIC) integrates deep neural networks (DNNs) to map high-dimensional images into compact latent representations, reducing re

Agents

Controllability in preference-conditioned multi-objective reinforcement learning

arXiv:2605.10585v1 Announce Type: new Abstract: Multi-objective reinforcement learning (MORL) allows a user to express preference over outcomes in terms of the relative importance of the objectives, b

agentsarxiv-cs-lg

Controlling Transient Amplification Improves Long-horizon Rollouts

arXiv:2605.08856v1 Announce Type: new Abstract: Autoregressive neural simulators now match classical solvers on short-horizon prediction of physical systems, yet their accuracy degrades rapidly when r

Convergence Analysis of Newton's Method for Neural Networks in the Overparameterized Limit

arXiv:2605.08352v1 Announce Type: new Abstract: A convergence analysis is developed for the regularized Newton method for training neural networks (NNs) in the overparameterized limit. As the number o

Coordinates of Capability: A Unified MTMM-Geometric Framework for LLM Evaluation

model-releasesarxiv-cs-cl

arXiv:2605.08522v1 Announce Type: new Abstract: The evaluation of Large Language Models (LLMs) faces a critical challenge in construct validity, where fragmented benchmarks and ad hoc metrics frequent

Core-Halo Decomposition: Decentralizing Large-Scale Fixed-Point Problems

arXiv:2605.08681v1 Announce Type: cross Abstract: We study solving large-scale fixed-point equation (x^star=ar F(x^star)) with decomposition. Standard strict decomposition assigns each agent a disjoin

CoreQ: Learning-Free Mismatch Correction and Successive Rounding for Quantization

arXiv:2602.05902v2 Announce Type: replace-cross Abstract: Post-training quantization (PTQ) enables efficient deployment of large language models by mapping pretrained weights to low-bit formats withou

Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation

arXiv:2605.09253v1 Announce Type: cross Abstract: While recent work in Reinforcement Learning with Verifiable Rewards (RLVR) has shown that a small subset of critical tokens disproportionately drives

CORP: Closed-Form One-shot Representation-Preserving Structured Pruning for Transformers

arXiv:2602.05243v2 Announce Type: replace-cross Abstract: Transformers achieve strong accuracy but incur high compute and memory cost. Structured pruning reduces inference cost, but most methods rely

researcharxiv-cs-cv

CORTEG: Foundation Models Enable Cross-Modality Representation Transfer from Scalp to Intracranial Brain Recordings

arXiv:2605.10337v1 Announce Type: new Abstract: Intracranial electrocorticography (ECoG) offers high-signal-to-noise access to cortical activity for brain-computer interfaces, yet limited per-patient

Cosine-Gated Adam-Decay: Drop-In Staleness-Aware Outer Optimization for Decoupled DiLoCo

arXiv:2605.09126v1 Announce Type: new Abstract: Asynchronous DiLoCo systems may receive pseudo-gradients computed several outer rounds earlier, yet the standard Nesterov outer optimizer does not expli

Applications

Count Anything at Any Granularity

arXiv:2605.10887v1 Announce Type: new Abstract: Open-world object counting remains brittle: despite rapid advances in vision-language models (VLMs), reliably counting the objects a user intends is far

applicationsarxiv-cs-cv

Applications

Counterfactual Stress Testing for Image Classification Models

arXiv:2605.10894v1 Announce Type: new Abstract: Deep learning models in medical imaging often fail when deployed in new clinical environments due to distribution shifts in demographics, scanner hardwa

applicationsarxiv-cs-cv

Tutorials

Counting Still Counts: Understanding Neural Complex Query Answering Through Query Relaxation

arXiv:2511.22565v2 Announce Type: replace Abstract: Neural methods for Complex Query Answering (CQA) over knowledge graphs (KGs) are widely believed to learn patterns that generalize beyond explicit g

tutorialsarxiv-cs-ai

CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving

arXiv:2605.10426v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving. However, existing reasoning mechanisms sti

Cplus2ASP: Computing Action Language C+ in Answer Set Programming

arXiv:2605.09528v1 Announce Type: new Abstract: We present Version 2 of system Cplus2ASP, which implements the definite fragment of action language C+. Its input language is fully compatible with the

CrackMeBench: Binary Reverse Engineering for Agents

arXiv:2605.10597v1 Announce Type: cross Abstract: Benchmarks for coding agents increasingly measure source-level software repair, and cybersecurity benchmarks increasingly measure broad capture-the-fl

CREATE: Testing LLMs for Associative Creativity

model-releasesarxiv-cs-cl

arXiv:2603.09970v2 Announce Type: replace Abstract: A key component of creativity is associative reasoning: the ability to draw novel yet meaningful connections between concepts. We introduce CREATE,

Cross-Cultural Transfer of Emoji Semantics and Sentiment in Financial Social Media

arXiv:2605.09414v1 Announce Type: new Abstract: Emojis are widely used in online financial communication, but it is unclear whether they provide transferable sentiment signals across languages, platfo

researcharxiv-cs-cl

Cross-Domain Lossy Compression via Constrained Minimum Entropy Coupling

arXiv:2605.09833v1 Announce Type: cross Abstract: This paper studies cross-domain lossy compression through the lens of minimum entropy coupling (MEC) with rate and classification constraints. In this

Cross-Family Universality of Behavioral Axes via Anchor-Projected Representations

arXiv:2605.09875v1 Announce Type: new Abstract: Large language models from different families use different hidden dimensions, tokenizers, and training procedures, making behavioral directions difficu

Agents

Cross-Modal RGB-D Fusion Transformer for 6D Pose Estimation of Non-Cooperative Spacecraft with Stereo-Derived Depth

arXiv:2605.08592v1 Announce Type: new Abstract: On-orbit servicing and active debris removal involving non-cooperative spacecraft require reliable pose estimation to supply accurate position and orien

agentsarxiv-cs-cv

Cross-Modal Semantic-Enhanced Diffusion Framework for Diabetic Retinopathy Grading

arXiv:2605.09242v1 Announce Type: cross Abstract: Automated grading of diabetic retinopathy (DR) faces several critical challenges: subtle inter-grade visual distinctions in fine-grained lesion patter

researcharxiv-cs-cv

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning

model-releasesarxiv-cs-cv

arXiv:2605.08839v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) requires a learning system to learn new classes while retaining previously learned knowledge. However, in real-world sc

Crosslingual On-Policy Self-Distillation for Multilingual Reasoning

arXiv:2605.09548v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable progress in mathematical reasoning, but this ability is not equally accessible across languages. E

safetyarxiv-cs-cl

CrossVL: Complexity-Aware Feature Routing and Paired Curriculum for Cross-View Vision-Language Detection

arXiv:2605.09802v1 Announce Type: cross Abstract: Vision-language models (VLMs) enable text-guided object detection but degrade severely under cross-view scenarios where ground and aerial viewpoints d

Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy

arXiv:2505.05707v2 Announce Type: replace Abstract: The integration of AI into daily life has generated considerable attention and excitement, while also raising concerns about automating algorithmic

safetyarxiv-cs-lg

Local Ai

Crystal Fractional Graph Neural Network for Energy Prediction of High-Entropy Alloys

arXiv:2605.08103v1 Announce Type: cross Abstract: High-entropy alloys (HEAs) have attracted growing attention for their exceptional mechanical and thermal properties arising from complex atomic config

local-aiarxiv-cs-ai

CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models

arXiv:2605.08960v1 Announce Type: cross Abstract: Crystal generative models mainly learn what stable crystals look like, with little explicit supervision for what makes them stable. We reveal a substa

CT-IDP: Segmentation-Derived Quantitative Phenotypes for Interpretable Abdominal CT Disease Classification

arXiv:2605.09002v1 Announce Type: cross Abstract: In this retrospective multi-institutional study, a quantitative phenotyping framework, CT-IDP (CT Image-Derived Phenotypes) was developed on the MERLI

CTQWformer: A CTQW-based Transformer for Graph Classification

arXiv:2605.09486v1 Announce Type: cross Abstract: Graph Neural Networks (GNN) and Transformer-based architectures have achieved remarkable progress in graph learning, yet they still struggle to captur

CUDABeaver: Benchmarking LLM-Based Automated CUDA Debugging

arXiv:2605.08455v1 Announce Type: new Abstract: Debugging CUDA programs has long been challenging because failures often arise from subtle interactions among hardware behavior, compiler decisions, mem

CUDAHercules: Benchmarking Hardware-Aware Expert-level CUDA Optimization for LLMs

arXiv:2605.08467v1 Announce Type: new Abstract: Large language models show promise for automated CUDA programming, however even the strongest coding models (e.g., Claude-Opus-4.6) may still fall short