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BridgeTwist: Twisting Schrodinger Bridges for Training-Free Conditional Sampling

arXiv:2610.18658v1 Announce Type: new Abstract: Generative models are often required to serve many downstream conditional tasks. In the training-free setting, a pretrained unconditional model is reuse

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BridgeWAM: Bridging Video and Action Experts via Latent Bridge Queries

DGX agent

arXiv:2610.14746v1 Announce Type: cross Abstract: World Action Models (WAMs) commonly couple video and action experts through dense, layer-wise Mixture-of-Transformers (MoT) attention, exposing action

tutorialsarxiv-cs-cv

14 Oct 2026

Bridging Risk Approximation Gaps in Model Predictive Task Sampling via In-Context Modeling

arXiv:2610.14913v1 Announce Type: cross Abstract: Reinforcement learning (RL) is essential for adaptive decision-making, e.g., robust Meta-RL and domain randomization (DR), and efficient post-training

BrowseSafe++: Benchmarking Prompt Injection Detection Across Web Contexts, Languages, and Browser Views

arXiv:2610.17457v1 Announce Type: cross Abstract: Browser agents need to make decisions and execute actions on behalf of users while consuming untrusted webpages, creating opportunities for malicious

BSIPipe: Bidirectional Sequence-Level Interleaved Pipeline Parallelism for Long-Sequence Large Language Model Training

arXiv:2610.14935v1 Announce Type: new Abstract: Pipeline parallelism has become a critical technique for training large language models. To address the throughput limitation of existing sequence-level

Building age estimation based on widely available multi-source1 geospatial data

arXiv:2610.16320v1 Announce Type: new Abstract: Building age is fundamental for urban infrastructure safety, operation, and planning, as it directly affects structural safety, and sustainable developm

Building Multilingual Datasets for Predicting Mental Health Severity through LLMs: Prospects and Challenges

arXiv:2409.17397v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly being integrated into various medical fields, including mental health support systems. However,

Building Trustworthy Household Robots through Open-World Safety

arXiv:2610.14034v1 Announce Type: new Abstract: Foundation models are expanding robot capabilities, enabling robots to handle more diverse objects, follow sophisticated instructions, and operate in co

safetyarxiv-cs-ro

ByteMark: A Unified Byte-Level Watermark for Detection Across Language Models

arXiv:2610.14427v1 Announce Type: cross Abstract: As watermarking of LLM-generated text enters deployment, users increasingly need to check text whose source is unknown. Existing detectors read the wa

C2Flight: A Closed-Loop Co-Simulation Framework for Early-Stage Evaluation of Heterogeneous UAV Computing Systems

arXiv:2610.14715v1 Announce Type: new Abstract: Early-stage design of heterogeneous UAV computing systems requires evaluating how algorithm, hardware, communication, and system topology affect closed-

researcharxiv-cs-ro

C^2LUE: Clinically Aligned Patient-Level Ultrasound Diagnosis via Evidence-Aware Multi-Instance Learning

arXiv:2610.14143v1 Announce Type: cross Abstract: Patient-level ultrasound diagnosis demands not only high predictive accuracy but also clinically aligned mitigation of asymmetric diagnostic risks. Wh

safetyarxiv-cs-cv

CacheLink: Functional KV Transport for Zero-Prefill Cross-Model Context Handoff

arXiv:2610.15509v1 Announce Type: new Abstract: KV caches make long-context serving efficient by reusing past computation, but model switching breaks this reuse and forces the receiver to prefill the

C{AE}NL: Collapse-Based Active Neural Learning with Objective-Aligned Representation Control

arXiv:2610.17509v1 Announce Type: cross Abstract: Active learning changes the representations used to acquire subsequent labels. We study this interaction through Collapse-Based Active Neural Learning

safetyarxiv-cs-cv

CAG-WAM: Causal Action-Gain World Action Models for Robot Manipulation

arXiv:2610.14720v1 Announce Type: new Abstract: World Action Models (WAMs) combine action generation with future prediction, allowing predicted futures to support action evaluation and robot decision

safetyarxiv-cs-ro

Calibrated alpha-Barycenters for Multiobjective f-Divergence Optimization

arXiv:2610.16819v1 Announce Type: cross Abstract: Matching one distribution to many targets can require repeated evaluations of every target. CAL-alpha-MODM aggregates a nonlinear predictor into one b

Calibrated Disambiguation for Partial Multi-label Learning

arXiv:2610.17635v1 Announce Type: cross Abstract: Partial multi-label learning (PML) aims to train a classifier on datasets whose instances are over-annotated with not only relevant labels but also ir

safetyarxiv-cs-cv

Calibrating LLM Explanations to Human Decisions with Reinforcement Learning

arXiv:2610.17306v1 Announce Type: cross Abstract: Explanations of LLM outputs are considered an important way to make model behavior legible, helping human users make good decisions on downstream task

Applications

CALM-5G: Event-Level Conformal Risk Control for Predictive Service-Assurance Alarms

arXiv:2610.17962v1 Announce Type: cross Abstract: Predictive service assurance converts forecasts of key performance indicators into alarms, and that conversion decides whether the assurance function

applicationsarxiv-cs-lg

CALM: Decentralized Control of Cooperative Aerial Manipulation with Multi-Agent Reinforcement Learning

model-releasesarxiv-cs-ro

arXiv:2610.18425v1 Announce Type: new Abstract: Cooperative aerial manipulation is difficult under wind, payload motion, and noisy goal broadcasts because multiple Unmanned Aerial Vehicles (UAVs) are

Can a Frontier VLM Serve as a Robot Policy for Memory-Dependent Manipulation?

model-releasesarxiv-cs-ro

arXiv:2610.17350v1 Announce Type: new Abstract: We use a frozen frontier vision-language model, GPT-6-Astra, directly as the robot policy on RoboMME, a benchmark of 16 memory-dependent manipulation ta

Agents

Can an Agent Learn to Predict Its Own Failure? Outcome-Grounded Confidence Calibration in LLM Agents

arXiv:2610.18614v1 Announce Type: cross Abstract: Controlling an acting LLM agent means deciding, at each step of a long-horizon task, whether to trust its next action, abstain, or hand off to a human

agentsarxiv-cs-lg

Can Machine Learning Potentials Be More Accurate Than the Methods They Are Trained On?

arXiv:2610.18027v1 Announce Type: cross Abstract: Although machine learning potentials (MLPs) generalize across configurational and chemical compound spaces, their accuracy is commonly believed to be

Can Renyi Accountants Beat Lossless Accountants for DP-SGD?

arXiv:2610.17120v1 Announce Type: new Abstract: Recent frameworks in differential privacy (DP), such as privacy loss distributions and f-DP, provide lossless representations of the privacy leakage of

Can Synthetic UAV Benchmarks Be Trusted? Real-Synthetic Validation for UAV Model Selection

arXiv:2610.14647v1 Announce Type: new Abstract: Synthetic UAV imagery can expand pre-deployment test evidence when task-specific Real data are scarce, but visual plausibility or semantic label alignme

Applications

Can We Trust This Evaluation? Instance-Level Reliability of LLM-as-a-Judge

arXiv:2610.14029v1 Announce Type: new Abstract: LLM-as-a-judge evaluation commonly aggregates multiple judges' decisions into a majority vote, yet this panel decision process does not guarantee that t

applicationsarxiv-cs-lg

CAPTOR: a vision-language foundation model for Chest-Abdomen-Pelvis Oncologic CT

arXiv:2610.14226v1 Announce Type: new Abstract: Purpose : To develop and evaluate CAPTOR, a deep-learning vision-language foundation model for oncologic chest-abdomen-pelvis (CAP) CT interpretation. M

researcharxiv-cs-cv

Tutorials

CartoVec: General-Purpose Location Embeddings from Open Map Data

arXiv:2610.17190v1 Announce Type: new Abstract: Geographic coordinates are common inputs in applied machine learning, but latitude and longitude describe position rather than the surrounding environme

tutorialsarxiv-cs-lg

Carve VFM: Carving Specialists out of Generalists, Automatic Task-Specific Compression of Vision Foundation Models

arXiv:2610.16813v1 Announce Type: new Abstract: Vision foundation models (VFMs) provide transferable visual representations, but their inference cost limits deployment in many settings. Fine-tuning sp

CAST-ASD: Causal Attention over Speakers and Time for Online Active Speaker Detection

arXiv:2610.14676v1 Announce Type: new Abstract: We present CAST-ASD (Causal Attention over Speakers and Time), a causal joint model for active speaker detection (ASD). Recent ASD models improve perfor

CAST: Real-Time Motion Capture for Any Skeleton Topology

arXiv:2610.14856v1 Announce Type: new Abstract: This paper studies the problem of recovering motion from monocular video, even when the target skeleton topology differs from the subject observed in th

CatPortrait: Taming Video Generative Models for Portrait Animation via In-context Conditioning

arXiv:2610.14957v1 Announce Type: new Abstract: Recent video generative models have shown strong capability in synthesizing high-quality and temporally coherent videos. Yet, adapting them for controll

researcharxiv-cs-cv

Local Ai

Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approach

arXiv:2610.18075v1 Announce Type: new Abstract: Selection bias is a difficult yet widespread problem in causal discovery, occurring whenever non-random selection processes lead to data that is not rep

local-aiarxiv-cs-lg

Causal Identification from Counterfactual Data: Completeness and Bounding Results

arXiv:2602.23541v3 Announce Type: replace-cross Abstract: Previous work establishing completeness results for counterfactual identification has been circumscribed to the setting where the input data b

CB-Agent: Concept-Based In-Context Learning for Sequential Action Selection

arXiv:2610.14574v1 Announce Type: cross Abstract: In-context learning (ICL) allows large language models to use demonstrations without updating their parameters. For interactive agents, demonstrations

Cepheid: Decoupling Scale from Structure for Cross-Family Motion Completion

arXiv:2610.18871v1 Announce Type: new Abstract: Sparse-to-dense motion completion recovers dense mesh motion from sparse vertex displacements. Residual priors trained on one geometry family frequently

Certified Robustness via Sparse Second-Order Cone Relaxations

arXiv:2610.16016v1 Announce Type: new Abstract: Certified robustness is critical for deploying neural networks in safety-sensitive settings, but computing exact certificates typically requires solving

CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection

arXiv:2610.14799v1 Announce Type: cross Abstract: Forecast-based models are fundamental to network traffic management and anomaly detection. However, it is essential to evaluate these forecasting mode

CFG-Policy: Unified Diffusion Policy Learning Framework via Classifier-free Guidance

arXiv:2610.14936v1 Announce Type: cross Abstract: Diffusion policies are increasingly used in robotic control and manipulation, vision-language-action models, and world-model-based agents. Their abili

ChampionKV: Head-Wise Adaptive Budget Allocation for KV Cache Compression

arXiv:2610.18026v1 Announce Type: new Abstract: Some observation-window-based KV cache compressors use prompt attention to identify important tokens but allocate uniform or predefined budgets across h

ChangeT^3: Evaluating Vision Test-Time Training for Remote Sensing Change Detection

arXiv:2610.15813v1 Announce Type: new Abstract: Bitemporal remote sensing change detection requires representations that preserve spatial detail while capturing contextual information relevant to chan

Channel Match: Calibrating Shape Latents for Few-Step 3D Generation

arXiv:2610.15321v1 Announce Type: new Abstract: Recent native 3D generators achieve high-quality asset creation by modeling structured 3D latents with flow-based transformers, but their high-resolutio

researcharxiv-cs-cv

Characterizing and Mitigating Distributional Bias in Physics-Informed Generative Models

arXiv:2610.16582v1 Announce Type: new Abstract: Physics-informed generative models augment a data-fitting objective with residual penalties that encourage physical constraints to hold in generated sam

Tutorials

Characterizing the Separability Gap in Prior-Data Fitted Networks

arXiv:2610.14989v1 Announce Type: new Abstract: Prior-data fitted networks (PFNs) are transformers pretrained on simulated datasets to approximate Bayesian posterior predictive distributions. Standard

tutorialsarxiv-cs-lg

Applications

Chasing Label Shifters: A Change-Aware Framework for Dynamic Graph Node Classification

arXiv:2610.17582v1 Announce Type: new Abstract: Entities in real-world systems often evolve over time: users shift between information consumption patterns, researchers migrate between fields, and fir

applicationsarxiv-cs-lg

Checking the Checkers: A Synthesis of Verifier Noise and Robustness in RLVR

arXiv:2610.17824v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a principal recipe for improving the reasoning of large language models (LLMs). It rest

ChemExplain: A Multimodal Benchmark for Explanatory Chemical Reasoning

arXiv:2610.17183v1 Announce Type: cross Abstract: Current chemistry benchmarks primarily evaluate outcome prediction or reaction pathway generation, but rarely test whether a model can explain why an

ChronoScribe: Semantic-Numerical Specialization for Instruction-Driven Time Series Forecasting

arXiv:2610.16345v1 Announce Type: cross Abstract: Recent advances in instruction-driven time series forecasting highlight a fundamental tension: systems must simultaneously master natural-language ins

Chunk-wise Model-based Policy Optimization

arXiv:2610.14564v1 Announce Type: cross Abstract: Model-based reinforcement learning has shown superior sample efficiency by leveraging the learned latent dynamics either in the form of derivative-fre