Search: “papers”

X-RACE: XAI-assisted Recurrent neural network Attribution for Channel Estimation

arXiv:2609.11211v1 Announce Type: cross Abstract: Deep learning models, notably Long Short-Term Memory (LSTM), have demonstrated promising performance in channel estimation for high-mobility vehicular

12 Sept 2026

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3D Euler-Angle Orientation Control for Two-Ray Fading Mitigation in Maritime Air-to-Sea Communications

DGX agent

arXiv:2609.11476v1 Announce Type: new Abstract: Maritime Air-to-Sea links are dominated by a line-of-sight ray and a sea-surface reflected ray whose destructive combination produces deep fades. Existi

model-releasesarxiv-cs-ro

11 Sept 2026

Agents

A Bellman Optimality Equation for Plasticity

arXiv:2609.10776v1 Announce Type: new Abstract: In continual reinforcement learning, carefully managing the stability-plasticity tradeoff remains a core challenge. Recent work by Abel et al. (2025) fo

agentsarxiv-cs-lg

A Multi-Stage Rule-Chaining Framework for Compositional and Interpretable Cognitive Reasoning

arXiv:2609.10654v1 Announce Type: cross Abstract: The Abstraction and Reasoning Corpus (ARC) benchmarks cognitive generalization, the ability to infer and apply abstract rules from limited examples. T

A Short Survey of Viewing Large Language Models in Legal Aspect

arXiv:2303.09136v2 Announce Type: replace Abstract: Large language models (LLMs) have transformed many fields, including natural language processing, computer vision, and reinforcement learning. These

safetyarxiv-cs-cl

An Empirical Measurement of Jailbreaking Evaluators

arXiv:2609.10594v1 Announce Type: cross Abstract: Expert evaluation of jailbreak responses is costly and difficult to scale, so the community increasingly relies on automated evaluators to determine w

safetyarxiv-cs-lg

Arbitrary Cipher Attacks Against Large Language Models Do Not Require Fine-Tuning

arXiv:2609.09553v1 Announce Type: cross Abstract: Large language model safety and security research is preoccupied with, among other things, detecting and preventing jailbreak attacks: alignment bypas

safetyarxiv-cs-ai

Are We Really Doing Few-Shot Learning? A Critical Examination of Pre-Training Assumptions

arXiv:2609.10851v1 Announce Type: new Abstract: Few-shot learning is commonly evaluated under protocols that pre-train a model on a large auxiliary set whose classes are disjoint from the target episo

safetyarxiv-cs-cv

AUC Maximization from Biased Positive-unlabeled Data with Confidence

arXiv:2609.10928v1 Announce Type: new Abstract: Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced binary classification. Although positiv

safetyarxiv-cs-lg

Automated Identification of Competing Narratives in Political Discourse on Social Media

arXiv:2609.11202v1 Announce Type: new Abstract: Social media platforms have become central to shaping political discourse, serving as arenas where narratives form and evolve, influencing public opinio

Local Ai

Bidirectional Multimodal Fusion of Sky Images and Time-Series for Solar Forecasting with Large Language Models

arXiv:2609.11135v1 Announce Type: new Abstract: Short-term photovoltaic (PV) power and global horizontal irradiance (GHI) forecasts are essential for effective dispatch, reserve scheduling, and grid o

local-aiarxiv-cs-lg

BridgeMatch: Conditional Transport Bridges in Matching Matrix Space for 3D Deformable Registration

arXiv:2609.11472v1 Announce Type: new Abstract: Reliable non-rigid point cloud correspondences are important for deformable anatomical registration, embodied perception and manipulation, and dynamic 3

researcharxiv-cs-cv

CARTS: Contextual Autoregressive Rank Transcoding Steganography for Full-Capacity Keyed Text Encoding

arXiv:2609.10744v1 Announce Type: cross Abstract: Autoregressive language models can be used to transform a payload text into a stegotext of identical token length by preserving per-position rank info

Local Ai

CEM-TUDASR: Computationally efficient multi-modality transformer based unsupervised domain adaptive super-resolution approach

arXiv:2609.11201v1 Announce Type: new Abstract: Wireless Capsule Endoscopy (WCE) enables non-invasive visualization of the gastrointestinal tract, but its miniaturized optics, sensor limitations, and

local-aiarxiv-cs-cv

CertDW: Towards Certified Dataset Ownership Verification via Conformal Calibration

model-releasesarxiv-cs-cv

arXiv:2506.13160v2 Announce Type: replace-cross Abstract: Deep neural networks (DNNs) rely heavily on high-quality open-source datasets (e.g., ImageNet) for their success, making dataset ownership ver

Certifying Lower Bounds for Risk-Sensitive Reinforcement Learning under Adversarial State Perturbations

arXiv:2609.10866v1 Announce Type: new Abstract: Reinforcement learning (RL) agents deployed in real-world environments are often vulnerable to adversarial perturbations in state observations, creating

CHRONOBERG: Capturing Language Evolution and Temporal Awareness in Foundation Models

arXiv:2509.22360v2 Announce Type: replace Abstract: Large language models (LLMs) excel at operating at scale by leveraging social media and various data crawled from the web. Whereas existing corpora

Agents

Cognitive Digital Twins: Ethical Risks and Governance for AI Systems That Model the Mind

arXiv:2606.23094v2 Announce Type: replace-cross Abstract: As AI systems become increasingly persistent and personalized, they make possible a class of technologies that we call cognitive digital twins

agentsarxiv-cs-cl

Complex-Text Robustness Evaluation and Failure Diagnosis for Low-Resource Multilingual Text-to-Speech

arXiv:2609.11545v1 Announce Type: new Abstract: Low-resource multilingual text-to-speech (TTS) systems have expanded language coverage, but their robustness under complex text inputs remains insuffici

Convex Optimization with Nested Evolving Feasible Sets (CONES) under Time-Varying Loss Functions

arXiv:2609.11207v1 Announce Type: new Abstract: Convex Optimization with Nested Evolving Feasible Sets (CONES)} was introduced in ite{CONESVaze} where the objective function (f) remains fixed but the

DefVINS: Visual-Inertial Odometry for Deformable Scenes

model-releasesarxiv-cs-cv

arXiv:2601.00702v3 Announce Type: replace-cross Abstract: Deformable scenes violate the rigidity assumptions underpinning classical visual--inertial odometry (VIO), often leading to over-fitting to lo

Detectable Only Where It Is Confounded: What Verified Duplication Counts Say About Membership Evidence in Language Models

arXiv:2609.10830v1 Announce Type: new Abstract: When a language model finds a sentence unusually cheap to predict, it is tempting to conclude that the sentence was in its training data. Almost every p

Detecting hallucinations in local models without eating VRAM: What we learned testing 1.5B to 120B models

Hey everyone, If you run local models via Ollama in production or personal projects, you've probably run into the hallucination problem: how do you know when a model is hallucinating without burning e

model-releasesr-ollama

Applications

Differentially Private EEG Feature Anonymization: A Privacy-Utility Case Study in Clinical Neurophysiology

arXiv:2609.11777v1 Announce Type: cross Abstract: Clinical electroencephalography (EEG) data are valuable for healthcare research and for developing artificial intelligence (AI)-based clinical decisio

applicationsarxiv-cs-lg

Distributed Physical Layer Authentication and Collaborative RSMA in Non-Terrestrial Networks via Graph Reinforcement Learning

arXiv:2609.09475v1 Announce Type: cross Abstract: Existing physical-layer authentication (PLA) schemes for non-terrestrial networks (NTNs) often rely on single-anchor verification, lack joint authenti

Do Agents Know When They Succeed? Calibrating Agent Confidence from Internal Representations

arXiv:2609.09448v1 Announce Type: new Abstract: As agentic systems getting adopted rapidly in safety critical applications, it is vital to measure the confidence associated with the agentic actions. I

safetyarxiv-cs-ai

Agents

Do large language models actually understand the world, or are they just very good at pretending? Does it even matter? Our recent special is…

Do large language models actually understand the world, or are they just very good at pretending? Does it even matter? Our recent special issue in the Royal Society, “World Models in Natural and Artif

agentsdavid-ha--x

E-CONAN (Entailment, CONtradition And Neutral) Benchmarks: Arabic Textual Entailment and Natural Inference Datasets

model-releasesarxiv-cs-cl

arXiv:2609.11334v1 Announce Type: new Abstract: Natural Language Inference processes pairs of sentences to extract their semantic relations. NLI has been a hot research topic, integrated as a main com

Empirical Evaluation of Membership Inference Attacks on NLP Text Classifiers: A Baseline Study on SST-2

model-releasesarxiv-cs-cl

arXiv:2609.10935v1 Announce Type: cross Abstract: Membership inference attacks (MIAs) try to determine whether a specific record was used to train a model, a privacy risk that matters in natural langu

Evaluating LLM-Simulated Conversations in Modeling Inconsistent and Uncollaborative Behaviors in Human Social Interaction

model-releasesarxiv-cs-cl

arXiv:2603.17094v2 Announce Type: replace Abstract: Simulating human conversations using large language models (LLMs) has emerged as a scalable methodology for modeling human social interaction. This

EVPeriscope: Extended Perception across Aerial and Ground Vehicles with Event-based Propeller Tracking

arXiv:2609.11920v1 Announce Type: new Abstract: Reliable relative localization between aerial and ground robots is a key requirement for tightly coordinated heterogeneous teams. This can be difficult

researcharxiv-cs-ro

EVTradeMatch: A Mobility-Aware Multi-Objective Matching Framework for EV--EV Energy Trading

arXiv:2609.10551v1 Announce Type: cross Abstract: Peer-to-peer energy trading among electric vehicles (EVs) can improve charging flexibility under limited charging infrastructure, but effective EV--EV

Explainability Assistant: A Conversational XAI Interface for Interpreting Energy Consumption Models

arXiv:2609.11860v1 Announce Type: cross Abstract: Energy consumption forecasting relies on increasingly complex machine learning (ML) models, such as Genetic Programming-based symbolic regressors, who

From Connectivity to Rewards: Dense Reward Learning with Directed State Graphs

arXiv:2609.10781v1 Announce Type: new Abstract: The integration of graphs with Goal-Conditioned Hierarchical Reinforcement Learning (GCHRL) has received increasing attention, as graphs naturally encod

safetyarxiv-cs-lg

From State Synchronization to Cognitive Self-Evolution: An Operational Architecture for Cognitive Digital Twins

arXiv:2609.09625v1 Announce Type: new Abstract: As Digital Twin (DT) systems evolve beyond state synchronization toward task-oriented and knowledge-driven operation, Cognitive Digital Twins (CDTs) hav

Gait-Dependent Effects on Quadruped Locomotion for Load-Carrying using Passive Mechanism

arXiv:2609.11059v1 Announce Type: new Abstract: Passive mechanical interfaces offer a lightweight alternative to actuated manipulators for quadruped payload carrying, but their impedance directly coup

researcharxiv-cs-ro

General Quantification of Covariate and Concept Shifts

arXiv:2609.11918v1 Announce Type: new Abstract: Generalization under distribution shift remains a core challenge in modern machine learning, yet existing learning bound theory is limited to narrow, id

Grounded Evaluation and Repair for NL-to-PDDL Problem Generation

model-releasesarxiv-cs-ai

arXiv:2609.09898v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown promise for translating Natural Language (NL) planning descriptions into PDDL problem instances. However, standa

Halo: Improving forecast accuracy through heteroscedastic estimation

arXiv:2609.10589v1 Announce Type: new Abstract: Heteroscedastic forecasting, where a network estimates a scale parameter alongside a location parameter, is normally motivated by uncertainty quantifica

Harness Robotic OS: A Unified Embodied-Agent Runtime for Closed-Loop Quadruped Inspection

arXiv:2609.11225v1 Announce Type: new Abstract: Autonomous property inspection requires more than robust robot navigation: a deployable system must connect heterogeneous sensing, reusable autonomy cap

safetyarxiv-cs-ro

Hierarchical Clustering Can Jointly Satisfy Richness, Consistency, and Scale Invariance

arXiv:2609.11173v1 Announce Type: new Abstract: Despite its ubiquity, clustering lacks a universally accepted definition of what is a cluster. Kleinberg's Impossibility Theorem formalizes this difficu

Tutorials

Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift

arXiv:2609.10994v1 Announce Type: new Abstract: Unlabeled-unlabeled (UU) learning allows us to learn a binary classifier from two sets of unlabeled data with different class-priors. It is a general fr

tutorialsarxiv-cs-lg

Improving 5G AI-RAN MCS Selection by Predicting Retransmissions

model-releasesarxiv-cs-ai

arXiv:2609.09324v1 Announce Type: cross Abstract: Link Adaptation (LA) in 5G NR is inherently reactive, relying on channel measurements and HARQ feedback that may become quickly obsolete when the chan

Kalman Filtering Based Flight Management System Modeling for AAM Aircraft

model-releasesarxiv-cs-ro

arXiv:2602.14948v2 Announce Type: replace Abstract: Advanced Air Mobility (AAM) operations are planned to utilize strategic flight planning services that predict temporal uncertainties to validate fli

Learning Intrusion Response Strategies for OT Systems

arXiv:2609.10298v1 Announce Type: cross Abstract: Cyberattacks against Operational Technology (OT) systems, which monitor and control industrial processes, pose an increasing threat to essential socie

LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics

arXiv:2609.11639v1 Announce Type: new Abstract: The energy transition is reshaping residential electricity consumption through the increasing adoption of distributed generation, electrified appliances

Local Ai

M3-Former: Multimodal Transformer with Mixture-of-Experts for Long-Term Vessel Trajectory Prediction

arXiv:2609.10559v1 Announce Type: cross Abstract: To address the challenges of behavioral multimodality, limited semantic utilization, and long-term error accumulation in vessel trajectory prediction,

local-aiarxiv-cs-cv

Meta-Learning for Classifier Selection in Image Datasets: A Feature-Driven Framework for Accuracy Prediction

arXiv:2609.11041v1 Announce Type: new Abstract: No Free Lunch theorem implies that any performance gains achieved by a classifier on a particular image distribution are necessarily offset by a loss of

researcharxiv-cs-cv