All papers

The paper presents a network utilizing self-attention and spatio-temporal convolutions for 3D skeleton-based human action recognition.

The paper proposes an uncertainty-aware multimodal descriptor fusing sequential camera and LiDAR data for robotic place recognition.

The paper develops a microprocessor-based smart wearable glass system with GPS monitoring to assist visually impaired individuals.

The paper proposes a metamorphic testing method with mutation operators to evaluate how retrieval-augmented generation systems handle changing document corpora.

The paper models epileptic dynamics using a multi-stable system to detect critical slowing down and design feedback control for seizure prevention.

The paper examines vulnerabilities of deepfake detection models to backdoor attacks and evaluates related defense strategies.

The paper studies cybersecurity threat detection using sequential deep learning models evaluated on the KDD network traffic dataset.

The paper summary contains webpage metadata rather than research text, though it mentions partial discharge localization for drone inspection using UHF detection.

The paper evaluates machine learning models, finding Random Forest predicts depression in menopausal women with high accuracy.

The paper proposes parallel delay chains to enable low-power neuromorphic circuits to detect anomalies in long electrocardiogram signals.

The paper evaluates machine learning models to detect active network attacks early with minimal preprocessing of raw traffic data.

The paper provides a synthetic dataset to detect passive wireless jamming attacks carried out using reconfigurable intelligent surfaces.

The paper proposes an adaptive strategy using software agents and competency-based ontologies to assist students in e-learning platforms.

The paper introduces an asymmetric 2D tensor parallelism framework to optimize large language model inference across hardware with uneven GPU capacities.

The paper presents a task token framework designed to maintain sub-second runtime for multi-agent logistics operations with hundreds of agents.

The paper presents a spatio-temporal attention framework to predict trajectories of multiple mixed agents in unsignalized traffic roundabouts.

The text contains publisher metadata and page navigation text without readable abstract details about the InsightChain supplier discovery agent.

The paper presents an autonomous framework using multiagent reinforcement learning to monitor, diagnose, and recover from failures in production AI systems.

The authors introduce CharXiv, a benchmark of realistic scientific charts designed to test descriptive and reasoning capabilities in multimodal language models.

The authors introduce CharXiv, a benchmark of realistic scientific charts designed to test descriptive and reasoning capabilities in multimodal language models.

The authors introduce CharXiv, a benchmark of realistic scientific charts designed to test descriptive and reasoning capabilities in multimodal language models.

The authors introduce CharXiv, a benchmark of realistic scientific charts designed to test descriptive and reasoning capabilities in multimodal language models.

The authors introduce CharXiv, a benchmark of realistic scientific charts designed to test descriptive and reasoning capabilities in multimodal language models.

The authors introduce CharXiv, a benchmark of realistic scientific charts designed to test descriptive and reasoning capabilities in multimodal language models.

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This survey provides an algorithmic overview and failure mode analysis of reinforcement learning techniques applied to language models.

This survey provides an algorithmic overview and failure mode analysis of reinforcement learning techniques applied to language models.

This survey provides an algorithmic overview and failure mode analysis of reinforcement learning techniques applied to language models.

Summary pending (next check).

Summary pending (next check).

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This paper presents reconstruction inversion techniques and hardware-software co-design to improve multi-patient temporal computed tomography.

This paper presents a computational method to correct for eye motion and optical aberrations in high-resolution retinal imaging.

This paper analyzes deprecated Android application programming interfaces and derives patterns to study how developers update them.

This paper analyzes deprecated Android application programming interfaces and derives patterns to study how developers update them.

This paper profiles the maintainability and structural code quality of model-generated Python programs using static analysis tools.

This paper introduces a benchmark to evaluate software engineering language agents on migrating code between Linux and Windows.

This paper proposes a framework to detect training data contamination in bug-fixing benchmarks for language models.

This paper introduces an approach to detect statistically significant software energy consumption regressions across commits in version histories.

This paper introduces an approach to detect statistically significant software energy consumption regressions across commits in version histories.

This paper develops machine learning models to detect, explain, and suggest alternatives for offensive language in online developer communities.

This paper develops machine learning models to detect, explain, and suggest alternatives for offensive language in online developer communities.

This paper presents a fine-tuning method to update language models for code comprehension as codebases change over time.

The paper extracts software requirements and architecture from code repositories and tests whether language agents can reconstruct projects from them.

The paper models mixed human-agent software teams to find cost-optimal spending allocations balancing human developer hours and automated coding agents.

The paper presents a residual world model to correct simulated actuator dynamics using proprioception to reduce the sim-to-real gap for humanoids.

The paper presents a visuo-inertial system estimating terrain context, locomotion mode, and gait speed for assistive wearable robotics.

The paper proposes a receding-horizon planning method to evaluate future camera viewpoint sequences for inspecting heavily occluded robotic targets.

The paper introduces a GPU-accelerated differentiable contact dynamics framework for simulating coupled deformable and rigid body physical interactions.

The paper presents a benchmark for vision-based 3D reconstruction of submerged sea ice using polar expedition and synthetic simulator data.

The paper introduces a framework that decouples task intent from robot dynamics to enable collective manipulation learning across multiple embodiments.

The paper compares adaptation strategies for vision-language-action models across diverse data scenarios, such as online, offline, and failure trajectories.

The paper introduces a tactile manipulation dataset and a foundation model combining visual planning with force-aware refinement for dexterous robots.

The paper introduces a mode-unified control framework using radial basis function interpolation to continuously control robotic prostheses across varying terrains.

The paper evaluates end-to-end vision-language-action policies for autonomous lunar rover docking under challenging surface lighting conditions.

The paper presents an action-reweighting learning framework for vision-language-action models to learn from human recovery demonstrations in robotic disassembly.