All papers

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.

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.

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.

The paper presents a robust model predictive control method using nonlinear reduced-order models that guarantees constraint satisfaction for full-order systems.

The paper develops a method to learn task-relevant constraints from visual demonstrations using sparse 3D keypoints for safe motion planning.

The paper presents probabilistic digital twins that model uncertain physical properties as probability distributions to guide human-robot interaction.

The paper introduces a residual learning framework with motion-aware gating to preserve expressive characteristics during humanoid robot whole-body control.

The paper evaluates an interactive interface connecting a physical master robot controller to a physical simulation using past successful trajectories as guidance.

The paper formulates a geometric refinement algorithm to smooth multi-usage pipe centerlines while maintaining topological clearance in additively manufactured manifolds.

The paper presents a virtual reality training system for vascular intervention combining dual-mode robot control with sensorless interaction estimation.

The paper proposes a navigation framework separating persistent spatial-semantic mapping from task-conditioned high-level decision policies for robots.

The paper examines how vision-language-action policies handle paraphrased instructions and evaluates ensembling and sampling techniques across different prompts.

The paper proposes a method using event-camera observations to selectively correct kinematic predictions for robust spacecraft pose tracking.

The paper introduces a meta-learning method that optimizes simulation parameter distributions to maximize downstream real-world robot manipulation performance.

The paper presents a hierarchical reinforcement learning framework to learn functional dexterous multi-finger robot manipulation directly on real hardware.

This paper presents an open-source procedural generator for creating customizable 2D and 3D environments with line-of-sight visibility analysis.

This paper presents a cooperative dual-arm planning framework that coordinates tabletop wiping with temporary object removal.

This paper introduces a visual roadmap framework built directly on 3D Gaussian Splatting renderings for image-goal navigation.

This paper proposes a world model planning framework to monitor execution and recover robot policies during deployment without retraining.

This paper presents a teleoperation platform and modular coordination framework for learning whole-body mobile manipulation policies.

This paper analyzes failures in mobile manipulation and introduces agentic interventions to coordinate planning, base movement, and manipulation.

This paper presents a simulation benchmark to evaluate wrist-force feedback integration strategies for vision-language-action policies.