AI Applications

BRS: Streamlining real-world whole-body manipulation for household activities. It introduces a robot suite tackling robot dexterity with bimanual coordination, navigation, and end-effector reach.

LONGCODEU: A new benchmark to challenge & enhance long code understanding in language models for software engineering!

SPIDER: A comprehensive pathology dataset boosts AI diagnostic models.

CognitiveDrone: A novel VLA model and benchmark for real-time cognitive UAV tasks, improving reasoning and control.

SpeQL: Near-instant results for ad-hoc queries using LLMs to predict and precompute, dramatically improving user experience.

LLM-driven framework enhances autonomous vehicle interactions with human drivers in real-time.

New dataset for automated ICD coding in Russian enhances clinical data accuracy.

DeepSolution enhances engineering design via tree exploration and bi-point thinking.

SoRFT enhances LLMs for issue resolving via subtask-oriented reinforced fine-tuning, outperforming other open-source models.

Sim-to-real RL recipe achieves robust vision-based dexterous humanoid manipulation without human demos!

Reflect VLM: Improving robotic manipulation via vision-language models with a novel reflection mechanism and a diffusion model for imagined futures.

Community moderation success relies on fact-checking!

RAD: 3DGS-based RL advances autonomous driving, achieving a 3x lower collision rate!

ARM4R pre-trains autoregressive robotic models using low-level 4D representations from human videos, achieving efficient transfer learning and improved task performance across various environments.

HUMANUP: A novel two-stage reinforcement learning framework enables real-world humanoid robots to autonomously recover from falls on various terrains.

FLAG-TRADER fuses LLMs & RL for enhanced financial trading, achieving superior performance compared to traditional methods by efficiently integrating multimodal data and adapting to market dynamics.

V2V-LLM leverages multi-modal LLMs for safer cooperative autonomous driving by fusing perception data from multiple vehicles, answering driving-related questions, and improving trajectory planning.

DexTrack achieves highly generalizable neural tracking control for dexterous robot manipulation by iteratively training a controller using high-quality demonstrations refined via homotopy optimization…

HMA: a novel approach for generating high-quality robotic videos 15x faster, enabling real-time policy evaluation and data augmentation for scaling robot learning.

Current pathology foundation models struggle with center variations; this paper introduces a robustness index to quantify this, revealing model biases and advancing robust model development.