DriveVer is a lightweight dual-head test-time verifier that predicts safety confidence scores and geometric refinement vectors for candidate trajectories, improving base planners on the NAVSIM benchmark.
Sce2drivex: A generalized mllm framework for scene-to-drive learn- ing,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
MARS introduces a four-agent MLLM system for risk-aware planning and personalized assistance in home robotics, claiming superior performance over state-of-the-art multimodal models on multiple datasets.
The paper provides the first comprehensive survey of multimodal chain-of-thought reasoning, including foundational concepts, a taxonomy of methodologies, application analyses, challenges, and future directions.
citing papers explorer
-
DriveVer: Lightweight Trajectory Evaluator as Test-Time Verifier for Autonomous Driving
DriveVer is a lightweight dual-head test-time verifier that predicts safety confidence scores and geometric refinement vectors for candidate trajectories, improving base planners on the NAVSIM benchmark.
-
MARS: Multi-Agent Robotic System with Multimodal Large Language Models for Assistive Intelligence
MARS introduces a four-agent MLLM system for risk-aware planning and personalized assistance in home robotics, claiming superior performance over state-of-the-art multimodal models on multiple datasets.
-
Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey
The paper provides the first comprehensive survey of multimodal chain-of-thought reasoning, including foundational concepts, a taxonomy of methodologies, application analyses, challenges, and future directions.