VirtueBench: Evaluating Trustworthiness under Uncertainty in Long Video Understanding
VirtueBench: Evaluating Trustworthiness under Uncertainty in Long Video Understanding explores VirtueBench is a benchmark to evaluate the trustworthiness of Vision-Language Models in long video understanding, highlighting the need for reliable and honest AI systems.. Commercial viability score: 7/10 in Video Understanding.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisProbing the Reliability of Driving VLMs: From Inconsistent Responses to Grounded Temporal ReasoningViability: 6.0
- Builds On ThisINFACT: A Diagnostic Benchmark for Induced Faithfulness and Factuality Hallucinations in Video-LLMsViability: 4.0
- Builds On ThisVAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-EvaluationViability: 5.0
- Prior WorkVideoZeroBench: Probing the Limits of Video MLLMs with Spatio-Temporal Evidence VerificationViability: 7.0
- Prior WorkAre Video Reasoning Models Ready to Go Outside?