#vlm-agents News & Analysis

5 articles tagged with #vlm-agents. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AIBearisharXiv – CS AI · Jun 97/10

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VisualLeakBench: Reproducible Action-Boundary Propagation Failures in Vision-Language Agents

Researchers introduce VisualLeakBench, a 500-image benchmark that reveals critical security vulnerabilities in vision-language agents, where sensitive information visible in screenshots and documents is propagated into tool arguments. Testing four production VLM systems shows baseline failure rates of 78.8% for personally identifiable information and 85.5% for unsafe text, with defensive prompts reducing PII propagation but leaving unsafe-text leakage at 52.6%.

AIBullisharXiv – CS AI · May 47/10

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Odysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning

Researchers introduce Odysseus, an open framework for training vision-language models (VLMs) to handle 100+ turn decision-making tasks using reinforcement learning, demonstrated through Super Mario Land gameplay. The work achieves 3x better performance than existing models while maintaining general capabilities, advancing the frontier of embodied AI agents.

AINeutralarXiv – CS AI · Jun 96/10

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OmniGameArena: A Unified UE5 Benchmark for VLM Game Agents with Improvement Dynamics

Researchers introduce OmniGameArena, a comprehensive UE5-based benchmark for evaluating vision-language model agents across diverse game environments (solo, PvP, cooperative), along with the Improvement Dynamics Curve methodology that tracks agent performance evolution through iterative refinement rather than single snapshots.

AINeutralarXiv – CS AI · Jun 26/10

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3DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Code

Researchers introduce 3DCodeBench, a comprehensive benchmark for evaluating vision-language models (VLMs) as procedural 3D modelers that convert text and image inputs into code for 3D modeling software. The study reveals that current advanced VLMs struggle primarily with API mismatches and geometric coherence, while identifying test-time scaling as an effective improvement method.

AINeutralarXiv – CS AI · May 126/10

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Mirror, Mirror on the Wall: Can VLM Agents Tell Who They Are at All?

Researchers introduced a benchmark testing whether vision-language model (VLM) agents can recognize themselves in mirrors, a cognitive capability that emerges only in some animal species. Results show self-identification through reflection occurs mainly in stronger VLMs, while weaker models fail to extract self-relevant information despite viewing their reflections, revealing that language-based self-reference alone does not guarantee grounded self-understanding.