AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Results characterize current agent capabilities across game-development activities and support combining runtime evidence with visual assessment, as well as identifying requirement omissions and gameplay logic errors as predominant implementation problems.
This work introduces CLBench-V, a benchmark for multimodal context learning that addresses the difficulty of localizing where context use breaks down by organizing tasks around three dimensions: context grounding, new information application, and new knowledge learning.
Experimental results demonstrate that S2V can consistently improve LVU performance across multiple LVU benchmarks, outperforming both general MLLMs and reasoning-based methods not only in LVU accuracy but also in training and inference efficiency.
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