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Confidently incorrect reports can mislead LLM agents just as effectively as accurate ones, revealing a critical vulnerability in network troubleshooting AI.
LLMs can leapfrog current network troubleshooting benchmarks by explicitly encoding structured diagnostic policies, rather than relying on free-form deliberation.
LLMs can now formulate significantly better penetration testing strategies, outperforming even GPT-5, thanks to a novel reasoning framework and targeted fine-tuning.
Adapting RGB-pretrained ViTs with viewpoint-conditioned feature selection leaps ahead in thermal vehicle re-identification, outperforming existing methods by a significant margin.
