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Personal GUI agents waste most of their retrieved history; this framework activates experience only when it's actually applicable, boosting performance and efficiency.
Top-performing models in automated pathology rely on structured representations and hierarchical diagnostics, not just vision-language models alone.
X$^2$Localizer boosts single-frame retrieval performance by over 4% while maintaining full-video accuracy, bridging the gap between evaluation benchmarks and real-world applications.
Adversarial techniques traditionally seen as threats are now being repurposed by content owners to proactively safeguard their visual assets from misuse.
Agentic LLMs are far more vulnerable to indirect prompt injection attacks than previously thought: AdapTools achieves over 2x improvement in attack success while significantly degrading system utility, even against strong defenses.
LLM agents can now defend against indirect prompt injection attacks without sacrificing task performance, thanks to a new method that surgically manipulates attention based on latent space analysis.