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Ranking rewards can be reused at test time to boost retrieval performance without accessing model weights or ground-truth labels.
Safety Sentry redefines safety in LLM interactions by enabling context-sensitive decision-making, significantly reducing user interruptions while enhancing safety outcomes.
CLIR uncovers 8x more unique bugs than existing fuzzing tools, revolutionizing compiler testing efficiency and effectiveness.
SIRI allows LLM agents to autonomously develop and internalize skills, achieving up to a 2.2% performance boost without external dependencies.
MT-EditFlow bridges the gap between local planning and global success in multi-turn image editing, achieving a significant performance boost over leading models.
Reference patches, typically discarded in software-engineering agent training, can be distilled into latent process graphs to guide trajectory curation, leading to more effective and efficient learning.
Finally, a gradually typed metaprogramming language with mutable references that *doesn't* let your variables escape scope, thanks to clever dynamic enforcement of environment classifiers.
Tool-using agents like Clawdbot are surprisingly vulnerable to seemingly harmless prompts, where minor misinterpretations can quickly escalate into high-stakes tool actions.
