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As AI agents increasingly act through external tools, tool results are no longer simple outputs but execution artifacts that must remain correctly associated with the context that produced them. Yet as results persist across caches, recovery mechanisms, and shared runtime state, this association can break, causing a re...
As large language models (LLMs) acquire advanced biological design capabilities, they offer substantial research benefits but simultaneously introduce severe dual-use risks, such as engineering enhanced pathogens or drug-resistant bacteria. While biosecurity frameworks recognize these threats, the actual risk of LLMs e...
This work introduces the Print Reader, a model trained with Semantic Mount-and-Read Tuning (SaRT) to describe frozen weight updates and demonstrates the feasibility of natural-language readout while pointing to reliability across updates as the next step.
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