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Evaluation has been central to advances in AI, and it is equally critical to autonomous discovery. Yet scaling autonomous discovery to real-world engineering challenges faces a fundamental bottleneck: the cost of evaluating progress. In real settings, a single true evaluation, such as a full benchmark or serving trace,...
Coding agents have become increasingly long-horizon, autonomous, reliant on general-purpose shell and maintain their own persistent memory for self-improvement. While these capabilities have made the agents powerful, they have also made them harder to defend against external adversaries. Defenses that restrict this arc...
Combee is proposed, a novel framework to scale parallel prompt learning for self-improving agents that speeds up learning and enables running many agents in parallel while learning from their aggregate traces without quality degradation.
Software development follows an implementation-verification loop in which developers or agents iteratively revise an implementation until an evaluator, such as a test suite, accepts it. The evaluator checks the implementation against a set of requirements under a model of the deployment environment. Yet even a formal p...
It is argued that an agent system should maintain an explicit representation of how it fails, induced from its own behavior and reusable wherever failure feedback is needed, and AdaMAST builds this representation by converting a target system's traces into a compact, evidence-grounded failure taxonomy.
A three-level taxonomy inspired by autonomous driving that distinguishes degrees of autonomy along a roadmap from today’s AI-assisted development workflows to fully autonomous software development in which AI systems autonomously identify demands and design, implement, verify, and maintain software without human oversi...
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