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User simulators enable interactive training and evaluation of AI assistants at scale. Yet current simulators struggle to reproduce the diverse behaviors of real users, leading to assistants that generalize poorly to underrepresented users. We identify user representations as an underexplored bottleneck in user simulati...
As vibe coding becomes increasingly capable and widespread, security vulnerabilities in even functionally correct solutions are a growing concern. When investigating functionally correct but insecure solutions, we find that the insecure agent is less than half as likely to conduct effective planning and testing for the...
The concept of in-context self-evolution is formalized and VALVE, a validated-gated framework for long-horizon skill optimization is introduced, which establishes finite convergence, provides theoretical guarantees for future-task gain and drawdown, and derive the validation and evaluation holdout sizes required for a...
This work introduces ExecCritic, combining a test--verify--revise scaffold with a role-specific reinforcement learning recipe for training agents within it, separating test construction from source-code repair.
On long-horizon complex search, TRACE substantially improves base-model tool-use ability using pure RL, without a cold-start supervised fine-tuning stage, an agentic mid-training stage, or training on live-web data.
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