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Learning from experience is essential for LLM agents to adapt to unfamiliar and dynmaic environments. Evaluating this ability is therefore important for understanding how effectively agents acquire and use new knowledge. Existing benchmarks have sought to evaluate this ability, but they primarily evaluate tasks whose r...
Large language models remain vulnerable to jailbreaks, and automated red teaming is the standard way to find jailbreaks in large language models at scale. Current methods either draw more samples at test time through search, rewriting, and tree expansion, or train a stronger attacker offline with reinforcement learning...
LLM-guided evolutionary methods, such as AlphaEvolve, have emerged as powerful approaches for challenging computational optimization problems, such as circle packing. However, prior work typically optimizes performance gain over a fixed number of iterations. We argue that practical optimization should maximize gain per...
Meta-TTL is introduced, a framework that formulates the discovery of effective self-improvement policies as a bi-level optimization problem and consistently outperforms existing baselines, suggesting that the optimized self-improvement policy encodes transferable meta-strategies that generalize beyond the training task...
This work proposes RealPref, a benchmark for evaluating natural preference-following in personalized user-LLM interactions, and finds that LLM performance drops significantly as context length grows and preference expression becomes more implicit, and that generalizing user preference understanding to unseen scenarios...
This work proposes a self-evolving test-time defense built around a persistent, cross-interaction rule memory that substantially reduces attack success rates while preserving benign utility, remains robust under an adaptive composite-wrapper attack, and does not increase over-refusal as the memory grows.
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