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LLMs can be forced to generalize beyond initial constraints by actively searching for adversarial test cases that expose logical divergences in generated code.
LLM agent progress increasingly hinges on better external cognitive infrastructure, not just stronger models.
Synergy's architecture lets agents evolve through experience by proactively recalling rewarded trajectories, hinting at a new way to build agents that learn and adapt in open, collaborative environments.
LMM-based GUI agents stick out like a sore thumb in human-centric mobile environments, but simple techniques can make them blend in without sacrificing utility.
By steering token selection at the logit level, LogitsCoder achieves more efficient and higher-quality reasoning chains for code generation, outperforming existing methods.
GUI agents learn faster and generalize better with a new reward shaping technique that dynamically adapts to successful exploration trajectories, outperforming fixed reward schemes.
