Background. A component replacement in a language-model agent changes an execution trajectory, potentially altering later observations, resource use, and recovery opportunities. Different evidence is needed to assess its task-level benefit and the contribution of local decision quality. Methods. This critical scoping r...
Reported improvements from tools and reusable skills in large language model agents refer to different comparisons. This critical narrative review examines what these evaluations estimate and which conclusions their designs support. The review checks the roles of one hundred cited papers and extracts focal evaluation d...
Scaling model-generated data is usually viewed as improving distillation: more examples should increase coverage, reduce noise, and produce stronger students. We show a second effect: larger datasets can make subtle teacher-specific signals easier to detect in the trained student, even when examples are off-task and ne...
Zhichen Dong, Zhi-Xuan Liu, Yuanjiu Fan, et al.· 1 citation
A semantic-entropy-based method, using task uncertainty to guide prompt optimization, which requires no training, works with black-box models, and integrates easily into existing prompt optimizers.
Shuyang Zhang, Zhixuan Liu, Zhichen Dong et al.· Annual Meeting of the Associ...· 2 citations
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