KbSD proposes self-distillation to calibrate agentic search
Published on June 30, 2026, the summary introduces KbSD, a self-distillation framework using dense token-level supervision for agentic search decisions. It reports no evaluation results.
KbSD proposes a self-distillation framework for agentic search. A teacher supplies hints as dense, token-level supervision to help calibrate decisions between using the model’s memory, retrieved evidence, or abstaining. The summary was published on June 30, 2026, and provides no experiment metrics or results.
The proposal may interest teams building retrieval agents who want to evaluate when to search or rely on model knowledge. To verify its scope and findings, consult the original publication and check whether it provides experimental details beyond those in this summary; the proposal alone is not evidence of performance.