Topic
cross-model
Dual-Agent Framework Translates Natural-Language Lab Protocols Into Robotic Execution
Researchers from an unnamed institution propose a dual-agent framework that translates natural-language microplate-based biological protocols into executable robotic commands. The system uses a Parser Agent and a Heterogeneous LLM Validation Agent with a self-correction loop, evaluated across 7 parsers and 3 validators on ELISA and Bradford assays.
LLM Confidence Is Epistemically Vacuous: New Method Detects Blind Spots in Clinical Data
A new study reveals that large language models (LLMs) fail to recognize their own knowledge limits on structured clinical data, outputting near-constant confidence scores regardless of accuracy. Researchers propose a cross-model calibrator using attribution divergence between LLM and XGBoost, reducing calibration error from 0.254 to 0.080 and improving accuracy from 49% to 75.3% without training.