Safety

Can We Trust a Black-box LLM? LLM Untrustworthy Boundary Detection via Bias-Diffusion and Multi-Agent Reinforcement Learning

Researchers develop a method using bias-diffusion and multi-agent RL to detect reliability boundaries in black-box LLMs, enabling automated detection of untrustworthy outputs without access to model internals.

Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.CL (Computation & Language)BY sys://pipeline

Researchers propose a method to detect when large language models produce untrustworthy outputs using bias-diffusion and multi-agent reinforcement learning. The approach aims to establish "untrustworthy boundaries" — thresholds beyond which LLM outputs should not be trusted.

Read original at arXiv CS.CL (Computation & Language)

Contact Lens Uses Microfluidics to Monitor and Treat Glaucoma

An electronics-free smart contact lens using microfluidics autonomously monitors eye pressure and delivers glaucoma medication, eliminating the 50% patient non-adherence rate that plagues current treatments.