Making Optimization Work When Labels Are Scarce [R]

Summary

https://www.gnosyslabs.com/case-studies/safety-classifier-sparse-labels Gnosys is an autonomous model engineer: it improves prompts and classifiers when ground truth is too sparse for conventional optimization. On ToxicChat, a public safety benchmark, under realistic label scarcity, it improved a classifier past both the team's starting point and GEPA (a standard prompt optimizer), across two runs of our current method. This note describes what we did, what we found, and where the method und

SpinGraph analysis pending — check back after processing.

• A system-level approach to prompt injection: separating instruction and data channels in LLM agents [P]
• How to describe a model that has higher accuracy with fewer #param and FLOPs? [D]
• ICML qr code visible [D]
• A map of the latest 11 million papers split by semantic similarity and time slices [P]
• [D] Simple Questions Thread
• ACL ARR May 2026[D]