Signals 4 · free daily AI digest

Stress-Testing Efficient Responsible-AI Evaluation: When Compute Savings Change Benchmark Conclusions

Paper recorded by Signals 4 on 2026-08-31 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-31 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.LG · 机器学习 · first seen 2026-09-01

Abstract

Efficient evaluation changes the protocol used to support claims about model behavior, yet it is rarely tested whether those claims remain stable after the evaluation itself is made cheaper. We stress-test conclusion robustness in responsible-AI benchmarking by evaluating three dense and mixture-of-experts models on BBQ and BBQ-V under seven conditions spanning batching, quantization, benchmark re

Read on arXiv →

#173 most recent of 215 cs.LG papers we have recorded · ↑ newer: "Train classical, deploy quantum" requires rethinking generalization · ↓ older: One Adapter, Many Tasks: Task-Conditioned Feature Transformations for
Cite this page: Stress-Testing Efficient Responsible-AI Evaluation: When Compute Savings Change Benchmark Conclusions: the #173 most recent of 215 cs.LG papers we have recorded (as of 2026-08-31). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/stress-testing-efficient-responsible-ai-evaluation-when-compute-savings-change-b.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.LG papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions