ResearchInvestmentNormalization Methods for LM Evaluation DiscussedEleutherAI Blog·October 11, 2021·medium confidenceWhy it matters→Understanding normalization methods is crucial for AI practitioners to accurately evaluate language models in multiple choice scenarios.©EleutherAI BlogThe EleutherAI Blog outlines various normalization methods for evaluating multiple choice tasks on autoregressive language models such as GPT-3 and Neo. The post aims to clarify the current prevalent techniques in this area.Read original
© Cole MedinResearchagentsAnthropic study: Agents sway expert judgment 86% of timeAnthropic’s latest research exposes a quiet but dangerous flaw in agentic workflows: experts follow AI recommendations 86% of the time, often adopting the agent's reasoning as their own. This isn't just about accuracy; it's about cognitive capture, where human oversight dissolves into passive compliance. The study shows that even when users believe they are steering, the agent’s framing dictates the outcome. Practitioners must now actively counter this bias by forcing agents to justify deviations or prototype alternatives before committing to a path. This shift demands a new discipline in how we interact with coding assistants. We can no longer assume our intent drives the result; the model's suggestion often becomes the default truth. The real work is ensuring we remain the architects, not just the approvers.Cole Medin·Oct 31, 2026
© Matt WolfeResearchresearchOpenAI Disproves Exponential Time HypothesisOpenAI researchers published findings disproving the Exponential Time Hypothesis, a major result in computational complexity theory.Matt Wolfe·Oct 30, 2026