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Steering Geometry: Validating Human Value Geometry in LLM Steering Space

As large language models (LLMs) are increasingly deployed in alignment-sensitive contexts, activation steering has emerged as a lightweight, inference-time alternative to fine-tuning methods (e.g., RLHF, DPO) for behavioral control. However, existing work typically validates steering on isolated behaviors, leaving it unclear whether steering vectors encode coherent semantic structure or merely…

We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.

Read the original at arxiv.org →

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Backtest 63.7%, production AUC 0.51 - a postmortem

Originally published at nbaproplab.com/learn/model-postmortem . A model that looks excellent offline and random in production is usually blamed on the serving code.

  • Model achieved 63.7% hit rate and 0.56 AUC in offline testing
  • Performance degraded to coin flip (AUC of 0.51) in production
  • Missing calibration snapshot caused train-serve mismatch

Give Your Coding Agent Eyes

Every coding-agent session starts blind: your agent rediscovers how your features work, and sometimes guesses wrong. AGENTS.md is the usual fix, but it loads every fact into every session — too thin…

  • Coding agents begin sessions blind, needing feature rediscovery.
  • AGENTS.md file loads relevant info, but can be sparse or heavy.
  • Catalog system improves efficiency by storing feature notes.

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