recall

Definition

Plain language

Of the things a system should have caught, how many it actually caught.

Why it matters: It measures how much a system misses, which is critical for safety monitors where letting a real attack slip through is costly.

For example, if there were 100 spam emails and the filter caught 80 of them, its recall is 80%.

Heard on the show

“You have to know something — the ideal gas law, an acid constant — and you have to do something with it: recall, substitute, compute.”

Mentioned in 29 episodes

1. 197
   Twin Problems Suggest AI Reasoning Gains Are Mostly Better Fact Recall
2. 192
   A 32B Open Model Matched Frontier Systems By Learning to Take Notes
3. 191
   How One Researcher Beat GPT-5.2 and Gemini 3 by Judging Their Answers, Not Improving Them
4. 187
   An 8-Billion Agent That Beats Models 80 Times Its Size By Looking Things Up
5. 186
   How a Frozen Model Went From 2% to 77% on Physics Puzzles — Without Retraining
6. 183
   Why You Can't Fine-Tune Foresight Into an AI Agent
7. 180
   The Bug Where Smart Assistants Read a Fact and Still Forget It
8. 169
   Why Better Bug Reports Can Make AI Coding Agents Worse
9. 145
   Building Forgetting Into a Language Model With One Extra Line of Code
10. 118
    Why the Best-Aligned AI Models Are the Easiest to Trick Into Producing Harm
11. 114
    Agents That Rewrite Their Own Weights Instead of Just Taking Notes
12. 108
    The Reasoning Cliff: Why Thinking Longer Makes Models Worse at Exact Step-by-Step Tasks
13. 102
    How to Catch an AI Attack That No Single Conversation Reveals
14. 100
    How a Prompt Wrapper Lets a Frontier Model Play Poker Like an Expert
15. 089
    When AI-Written Papers Read Well But the Evidence Underneath Is Broken
16. 086
    Why Frozen-Weight Agents Still Get Worse Over Time
17. 085
    Why Long-Context Models Might Need Compute, Not Capacity, Before Eviction
18. 077
    Reading a Model's Confidence Curve to Decide When Chain-of-Thought Is Worth It
19. 074
    How a Fifteen-Hundred-Dollar Training Run Matched Llama and Gemma on Reasoning
20. 062
    Treating Hallucinations as Exploits: A Gate-Based Architecture for Agent Safety
21. 057
    How Uber Caught 206 Leaked Credentials With an LLM-Powered Security Stack
22. 040
    Two Frozen Models Learn to Whisper: Coupling Through Hidden States
23. 038
    How LLMs Get Persuaded: One Attention Head, A Tetrahedron, And A Single Dial
24. 037
    Why Hallucination Detectors Miss Stale Facts: A Geometric Story About What Models Know But Don't Say
25. 036
    Sparse Attention Was the Wrong Frame. Treat It as Geometry Instead.
26. 033
    Echo: The Paper Arguing You Never Needed a KV Cache for Retrieval
27. 032
    A Sticky-Note for Every Layer: Letting Transformers Remember What They Were Just Thinking
28. 031
    When Your AI Assistant Won't Let Go of Old Facts About You
29. 023
    Why a Small Agent Confidently Overwrites Memories It Doesn't Understand

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