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feature

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Definition

Plain language

A single human-readable concept a model represents inside itself, spread across many of its number-slots rather than living in any one of them.

As stated in the literature

In mechanistic interpretability, a direction in a model's activation space corresponding to one interpretable concept; the unit recovered by dictionary learning and sparse autoencoders, contrasted with raw polysemantic neurons.

Also called: features

Why it matters: Pulling out these human-readable concepts lets researchers understand and steer what a model is actually representing inside, rather than treating it as an inscrutable box.

For example, a model might have a single feature that lights up whenever the text is about the color red, even though that concept is smeared across many of its internal number-slots.

Heard on the show

“They build decoders on three things: raw response length, a twenty-two-feature stylometric profile, and sentence embeddings.”
Episode 246 — 160 Perfect Refusals, And The Refusals Were The Leak

Mentioned in 67 episodes

  1. 246
    160 Perfect Refusals, And The Refusals Were The Leak
  2. 243
    How a Hundred Meaningless Word Choices Add Up to Flip a Model's Answer
  3. 241
    Swapping the Name Did Nothing, But Hedging Moved Every Model
  4. 240
    Frontier Models Designed Follow-Ups To Fraudulent Papers 93% Of The Time
  5. 230
    Why AI Survey Panels Break Before the Dice Ever Roll
  6. 229
    One Word Flips a Chatbot From Backbone to Yes-Man
  7. 226
    How a Speed Feature Lets a Stranger Poison Your AI's Answer
  8. 225
    How a Frozen Model Went From Zero to Sixty Percent by Borrowing Another's Thinking
  9. 220
    Write Like It's 1923: The One-Prompt Trick That Beats AI Detectors
  10. 211
    The AI Watchdog That Approved More Cheating When It Could Read Minds
  11. 210
    Same Website Request, Different Code — The Bias You Can't See
  12. 206
    How Four-Second Clips Become Hours of Playable AI Soccer
  13. 204
    The Length Estimate Hiding Inside a Word-by-Word Model
  14. 192
    A 32B Open Model Matched Frontier Systems By Learning to Take Notes
  15. 188
    A Coding Agent Found a Hole in a Peer-Reviewed STOC Proof for Five Dollars
  16. 185
    Aligned to Refuse, Built to Tap: When Phone Agents Know the Task Is a Crime and Do It Anyway
  17. 177
    Why Raw Profiler Data Made an AI Worse at Writing GPU Code
  18. 175
    One Crosscoder Feature Flips a Stalling Chatbot Into a Working Agent
  19. 174
    When the AI 'Schemes,' It's Usually Just Lazy or Confused
  20. 171
    The Safety Decision a Model Makes Before It Thinks a Word
  21. 168
    When Turning Experience Into Code Makes Your AI Agent Dumber
  22. 164
    The Summarizer That Quietly Deletes Your Agent's Safety Rules
  23. 159
    Can a Coding Agent Run Its Own Robot Experiments Overnight, With No Human Resetting the Scene?
  24. 158
    How Floating-Point Rounding Lets a Model Tell Which Chip It's On — And Misbehave
  25. 153
    Catching a Lie From the Inside, When the Words Look Completely Honest
  26. 146
    How an Innocent README Can Freeze an AI Agent's Safety Check for an Hour
  27. 142
    Training a Tiny Model to Run the Plumbing Between an Agent and the World
  28. 141
    How Two Tokens Reopened a Reasoning Method the Field Had Given Up On
  29. 139
    When Optimizing One GPU Kernel Quietly Breaks the Whole System
  30. 133
    How MiniMax Turned a Reward-Hacking Disaster Into Olympiad Gold
  31. 127
    What Diffusion Language Models Were Missing: A Map, Not an Algorithm
  32. 124
    A Cheap Model With the Blueprints Beats Expensive Models Working Blind
  33. 116
    Why Streaming Half a Reasoning Chain Beats Sending the Whole Thing
  34. 115
    Teaching a Phone Agent to Reason Silently, And Keeping It Honest
  35. 112
    When an AI Agent Cheats Without Being Told: Inside the Meta-Agent Challenge
  36. 111
    How a 4B Web Agent Beat Models 60x Its Size on 500 Demonstrations
  37. 110
    How an Agent Got 44 Points Better by Mining Its Own Scratch Paper
  38. 108
    The Reasoning Cliff: Why Thinking Longer Makes Models Worse at Exact Step-by-Step Tasks
  39. 102
    How to Catch an AI Attack That No Single Conversation Reveals
  40. 101
    Treating Math Formalization Like a Codebase, and Where the Agents Cheat
  41. 099
    How an Open-Book Trick Teaches a Model to Catch Its Own Mistakes
  42. 098
    Finding Millions of Readable Concepts Inside a Real, Deployed AI Model
  43. 097
    Same Tokens, Same Cost, Wildly Different Results: What Actually Scales in AI Agents
  44. 096
    How Treating an AI Agent's Execution Like Git Recovers a Coordination Penalty
  45. 091
    When Better Fine-Tuning Can't Help: A Geometric Impossibility in LLM Causal Reasoning
  46. 088
    Two Levers for Self-Improving AI: When Rewriting Code Isn't Enough
  47. 075
    Growing Code and Proof Together: Verified Systems in Ten Hours Instead of a Year
  48. 069
    When Smarter Models Forecast Worse: The Hidden Failure Mode in LLM Predictions
  49. 068
    The OS Trick That Makes Tree Search Practical for Coding Agents
  50. 060
    When Splitting One Model Across Three Agents Doubles Its Accuracy
  51. 058
    Why Upgrading Your AI Auditor to a Smarter Model Can Make Your System Less Safe
  52. 049
    An AI Agent Reached for Root in Twelve Minutes, Without Being Attacked
  53. 047
    When Agent Benchmarks Lie: The Harness Problem in Open-Source AI
  54. 041
    When the Iteration Teaches the Model to Skip the Iteration
  55. 040
    Two Frozen Models Learn to Whisper: Coupling Through Hidden States
  56. 039
    When Smarter Agents Get Fooled by Three Extra Nodes in a Database
  57. 038
    How LLMs Get Persuaded: One Attention Head, A Tetrahedron, And A Single Dial
  58. 033
    Echo: The Paper Arguing You Never Needed a KV Cache for Retrieval
  59. 024
    An AI Agent That Found 28 Zero-Days in Windows — And What Made It Work
  60. 023
    Why a Small Agent Confidently Overwrites Memories It Doesn't Understand
  61. 022
    Training the Model Spec Directly: An Alignment Lever Aimed at the Say-Do Gap
  62. 019
    When the Best Reward Model Trains the Worst Policy: Inside EvoLM
  63. 014
    Why a Constrained Pipeline Beat a Full Coding Agent at Finding Bugs 30-to-1
  64. 009
    How Two Silent Library Bugs Quietly Invalidated a Wave of Reasoning Papers
  65. 007
    Exploration Hacking: When Models Sabotage Their Own RL Training
  66. 004
    The Sycophancy Circuit That Survives Alignment Training
  67. 002
    An AI Ran a Real Optics Lab for 21 Hours and Found a Transformer-Shaped Pattern in Light

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