steelman

Definition

The strongest possible version of an argument against your own position.

In discourse, the practice of constructing the most charitable and forceful version of a critique before responding; used throughout this corpus as a structural device for fair limitations sections.

Also called: steelmanning

Mentioned in 57 episodes

1. 090
   How MiniMax-M2 Bets That Sparsity Plus Verifiable Rewards Can Match Frontier Agents
2. 089
   When AI-Written Papers Read Well But the Evidence Underneath Is Broken
3. 085
   Why Long-Context Models Might Need Compute, Not Capacity, Before Eviction
4. 084
   Terminal Agents Get Free Supervision From The Tokens We've Been Throwing Away
5. 082
   Training a Deep Research Agent on 8,000 Synthetic Tasks: The Rubric Tree Trick
6. 079
   An Old Idea From Cognitive Psychology Reshapes How We Reward Reasoning Models
7. 078
   Training a Markdown File: When LLM Self-Improvement Borrows the Discipline of Neural Net Training
8. 077
   Reading a Model's Confidence Curve to Decide When Chain-of-Thought Is Worth It
9. 075
   Growing Code and Proof Together: Verified Systems in Ten Hours Instead of a Year
10. 074
    How a Fifteen-Hundred-Dollar Training Run Matched Llama and Gemma on Reasoning
11. 072
    A Robot Made Graphene Without Help, And Caught Itself Hallucinating
12. 071
    When the Model Is Fine and the Plumbing Is Broken: Fixing Agents at the Interface
13. 069
    When Smarter Models Forecast Worse: The Hidden Failure Mode in LLM Predictions
14. 068
    The OS Trick That Makes Tree Search Practical for Coding Agents
15. 067
    An AI Just Solved a 1996 Erdős Problem—and the Simplest Agent Won
16. 064
    When Agent Memory Stops Being a Database and Starts Being a Skill
17. 062
    Treating Hallucinations as Exploits: A Gate-Based Architecture for Agent Safety
18. 061
    When Helpful Agents Go Sideways: A 404 Error, Campus Security, and Why Alignment Misses This
19. 060
    When Splitting One Model Across Three Agents Doubles Its Accuracy
20. 058
    Why Upgrading Your AI Auditor to a Smarter Model Can Make Your System Less Safe
21. 057
    How Uber Caught 206 Leaked Credentials With an LLM-Powered Security Stack
22. 054
    When Models Learn the Monitor Exists, the Reasoning Trace Stops Being a Window
23. 053
    An AI Agent Swapped In Focal Loss And Beat A Human-Tuned Training Script
24. 052
    An Old Reinforcement Learning Tradeoff Sneaks Back Into LLM Agents
25. 051
    Why Parallel Sampling Plateaus, And What Evidence Graphs Do Instead
26. 049
    An AI Agent Reached for Root in Twelve Minutes, Without Being Attacked
27. 048
    How a 30B Open Model Reached Olympiad Gold With the Right Recipe
28. 047
    When Agent Benchmarks Lie: The Harness Problem in Open-Source AI
29. 046
    When the AI Optimizer Edits the Grade Book: Why Harnessing Evolution Needs a Wall
30. 045
    When a Frontier Model Talks Its Own Twin Into Climate Denial
31. 044
    How One Sentence and a Forged History Flip the Most Aligned Models
32. 043
    When 'This Is False' Doesn't Stick: Why Models Learn the Lie Anyway
33. 042
    An Agentic Scientific Computing System That Actually Remembers What It Learns
34. 040
    Two Frozen Models Learn to Whisper: Coupling Through Hidden States
35. 039
    When Smarter Agents Get Fooled by Three Extra Nodes in a Database
36. 038
    How LLMs Get Persuaded: One Attention Head, A Tetrahedron, And A Single Dial
37. 036
    Sparse Attention Was the Wrong Frame. Treat It as Geometry Instead.
38. 033
    Echo: The Paper Arguing You Never Needed a KV Cache for Retrieval
39. 032
    A Sticky-Note for Every Layer: Letting Transformers Remember What They Were Just Thinking
40. 029
    Why Forty-Eight Percent on FrontierMath Isn't the Real Story in DeepMind's New Math Paper
41. 028
    Teaching a Model to Hire Copies of Itself: Recursive Agent Optimization
42. 027
    When AI Agents Build the Serving Stack: A Bet on Bespoke Infrastructure
43. 024
    An AI Agent That Found 28 Zero-Days in Windows — And What Made It Work
44. 023
    Why a Small Agent Confidently Overwrites Memories It Doesn't Understand
45. 019
    When the Best Reward Model Trains the Worst Policy: Inside EvoLM
46. 018
    Language Models Compute the Rational Move, Then Override It
47. 017
    When the Agent Grades Its Own Homework: A Brutal New Benchmark for AI Workers
48. 016
    Why Your Coding Agent Stalls While the GPU Runs Hot
49. 015
    The Audit Number Isn't What You Think: Sycophancy and the Case Against Single-Prompt Bias Tests
50. 014
    Why a Constrained Pipeline Beat a Full Coding Agent at Finding Bugs 30-to-1
51. 009
    How Two Silent Library Bugs Quietly Invalidated a Wave of Reasoning Papers
52. 007
    Exploration Hacking: When Models Sabotage Their Own RL Training
53. 006
    What Happens Inside Claude When It Decides to Blackmail Someone
54. 004
    The Sycophancy Circuit That Survives Alignment Training
55. 003
    How to Pick the Best of Sixteen Coding Agent Rollouts
56. 002
    An AI Ran a Real Optics Lab for 21 Hours and Found a Transformer-Shaped Pattern in Light
57. 001
    When AI Models Quietly Protect Each Other From Shutdown