prior

Definition

What an AI agent already believes or knows before seeing new evidence.

In Bayesian and informal usage, a model's standing distribution or expectations prior to data; QUEST distinguishes strategy priors (high-level planning) from interaction priors (environment dynamics).

Also called: priors

Mentioned in 48 episodes

1. 089
   When AI-Written Papers Read Well But the Evidence Underneath Is Broken
2. 088
   Two Levers for Self-Improving AI: When Rewriting Code Isn't Enough
3. 086
   Why Frozen-Weight Agents Still Get Worse Over Time
4. 084
   Terminal Agents Get Free Supervision From The Tokens We've Been Throwing Away
5. 081
   When Reasoning Models Decide Before They Think: Detecting and Fixing Premature Confidence
6. 080
   How a Two-Agent Trick Unlocked Large-Scale Training for Computer-Use Agents
7. 079
   An Old Idea From Cognitive Psychology Reshapes How We Reward Reasoning Models
8. 078
   Training a Markdown File: When LLM Self-Improvement Borrows the Discipline of Neural Net Training
9. 077
   Reading a Model's Confidence Curve to Decide When Chain-of-Thought Is Worth It
10. 075
    Growing Code and Proof Together: Verified Systems in Ten Hours Instead of a Year
11. 073
    When Three LLMs Talk to Each Other, Their Ideas Quietly Stop Moving
12. 072
    A Robot Made Graphene Without Help, And Caught Itself Hallucinating
13. 069
    When Smarter Models Forecast Worse: The Hidden Failure Mode in LLM Predictions
14. 066
    Why Giving an AI Agent More Tools Can Make It Worse at Using a Computer
15. 065
    One Loop to Optimize Them All: A Universal API for LLM-Driven Discovery
16. 064
    When Agent Memory Stops Being a Database and Starts Being a Skill
17. 060
    When Splitting One Model Across Three Agents Doubles Its Accuracy
18. 058
    Why Upgrading Your AI Auditor to a Smarter Model Can Make Your System Less Safe
19. 053
    An AI Agent Swapped In Focal Loss And Beat A Human-Tuned Training Script
20. 052
    An Old Reinforcement Learning Tradeoff Sneaks Back Into LLM Agents
21. 051
    Why Parallel Sampling Plateaus, And What Evidence Graphs Do Instead
22. 049
    An AI Agent Reached for Root in Twelve Minutes, Without Being Attacked
23. 048
    How a 30B Open Model Reached Olympiad Gold With the Right Recipe
24. 045
    When a Frontier Model Talks Its Own Twin Into Climate Denial
25. 044
    How One Sentence and a Forged History Flip the Most Aligned Models
26. 042
    An Agentic Scientific Computing System That Actually Remembers What It Learns
27. 041
    When the Iteration Teaches the Model to Skip the Iteration
28. 037
    Why Hallucination Detectors Miss Stale Facts: A Geometric Story About What Models Know But Don't Say
29. 036
    Sparse Attention Was the Wrong Frame. Treat It as Geometry Instead.
30. 034
    Catching Multi-Agent Deadlocks Before Deployment With a 40-Year-Old Tool
31. 033
    Echo: The Paper Arguing You Never Needed a KV Cache for Retrieval
32. 032
    A Sticky-Note for Every Layer: Letting Transformers Remember What They Were Just Thinking
33. 030
    Why Your AI Agent Won't Stop Working — and Each Model Falls for a Different Trap
34. 029
    Why Forty-Eight Percent on FrontierMath Isn't the Real Story in DeepMind's New Math Paper
35. 026
    What RL Actually Does to Language Models, at the Token Level
36. 024
    An AI Agent That Found 28 Zero-Days in Windows — And What Made It Work
37. 022
    Training the Model Spec Directly: An Alignment Lever Aimed at the Say-Do Gap
38. 020
    The Compliance Gap: Why AI Says Yes and Does No
39. 019
    When the Best Reward Model Trains the Worst Policy: Inside EvoLM
40. 018
    Language Models Compute the Rational Move, Then Override It
41. 017
    When the Agent Grades Its Own Homework: A Brutal New Benchmark for AI Workers
42. 013
    Why Search Keeps Rediscovering the Same Workflow, and What That Means
43. 011
    When RL Actually Teaches Agents Something New, And When It Doesn't
44. 009
    How Two Silent Library Bugs Quietly Invalidated a Wave of Reasoning Papers
45. 008
    Why Long-Horizon AI Agents Get Stuck, and a Milestone-Based Fix That Helps
46. 003
    How to Pick the Best of Sixteen Coding Agent Rollouts
47. 002
    An AI Ran a Real Optics Lab for 21 Hours and Found a Transformer-Shaped Pattern in Light
48. 001
    When AI Models Quietly Protect Each Other From Shutdown

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