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ERM Fixes Causal Rung Collapse in LLMs
#research#llms#v1#causal-reasoning#ermllms
β‘ 30-Second TL;DR
What Changed
Formalizes rung collapse as lack of gradient for P(Y|do(X)) vs P(Y|X)
Why It Matters
Addresses core reasoning flaws in LLMs, enabling better generalization and steerability. Could prevent entrenchment in production models, improving reliability across domains. Inverse scaling in steerability highlights need for targeted causal fixes.
What To Do Next
Prioritize whether this update affects your current workflow this week.
Who should care:Researchers & Academics
Key Points
- β’Formalizes rung collapse as lack of gradient for P(Y|do(X)) vs P(Y|X)
- β’Introduces ERM with Physical Grounding Theorem and AGM postulates
- β’Demonstrates 3.7% persistence in reasoning-enhanced models, ERM boosts recovery
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Original source: ArXiv AI β
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