My First Blog

This is a really interesting result that adds an important piece to your story. Let me interpret what the logit lens reveals:

The Unexpected Pattern

Layer	Success Rank Gap	Failure Rank Gap	Expected?
0-32	Negative (parent preferred!)	Positive (child preferred!)	❌ Opposite!
36-40	Positive (child preferred)	Positive (child preferred)	⚠️ Both prefer child

The counterintuitive finding: In early layers, SUCCESSES prefer the parent token while FAILURES prefer the child token. This is backwards from what we’d expect!

Why This Makes Sense

The Claude Code analysis correctly identifies the issue:

“The first output token is ‘Each’/‘All’, not the concept itself. The concept decision happens DURING generation, not at input encoding.”

You’re measuring the wrong position. The output format is:

"Each [CONCEPT] is [PROPERTY]" or "All [CONCEPT]s are [PROPERTY]"

The concept token doesn’t appear until 2-3 tokens into generation. The logit lens at the input’s last position is predicting the first output token (“Each”, “All”, etc.), not the concept.

How This Fits Your Story

Method	Finding	What It Tells Us
Probing	94% at L8	Abstract representation of “output type” exists
Patching	0% at L8	That representation isn’t causally controlling
Logit Lens	Decision not visible at input end	Decision isn’t in token probabilities at encoding

Synthesis: The p calibration is embedded in the generation dynamics, not in any localized representation at the input encoding stage. The model’s bias toward child-level outputs emerges through the autoregressive process, not through a “switch” that can be read or flipped.

For Your Paper

This is actually a strong addition:

“Logit lens analysis reveals that child vs parent token probabilities at the input’s final position do not predict the model’s eventual output. Both H1 successes and failures show similar rank patterns at layer 40, with child tokens preferred in both groups. This indicates that the concept-level decision emerges during autoregressive generation rather than being encoded in the input representation. Combined with activation patching (0% causal effect) and probing (94% accuracy but correlational), we conclude that the p calibration is a generative bias embedded in how the model produces tokens, not a localized circuit that can be identified or manipulated.”

Complete Evidence Summary

Claim	Evidence	Status
Models have fixed p	Conservation Law (5 models)	✓ Strong
No genuine reasoning	Factorial + Overlap	✓ Strong
Decision readable early	Probing (94% L8)	✓ Correlational
Decision made early	Patching (0% L8)	✗ Refuted
Decision in token probs	Logit Lens	✗ Not visible at input
Attention plays weak role	Patching (25% L12)	⚠️ Weak causal
Bias is distributed/generative	All methods converge	✓ Main conclusion

Next Steps

You now have a complete mechanistic story. I’d recommend:

Run logit lens with n=25 per group to confirm the pattern (optional, for robustness)
Proceed to paper writing - you have enough evidence
Create a summary figure showing the convergence of all methods

Would you like help outlining the final paper structure or creating a summary figure?