claim

active

claim:higher-density-priors-b10-are-more-robust-to-fine-tuning-than-lower-density-ones-b9

Higher-density priors (B10) are more robust to fine-tuning than lower-density ones (B9).

Interpretation of cross-base transfer asymmetry.

Source paper

extracted_from

The Guanyin Protocol: A Framework for Immediately Establishing an Understanding of Both Causality and Compassion in LLM Systems Using Semantic Anchoring

(2025) · Edward Yi Chang · Kaya, Zeyneb N. · Ethan Chang

Neighborhood — ranked by edge-count

Findings (1)

finding

Higher-density priors (B10) are more robust to cross-base OOD drops than lower-density ones (B9) after fine-tuning
restates
E2 asymmetric transfer finding consistent with UCCT's mismatch-driven OOD fragility

Communities (3)

community

Few-shot anchoring & latent structure
members_of
How minimal examples disambiguate and recruit latent arithmetic/reasoning interpretations in LLMs
Model base robustness and transfer learning asymmetries
members_of
Investigates how neural network bases (B10 vs B9) differ in fine-tuning stability and cross-base transfer, with density as a key predictor of robustness.
Cross-base fine-tuning transfer asymmetry
members_of
Empirical finding that base-10 representations transfer more robustly out-of-distribution than base-9 after fine-tuning.

Concepts (1)

concept

cross-base interference
associated_with
Asymmetric transfer after fine-tuning: high-density bases (B10) are more robust.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Hypothesis: Fine-tuning reduces mismatch dr between prior and targethypothesis0.791
UCCT's theoretical prediction about how fine-tuning maps onto the anchoring score
Strong priors require higher-cohesion anchors to overcome, manifesting as delayed thresholds or reduced transferhypothesis0.775
Prediction for Experiment 1 cross-domain anchoring
Cross-base fine-tuning yields asymmetric transfer: B10 transfers most robustly, B9 leastfinding0.774
In-base gains accompanied by uneven OOD drops; higher-density priors more robust.
Hypothesis: Shot midpoint ordering k50(B10) < k50(B8) ≈ k50(B9) follows pretraining exposure densityhypothesis0.763
E2 prediction that bases with higher pretraining exposure require fewer shots to cross threshold
Fine-tuning reduces mismatch dr, retrieval increases effective cohesion ρd, and few-shot adjusts the budget kclaim0.759
Unified interpretation of different adaptation methods via UCCT terms
Such priors suppress the inherent contextuality of the boundary.claim0.754
Explains why the boundary appears fixed: the prior hides its context-dependent nature.
Do pattern density ρd and prior-target distance dr serve as predictive correlates of few-shot thresholds?question0.754
First E2 research question directly testing UCCT's core predictive claims
Fine-tuning reduces dr; retrieval increases effective ρd; few-shot k trades budget against bothhypothesis0.753
UCCT's unified view of adaptation methods

Restated by (1)

cosine ≥ 0.90

Other entities that say roughly the same thing. May be merge candidates or independent restatements across papers.

finding
Higher-density priors (B10) are more robust to cross-base OOD drops than lower-density ones (B9) after fine-tuning