hypothesis

active

hypothesis:hypothesis-peak-alignment-location-s-max-and-normalized-trajectory-area-aus-n-predict-shot-midpoints-50

Hypothesis: Peak alignment location S_max and normalized trajectory area AUS_N predict shot midpoints θ50

E3 prediction that internal geometry provides a bridge to behavioral thresholds

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

Papers (1)

paper

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

Findings (3)

finding

Larger S_max correlates with smaller θ50 across backbones in E3 (negative association consistent across pooling and metric choices)
associated_withsupports
Key geometry-to-behavior bridge finding in E3; robust to pooling choice, cosine vs. L2, and frozen external encoder
LLaMA E3 geometry summary: S_max = −1.896 ± 0.211, AUS_N = −2.119 ± 0.198, peak layer ℓ* = 10 [IQR 0.384]
supports
Seed-pooled geometry statistics for LLaMA in E3, providing quantitative basis for geometry-to-behavior correlate
Correlation between layer-wise scores and task accuracy ρ = −0.73 (p < 0.001) on LLaMA
supports
Core E3 finding validating S as a predictor of anchoring effectiveness

Artifacts (1)

artifact

Semantic Anchoring in LLMs: Thresholds, Transfer, and Geometric Correlates
introduces
Main paper presenting UCCT and semantic anchoring framework.

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.

Peak anchoring Sbmax and normalized area AUSN correlate with per-item success and internal shot midpoints θ50, providing a geometry-to-behavior bridge.claim0.826
Main interpretation of E3.
Hypothesis: Shot midpoint ordering k50(B10) < k50(B8) ≈ k50(B9) follows pretraining exposure densityhypothesis0.822
E2 prediction that bases with higher pretraining exposure require fewer shots to cross threshold
S = ρd - dr - log k predicts shot midpoints across different bases, tasks, and modelsclaim0.796
Predictive practical utility claim.
Shot midpoint ordering k50(B10) < k50(B8) ≈ k50(B9) tracks pretraining exposure densityclaim0.783
Interpretation that pattern density from pretraining determines few-shot requirements
Normalized Area Under Anchoring Trajectory (AUS_N)concept0.783
Mean of S(ℓ) across layers; weaker geometry correlate with θ50
Shot midpoints follow k50 ∝ dr/ρd; higher cohesion and lower mismatch yield fewer required examplesclaim0.778
Core quantitative prediction of UCCT validated by E2 threshold ordering
Shot midpoint ordering k50(B10) < k50(B8) ≈ k50(B9) and transition widths correlate with mismatch D(P0∥PT)hypothesis0.776
Testable prediction for Experiment 2
Layer-wise geometry summaries (Sbmax, AUSN) predict internal few-shot thresholds θ50claim0.759
Claim that geometry-to-behavior correlates exist