hypothesis

active

hypothesis:hypothesis-shot-midpoint-ordering-k50-b10-k50-b8-k50-b9-follows-pretraining-exposure-density

Hypothesis: Shot midpoint ordering k50(B10) < k50(B8) ≈ k50(B9) follows pretraining exposure density

E2 prediction that bases with higher pretraining exposure require fewer shots to cross threshold

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

B10 shot midpoint k50 = 0.28 ± 0.05 shots with accuracy 94.8 ± 1.2%
associated_withsupports
Lowest threshold condition in E2; near-zero/one-shot threshold consistent with high pretraining density

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.

Shot midpoint ordering k50(B10) < k50(B8) ≈ k50(B9) tracks pretraining exposure densityclaim0.937
Interpretation that pattern density from pretraining determines few-shot requirements
Shot midpoint ordering k50(B10) < k50(B8) ≈ k50(B9) and transition widths correlate with mismatch D(P0∥PT)hypothesis0.895
Testable prediction for Experiment 2
k50 ordering: B10 (0.28) < B8 (1.83) < B9 (2.91) follows pretraining densityfinding0.851
Monotone ordering consistent with k50 ∝ dr/ρd.
Shot midpoints follow k50 ∝ dr/ρd; higher cohesion and lower mismatch yield fewer required examplesclaim0.830
Core quantitative prediction of UCCT validated by E2 threshold ordering
Hypothesis: Peak alignment location S_max and normalized trajectory area AUS_N predict shot midpoints θ50hypothesis0.822
E3 prediction that internal geometry provides a bridge to behavioral thresholds
The ordering of few-shot thresholds k50 and transition widths aligns with k50 ∝ dr/ρd.claim0.810
Interpretation of E2 results.
S = ρd - dr - log k predicts shot midpoints across different bases, tasks, and modelsclaim0.791
Predictive practical utility claim.
Higher-density priors (B10) are more robust to fine-tuning than lower-density ones (B9).claim0.763
Interpretation of cross-base transfer asymmetry.