claim

active

claim:shot-midpoints-follow-k50-dr-d-higher-cohesion-and-lower-mismatch-yield-fewer-required-examples

Shot midpoints follow k50 ∝ dr/ρd; higher cohesion and lower mismatch yield fewer required examples

Core quantitative prediction of UCCT validated by E2 threshold ordering

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 (2)

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
Dense but off-task anchors yield high ρd AND high dr; behavior does not improve, consistent with mismatch dominating S
supports
E3 negative control validating that both ρd AND dr must be favorable for S to exceed Sc

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) and transition widths correlate with mismatch D(P0∥PT)hypothesis0.852
Testable prediction for Experiment 2
Shot midpoint ordering k50(B10) < k50(B8) ≈ k50(B9) tracks pretraining exposure densityclaim0.842
Interpretation that pattern density from pretraining determines few-shot requirements
Hypothesis: Shot midpoint ordering k50(B10) < k50(B8) ≈ k50(B9) follows pretraining exposure densityhypothesis0.830
E2 prediction that bases with higher pretraining exposure require fewer shots to cross threshold
The ordering of few-shot thresholds k50 and transition widths aligns with k50 ∝ dr/ρd.claim0.823
Interpretation of E2 results.
shot midpoint k50concept0.816
Number of in-context exemplars to reach 50% accuracy in E2.
S = ρd - dr - log k predicts shot midpoints across different bases, tasks, and modelsclaim0.803
Predictive practical utility claim.
Fine-tuning reduces mismatch dr, retrieval increases effective cohesion ρd, and few-shot adjusts the budget kclaim0.788
Unified interpretation of different adaptation methods via UCCT terms
Hypothesis: Peak alignment location S_max and normalized trajectory area AUS_N predict shot midpoints θ50hypothesis0.778
E3 prediction that internal geometry provides a bridge to behavioral thresholds