claim

active

claim:peak-anchoring-sbmax-and-normalized-area-ausn-correlate-with-per-item-success-and-internal-shot-midpoints-50-providing-a-geometry-to-behavior-bridge

Peak anchoring Sbmax and normalized area AUSN correlate with per-item success and internal shot midpoints θ50, providing a geometry-to-behavior bridge.

Main interpretation of E3.

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 (7)

finding

Meta-LLaMA-3.1-8B-Instruct shows optimal anchoring at layer 9 (S ≈ −1.90, median peak layer ℓ* = 10 [IQR 0.384])
supports
E3 result establishing the Goldilocks zone at mid-layers for LLaMA architecture
Correlation between layer-wise S scores and task accuracy: ρ = -0.73, p < 0.001
supports
Shows S predicts anchoring effectiveness.
Math and code tasks show strongest mid-layer anchoring on LLaMA (S ≈ −1.65 at layers 8-12)
supports
Task-specific E3 finding showing compositional reasoning requires deeper processing
LLaMA-3.1-8B: Sbmax = -1.896 ± 0.211, AUSN = -2.119 ± 0.198, peak layer ℓ* = 10 (median)
supports
Seed-pooled geometry-only statistics (per-dev z units).
Larger Sbmax associated with smaller θ50 in E3 sweep
supports
Geometry-to-behavior correlate within E3.
AUSN mean -2.119 ± 0.198
supports
Normalized area under S(ℓ) averaged over seeds.
Sbmax mean -1.896 ± 0.211
supports
Geometry summary peak anchoring score averaged over seeds.

Communities (3)

community

Few-shot anchoring & latent structure
members_of
How minimal examples disambiguate and recruit latent arithmetic/reasoning interpretations in LLMs
Layer-wise geometry predicting few-shot learning
members_of
Silhouette-based metrics (Sbmax, AUSN) across LLM layers predict task accuracy and few-shot thresholds.
Neural activation geometry and behavioral prediction
members_of
Quantifies relationships between layer-wise activation statistics (Sbmax, AUSN) and task performance metrics in LLMs, bridging internal representation geometry to behavioral outcomes.

Artifacts (1)

artifact

Semantic Anchoring in LLMs: Thresholds, Transfer, and Geometric Correlates
supports
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.

Hypothesis: Peak alignment location S_max and normalized trajectory area AUS_N predict shot midpoints θ50hypothesis0.826
E3 prediction that internal geometry provides a bridge to behavioral thresholds
Layer-wise geometry summaries (Sbmax, AUSN) predict internal few-shot thresholds θ50claim0.822
Claim that geometry-to-behavior correlates exist
peak anchoring Sbmaxconcept0.800
Maximum layer-wise anchoring score across layers.
AUS_N is a weaker correlate of θ50 than S_max across E3 backbonesfinding0.793
E3 finding distinguishing the two geometry summaries; breadth less predictive than peak height
Normalized Area Under Anchoring Trajectory (AUS_N)concept0.781
Mean of S(ℓ) across layers; weaker geometry correlate with θ50
Larger S_max correlates with smaller θ50 across backbones in E3 (negative association consistent across pooling and metric choices)finding0.774
Key geometry-to-behavior bridge finding in E3; robust to pooling choice, cosine vs. L2, and frozen external encoder
Mid-layers (6-15) achieve peak anchoring because semantic structure differentiates while maintaining coherence, forming a Goldilocks zoneclaim0.770
Interpretation of E3 layer-wise results; motivates targeted UCCT interventions at layers 8-12
Do layer-wise geometric signatures (τ_peak, AUS_N) correlate with behavioral thresholds (k50)?question0.768
E3 research question testing whether internal representations provide a geometry-to-behavior bridge