claim

active

claim:layer-wise-geometry-summaries-sbmax-ausn-predict-internal-few-shot-thresholds-50

Layer-wise geometry summaries (Sbmax, AUSN) predict internal few-shot thresholds θ50

Claim that geometry-to-behavior correlates exist

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 (3)

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
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).

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.
Mid-layer representation geometry in neural networks
members_of
Studies how internal layer-wise geometric properties (anchoring, clustering trajectories, geometry summaries) peak in middle layers and predict downstream task performance across LLMs and shallow networks.

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.

Do layer-wise geometric signatures (τ_peak, AUS_N) correlate with behavioral thresholds (k50)?question0.831
E3 research question testing whether internal representations provide a geometry-to-behavior bridge
Geometry summaries (Sbmax, AUSN)method0.825
Peak anchoring (Sbmax) and normalized area under the S(ℓ) curve (AUSN) used to summarize trajectory.
Peak anchoring Sbmax and normalized area AUSN correlate with per-item success and internal shot midpoints θ50, providing a geometry-to-behavior bridge.claim0.822
Main interpretation of E3.
Layer-wise geometry shows early dip, mid-layer alignment, and late standardization across tasksclaim0.785
Qualitative pattern from E3.
AUS_N is a weaker correlate of θ50 than S_max across E3 backbonesfinding0.776
E3 finding distinguishing the two geometry summaries; breadth less predictive than peak height
The ordering of few-shot thresholds k50 and transition widths aligns with k50 ∝ dr/ρd.claim0.775
Interpretation of E2 results.
Few-shot thresholds and transition widths track ρd/dr at fixed computational complexityclaim0.766
E2 main interpretive claim.
Mid-layers (6-15) achieve peak anchoring because semantic structure differentiates while maintaining coherence, forming a Goldilocks zoneclaim0.766
Interpretation of E3 layer-wise results; motivates targeted UCCT interventions at layers 8-12