finding

active

finding:meta-llama-3-1-8b-instruct-shows-optimal-anchoring-at-layer-9-s-1-90-median-peak-layer-l-10-iqr-0-384

Meta-LLaMA-3.1-8B-Instruct shows optimal anchoring at layer 9 (S ≈ −1.90, median peak layer ℓ* = 10 [IQR 0.384])

E3 result establishing the Goldilocks zone at mid-layers for LLaMA architecture

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

Claims (4)

claim

Peak anchoring Sbmax and normalized area AUSN correlate with per-item success and internal shot midpoints θ50, providing a geometry-to-behavior bridge.
supports
Main interpretation of E3.
Layer-wise geometry summaries (Sbmax, AUSN) predict internal few-shot thresholds θ50
supports
Claim that geometry-to-behavior correlates exist
Layer-wise anchoring peaks in a 'Goldilocks zone' between early and late layers.
supports
Qualitative characterization of optimal anchoring depth.
Mid-layers (6-15) achieve peak anchoring because semantic structure differentiates while maintaining coherence, forming a Goldilocks zone
supports
Interpretation of E3 layer-wise results; motivates targeted UCCT interventions at layers 8-12

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.

Concepts (2)

concept

Meta-Llama-3.1-8B-Instruct
associated_with
Backbone model used in E3 geometry analysis.
Three-Stage Layer Trajectory
supports
Empirically observed pattern in E3: early enrichment (ρd dips), mid-layer alignment (dr falls), late standardization (re-clustering)

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.

Math and code tasks show strongest mid-layer anchoring on LLaMA (S ≈ −1.65 at layers 8-12)finding0.890
Task-specific E3 finding showing compositional reasoning requires deeper processing
Peak Layer-wise Anchoring Score (S_max)concept0.798
Maximum of S(ℓ) across layers; geometry summary used to predict θ50
Peak layer ℓ* median 10, IQR 0.384finding0.798
Median layer where S(ℓ) peaks, across seeds.
LLaMA-3.1-8B: Sbmax = -1.896 ± 0.211, AUSN = -2.119 ± 0.198, peak layer ℓ* = 10 (median)finding0.795
Seed-pooled geometry-only statistics (per-dev z units).
Layer 24 (indexed at 8) of LLaMA3.1-8B on Hinting satisfies Criteria 1 and 2 under both IIT 3.0 and IIT 4.0 (temporal permutation).finding0.794
One of the most promising cases; approximately corresponds to the 2/3 layer of LLaMA3.1-8B.
The case at approximately the 2/3 layer of LLaMA3.1-8B (Layer 24, satisfying Criteria 1 and 2) aligns with prior studies showing the 2/3 layer optimally predicts human brain activity.finding0.791
Connects this study's results to Schrimpf et al. 2021 and Caucheteux et al. 2022/2023 findings on brain-LLM alignment.
Optimal activation capping layers for Llama 3.3 70B are layers 56-71 (out of 80) at 25th percentile capfinding0.789
Specific implementation finding for Llama capping parameters
LLaMA E3 geometry summary: S_max = −1.896 ± 0.211, AUS_N = −2.119 ± 0.198, peak layer ℓ* = 10 [IQR 0.384]finding0.775
Seed-pooled geometry statistics for LLaMA in E3, providing quantitative basis for geometry-to-behavior correlate