claim

active

claim:layer-wise-trajectories-show-early-enrichment-mid-layer-alignment-and-late-re-clustering

Layer-wise trajectories show early enrichment, mid-layer alignment, and late re-clustering.

Qualitative geometry pattern.

Source paper

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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

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
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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.

Layer-wise geometry shows early dip, mid-layer alignment, and late standardization across tasksclaim0.838
Qualitative pattern from E3.
Layer-wise anchoring peaks in a 'Goldilocks zone' between early and late layers.claim0.787
Qualitative characterization of optimal anchoring depth.
Single-layer analyses can be misleading because early-layer truth directions may reflect surface features with limited cross-task generalization.claim0.786
Methodological critique of prior work that fixed a single layer for truth probing.
layer-wise trajectory analysismethod0.781
Computing per-layer S(ℓ) to summarize geometry.
For simple factual tasks F0-F3, probe directions show a sharp geometric transition in middle layers, with late-layer probes converging to high cosine similarity; A3 and F4-F5 show no clear transition.finding0.777
Geometric evidence for convergence to stable truth directions only for simpler tasks.
The model converges to a more stable truth direction in middle-to-late layers, as evidenced by increasing cosine similarity between layer-wise probes.claim0.773
Supported by the geometric transition visible in cosine similarity heatmaps for F0-F3.
Mid-layers (6-15) achieve peak anchoring because semantic structure differentiates while maintaining coherence, forming a Goldilocks zoneclaim0.763
Interpretation of E3 layer-wise results; motivates targeted UCCT interventions at layers 8-12
Layer-wise anchoring trajectoryconcept0.762
Plot of per-layer anchoring score S(ℓ) across model depth, revealing early dip, mid-layer peak, late standardization.