Math/code tasks S ≈ -1.65 at layers 8–12

Task-specific peak anchoring score for structured reasoning domains.

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 (1)

claim

Layer-wise anchoring peaks in a 'Goldilocks zone' between early and late layers.
supports
Qualitative characterization of optimal anchoring depth.

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.

Math and code tasks require deeper processing to bind complex patterns, as evidenced by strongest mid-layer anchoring at layers 8-12claim0.814
Task-specific interpretation of E3 anchoring pattern differences
deeper layers (16–28)concept0.779
Layers where anchoring weakens systematically due to representational drift.
Math and code tasks show strongest mid-layer anchoring on LLaMA (S ≈ −1.65 at layers 8-12)finding0.773
Task-specific E3 finding showing compositional reasoning requires deeper processing
For a given task, the number of all sequences which work is tiny by comparison with the huge number of all possible sequences; less than a trillionth of all 6 × 10^23 possible sequences actually work well enough.claim0.771
A combinatorial argument that good sequences are astronomically rare, emphasizing the difficulty of discovery.
Thought detection peaks at ~2/3 layer depth; intention checking peaks at ~1/2 layer depth.finding0.768
Lindsey (2026) differential layer performance explained by Janus's path combinatorics — different tasks use different path distributions.
Correlation between layer-wise S scores and task accuracy: ρ = -0.73, p < 0.001finding0.766
Shows S predicts anchoring effectiveness.
Factual tasks F0-F3 reach near-perfect AUROC in early-to-mid layers of Llama-3.1-8B; arithmetic tasks A1-A3 emerge much later; counting tasks F4-F5 emerge late similar to arithmetic.finding0.761
Core empirical finding about layer-dependent truth direction emergence across task types.
Layer-wise geometry summaries (Sbmax, AUSN) predict internal few-shot thresholds θ50claim0.759
Claim that geometry-to-behavior correlates exist