claim

active

claim:no-single-layer-is-universally-optimal-for-probing-truth-directions-different-tasks-peak-at-different-layers

No single layer is universally optimal for probing truth directions; different tasks peak at different layers.

Argues against the single-layer analysis approach of prior work.

Source paper

extracted_from

Testing the Limits of Truth Directions in LLMs

(2026) · Angelos Poulis · Mark Crovella · Evimaria Terzi

Neighborhood — ranked by edge-count

Findings (1)

finding

The between-to-within-class variance ratio peaks at different layers for different tasks, confirming no single layer is universally optimal.
supports
Supports the claim against single-layer probing approaches used in prior work.

Claims (1)

claim

Universality claims for truth directions are more limited than previously assumed, with significant differences observable for various model layers, task difficulties, task types, and prompt templates.
supports
Overarching conclusion summarizing the paper's contribution relative to prior universality claims.

Questions (1)

question

Where inside the LLM should we look for an accurate truth direction that will generalize the most across tasks?
answered_by
One of the three guiding research questions of the paper.

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.

Single-layer analyses can be misleading because early-layer truth directions may reflect surface features with limited cross-task generalization.claim0.843
Methodological critique of prior work that fixed a single layer for truth probing.
Truth directions emerge in earlier layers for factual tasks and later layers for arithmetic tasks.claim0.843
Core empirical claim about the layer-dependence of truth direction emergence as a function of task type.
Truth-related directions reliably emerge at 60–75% of normalized layer depth in Qwen and Gemma modelsfinding0.815
Experiment 1 finding localizing where truth can be causally mediated
The model converges to a more stable truth direction in middle-to-late layers, as evidenced by increasing cosine similarity between layer-wise probes.claim0.812
Supported by the geometric transition visible in cosine similarity heatmaps for F0-F3.
Truth direction universalityconcept0.807
The claim that truth directions are consistent and generalizable across layers, tasks, and prompt formats in LLMs.
Truth directions fail to generalize to harder tasks (F3-F5) regardless of prompt template because activations remain highly entangled.claim0.799
Establishes task difficulty as a hard limit that instructions cannot overcome.
We hypothesize that explicitly instructing the model to evaluate the correctness of the given statement may change the geometry of truth directions.hypothesis0.794
Motivating hypothesis for Section 5's investigation of prompt template effects.
The need for genuine counting over lists of more than two elements introduces the key limitation of truth directions.claim0.792
Identified as the exact computational operation that breaks truth direction generalization.