claim

active

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

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.

Overarching conclusion summarizing the paper's contribution relative to prior universality claims.

Source paper

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Testing the Limits of Truth Directions in LLMs

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

Neighborhood — ranked by edge-count

Papers (1)

paper

Testing the Limits of Truth Directions in LLMs
introduces

Claims (4)

claim

Linear truth directions in LLMs are reliable primarily in factual recall cases and break down when truth assessment depends on computing and storing intermediate results.
supports
Central empirical conclusion of the paper about the fundamental limits of truth directions.
The model appears to encode truth differently under passive versus active truth evaluation prompts.
supports
Key finding from Section 5 based on low cosine similarity between no-prompt and ask-correct probes.
No single layer is universally optimal for probing truth directions; different tasks peak at different layers.
supports
Argues against the single-layer analysis approach of prior work.
Single-layer analyses can be misleading because early-layer truth directions may reflect surface features with limited cross-task generalization.
supports
Methodological critique of prior work that fixed a single layer for truth probing.

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.

Truth directions fail to generalize to harder tasks (F3-F5) regardless of prompt template because activations remain highly entangled.claim0.828
Establishes task difficulty as a hard limit that instructions cannot overcome.
Truth direction universalityconcept0.826
The claim that truth directions are consistent and generalizable across layers, tasks, and prompt formats in LLMs.
We hypothesize that explicitly instructing the model to evaluate the correctness of the given statement may change the geometry of truth directions.hypothesis0.821
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.claim0.813
Identified as the exact computational operation that breaks truth direction generalization.
Truth directions emerge in earlier layers for factual tasks and later layers for arithmetic tasks.claim0.807
Core empirical claim about the layer-dependence of truth direction emergence as a function of task type.
Are the discovered truth directions robust to architectural variation and fine-tuning differences across model families?question0.803
Open question on generalization beyond Gemma and Qwen families
Multiple semantically adjacent truth directions make models more vulnerable to manipulations that shift outputs without obvious signs in the primary truth directionclaim0.802
Safety implication derived from multi-dimensional truth structure finding
Truth-related directions reliably emerge at 60–75% of normalized layer depth in Qwen and Gemma modelsfinding0.795
Experiment 1 finding localizing where truth can be causally mediated