finding

active

finding:the-difficulty-boundary-for-truth-directions-replicates-across-all-four-tested-models-llama-3-2-3b-llama-3-1-8b-gemma-2-2b-gemma-2-9b-generalization-to-f3-f5-remains-consistently-low-regardless-of-model-size-or-family

The difficulty boundary for truth directions replicates across all four tested models (Llama-3.2-3B, Llama-3.1-8B, Gemma-2-2b, Gemma-2-9b); generalization to F3-F5 remains consistently low regardless of model size or family.

Establishes generalizability of the core difficulty-boundary finding across model families.

Source paper

extracted_from

Testing the Limits of Truth Directions in LLMs

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

Neighborhood — ranked by edge-count

Claims (1)

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.

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.

The generalization improvement from explicit instructions observed in Llama models (A1-A3 to F0-F2) is more pronounced for F3-F5 to F0-F2 in Gemma models.claim0.860
Shows the instruction effect, while shifting geometry, may not produce consistent generalization effects across model families.
Truth-related directions reliably emerge at 60–75% of normalized layer depth in Qwen and Gemma modelsfinding0.851
Experiment 1 finding localizing where truth can be causally mediated
Llama 3.1 405B shows 14% compliance gap in minimal helpful-only replication but smaller Llama and Mistral models show no gapfinding0.815
Replication across open-weight models supports scale-emergence finding
Fine-tuning Llama-3.1-8B on self-correction examples increases multi-attempt rate proportionally with training data ratiofinding0.812
Shows behavioral pattern of self-correction is trainable in smaller models
Llama-3.3-70B exhibits internal consistency-checking mechanisms that operate during inferenceclaim0.811
Central interpretive claim of the paper supported by causal ablation and activation evidence
Truth directions fail to generalize to harder tasks (F3-F5) regardless of prompt template because activations remain highly entangled.claim0.804
Establishes task difficulty as a hard limit that instructions cannot overcome.
Logit self-report drift positive for all three LLaMA sizes (turn slopes 0.159, 0.038, 0.141; all p<10⁻²⁰) but does not increase monotonically with scalefinding0.803
Unlike probe drift, report drift magnitude does not follow a clean scaling law; size-slope is negative
Llama-3.3-70B shows multi-attempt rate of 7.4% vs. ≤1.2% for all other models testedfinding0.802
Supporting finding showing ESR is driven by both higher multi-attempt rates and comparable improvement rates