Papers by Stefano Civelli
A Shared Geometry of Difficulty in Multilingual Language Models (2026.acl-short)
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| Challenge: | Large language models encode problem difficulty as an internal signal that can be linearly decoded from their residuals. |
| Approach: | They train linear probes on the AMC subset of the Easy2Hard benchmark, translated into 21 languages, and found difficulty-related signals emerge at two distinct stages of the model internals. |
| Outcome: | The results show that difficulty-related signals emerge at two distinct stages of the model internals, corresponding to shallow (early-layers) and deep (later-layer) representations, that exhibit functionally different behaviors. |