Shortcut Before Circuit: Document Statistics Time In-Context Conflict Resolution
When a context asserts two values for one fact, a model commits to a cue -- recency, repetition, position -- but natural data rarely makes these disagree, so behavior cannot reveal which. We train 26M-parameter transformers on a synthetic language where recency and rarity are exactly coextensive, and separate them with a minimal causal edit that inverts one cue while holding the truth, token count and answer position fixed. All 75 runs reach accuracy >= 0.999, including where the trivial heuristic fails, so no held-in evaluation distinguishes them. Under intervention the per-cell readout does not replicate: 13 of 25 cells differ by more than 0.3 in sign fraction across three seeds, the la...
| Authors | Yijun Liao, Fanwei Liang |
|---|---|
| Primary category | cs.CL |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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