Ambiguous 2-shot anchors yield four distinct interpretations across M1-M4 (P_abs-mult, P_add x2, P_signed-mult)

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• Ambiguous anchors (33-27=60, 11-9=20) yield four distinct arithmetic interpretations across M1-M4finding0.820
  Models produce different answers (240, 138, -240) from the same ambiguous prompt
• Adding a single disambiguating example (12−9=21) aligns divergent M1-M4 interpretations under tested seedsfinding0.740
  E1 finding consistent with threshold-crossing: near-threshold state resolved by one additional anchor
• Calibrated few-shot prompting was a surprisingly weak baseline for truth classification compared to linear probesfinding0.728
  Unexpected finding that behavioral baseline underperforms representational probing approaches
• Two exemplars (2−3=5, 7−4=11) induce reinterpretation of '−' as addition on held-out queries across mainstream LLMsfinding0.726
  E1 qualitative finding demonstrating anchor rebinding of strong arithmetic prior
• 2-shot reinterpretation of '-' yields 23 for 15-8 on held-out queryfinding0.723
  E1 qualitative: two exemplars (2-3=5, 7-4=11) cause LLMs to output 23 for 15-8.
• MDS injections align with the Linear Representation Hypothesis: target trait varies near-linearly with alpha in open-ended generationclaim0.723
  Theoretical alignment claim backed by OLS R2 analysis showing 96.15% of trends have R2>=0.75
• Shot midpoints follow k50 ∝ dr/ρd; higher cohesion and lower mismatch yield fewer required examplesclaim0.719
  Core quantitative prediction of UCCT validated by E2 threshold ordering
• Probes trained under different explicit instruction prompts (ask-correct, ask-t/f, ask-able, ask-arith) are highly aligned with each other in cosine similarity.finding0.719
  Shows the passive vs. active divide is more important than the specific wording of instructions.