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Neighbor information improves individual prediction in all 16 public-data settings and pooled collective forecasts on held-out questions, although collective gains depend on transfer conditions.
Ground factual answering makes truth an absorbing boundary and eliminates the effect, suggesting that the instability is specific to signed-belief tasks while grounded verification remains stabilizing.
An [interaction order x memory] taxonomy is introduced that maps perception and memory to an effective theory and a predicted $N$-trend of the surrogate error, and the two refuted predictions are themselves matched quantitatively by the theory with no free parameters.
This work models a cheap swarm of unreliable agents as a consensus on a graph in which each oracle pins one node toward the truth at a cost-coupled, concave strength, and measures quality by the coherence H(R)=tr M(R)^{-1}.
This paper treats hallucination as a temporally extended span and detects it by sequence labeling: each token is scored from a 33-dimensional feature stream that fuses text statistics, Natural Language Inference entailment, and language model surprisal, with no access to model internals.
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