# StateForge

The work introduces IntentFlux, an executable benchmark that rewrites verifiable tasks into multi-turn dialogues with controlled intent changes (Variant replacements and Decoy withdrawals) while preserving the original graders, observes in a 627-case calibration that mean task score falls from 0.476 to 0.384 as superseded and withdrawn information accumulates, finds that across eight models the fully-correct rate is lower when the same final task must be recovered from an evolving dialogue than when given directly in a single turn, and further introduces StateForge, which explicitly maintains the active requirement state before generation and raises General-Test mean score from 0.367 to 0.

The work introduces IntentFlux, an executable benchmark that rewrites verifiable tasks into multi-turn dialogues with controlled intent changes (Variant replacements and Decoy withdrawals) while preserving the original graders, observes in a 627-case calibration that mean task score falls from 0.476 to 0.384 as superseded and withdrawn information accumulates, finds that across eight models the fully-correct rate is lower when the same final task must be recovered from an evolving dialogue than when given directly in a single turn, and further introduces StateForge, which explicitly maintains the active requirement state before generation and raises General-Test mean score from 0.367 to 0.