Public articles linked to the same research event.
arXiv The authors introduce a theoretical framework for contextual influence through inference dynamics, showing that under a controlled single-round setting repeated contextual assertions do not cause unbounded drift but instead drive internal inference trajectories to stable, query-dependent limits, so repetition does not act as accumulating evidence and may fail to change a prediction or make a flip inevitable; they validate representation-level dynamics against output-level answer shifts across six models and several QA benchmarks.
The authors introduce a theoretical framework for contextual influence through inference dynamics, showing that under a controlled single-round setting repeated contextual assertions do not cause unbounded drift but instead drive internal inference trajectories to stable, query-dependent limits, so repetition does not act as accumulating evidence and may fail to change a prediction or make a flip inevitable; they validate representation-level dynamics against output-level answer shifts across six models and several QA benchmarks.
The authors introduce a theoretical framework for contextual influence through inference dynamics, showing that under a controlled single-round setting repeated contextual assertions do not cause unbounded drift but instead drive internal inference trajectories to stable, query-dependent limits, so repetition does not act as accumulating evidence and may fail to change a prediction or make a flip inevitable; they validate representation-level dynamics against output-level answer shifts across six models and several QA benchmarks.
The authors introduce a theoretical framework for contextual influence through inference dynamics, showing that under a controlled single-round setting repeated contextual assertions do not cause unbounded drift but instead drive internal inference trajectories to stable, query-dependent limits, so repetition does not act as accumulating evidence and may fail to change a prediction or make a flip inevitable; they validate representation-level dynamics against output-level answer shifts across six models and several QA benchmarks.