Three papers from our lab at #NeurIPS2026, spanning LLM reasoning faithfulness, belief formation, and interpretability of MoEs: ⭐ Faithfulness Metrics Don't Measure Faithfulness: A Meta-Evaluation with Ground Truth arxiv.org/abs/2605.25052 We introduce a benchmark for CoT faithfulness metrics, and find that existing metrics are either close to random or prohibitively expensive to run @GurYoav @anmarasovic ⭐ Indications of Belief-Guided Agency and Meta-Cognitive Monitoring in Large Language Models arxiv.org/abs/2602.02467 An interdisciplinary collaboration applying the HOT-3 indicator of consciousness to LLMs using interpretability tools, revealing surprising patterns in belief formation and action selection in LLMs @noam_steinmetz @GoldsteinYAriel @Liad_Mudrik ⭐ Routers Learn the Geometry of Their Experts: Geometric Coupling in Sparse Mixture-of-Experts arxiv.org/abs/2605.12476 We find a geometric coupling between routers and their corresponding experts in MoEs, explaining how routers form assignments that support an effective division of labor @sageaarc @noyahochwald