To tackle the tendency of existing multimodal models to overemphasize vision—often misjudging an object's physical properties when visual and tactile information conflict—the Daimon Robotics team, in collaboration with several universities, has proposed Touch-R1, the first tactile-anchored physical reasoning model. This work systematically introduces reinforcement learning into the field of tactile reasoning for the first time. The team also built TouchReason-1M, a large-scale cross-sensor tactile reasoning dataset, and TouchReason-Bench, an evaluation benchmark, together forming a complete infrastructure for tactile reasoning. Touch-R1 adopts a three-stage training pipeline and, through a reinforcement learning strategy designed specifically for touch, achieves consistent reasoning of physical properties across sensors and can correct visual misjudgments based on tactile evidence. On TouchReason-Bench, the 9 core performance metrics of Touch-R1-7B substantially outperform mainstream multimodal models such as GPT-4o as well as previous touch-specific models. This research elevates touch from an auxiliary perception modality to a core modality of embodied foundation models, providing underlying capability support for physical interaction scenarios such as material recognition, precision assembly, and flexible object manipulation. The paper has been accepted to NeurIPS 2026.