Ultrathin multimode fibers (MMFs) promise endoscopes with hair-scale diameters for accessing sub-millimeter anatomy, but in MMF far-field imaging the required small collection aperture drives speckle-dominated measurements that rapidly degrade image fidelity. Here we present Speckle Clean Network (SCNet), a physics-guided foundation model for universal speckle removal that makes photon-limited, single-fiber collection compatible with high-fidelity reconstruction across diverse scattering conditions without target-specific retraining. SCNet combines a Mixture of Experts (MoE) architecture with material-aware routing, wavelet-based frequency decomposition to separate structure from speckle across sub-bands, and a curriculum-style optimization that progressively enforces spectral consistency before spatial fidelity. Using an ultrathin dual-fiber holographic probe, we deliver wavefront-shaped illumination through one MMF and collectbackscattered photons through a parallel MMF. We validate SCNet on 3D plastic objects over varying working distances, resolve 5.66 lp/mm on a paper USAF target, and restore fine structures on leaves and metal surfaces. On rabbit heart and kidney tissues, SCNet improves recovery of low-contrast anatomical texture under the same ultrathin collection constraint. We further compress SCNet through multi-teacher distillation to reduce computation while preserving reconstruction quality, enabling inference at 60 FPS. This work effectively decouples image quality from probe size, establishing a speckle-free ultrathin endoscopy for stand-off imaging in confined spaces.