Topic
geometry
New Causal Discovery Algorithms BRIDGE and SKFM Use Lie Bracket Geometry to Uncover Latent Confounders
A new arXiv paper introduces two causal discovery algorithms, BRIDGE and SKFM, that use Lie bracket geometry to infer latent confounders. Built on Kan-Do-Calculus, these methods collapse the super-exponential space of possible DAGs and offer a new paradigm for causal discovery.
DOG-DPO: Training-Free Geometric Data Selection Boosts LLM Safety Alignment with 11% of Data
Researchers propose DOG-DPO, a training-free data selection framework for LLM safety alignment that treats preference pairs as geometric directions. By decomposing multi-dataset geometry and maximizing diversity-based coverage, it achieves strong utility-robustness trade-off using only 11% of preference pairs, recovering most safety gains of full-data training while being teacher-free, training-free, and substantially faster than traditional selection methods.
DH-V2: Geometry-Based Sampler Achieves 1,433x Compression for Edge Perception
Researchers present Double-Helix Vision (DH-V2), a geometry-based visual sampler that compresses 2D images into compact 1D signals using golden-ratio-inspired spiral trajectories. At 4K resolution, it achieves a 1,433x compression ratio while running in 0.52ms on CPU-only hardware, and includes a JSON-serializable Robotics API for bandwidth-constrained perception.