📢Aug 31 (Mon): From Interface to Inference: Eliciting Any-Order Inference from Any-Order Models 🤔Many discrete reasoning tasks, such as code generation, are inherently non-causal. Programmers naturally move between high-level structure and local details in a process called any-order inference. While masked diffusion models offer a native any-order prediction interface, maximizing their potential is difficult due to a core limitation: 1️⃣ This any-order interface does not automatically yield actual any-order inference 2️⃣ Fixed-canvas, token-level models suffer from "positional uncertainty"—they may know what semantic component should appear, but not where to place it 🔑To address the interface-inference gap, the authors propose two complementary approaches designed to natively support any-order inference without relying on hand-designed mechanisms. 🔧The framework tackles positional uncertainty through two methods: (1) Insertion-based masked diffusion (building on FlexMDM), which relaxes fixed-position commitments via insertions to enable generation across non-contiguous regions, and (2) Latent-space masked diffusion (LatentMDM), which shifts prediction to coarser semantic segments to enable search over latent generation orders. 📈The resulting models—a 7B FlexMDM trained for Python coding and a 125M LatentMDM for GSM8K—successfully induce distinct any-order inference behaviors. The study demonstrates that overcoming positional uncertainty through these approaches significantly improves downstream performance. This Monday, the joint first authors Seunggeun Kim (@seunggeun_kr), Jaeyeon Kim (@Jaeyeon_Kim_0), Taekyun Lee (taekyunl.github.io), and Yuyuan Chen (yuyuanchen0.github.io) will present their recent paper on the FlexMDM and LatentMDM frameworks.