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New research frames insertion language models with Markov chains and learnable dynamics

By PulseAugur Editorial · [2 sources] ·

Two new research papers explore advancements in insertion language models (ILMs), a method for generating sequences by inserting tokens. The first paper introduces a continuous-time Markov chain framework to derive a diffusion-style denoising objective for ILMs, showing it can be competitive with existing methods while offering more sampling flexibility. The second paper proposes LoFlexMDM, an ILM that learns data-dependent insertion orders, improving generation quality on molecular tasks. AI

IMPACT These papers advance sequence generation techniques, potentially improving performance in areas like molecular design and general language modeling.

RANK_REASON Two academic papers published on arXiv detailing new frameworks and models for sequence generation.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research frames insertion language models with Markov chains and learnable dynamics

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Dhruvesh Patel, Benjamin Rozonoyer, Soumitra Das, Tahira Naseem, Tim G. J. Rudner, Andrew McCallum ·

    A Continuous-Time Markov Chain Framework for Insertion Language Models

    arXiv:2606.10199v1 Announce Type: cross Abstract: Insertion Language Models (ILMs) offer several advantages over left-to-right generation and mask-based generation. However, existing formulations of insertion-based generation have largely been ad-hoc. In this paper, we derive a d…

  2. arXiv cs.LG TIER_1 English(EN) · Dhruvesh Patel, Benjamin Rozonoyer, Gaurav Pandey, Tahira Naseem, Ram\'on Fernandez Astudillo, Andrew McCallum ·

    Insertion Based Sequence Generation with Learnable Order Dynamics

    arXiv:2602.18695v2 Announce Type: replace Abstract: Existing insertion-based masked diffusion models that generate sequences by interleaving token insertion with unmasking use fixed schedules that are not dependent on the data. For structured sequences like graphs and molecules, …

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