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OshriNap/README.md

Oshri Naparstek

Research Scientist · IBM Research

Multimodal AI · Language Generation · Learning & Optimization

Website · Google Scholar · IBM Research · LinkedIn


I study how AI systems represent information, generate language, and turn reasoning into useful computation. My work connects mathematical ideas with models, experiments, and tools people can explore.

At IBM Research Haifa, I work on multimodal learning and document understanding. I co-authored Granite Vision and REAL-MM-RAG. My background spans a PhD in Electrical Engineering, distributed optimization, and reinforcement learning.

Explore my work

What if a language model could refine a token before committing to it?

Autoregressive language generation through continuous prediction in embedding space, with discrete tokens produced by projection at commitment time. An optional “liquid tail” allows local refinement before commitment. Originally introduced as Token Maturation.

Paper · Project & generation demos

Tools for working with AI

Project What it does
Graph Map Collaborative knowledge graphs for AI conversations, with an MCP server and an interactive browser interface.
Skill Editor A visual editor for agent workflows, linking a node graph to the underlying Markdown source.
Evolution Agent An experimental code optimization framework combining genetic programming with a multi-tier LLM strategy.

Selected research

  • Granite Vision — A lightweight, open-source vision-language model for enterprise document understanding. Co-author.
  • REAL-MM-RAG — A benchmark for realistic multimodal document retrieval, including table-heavy documents and rephrased queries. ACL 2025 · Co-author.
  • Complexity as Advantage — A regret-based perspective on emergent structure and observer-dependent complexity. Sole author.

More publications →

Ideas I'm exploring

  • Continuous language generation: separating the evolution of a representation from the decision to emit a token.
  • Cognitive offloading: turning repeated AI reasoning into reusable, verifiable computation.
  • Representation and complexity: understanding what makes a pattern useful to one observer and invisible to another.

I write about these questions and build interactive explanations at oshrinaparstek.com.


Outside research: music, macro photography, and freediving. For research conversations and collaboration, find me on LinkedIn.

Popular repositories Loading

  1. skill-editor skill-editor Public

    Visual node-graph editor for Claude Code skills - Blender-style canvas for editing skill workflows

    TypeScript 1

  2. CLIP-featurevis CLIP-featurevis Public

    Forked from openai/CLIP-featurevis

    code for reproducing some of the diagrams in the paper "Multimodal Neurons in Artificial Neural Networks"

    Python

  3. ddim ddim Public

    Forked from ermongroup/ddim

    Denoising Diffusion Implicit Models

    Python

  4. mixture-of-experts mixture-of-experts Public

    Forked from davidmrau/mixture-of-experts

    PyTorch Re-Implementation of "The Sparsely-Gated Mixture-of-Experts Layer" by Noam Shazeer et al. https://arxiv.org/abs/1701.06538

    Python

  5. Token-maturation Token-maturation Public

    Token Maturation: an autoregressive LLM operating entirely in continuous embedding space. Decouples prediction from commitment via a "liquid tail" that matures through iterative refinement before d…

    HTML

  6. test_project test_project Public