mlsanityData to 14 Oct 2026

Continuous Interaction Diffusion: A Diffusion-Native Runtime for Asynchronous Tool-Augmented Reasoning

arXiv 2608.10438 ↗First posted 11 Aug 2026cs.AIcited by 0

On the map: LLM reasoning → Diffusion reasoning and test-time scaling

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Abstract (arXiv)

Large language models increasingly rely on external tools to access up-to-date information, perform computation, and interact with the outside world. For autoregressive models, tool use naturally fits the generation process: the model emits a tool call, waits for the result, and then continues generating. Diffusion language models (dLLMs), however, reason by repeatedly refining many parts of their output in parallel, making this stop-and-resume interaction pattern unnecessarily restrictive. It can force tool decisions before the model's reasoning has stabilized, delay useful observations until a discrete call finishes, and introduce redundant refinement and tool execution, potentially hurting both task accuracy and inference efficiency. We introduce Continuous Interaction Diffusion (CID), a diffusion-native model--runtime architecture that integrates tool interaction into iterative denoising. CID separates a model-read-only fact channel, a thought channel represented by a Typed Cognitive Tensor, and a display channel. Information needs can emerge before a textual or JSON call is fully serialized, allowing perceptual bindings to launch external reads while denoising continues. Returned results are projected into the evolving thought state and can revise earlier cognition and display regions. Persistent bindings reuse static results without repeated external execution and refresh changing sources when needed. CID is designed to expose evidence earlier, overlap tool latency with model computation, reduce duplicate external work, and preserve useful computation after new evidence arrives. We formalize the architecture, runtime, and training objectives, and define an evaluation protocol for task quality and end-to-end efficiency. This first paper focuses on read-only tools and makes no empirical performance claims.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Diffusion reasoning and test-time scaling, one of 17 directions in LLM reasoning. Builds on 19 of its 22 references that are in the map. First posted on arXiv 11 Aug 2026.

Builds on

19 of 22 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models12 Feb

Other references · 18

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  3. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,719
  4. 2022Diffusion-LM Improves Controllable Text Generationcited by 1,027
  5. 2025Large Language Diffusion Modelscited by 912
  6. 2024Simple and Effective Masked Diffusion Language Modelscited by 848
  7. 2024Training Large Language Models to Reason in a Continuous Latent Spacecited by 797
  8. 2025Dream 7B: Diffusion Large Language Modelscited by 544
  9. 2022DiffuSeq: Sequence to Sequence Text Generation with Diffusion Modelscited by 450
  10. 2025Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Modelscited by 409
  11. 2025Don't Settle Too Early: Self-Reflective Remasking for Diffusion Language Modelscited by 37
  12. 2025LaDiR: Latent Diffusion Enhances LLMs for Text Reasoningcited by 29
6 more
  1. 2026Continuous Latent Diffusion Language Modelcited by 14
  2. 2024Asynchronous Tool Usage for Real-Time Agentscited by 13
  3. 2026Tool Calling is Linearly Readable and Steerable in Language Modelscited by 10
  4. 2025Planned Diffusioncited by 9
  5. 2026DLLM-Searcher: Adapting Diffusion Large Language Model for Search Agentscited by 8
  6. 2026The Bitter Lesson of Diffusion Language Models for Agentic Workflows: A Comprehensive Reality Checkcited by 6

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  1. 2026MetaState: Persistent Working Memory Enhances Reasoning in Discrete Diffusion Language Models
  2. 2026Hierarchical Continuous Diffusion Language Models
  3. 2025Beyond Surface Reasoning: Unveiling the True Long Chain-of-Thought Capacity of Diffusion Large Language Models
  4. 2026Think First, Diffuse Fast: Improving Diffusion Language Model Reasoning via Autoregressive Plan Conditioning
  5. 2025Advancing Reasoning in Diffusion Language Models with Denoising Process Rewards
  6. 2025Diffuse Thinking: Exploring Diffusion Language Models as Efficient Thought Proposers for Reasoning
  7. 2026Reasoning with Latent Tokens in Diffusion Language Models
  8. 2025Planner and Executor: Collaboration between Discrete Diffusion And Autoregressive Models in Reasoning