Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning

Dimensions overall score 7.0

• A unified framework for retrieval-augmented generation that embeds retrieval control directly into the decoding process, outperforming strong baselines and competing with GPT-4o with fewer parameters. Instead of treating retrieval as an external intervention, we express retrieval decisions within token-level decoding, enabling end-to-end coordination without additional controllers or classifiers.problem 35%
  Evidencepartial

  A unified framework for retrieval-augmented generation that embeds retrieval control directly into the decoding process, outperforming strong baselines and competing with GPT-4o with fewer parameters. Instead of treating retrieval as an external intervention, we express retrieval decisions within token-level decoding, enabling end-to-end coordination without additional controllers or classifiers.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

• We revisit retrieval-augmented generation (RAG) by embedding retrieval control directly into generation. Instead of treating retrieval as an external intervention, we express retrieval decisions within token-level decoding, enabling end-to-end coordination without additional controllers or classifiers.method 35%
  Evidencepartial

  We revisit retrieval-augmented generation (RAG) by embedding retrieval control directly into generation. Instead of treating retrieval as an external intervention, we express retrieval decisions within token-level decoding, enabling end-to-end coordination without additional controllers or classifiers.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

• ScienceToStartup currently rates this 7.0/10 on the public viability pass. This design tightly couples retrieval and reasoning and supports dynamic multi-step inference with on-the-fly evidence integration. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.result 35%
  Evidencepartial

  ScienceToStartup currently rates this 7.0/10 on the public viability pass. This design tightly couples retrieval and reasoning and supports dynamic multi-step inference with on-the-fly evidence integration. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

• Retrieval-Augmented Generation moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.why now 35%
  Evidencepartial

  Retrieval-Augmented Generation moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

Competitive landscape

A unified framework for retrieval-augmented generation that embeds retrieval control directly into the decoding process, outperforming strong baselines and competing with GPT-4o with fewer parameters.