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

• URL: http://arxiv.org/abs/2604.11407v2
• Date: Sun, 19 Apr 2026 03:48:59 GMT
• Title: Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning
• Abstract summary: We revisit retrieval-augmented generation (RAG) by embedding retrieval control directly into generation.<n>Instead of treating retrieval as an external intervention, we express retrieval decisions within token-level decoding.<n>GRIP is a unified framework in which the model regulates retrieval behavior through control-token emission.
• Score: 32.86954579218087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Under the paradigm of Retrieval as Generation, we propose \textbf{GRIP} (\textbf{G}eneration-guided \textbf{R}etrieval with \textbf{I}nformation \textbf{P}lanning), a unified framework in which the model regulates retrieval behavior through control-token emission. Central to GRIP is \textit{Self-Triggered Information Planning}, which allows the model to decide when to retrieve, how to reformulate queries, and when to terminate, all within a single autoregressive trajectory. This design tightly couples retrieval and reasoning and supports dynamic multi-step inference with on-the-fly evidence integration. To supervise these behaviors, we construct a structured training set covering answerable, partially answerable, and multi-hop queries, each aligned with specific token patterns. Experiments on five QA benchmarks show that GRIP surpasses strong RAG baselines and is competitive with GPT-4o while using substantially fewer parameters.

Related papers

• ORDER: Task-Conditioned Routing for Retrieval-Augmented Generation [3.6028767235872965]
  In this paper, we introduce a query-conditioned RAG framework that jointly adapts indexing and retrieval to the incoming query.<n>We evaluate our framework on large-scale, heterogeneous historical archives and show that conditioning both indexing and retrieval on the query consistently outperforms both naive baselines and strong state-of-the-art RAG systems.
  arXiv  Detail & Related papers  (2026-09-15T11:23:16Z)
• Collaborative Memory Augmentation for Generative Recommendation [49.76630658239287]
  We propose textbfOMEGA, a cOllaborative MEmory augmentation framework for Generative recommendAtion.<n>We first introduce a latent context compression method that utilizes learnable query tokens to distill sequential user behavior into compact representations.<n>These compressed representations are aggregated into a collaborative memory bank, serving as an explicit repository of global behavioral patterns.
  arXiv  Detail & Related papers  (2026-08-02T15:34:43Z)
• Harness-G: A Graph-Structured Harness for Search Agents [17.554676400013424]
  Reinforcement learning (RL) search agents commonly model retrieval as free-form natural-language query generation.<n>We propose Harness-G, a graph-structured retrieval framework that redesigns this interface.<n>It reformulates free-form query generation as finite action selection, while the environment constructs the menu, tracks retrieval state, and validates and executes each choice.<n>It achieves the highest average F1 at both evaluated model scales, outperforming the strongest baseline, Graph-R1, by 10.74 points at 1.5B and 3.98 points at 3B.
  arXiv  Detail & Related papers  (2026-07-30T04:05:05Z)
• Integrating Chain-of-Thought into Generative Retrieval: A Preliminary Study [31.167867050243256]
  ThinkGR is a unified framework that interleaves chain-of-thought with docid generation, enabling iterative thinking and retrieval within a single generative process.<n> Experiments on four multi-hop retrieval benchmarks demonstrate that ThinkGR achieves state-of-the-art performance with an average improvement of +6.86%.
  arXiv  Detail & Related papers  (2026-05-21T11:48:35Z)
• RAGR: Review-Augmented Generative Recommendation [61.29879991713178]
  Review-Augmented Generative Recommendation (RAGR) is a novel GR framework that incorporates review feedback directly into the generative user sequence.<n>RAGR yields consistent and significant gains over strong GR backbones across all metrics.
  arXiv  Detail & Related papers  (2026-05-17T05:21:23Z)
• Agentic DAG-Orchestrated Planner Framework for Multi-Modal, Multi-Hop Question Answering in Hybrid Data Lakes [5.276278723196607]
  Agentic DAG-Orchestrated Transformer (A.DOT) Planner is a framework for multi-modal, multi-hop question answering.<n>A.DOT compiles user NL queries into directed acyclic graph (DAG) execution plans spanning both structured and unstructured stores.<n>System decomposes queries into parallelizable sub-queries, incorporates schema-aware reasoning, and applies both structural and semantic validation.
  arXiv  Detail & Related papers  (2026-03-15T05:34:16Z)
• Multi-Step Semantic Reasoning in Generative Retrieval [50.65947993017158]
  Generative retrieval (GR) models encode a corpus within model parameters and generate relevant document identifiers for a given query.<n>Existing GR models struggle with complex queries in numerical contexts, such as those involving semantic reasoning over financial reports.<n>We propose ReasonGR, a framework designed to enhance multi-step semantic reasoning in numerical contexts within GR.
  arXiv  Detail & Related papers  (2026-03-12T18:38:50Z)
• Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage [89.58253972744531]
  Retrieval-augmented generation (RAG) systems combine document retrieval with a generative model to address complex information seeking tasks like report generation.<n>We investigate whether upstream retrieval metrics can serve as reliable early indicators of the final generated response's information coverage.<n>Our findings demonstrate strong correlations between coverage-based retrieval metrics and nugget coverage in generated responses at both topic and system levels.
  arXiv  Detail & Related papers  (2026-03-09T18:20:20Z)
• RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learning [69.87510139069218]
  Retrieval-Augmented Generation (RAG) integrates non-parametric knowledge into Large Language Models (LLMs)<n>Recent progress has advanced text-based RAG to multi-turn reasoning through Reinforcement Learning (RL)<n>We introduce model, an RL-based framework that enables LLMs to perform multi-turn and adaptive graph-text hybrid RAG.
  arXiv  Detail & Related papers  (2025-12-10T10:05:31Z)
• LeanRAG: Knowledge-Graph-Based Generation with Semantic Aggregation and Hierarchical Retrieval [10.566901995776025]
  LeanRAG is a framework that combines knowledge aggregation and retrieval strategies.<n>It can mitigate the substantial overhead associated with path retrieval on graphs and minimizes redundant information retrieval.
  arXiv  Detail & Related papers  (2025-08-14T06:47:18Z)
• TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning [7.706148486477738]
  Retrieval-Augmented Generation (RAG) has demonstrated considerable effectiveness in open-domain question answering.<n>Existing RAG approaches exhibit critical limitations when applied to heterogeneous documents.
  arXiv  Detail & Related papers  (2025-06-12T06:16:49Z)
• ImpRAG: Retrieval-Augmented Generation with Implicit Queries [34.72864597562907]
  ImpRAG is a query-free RAG system that integrates retrieval and generation into a unified model.<n>We show that ImpRAG achieves 3.6-11.5 improvements in exact match scores on unseen tasks with diverse formats.
  arXiv  Detail & Related papers  (2025-06-02T21:38:21Z)
• Chain-of-Retrieval Augmented Generation [91.02950964802454]
  This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer.<n>Our proposed method, CoRAG, allows the model to dynamically reformulate the query based on the evolving state.
  arXiv  Detail & Related papers  (2025-01-24T09:12:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.