Interact-RAG: Reason and Interact with the Corpus, Beyond Black-Box Retrieval
Authors: Yulong Hui
Chao Chen
Zhihang Fu
Yihao Liu
Jieping Ye
Huanchen Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Retrieval-Augmented Generation
LLM Agent
LLM Reasoning
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Retrieval-Augmented Generation (RAG) has significantly enhanced LLMs by incorporating external information. However, prevailing agentic RAG approaches are constrained by a critical limitation: they treat the retrieval process as a black-box querying operation. 
This confines agents' actions to query issuing, hindering its ability to tackle complex information-seeking tasks. To address this, we introduce Interact-RAG, a new paradigm that elevates the LLM agent from a passive query issuer into an active manipulator of the retrieval process. We dismantle the black-box with a Corpus Interaction Engine, equipping the agent with a set of action primitives for fine-grained control over information retrieval. To further empower the agent on the entire RAG pipeline, we first develop a reasoning-enhanced workflow, which enables both zero-shot execution and the synthesis of interaction trajectories. We then leverage this synthetic data to train a fully autonomous end-to-end agent via Supervised Fine-Tuning (SFT), followed by refinement with Reinforcement Learning (RL). Extensive experiments across six benchmarks demonstrate that Interact-RAG significantly outperforms other advanced methods, validating the efficacy of our reasoning-interaction strategy.

Published as a conference paper at ICLR 2026
INTERACT-RAG: REASON AND INTERACT WITH THE CORPUS, BEYOND BLACK-BOX RETRIEVAL
Yulong Hui', Chao Chen", Zhihang Fu?, Yihao Liu', Jieping Ye", Huanchen Zhang
Tsinghua University "Alibaba Cloud
ABSTRACT
Retrieval-Augmented Generation (RAG) has significantly enhanced LLMs by in- corporating external information. However, prevailing agentic RAG approaches are constrained by critical limitation: they treat the retrieval process as a black- box querying operation. This confines agents' actions query issuing, hindering its ability to tackle complex informationn-eekinng tasks. To address this, we intro- duce Interact-RACE a new paradigm that elevates the LLM agent from a passive query issuer into an active manipulator of the retrieval process. We dismantle the black-box with a Corpus Interaction Engine, equipping the agent with a set of action primitives for fine-grained control over information retrieval. To fur- ther empower the agent on the entire RAG pipeline, we first develop a reasoning- enhanced workflow, which enables both zero-shot execution and the synthesis of interaction trajectories. We then leverage this synthetic data to train a fully au- tonomous end-to-end agent via Supervised Fine-Tuning (SFT), followed by re- finement with Reinforcement Learning (RL). Extensive experiments across six benchmarks demonstrate that Interact-RAG significantly outperforms other ad- strategy.
vanced methods, validating the efficacy of our
INTRODUCTION
Large Language Models (LLMs) have shown advancements in natural language understanding and generation but are constrained by their training data, which can be static, outdated, or lack domain- specific knowledge (Huang et al.) 2025). Getrieval-Augmenter Generation (RAG) has emerged as prevailing solution this limitation (Lewis et al. 2020; Gao et al. (023). By retrieving information from external corpora, RAG systems enable LLMs to access up-to-date information, incorporate
specialized knowledge, and reason over proprictary data (Hui et al. 2024 Li ct al. 2025f).
The development of RAG has progressed through three stages. The initial approach, Static RAG, performs a single retrieval to fetch relevant documents for the LLM (Gao et al. (023).. To handle more complex tasks, Iterative RAG frameworks were introduced. These systems employ multi- step retrieval pipelines to progressively gather information (Trivedi et al., 2023) Jiang et al., 2023] Chan al. 2024). The current frontier Agentic RAG, which autonomously orchestrate the entire workflow with more flexibility (Gao et al. 2025 Li et al.) 2025ch. The agent decides when retrieve, what to query, and how to analyze the retrieved in- formation (Singh et al. 2025| et al. 2025). Advanced implementations include prompt-driven multi-agent workflows (Nguyen et al. [025 Li al.) 2025b) and other end-to-end trained agents using Supervised Min-Tuning (SFT) and Reinforcement Learning (RL) to improve reasoning and adaptability (Jin et al., 2025a Zheng et al., 2025 Qian & Liu 2025).
an LLM-centric agent to
Despite these advances, existing agentic RAG frameworks share critical limitation: they treat the retrieval process as an opaque black-box. The agent confined to issuing query and passively receiving text chunks, typically from an embedding-based semantic retriever (Gao et al. 2025 Jin et al. 2025a). This paradigm prevents the agent from inspecting the internal state of the re- trieval process, thereby forcing it to relinquish fine-grained control over the process. Consequently, the agent's 's exploration is restricted to a trial-and-erro loop of query reformulation, which lim- its the breadth, depth, and overall efficacy of its information seeking. For example, when asked,
Huanchen Zhang is also affi liated with the Shanghai Qi Zhi Institute. Corresponding author.

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Published as a conference paper at ICLR 2026
User Query
To answer this first query for "the relense date of xx", then
The information is insufficient
I will search again with the query "when was the xox released
X
Which film was released first, Death of Failure to Launch?
Query
Text-Chunks
Query
Text-Chunks
Black-Box RAG
Embedding- Based Semantic Retriever
Reasoning
Reasoning
should first identify release date of Then Finally...
The information insuf Let's 's refine the action. The noisy doc should be excluded Since I will rely more on the exact-search wwll.
Agent
First, the semontic search can ...
exact seanch should include.. will
also anchor on specific entity
Interact-RAG
Actions
Feedback
Actions
Feedback
Corpus
Multi-aaceted Retrieval: semantic-scarde
Anchored Matching:
Context Shaping:
Interaction
indute-docs exclude-docs adjjst scale
exact-search
entily-match
Engine
weighted-fusie fusion
Figure 1: A brief demonstration of Interact-RAG. It empowers the agent with fine-grained control over the information-seekien seeking process, leveraging a set of interactive actions. In contrast, conven- tional RAG is confined to ineffective loops of query issuing.
"Which film was released first, The Jaws of Death or Failure to Launch?", agent might first query for the "release date of The Jaws of Death". This retrieval may fail if the supporting evidence is phrased differently (e.g.. "".Thee Jaws of Death is 1976 thriller film...) if the retriever is distracted by semantically similar but irrelevant entities (e.g., a film named The Hound of Death). Faced with such a failure, existing agents can only resort repeatedly paraphrasing the query (e.g.. "when was The Jaws of Death released"). This often leads to an inefficient loop of blind guessing that fails to obtain the necessary information.
To overcome this limitation, we introduce Interact-RAG, a novel paradigm that transforms the agent from a passive query issuer to an active participant in the retrieval process. Our core idea is to dismantle the retrieval "black box" by providing the agent with transparent and fine-grained control over its information seeking. To achieve this, firstly, we propose a Corpus Interaction Engine, which equips the agent with versatile set of Interaction Primitives, categorized into three action types: (1) Multi-Faceted Retrieval, which allows the agent to employ diverse retrieval strategies (e.g., semantic, exact) and adaptively fuse their results with different weights; (2) Anchored Match- ing, which focuses the search on a specific entity to mitigate distraction from irrelevant content; (3) Context Shaping, which enables the agent proactively manage the retrieval context by retaining efficient documents and adjusting the retrieval scope. This suite of primitives serves as a foundation for the fined-grained control, beyond simple query reformulation (as shown in in Figure I
However, just providing these interactive capabilities is insufficient. Empowering the LLM to ac- tively and strategically master the interactive pipeline remains challenging. First, it is difficult to directly instruct an LLM to manage the intricate multi-step process. To address this, we design reasoning-enhancee workflow that decomposes the task into three modules: a global planner, an adaptive reasoner, and an executor. This approach not only provides a robust, training-free solution but also synthesizes high-quality agent trajectories for subsequent training. Second, achieving full autonomy requires the model to internalize the strategic policies. Therefore, we leverage the synthe- sized trajectories and apply Supervised Fine-Tuning (SFT), followed by refinement with Reinforce- ment Learning (RL). As shown in Figure I exccuting the entire pipeline, without relying on an explicit multi-module architecture.
we finally yield a unified, end-to-end agent capable of
We conduct extensive experiments six challenging RAG benchmarks. Our final trained Interact- RAG agent significantly outperforms other advanced RAG approaches, achieving a relative improve- ment of 22.5%. Ablation studies and detailed analysis further validate the efficacy of our proposed methods. This work sheds light on future exploration to build effective RAG systems with agent- driven interactive retrieval and reasoning enhancement.
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Published as a conference paper at ICLR 2026
2 PRELIMINARY
2.1 RAG FORMULATION
External Information. The external information in RAG is often represented as visible corpus C {d1,d2,... .2...,, d typically consisting of N documents or segmented text chunks.
Task Formulation. For RAG system, the core objective is to produce factual and useful response A to user query Q, utilizing the retrieved information from the external corpus C.
Basic Pipeline. The RAG process typically consists of two main stages: retrieval and generation. Given a user query Q and the corpus C, retriever R selects some relevant chunks C" c C, which is often based on embedding similarity. Subsequently, a LLM 9 generates the response Y, conditioned on both the query Q and the retrieved context C². The process can be formalized as:
C' R(Q,C,,,,, Y -QQ C').
2.2 END-TO-END RAG AGENT
To overcome the rigidity of static pipelines, recent works frame RAG sequential process driven by an LLM agent, TLLM- Given a query Q, the agent continuously searches the information from a corpus C. At each step I, it generates action a, based the history: a, MI mH. (H,-1), where the history H,-1 contains prior thoughts, actions and retrieved information (with Ho Q).
The actions of agent often include: (1) seare rch (gr): issuing query d retrieve evidence I, from the corpus; (2) answer (Y); concluding the final answer Y. When search action is invoked, the information retrieved and appended the history, following the action a;:
H₂ Hi-1 + (ax. Ii)
where denotes the concatenation operation. And typical agent trajectory can be visualized as: [thought] - [search] >> [info] - [thought] [search] - [info] - [thought] -> [answer]
In this trajectory, each [thought]-[seann [search] or [thought]-[answe)) corresponds an action a, [info] represents the retrieved information 4t, and their accumulated history H, is iteratively fed to the LLM for subsequent decisions.
METHODOLOGY: INTERACT-RAG
In this section, we introduce Interact-RAc RAG with three core components: (1) a corpus interaction engine that supports the fine-grained information control; (2) reasoning-enhanced workflow that enables both zero-shot solution and data synthesis; and (3) training pipeline using SFT and RI. to produce an autonomous end-to-end agent.
3.1 INTERACTIVE ENGINE AND PARADIGM
RAG systems typically treat the information retrieval as a black-box semantic-query-senne To address this, propose the Corpus Interaction Engine, which equips the agent with versatile set of Interaction Primitives. This allows the agent to navigate the information corpus C in a human- like manner, with fine-grained reasoning and manipulation. We define the agent's 's action space Aci (corpus interaction) to include these primitives, which can be categorized into three classes:
1) Multi-Faceted Retrieval. Primitives in this category offer diverse retrieval strategies locate query related text passages, balancing semantic relevance with lexical precision.
semant ic search (querys): Performs a dense retrieval, using embedding similarity to find semantically related documents.
exact search (keywords_): Executes a sparse retrieval based on exact keywords ranking, ideal for finding specific terms, names, phrases.
weighted fusion (w,, w.): Sets the fusion weights for semantic and exact search strate- gies, enabling the agent to flexibly combine their strengths based on the context of the query.
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2) Anchored Matching. This allows the agent to focus its search specific, identified entity, thereby retrieving highly relevant information and minimizing distraction from noisy context.
ent entity match (entity): Retrieves information segments that are strongly associated with specified entity, ensuring the results are centered around a key subject.
3) Context Shaping. These actions enable the agent to sculpt the information context dynamically.
include.docs (doc_ids) Guarantees the inclusion of specified documents in subsequent retrieval steps, ensuring critical information is not missed.
exclude ldlccsdeedcc (doc.ids): Filters out irrelevant documents from subsequent searches, pre- venting noisy distractions.
adjust.sces _cccle e (n): Adaptively adjusts the scale of the retrieved information (e.g., the number of text chunks) match the different complexity of the sub-problem.
Agent Interaction Pipeline. Within the Interact- AA pipeline, the LLM agent orchestrates the decision-making process (as shown Figure D) At each step t, given the previous history, the LLM will generate structured output that includes: (1) reasoning thought that rationalizes the current state and strategy, and (2) a suite of concurrent actions Ar { Aty....} C Aci. These actions are formulated the parameterized function call, encapsulated within structured tags (e.g., The Corpus Interaction Engine then parses and executes the actions,
<tool coloo_. </toll///looall>>> returning a consolidated response to the LLM. This response, wrapped in tags like <tool response">, contains the aggregated retrieved content and critical metadata (e.g., source document id, similarity scores for each search strategy). This interactive feedback allows the agent to perform sophisticated strategic analysis and dynamically refine the next actions.
Implementation. Our engine is designed for computational efficiency with small overhead. We implement primitives like exact_searet, and entity_mateet ma by leveraging the Full-Text Search (FTS) modules in relational databases (SQLite 2025). While this builds an additional text index, the computational cost is negligible. Furthermore, the Context Shaping primitives are imple- mented through simple filters. It is worth noting that while the agent may invoke multiple strategies in a single iteration, our engine avoids generating multiple large contexts. Instead, it produces a single consolidated context by aggregating these primitives. Further implementation details and validation are provided in Appendix D.2
3.2 REASONING-ENHANCEE WORKFLOW
Directly prompting LLM to master the entire interactive pipeline is challenging. Therefore, we develop a reasoning-enhanee workflow, decomposing the agent action into a hierarchical and iterative structure. It not only serves robust training-free solution but also generates high-quality data to train our end-to-end agent. As shown in Figure 2 the workflow contains three collaborative modules: a global-planner, an adaptive-reasoner, and an executor.
1) Global-Planner. Given a user query, the global-planner analyzes the problem and decomposes into a primary step-by-step execution plan, providing high-level strategic roadmap.
2) Adaptive-Reasonee This component acts as the cognitive core of the workflow. At each step, it first analyzes the current state, including the previous actions, gathered information, and the objec- tive from the planning road-map. After the analysis, it adaptively issues one of two directives:
Proceed: If the current sub-task is progressing well and the retrieved information sufficient, it instructs the Executor to proceed to the next step conclude the final response.
Reflect & Refine: If the process encounters obstacle (e.g., insufficient information), the reasoner will enter a reflection phase. It diagnoses the issue and refines the interaction strategy for the next action. For example, it might rely more on or use
to locate precise terms,
to filter out misleading documents.
Additionally, the reasoner is instructed to adjust the primary plan when necessary. This ensures flexibility, allowing changes without rigidly adhering to the initial roadmap.
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Interaction Engiene
FeedBack
Actions
Proceed-
User Query
Plan
Analyzo-+
Answer
Global Planner
Adaptive Reasoner
Retine
Executor
Figure 2: An illustration of our
workflow.
3) Executor. Following the directives from the reasoner, the executor translate the strategy into concrete, structured action. It generates the precise function call for the interaction primitives with appropriate parameters. Once all sub-tasks are complete, the Executor will generate the final answer.
This modular design clearly decouples high-level planning, detailed reasoning and precise execution. For general-purpose LLM, this separation is critical, as the well-defined and focused tasks elicit more reliable output. This workflow yields two significant advantages. First, as training- free method, it enhances the stability and logical coherence of zero-shot RAG. Second, it serves data synthesis engine to train the autonomous agent. With logically-structuree modules, the LLM operates in non-reasoning mode to produce clean reasoning traces, free from the verbose and irrelevant thinking content, which is common in native large reasoning models (LRMs).
3.3 END-TO-END AGENT TRAINING
To develop autonomous, end-to-end end LLM agent that internalizes reasoning, adopt two-stage training process involving supervised fine-tuning (SFT) followed by reinforcement learning (RL.).
Trace Sampling and Fine-Tuning. The initial SFT stage aims to teach the LLM the fundamental mechanics, such as planning, reasoning, and mastering the interactions. We leverage our reasoning- enhanced workflow to generate a collection of trajectories based QA pairs. To ensure the data quality, we retain only successful trajectories, where the agent's 's final answer matches the ground truth. The agent is then fine-tune fine-tuned on these nigh-quality trajectories. The training objective is to predict the sequence of thoughts and actions in an auto-regressive manner. During loss calculation, mask out the tokens of retrieved information, avoiding the distraction during learning.
Policy Refinement with Reinforcement Learning. We then employ RL to enable superior strate- gies through active exploration. We adopt Group Relative Policy Optimization (GRPO) (Shao al. 2024), an advanced optimization algorithm, to further refine the agent's 's policy Tg. During the policy updating, also mask out the tokens of retrieved information.
1) RL Objective: Given a question from the dataset q E Dq, the agent generates a group of trajectories [T.) And the policy Te is updated using the following objective function:
JCRPO E [n~Do:
min (po(a(") clip (p((aa))) +c) A(T:)) - DKI (Noll Teet)
Ro(al (9)
where a, means the agent action, pe (a({)
the importance sampling ratio, and the
Teay (a) [2.
advantage Å(T:) calculated by normalizing the rewards within sampled group. This objective encourages updates towards high-reward trajectories while stabilizing training.
We design a outcome reward R(T) to guide the agent, based on both the syntactic validity and answer accuracy of its trajectory
2) Reward Function:
R(T) = -1 + I[Taana) + I[Tvana] I[yans}
Here, each trajectory incurs an initial penalty of -1. The agent should generate a format-coherent output overcome this penalty: TT.)) denotes the indicator function, which returns 1 if its enclosed
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condition is true, and 0 otherwise. First, the term [( grants +1 reward if r is syntactically valid, thereby neutralizing the initial penalty. Syntactic validity encompasses the entire action se- quence structure, the reasoning format, and the tool call syntax. Second, [[(Taaii) HBamm) provides a +1 reward for task success, where the final answer Yans matches the ground-truth. This reward is gated by the trajectory's validity, ensuring that only well-formed output be rewarded. It is worth noting that, the inclusion of the "-1 initial penalty" was primarily intended for intuitive logic. Mathematically, this constant term is canceled out during the group-based normalization process in GRPO and does not influence the advantages or the actual training.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Datasets. We conduct experiments across six prominent and standard RAG benchmarks. These include two single-hop question-an annseriin datasets, Natural Questions (NQ) (Kwiatkowski et al.]
2019) and PopQA (Mallen et al.) (023), and four multi-hop question-answering datasets: Hot- potQA Yang et al. 2018). 2WikiMuliHopQAA (2Wiki) (Ho et al.] 2020). MuSiQue (Trivedi) et al., ) and Bamboogle (Press et al.). More dataset details are in Appendix D.I Besides, for more comprehensive evaluation over non-wiki domains, we conduct additional experiments on
the MultiHop-RAll RAG benchmark (Tang& & Yang 2024 (more details in Appendix
Baselines. We compare our method against a diverse suite of baselines, covering paradigms of non-RAG, static, iterative, prompt-driven multi-agent, and end-to-end trained agents. Specifically, we include: (1) Direct: Answers questions directly via Chain-of- Thoouhhh without external in-
formation. (2) Standard RAG: static RAG method that performs single retrieval. (3) IR- CoT (Trivedi et al. 2023): ; A representative iterative RAG method using intermediate thought-chain steps to formulate queries for multi-step retrieval. (4) MA-RAG (Nguyen et al. 2025): A multi- agent framework with agent collaboration. (5) Search-O1 (Li et al. (025b): An agentic framework with a reasoning-enhanced workflow. (6) Search-R1 (Tin al.] 2025a): An end-to-end approach that uses RL to generate multi-turn search queries after reasoning. (7) SimpleDeepSearcher (S- DeepSearcher) (Sun et al. 2025): An end end-to-end approach that fine-tunes a LLM on synthesized high-quality data. (8) R-Search (Zhao et al. 2025); An end-to-end approach that trains au- tonomous agent via RL, using optimized multi-reward signals.
Experimental Details. Following previous works (Jin et al. 2025a Qian & Liu 2025). we process the 2018 Wikipedia dump as the retrieval corpus. We employ the b5-base-v2 (Wang et al. 2022) model as the retriever, fetching the top relevant chunks. To ensure a fair comparison, the same corpus and retriever across all methods, and re-evaluate all baselines under this unified setting. For all experiments, we use Qwen3-8B by default (Yang al., 2025), recent instruction- tuned model. For training-driven baselines (i.e., Search-RI, S-DeepSearcher, and R-Search), we utilize their official checkpoints trained on Qwen-2.5-7B, since their 8B versions are not available yet. To ensure the comprehensiveness, we also report our results on Qwen2.5-7B in main results.
maintain
We train our agent on the combined training splits of NQ. HotpotQA, and MuSiQue, and evaluate on the test splits of all six benchmarks. This setup enables the generalization on both in-distribution and out-of-distribution (PopQA, 2Wiki, Bamboogle). For the training process, we first employed Qwen-Plus to synthesize 4.8K agent trajectories for SFT. Subsequently, we utilized 7.1K question- answer pairs for the RL phase. More details are in Appendix D.3
4.2 MAIN RESULTS
We evaluate Interact-RAG on six benchmarks, with the main results in Table ??. Our findings highlight three key advantages of our approach. First, Interact-RAG consistently achieves best per- formance across all datasets. On average, it improves the EM-score by 9.7 points (22.5% % relative gain) over the second-best method, Search-RI. And our 7B version also achieves a relative im- provement of 10.1%. Notably, our Interact-RAG was trained on 12K QA data, a small fraction of the 170K QA pairs used for Search-R1. This data disparity also explains Search-RI 's higher re- sults on the NQ dataset using the 7B model. Second, the performance gains are more pronounced on complex multi-hop QA tasks. For instance, on Musique, Interact-RAAe delivers a 36.4% rela- tive improvement. Concurrently, it maintains strong performance single-hop benchmarks like

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Published as a conference paper at ICLR 2026
Multi-Hop QA
Single-Hop QA
AVG
Method
HotpotQA
2Wiki.
Musique
Bamboogle
NQ
PopQA
EM
F1
EM
FI
EM
FI
EM
FI
EM
F1
EM
FI
EM
FI
Results Qwen2.5-7B Backbone
Direct
17.8
26.2
22.6
27.7
4.3
9.8
19.6
30.2
16.1
23.5
18.6
21.2
16.5
23.1
Std-RAG
29.8
41.5
28.0
34.1
9.5
14.4
18.4
26.8
35.1
44.9
34.6
41.2
25.9
33.8
IR-CoTT
19.3
36.6
20.0
37.5
5.2
14.4
18.2
30.0
16.5
27.5
24.6
34.8
17.3
30.1
MA-RAG
35.0
44.7
39.6
46.9
13.1
19.0
40.8
50.9
29.5
39.9
33.3
39.0
31.9
40.1
Search-ol
33.6
46.3
39.9
49.6
14.7
21.7
32.0
45.3
33.7
43.8
36.3
43.1
31.7
41.6
Search-R1
45.2
60.1
50.9
58.2
25.5
34.1
42.2
55.6
45.3
54.7
49.3
53.6
43.1
52.7
R-Search
38.2
51.0
58.8
19.4
28.0
36.0
52.1
36.8
42.8
46.2
38.7
48.0
S S-DeepSeacch
40.2
53.6
54.0
61.8
18.6
25.8
46.2
57.4
37.0
46.6
40.6
46.1
39.4
48.5
Interact-RAG-7B
47.8
61.6
63.6
71.0
30.9
39.5
47.6
61.1
43.7
52.9
51,6
54.4
47.5
56.8
Results on Qwen3-8B Backhone
Direct
23.6
33.0
26.4
32.5
5.9
13.3
33.2
49.1
19.8
30.4
20.5
25.0
21.6
30.6
Std-RAG
37.6
50.6
35.9
41.2
13.7
21.0
26.8
35.0
37.1
47.8
37.3
44.6
31.4
40.0
IR-CoT
30.8
33.6
42.6
12.9
20.3
22.8
32.6
33.0
31.7
38.1
27.5
36.8
MA-RAG
39.3
51.7
45.5
53.1
18.0
25.1
35.6
49.3
34.6
46.2
40.2
45.8
35.5
45.2
Search-ol
23.1
30.2
28.0
13.8
33.1
33.1
38.4
26.4
33.1
Search-R11
45.2
60.1
50.9
58.2
25.5
34.1
42.2
55.6
45.3
54.7
49.3
53.6
43.1
52.7
R-Search*
38.2
51.0
58.8
64.4
19.4
28.0
52.1
36.8
46.5
42.8
46.2
38.7
48.0
S- S-DeepSearch!
40.2
53.6
54.0
61.8
18.6
25.8
46.2
57.4
37.0
46.6
40.6
46.1
39.4
48.5
Interact-RAG
51.6
66.7
69.6
76.4
34.8
43.9
54.0
65.5
50.9
60.7
56.0
60.2
52.8
62.2
Table 1: Overall performance in Exact Match (EM) and Fl scores across various benchmarks. Bold and underline denote the best and second-best performance. For the Qwen3-8B results, methods marked with a dagger (1) use their official 7B models, due to the lack of 8B versions. Notably, our interactRRGG was trained on 12K QA data, a small fraction of the 170K QA pairs used for Search- RI. This data disparity also explains Search-RI's higher results on the NQ dataset when using the 7B model.
Method
2Wiki.
Musique
PopQA
Interact-RAN -AG
69.6
56.0
w/o Interaction
63.4 (-8.9%)
30.1 (-10.9%)
50.2 (-10.4%)
w/o SFT
59.0 (-15.2%)
26.4 (-21 11.9%)
52.2 (-6.8%)
65.2 (-6.3%)
28.1 (-16.9%)
45.6 (-18.6%)
Table 2: Ablation study on Interact-RAG, reported in Exact Match (EM) scores. The 2Wiki and Musique are multi-hop-QA datasets, while PopQA is single-hop.
NQ and PopQA, with relative improvements of 11.0% and 12.3% EM scores. This validates the effectiveness of our interaction-reasoninn paradigm in tackling complex challenges. Third, our trained agent demonstrates great generalization. Trained with train-splits of HotpotQA, Musique, and NQ, it achieves consistent improvements on both in-distribution and out-of-distribution bench- marks. This indicates that the learned capability are not task-specific, underscoring the robustness and generalizability of our approach.
4.3 ABLATION STUDY
As shown in Table 2 we conduct an ablation study on Interact-RAG.
Efficacy of the Interaction Paradigm. The "woo Interaction" variant means the black-box retrieval deployed, mirroring the paradigm of typical agentic RAG systems. this configuration, the agent is restricted issuing queries to semantic retriever, without any other interaction. The

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Method
2Wiki
Musique
PopQA
Interact-RAG
69.6
34.8
56.0
w/o All-Interaction
63.4
30.1
50.2
w/o Multi Multi-faccted Retrieval
66.0
34.6
55.1
w/o Anchored Matching
66.3
34.4
53.4
w/o Context Shaping
68.8
33.6
55.2
Table Fine-grained ablation study on interaction primitives, reported in Exact Match (EM) scores.
Training freeinng-feeeeae Method
2Wiki.
Musique
PopQA
MA-RAG
45.5
18.0
40.2
Interact- RAG- Workflow
60.1
24.1
43.6
w/o Interaction
56.3
18.8
38.7
w/o Workflow
52.0
21.8
40.0
Table Ablation performance our training-free workflow, with MA-RAG (Nguyen et 2025) as baseline reference. Results reported in Exact Match (EM) scores.
corresponding results clearly show a marked performance drop. This finding underscores the critical value of our interactive paradigm, confirming that equipping the agent with fine-grained control over information-seeking process essential and effective.
Impact of the Training Strategy. For two-phase training, removing SFT leads to severe perfor- mance drops, especially on challenging datasets like Musique (-21.9%). This highlights its role in building fundamental mechanics of planning, reasoning, and iterative interaction. Similarly, omit- ting RL also causes marked declines, as RL is essential to develop more strategic policies. These results demonstrate that while SFT establishes the patterns of reasoning and interaction, RL further optimizes the agent's ' policy to achieve better performance. (More discussion in Section 4.55)
Ablation Interaction Primitives. To isolate the specific contributions of each component, conduct fine-grained ablation study on the interaction primitives (Table 4.3). The results reveal strong synergistic effect: while removing the single module degrades performance, the complete absence of interaction leads to the most significant drop. This confirms that these primitives function collectively surpass black-box retrieval. Furthermore, the contributions of different components vary across data patterns, showing the comprehensiveness of our design. For example, Retrieval and Anchored Matching are vital for 2Wiki, which may demand explicit facts. And Con- text Shaping proves more impactful on Musique, where the prevalence of distractors requires robust context scaling.
4.4 TRAINING-FREI SCENARIOS
In scenarios with limited training resources requiring zero-shot deployment, training-free solu- tions are practically important. Therefore, we evaluate our training-free approach, termed Interact- RAG-Workflow. As shown Table 4 our approach consistently outperforms MA-RAG across various benchmarks, underscoring the intrinsic effectiveness of our even without model training. To better understand the impact of individual components, we con- duct two ablation studies. First, removing the interaction (i.e, resorting to black-box query-search) leads to a significant performance drop, highlighting the critical role of fine-grained retrieval con- trol. Second, "w/o workflow" means omit reasoning enhanced workflow and directly instruct reasoning-enhanced
paradigm
the LLM through an end-to-end prompt (detailed in Appendix D.4). This also results in performance degradation, confirming the effectiveness of our workflow to orchestrate the entire RAG process.
4.5 DETAILED ANALYSIS
Efficiency of Information Retrieval. We further assess the retrieval process by measuring the number of action iterations. We compare our Interact-RAG against two query-only methods: an ablation variant restricted only the query-search action (termed as Ours-Search) and the Search-
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Ours Interact (EM)
Ours: Interact (Iter)
Ours-Searth (EM)
Ours Search ([ter)
Search-R] (E.M)
Search-Rl RI (Tee)
45
Action Iterations
40-
EM Score
40-
30
25-
SFT +RL
RL-only
SFT-oniy
Musique
2Wiki Datasets
PopQA
15
Training Steps (k)
Figure 3: Comparison of the retrieval itera- tions and the accuracy.
Figure Performance during RL train- ing. Measured on a sampled subset.
EM Score
Multi-aaceled Retrieval
34.80
3..1033166
Action Counts
Adjust Scale Shaping
28.10
1,82
1.80
1.70
0.90
0.78
15
0.31
0.40
0.21
0.02
Non-Trained
Afte-SFT
FullSFF-RL
Agent Training Stages
Figure 5: Action invocation status in different training stages, Measured the Musique dataset.
RI method. The results in Figure 3 indicate that Interact-RAG always achieves the highest EM scores with the minimum action iterations. This is particularly pronounced on complex multi-hop datasets (2Wiki and Musique), where tasks demand more intricate information seeking. This finding validates the core advantage of our paradigm: by providing the agent with fine-grained control, it can navigate the information space in less iterations, avoiding inefficient trial-and-error loops. (A case study is in Fig
Training Dynamics in RL. Figure depicts the RL training dynamics, with EM scores evaluated on sampled subset of the six test datasets. We compare the two-stage SFT+RL approach with RL-only method. Starting from the SFT checkpoint, the SFT+RL model demonstrates a consistent improvement after initial warm-up phase, ultimately converging contrast, the RL-only agent shows faster progress within the first 40 steps and then its development slows, resulting marginal improvements over the SFT-only baseline (dashed line) and falling significantly behind the SFT+RL model. This highlights the critical role of the two-stage training. SFT provides the agent with crucial foundational capability and strategic solution paths. Without this prior, the RL-only agent struggles to master the complex retrieval strategies from scratch.
high-performance level. In
Interaction Patterns Across Training Stages. To understand how our training shapes the agent's behavior, Figure 5) shows the statistics of interaction across different stages. (1) agent relies solely on an end-to-end prompt, exhibiting limited engagement. It averages only 1.82 turns, with minimal invocation of interactive actions. This confirms that, without training, the LLM struggles to autonomously master the iterative information-seeking process. (2) SFT Stage: After SFT, the agent learns the fundamental processing patterns. The number of interaction turns rises to 3.61, indicating that SFT instills reasoning strategies and equips the agent to better engage with the Corpus Interaction Engine. (3) RL Stage: While the number of interaction turns decreases, the EM score improves significantly. This reflects the agent's ' transition to a more strategic policy, enhancing both efficiency and accuracy through improved reasoning and appropriate retrieval actions. (4)
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Detailed Observations: After the RL exploration, the frequency of Entity-Match increases sharply- This suggests the agent has learned to prioritize precise and anchored searches. In contrast, the use of Doc-Shaping decreases, because the agent's improved retrieval precision reduces the necessity for subsequent noise filtering. In summary, this progression analysis highlights the rationality and effectiveness of interaction paradigm and training pipeline.
RELATED WORK
5.1 RETRIEVAL--UGGENNTN GENERATION
Retrieval-Augmente Generation (RAG) is prevailing method to enhance LLMs with external in- formation (Lewis et al., 2020). Basic RAG relies on static embedding -aatingg---eddddd-- retrieval (Lewis al. 2020 Li et al. 2025e d). which may suffer from information omission (Gao et al., (023). To ad- dress this, various studies propose tree-based or graph-based index to improve retrieval robustness
(Jin et al. 2025b Edge et al.] 2024 Luo et al. 2025 Another direction focuses on improving the retrieval pipeline. Iterative RAGs were introduced to progressively refine information through
multi-step retrieval (Trivedi et al. 2023: Chan et al. 2024
Hui et a 2025). Recent agentic methods provide more flexibility, where the LLM autonomously orchestrates the entire RAG pipeline (Gao al. 2025). Methods such as MA-RAG (Nguyen al., 2025). Search-O1 (Li et al. (025b). and
MCTS-RAG (Hu et al.] (025) implement prompt-driven strategies, leveraging multiple agentic mod- ules. End-to-end approaches like Search-RI (Jin et al.) 2025a). InForage (Qian & Liu 20225), and SimpleDeepSearcher (Sun et al. (2255) adopt SFT and RL to create fully autonomous agents. De- spite the effectiveness of above approaches, they often operate within black-box retrieval paradigm, limiting the analysis and control. Addressing this, our work explores an interactive framework with fine-grained retrieval manipulation, supporting improved reasoning and adaptability.
5.2 REASONING-ENHANCCE LLM AGENT
Enhancing LLMs with reasoning has become prevailing research focus (Xu et al. 2025 The strategics span prompting- based approaches like ofain-of-Thought Wei et al. (2022), ) and training- optimized models like OpenAl o1/o3/04 (Jaech al. 2024) and DeepSeek-RI RI (Guo et al. 2025
To support broader scenarios, various works leverage reasoning to improve the performance of L.L
agents(Ferrag et al. 2025), training them to use tools and solve complex problems (Lu et al. 2025b
Shen et al.] 2025). They explore various dimensions, including the construction of high quality
training data (Li al. 2025a Shi et al. 2025). the refinement of reward signals (Zhao
2025
Qian & Liu 2025), ) and the optimization of reinforcement learning algorithms (Dong et al. 2025 Lu
iet | 2025a). While these works have made great advances concentrating on the agent's training, our
focus is distinct: we redesign the interaction paradigm for RAG agents and leverage the reasoning capability to enable fine-grained manipulation.
CONCLUSION
In this paper, we identify the limitation simple black-box retrieval, and introduce Interact-RAG, a new paradigm empowering LLM agents with fine-grained control over the information-sseking pro- cess. Our approach features an underlying Interaction Engine, reasoning-enhaneed workflow and two-stage training pipeline, finally yielding a unified, end-to-end interactive RAG agent. Exten- sive experiments show Interact-RA. -ee significantly outperforms advanced baselines, validating the effectiveness of reasoning-interaction paradigm. This work offers a promising direction for creating more powerful, transparent, and interactive RAG systems.
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ETHICS STATEMENT
This work adheres to the ethical guidelines set forth by ICLR 2026. We have conducted our research with commitment to avoiding harm, ensuring honesty and transparency in methodology and reporting. We have made concerted efforts to identify and mitigate potential biases in our data and algorithms ensure fairness. Furthermore, our research respects individual privacy, and have complied with all applicable regulations and ethical standards regarding data use.
REPRODUCIBILITY STATEMENT
In this work, we present Interact-RAG, a new paradigm empowering LLM agents with fine-grained control over the information-seeklnn process. To ensure reproducibility and facilitate further re- search, we provide the source code in the supplementary materials. Additionally, we offer compre- hensive documentation in Section 4.1 and Appendix [D] covering dataset details, training parameters, environment constructions, and experimental configurations.
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A THE USE OF LLMS
This paper utilized LLMs only for language polishing in parts of the text.
B LIMITATIONS
Despite the effectiveness of Interact-RAG. acknowledge limitations: (1) RL Optimization: Our RL stage currently relies on outcome-hasee rewards. While this aligns with some established GRPO practices, incorporating granular process rewards or advanced RL algorithm could further enhance learning efficiency. (2) Test-time Cost: Our explicit Chain-of-Thought reasoning trajec- tory incurs higher latency and token usage, compared to non-reasoning modes. This test-time scal- ing reflects the trade-off inherent in reasoning models, where increased compute is exchanged for superior accuracy. future work, we will explore more advanced RL designs and optimize cost-effectiveness.
C ADDITIONAL DEMONSTRATION AND EXPERIMENTS
C.1 CASE STUDY
As illustrated in Figure 6 Search-R1, which relies black-box query search, can fall into query loops, hindering its ability to efficiently retrieve evidence. In contrast, our Interact-RAG utilizes granular interactive actions effectively address this challenge.
Question: What's the birth date of the director of film Want Someone To Eat Cheese With?
Search-Ri
Interact-RAG
First, both find director Jeff Garlin
First, both find the director is Jeff Garlin
<thinksI found that the director of the film
<thinks We find that Want Someone to Eat Cheese With" film directed by Jeff Garlin, To find his birth date, we can include exact keywords. And we can also focus his with entity match ... </thinks
Want Someone To Cheese With" Jeff Garlin.
Now I need to find what's his birth date </think>
<search> Jeff' Garlin birth </search>
<tool calls ("entity match": "Jeff" Garlin",
<information> In March 2018, 8, Garlin was one of the actors who cinformation>
"exact query keywords": ["Jeff Garlin", "bom"), "semantic query": "What is Jeff Garlin's birth date?") </tool_<cll>> call>
<think> found out that the birth year is still not
available. need to find this information again.
<tool response> Jeff' Garlin (born June 1962)
</think>
concrican...</tor response>
<search> What's the birth date of Jeff' Garlin? </search>
<thinks We have found that the director is Jeff Garlin, was born on June 5, 1962 </think> ... Answer ...
<information> 2013, Garlin signed the ABC sitcom "The Goldbergs" <linformation>
<think> The birth date is still un kookwow...... </think>
Case study under multi-hop query, comparing Interact-RAG and Search-R1. The results highlight that Search-R1, relying on black-box query-search, can become trapped in query loops, failing to retrieve efficient evidence. In contrast, our approach leverages granular interactive actions to directly resolve the issue. Both the exact keyword "born" and the anchored entity-match are helpful to retrieval the desired information.
Figure 6:
C.2 GENERALIZATION TO OTHER DOMAINS
For more comprehensive evaluation over other non-wiki domains, conducted additional experi- ments on the MultiHop-RACE benchmark (Tang & Yang 2024). Its corpus consists of news covering diverse fields such technology, health, and business. As shown in the Table 5 Interact-RAG significantly outperforms baselines in this out-of- diito---istriuttion setting. This further demonstrates the effectiveness and robustness of our interaction-reasonin paradigm.
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Method
EM
F1
7B Models
Std-RAG-7B
57.5
59.4
MA-RAG-7B
50.2
52.3
Search-R 1-7B
73.0
74.1
Interact-RAG-7B
79.4
80.4
8B Models
Std-RAG-8B
63.4
65.1
MA-RAG-8B
66.3
68.7
Interact-RAG-8B
81.4
82.3
Table 5: Performance results on the MultiHop-RAG benchmark (non-wiki domain).
Method
2Wiki
Musique
HotpotQA
Stardard--Rttrieval
0,434
0.127
0.502
MA-RAG-7B
0,620
0.256
0.573
MA-RAG-SB
0.631
0.277
0.581
Search-R1-7B
0.608
0.256
0.609
Anteract-AAG-75
0.686
0.316
0.632
Interact-RAG-8B
0.706
0.335
0.637
Table 6: Retrieval quality evaluation. We report the coverage of supporting facts within the retrieved context.
C.3 RETRIEVAL QUALITY
To assess retrieval quality beyond final answer accuracy, we evaluate the coverage of supporting facts across three multi-hop benchmarks, since they provide human-annotated evidence. We calculated the proportion of supporting facts covered within the retrieved context. As shown in Table O our Interact-RAG consistently outperforms other baselines. This confirms that our active interaction paradigm effectively improves the information retrieval and gathering process.
C.4 COST-PERFORMANCE TRADE-OFF ANALYSIS.
Table C.4 provides a detailed comparison computational costs on the 2WikiMultiHopQA dataset. The evaluation conducted on the NVIDIA A100 GPU utilizing the vLLM inference engine, and the results are averaged per question. To ensure the consistent comparison, the methods are all based the Qwen2.5-7B model. We acknowledge that Interact-RAG incurs higher latency and token consumption compared to standard baselines. This increase stems from the explicit reasoning trajectory. However, this design aligns with the emerging paradigm of reasoning models and test- time scaling (e.g., OpenAl-ol (Jaech et al. 2024), DeepSeek-RI (Guo et al.) 2025)), where increased inference compute is necessary trade-off for superior accuracy complex tasks.
Furthermore, we conducted a control experiment applying a Best-of-N (N 3) sampling strategy the strong baseline, Search-RI. While this approach increased the computational cost by over 3x, the performance only improved from 50.9 to 56.2, still falling short of ours. This demonstrates the effectiveness of Interact-RAG.
C.5 FUSION POLICY FOR MULTI-FACETED RETRIEVAL
In the multi-faceted retrieval, we need to fuse the results from two distinct strategies. In our imple- mentation, we apply min-max normalization to both scores, and then perform the weighted aggrega- tion. where the weight is assigned by the LLM agent. Now, compare fusion policy with two standard rank fusion methods, including Reciprocal Rank Fusion (RRF) and CombMNZ (Cormack) et al. 2009). As shown in Table 8, implementation yields performance comparable slightly
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Method
EM Score
Latency (8)
Tool Time (s)
Out Tok.
In Tok.
MA-RAG-7B
39.6
15.1
5.09
181
3812
Search-RI-77
50.9
17.2
5.61
192
4289
Interact-RAG-7B
63.6
36.4
4.67
713
5216
Table 7: Detailed computational cost analysis on the 2WikiMultiHopQA dataset. We report the Exact Match (F.M) score alongside average wall-clock latency, tool execution time, and accumulated token consumption per query.
Fusion Method
2Wiki
Musique
PopQA
RRF
68.7
34.6
55.3
CombMNZ
69.8
32.9
56.0
Interact-RAG
69.6
34.8
56.0
Table 8: Comparison of different fusion strategies (reported in EM Scores).
superior. And we would like to clarify that the fusion mechanism is small functional design in our interaction engine, its variations do not significantly alter the overall performance.
C.6 OVERHEAD OF OUR CORPUS INTERACTION ENGINE
For the memory footprint, on a corpus of 21.8K documents (280K chunks), the standard vector database (based Chromadb (core team 2025 occupies 2.3GB. And our additional interaction component (based on SQLite FTS-based (SQLite 2025)) occupies only 0.3GB, representing a neg- ligible memory addition. Besides, we evaluate the deployment performance of engine using the 2Wiki dataset, in an 8-core CPU environment with 96 concurrent request clients. For the wall-clock time, our interaction processing latency is 1.82s per iteration, only 3% higher than the standard vector-based based retrieval (1.76s), which is negligible.
D ADDITIONAL IMPLEMENTATION DETAILS
D.1 DATASET DETAILS
For the training phase, our data sourced from the combined training splits of NQ, HotpotQA, and MuSiQue. This collection includes both single-hop (NQ) and multi-hop (HotpotQA, MuSiQue) question-answering data. Following the workflow described in Section 3.2, we synthesized 4.8K agent trajectories for Supervised Fine-Tuning (SFT). Subsequently, for the Reinforcement Learn- ing (RL) phase, we started with 9K question-answer pairs and filtered out some overly simplistic questions (measured by the pass rate), resulting in curated set of 7.4K pairs. For the evaluation phase, our test set was constructed by randomly sampling 500 question-answer pairs from each of six distinct datasets. An exception 125 available test instances due to its limited size.
made for the Bamboogle dataset, from which used all
D.2 MORE DETAILS OF CORPUS INTERACTION
Our Corpus Interaction Engine designed to support agent interactions. It parses LLM --Meeeeeerteed tool-calling response, executes the specific operations, and returns the feedhack. The implementa- tion lightweight, intentionally avoiding the overhead of heavy operations or extra L.M invoca- tions.
The core functionalities are realized as follows: (1) For Semantic-Search, we implemented a re- triever based on the e5-base-v2 model (Wang et al. 2022). using the prevailing ChromaDB (core) team 2025) as our underlying vector database. (2) Exact-Search is built upon the Full-Text Search (FTS) module of SQLite database (Bhosale al. 2015), which returns results ranked by the BM25 scores between query keywords and text chunks. (3) the Fusion Stage of semantic and exact search, first apply the min-max normalization over the scores of the top-20 chunks from each
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search strategy. These scores are then aggregated via weighted sum, according the weight spec- ified by the agent, and the high-scoring chunks are ultimately returned. (4) For Satiit--atcchin,. the LLM agent is responsible and capable provide the specific entity keywords, based the query and history context. Then, we utilize SQLite FTS to retrieve sentences containing the entity terms, where the words are normalized for comparison. After that, retain and append the three most relevant small sentences based on the current sub-query. (5) Simpler actions like Include-Dov and Exclude-Docs are handled directly through basic filtering operations. Regarding filter persistence, the LLM is instructed to manage the state by reissuing filter instructions if needed.
The engine follows deterministic pipeline resolve concurrent actions: (1) Retrieval scores are fused to form a candidate list. (2) Doc-filters (includc/exclude) are applied to explicitly retain or remove chunks. (3) Top-ranked chunks are selected according to the chunk budget. (4) Unique entity-specific short snippets are appended.
It worth noting that while the agent may invoke multiple strategies in a single iteration, our engine avoids generating multiple large contexts. Instead, it produces single consolidated context by ag- gregating these primitives. Specifically, multi-strategy retrieval yields a unified chunk set via score fusion, and Entity Match appends only concise snippets with negligible addition. While context- shaping may dynamically change the number of chunks, our ablation studies (Table 4.3) demon- strate that Interact- -RAG still maintains strong performance without this module. Collectively, these suggest that our performance gains stem from ane-grained interaction strategies, rather than simply inflating the retrieval volume.
D.3 EXPERIMENTAL DETAILS
All our experiments were conducted on a cluster of 8 NVIDIA A (80GB) GPUs To ensure generality and alignment, action pipeline is implemented using the official reasoning and tool- use template from Qwen3 (Yang et al. 2025). which inherently utilizes <think>, <tool_call>, and <tool response> tags. In the Supervised Fine-Tuning (SFT) stage, we employ the Alama-Factory framework (Zheng et al. 2024). training for 2 epochs with a learning rate of 2 x 10-5 and a batch size of 128. Following this, the agent is refined through Reinforcement Learning (RI.) using the verl framework (Sheng et al. 2025). The RL phase involves multi-turn agent training for 2 epochs, with a policy learning rate
10 6 batch size of 128, a maximum of 7 interaction turns, and the rollout-num of 8. During the RL training, we observed the format error rate of 2% at the beginning. And this rate declined to 1.1% at step 50 and dropped 0.04% by the final step (i.e., step- 110), which also demonstrates the effectiveness of our RL optimization.
For our evaluation, we enabled Qwen3's native thinking mode (Yang et al. 2025) for non-RAG and standard-RAG baselines to maximize their reasoning capabilities, while disabling it for prompt- based methods like IR-CoT and MA-RAG to ensure strict format adherence. All end-to-end trained agents, including Interact-RAG: operated with their innate reasoning enabled. Furthermore, we addressed a corpus limitation: using the generic 2018 Wikipedia dump as a corpus often causes mismatches with QA benchmarks (e.g.. entity name ambiguity, missing evidence). We therefore constructed a more faithful corpus as follows: for benchmarks with candidate passages, we used their metadata to obtain the corresponding documents from the 2018 Wikipedia snapshot, which mitigates the name ambiguity. If the document was unavailable, we used the provided passages directly. For benchmarks lacking explicit evidence (e.g.. Bamboogle), we generated synthetic queries from the question and ground-truie answer to retrieve the top 20 most similar passages via a retriever. Our final evaluation corpus consists of approximately 280,000 text chunks, with each chunk averaging 100 words. We will release this corpus to facilitate further research.
D.4 LLM PROMPTS
To ensure generality and alignment, our action pipeline is implemented using the official reasoning and tool-use template from Qwen3 (Yang et al. 2025). Therefore, we don't need to specify special tags or define explicit rules for the model's output structure. Actions described in Section 3.1 can simply be injected as tool-use arguments, where the template automatically formats the inputs into the required structure, and the model inherently generates standardized reasoning and tool calls. Therefore, we just need to craft the task prompt, the details of which are provided below.
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End-to-End End Prompt for Interactive RAG Agent
You strategic Al research assistant. Your task is to questions leveraging a search tool. You must operate in a systematic, iterative loop of planning, acting, and analyzing.
## Your Research Process
### 1. Understand & Plan:
Understand the user's question and create search plan.
First. thoroughly analyze the user's question. Identify key concepts, entities, and any constraints.
If the question is straightforward, formulate single, comprehensive search query that is most likely to yield the final answer.
If the question is complex, break it down and define the clear and specific sub-tasks. Please outline the sub-questions and desired outcomes for each step.
If some sub-tasks can be executed parallelly, you should point out it. You should first perform thinking, and output the primary plan in the list format.
### 2. Execute the Search
Based on the current state and previous analysis, call the execute_searde pllan tool to perform the search.
The parameter semantic query is primary and required. It should be a clear and concise query to search the needed information.
There are also several optional parameters to improve the search results. You should per- form analysis and adapt the parameters actively and reasonably.
### 3. Observe & Iterate
Analyze the retrieved context, and decide the next action.
If the received context is not good, you should reflect to improve the search, and execute the search tool again with the refined parameters.
If you get sufficient information for the uub-question, you proceed to the next sub-task with another search execution. You should make sure search for current step is enough, don't be overly confident about some noise.
If you have gathered sufficient evidence to construct a complete answer for the whole question, you should conclude the final answer with more function-calls. You don't need to follow the primary search plan strictly. You can adapt your strategy based the retrieved context and your analysis.
## Final Answer Formulation Once you have enough evidence get the final answer, you just conclude it. The final answer must be concise and direct words.
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Prompt for the Global-Planne, within our Workflow
You expert research assistant. focused high-level planning. There's search tool available to you to fetch information. Your core goal is to plan process to answer the user's query.
## Your Planning Process:
Thoroughly analyze the user's question. Identify key concepts, entities, and any con- straints.
the question direct or straightforward. formulate single, comprehensive search query that is most likely to yield the final answer. Direct question example: "when was the last time france hosted the olympics".
If the question is complex, break it down and define the clear and specific sub-tasks. Develop specific plan to guide the research process, outlining sub-questions for each step. Sub-tasks must be simple and direct; if not, further divide them into smaller steps. Some sub-tasks may be executed parallelly, you should point out it.
## Other Requirements:
In the analysis and planning, do not include your uncommon internal knowledge, as it may be inaccurate. Do not try answer the question by yourself, just provide the research plan.
## Your expected output
You should first perform the concise thinking as described above, and then output the anal- ysis and output the research plan. The analysis should be organized in a natural language format, with fluent and connective expressions (e.g., Okay, Then, Therefore). And the pri- mary plan should be in list format as:
Primary Plan: 1. Determine the director of the film Polish Russian War'. 2. Identify the birthplace of that director. 3. Formulate the final answer.
Prompt for the Adaptive-Reasonee within our Workflow
You are an expert research strategist. Your task to analyze the state of a research query, evaluate the latest search results, and devise the next best step. You should only generate the plan for the next action, not execute it or answer it.
## Your Instructions:
You should first briefly summarize the relevant key findings from the previous search. And state what information has been gathered and what is still missing, Based on the observation, you should reasonably choose one of the following three paths, then analyze and propose the next step.
A) Proceed: Choose this path if the last search successfully answered the current sub- question. State the key information that was found, then propose the next logical search with appropriate parameters. You can propose up to two parallel searches if needed. B) Conclude: Choose this path the whole tasks are resolved and you have sufficient in- formation answer the user's original query. Announce that the research is complete and provide a concise summary of all key findings.
C) Reflect & Refine: Choose this path if the previous search ineffective (e.g., irrelevant, incomplete, or low-quality results). First, briefly explain why the search failed, Then, think reasonably and propose refined search action with improved parameters. If sub-task remains unresolved after 3 attempts, consider moving on to the next one. Do not include your uncommon internal knowledge, as it may be inaccurate.
## Output Format:
For both PROCEED and REFINE step, you should concisely and reasonably analyze and suggest the parameters for the next search.
You should strictly format your entire output in natural language * Please use more fluent and connective expressions.
You don't 't need to conclusively list the parameters at the end. Please make your output concise but clear.
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Prompt for the Executor within our Workflow
You specialized searching execution agent. You will be presented with a user's query and prior search results with analysis. Your sole purpose is to perform one two specific actions: either call the execute search plan tool provide the final answer. ## Your Actions (Choose ONE):
Execute Search:
- Based on the provided instructive analysis, you should identify the proper query and pa- rameters.
Your task is to call the tool with the appropriate parameters.
Note:
The semantic_queny parameter is required. It should be clear and specific. If the previous instructions do not provide query, you should formulate one.
There are also several optional parameters to refine search results.
You can make up to seperate calls in one turn, needed (i.e., some sub-tasks can be executed parallelly).
Formulate the Final Answer:
Based on the provided information and the analysis, if the evidence is sufficient to answer the user's whole original question, you should provide a final answer. The final answer must be concise and direct words
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Reviews:
[{"review_id": "XwwAahQjZv", "reviewer": "Reviewer_rrcZ", "summary": "This paper addresses a critical limitation in contemporary Retrieval-Augmented Generation (RAG) systems, specifically within the \"Agentic RAG\" paradigm. The central problem identified is that existing LLM agents treat the retrieval process as an \"opaque black-box\". This confines the agent to the role of a \"passive query issuer,\" which must rely on inefficient \"trial-and-error loops\" of query reformulation to find information, a method that frequently fails in complex, multi-hop information-seeking tasks.\nThe paper hypothesizes that empowering the LLM agent with fine-grained, transparent control over the retrieval process will lead to significantly more effective and efficient information seeking. To test this, the authors introduce Interact-RAG, a new paradigm that transforms the agent into an \"active manipulator\" of the retrieval process.", "questions": "1.  [most urgent point ] Were the baseline models (Search-R1, R-Search, etc.) re-evaluated on the \"faithful corpus\"?\n2. Could the authors please justify or rephrase the \"lightweight\" claim? Given the architectural requirement for two distinct search indexes (a vector database and an FTS module) , the system is architecturally more complex than standard RAG. Perhaps \"computationally efficient\" (referencing the fewer iterations in Figure 3)  is a more accurate term than \"lightweight\" (which implies low architectural overhead)", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The questions posed by this paper are critical to the advancement of LLM agents. The core problem—that agents are \"stuck\" in inefficient query reformulation loops and lack fine-grained control over their tools —is a widely recognized and significant unsolved challenge in the field. This paper tackles this gap head-on, addressing the fundamental limitations of the agent-retriever interface.\n2. The paper is exceptionally rigorous. The design of the Interact-RAG-Workflow as a dual-purpose system—serving as both a strong zero-shot agent and a high-quality data synthesizer—is an elegant and insightful solution to the data bottleneck problem in agent training.\n3. The authors' thoroughness is a key strength. The ablation studies (Table 2)  and detailed behavioral analysis (Section 4.4)  anticipate and preemptively answer nearly every major question a reviewer might have about why the system works. The analysis of learned interaction patterns in Figure 5, for instance, provides a fascinating window into the \"mind\" of the trained agent, showing how its policy evolves to favor precision (e.g., Entity-Match).", "weaknesses": "1. This is the paper's most significant weakness. The authors admit in Appendix C.3 that the standard 2018 Wikipedia dump has \"mismatches\" and \"missing evidence,\" leading them to \"construct a more faithful corpus\". This is a major methodological decision that clouds the results. The paper does not explicitly state that the baselines (Search-R1, R-Search, etc.) were re-evaluated on this new \"faithful corpus.\" If they were not, the 22.5% gain and SOTA claims are confounded, as the comparison would not be apples-to-apples. This corpus advantage could be responsible for a portion of the performance gain.\n2. The reward function $R(\\tau) = -1 + \\mathbb{I}\\{\\tau_{valid}\\} + \\mathbb{I}\\{\\tau_{valid}\\} \\cdot \\mathbb{I}\\{y_{ans}\\}$ 1 is highly sparse. It only provides a positive signal at the end of a (potentially long) trajectory. The paper does not discuss why this sparse signal was sufficient for the agent to learn complex, multi-step strategies (like prioritizing Entity-Match, as seen in Figure 5).1 The implicit answer, supported by the failure of the RL-only \"w/o SFT\" agent 1, is that the SFT stage 1 does almost all the heavy lifting. The RL stage merely refines an already-strong policy. The paper's true methodological contribution may be the SFT data-generation pipeline, with the RL refinement being a final optimization step.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dJJDf6DsSH", "reviewer": "Reviewer_6cgB", "summary": "The paper proposes Interact-RAG, which exposes retrieval as controllable “interaction primitives” (semantic/exact search, weighted fusion, entity match, context shaping) instead of a black-box query. A reasoning-enhanced workflow (planner–reasoner–executor) is used for zero-shot execution and data synthesis, followed by SFT and RL to train an end-to-end agent. Experiments on six QA benchmarks show sizable gains over agentic baselines.", "questions": "Please address the concerns in Weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This paper has practical significance in addressing a key limitation in agentic RAG—namely, the lack of retrieval control—making it potentially useful for real-world systems.\n\n2. The paper is clearly written and easy to follow, with well-structured explanations that make the methodology and experiments accessible.", "weaknesses": "1. The proposed Corpus Interaction Engine appears incremental rather than fundamentally novel; it mainly uses additional retrieval modes. This does not fully realize the claim in intro section:  “transforming the agent from a passive query issuer to an active participant,”.\n\n2. Missing fine-grained ablations: Ablation studies for the specific components (Multi-Faceted Retrieval, Entity Match, Adjust Scale, and Doc Shaping) are absent, making it unclear which modules drive the observed performance gains and by how much.\n\n3. Backbone and retriever parity: Please report results with matched backbones (same LLM family/size and the same retriever). The paper uses e5-base-v2—what retrievers are used for each baseline, and are they consistent? Without parity, improvements may reflect model or retriever differences rather than the proposed method.\n\n4. Cost analysis: Because the method’s reasoning trajectory introduces additional steps, the extra wall-clock time and token costs should be analyzed in detail (end-to-end latency, tool-call time, and total token consumption).\n\n5. Retrieval step inflation: From Figures 5 and 6, a single output trajectory in Interact-RAG can issue multiple tool_call operations, and both Multi-Faceted Retrieval and Entity Match can trigger several retrieval steps. Compared to Search-R1 and other baselines, how many retrieval steps are taken in total? Could the performance gains be primarily due to increased retrieval volume rather than a better strategy? Please quantify and control for the number of retrievals.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "svbogWh9RW", "reviewer": "Reviewer_DdfH", "summary": "Interact-RAG introduces a new paradigm for Retrieval-Augmented Generation that breaks the black-box treatment of retrieval and turns the LLM agent into an active manipulator of the corpus. It provides a lightweight Corpus Interaction Engine with fine-grained action primitives—multi-faceted retrieval (semantic, exact, weighted fusion), anchored matching (entity-focused search), and context shaping (include/exclude docs, adjust scale)—and pairs this with a reasoning-enhanced workflow (global planner, adaptive reasoner, executor) for robust zero-shot execution and data synthesis. The synthesized trajectories are used to train an end-to-end agent via supervised fine-tuning, then refined with reinforcement learning (GRPO). Across six QA benchmarks, Interact-RAG achieves state-of-the-art EM/F1, with especially strong gains on multi-hop tasks and improved efficiency (fewer action iterations). Ablations show the interaction primitives, SFT, and RL are all critical, and the training-free workflow alone outperforms strong multi-agent baselines.", "questions": "Fusion mechanics: How are dense and sparse scores normalized before fusion? Are the fusion weights learned, fixed, or chosen by the agent per step? Do you constrain weights (e.g., to [0,1] or sum to 1)? Have you compared your fusion against standard rank fusion methods (RRF, CombSUM/CombMNZ)?\n\nEntity match implementation: How is the “entity” identified and canonicalized (prompted string, NER, or entity linking)? How do you handle aliases, disambiguation, and collisions across similarly named entities? Any safeguards against retrieving wrong-entity passages?\n\nConcurrent actions and precedence: When the agent issues multiple actions at a step (e.g., include/exclude + retrieval), what is the execution order and conflict resolution? How are results aggregated, and how do stateful filters (include/exclude) persist across future steps?\n\nOn the “-1 initial penalty” in the reward: In GRPO, if returns are centered/normalized within each group (at least subtracting the group mean, often dividing by the standard deviation), adding a constant to all trajectories (e.g., -1) cancels out and should not affect the advantage or policy updates. Does your implementation include any components that depend on absolute returns (e.g., uncentered advantages, value-function training on absolute returns, cross-group thresholds for filtering/early stopping, running-mean normalization) that would make this bias meaningful? If not, could the -1 be removed or replaced with a more discriminative shaping reward (e.g., graded penalties for format errors, intermediate retrieval-quality rewards), and can you report the impact on training stability and sample efficiency?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "Originality: Proposes a corpus interaction engine with actionable primitives (multi-faceted retrieval, anchored matching, context shaping) and a hierarchical reasoning workflow (planner–reasoner–executor), moving beyond black-box query reformulation. The integration of training-free trajectory synthesis with SFT+RL is thoughtfully designed.\n\nQuality: Empirical evaluation is thorough, covering diverse datasets, strong baselines, and ablations that isolate each component’s contribution. Analyses of efficiency (action iterations) and RL training dynamics add credibility. Implementation choices are pragmatic (lightweight FTS, clear tool-call formats).\n\nClarity: Motivation and problem framing are clear; the pipeline and action space are well explained; figures and tables help understanding; methodology is presented with enough detail to reproduce key components.\n\nSignificance: Demonstrates sizable, consistent gains—especially on complex multi-hop tasks—showing the practical value of interactive retrieval. The paradigm is likely to influence future RAG agent design, with both training-free and trained variants offering immediate utility.", "weaknesses": "Scalability and deployment realism: The interaction engine relies on SQLite FTS and simple filters. This keeps things lightweight but raises questions about performance on large or sharded corpora, multi-tenant settings, and streaming updates. There is no wall-clock or throughput/latency analysis, nor memory/compute footprint or cost per question. Without these, claims about efficiency (fewer iterations) are not tied to practical runtime advantages.\n\nRetrieval quality metrics: The paper focuses on EM/F1. For multi-hop QA (e.g., HotpotQA), retrieval-oriented metrics (recall@k of supporting docs, precision of included context, coverage of hops) would strengthen the evidence that the interaction engine improves retrieval quality, not just final answers.\n\nScore fusion calibration: Weighted fusion between dense and sparse scores is central, but the paper does not detail score normalization/calibration (dense cosine vs. BM25/FTS scores are not directly comparable), how weights are chosen/updated by the agent, or whether any safeguards (e.g., monotonicity, bounds, or learned scaling) are used. This may lead to unstable retrieval quality across corpora.\n\nEvaluation fairness and backbone effects: The main results use Qwen3-8B while several baselines are reported with 7B checkpoints. Although 7B results for Interact-RAG are in the appendix, fairness is harder to assess from the main text. Bringing same-backbone (7B) head-to-head comparisons into the main results would improve confidence that gains are due to the method rather than model size.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wJZj6QxZQY", "reviewer": "Reviewer_Xirv", "summary": "The paper introduces Interact-RAG, a paradigm that addresses the black-box limitation in agentic Retrieval-Augmented Generation by granting the LLM agent direct, fine-grained control over the retrieval process through a lightweight Corpus Interaction Engine. This engine provides three categories of action primitives: Multi-Faceted Retrieval, Anchored Matching, and Context Shaping. To enable effective use, the authors propose a modular reasoning-enhanced workflow with a global planner, an adaptive reasoner for proceed and reflect-refine decisions, and an executor, which supports zero-shot execution and synthesizes clean trajectories. These trajectories are used to train an end-to-end agent through Supervised Fine-Tuning on a collection of successful traces, followed by Group Relative Policy Optimization reinforcement learning with a validity-gated accuracy reward. On six benchmarks, the Qwen3-8B-based Interact-RAG achieves higher exact match and F1 scores than existing baselines, showing substantial relative gains and particularly strong improvements on multi-hop reasoning tasks.", "questions": "- What is the per-primitive ablation (EM/F1 drops when removing each category) on HotpotQA and MuSiQue, to quantify if anchored matching alone closes the gap to Search-R1?\n- Why mask retrieved tokens only during SFT loss but not RL—does this cause distribution shift, and how many RL trajectories were invalid due to format errors?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The global-planner → adaptive-reasoner → executor decomposition produces coherent traces without verbose LRM noise.\n- Consistent SOTA across in- and out-of-distribution.\n- lightweight engine adds negligible overhead while enabling actions like adjust_scale for sub-task scoping.", "weaknesses": "- Experiments use only 2018 Wikipedia (E5 retriever, top-3 chunks default); no evaluation on larger/dynamic corpora (e.g., 2025 dumps) or non-Wiki domains, where FTS indexing might scale poorly or entity_match fail on ambiguous entities.\n- The outcome-only reward ignores process quality—e.g., penalizes all invalid formats equally but doesn't credit efficient trajectories (fewer steps) or penalize over-reliance on exclude_docs, potentially encouraging brittle strategies not exposed in the 7.1K RL questions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)