GPS: Graph-guided Proactive Information Seeking in Large Language Models
Authors: Ruiqing Li
Yifeng Xu
Xinke Jiang
Zhibang Yang
Xinyu Ma
Yue Fang
Junfeng Zhao
Yasha Wang
Xu Chu
Venue: ICLR 2026 Poster (2026)
Keywords: Large Language Models
Retrieval-augmented generation
Reinforcement learning
Clarification questions
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Equipping Large Language Models (LLMs) with the ability to proactively ask clarifying questions is essential to mitigate ambiguity when faced with underspecified user queries in retrieval-augmented generation (RAG) systems. However, existing methods often neglect the rule-based reasoning structures embedded in the retrieved knowledge that are central to ambiguity, making it challenging to learn an effective and efficient question-asking strategy. To address these issues, we introduce \textbf{GPS}, a two-stage framework for enhancing proactive information seeking abilities of LLMs in RAG systems. In the reasoning stage, we propose a Directed Acyclic Graph (DAG) reasoning structure with theoretical guarantees of logical completeness, which facilitates capturing all conditional logic in the retrieved knowledge and supports effective clarification. In the clarification stage, we design a traversal-based algorithm that dynamically prunes the DAG based on user responses, enabling efficient clarification. To further enhance DAG construction, we first propose a conditional paths guided data synthesis method to address data scarcity challenge, then we apply a clarification-oriented reinforcement learning method with a hybrid reward that jointly considers effectiveness and efficiency to optimize the LLM. Experiments on three benchmarks demonstrate that \textbf{GPS} outperforms baseline methods in both success rate and clarification efficiency.

Published as a conference paper at ICLR 2026
GPS: GRAPH-GUIDED PROACTIVE INFORMATION SEEKING IN LARGE LANGUAGE MODELS
Ruiqing Li Yifeng Xu Xinke Jiang' :2, Zhibang Yang" ?2, Xinyu Mak Yue Fang
Junfeng Zha Yasha Wang 12,5; Xu 2,3,4
National Engineering Research Center For Software Engineering, Peking University, Beijing, China
2 School of Computer Science, Peking University, Beijing, China
3 Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing, China
4 Center on Frontiers of Computing Studies, School of Computer Science, Peking University
5 Peking University Information Technology Institute (Tianjin Binhai), Tianjin, China
6 Big Data Technology Research Center, Nanhu Laboratory Irgestu. pkk..kkkee.e.. wuu..edu.com en, chu.xu@pku.edu cn
ABSTRACT
Equipping Large Language Models (LLMs) with the ability proactively ask clarifying questions is essential to mitigate ambiguity when faced with underspec- ified user queries in retrieval-augmented generation (RAG) systems. However, existing methods often neglect the rule-based reasoning structures embedded in the retrieved knowledge that are central to ambiguity, making it challenging to learn an effective and efficient question-asking strategy. To address these issues, we introduce GPS, a two-stage framework for enhancing proactive information seeking abilities of LLMs in RAG systems. In the reasoning stage, propose a Directed Acyclic Graph (DAG) reasoning structure with theoretical guarantees of logical completeness, which facilitates capturing all conditional logic in the re- trieved knowledge and supports effective clarification. In the clarification stage, we design traversal-basee algorithm that dynamically prunes the DAG based on user responses, enabling efficient clarification. To further enhance DAG construc- tion, first propose conditional paths guided data synthesis method to address data scarcity challenge, then we apply cotrification-oriinne ing method with hybrid reward that jointly considers effectiveness and efficiency to optimize the LLM. Experiments on three benchmarks demonstrate that GPS outperforms baseline methods in both success rate and clarification efficiency. |
reinforcement learn-
INTRODUCTION
Kezaledge
Consider a user seeking information about disability benefits eligibility and asks question: "Am I eli- gible for disability premium?" While this question seems straightforward, the actual eligibility depends on multiple unstated conditions: income level, dis- ability severity, and age. Without this critical infor- mation, even the most advanced vetrivall-augment generation (RAG) systems may provide incorrect or misleading answers. This scenario, illustrated in
Disabillty premium elightlity
you you Srnnnd the disahiliis premium
Employ Suppoet
get the presium you satstty Sollowing:
under pession credit
you' be getting Disaiiiity Living Allewance (BILA)
been maable work
IN sa Information
Can mottre decliity IT nut carserty worting!
get Income-rriatee ESS.A Ja under penciun crodit ago I've bern arabic to wark for 3 years.
You sumcea Juckkity pecsions X
Figure D exemplifies fundamental challenge real-world question-answeriin systems: how Al systems proactively identify and gather missing in- formation to provide accurate responses?
An illustration of an underspecified user query about disability benefits eligibil- ity, where missing key information (e.g., in- come level, disability severity, age) may lead to incorrect LLM responses.
Figure I:
This ambiguity primarily arises from underspecified user queries, which frequently occur in real-world
"Corresponding Author
'Our code is open-sourced at httph::/ggtthub...ottttt http://ttthub..om////////// /

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Published as a conference paper at ICLR 2026
settings due to users' limited domain knowledge (Zhang et al. 2024, Deng al. 2023a Kim al.
2024) or natural tendency to omit seemingly obvious details (Zipt)
1949). While existing RAG
methods excel at retrieving relevant documents (Ren et al., [222]] Lewis et al., 2020 Zhao et al.
2025 Xu et al., 2025) Zhang et al. 2025b), they fundamentally assume that user queries contain sufficient information, but this assumption often fails in practice.
promising solution is to equip Large Language Models (LLMs) in RAG systems with the ability to proactively ask clarifying questions when faced with underspecified queries. Currently, there are two main approaches: prompting and fine-tuning. Prompting methods (Deng et al. 2023b; Kuhn
al. 2023; Hu et al., 2024; Kobalezyk al., 2025) utilize the reasoning capabilities iteratively identity ambiguity and generate clarification questions. However, their performance is constrained by the capability of L.LMs as small-scale I.LMs often struggle to identify ambiguities (Zhang et al. 2024). The better way is to fine-tune LLMs by multi-turn clarífication dialogue data
LLMs to
collected through human annotation (Qian et al. 2024] Chen et al. 2024) or self-sampling strategies (Andukuri et al. 2024, Zhang al., (025a). However, the former is costly to obtain, while the latter imposes no constraints on the clarification search space, potentially leading to irrelevant or redundant interactions. Therefore, it is necessary to develop effective and efficient method to reach our goal.
We propose that the key to resolving ambiguity in underspecified queries lies in explicitly model- ing the conditional reasoning structures within retrieved documents. Unlike existing methods that treat clarification typically encode knowledge as conditional rules-if-then statements that map combinations of con- ditions to conclusions. By extracting and representing these rules a Directed Acyclic Graph (DAG), we can systematically identify all conditions relevant to the user's query and guide clarifi- cation dialogues through efficient traversal strategies.
open-ended dialogue problem, observe that domain-specific documents
However, realizing this vision presents three fundamental challenges. (C1) How can we design a reasoning structure that captures all logical dependencies while remaining computationally tractable? The structure must be expressive enough to represent arbitrary Boolean functions yet ef- ficient enough for real-time interaction. (C2) How can we train models to extract such structures when existing datasets lack annotations for conditional reasoning? Current QA benchmarks rarely include underspecified queries or their missing conditions. (C3) How can we optimize the extracted structures for both correctness and interaction efficiency? Users will abandon systems that require excessive clarification rounds.
To address (C1), we propose conditional reasoning DAG structure, which is theoretically guaran- teed to be logically complete to express any Boolean function via disjunctive normal form (DNF). Besides, the DAG allows for subgraph sharing across reasoning paths and supports dynamic prun- ing based on user responses, enabling ((r) average-case clarification complexity, where r < the average reasoning depth rather than the total number of conditions k. To address (C2), we propose a conditional path guided data synthesis method to generate usable dataset for both training and evaluation. This method generates question-answer pairs with associated missing conditions along each conditional path from document. A filtering mechanism based on the necessity of the missing conditions further applied to retain high-quality examples. To address (C3), We propose Marification-orienee oriented reinforcement learning method to enhance L.M'' ability to extract DAG structures for effective and efficient clarification. We design a hybrid reward that encourages the LLM to prioritize DAG that leads to correct answer and requires fewer interaction.
Our main contributions can be summarized as follows:
Novel Framework:
We introduce GPS (Graph-guided Proactive Information Seeking), the first framework to explicitly model conditional reasoming structures for clarification in RAG systems. Theoretical Foundation: We prove that our DAG-based representation achieves logical com- pleteness while enabling O(r) average-case clarification complexity, where r & k is the average reasoning depth rather than the total number of conditions k.
Practical System: We develop a complete pipeline including (i) Conditional path guided syn- thetic data generation to address training data scarcity, (ii) clarification-orintte reinforcement learning that jointly optimizes for accuracy and efficiency, and (iii) dynamic traversal algorithms that reduce user interaction burden.
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Extensive experiments three benchmarks demonstrate that GPS achieves average improvement of 7.5% % in success rate and 4.2% % in clarification efficiency over the best baseline method.
Empirical Validation:
2 RELATED WORK
Clarification in LLMs Currently, there are two main approaches to enhance the ability of LLMss to proactively ask clarifying questions: prompting and fine-tuning. Prompting methods (Deng et al, 2023b Kuhn et al. 2023) utilize the reasoning capabilities of LLMs to iteratively identify ambi- guity based on the conversation history and choose to either ask clarification questions or generate response. However, their performance is constrained by the capability of LLMs small-scale LLMs often struggle to identify ambiguities (Zhang et al.) (022)) and as the conversation history grows longer, the risk of lost-in-the-middle increases (Liu al. 2024). Another line of work is to fine-tune LLMs with multi-round conversation data (Qian et al.) 2024; Chen et al., 2024). Yet these approaches rely on access to
conversation data, which is expensive to col-
expensst
lect in practice. Some methods (Andukuri et al., 2024; Zhang et al., 2025a) explore self- improve paradigm for sampling conversation data and use the accuracy of final responses to filter low-quality clarification data. Nevertheless, these methods typically imposes no constraints on the clarification search space, potentially leading to irrelevant or redundant interactions. Therefore, it is necessary to develop an effective and efficient method for proactive clarification.
Graph-based Reasoning in NLP
Recent work has explored structured representations for multi-
hop reasoning (Besta et al. 2024). knowledge graph integration (Ren al. 2020 Li al. 2025) and
neural-symbolic reasoning Xu et al. 2024). Query2Box (Ren et al. 2020) reasons over know ledge
graphs by embedding multi-hop logical queries geometric boxes in vector space. [i et al. (2025)
proposes to inject LLMs with structured knowledge by encoding knowledge graphs via graph neural (eNNsrks(GNNss), Xia al. (2025) proposes a novel fine-tune framework stimulating the ability of LLMs to perform complex reasoning on knowledge graphs. However, these methods primarily en- code entities, relations, or intermediate reasoning steps for specified queries, with the objective of improving multi-hop reasoning performance or the faithfulness of logical inference. Our work is explicitly designed for underspecified queries and uniquely combines graph-based reasoning with proactive clarification.
PROBLEM FORMULATION
In this paper, we aim to enhance LLMs' ability to proactively ask clarification questions when facing underspecified user query RAG scenarios. Rigorously, given a user query 4, the retrieved relevant document d = Retrieve(g), and the user's background context S which is not observable to the LLM, we denote by C₁ . C.. the set of user-specific condition variables in d, each condition variable e; takes values from a finite value set Ve: We divide CJ into two disjoint subsets:
Cnoonn (4) C Cd: the subset of known condition variables with values provided in query q- Cmiss(g) = Ca ) Oknown (g): the subset of missing condition variables specific to query g. with values depend on the hidden user's background S and are necessary to determine the answer.
Let A {ai..... am) denote the set of possible answers. The final answer a E A is determined by Cmiss()) through set of latent logical constraints R encoded in (e.g.. eligibility rules). Our objec- tive is to enhance L.LMs' ability to proactively elicit the values of Cmise (q), so that an unambiguous answer can be inferred.
METHODOLOGY
In this section, we introduce GPS, two-stage framework for proactive clarification. In the rea- soning stage, a Reasoner LLM θn captures the conditional structure documents as a DAG. In the clarification stage, Clarifier LLM Oc interacts with User-Simulator LLM Gu, dynamically
For example, condition variable e;
"marital status" may have Ve, (Single, Married, Divorced).
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Shep Conditional Guided Data Synthesis
Docmant
Wisiig Information
DeepSeck
Logic
Sompling
Verifier
Synthetic
Step 2: Clarification-Oriiaii Oriented Reinforcement Learning with Hybrid Reward
Update
Model
Group
Computation
Reward Function
Group DABs
Reasening Stage
Accunccy
Reward
V.S.
Rg
Efficincy Reverd
Rut
BAG A71
Information
Funchion
Clarification Stage
Struchone
Entropy
Figure 2: The overview of our method pipeline.
pruning the DAG during traversal to elicit values of miss To further improve DAG quality, GPS employs the Conditional Path Guided Data Synthesis procedure construct training dataset and uti- lizes Clarification-oriente Reinforcement Learning to optimize the Reasoner. The overall pipeline illustrated in Figure
4.1 CONDITIONAL REASONING DAG CONSTRUCTION
To construct structure which is both logically complete and clarifiaation-effiiinn we define conditional reasoning DAG structure G = (W, E), where:
Each non-terminal node ne. E N represents a user condition variable o; E C'a, each terminal node Nam E / represents a possible answer am E A.
Each edge Ei.j = (Me, M) is labeled with a condition value E Ve, denoting a possible value the predecessor node. The outgoing edges from each node are mutually exclusive and collectively exhaustive.
If a node has single predecessor node, it implicitly forms an AND relation with its predecessor condition. If node has multiple predecessor nodes, forms an OR relation all predecessor conditions.
We first analyze the logical completeness of our proposed conditional reasoning DAG structure as follows. The clarification efficiency will be discussed in 4.2
Proposition 1. For any finite- valued function [[ V; - A over condition variables (e there exists a conditional reasoning DAG G such that, for each am E A, every root-to-leaf path ending am corresponds to conjunction in the disjunctive normal form (DNF) of the indicator function 1[g(-) = am), and the union of all such paths encodes the full DNF of 1[g()) am)-
See Appendix for complete proof. The proposition theoretically guarantees that our conditional reasoning DAG structure is expressive enough to represent any finite-valued function, allowing for comprehensive detection of missing conditions and effective clarification. In the reasoning stage, prompt the Reasoner LLM O₂ to construct a conditional reasoning DAG based on the user query and the retrieved document. The detailed prompt is provided in Appendix C.1

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4.2 DYNAMIC TRAVERSAL- BASED CLARIFICATION
Given the constructed conditional reasoning DAG 9 - (W we propose dynamic traversal- based clarification approach that generates clarification questions in a topological order prioritize essential conditions and dynamically prune inconsistent paths. Formally, let degin (nt) denote the in-degree of node neg we define the candidate clarification set U:
U1 = {n; E E degin (m:) = 0, i Cknown (4)):
U2 (n, E W [mn. R2,1) E & u. E Cknowa(g), (n..(n.)) = 0}.
U = U1 2
(1)
Nodes in U are eligible for clarification as they are not blocked by unresolved predecessors. To determine the initial order of the candidate set, we estimate the expected cost based on the remaining depth for each n, U:
{(n.) = PR
Σ
len(p)
PEPa:
where Pn, is a set of paths from n, to leaf node, and len(p) is the number of missing condition variables in path p.
Our dynamic traversal- based clarification consists of three steps:
1. Initialization: construct the candidate set of missing conditions and start with empty dialogue history H;
2. Iterative clarification: select the most informative condition, ask a clarification question via
Oc, obtain user-specific answer from Oy, and update by following the consistent path;
3. Final answering: once a terminal condition reached or no candidates remain, Oc produces the final answer à conditioned on H.
Through dynamic traversal-based clarification process, the dialogue history H constitutes the elicited values for the missing condition set Ciiss, which are required resolve the ambiguity. We present the algorithmic pseudo-code of dynamic traversal-based clarification in Appendix D We also analyze the efficiency of GPS and obtain that the expected number of clarification turns depends only on the small set of conditions along the true reasoning path, rather than the full set of conditions present in the document. Detailed analysis is provided the Appendix B
4.3 CLARIFICATION-ORIENT REINFORCEMENT LEARNING
Enhancing the Reasoner LLM's ability to construct accurate conditional reasoning DAG is essential for the overall performance of the GPS framework. To achieve this, we first propose Conditional Path Guided Data Synthesis procedure to address the data scarcity challenge. Based on the synthetic dataset, we design Clarification- OOriented Reinforcement Leurning approach with a hybrid reward that integrates clarification effectiveness and efficiency, encouraging the Reasoner LLM to extract DAG structures that lead to correct answer and require fewer interaction.
4.3.1 CONDITIONAL PATH GUIDED DATA SYNTHESIS
We synthesize our dataset based on ConditionalQA (Sun al. 2022 a reading comprehension dataset that includes long-context documents containing complex logic rules, along with well- specified and underspecified queries with human-annotated missing conditions. However, a key limitation of ConditionalQA dataset is that only 550 out of 2,247 samples (24.5%) are underspeci- fied, making it difficult to train models that generalize well in proactive information seeking task. To address this, propose Conditional Path Guided Data Synthesis method augment high-quality underspecified training data. The synthesis process consists of two steps: Problem Generation and Verification.
Problem Generation First, we prompt advanced LLMs such as DeepSeek-R2 (Guo et al. 2025 to generate underspecified questions with multi multi-conditional reasoning paths from document d. The prompt used for this task can be found in Appendix C.2 Each item contains three parts:
An underspecified question y that admits multiple plausible answers;
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A set of missing conditions C with value domains VetCeo;;
A set of conditional paths p = {(v, a)), where v E Ilec Ve is complete assignment, a E A is the unique answer determined by v.
Verification Each synthetic data instance is represented as (gi, Vi, dz,4;). To ensure data quality, we introduce filtering mechanism based on the necessity of the missing conditions. For each instance, we prompt Verifier LLM to predict the answer both with and without access to the missing conditions. We retain the instance only the full-input prediction dfull matches the gold answer ats while the masked imput prediction Gpartial does not. This filtering preserves cases where missing information is essential, yielding a high-quality dataset D {%. Vs, d3, ag him1-
4.3.2 HYBRID REWARD: EFFECTIVENESS EFFICIENCY, AND ENTROPY
Setup. Our goal is to train the Reasoner LLM Ø to generate high-quality conditional reason- ing DAGs that enable fixed Clarifier LLM θc to complete proactive clarification with minimal interaction. Motivated by recent progress of RI. for reasoning (uuo et al. 2025). we formulate the DAG-constructioon problem generates and parses a DAG given query and document d (we write 0 as the Reasoner parameters OR for brevity), the DAG deterministically induces clarification trajectory (which condition to ask next and which edge to follow), and yielding scalar reward. We adopt GRPO Guo et al. 2025) algorithm to optimize TT..
reinforcement learning task: the policy To ( q. d) autoregressively
GRPO objective. For each query and retrieved document d, GRPO samples h DAGs {or Tu (:1g, d) with rewards {ri}/-1- defines the advantage A; as
A mean ((r1,..... Tn.)
(3)
and optimizes the clipped policy objective with reference KI.:
min To(ailg) A., clip me_ Tea (oilg)
(G) Eg for mnme (11)
mo 1 1- 1- +c)
(4)
DeI.(Tr (Tol Teor)
with
Tree (oil4) To(o.l))
Teat (oi(()
(Too (iett)
log
(o(0))))
GRPO calculates
advantages within h samples {o for the same input, so higher- reward generated DAG samples are upweighted and lower-reward ones are downweighted.
Hybrid reward function. To optimize the policy toward high-quality DAGs, we design a hy- brid reward function that jointly promotes correctness, low interaction cost, and structurally non- redundant.
Effectiveness reward encourages correct final answers after clarification. Let à be the predicted answer after clarification based on DAG o and a; the ground truth:
1, Evaluator(à; a;) True,
Face,i
(6)
0, Evaluator(a,, a.) = False.
Efficiency reward encourages fewer clarification turns. Let I, be the number of turns induced by Ot, and fmax = max L within the GRPO group. We set the coefficient a 0.5:
Tefl.i = 1 fmax
Structural quality reward a measures how effectively DAG converts intermediate branching uncertainty into discriminative power over the final conclusions. We formalize this intuition using informationn-comvrsiii efficiency measure.

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(N.E) be clarification DAG with condition nodes C' and conclusion (leaf) nodes L. We assume uniform branching: at any condition node, the outgoing probability mass is evenly split among all children. Let R S C be condition roots with no predecessors. Each root receives initial mass R/ For any condition node n. E C, its outgoing mass splits uniformly across children:
Forward probability propagation. Let G
P(u) [(u)]
P(v) Σ
(8)
u:(u~vVEE
where Fu)) is the set of children of u. This yields a forward-reachabilig distribution P(n) over all nodes. We can obtain the mass P(t) any leaf node E L and the normalized leaf distribution is:
P(()
P(() Eeer P(e)
Entropy of graph splits and leaf. The graph split entropy reflects the total uncertainty injected by intermediate splits:
H grapph
Σ P(n) log F(n)),
(10)
nEC
and the leaf entropy the Shannon entropy of the normalized leaf distribution:
Heaf
P(0) log P(t).
(11)
FEL.
Information-conversioo efficiency.
We define the structural quality reward Tn, 2
Heaff
H Hgraph > 0,
H graphh
Tni
1,
H grapph = 0 A A P(()
(12)
0,
otherwise.
We provide illustrative instances in Appendix G.3 demonstrating the rationality of the structural
quality reward.
Overall reward. The in sample's total reward is calculated as follows:
Taces
(reff + +r,a)
(13)
We provide the pseudo-code of overall inference procedure of GPS in Appendix t and training procedure of the Reasoner in Appendix F and additionally offer an illustrative example of the two- stage proactive information-sedt seeking process in Appendix
EXPERIMENT
5.1
EXPERIMENTAL SETUP
Dataset We constructed the GPS training dataset based on ConditionalQA (Sun et al., (022) and test method the following three datasets. The detailed statistics of the datasets intro- duced in Appendix
Synthetic is the test split of our conditional path guided synthetic dataset, consisting entirely of underspecified queries.
ConditionalQA (Sun et al. 2022) includes both well-specified queries and underspecified queries. It provides annotation for each question-answer pair along with the document and the correspond- ing missing conditions. For well-specified queries, the missing conditions are empty.
conversational QA dataset that also includes well-specified queries and underspecified queries based on rules expressed in natural language text. For under- specified queries, it provides annotated clarification dialogues. We concatenate the clarification dialogue the missing conditions. Compared to typical datasets in RAG scenarios, ShARC fea- tures much shorter documents and a restricted answer space limited to yes or no. We use ShARC evaluate the generalization ability of our method.
ShARC [Verma et al.) 2020)
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Baselines We adopt the following state-of-ther -o-----fftttrr---e approaches compared baselines.
Base Method answers user query directly based on the relevant document, which can consid- ered as fundamental framework in RAG.
ProCoT (Deng et al. 2023b) leverages Chain of Thought prompting scheme to judge whether the user query is underspecified and generate a clarification question if needed.
UoT (Hu et al. 2024) proposes Uncertainty of Thought prompting, which enhances LLM reason- ing by explicitly modeling and reducing uncertainty during the reasoning process.
BED-LLM (Kobalczyk et al. 2025) uses Bayesian Experimental Design to pick the clarification question that maximizes information gain.
Adaptive-BED-LLMM BED-LLM an ambiguity- adaptive variant BED-LLM for fairer comparison, as the original method always chooses ask clarification questions. It answers directly when multiple initial answers are semantically consistent, otherwise performs BED-based clarification.
Adaptive-
Clarify-DPP (Zhang et al., 2025a is a fine-tuning based method. It leverages a self-improve method to collect training data and filter data by gold answer.
ChaptiveCCarift-DDOOO
ambiguity-adaptive variant of Clarify-DPO based the Clarify-
or or-iirect Answer strategy proposed in (Zhang et al. (2025a). The model learns to choose either generating clarification question or directly answering.
Models We evaluate the performance using Llama3-80 Barmaa-&B-Innttii 8B-Instruct (Grattafioni et al. 2024) and Qwen2.5-7B-Instruet Yang et al. 2025) as backbone models.
Evaluation Metries lowing four metrics:
We evaluate the model's proactive information seeking ability using the fol-
Success Rate (SR). Following previous studies Hu et al. 2024 Qian et al. 2024). we use this metric measures the effectiveness of clarification process by computing the proportion the cor- rect predictions after clarification. We employ evaluator LLM judge the semantic alignment between the predicted answer and the ground-truth answer. The evaluation prompt is provided in Appendix C.3
Weighted Clarification Turns (WCT). Previous studies commonly evaluate clarification effi- ciency using Mean Clarification Turns (MCT), defined as the average number clarification questions (Hu al. 2024 Qian al., 2024). However, the desired behavior of proactive in- formation seeking model is to prioritize correct clarification before optimizing efficiency, and MCT alone cannot capture success-conditioned efficiency. Inspired by prior evaluation proto- cols (Yokoyama et al. 2021), we introduce the Weighted Clarification Turns (WCT):
WCT
Pauccess
MCT success + Pfailed Tmaax
(14)
where Psuceess and Ptaited denote the proportions of successful and failed samples, MCT success is the mean clarification turns over successful samples, and Tmax = 10 is the maximum clarification- turn budget in experiments. Lower WCT indicates more efficient clarification while preserving correctness.
F1 score for Clarification Need Prediction Accuracy (CNP). Following previous studies (Deng et al. 2023b /Zhang al. 2025a), compute the F1 score of CNP for evaluating the model's ability to identify the necessity of clarification.
5.2
PERFORMANCE COMPARISON
Table I presents the performance comparison of different methods across three benchmarks: Syn- thetic, ConditionalQA, and ShARC. We summarize key findings below:
Training for proactive information seeking is essential. The Base Method yields low success rates on Synthetic and ShARC, where underspecified queries are more prevalent, indicating its lim- ited ability to handle missing conditions. Baselines equipped with proactive clarification consistently improve SR over the Base Method. However, purely prompt-basee methods sometimes fail to sur- pass the Base Method. For example, ProCoT occasionally results in degraded performance, likely
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Table 1: Performance comparison on three datasets. Columns report SR (Success Rate, %), Weighted Clarification Turns (WCT), and FI score (%). Bold indicates the best result, while underline denotes the second-best results.
Method
Synthetic WCT (4)
ConditionalQA WCT ())
ShARC WCT (4)
SR (t)
(
SR (t)
(1)
SR (t)
(1)
Qwen2.5 Swen2.5-7B-Instuuct -I-strcctt
Base Method
21.2
7.88
0.0
70.3
2.98
0.0
49.3
5.08
0.0
ProCoT
42.5
6.07
50.9
71.6
2.95
10.4
62.6
4.06
51.3
UoT
32.8
7.05
89.2
60.3
4.25
28.2
70.5
3.25
83.8
BED-LLM
40.9
6.41
100.0
52.8
5.26
37.6
62.2
4.22
66.7
Adaptive-BEII BED-LLM
34.6
95.2
50.2
28.6
59.4
4.56
71.0
Clarify-DPO
39.2
4.67
100.0
72.0
3.52
37.6
78.5
2.93
66.7
Adaptive-Clarify-Dee
32.6
7.04
96.2
69.9
3.02
0.0
70.0
2.99
0.0
GPS
60.2
4.59
96.4
73.4
36.7
79.3
2.41
87.5
LLAMA3-8P CAAMMMA----Instiin -I-nstrrctt
Base Method
30.8
6.92
0.0
62.8
3.72
0.0
56.6
4.34
0.0
ProCoT
28.3
7.62
29.2
66.3
25.7
53.7
5.16
35.6
UoT
29.7
7.36
90.9
64.6
4.03
31.7
68.3
3.67
69.9
BED-LLM
39.6
6.53
100.0
47.2
6.02
37.6
64.0
4.20
66.7
Adaptive-BED-LLMN
35.6
96.2
44.5
31.4
67.8
67.0
Clarify-DPO
53.2
5.21
100.0
66.3
4.03
37.6
82.7
2.55
66.7
Adaptive-Clane CaareeC-laryyyr-ea
31.3
7.16
95.5
67.7
3.23
0.0
68.8
3.12
0.0
GPS
56.5
5.02
96.2
74.6
2.89
28.0
75.8
82.5
due to limited backbone capacity and the inherent complexity of conditional reasoning, consistent with prior observations (Zhang 2024
GPS achieves the best balance between effectiveness and efficiency. GPS consistently improves SR across baselines. With LLaMA-3-$B-Instnee 10.4% over the second-best method, and 4.5% with Owee2.5--B-Innstrun which jointly reflects correctness and efficiency, GPS attains the lowest WCT nearly all settings, indicating higher accuracy at lower effective clarification cost.
GPS achieves an average relative SR gain of Moreover, under WCT,
Strong generalization to ShARC. GPS also generalizes well the ShARC dataset, where it consis- tently outperforms prompt-basee based methods and Base Method, and achieves performance comparable the Clarify-DPO method, despite the fact that Clarify-PPO trained directly on ShARC. This highlights the strong generalization ability of GPS across different benchmarks.
5.3 ABLATION STUDY
To evaluate the contribution of each component GPS, we conduct an ablation study using Qwen2.5--BBIInsrruct as the backbone LLM. Results are reported in Table 2 on both the Synthetic and ConditionalQA datasets.
The full GPS model achieves the best overall performance across both datasets. Removing rein- forcement learning (w/o RL) leads to notable drop in SR and a clear increase in WCT, particularly on the Synthetic dataset, highlighting the importance of policy optimization. Ablating either the Efficient Reward or the Structural Quality Reward consistently degrades performance, increasing WCT and reducing SR, which indicates that jointly modeling correctness and efficiency is erucial. Finally, disabling dynamic traversal results higher WCT on both datasets, suggesting its role in reducing effective clarification cost by guiding more efficient clarification paths.
5.4 QUALITATIVE ANALYSIS
Figure presents comparison between strong baseline Clarify-Do and our method GPS on an underspecified query about policy eligibility. Clarify-DPO selects clarification questions based

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Published as a conference paper at ICLR 2026
Table 2: Ablation results of GPS with Qwen2.5-7B-Instmn
Synthetic
ConditionalQA
Method
SR1
WCT1
FIf
|RR
WCT,
Fl
GPS
60.2
4.59
96.4
73.4
2.91
36.7
w/o RI.
52.2
5.57
96.8
67.7
3.63
23.1
w/o Efficient Reward
59.0
5.06
97.1
70.7
3.58
28.9
w/o Structural Quality Reward
56.1
5.32
96.3
70.3
3.61
29.1
w/o Dynamic Traversal
59.6
5.19
96.4
71.2
3.63
22.2
implicit reasoning, which leads to missing essential condition about income-related ESA, and the resulting clarification path does not cover all necessary branches. This omission causes the model to produce an incorrect final answer.
In contrast, GPS first constructs conditional reasoning DAG that explicitly enumerates all relevant conditions. The dynamic traversal module then identifies the most informative condition to clarify, removes inconsistent branches based on user responses, and narrows the search to the uniquely valid leaf node. This produces a clarification trajectory that is both minimal and logically complete. For more qualitative analysis, please refer to Appendix G
Document
User Background
Divability premium eligibilitt
already get income rolated ESA, aa under persion credit age. Idnntt DI or PIP. been anable wark monhs..
you diabiiiyy premiunt
Employment Support (ESA),
you
get the premium you
pensien credit age getting:
Disability Living Allowance (DLA)
Personal Independence Payment (PIP)
If you do quality tloough the shove, still the premium of
Query
you've been unable work for
dlgiitee for disabillyy pezmium!
Clarify-DPO
USA!
Query: llighle disiiility promium??
Clartly Q1AAre you under pension credit creditage?
Clarity AL: Yes.
punkan age?
Canduasen cliaile
Answer: Elighte
Ceatt sargrinng BlA?
GPS
Query:Aun elighte for disability pezmium!
Coudle
Condusion
Clarity Ql(Cond1): income relaned USA?
Clacity You.
libee yealhus
Anmwor(Cnnevvieat) NOT cligitic
Figure 3: Comparison of Clarify-DPO and GPS policy eligibility example. Clarify-DPO asks an incomplete set of clarification questions and reaches an incorrect answer. GPS constructs a condi- tional reasoning DAG and identifies the correct clarification path, producing the correct conclusion.
CONCLUSION
In this paper, we propose GPS, a two-stage framework for enhancing proactive information seeking abilities of LLMs in RAG systems. In the reasoning stage, propose DAG reasoning struc- ture with theoretical guarantees of both logical completeness and clarification efficiency. In the clarification stage, design traversal-based -based algorithm that dynamically prunes the DAG based on user responses, enabling efficient clarification. To further enhance DAG construction, we pro- pose a conditional path guided data synthesis method to address data scarcity challenge, then apply a ceriification-oricnn reinforcement learning method with a hybrid reward that jointly con- siders effectiveness and efficiency optimize the LLM. Extensive experiments on three benchmarks demonstrate the effectiveness and efficiency of GPS in handling underspecified queries.
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ACKNOWLEDGMENTS
This work is supported by the National Natural Science Foundation of China (No. 62576013).
REFERENCES
Chinmaya Andukuri, Jan-Philipp Fränken, Tobias Gerstenberg, and Noah D. Goodman. Star-gate: Teaching language models to ask clarifying questions. CoRR, abs/2403. 19154, 2024. doi: 10. 48550/ARXIV.2403.9999
https://////o http://..o.......//////..... 48550//a51111. 240....24400. 19154
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michat Podstawski, Lukas Giani- nazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, and Torsten Hoe- fler. Graph of thoughts: solving elaborate problems with large language models. In Proceedings of the Thirty-Eightt AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Ad- vances in Artificial Intelligence, 87735-887-9. doi: 10. (609//aai..3381166. 29720. URL htt.::ttp/://tttm http://dooooog//////////oo 0609/ v381 66.1166.10 29720
AAAI Press, 2024. ISBN 978-1-
Maximillian Chen, Ruoxi Sun, Sercan Ö. Arik, and Tomas Pfister. Learning clarify: Multi-turn conversations with action-based contrastive self-training. CoRR, abs/2406.00022,, 2024. doi: 10. 48550/ARXIVV2406600
URL httttt:pp:://t httg///oooooo//////////o wtt.or///..../////..///r//e/.///
48550/ 240. 04222.0222
Yang Deng, Wenqiang Lei, Minlie Huang, and Tat-Seng Chua. Rethinking conversational agents in the era of Ilms: Proactivity, non-collaborativitt. and beyond. In Qingyao Ai, Yiqin Liu, Alis- tair Moffat, Xuanjing Huang, Tetsuya Sakai, and Justin Zobel (eds.), Anmual International ACM SIGIR Conference Research and Development in Information Retrieval in the Asia Pacific Re- gion, SIGIR-AP 2023, Beijing, China, November 26-28, 2023, pp. 298 301, ACM, 2023a. doi: URLpttps: http://oii.oooo /tttps://doi.org//11 10. 1 14/ 362491444888 32222449...669954 3629548
10.1145/3624918.66 83629554.
Yang Deng. Lizi Liao, Liang Chen, Hongru Wang. Wenqiang Lei, and Tat-Seng Chua. Prompt- ing and evaluating large language models for proactive dialogues: Clarification, target- guided, and non-collaboration. In Houda Bouamor, Juan Pino, and Kalika Bali (eds.), Find- ings of the Association for Computational Linguistics: EMNLP 2023, Singapore, Decem- ber 6-10, 2023, pp. 10602-10621. Association for Computational Linguistics, 2023b. doi: 10.18653/W1/2233FFDDDN -EIGGG---M.PP....... 2023. 00233..nndinss-mmmmpp..
URL
Aaron Grattafiori, Abhimanyu Dubey, Abhinay Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Ko- renev, Arthur Hinsvark, Arun Rao, Aston Zhang, Aurelien Rodriguez, Austen Gregerson, Ava Spataru, Baptiste Roziere, Bethany Biron, Binh Tang, Bobbie Chern, Charlotte Caucheteux, Chaya Nayak, Chloe Bi, Chris Marra, Chris McConnell, Christian Keller, Christophe Touret, Chunyang Wu, Corinne Wong, Cristian Canton Ferrer, Cyrus Nikolaidis, Damien Allonsius, Daniel Song, Danielle Pintz, Danny Livshits, Danny Wyatt, David Esiobu, Dhruv Choudhary, Dhruv Mahajan, Diego Garcia-Olss Diego Perino, Dieuwke Hupkes, Egor Lakomkin, Ehab AlBadawy, Elina Lobanova, Emily Dinan, Eric Michael Smith, Filip Radenovic, Francisco Guzmán, Frank Zhang, Gabriel Synnaeve, Gabrielle Lee, Georgia Lewis Anderson, Govind That- tai, Graeme Nail, Gregoire Mialon, Guan Pang, Guillem Cucurell, Hailey Nguyen, Hannah Kore- vaar, Hu Xu, Hugo Touvron, Iliyan Zarov, Imanol Arricta Ibarra, Isabel Kloumann, Ishan Misra, Ivan Evtimov, Jack Zhang, Jade Copet, Jaewon Lee, Jan Geffert, Jana Vranes, Jason Park, Jay Ma- hadeokar, Jeet Shah, Jelmer van der Linde, Jennifer Billock, Jenny Hong. Jenya Lee, Jeremy Fu, Jianfeng Chi, Jianyu Huang, Jiawen Liu, Jie Wang, Jiecao Yu, Joanna Bitton, Joe Spisak, Jong- soo Park, Joseph Rocca, Joshua Johnstun, Joshua Saxe, Junteng Jia, Kalyan Vasuden Alwala, Karthik Prasad, Kartikeya Upasani, Kate Plawiak, Ke Li, Kenneth Heafield, Kevin Stone, Khalid El-Arini, Krithika Iyer, Kshitiz Malik, Kuenley Chiu, Kunal Bhalla, Kushal Lakhotia. Lauren Rantala- Hentala-Yeary, Laurens der Maaten, Lawrence Chen, Liang Tan, Liz Jenkins, Louis Martin, Lovish Madaan, Lubo Malo, Lukas Blecher, Lukas Landzaat, Luke de Oliveira, Madeline Muzzi, Mahesh Pasupuleti, Mannat Singh, Manohar Paluri, Marcin Kardas, Maria Tsimpoukelli, Mathew

---

Published as a conference paper at ICLR 2026
Oldham, Mathieu Rita, Maya Pavlova, Melanie Kambadur, Mike Lewis, Min Si, Mitesh Ku- mar Singh, Mona Hassan, Naman Goyal, Narjes Torabi, Nikolay Bashlykov, Nikolay Bogoy- chev, Niladri Chatterji, Ning Zhang. Olivier Duchenne. Onur Çelebi, Patrick Alrassy, Pengchuan Zhang, Pengwei Li, Petar Vasic, Peter Weng. Prajjwal Bhargava, Pratik Dubal, Praveen Krishnan, Punit Singh Koura, Puxin Xu, Qing He, Qingxiao Dong, Ragavan Srinivasan, Raj Ganapathy, Ra- mon Calderer, Ricardo Silveira Cabral, Robert Stojnic. Roberta Raileanu, Rohan Maheswari, Ro- hit Girdhar, Rohit Patel, Romain Sauvestre, Ronnie Polidoro, Roshan Sumbaly, Ross Taylor, Ruan Silva, Rui Hou, Rui Wang, Saghar Hosseini, Sahana Chennabasappa, Sanjay Singh, Sean Bell, Seohyun Sonia Kim, Sergey Edunov, Shaoliang Nie, Sharan Narang, Sharath Raparthy, Sheng Shen, Shengye Wan, Shruti Bhosale, Shun Zhang. Simon Vandenhende, Soumya Batra, Spencer Whitman, Sten Sootla, Stephane Collot, Suchin Gururangan, Sydney Borodinsky. Tamar Herman, Tara Fowler, Tarek Sheasha, Thomas Georgiou, Thomas Scialom, Tobias Speckbacher, Todor Mi- haylov, Tong Xiao, Ujjwal Karn, Vedanuj Goswami, Vibhor Gupta, Vignesh Ramanathan, Viktor Kerkez, Vincent Gonguet, Virginie Do Vish Vogeti, Vítor Albiero, Vladan Petrovic. Weiwei Chu, Wenhan Xiong. Wenyin Fu, Whitney Meers, Xavier Martinet, Xiaodong Wang, Xiaofang Wang, Xiaoqing Ellen Tan, Xide Xia, Xinfeng Xie, Xuchao Jia, Xuewei Wang. Yaelle Gold- schlag, Yashesh Gaur, Yasmine Babaei, Yi Wen, Yiwen Song, Yuchen Zhang, Yue Li, Yuning Mao, Zacharie Delpiere Coudert, Zheng Yan, Zhengxing Chen, Zoe Papakipos, Aaditya Singh, Aayushi Srivastava, Abha Jain, Adam Kelsey, Adam Shajnfeld, Adithya Gangidi, Adolfo Victoria, Ahuva Goldstand, Ajay Menon, Ajay Sharma, Alex Boesenberg, Alexei Baevski, Allie Feinstein, Amanda Kallet, Amit Sangani, Amos Teo. Anam Yunus, Andrei Lupu, Andres Alvarado, An- drew Caples, Andrew Gu, Andrew Ho, Andrew Poulton, Andrew Ryan, Ankit Ramchandani, An- nie Dong, Annie Franco, Anuj Goyal, Aparajita Saraf, Arkabandhu Chowdhury, Ashley Gabriel, Ashwin Bharambe, Assaf Eisenman, Azadeh Yazdan, Beau James, Ben Maurer, Benjamin Leon- hardi, Bernie Huang, Beth Loyd, Beto De Paola. Bhargavi Paranjape, Bing Liu, Bo Wu, Boyu Ni, Braden Hancock, Bram Wasti, Brandon Spence, Brami Stojkovic, Brian Gamido, Britt Mon- talvo, Carl Parker, Carly Burton, Catalina Mejia, Ce Liu, Changhan Wang, Changkyu Kim, Chao Zhou, Chester Hu, Ching-Hsiang Chu, Chris Cai, Chris Tindal, Christoph Feichtenhofer, Cynthia Gao, Damon Civin, Dana Beaty, Daniel Kreymer, Daniel Li, David Adkins, David Xu, Davide Testuggine, Delia David, Devi Parikh, Diana Liskovich, Didem Foss, Dingkang Wang, Due Le,, Dustin Holland, Edward Dowling, Eissa Jamil, Elaine Montgomery, Elconora Presani, Emily Hahn, Emily Wood, Eric-Tuan Le, Erik Brinkman, Esteban Arcaute, Evan Dunbar, Evan Smoth- ers, Fei Sun, Felix Kreuk, Feng Tian, Filippos Kokkinos, Firat Ozgenel. Francesco Caggioni, Frank Kanayet, Frank Seide, Gabriela Medina Florez, Gabriella Schwarz, Gada Badeer, Georgia Swee, Gil Halpern, Grant Herman, Grigory Sizov, Guangyi, Zhang, Guna Lakshminarayanan, Hakan Inan, Hamid Shojanazeri, Han Zou, Hannah Wang. Hanwen Zha, Haroun Habeeb. Harri- son Rudolph, Helen Suk, Henry Aspegren, Hunter Goldman, Hongyuan Zhan, Ibrahim Damlaj, Igor Molybog. Igor Tufanov, Ilias Leontiadis, Irina-Elena Veliche. Itai Gat, Jake Weissman, James Geboski, James Kohli, Janice Lam, Japhet Asher, Jean-Baptiste Gaya, Jeff Marcus, Jeff Tang. Jen- nifer Chan, Jenny Zhen, Jeremy Reizenstein, Jeremy Teboul, Jessica Zhong, Jian Jin, Jingyi Yang. Joe Cummings, Jon Carvill, Jon Shepard, Jonathan McPhie, Jonathan Torres, Josh Ginsburg, Jun- jie Wang, Kai Wu, Kam Hou U, Karan Saxena, Kartikay Khandelwal, Katayoun Zand, Kathy Matosich, Kaushik Veeraraghavan. Kelly Michelena, Keqian Li, Kiran Jagadeesh, Kun Huang, Kunal Chawla, Kyle Huang, Lailin Chen, Lakshya Garg, Lavender A. Leandro Silva, Lee Bell, Lei Zhang. Liangpeng Guo, Licheng Yu, Liron Moshkovich, Luca Wehrstedt, Madian Khabsa, Manav Avalani, Manish Bhatt, Martynas Mankus, Matan Hasson, Matthew Lennie, Matthias Reso, Maxim Groshev, Maxim Naumov, Maya Lathi, Meghan Keneally, Miao Liu, Michael L. Seltzer, Michal Valko, Michelle Restrepo, Mihir Patel, Mik Vyatskov, Mikayel Samvelyan, Mike Clark, Mike Macey, Mike Wang, Miquel Jubert Hermoso, Mo Metanat, Mohammad Rastegari, Munish Bansal, Nandhini Santhanam, Natascha Parks, Natasha White, Navyata Bawa, Nayan Singhal, Nick Egebo, Nicolas Usunier, Nikhil Mehta, Nikolay Pavlovich Laptev, Ning Dong, Norman Cheng, Oleg Chernoguz, Olivia Hart, Omkar Salpekar, Ozlem Kalinli, Parkin Kent, Parth Parekh, Paul Saab, Pavan Balaji, Pedro Rittner, Philip Bontrager, Pierre Roux, Piotr Dollar, Polina Zvyagina, Prashant Ratanchandani, Pritish Yuvraj, Qian Liang, Rachad Alao, Rachel Ro- driguez, Rafi Ayub, Raghotham Murthy, Raghu Nayani, Rahul Mitra, Rangaprabhu Parthasarathy, Raymond Li, Rebekkah Hogan, Robin Battey, Rocky Wang, Russ Howes, Ruty Rinott, Sachin Mehta, Sachin Siby, Sai Jayesh Bondu, Samyak Datta, Sara Chugh, Sara Hunt, Sargun Dhillon, Sasha Sidorov, Satadru Pan, Saurabh Mahajan, Saurabh Verma, Seiji Yamamoto, Sharadh Ra- maswamy, Shaun Lindsay, Shaun Lindsay, Sheng Feng. Shenghao Lin, Shengxin Cindy Zha,
12

---

Published as a conference paper at ICLR 2026
Shishir Patil, Shiva Shankar, Shuqiang Zhang, Shuqiang Zhang, Sinong Wang, Sneha Agarwal, Soji Sajuyigbe, Soumith Chintala, Stephanie Max, Stephen Chen, Steve Kehoe, Steve Satter- field, Sudarshan Govindaprasad, Sumit Gupta, Summer Deng, Sungmin Cho, Sumny Virk, Suraj Subramanian, Sy Choudhury, Sydney Goldman, Tal Remez, Tamar Glaser, Tamara Best, Thilo Koehler, Thomas Robinson, Tianhe Li, Tianjun Zhang, Tim Matthews, Timothy Chou, Tzook Shaked, Varun Vontimitta, Victoria Ajayi, Victoria Montanez, Vijai Mohan, Vinay Satish Ku- mar, Vishal Mangla, Vlad Ionescu, Vlad Poenaru, Vlad Tiberiu Mihailescu, Vladimir Ivanov, Wei Li, Wenchen Wang, Wenwen Jiang, Wes Bouaziz, Will Constable, Xiaocheng Tang, Xiao- jian Wu, Xiaolan Wang. Xilun Wu, Xinbo Gao, Yaniv Kleinman, Yanjun Chen, Ye Hu, Ye Jia, Ye Qi, Yenda Li, Yilin Zhang, Ying Zhang, Yossi Adi, Youngjin Nam, Yu, Wang, Yu Zhao, Yuchen Hao, Yundi Qian, Yunlu Li, Yuzi He, Zach Rait, Zachary Vitito Zef Rosnbrick, Zhao- duo Wen, Zhenyu Yang. Zhiwei Zhao, and Zhiyu Ma. The llama 3 herd of models, 2024. URL. ht https: //apxii////vv..//// owg/arg//as..oooooo 2407.21783
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi. Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei Feng, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Deli Chen, Dongje Ji. Erhang Li. Fangyun Lin, Fucong Dai. Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang. Honghui Ding, Huajian Xin, Huazuo Gao, Hui Qu, Hui Li, Jianzhong Guo, Jiashi Li, Jiawei Wang, Jingchang Chen, Jingyang Yuan, Junjie Qiu, Junlong Li, J. L. Cai, Jiaqi Ni, Jian Liang, Jin Chen, Kai Dong, Kai Hu, Kaige Gao, Kang Guan, Kexin Huang, Kuai Yu, Lean Wang, Lecong Zhang, Liang Zhao, Litong Wang, Liyue Zhang, Lei Xu, Leyi Xia, Mingchuan Zhang, Minghua Zhang, Minghui Tang. Meng Li, Miaojun Wang, Mingming Li, Ning Tian, Panpan Huang, Peng Zhang, Qiancheng Wang. Qinyu Chen. Qiushi Du, Ruigi Ge, Ruisong Zhang, Ruizhe Pan, Runji Wang, R. J. Chen, R. L. Jin, Ruyi Chen. Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shengfeng Ye, Shiyu Wang, Shuiping Yu, Shunfeng Zhou, Shuting Pan, S. S. Li, Shuang Zhou, Shaoqing Wu, Shengfeng Ye, Tao Yun, Tian Pei, Tianyu Sun, T. Wang, Wangding Zeng. Wanjia Zhao, Wen Liu, Wenfeng Liang, Wenjun Gao. Wenqin Yu, Wentao Zhang. W. L. Xiao, Wei An, Xiaodong Liu, Xiaohan Wang, Xiaokang Chen, Xiaotao Nie, Xin Cheng, Xin Liu, Xin Xie, Xingchao Liu, Xinyu Yang. Xinyuan Li, Xuecheng Su, Xuheng Lin, X. Q Li, Xiangyue Jin, Xiaojin Shen, Xiaosha Chen, Xiaowen Sun, Xiaoxiang Wang, Xinnan Song, Xinyi Zhou, Xianzu Wang. Xinxia Shan, Y. K. Li, Y. Q. Wang, Y. X. Wei, Yang Zhang, Yanhong Xu, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Wang, Yi Yu, Yichao Zhang, Yifan Shi, Yiliang Xiong, Ying He, Yishi Piao, Yisong Wang, Yixuan Tan, Yiyang Ma, Yiyuan Liu, Yongqiang Guo, Yuan Ou, Yuduan Wang, Yue Gong, Yuheng Zou, Yujia He, Yunfan Xiong, Yuxiang Luo, Yuxiang You, Yuxuan Liu, Yuyang Zhou, Y. X. Zhu, Yanhong Xu, Yanping Huang, Yaohui Li, Yi Zheng. Yuchen Zhu, Yunxian Ma. Ying Tang. Yukun Zha, Yuting Yan, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhean Xu, Zhenda Xie, Zhengyan Zhang, Zhewen Hao, Zhicheng Ma, Zhigang Yan. Zhiyu Wu, Zihui Gu, Zijia Zhu, Zijun Liu, Zilin Li, Ziwei Xie, Ziyang Song, Zizheng Pan, Zhen Huang, Zhipeng Xu, Zhongyu Zhang, and Zhen Zhang. Deepseek-rl: Incentivizing reasoning capability in 1lms via reinforcement learning. 2025. URL WtRLhhtps::// http::://rxvvvv.oo. abs/////////// /501 12948
Zhiyuan Hu, Chumin Liu, Xidong Feng, Yilun Zhao, See-Kiong Ng. Anh Tuan Luu, Junxian He, Pang Wei W. Koh, and Bryan Hooi. Uncertainty of thoughts: Uncertainty-aware plan- ning enhances information seeking in llms. In Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tomezak, and Cheng Zhang (eds.), Ad- vances in Neural Information Processing Systems 38: Annual Conference on Neural Infor- mation Processing Systems 2024, NeurlPS 2024, Vancouver. BC. Canada, December 10 15, 2024, 2024. httt: wwt.ppaawrsnipp....pppppppp ce/paper files/ halles/paper//22//eae 2b0e 44bd8 28
html
Hyuhng Joon Kim, Youna Kim, Cheonbok Park, Junyeob Kim, Choonghyun Park, Kang Min Yoo, Sang-goo Lee, and Taeuk Kim. Aligning language models to explicitly handle ambiguity. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, tics. doi: Miami, Florida, USA, November 2024. Association for Computational Linguis- URL ht aattps://acLattholooyy. www.tthwhwoooooo../22.....
pp. 1989-2007,
eemn 1 ma i n. 1197
13

---

Published as a conference paper at ICLR 2026
Kasia Kobalczyk, Nicolás Astorga, Tennison Liu, and Mihaela van der Schaar. Active task dis- ambiguation with llms. In The Thirteenth International Conference on Learning Represen- tations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net, 2025. URL https:
//openreview. net. formm?? http://fooommmmmm.D00AAA d=
Lorenz Kuhn, Yarin Gal, and Sehastian Farquhar. Clam: Selective clarification for ambiguous questions with generative language models. In ICML Workshop Deployable Generative Al, 2023. URL
net for//foruiee forum?id wttp://teetd/////nnneeeaoaoo VoW.dddddwwwggsso
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. Retrieval-augmente H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (eds.), Advances in Neu- ral Information Processing Systems, volume 33, pp. 9459-9474. Curran Associates, Inc., neuri cc wwwpp/appprrppprr waf.elllleeee htss/aper/22222 paf
generation for knowledge-intensive nip tasks. In
2020. URL
File/ 211066669222220000000222555588/2
Zichao Li, Zong Ke, and Puning Zhao. Injecting structured knowledge into LLMs via graph neural networks. In Hao Fei, Kewei Tu, Yuhui Zhang, Xiang Hu, Wenjuan Han, Zixia Jia, Zilong Zheng, Yixin Cao, Meishan Zhang. Wei Lu, N. Siddharth, Lilja Øvrelid, Nianwen Xue, and Yue Zhang (eds.), Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025), pp. 16-25, Vienna, Austria, August 2025, Association for Computational Linguis- tics. ISBN 777-8-89116-2885
URL ac www.olanhhoooooooooooooo otg/ x1 x11m- 3/
Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. Lost in the middle: How language models use long contexts. Transactions of the Association for Computational Linguistics, 12: 221157177, 2024. doi: 10.1162/tacl a 00638. URL
ogy.ooy..oooooo..............oo. wwg..orgg/0220. tacl- 1.
Cheng Qian, Bingxiang He, Zhong Zhuang, Jia Deng, Yujia Qin, Xin Cong, Zhong Zhang, Jie Zhou, Yankai Lin, Zhiyuan Liu, and Maosong Sun. Tell me more! towards implicit user in- tention understanding of language model driven agents. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Com- putational Linguistics (Volume 1: Long Papers), pp.
Bangkok, Thailand, August 2024. Association for Computational Linguistics. doi: 10.18653/v1/2024..cu ooy.
URL
617
Hongyu Ren, Weihua Hu, and Jure Leskovec. Query2box: Reasoning over knowledge graphs in vector space using box embeddings. In 8th International Conference on Learning Represen- tations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, 2020. URL https: opw.review.........eoeoeee net/ wtt.://ww.eee/orummaa---------- Bdg
Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hua Wu, Ji-Rong Wen, and Haifeng Wang. Investigating the factual knowledge boundary of large language mod- els with retrieval augmentation. In Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa, Barbara Di Eugenio, and Steven Schockaert (eds.), Proceedings of the 31st In- lernational Conference on Computational Linguistics, COLING 2025, Abu Dhabi, UAE, Jan- uary 19-24, 2025, pp. 3697-3715. Association for Computational Linguisties. 2025. URL http::://aclaanhhloogoo
wttp://225..o11ngmaaai.... 250/
Haitian Sun, William Cohen, and Ruslan Salakhutdinov. ConditionalQAA:
A complex reading com- prehension dataset with conditional answers. In Smaranda Muresan, Preslav Nakov, and Aline
Villavicencio (eds.), Proceedings of the 60th Annual Meeting of the Association for Computa- tional Linguistics (Volume 1: Long Papers). pp. 3627-3637, Dublin, Ireland, May 2022, As- sociation for Computational Linguistics. doi: 10, 18.11813/1 1/2022..1oo222 www.loggy.r.///220/ [/aalaattaooooy..orr//22..... otcp://ooo...0222221----..-----0 2537
URL https:
Nikhil Verma, Abhishek Sharma, Dhiraj Madan, Danish Contractor, Harshit Kumar, and Sachindra
Joshi. Neural conversational qa: Learning to reason v.s. exploiting patterns, 2020. URL http::
14

---

Published as a conference paper at ICLR 2026
Tianle Xia, Liang Ding, Guojia Wan, Yibing Zhan, Bo Du, and Dacheng Tao. Improving com- plex reasoning over knowledge graph with logic-aware curriculum tuning. In Toby Walsh, Julie Shah, and Zico Kolter (eds.), AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25 March 4, 2025, Philadelphia, PA, USA, pp. 12881-12889, AAAI Press, 2025. doi: 10. V331/AAAALV11121.... URL wttp:/ttt////ettt org /10 16097 1609/ aaai. v39112 33405
Jundong Xu, Hao Fei, Liangming Pan, Qian Liu, Mong-Li Lee, and Wynne Hsu. Faithful log- ical reasoning via symbolic chain-of-thought. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computa- tional Linguistics (Volume Long Papers), pp. 13326-15 13365, Bangkok, Thailand, August lonno...2cc.. 720. URL
2024. Association for Computational Linguistics. doi: atttp::/aclaattiee woeethooogg..... wwt.oogg/2222. acl-l22..cllooonn 7207]
Yongxin Xu, Ruizhe Zhang, Xinke Jiang. Yujie Feng, Yuzhen Xiao, Xinyu Ma, Runchuan Zhu, Xu Chu, Junfeng Zhao, and Yasha Wang. Parenting: Optimizing knowledge selection of retrieval- augmented language models with parameter decoupling and tailored tuning. Proceedings of the 63rd Annual Meeting of the Association for Compulational Linguistics (Volume 1: Long Papers), pp. 11643-11662, 2025.
An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang. Jiaxi Yang. Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tianyi Tang, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu. Qwen2.5 technical report, 2025. URL https: //axvv/.//xvvv.o/ org/ /t/p/////2/211/ 2//.//122111111 15115
Naoki Yokoyama, Sehoon Ha, and Dhruv Batra. Success weighted by completion time: A dynamics-aware evaluation criteria for embodied navigation. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1562- 1599. IEEE Press, 2021. doi: 10.1109/R0051166 26.1166662219337733 2021 96367433373
URL https: /tpps://doi. http:///...rr/11111111111///1111 100.111090/R5111611
Michael Jq Zhang, W. Bradley Knox, and Eunsol Choi. Modeling future conversation turns to teach Iims ask clarifying questions. In The Thirteenth International Conference on Learn- ing Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net, 2025a. URL net Foror
Ruizhe Zhang, Yongxin Xu, Yuzhen Xiao, Runchuan Zhu, Xinke Jiang, Xu Chu, Junfeng Zhao, and Yasha Wang. Knowpo: Knowledge-aware preference optimization for controllable knowledge selection aerreeval-augmenned language models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 25895-25903,, 2025b.
Tong Zhang, Peixin Qin, Yang Deng, Chen Huang, Wenqiang Lei, Junhong Liu, Dingnan Jin, Hon- gru Liang, and Tat-Seng Chua. CLAMBER: A benchmark of identifying and clarifying ambigu- ous information needs in large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). ACL 2024, Bangkok, Thailand, August 11-16, 2024, pp. 10746-10766. Association for Computational Linguisties, 2024. doi: 10.1 2024. acll
http:///oooorg//////// org 18653/v1
ong...... 578
Xuejiao Zhao, Siyan Liu, Su-Yin Yang, and Chunyan Miao. Medrag: Enhancing retrieval- augmented generation with knowledge graph-elicited reasoning for healthcare copilot. In Pro- ceedings of the ACM on Web Conference 2025, WWW '25, pp. 4442-4457, New York. NY, USA. 2025. Association for Computing Machinery. ISBN 9798400712746 doi: 10.1145/3696410. 3714782. URL https: /ww. org.oorrg100 100. 11111111113111 3714782
George Kingsley Zipf. Human behavior and the principle of least effort. 1949.
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A PROOF OF PROPOSITION 1
Proposition For any finite-valued function g - V; + A over condition variables {cal fimi there exists a conditional reasoning DAG 9 such that, for each am E A, every root-to-leaf path ending at am corresponds to a conjunction in the disjunctive normal form (DNF) of the indicator function 1 (g(()) = am. and the union of all such paths encodes the full DNF of (g(-) am)-
Proof. Let g: I V; + be a total function over a finite domain, where each condition variable Cg takes values in finite set V.. For an arbitrary value om E A, we define the indicator function:
k v
f(v) = [[((v) am),
II V- i=1
(15)
Since the domain of g finite, f can be expressed in disjunctive normal form (DNF);
f(v) = V (Aa= where Com. = {v gg(v) = a.
(16)
vECom i=1
We construct conditional reasoning DAG (W, such that:
Each internal node corresponds to condition variable cz
Each edge from a node c; is labeled by value E Vi;
Each root-to-leaf path encodes a conjunction - (c: = 2t) for some v Cam
Each leaf node is labeled with am-
Formally, for each (vi. (vi....,,u) uk) E Com, we construct path Pv (no, 71, m,..., nik) where:
Ao is the root node,
for each j = 1,..., k. node n, is labeled with Cx() for some fixed total order over [ki].
edge (n--1, n2) is labeled by Ux())
the final node ny connects a terminal node labeled with am
By construction, the union of all such root-to-leaf paths exactly encodes the DNF of [[g(-) = am
B ANALYSIS OF CLARIFICATION EFFICIENCY.
To analyze the efficiency of our clarification strategy, note that the worst-case number of clarifica- tions bounded by the total number of condition variables k. However, in practice, each conclusion only small subset of these variables. We denote the average number of ditions along a valid reasoning path as r < k. Our dynamic traversal algorithm prunes inconsistent branches based on user responses, and selects the most cost-effeetive clarification at each step. As result, the expected number of clarification turns is reduced to O(r). Moreover, the DAG structure allows for node sharing across multiple paths, which enables information reuse and further reduces the overall number of clarification turns below r in settings with high condition overlap across paths.
typically depends
C PROMPTS
This section presents the prompts used our method, including DAG extraction prompt for clarifi- cation, conditional path guided data synthesis prompt for DAG construction and evaluation prompt.
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C.I DAG EXTRACTION PROMPT FOR CLARIFICATION
DAG extraction prompt for clarification
Given a user question and relevant document that are useful for answering the question, your task is to:
Based on the passage, decide whether the user question
has multiple conditional answers that are only applicable when certain user-speci
conditions apply.
2. Then, build graph (DAG) to represent all possible conditional reasoning paths, The node and edge of the DAG should be json format as follows:
Node format:
"node id": unique integer ID.
"node type": either "Condition" "Conclusion", "Conclusion" nodes must be terminal nodes with no outgoing edges.
"node content"; if the current node is Condition node, the content should be a clarification question about the conditional judgement; if the current node is Conclusion node, the content should be a statement about the final answer to the user's question.
"pre node id": node, if a node has multiple predecessor nodes, the predecessor nodes are in OR relationship.
list of the predecessor nodes of the current
Edge format:
"from": the starting node id of the edge, must be Condition node.
"to": the ending node id of the edge.
"label": the label of the edge, should be the answer of the starting Condition node's s clarification question.
Your output must contain only two parts:
[nodes] A list of all nodes. Each node must follow json format above. [nodes]
[edges] A list of all edges. Each edge must follow json format above. [edges]
Notice: Your output must include the above two parts with complete and properly closed tags.
Now, let's begin:
The user question is: [query here]
The document is: [document here]
Cutput:
C.2 CONDITIONAL PATH GUIDED DATA SYNTHESIS PROMPT
Conditional path guided data synthesis prompt
Your task is to extract structured decision problems from the following policy document. These problems must meet the following criteria:
1. The question has multiple possible answers (not just yes/no).
2. The answer depends on two or more user-specific conditions.
3. Different combinations these conditions lead to different
answers.
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For each decision problem you identify, extract the following fields:
"question": concise question that summarizo zes the decision in the first person.
"conditions": list of all relevant condition checks
in natural language. These should be simple yes/no-type evaluations.
"outputs": list of reasoning paths. Each path should
contain:
- combination of condition values (e.g., "A"; "Yes", "B": "No")
the resulting answer
brief natural language explanation of the reasoning
Use the following output format:
"question": "...",
"conditions" [" ["..."), "],
"outputs":
{
"combination": ("...": "...", "...": "..."h,
"answer";
reassn":
Notice: Your output must contain only the list with no other words!
Now process the follewing document and extract all such multi-conditinnal decision problems. The document is: [document here]
C.3 EVALUATION PROMPT
Evaluation prompt
Given a question, candidate answer, and ground truth answer, your task is to determine whether the candidate answer is semantically consistent with the ground truth answer based the following criteria:
Semantic Consistency Rules 1. If the ground truth answer contains a single definite conclusion, the candidate answer should express the same conclusion.
2. The candidate answer must not introduce any conclusions that contradict the ground truth answer.
Output Format
Your output should consist of two parts: reasoning part and conclusion part. The reasoning part should explain your judgment process. The conclusion part's content is "yes" if two answers are considered semantically consistent, otherwise "no".
The question is: [question here]
The ground truth answer is: [ground truth answer here]
The candidate answer is: [candidate answer here]
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D ALGORITHM OF DYNAMIC TRAVERSAL-BASED CLARIFICATION
Alg. D shows the detailed procedure of dynamic traversal-based clarification.
Algorithm Dynamic Traversal-Bars Clarification
Require: DAG G = (N, Clarifier LLM Oc, User Simulator LLM Oy with access to back- ground S, known condition set Knowwa (4)
Ensure: Final answer à
Initialize dialogue history H
2: Compute candidate set U according to Eq.
3: while + 0 do
4: Select n4 according to Eq.
5: Generate clarification question qn, ~ (n,).)
6: Obtain user response an, (gn, S)
7: if (n,, nz. v) E &, V. = On; then
8: Record (n, into H
9: Continue traversal to
else
11: Remove n, from U and continue with next candidate end if
13:
end while
return (H)
E ALGORITHM OF GPS INFERENCE PROCESS
Alg. 2 shows the overall inference pipeline of GPS, which consists of conditional reasoning DAG construction and dynamic traversal-based clarification (Alg.
Algorithm GPS Inference: Graph-guided Proactive Clarification
Require: User query g, retrieved document d, Reasoner LLM OR. Clarifier LLM Oc User (or Simulator) Oy with background S
Ensure: Final answer à
1: // Reasoning stage: DAG construction
2: Construct a DAG-extraction prompt PDAG (g. d) (see Appendix C.1
3: Generate structured DAG description y 2 (PDAGG d)).
4: Parse into a conditional reasoning DAG G (N, E) = PARSE(y).
5: // Clarification stage: dynamic traversal (Alg. 1
6: Identify the known condition set Cknown (q) + Oc d)
7: + ATW.MCCTRAEERSSLLLARIRIEC
(, θc, Uv Cknowa (owwa(()),5 (q),
8: return
F ALGORITHM OF THE REASONER TRAINING PROCESS
Alg. summarizes the clarification-orientee reinforcement learning procedure used to train the Rea- soner LLM OR with hybrid rewards over synthetic conditional-pal data.
G CASE STUDY
G.1
QUALITATIVE COMPARISON BETWEEN GPS AND BASELINE METHODS
Figure illustrates the different clarification processes adopted by GPS and ProCoT on the same underspecifted query from the Synthetic dataset. GPS successfully identifies the correct conditional
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Algorithm 3 Clarification-Orientte RL for DAG Extraction
Require: Document collection 2, data synthesis module SYNTH, initial Reasoner LLM O (0) Clar- ifier LLM θc. User Simulator ey RL iterations
Ensure: Trained Reasoner LLM f
// Conditional-paat guided data synthesis
2: S + (D)
to 3: for too do
4: Sample minibatch B C
5: for each (q. d, a, Cuiss) E B do
6:
DAG extraction by current Reasoner
G= 8N ~ ( (q.d)
7:
8:
I/ Simulated clarification and answer prediction
9:
à. Tetar t DYNAMICTRAVEERR CNNAMITTRNNERSAALLAARFICCCTTTTT
Oc. Ou, Cknowa (g), S)
10:
// Hybyri reward computation
11:
Compute total reward as in Eq. 13
12:
Store (4, d, G, R) for RL update
end for
14:
I/ RL update of Reasoner
θ -RLUPDATE(0 (t-1) {lg. d, 9, (ad.)))
16:
end for
return θ (TT)
rules from the document (highlighted in red) and constructs a conditional reasoning DAG to guide the clarification process through traversal, ultimately leading the correct answer. In contrast, Pro- CoT is distracted by irrelevant information in the document (highlighted in orange), asks unrelated clarification question, and consequently derives an incorrect answer.
Decumsent
apely help with the cost of finter higher oduration the
following
fonther money pay suition and your meimeraacce
parcst dial on afler January 1930
unliengnduatte counc uniooniiy other higher cducation
you're 16 or over and time educacinn
instiution (suck as collage school) this can include study aboood of coune
you ur survising yuncei rusivve bencevsment banefit frum the Amnal Fonaz-
Compensatoon scheme, War Pensoon scheme or Armed Forces Attibutable Benefis
a higher level technical education counse at gualification levels or
Query
am 18 years
college and time My sligble say scholanship univenity?
July 2001.
Background
Geher diad noult
Neithe my
Comperatiion War
Proactive
Qeery be dliill for any scholandip universit??
will be di ligitle for any scholanshpp for the miverstyy
Quiry
(Candl): la your pareat a vetenas who died as aecult
Canal
Clarify Q1: Are plansing apply for bigher
of thei service is the armed firess? You (Cand2y Dil your peecet tie un ofter Jamusry
education scholarship, euch undergraduate
Yo
Concloizz!
anlverslty?
19999 -s Yes
higher
plenring spply Clarily scholastip...
(CandB) Are
Yox
over and in fill-ime education?
Nu
my odacstion oppottailiss crsure focus fully on my sidics without undrs firaniial aress.
Clarity Q[Cond4):
benefits surviving
Conckoias?
Campensation
Amower: You, you would be digible for the schobrohig ship However, you need to confinn your
Armed Altrtbutable Benelits
Charify A:
slighiity with the benefite you are moxtiving
CnnerrConettdonn) You are not eligibie the schslarshig.
Cecc
Figure 4: Comparison GPS and ProCoT on an underspecified query from the Synthetic dataset. GPS extracts the correct conditional rules and uses the resulting DAG to ask the necessary clar- ification and reach the correct answer, whereas ProCoT focuses on irrelevant details and asks an unrelated question, leading to incorrect conclusion.
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Figure illusttates that GPS provides more reliable clarification process than UoT. UcT selects clarification questions based on uncertainty signals but lacks an explicit representation of the full decision structure. As result, it checks only the first two conditions and stops once uncertainty appears reduced, which causes it to miss a decisive eligibility factor and produce an incorrect con- clusion. In contrast, GPS constructs the conditional reasoning DAG, queries all necessary conditions through dynamic traversal, and therefore arrives at the correct final answer.
Document
Query
your ciromnstaanes
cancel Marruge Alfonsnce if of the fellowing
your relaiiosiity cods- because you ve divocoed, eodel ('dassolved"' your divll parterslip or legilly separated longer slihhtle
changes
Mist ooocel Allwaace?
you kenger chein
User Background
I've bee refeeting on my ournent staetion and warted to encare everthingg is onder My relationship stable, there lout't been ars divoree, disolation legal appertiin. Aukitinnally au incume rxmain sams, so Ia cluzbll henelita bozn receivirs Laka continue chimm these benefia as they provile cocncial support for child me to aake
deaäh are up date and that sill mecinn all reqcrenenti.
Uot
GPS
Must eazcel
Allowance?
Cluily Ql(Candyy there
Clarify Ql(Condl) to diveno, disutttion cral puttoraiin logd
relutiondip eoded
Frelh
thst make incligible Marrage Alowato??
Clurtty A No
Cheifty Al: Ne
eeperatae?
NJ
Clurlyy QbcCoed Has your relatconkis anded dae didrom dowokron af's cisil parmendepp.o or epal
Cheifó @2(Codd2), Has your incusee chasacd, making you longer tliglle for MarMany
equertiinn
Gimelaree?
(U(Caed)): lenger
Clarity
Missing Cond3
Cinchnaldly Yea, axat cenecl Somsage Allowance
Figure 5: GPS provides more reliable clarification process than UoT. UoT queries only part of the relevant conditions and terminates prematurely, which causes it to miss decisive eligibility factor and produce an incorrect conclusion. GPS constructs the full conditional reasoning DAG, queries all necessary conditions through structured traversal, and therefore arrives at the correct final answer.
G.2 CAPABILITY OF GPS IN MODELING NESTED CONDITIONAL LOGIC
Figure 6 demonstrates that the underlying rule structure is not a flat sequence of conditions but genuinely nested logical hierarchy. The eligibility decision depends on multiple interacting sub- rules: initial branch based on registration date, a second layer involving engine standard, and filter. In parallel, separate subtree handles converted or re-engined vehicles, further routing to different authorities depending subsequent conditions. These rule blocks depend on one another in a layered manner, where the outcome of one condition determines which deeper sub-rule becomes applicable- defining characteristic of nested logic.
third layer contingent on the presence
Under our framework, such hicrarchical dependencies map cleanly into a DAG. Conjunctive de- pendencies appear chained edges, disjunctive alternatives as branching nodes, and intermediate outcomes naturally serve as parent nodes for deeper conditional layers. As stated in Proposition 1, any finite conditional rule system with nested structure can be transformed into such DAG without loss of logical fidelity. The figure illustrates this concretely: GPS successfully captures all nested branches in a structurally precise DAG, confirming that the method faithfully models and traverses multi-level logical hierarchies rather than only simple cundition-conclune patterns.
G.3 IMPACT OF STRUCTURAL QUALITY REWARDS ON DAG CONSTRUCTION
Figure illustrates how the proposed structural quality reward rn distinguishes between well- structured and struty-structured clarification DAGs. The left DAG generated by GPS forms a clean hierarchical decision structure: each clarification introduces meaningful discrimination, branches do not recombine, and each split contributes directly to narrowing the final conclusions. As a result, its split entropy is fully converted into leaf-level discriminative power, yielding ry 1.
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Document
Contact DVLAiffie vehicle las ether
Eligibility
bocn titted or conventd to solely petro)
Your vhiice can be usted for Law Emiscura Ceriifictte if
lud sppeoved corvesiun
regisered the UK beftee Octoerr 2006
Contact Tranpor far London (TIL) if your vehidle his been 're-engined" meet Low
fited with full firor to on Fiiiiion Zone emiveions evenbeekk
Fmiscon Zone standans.
You not real a Low Emiiiioon Canifiiati velicke with Furo ongine.
Book Lon Emissions Certificate test
Converted engined vehlcles
Query
Ia whidde Low Coriiicale (LBC))
GPS
Canclatioal: Not givle for EC
de Ieffef Outober 2006?
Ye
Canciacion2: lllg for LIC.
you vchidi
Ulze vehide todian appervel paussaaasiee
Yea
Condimiou Corner DVT Butterr interniies
Coudt Rosee siteeta with
Castl tiee your viiide hore
Yos
Conclationd: Lectad: inaapart London (TI1 for farher information
Yo
Figure 6: GPS successfully models a multi-layer nested logical hierarchy: conjunctive chains, dis- junctive branches, and deeper sub-rules are all represented in unified DAG structure.
In contrast, the right DAG generated by backbone model exhibits redundant branching: several clarifications produce splits that later merge, creating patterns where injected uncertainty does not contribute distinguishing final leaves. This causes the graph-level split entropy H graph increase while the leaf entropy Heatf remains low, yielding a substantially reduced score of ri 0.46.
This case demonstrates that the structural quality reward effectively penalizes DAGs whose inter- mediate clarifications do not help refine the final conclusion space, and correspondingly encourages models produce non-redundant clarification structures.
H ILLUSTRATION OF THE OVERALL GPS REASONING AND CLARIFICATION WORKFLOOW
Figure provides a concise end-to-end illustration of the GPS framework. Given user query and its associated document, the Reasoner first extracts all condition -eeeedennn rules and generates conditional reasoning DAG, where internal nodes represent clarification conditions and leaf nodes represent possible conclusions. Based on this DAG, the Clarifier interacts with the user in a traversal manner, issuing only the condition queries necessary eliminate incompatible branches. As user responses progressively constrain the DAG, the traversal converges unique conclusion, from which the final answer is produced.
This example illustrates how GPS combines document-groundeer rule extraction with adaptive clar- ification to resolve underspecified query effectively.
I IMPLEMENTATION DETAILS
The experiments are conducted on machine equipped with 8 NVIDIA A800 GPUs. For GRPO, we apply LoRA and set the rank LoRA to 64. The training epoch set to 1, the batch size is set to 32 and the learning rate is 3e-6. The hyperparameter in the hybrid reward is set to 0.5.
ANALYSIS OF PERFORMANCE ON TWO TYPES OF QUERIES
Table 3 reports the SR (%) different methods on ConditionalQA and ShARC, separately for un- derspecified and well-speciffed queries. On ConditionalQA, GPS consistently outperforms all base-
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Document
Get a deed pull
doaa poll legal decumon thi provic change of romme You can change any puet of your rame, abd er ramore nemoe and hyplen, or change queling
There are 2 ways get doad poll You can siherr
Make us 'uncarslled' duod pall yoursel
You need to 16 over make your own deed poll.
Apply Sor on "cnrolled" deed pell
You rast apply to Royal Courts Justice to get on "cavllled deed pell using die deed poll process. It costs 542 44,
Yoo can only encoll your own nome change yf 18 over. The process i differeor to clange the same of'a child under 18.
Query
What type deal pall de nead chaue samse
Qwen2.5-778 In-iituutttt
Instruct + GPS
Qwen2.578-Iastrrut
Coedi yoe Bee pernavelly
Coedl: la sune fien iceal parpee"
thir
Comd2: you year
Conclasbol: Yoa connet change your sarss by faed pull
Coetll sge ovemans?
Crstt loiag--s ovcree?"
Coedit 110
Ne
ald older
Contiedee2 tryou an tider 16, you carsor change your came by derd pall
You
No
Canchnkent laserolled as 'caellld teal
Condudinn You mike or tnwertlie food pali youndlt.
Conduddadd You
Lecosllof fad pill younalt
Yeans faet pell yourall apply mnolled faed pall.
Tyl Hesst Harrps
Tal Barpp Maraph 0.46
Figure 7: Comparison of structural quality between two clarification DAGs. The GPS-generated structure (left) forms clean, monotonic decision refinement and achieves ry 1. The baseline (right) contains redundant branching and split-merge patterns, which inflate Hgraph without increasing Hleat resulting low rn 0.46. The structural quality reward explicitly captures this efficiency gap and drives learning toward well-structured clarification DAGs.
Document
Disability elighllity
yoou income related Employyent Support (ESA), you gannot get the disebillly premiumt.
get the premium you
peasion credit age Of getting
Dosialityy Living Allowance (DL.A)
Personal Independence Pegt (PIP)
you do not quality dhrough the above, may still get the peeniuu If 've been unable to work for at Jeast
How Claim
Query
Background
Eeighii for disability premium?
Loe't pa moore edaadd ISA,
Reasoner
under pesdis crutlt ags
Fre cnable wokk fur manlls.
Cenifer
Wser Dendettr
GPS-DAG
Deyoorge FSA?
UPS-Clariffcation Turns
Query: Am dighll for duability premium?
peiiee coeil codiliget
Condusienl: NOT digitle
QIrCendT): Do you
Charify Q2Cand2] Are you under pensiin crefit croditapy?? agy?
CiedD
Clarify A2: No.
Clarlly QRCand3): Are you getting DLA?
Clarify A3. No.
Coudl Bodee.xaeyeuuaeeeo geiee
Conchniend digitlee
Clarify Q4CCand4): Ame you getting MP?
Clartly
Q5Cadd5), Have you unbble lesst
suble nork Ber::
No
Clarity
AnswerCancuuienl)) I): NOT algihle
Figure 8: Overview of the GPS workflow. The Reasoner first extracts condition -eeenddet rules from the query-document pair and produces a conditional reasoning DAG. The Clarifier then per- forms multi-turn clarification by traversing the DAG, pruning incompatible branches based on user responses, and converging valid conclusion.
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lines, achieving the highest SR in both well-specified (72.7) and underspecified (81.1), demonstrat- ing the offectiveness DAG-guided clarification. ProCoT also performs competitively on under- specified queries (69.8), surpassing Base Method and Clarify-DPO, In contrast, BED-LLM shows consistently poor performance, especially well-specified queries (46.6). On ShARC, Clarify-DPO achieves the best performance underspecified queries (91.4), while GPS remains strong and bal- anced across both settings (71.6/80.0) Interestingly, Base Method and ProCoT collapse on ShARC underspecified queries (32.3), suggesting limited cross-domain generalization. Overall, these results highlight the robustness of GPS in identifying and resolving ambiguity.
Table 3: Success Rate (SR, %) well-specified underspecified queries across ConditionalQA and ShARC with the LLaMA backbone. Bold denotes the best result and underline the second-best
ConditionalQA
ShARC
Method
Well-specified
Underspecified
Well-specified Underspecified
Base Method
63.6
60.4
80.9
32.3
UoT
64.8
32.9
69.1
67.7
Clarify-DPO
68.2
53.9
74.1
91.4
ProCoT
65.3
69.8
75.2
32.3
BED-LLM
46.6
40.5
64.4
63.6
GPS
72.7
81.1
71.6
80.0
K DATASET DETAILS
We first construct Synthetic dataset consisting of 2575 underspecified queries based on documents from ConditionalQA.. These samples are split into training and testing sets using 8:2 ratio, with 2060 samples used for training and the remainder for testing. For overall training, combine the Synthetic training split with the underspecified queries of the Conditional training set, resulting in total of 3250 training samples. For evaluation, test split, the ConditionalQA test set, and the ShARC test set. We present the detailed size of our training dataset and three evaluation benchmark datasets in Table
three evaluation datasets: the Synthetic
Table 4: Dataset Statistics.
Sources
Underspecified
Well-specified
Total
Training data
Synthetic
3250
3250
In-domain test data
Synthetic
515
515
ConditionalQA
53
176
229
In-domain Total
568
176
744
Out-of-domain test data
ShARC
675
675
1350
L THE USE OF LARGE LANGUAGE MODELS (LLMS)
In this work, Large Language Models (LLMs) mainly used auxiliary tools rather than components of the proposed method. Specifically, we leverage LLMs for two purposes: (i) gram- mar checking and language polishing of academie writing; and (ii) providing suggestions for code
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Published as a conference paper at ICLR 2026
debugging, particularly in identifying possible causes of error messages and offering potential fixes. These uses of LLMs help streamline the writing and coding workflow, but they do not influence the methodological design or experimental results of this paper.
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Reviews:
[{"review_id": "np4qzDolaN", "reviewer": "Reviewer_Zgt8", "summary": "The paper presents GPS, a two-stage framework for improving proactive clarification in retrieval-augmented generation (RAG) systems when user queries are ambiguous or underspecified. The method explicitly models conditional reasoning dependencies using a Directed Acyclic Graph (DAG) that captures logical structures across retrieved documents. The first stage constructs the DAG with theoretical guarantees of logical completeness, while the second stage performs dynamic traversal to prune inconsistent reasoning paths based on user feedback. To mitigate data scarcity, the authors propose a conditional path-guided data synthesis strategy and optimize DAG extraction using clarification-oriented reinforcement learning with hybrid rewards balancing accuracy and efficiency. Experimental results are conducted on Synthetic, ConditionalQA, and ShARC benchmarks.", "questions": "Since the improvement in Success Rate is only marginal, it would strengthen the paper to include qualitative comparison examples that clearly demonstrate the advantages of GPS in constructing higher-quality and more logically complete DAGs compared to existing methods. Such examples are essential to highlight why graph construction is both necessary and beneficial, beyond quantitative gains—showing how the proposed approach leads to more accurate condition–conclusion reasoning, better clarification paths, and enhanced interpretability relative to baselines.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 3, "strengths": "+ GPS introduces a novel approach by integrating graph-based reasoning with proactive clarification in retrieval-augmented generation systems, which significantly advances the state of the art in handling underspecified queries.\n+ The framework is supported by theoretical foundations, including formal guarantees of logical completeness for the constructed DAG, ensuring systematic and reliable reasoning.\n+ Empirical results demonstrate that GPS outperforms baseline methods on Synthetic and conditional QA dataset on Success rate, and achieve comparable results on OOD dataset ShARC, showing generalization capabilities.", "weaknesses": "- The performance across the three benchmarks does not show clear and consistent superiority on all evaluation metrics, suggesting room for further improvement in robustness and overall gain.\n- The methodological exposition could be clearer. the paper would benefit from more detailed algorithmic descriptions and richer examples to illustrate how conditional relationships are captured and resolved.\n- The examples provided involve relatively simple condition–conclusion links; it remains unclear whether the DAG-based reasoning can handle more complex logical hierarchies, such as when a conclusion becomes a condition in a nested structure.\n- Interestingly, on in-domain data, GPS exhibits only marginal gains and performs worse than Clarify-DPO in clarifier prediction accuracy. This suggests that the reasoner may struggle to interpret graph-based representations effectively when determining whether a query requires clarification. It would be valuable to explore enhancing the reasoner’s capability through joint training on synthesized DAG–QA pairs, allowing it to better align graph structures with clarification decisions.", "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": "F5nts19vxy", "reviewer": "Reviewer_yNPb", "summary": "This paper tackles ambiguity in RAG by teaching LLMs to proactively ask better clarifying questions. It proposes GPS, a two-stage approach: first, represent the retrieved knowledge with a Directed Acyclic Graph so the model can reason over conditional rules in a logically complete way; second, traverse and prune that graph interactively based on user answers to keep clarification efficient. To make this practical, the authors generate training data that reflect conditional paths and further fine-tune with a clarification-oriented RL objective balancing effectiveness and efficiency.", "questions": "If the retrieved document is incomplete or underspecified, can the model leverage its own parametric knowledge to supplement missing conditions during DAG construction and clarification?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Rule-structured reasoning: Modeling conditional rules as a graph is a clean, principled way to surface ambiguity and drive targeted clarification, rather than ad-hoc question asking.\n\n2. Theory with practical bite: The logical completeness guarantee plus average-case O(r) clarification complexity gives both soundness and efficiency.\n\n3. End-to-end system design: A coherent pipeline—conditional-path data synthesis, clarification-oriented RL with a hybrid (accuracy/efficiency) reward, and dynamic traversal—aligns training with the actual interaction objective.", "weaknesses": "1. Baseline fairness (Clarify-DPO): The original Clarify-DPO does not has the RAG part. It’s unclear whether Clarify-DPO had access to retrieved documents (true RAG) or only engaged in Q&A without retrieval. If the latter, the comparison is unfair; if the former, the paper should specify how evidence was integrated to ensure parity.\n\n2. Training data parity: GPS is trained on ConditionalQA (same policy domain as ShARC). Were baselines also trained on exactly the same splits and sources?\n\n3. Efficiency evidence gap: The claim of higher efficiency isn’t supported by the experiments. There is no comparison in the aspect of efficiency with the baselines. \n\n4. Domain generalization: Evaluations center on rule-heavy policy/regulation datasets; it’s unclear how GPS transfers to domains where rules are fuzzier (open-ended QA, multi-hop encyclopedic tasks) or where conditional structures are incomplete/noisy.", "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": "v7FtOehjrQ", "reviewer": "Reviewer_1Jai", "summary": "The paper introduces GPS, a two-stage framework that enables large language models in retrieval-augmented generation systems to proactively clarify underspecified user queries by modeling conditional logic in retrieved documents as a directed acyclic graph. In the reasoning stage, a Reasoner LLM constructs a logically complete graph that captures the AND and OR relationships among condition variables and possible answers. In the clarification stage, a Clarifier LLM dynamically traverses the graph, selecting questions from a candidate set of nodes based on their expected remaining depth, and prunes inconsistent paths according to user responses to achieve efficient clarification with only a few turns.\n\nTo train the Reasoner, the authors propose conditional path-guided synthesis that augments ConditionalQA by generating underspecified queries with multiple path assignments and filtering them through a Verifier LLM to ensure consistency. They further apply reinforcement learning with a hybrid reward combining accuracy, efficiency, and structural quality to enhance reasoning precision and interaction efficiency.", "questions": "- How often does the Reasoner generate invalid directed acyclic graphs—for instance, containing cycles, incomplete edge coverage, or mismatched condition variable domains—and what fallback mechanism is applied during traversal if graph parsing fails or if the candidate set becomes empty prematurely?\n- Regarding the efficiency reward, what specific value of α was used, and how does the model’s performance degrade when user responses deviate from the simulator assumptions, such as providing evasive answers or multi-valued inputs outside the defined variable domains?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- Proposition 1 rigorously proves logical completeness via DNF encoding, with every root-to-leaf path corresponding to a conjunction. \n- Conditional path-guided generation from documents produces underspecified queries with explicit missing conditions and corresponding reasoning paths, each consisting of variable–answer pairs. These queries are filtered based on necessity—retained only if a Verifier model can answer correctly when given the full conditions but fails when any are masked. This process yields high-quality augmentations of ConditionalQA’s limited underspecified samples, expanding the dataset significantly without requiring human annotation.", "weaknesses": "- Filtering retains samples only when the Verifier LLM predicts the correct answer under full conditions but fails under partial ones. However, this approach discards nuanced cases involving partial ambiguity resolution and inherits the biases of the Verifier model, such as inconsistencies observed in DeepSeek-R1. \n- There is no inter-annotator agreement or human validation for the roughly 75.5% of samples discarded during ConditionalQA augmentation.", "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": "hEyR80jlk4", "reviewer": "Reviewer_behg", "summary": "The GPS (Graph Guided Proactive Information Seeking) framework proposed in this paper addresses the issue of active clarification in RAG systems when LLM processes queries with insufficient information. It innovatively introduces a conditional inference structure based on directed acyclic graphs (DAGs) and combines conditional path guided data synthesis with clarification oriented reinforcement learning, effectively balancing the effectiveness and interaction efficiency of clarification.", "questions": "1. Why was the method in this article not compared with the GraphRAG series models?\n\n2. The second stage of this method uses models around 7B, which is too small compared to the deepseek used in the first stage. Should we use LLMs of the same scale? Existing mainstream methods also tend to use larger models", "limitations": "", "rating": 2, "confidence": 4, "soundness": 1, "presentation": 2, "contribution": 1, "strengths": "1. The design of DAG conditional reasoning structure combines logic and efficiency, breaking through the limitations of traditional prompting methods that rely on LLM spontaneous reasoning, and providing structured logical support for active clarification.\n\n2. The conditional path guided data synthesis method not only optimizes the logical correctness of DAG, but also suppresses redundant interactions and structural redundancy, achieving multi-objective collaborative optimization.", "weaknesses": "1. The specific implementation logic for extracting DAG is not clear, such as \"how to parse conditional variables and logical relationships from unstructured documents\" and \"when there are fuzzy rules in the document.\n\n2. Suggest adding parameter sensitivity experiments to clarify the optimal parameter selection strategy; Fully derive the calculation process of structural quality rewards and verify its rationality through examples.\n\n3. In the relevant work section, there was no in-depth comparison of the essential differences between GPS and existing models in \"structured inference targets\" In addition, many SOTA methods have already studied knowledge graphs and their logic. What is the difference between this article and them?", "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)