Hybrid Deep Searcher: Scalable Parallel and Sequential Search Reasoning
Authors: Dayoon Ko
Jihyuk Kim
Haeju Park
Sohyeon Kim
Dahyun Lee
Yongrae Jo
Gunhee Kim
Moontae Lee
Kyungjae Lee
Venue: ICLR 2026 Poster (2026)
Keywords: Multi-hop QA
RAG
Reasoning
Primary area: foundation or frontier models, including LLMs
TL;DR: We propose HybridDeepSearcher, a scalable search agent that dynamically integrates parallel and sequential strategies,trained on HDS-QA,a novel hybrid-hop dataset with supervised trajectories.

Abstract: Large reasoning models (LRMs) combined with retrieval-augmented generation (RAG) have enabled deep research agents capable of multi-step reasoning with external knowledge retrieval. However, previous methods that extend reasoning with single-query search steps struggle to scale to complex tasks demanding broad document exploration. Meanwhile, approaches that generate multiple independent queries simultaneously may limit deeper, sequential reasoning. To address these limitations, we propose HybridDeepSearcher that dynamically integrates parallel and sequential search strategies to enable effective search scaling. To support training, we introduce HDS-QA, a novel dataset that seamlessly integrates broad parallel search with sequential search reasoning, providing answer trajectories in the form of reasoning-query-retrieval loops with parallel sub-queries. Across all five benchmarks, our approach significantly outperforms the state-of-the-art, improving F1 scores by +15.9 on FanOutQA and +11.5 on a subset of BrowseComp. Further analysis reveals that HybridDeepSearcher effectively scales performance with additional test-time search resources and demonstrates robustness on questions requiring more evidence, achieving higher evidence coverage. We include the code in the supplementary materials and will release the dataset and code publicly.

Published as a conference paper at ICLR 2026
HYBRID DEEP SEARCHER: SCALABLE PARALLEL AND SEQUENTIAL SEARCH REASONING
Dayoon k
Jihyuk Kim²
Haeju Park²
Sohyeon Kim'
Dahyun Lee² Yongrae Jo
Gunhee Kim
Moontae Lee
Kyungjae Lee
'Seoul National University LLG Al Research
'University of Illinois Chicago "University of Scoul
layoon.ko@vision.snng
httpes//aaprrideeppparccee....d io/
ABSTRACT
Large reasoning models (LRMs) combined with retrieval-augmented generation (RAG) have enabled deep research agents capable of multi-step reasoning with external knowledge re- trieval. However, we find that existing approaches rarely demonstrate text-time search seal- ing. Methods that extend reasoning through single-query sequential search suffer from lim- ited evidence coverage, while approaches that generate multiple independent queries per step often lack structured aggregation, hindering deeper sequential reasoning. We propose hy- brid search strategy to address these limitations. We introduce HybriddeeppSarcher: a structured search agent that integrates parallel query expansion with explicit evidence ag- gregation before advancing to deeper sequential reasoning. To supervise this behavior, we introduce HDS-QA, novel dataset that guides models to combine broad parallel search with structured aggregation through supervised reasoning qqerr-reerreee trajectories containing parallel sub- sub-queries. Across five benchmarks, Hy SiypeieDeeeeerrcher significantly outperforms the state-of-the-art, improving Fl scores by +15.9 on FanOutQA and +9.2 on BrowseComp'. Further analysis shows its consistent test-time search scaling: performance improves as ad- ditional search turns calls are allowed, while competing methods platcau.
INTRODUCTION
Large reasoning models (LRMs), such OpenAI o3 (OpenAl 2025) and DeepSeck-RI (Guo al., 2025), have demonstrated that allocating additional computation at inference time, commonly re- ferred to test-time scaling, can substantially improve performance. By generating longer reason- ing traces through additional tokens, these models achieve stronger results on complex tasks.
Following this line of work, recent studies (Li et al. 2025 Jin et al. 2025 Zheng al. 2025
Gao et al. 2025a) have developed retrieval -aggeeeeee generation (RAG) into deep search agents by incorporating multiple retrieval turns into the reasoning process. These agents operate in iterative loop that issues one or more queries, retrieves relevant information, and integrates the retrieved content into subsequent reasoning steps.
However, we observe that simply extending the search chain does not yield proportional perfor- mance gains as information requirements increase. This limitation is illustrated in Figure D where we evaluate the test-time scaling behavior of existing search agents on BrowseComp'. For instance, Search-R1 (Jin et al. 2025), trained with GRPO (Shao et al., (024) for sequential search with one query per turn, performs about 4.7 turns average but achieves limited performance. Because it issues only single query at each step, the total number of search calls remains around 4.6, which restricts comprehensive document exploration. Conversely, RAG-RI (Tan et al.] 2025). trained with GRPO for parallel search with multiple queries per turn, issues more queries overall (about 8.2 search calls) and gathers more information. However, it still uses only about 4.3 turns average and achieves only marginal improvement over the sequential search baseline.
Work was done during internship at L.G Al Research.
CCorrsspondigg author.

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loops that Published as a conference paper at ICLR 2026
x
Search-ol x
Search-R1
x
R1-Searcher
x
DeepResearcher
X
RAG-R1
Ours
15
Mc
M,
F1 Score
10
2.3 Turns
Turns
F1 Score
Calls
0.0 F1
My
x X
M
x
X
6
8 12
16
Average Number of Search Turns
Average Number Search API Calls
Figure 1: Test-time Search Scaling on BrowseComp'. For our method, evaluation is conducted by scaling two types of search resources: (1) latency measured by the maximum number of search turns (Mr = 1, 4, 8), and (2) search cost measured by the maximum number of search calls (Mc = 2, 2,4, 16). The x-axes report the average number of tums/calls actually used under each budget. Our model is required to output final answer once either resource limit is exhausted. For other baselines, we allow maximum of 10 turns with unlimited API call limits. The results on the other benchmarks are provided in
These results suggest that existing approaches, although designed to scale search during reasoning, fail to achieve consistent test-time search scaling. Specifically, models trained to extend reasoning through additional sequential search steps encounter information bottleneck because each step retrieves only a single piece evidence, limiting coverage growth. Models trained to generate multiple parallel queries per turn suffer from synthesis difficulty because they have not been trained to aggregate large sets of retrieved evidence without structured reasoning, often leading to premature termination. This observation raises fundamental question: what search strategy enables genuine test-time search scaling?
In this work, we propose hybrid search enable test-time search scaling. For scalable search, the search agent should (i) generate multiple queries at each step to close knowledge gaps without intro- ducing an information bottleneck, and (ii) explicitly aggregate the retrieved evidence for subsequent sequential reasoning. Consider the question: "Out of all feature-length theatrical films directed by John Carpenter before 2015, which has the longest running time?" Once the filmography iden- tified, a search agent needs to retrieve all runtimes and comparing them globally. This naturally calls for parallel query expansion to collect the runtimes one step, followed by aggregation to determine the maximum.
To instill this structured behavior, we introduce HDS-QA, a novel training dataset that teaches mod- els to (i) generate broad parallel queries and (ii) explicitly aggregate results before continuing quential reasoning. While prior approaches rely on outcome-reward online reinforcement learn- ing with substantial computational resources, we demonstrate that direct supervision of this hybrid search strategy alone enables true test-time search scaling.
To the best of our knowledge, this is the first dataset that (i) increases the breadth of parallel search by supporting beyond two parallel sub-queries, and (ii) explicitly incorporates these broad paral- lel search results into sequential search reasoning. This enables scalable search through structured parallel-to-seqeeme
reasoning. We generate these questions through a carefully designed auto- matic pipeline and curate answer trajectories in the form of geasonnng-quey--eetie include parallel search queries, resulting in 2,111 question-answer pairs.
We fine-tune an LRM on HDS-QA to build HybriddeeppSearcher which demonstrates consistent and monotonic search scaling as shown in Figure I To assess this capability, we scale two test-time search resources: (i) search turns from one to eight and (ii) search calls from two to sixteen. Fl improves by 3.8x (from 4.0 to 15.1) when increasing the turn limit from My=1 to My-8, and by 4.4x (from 3.4 to 15.1) when increasing the call limit from Mc=2 to 1c= In contrast, existing baselines plateau despite comparable or greater search budgets. HybridDeepSearcher achieves up to a threefold improvement over the state-of-the-art under matched resource constraints.
Additionally, experimental results reveal three key findings: First, it significantly outperforms all baselines across all five benchmarks, doubling accuracy on FanOutQA (7 evidence on average). Second, across all benchmarks, it consistently improves search turns or calls increase, while other baselines remain stagnant or even fail to improve. Third, as the number of required evidence increases, our model shows minimal performance loss, while others suffer from significant decline.
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Published as a conference paper at ICLR 2026
In summary, contributions are follows:
We introduce HDS-QA, novel dataset that teaches models integrate broad parallel search with structured aggregation to enable scalable sequential reasoning.
We propose HybridDeepSearcher, a structured search agent trained on HDS QA that integrates parallel query expansion with explicit aggregation, enabling scalable and efficient search.
HybridDeepSearcher exhibits test-time search scaling, improved search efficiency, broader idence coverage, and robustness to increasing evidence requirements.
2 RELATED WORK
Sequential vs. Parallel Search. Iterative sequential search (Trivedi et al. 2023 Yao et al. 2023
Shao et al. 2023) has been effective for early MHQA with predefined linear reasoning paths, where question is decomposed into interdependent sub-questions processed sequentially. For instance, IRCoT (Trivedi et al.) 2023) iteratively generates chain-of-thought sentence based on retrieved documents and performs subsequent retrieval using the sentence query.
Recent work (I.i et al. 2025 Jin et al. 2025 Song et al.) 2025 Chen et al., 2025a) has developed search agents integrating LRMs with RAG to orchestrate multi-step reasoning with external retrieval. Search-ol (Li et al.) 2025) introduces a prompt-based agentic RAG framework leveraging Reason- in-Documents for inline synthesis, while Search-RI (Jin et al. 2025) and DeepResearcher (Zheng et al. 2025) use Group Relative Policy Optimization (GRPO) (Shao et al., (002)) to incentivize enhanced search and reasoning. However, these approaches emphasize scaling reasoning via RL while leaving search scaling largely unaddressed.
Concurrnt works (Tan al., 2025 Zhao et al. 2025) explore multi-query generation, but they trained on questions that resort solely to either sequential or parallel search. Our contribution the integration of broader parallel search into sequential search reasoning to scale search.
Task Decomposition. The decomposition of parallel and sequential search is closely related to task decomposition. For MHQA tasks, GenDec (Wu et al. (2024) decomposes questions into sub- queries, while Plan*RAG (Verma et al., 2025) constructs directed acyclic graphs of sub-queries. However, both methods are static and cannot adapt to intermediate retrieval results, often leading to incomplete evidence coverage.
Beyond static methods, several approaches Zhu et al. 2024a Prasad et al. 2024 Lee & Kim 2023) explore dynamic decomposition across various tasks, such as web navigation. ReDel (Zhu et al. 2024a) implements a recursive multi-agent framework in which agents decompose tasks and delegate sub-tasks on the fly, producing both parallel and sequential sub-tasks. Similarly, ADaPT (Prasad et al.) 2024) generates an initial plan and invokes an external verifier to trigger further hi- erarchical decomposition when the plan fails. These methods employ prompt-basee strategies with proprietary large language models (LLMs), such as GPT-4.
These works primarily focus on how decompose given task effectively. However, it is equally crucial effectively synthesize the results obtained from decomposed queries for subsequent re- trieval steps in search scaling. Our dataset addresses both decomposition and synthesis by integrat- ing parallel search with sequential search reasoning.
Question Answering Datasets. In the early stages of MHQA research, datasets such as Hot- potQA (Yang et al. 2018) and 2WikiMulliHopAA (Ho et al., 2020) were widely used train and evaluate the retrieval and reasoning capabilities of LLMss. As models have advanced, more chal- lenging benchmarks have emerged test increasingly complex reasoning over broader evidence coverage. MuSiQue (Trivedi al. 2022) increases sequential complexity by chaining single-hop questions, extending reasoning from two to four hops. FanOutQA (Zhu et al.) (024b) evaluates fan-out style questions that require simultaneous, retrieval across multiple independent entities.
More recently, FRAMES (Krishna et al. 2025) has been proposed to evaluate factual accuracy, retrieval ability, and reasoning generating final answers, while BrowseComp (Wei et al., 2025) poses complex questions that demand integrating multiple factual pieces that are often difficutt to locate the web. These benchmarks reflect the growing complexity of evaluation tasks.
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Search & Beesoning Workflow
Single- Hop Q who is the miin vecalist of (Queen)
1. Panaliel anarch identify the rack band
RRock band with dramatic live performances and exceptional vocal range?
Fredcle Hertury
TRock hand with an innovalive bland operalic style and rock*
"Rock band whose aibum is poplutar papular sporting venue anthem"
Parallel Hop
Sequentiaal
Hap
2 Sequential search to find out the main vocal of the band
HDS QA Quastion Generation Pipuline
Who the vocallst the Gueen?
Collect related questions about (Queen)
Extract stics (Queen
Compose question (Queen)
Integrate the into
Google's "Roapia Also Ask"
Prompt: Summorize each description
(recept: Usiigg chorecteristis of "Clusen"
Promett Integrate irte Ceorry single integrined
cene lne characteriati about the
generate comples. rppist queation that
Who the main vocolasi Queen?
"uuee""
information.
Who has the nighost voice nn Queen?
Queen''s vocalist has exceptional vocal
hight related to the gleen antly.
anower Avdid using pconouns names
Check it uranowenbbee with the top cocumerns.
Frediie Hanoury's Voice
range with dramatic live performances.
What most euccessful Cueen
song? ?
shows innovative blend
C Checkhhttee whe top documennts
unanswerable
vocallit rack band known
Hybrid-HHpp Who
styla.
Paralle: Hop a: which rock band known for
dramatic live performances and
upy
dramatic live performances and exceptional
exceptional vocal range, an Innovative
Overlapoed document!
vocal rangs, an innovative bland of operatic
blend of openatic style and rock, and famous for crcating a legondary album
Which Dueen
events? Q We Are the Champlons
sporing
Queen's legundary album famous in
poputar sporting varue anther....
style and mack, and famoua for creating
legendary album with universally papular
with universally popular sporting venue anthem?
sporting arthem?
Figure 2: Pipeline for HDS-QA question generation.
Compared to recent benchmarks, progress training datasets (e.g., HotpotQA) has lagged behind in the number of hops and required evidence (at most two), leaving models unable to keep pace with increasingly complex tasks that demand processing numerous pieces of retrieved information. Con- current synthetic-daie data efforts (Wu et al. 2025 Gao et al. [2025b] Liu et al. (225) also increase task complexity, but they primarily construct questions centered on deeper or longer sequential browsing trajectories. In contrast, our training dataset is specifically designed to provide questions that in- volve (i) a greater breadth of parallel sub-queries and (ii) seamless incorporation of parallel search results into subsequent sequential search steps. Such explicit parallel-sequentic decomposition is currently underrepresented in existing training datasets, and HDS-QA directly fills this gap.
APPROACH
We propose HybridDeepSearcher, an LRM capable of adaptively handling both parallel-hop and sequential-hop search strategies. In parallel-hop queries, multiple searches can be executed simul- taneously without interdependence, whereas sequential-hop queries require step-by-step execution, where each query depends on the result of the previous one. To train the model for such flexible search reasoning, we introduce a novel supervised dataset, HDS-QA
3.1 HDS-QA
HDS-QA QA provides complex questions that require both parallel- and sequential- reasoning, along with iterative wewpiiin-uueriingeeeeeeeee enabling supervised training. All prompts used are presented in Appendix
trajectories to derive the correct answer for each question,
Question Generation. As illustrated in Figure 2 our question- generation pipeline involves four key steps. We use Qwen3-32B (Yang et al. 2025 across all processes in generating questions.
1. Entity extraction and related question collection: Starting from a single-hop seed NQ ques- tion Kwiatkowski et al. (2019) (e.g.. Who is the main vocalist of Queen?). we extract a central entity (e.g., Queen) via prompting. We then query Google's People Also Ask feature using the seed question to collect multiple related questions about the entity. To ensure diversity, we lect only the queries that retrieve distinct top-ranked documents. As shown in the Figure [ the related question "When did Queen break up" is not adopted since it retrieves the same document as "What was the most successful Queen song?" ?"
2. Entity characteristic summarization: We summarize the retrieved documents for each related question into concise statements (three to five) representing the entity's key characteristics. We leverage the prompt for the
module from Search-ol (Li ct al. 2025).
3. Parallel-hop question formulation: Using these characteristics, we compose a parallel-hop question implicitly referencing the entity. We prompt the model to avoid explicitly mentioning entities closely associated with the central entity, ensuring the necessity for parallel hops.
4. Integration into hybrid-hop questions: Finally, we replace the entity in the seed single-hop question with the parallel-hop question, introducing an additional sequential hop. To ensure that both parallel and sequential hops are genuinely required, we verify that neither the parallel-hop question nor the final hybrid-hop question can be directly answered from single retrieval step.
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Figure 2 also illustrates the workflow for solving the example question. In this example, the model is supposed to perform sequential reasoning in two steps "eqquential-hops) (i) identifying the rock band and (ii) finding its main vocalist. During the first step. identifying the rock band necessitates issuing three simultaneous queries (parallel-hops). Following this pipeline, we generate a total of 1,987 hybrid-hop questions.
Ansjer-trajectory Generation. We create answer trajectories through iterative loops of reason- ing, querying, and retrieval. Inspired by the prompting strategy [i et al. (2025), we prompt the Qwen3-32B model to iteratively perform
netriiavl steps, emitting multiple par- allelizable queries simultaneously during each querying step until a final answer is produced. In the prompt, we include carefully designed demonstration of an answer trajectory based on the ques- tion F We retain a trajectory in the dataset only if its final answer is correct. Importantly, trajectory may still contain incorrect steps, but if it ultimately recovers and produces the correct answer, it can provide valuable supervision by demonstrating how to recover from errors.
To ensure diversity in trajectories, we perform inference four times for each question and retain all successful trajectories. This allows the model to learn various valid reasoning traces and adapt flex- ibly based retrieved search results. From 7,948 total attempts (1,987 questions 4 inferences), we collect 2, 21111 successful trajectories. At the question level, 773 of the 1,987 questions receive at least one correct answer across their four attempts, resulting in pass @4 of 38.9% (773 / 1,987).
Although the HDSSQA question generation pipeline is fixed, it provides a controlled and reliable setup that explicitly requires both parallel and sequential reasoning. As shown in Appendix B the resulting HDS-QA trajectories exhibit substantial diversity in both sequential depth and parallel width, indicating highly varied emergent reasoning behaviors. This suggests that HDS- QA questions function as a stable testbed in which the model must dynamically determine when to invoke parallel retrieval and when to engage sequential reasoning.
3.2 HYBRIDDEEPSEARCHER
We fine-tune Qwen3-8B on HDS-QA to create the HybridDeepSearcher model. This section de- scribes how HybridDeepSearcher operates through iterative reasoning, querying, and retrieval steps, as shown in Table
Reasoning. The model begins by reasoning within the (think) and (/think) tokens.
Querying. Based on the reasoning outputs, the model generates sequential or parallel queries within the (Ibegin avegin queries)>| and Klend search queries|) tokens, separated by ":\n".
Retrieval. Each query is executed via web search API, and the retrieved documents are sum- marized using an external model through an API call. Following Li et al.] (2025) and [Zheng et al. (025), employ a summarizer because web search results are highly noisy and often contain large amounts of irrelevant or duplicated content. Specifically, we adopt the summarization prompt from Li al. (025)). For each query qi the summarizer produces summary s;, and we concatenate them in the form of "q:: s,". All such query-summary pairs are joined with newline separators,
Example
Q. Out of leanure-length theatricul films John Curpenter directed before 2015, which the longest running time?
Hy HybridDeepSearhher Response
think) The goal to... I'll start by searching for list of John Carpenter's 's feature films before 2015. (/hinkk
beggin search queries) John Carpenter's 's feature films before 2015:) List of John Carpenter... \n [ ([end search queries)
begin search search_results]] John Curpenter's feature films before 2015: John Carpenter's soppetter's.... ...A/m List of John... [lend_search search_resultss >
( the running time of each film and identify the longest one. seurch for the running times of these films, [/hinkk]
(/hegin search search.querrisss Running of Dark Star) Running time of Glosts Mars [[jndd quarch_queriess)
(eegin [heeginsearrchpgg (eccurcc_ssssss__ Running time of Dark Star: The running..... \a Running time of Ghosts of hrond_searchreeaee
( From this list, the longest running time is 115 minutes for Starman (1984). (Ahink)
FFnnal * ++ Starman (1984)
Table 1: An example of HybridDeepSSarcher on FRAMES.
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Published as a conference paper at ICLR 2026
and the final aggregated result is wrapped between the special tokens ( cend_seerch_resuue results tokens.
[begin.search.resatt)) and
Afterward, the model resumes reasoning, and may repeat additional querying-retrieval cycles. Once sufficient information is gathered, it produces final answer.
4 EXPERIMENTAL SETUP
We evaluate our approach in both sequential and parallel search reasoning tasks, comparing its performance with several baseline models.
Datasets. We evaluate our method on five QA benchmarks, covering both sequential and parallel search reasoning scenarios:
MuSiQue (Trivedi et al. 2022): Consists of questions generated by chaining multiple single- hop questions. Sequential hops range from 2 to 4, with some questions containing most two parallel hops.
(Zhu et al. entities and aggregate extensive information across many documents.
FanOutQA
2024b): Contains fan-out style questions requiring the model to identify
FRAMES (Krishna et al. 2024): Evaluates complex multi-hop retrieval strategies as well as the model's factuality and reasoning capabilities, requiring the integration of information from multiple sources.
MedBrowseComp (Chen et al. 2025b): Features medical fact-seeking tasks with web browsing to deliver concise, verifiable answers, simulating real-world medical research scenarios.
BrowseComp (Wei et al. 2025): : Assesses the model's persistence in searching, collecting, and
verifying information with inverted and complex questions, which are difficult resolve but easy to verify. As many BrowseComp questions require exhaustive browsing, we curated practical yet challenging subset of 150 questions (BrowseComp') that OpenAl o3 can solve within five-minute web-search limit. Specifically, we ran o3 with web search and retained questions it answered correctly within five minutes, yielding 150 question-answer pairs.
Evaluation Metrics. To evaluate the effectiveness and efficiency of our model, we use the follow- ing metrics:
F1: We report the word-level Fl score as measure of the accuracy model responses. For FanOuiQA, we also report the BLEURT score, learned semantic similarity metric, in accor- dance with the dataset's established evaluation protocol.
Acc (Model judge accuracy): Accuracy assessment generated by the model. For FanOutQA, we follow the prompt provided in Zhu et al. (2024b). For other cases, we use the prompt from Zheng et al. (2025), with Qwen3-32B to perform scoring.
# Turn: We report the average number of search turns per response, measuring inference latency.
AUC (Area Under Accuracy-Turn Curve): Measures efficiency as the area under the aceu- racy-turn curve (Figure B). capturing the trade-off between accuracy and latency. Accuracy is computed from the mean Acc over search turns, assigning if a question remains unanswered. Formally, let Q be the set of evaluation questions, and T the maximum number of turns. For each qi E Q, define
[Ace(g:), if di is answered within turns, otherwise.
(r(g
0.
Then the AUC is
1 AUC
se(g:) g.E
(I)
T
t=1
Thus, higher values indicate better efficiency.

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Published as a conference paper at ICLR 2026
MuSiQue
FanOuiQA
FRAMES
MedBrowseComp Acc & 4Tom AUC
BrowseComp'
Ace #Tum AUC
Fl
BLEURT Ace 4 4'uum AUC
Fl
Ace à Tum AUC
Acc e TTTm AUC
Non-iteratire
Nalve Gen
12.8 16.4
10.9
27.5
3.2
14.0
17.5
8.0
11.9
0.0
0.0
Standard
21.
16.3
0.0
Sequential Search
Search-ol
23.4 318
3.7
0.26
26.7
32.9
5.2
54.2
48.6
4.3
21.6
4.7
0.16
200
5.2
0.02
Search-RI
26.6
29.1
3.2
0.23
10.1
23.1
1.2
4.3
0.01
27.3
34.8
4.0
0.25
18.8
21.6
4.0
0.16
4.7
4.7
0.03
RI-Scarcher
25.1 28.5
2.7
0.21
18.8
30.2
2.5
3.1
16.0
19.0
2.8
0.15
15.8
24.4
3.1
0.20
2.0
33
0.02
DeepResearcher
21.7
23.4
3.4
0.19
26.4
35.4
6.45
3.6
01.0
28.5
36.6
3.2
0.30
14.7
26.1
4.3
0.20
4.5
2.7
3,3
0.02
RAG-RI
32.4
0.29
36.7
28.2
0.24
0.02
Hybrid Search (Ours)
HyhrilIeeepSaurcher
35.1
0.30
54.0
3.4
30.4
0.26
0.09
W/ 2een2.557B-Immd
28.1 32.6
2.8
0.26
37.4
43.4
17.4
3.4
0.13
39.0
52.4
3.4
0.42
23.2
32.7
3.3
0.25
8.4
6.0
64
0.04
Table 2: Comparison of answer accuracy on MuSiQue, FanOutQA, FRAMES, MedBrowseComp. and BrowseComp'. Best results in each column are marked in bold. AUC represents the area under the accuracy-turn curves (Figure 3 higher values indicate greater effectiveness with fewer search turns. BrowseComp' is a subset of BrowseComp consisting of 150 questions that are solvable by OpenAl o3 using web search within a 5-minute limit. We use Qwen3-88 8B for Naïve Gen, Standard RAG, and Search-ol.
Baselines.
We compare our method against multiple baselines, categorized into three groups:
Non-iterative: (i) Naïve Generation: inference without retrieval; (ii) Standard RAG: retrieves documents directly based on the input question.
Sequential search baselines: These methods implement iterative single-query strategies. (iii) Search-ol (Li et al., 2025): Prompt-based iterative baseline: (iv) Rearch-R1 (Jin et al., (025) and (v) R1-Scarcher (Song et al. 2025); Trained with GRPO on single-hop (e.g.. NQ) or multi- hop QA tasks (e.g., HotpoiQA), using Qwen2.5-775 Qwen2.5-7B-Instrrec (Qwen et al. 2025) as backbone.
Parallel search baselines: These methods implement iterative multi-query strategies. (vi) Deep- Researcher (Zheng et al. 22025) and (vii) RAG-R1 (Tan et al. 2025 trained with GRPO on single- and multi-hop tasks, employing Qwen2.5-7B-Instee
Experimental Details. We employ Qwen3-8B Yang et al. 2025) for all prompt-based baselines (i, ii, iii), enabling thinking mode for these models. All iterative methods (iii-vii) are allowed up to 10 search turns, performing reasoning after each retrieval step. Queries are executed via web search using the Jina AI To summarize retrieved documents, we utilize the Qwen3-32B model for baselines (iii) Search-oT and (vi) DeepReseer as well ours. For training HybridDeepSearcher, we fine-tune Qwen3-8B on 2.111 HDS-QA question-answe answer trajectory pairs, randomly split into 95% training and 5% validation, for epoch with learning rate of 3e-5, batch size of 32, and gradient accumulation over 32 steps. All parameters undergo fine-tuning, and we masked the tokens between search results tokens, not applying gradient updates the search results prevent the model from memorizing them. Further experimental details appear in Appendix A
RESULTS
Table 2 compares HybridDeepSearcher with the basclines in terms of answer Accuracy (FI and Acc), average number of search turns, and AUC. We also provide qualitative analyses by comparing our method with other baselines in Appendix F please refer to it for detailed examples.
HDS-QA enables HybridDeepSSarcher to consistently achieve the best answer Accuracy across benchmarks (Table 2). Naïve generation performs poorly, confirming that these bench- marks require external knowledge beyond what LRMs encode. Standard RAG improves slightly, but its single-pass retrieval cannot adapt to missing information during reasoning

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MusiQue
FanOutQA
FRAMES
MedwseooCoomp
BrowseComp
20
15
15
40
20-
Mean ACC (0 if unnnsseredd
20
10
10
20
10
0-
10
10
4
6
8
10
2
4
Number Turn (Latency)
60-
60
30
30
15
10
20
16
12
0-
Number of Search Calls (Search Cost) R1-Searcher
Searchel1
Search-R1
Respahscar
RAG-R1
Figure 3: Trade-off between effectiveness and efficiency. We compare mean Ace scores by the number of search turns (upper) and search API calls (lower). At each turn or API call, we compute mean Ace scores across all datapoints, assigning a score of if unanswered within the allowed turns or calls.
Iterative single-query baselines substantially outperform standard RAG, particularly on the MuSiQue dataset, but struggle on FanOutQA and FRAMES, which require retrieving broader and more disjoint pieces of information. In these cases, multi-query baselines, DeepResearcher and RAG-RI, achieve comparable or superior Accuracy with fewer search turns (i.e., lower latency). These results indicate that the ability generate multiple queries in parallel is crucial for efficiently scaling search in scenarios requiring broader information retrieval, while iterative querying is effec- tive in focused, narrow settings.
Nonetheless, multi-query baselines still underperform HybridDeepSearcher in both Fl and Acc, reflecting their suboptimal use of parallel search. This limitation may stem from their training data: as most are trained on HotpotQA, which involves only two sequential or parallel hops without hybrid integration. Consequently, these models show little improvement on BrowseComp', which demands persistent search. In contrast, HybridDeepSSearcer, trained on HDS-QA with explicit hybrid supervision, consistently achieves the highest Accuracy across all benchmarks, including MedBrowseComp and BrowseCompl demonstrating generalizability.
For fairer comparison, we also train Qwen2.5-7B-Instrus on HDS-QA, since all iterative search baselines except Search-ol use it as the backbone. This model surpasses the state of the art across trajec-
all benchmarks. Notably, it is trained only via supervised fine-tuming on puralel--squentee tories, without any RL (c.g., GRPO) for reasoning, unlike most basclines. These results suggest that scalable hybrid search behavior is learnable from supervision alone, indicating that current RI. approaches such GRPO may not provide the most effective signal for search scaling.
HybridDeepSSerchhr shows a strong efficiency, balancing between effectiveness and latency (Table We introduce the AUC metrie measure the trade-off between effectiveness and la- tency, noted in Eq. Across all benchmarks, ours achieves the highest AUC value. Although RAG-RI consumes significantly fewer turns to solve problems compared to other baselines, its lower performance results in lower AUC value compared to ours. This is because RAG-R1 fails to leverage additional turns, plateauing after about 2-3 turns, as illustrated in the first row of Figure
HybridDeepSearcher scales performance with increased resource utilization (Figure Fig- presentss
ure presents mean Ace scores with respect to search turns (or search API calls), illustrating the re- lationship between model performance and latency (or search costs), respectively. Regarding search turns (upper), ours consistently achieves the highest Acc scores across most turns. Although RAG- RI demonstrates better performance in the initial two turns on MuSiQue and FRAMES. does not exhibit further improvement with additional turns. In contrast, progressively enhances its performance with subsequent turns. Especially on BrowseComp', unlike other baselines, ours con- sistently benefits from utilizing more turns.
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In terms of API search calls (lower), ours initially shows lower performance compared to other baselines when fewer calls are utilized. Nevertheless, while other baselines reach a performance plateau after approximately eight calls, ours continues improve performance the number of search API calls increases, particularly on FanOutQA and BrowseComp'. These datasets require persistent information gathering for verification or comparison tasks, thus demanding robust search capabilities. Ours fulfills this requirement by effecctvveyy parallelizing multiple queries within fewer turns, enabling scalable query handling.
HybridDeepSeerccher significantly enhances the LRM's search capability (Table We also examine the search capability of iterative search models, a core competency of LRMs in the RAG paradigm. Specifically, we investigate whether the gold evidence documents (i.e., Wikipedia links) annotated in MuSiQue, FanOutQA, and FRAMES datasets are retrieved using queries generated by models. We the Wikimedia API to retrieve the top-10 Wikipedia links to calculate coverage. Specifically, we compute the set intersection between the gold evidence links and all retrieved links. Formally, the mean evidence coverage calculated follows:
Un DD1
Evidence Coverage =
Q.EQ
where di E Q is question in the dataset, D; is the set of gold annotated links for the dt, and U; is the union of links retrieved by any of the model's queries for qt. The results are reported in Table where ours outperforms all the baselines across all three benchmarks. The performance gap is most pronounced in FanOutQA, which has the highest number of annotated evidence links among the three datasets. This demonstrates that ours can effectively scale the search to retrieve all necessary evidence.
HybridDeepSearcher is more robust on questions requiring extensive evidence (Figure Figure 4 reports Acc scores grouped by the number of gold evidence documents MuSiQue, FanOutQA, and FRAMES. We compare against Search-ol and RAG-RI, representing strong single- query and multi-query iterative baselines. As the number of required evidence increases, questions become more challenging due to incomplete coverage. Nevertheless, HybridDeepSSeacher exhibits robustness. with consistently smaller performance drops. In particular, on FRAMES, it maintains stable performance even when increasing from three to five or more evidence documents, whereas the baselines degrade significantly as evidence requirements grow. These results highlight that inte- grating parallel and sequential search captures both the breadth and depth of information, enabling robust scaling on complex questions.
MuSiQue
FanDutQA
FRAMES
Evidence Coverage Rute
Search-ol -H-Ours
25
60
MuSiQue
FanOutQA
FRAMES
Searc--ol
33.4
38.3
44.8
30-
20.
Seurc--11
R1-Searcher
34.2
35.6
38.6
40
DeepResearcher
38.8
49.9
49.0
RAG-RI
74
HybrieDeeeSSnncher
40.7
61.0
55.8
Number of Gold Evidence
Table 3: Comparison of search capability with the evidence coverage rate.
Figure Ace grouped by the number of gold evi- dence MuSiQue, FanOutQA, and FRAMES.
DISCUSSION
In this section, we further examine (i) whether the performance gain arises from hybrid search behavior rather than merely fine-tuning HDS-QA, and (ii) whether reinforcement learning can further improve hybrid search.
Does the performance gain come from HDS-QA fine-tuning hybrid search behavior? To iso- late the effect of hybrid search, we construct an ablation that removes all parallel querying behavior. We train model trajectories that issue exactly one query per reasoning step while keeping the

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MuSiQue #Turn
FanOuiQA AUC
FRAMES
ModBrowseComp #Tum
BrowscCompl
Acc
AUC
Acc
#Tum
Acc
#Tum
AUC
Acc
AUC
Acc
#Tum AUC
Sequential Search
Search-ol
31.8
3.7
0.26
8.7
5.2
0.06
48.6
4.3
0.37
21.6
4.7
0.16
0.02
Ours (single-query)
21.6
2.9
0.18
8.7
4.5
0.06
33.8
3.2
0.25
26.8
4.7
0.10
0.04
Hybrid Search
Ours
35.1
3.3
0.30
20.0
3.1
0.15
54.0
3.4
0.44
30.4
3.4
0.26
13.3
6.0
0.09
Ours (GRPO)
36.3
4.0
0.27
20.9
4.3
0.15
57.2
4.1
0.42
31.1
4.1
0.23
14.0
6.7
0.08
Table 4: Comparison Acc, Turn, and AUC across five benchmarks for Search-ol, our single- query fine-tuned model, Hyprade and HebridDeeepSearcher with GRPO.
underlying questions unchanged. Using the same data-generation pipeline, this produces 2. lk valid single-query trajectories, providing controlled comparison against HybridDeepSeaacher.
As shown in Table 4 the single-query variant underperforms HebrrdDeepSeearhher across all bench- marks, and in some cases even falls below Search-ol. These results show that the gains stem not just from exposure to the questions but, more importantly, from the hybrid search behavior itself, which improves generalization and search scalability.
Can reinforcement learning further improve hybrid search? Since prior work (Jin et al. 2025 Tan al.) 2025) suggests that reinforcement learning methods such as GRPO (Shao al. 2024) can help models scale their search behavior, we investigate whether GRPO can further enhance HybridDeeppSearcee. We apply GRPO on top of HebridDeepSSeacher and evaluate the effects on the same set of benchmarks, using binary correctness reward from an LLM-as-a-judge (Qwen3- 4B-Instruct), indicating whether the final answer is correct.
Table 4 shows that GRPO yields modest but consistent accuracy improvements across four bench- marks. However, these gains are accompanied by increased average search depth. As result, the AUC, which reflects both accuracy and search efficiency, decreases compared to the supervised model. Overall, these findings indicate that while GRPO can improve final answer quality, it also induces deeper search trajectories, making less efficient than supervised hybrid-search training alone. This pattern suggests that GRPO's s tendency to lengthen search trajectories does not substan- tially scale search, highlighting the need for more effective RL formulations for search scaling.
7 CONCLUSION
In this work, we address the challenge of scaling search. We propose a hybrid approach that inte- grates parallel and sequential search reasoning. To train models to effectively utilize this strategy, construct HDS-QA via a carefully designed automatic pipeline, which generates questions that explicitly integrate broad parallel search into subsequent sequential reasoning. The dataset also in- cludes answer trajectories represented as iterative reasoning query retrieell loops involving parallel sub-queries.
Through fine-tuning on HDS-QA we develop HybridDeepSearcher, a model capable of seam- lessly combining parallel and sequential search strategies. Experiments show that Hybrid- DeepSearcher achieves significant performance improvements and superior efficiency as well. Fur- ther analysis demonstrates test-time search scaling, utilizing more search turns calls for additional performance improvements, unlike all other baselines. Additionally, its sub-queries cover more ev- idence, resulting in a larger performance gap over the state-of-the of-the-art questions requiring more evidence.
Looking ahead, we plan to investigate preference optimization methods for scaling search and to tend these insights to multi-agent systems, where concurrent agents may further enhance efficiency and scalability.
ACKNOWLEDGEMENT
This work was supported by LG AT Research.
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A EXPERIMENTAL DETAILS
A.I EVALUATION DATASET.
We use 512 datapoints from the MuSiQue dev set, following Zheng et al. (2025), the entire 310 datapoints from the FanOutQA dev set, all 824 datapoints from the FRAMES test set, all 605 data- points from the MedBrowseComp evaluation set, and 150 selected datapoints from BrowseComp as described in the main text.
A.2 COMPUTATION.
In training HybridDeepSearcher, we use eight NVIDIA A100 40GB GPUs; fine-tuning Qwen3-8B takes approximately 30 minutes. During inference, each generated query involves one Jina Search API call across all baselines and our method. Additionally, one LLM (Qwen3-32B) summarization API call is made per generated query for Search-ol, DeepResearcher, and our method. For gener- ating LLM responses, we utilize vL.LM on an A100 40GB GPU. For GRPO, we use eight H100 80 GPUs.
A.3 HYPERPARAMETERR.
Following previous work (Li et al. 2025), set the maximum number of search turns to 10. Dur- ing inference with vLLM, we set tensor tensor.paralat www.ppaaaalllllleeeeeeeeeeeeeeee
to True, max-nun_seqs 16, temperature 0.6, and top-p 0.95, following the guidelines pro- vided in the Qwen3 technical report. For GRPO, we use a global batch size of 32, a rollout of 16 per question, and 20 steps.
to 4, enforce.eages
A.4 LLM USAGE
We have used LLMSs to polish writing for grammar correction and rephrasing.
B ANALYSIS OF REASONING TRAJECTORY DIVERSITY IN HDS-QA
This section provides additional statistics that clarify the diversity of reasoning trajectories induced by question generation pipeline.
Our question generator adopts a minimal structural template consisting of one parallel hop and one sequential hop. While the surface form is fixed, the realized reasoning trajectories vary substan- tially in both: (i) parallel width (measured per turn) and (ii) sequential depth (measured per question).
B. PARALLEL RETRIEVAL WIDTH (PER TUrN)
Parallel width is measured at the turn level. "Width" denotes the number of parallel queries issued single turn, and the count corresponds to the number of turns exhibiting that width.
# Parallel Queries (Width)
2
3
4
5+
Turns
586
1958
2044
166
36
Table 5: Distribution of parallel retrieval width per turn.
These statistics show that many turns involve multi- biatt--branchhh retrieval (width 2 2), with substantial number requiring three parallel queries. This indicates that even under a fixed template, the system frequently expands laterally gather multiple pieces of evidence within a single reasoning step.
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B.2 SEQUENTIAL REASONING DEPTH (PER QUESTION)
Sequential depth is measured at the question level. "Depth" denotes the number of sequential reasoning steps required to reach the final answer.
Sequential Depth
3
5+
# Questions
211
1319
424
119
38
Table 6: Distribution of sequential reasoning depth per question.
The distribution demonstrates meaningful variation in reasoning depth, including multi-step chains beyond three hops.
Overall, although the question template is structurally minimal, the induced reasoning trajectories exhibit substantial diversity in both lateral expansion (parallel width per turn) and longitudinal chain- ing (sequential depth per question). This structural diversity enables the supervised model to adap- tively handle varying degrees sequential and parallel search reasoning.
C ADDITIONAL EXPERIMENTS
C.I EXTENDED ANALYSIS OF TEST-TIME SEARCH SCALING ON ADDITIONAL DATASETS
We extend the analysis of test-time search scaling (initially shown in Figure of the main text) to additional datasets. The results are presented in Figures [5a| and 5b] Specifically, we control two search budgets: (i) the number of search turns (Mr [1,2, 8D. and (ii) the number of search API calls (Mc E [2, 4, 8, 16)). While other baselines are not constrained by these budgets, our method is required
produce final answer once either budget exhausted. In detail, when the
number of proposed parallel queries exceeds the remaining Mc. we execute only the first subset of queries up to the remaining budget. Additionally, although the MedBrowseComp dataset contains unanswerable questions, we compute performance scores using only the answerable questions for fair comparisons across budget settings, as lower-budget scenarios may disproportionately benefit from the presence of unanswerable questions.
Regarding the number search turns, our model generally achieves comparable performance even with fewer turn budgets. Although RAG-RI slightly outperforms ours on MuSiQue and FRAMES under lower turn budgets, it does not significantly benefit from utilizing larger turn budgets. In contrast, model effectively scales its performance with increased number of turns, eventually surpassing RAG-RI.
In terms of the number of search API calls, method consistently outperforms the bascline FanOutQA and MedBrowseComp. even when using fewer API calls. However, on MuSiQue and FRAMES, our approach initially exhibits lower performance than other baselines when fewer than search calls are used. Nevertheless, our method can effectively scale performance with an increased number of calls, achieving comparable or superior results-particularly when leveraging parallel search strategies.
Overall, these results indicate that integrating sequential and parallel search not only reduces latency and achieves competitive performance with fewer turns but also effectively scales performance when additional budget available. This improvement arises because our method dynamically adjusts re- trieval strategies and employs adaptive workflows to efficiently manage large numbers of documents for complex questions.
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MuSiQue M
MuSiQue
35-
16
30-
F1 Score
25
M:
X
26
No
20
20
14.
F1
15-
H1
10-
10
Aec
2
5
10
Average Number Search Turns
Average Number of Search Calls
FanOutQA
FanOutQA
50
50-
F1 Scooe
40-
MC
F1 Score
Me-B B
Mc-15
30-
30
X
20-
10
Average Number of Search Turns
Average Number of Search Calls
FRAMES
FRAMES
Mc
40-
F1 Score
Score
30-
X
10
10.
6 Average Number of Search Calls MedBrowseComp
10
Average Number of Search Turns
MedBrowseComp
30
Searche1
Search-R R1
25
RL Searcher
F1 Score
Mc
Mt
Mc
20-
RAG
X
Mc 2/
X
x
Searchrr
DoepResaadder
10
RAG441
Average Number of Search Turns
Average Number of Search Calls
(a) Search Turns
(b) Search API Calls
Figure 5: Test-Tine Time Search Scaling results: (a) number of turns and (b) number of API calls.
C.2 EFFECT OF THE GENERATED-TOKEL BUDGET ON MEAN MBE SCORES
We investigate how the mean MBE score when the number of tokens the LLM generates increases. As Figure 4 in the main body, we assign unanswered within the allowed tokens. Specifically, only tokens produced by the model itself are counted; tokens originating from retrieved search snippets are excluded.
As shown in Figure 6 ours benefits consistently from larger token budget, with especially pro- nounced gains on FanoutQA, BrowseComp'. In contrast, RAG-RI gains almost no benefit from additional tokens, demonstrating limited scalability. Search-ol and DeepResearcher improve the number of generated tokens grows, but they start from a much lower baseline, indicating that they require considerably more inference cost to achieve competitive performance.
D ABLATION STUDY
D.1 MODEL SCALE
To examine whether approach generalizes beyond 7-8B models, conduct experiments using both a smaller backbone (Qwen3-43) and a larger backbone (0wen3-32B). We compare models fine-tuned on HDS-QA against Search-o1, and the results are summarized in Table
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Mean ACC (0 if unanswered)
MuSiQue
FanOutQA
FRAMES
MedBrowseComp
BrowseComp
40
30
30
20
20
20
20
10
10
5-
300
900
1500
300
900
1500
3.00
900
1500
300
900
1500
3.0
900
1500
Number of Generated Tokens (LLM Cost)
Search-ol
Search-R1
R1-Searcher
DeepResearcher
RAG-R1
Ours
Figure 6: Comparison of Mean MBE Scores by the Number of Generated Tokens
Across both model scales, HDS-QA yields substantial gains over Search-ol. These results indicate that HDS-QA provides effective supervision by supplying coherent and reliable hybrid search traces, regardless of model size. Importantly, the hybrid-search trajectories used in these experiments were generated by the same 32B model, demonstrating that teacher model larger than the student not required.
MuSiQue
FanOutQA
FRAMES
MedBrowseComp
BrowseComp
Qwen3-4B
Scarch-ol
28.5
8.1
45.2
19.2
2.0
Ours
33.4
17.7
51.8
30.4
5.3
Qwen3-32B
Search-ol
35.9
17.7
58.1
33.2
6.0
Ours
37.5
30.0
59.8
36.0
15.4
Table 7: Performance across different model sizes.
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D.2 SUMMARIZER
To analyze the effect of a summarizer, we conduct the same experiments on HybridDeepSearcher using (i) smaller summarizer and (ii) summarizer. As in the main experiments. we the 3wen3-325 model as a summarizer for HybridDeepSearcher, and adopt Qwen3-8F 8B model as lightweight alternative.
Table 8 shows the results. Both the 8B and no-summarizer variants outperform the strongest base- line, RAG-RI, showing only modest average drops of 1.2 and 0.7 points, respectively. These results indicate that large 32B summarizer is not strictly necessary for strong performance. Hybrid- DeepSearcher remains robust, even with weaker or absent summarizer, highlighting its practicality of our approach.
MuSiQue
FanOutQA
FRAMES
MedBrowseComp
BrowseComp'
Parallel Search
DeepResearcher RAG-RI
23.4
6.45
36.6
26.1
32.4
10.0
45.6
28.2
2.0
Ours
Seepareeeeer
35.1
20.0
54.0
30.4
13.3
w/ 8B Summarizer
37.6
16.1
55.6
29.5
11.9
w/o Summarizer
15.8
51.1
33.4
10.5
Table 8: Ablation study with a summarizer.
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Question: who does seth macfarlane play on american dad Published as a conference paper at ICLR 2026
E РROMPTS
Prompt for Entity Extraction The prompt below extracts proper nouns from given single-hop question-answer answer pair to identify the central entity. These entities serve as the anchor for retrieving related questions in our dataset construction process.
Prompt for Entity Extraction
**Task Instruction:*
Identify and list all proper nouns (names of specific people, places, characters, titles, etc.) from the provided **Question** and Answer**
#Guideliness#*
** Analyze the Input:**
Review both the question and answer carefully.
Extract proper nouns that refer to specific entities.
**Qutput #ormat:**
Provide the results strictly following this JSON format:
"question": ["Proper nouns from the question"],
"answer": ["Proper nouns from the answer"]
}}
**Fxmpple***
Input:
Answer: stan smith and roger
Output:
{(
"question": ["Seth MacFarlane". "American Dad"),
"answer": ["Stan Smith", "Roger"]
}}
**Inputs:** **
**Question:#a
{question}
** * (answer)
Now, extract proper nouns from the provided qussti pair.
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Published as a conference paper at ICLR 2026
Prompt for Documents Summarization Inspired the Search-ol Reason-in- -nnuummnn- mod- ule, this prompt instructs the model to review the retrieved web pages, identify factual information relevant to each related People Also Ask query, and generate clear, concise answer. The response should directly address the query and reference both the source pages and the provided reference entity for proper grounding.
Prompt for Webpage Reasoning
### Task Instruction:
You are tasked with reading and analyzing web pages based the following inputs: **Search Query**, **Searched Web Pages**, and **Reference Entity**, Your objective is provide sentences that directly answer the **Search Query**, using relevant information found in the #Sarrched Web Pages** and grounding the answer in the context of the **Reference Entity**.
### Guidelines:
1. ** *nnllyze the Searched Web Pages:**
Carefully review each searched web page.
Identify the most relevant factual information to directly answer the **Search Query**
2. **Formulate A
Summarize your analysis in one clear, accurate, and grammatically correct sentence that explicitly addresses the **Search Query** The answer ranges from to 3 sentences.
Ensure that the answer clearly references the provided ** **Reference Entity**
**Output #ormat:**
"**If helpful information is found:** Present your answer in to 3 sentences beginning with:
***Final Information
**If no helpful information is found:** Output the following:
F**aiaal Information** No helpful information found.
### Inputs:
** Searchh +uery:#
|earch--uery)
Searched Web Pages:** (ocument]
Refference Entity:**
[reference.eniity]
Analyze each web page and clearly answer the query "(search.query) in to 3 sentences.
20

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Published as a conference paper at ICLR 2026
Prompt for Entity Characteristics Summarization The prompt below further summarizes the retrieved documents' summarization about a given entity into concise statements that preserve the essential information. These summaries are intended to serve input for generating parallel-hop questions that indirectly refer to the target entity.
Prompt for Clue Summarization
#H# Task Instruction:
You are given an entity and a list of clues about the entity. Your task is to summarize each clue into concise clue about the entity, but remain the key information of the clue.
### Guidelines:
*Summarize Clues:*
Summarize each clue into concise clue.
Remain the key information the clue.
### Inputs: **Entity:**
{entity}
** Input Clues:** list)
### Output Format: Summarized Clues:
" [(cllue 1 summary) } yr, [llu summary) )r
Prompt for Complex Question Generation This prompt generates complex, implicit question using a list of summarized clues. The question should logically lead to the target entity without explicitly naming it, enabling parallel-hop reasoning step.
Prompt for Complex Question Generation
### Task Instruction:
You are provided with an entity and of clues. Then, generate complex, implicit question that logically guides to the provided entity the correct answer, without explicitly naming it or the related entities removed from the clues.
### Guidelines:
1. ** Analyze the Clues:*
Carefully examine each clue provided.
Identify unique characteristies context from these clues that indirectly lead to the given entity.
**Generate a Complex Question:*s
Formulate an insightful, implicit question.
Your question should guide logically towards the entity, encouraging deduction.
Avoid using pronouns or names in the clues that are highly related the given entity.
### Example:
21

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Published as a conference paper at ICLR 2026
#ntitty;**
Queen
**Cluee:**
Known for energetic and theatrical live performances.
2. Freddie Mercury was famous for wide vocal range.
Famous for blending rock with operatic and theatrical styles.
4. Produced the legendary album "A Night at the Opera." Noted for the iconic anthem frequently performed at sports events.
**Correct Output:**
**Complex Question:** e Which celebrated rock band, recognized for energetic and theatrical live performances and a lead singer renowned for his exceptional vocal range, is famed for an innovative blend of operatic style and rock, creating legendary album that includes universally popular anthem commonly heard in sporting venues?
### Now Complete the Task:
**nitty:**
{entity)
Selected Clues:** (input_istt
### Output Format:
*Complex Question:** {(complex_questionn }
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Prompt for Question Integration The prompt below demonstrates how construct hybrid-hop -hop question by integrating parallel-hn hop question into seed single-hop question, replacing the central entity.
Prompt for Question Integration
**Task Instruction:** **
You have two questions provided inputs (**Q1** ** and **Q2**), Your task is to integrate the descriptive content of **Q2** (which answers the entity entity) into #*Q1 by replacing only the specified entity (entity) in **Q1**
*Guiddlines:**
**Identify E
Clearly identify the entity (entity) within Q1 to replace.
2. **Itegrationn Procedure
Replace only the entity (entity) from QI with the descriptive content of Q2.
The result must be one cohesive, grammatically correct, and logically coherent question.
Do not concatenate two separate questions. Instead, integrate smoothly.
**Output #ormat:**
Clearly present single integrated question.
** **Exmple:**
**Inputs:** **
**Q1:** Who is the lead vocal in Queen?
**Q2:** Which celebrated rock band, recognized for dramatic live performances and lead singer renowned for his exceptional vocal range, is famed for an innovative blend of operatic style and rock, creating legendary album that includes universally popular anthem commonly heard in sporting venues? (Answer: Queen)
**Output:** **
** #Itterrated Question:** Who is the lead vocal in the rock band, recognized for dramatic live performances and lead singer renowned for his exceptional vocal range, is famed for an innovative blend of operatic style and rock, creating a legendary album that includes universally popular anthem commonly heard in sporting venues?
Now Complete the Task:**
** Inputs: **
**Q1 Ql:** {question_1}
** *2:**
[question.2] 2) (Answer: {entity])
*Output #
** *IItegrated Question:** {(integrated, {
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Published as a conference paper at ICLR 2026
Prompt for Model Response Generation The prompt below instructs the model to perform multi-step reasoning and search in order to assess whether given question can be answered in single retrieval step. It guides the model to emit search queries when needed, interpret retrieval results, and iteratively construct answer trajectories that validate the necessity of multi-hop infer- ence. The resulting answer trajectories used to construct training dataset, retaining only those whose final answers are correct.
Prompt for Response Generation
### Task Instruction:
You will be given a question. Your task to generate a detailed and step-by-step reasoning answer with parallel web search queries.
### Guidelines for Reasoning Answer Generation:
For each step, explicitly perform the suggested queries, using: bheginseaarh..qeriie
query 1; query 2:
eendssearchh.cc queries >>
When you have multiple queries that can be parallelized, put them together in one aern_search_quuiieeeee > and
block separated by semicolons. The search results followed by the search queries between
begin search results >>..< end search results >. So, clearly confirm findings at each intermediate step and state concise conclusion before moving to the next step.
Use natural and conversational reasoning transitions (e.g., "First", "Wait", "Next", "Alternativel"" improve readability.
Clearly present the final answer at the end of the reasoning as follows:
Final *nswer;**
|
### Example
##Inuts::*
Question:
Who is the lead vocal in the rock band recognized for dramatic live performances, lead singer renowned for exceptional vocal range, an innovative blend of operatic style and rock, and legendary album featuring anthem frequently heard at sports events?
**Utput:***
<think>
Okay, let's analyze the clues step by step. The question asks find the lead vocalist of a rock band known for dramatic live performances and operatic-rock fusion, the lead vocalist's exceptional vocal range, and an universally recognized sports anthem. Let's start by searching for the rock band and then the lead vocalist.
Initially, several bands might fit this description, so I should perform some searches to narrow this down:
</think>
Rock band known for dramatic live performances and operatic-rock fusion; Rock band with lead vocalist known for exceptional vocal range; Rock band anthem commonly played at sports events
sed_seearh..ueriess
begin search results
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Published as a conference paper at ICLR 2026
The descrihed band is Queen.
< <nd search results >
Given these strong clues, it could directly point towards Queen, particularly due to their operatic style, famously exemplified in the legendary album "A Night at the Opera". Indeed, Queen's anthems like "We Will Rock You" and "We Are the Champions" are frequently played at sporting events. Their vocalist, Freddie Mercury, is widely celebrated for his exceptional vocal range and theatrical stage presence.
To confirm this clearly, let's verify the lead vocalist of Queen explicitly:
begin beginnsearch. search.queries > The lead vocalist of Queen
www.ssearrhh.uiriee >
begin search results
The lead vocalist of Queen is Freddie Mercury.
www.sscrrrrruuuleee |>
Given these confirmations, the band described by the clues is indeed Queen, and their iconic lead vocalist was Freddie Mercury.
**Final #ns
\boxeeFFeeddie Mercury
### Inputs:
** Question:#*
{question)
Now, generate your detailed reasoning based the provided question.
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Published as a conference paper at ICLR 2026
F CASE STUDY
From Table 9 to Table 19 we present examples of outputs from our HybridDeepSeearhhe on the MuSiQue, BrowseComp. FRAMES, respectively. For each case, we selectively excerpt a portion of the raw model output to fit the page limit. The model reasoning steps are enclosed within (think) and ( Search queries generated by the model are enclosed within
and while the refined search results are enclosed within ([begin_search search r and (Jend search results)). We observe that our trained model effectively leverages parallel query- ing to retrieve relevant information, enabling accurate answer generation with reduced context. In contrast, existing methods often rely on sequential querying, which results in longer contexts that hinder correct answer generation, accumulate retrieval errors, or fail to incorporate necessary con- straints during the query generation stage.
wand_search.gupe
26

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Published as a conference paper at ICLR 2026
An example on MuSiQue dataset answered by ours, with special symbols used in the search queries and search results.
Table 9:
Example from MusiQue Dataset
Question:
When did the first mosque open where Federated States of Micronesia Maritime Boundary Treaty was signed?
Answer: September 2012
Model Output by Ours
(think]
Let's break down the question. The task is to find out when the first mosque opened where the Federated States of Micronesia (FSM) Maritime Boundary Treaty was signed. This requires key pieces information: where was the FSM Maritime Boundary Treaty signed?es
2. When was the first mosque built that location?**
I'll start determining the location of the FSM Maritime Boundary Treaty. Since this is treaty. it was likely signed in specific city country. I'll search for location of the treaty signing.
(Ahink)
([begin search queries] Where was the FSM Maritime Boundary Treaty signed?? Location of the FSM Maritime Boundary Treaty signing
(lenchsearchhua )
[beggn_scarch_c search results)
Where was the FSM Maritime Boundary Treaty signed?: The FSM Maritime Boundary Treaty with the Marshall Islands was signed *MMajuro, Marshall Islands** on **5 July 2006**, confirmed by Wikipedia page on the Marshall Islands FFdderrtd States of Micronesia Maritime Boundary Treaty...
(/end search results)).
(think)
Now that know the FSM Maritime Boundary Treaty was signed in *Majuro, Marshall Islands**, 1 need to determine when the first mosque was built in Majuro. This is a historical question, so I'll search for the construction date of the first mosque in Majuro. (Ahinnk)
[eeeic swarch queries)) When was first mosque built in Majuro. Marshall Islands?;
Construction date of the first mosque in Majuro
scarch scarch queries)) }
([begin_scarce sergin_scarc_.og jesults
When first mosque built Majuro, Marshall Islands?: The mosque in Majuro, Marshall Islands, named Bait Ahad Mosque (also referred to as Bacr-Ul-Ahad Mosque), was built and inaugurated **2012**, Multiple sources confirm that the mosque was officially opened in **September 2012**, with specific mentions of ##21 September 2012** the opening date...
searchscarcreesull
(think)
So the FSM Maritime Boundary Treaty was signed in **Mauuro, Marshall Islands on #95 July 2006*s, and the first mosque in Majuro was huilt and inaugurated ##21 September 2012 This means the mosque was built **a the treaty signed. The question asks for the date first mosque opened where treaty was signed, which in Majuro. answer #e21 September 2012a9, (Ahink)
Final answer:
21 September 2012
27

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Published as a conference paper at ICLR 2026
Table 10: example on MuSiQue dataset answered by RAG-RI, with special symbols used in the search queries and search results.
Example from MuSiQue Dataset
Question:
When did the first mosque open where Federated States Micronesia Maritime Boundary Treaty signed?
Answer: September 2012
Model Output
(think)
answer question. need to identify two key pieces information:
1. The location where the Federated States of Micronesia Maritime Boundary Treaty was signed.
2. The date when the first mosque opened in that location.
I do not have immediate information either of these points, I will need search for them.
(Athink)
search>
Federated Micrnnesia Boundary Treaty mosque Palau opening
# Incorrectly parallelize sequentially dependent query
</eearhh>
information>
[f "iitle::
"Marshall
Islands
Foderated
States
of
Micronesia
Maritime
Boundary', 'url':
https://e.wwkkipeeiii... wttp:////wwwwi//aaaaaalllaaaaa Fedannt,,Fsserteeed....... Sf Batrronesia_Mrrttime Rarr "snippet's
"The Boundary Treaty treaty Location, Majuro, Marshall Islands.
Marshall Islands Federated of Micronesia
Parties. Marshall
</search>
(
Based on seurch results, the capital of is Ngerulmud, most the population lives Koror. The mosque
Palau is located in Koror. Now, need to find the specific date when the first mosque in Koror opened.
(/think)
<search>>
First mosque Koror Palau opening date
</search>
<information>
[ (("title:: 'Islam Palau Wikipedia', ; 'url': 'https://enn http://ww..kkpedddiiingg/iiiiiaa in Palau', 'snippet': 'Islam minority religion
in Palau. Palau Central Jame Masjid, Koror. Contents. History; Demographics; Islamic Society; See also; References'
</informaton>>
(
The search results did not provide a specific date for the opening the first mosque in Koror, Palau. However, can infer that the first mosque in Koror is the Palau Central Jame Masjid. will refine search find the exact opening date of this mosque. (/hink)
<searchs
Palau Central Jame Masjid opening date
</search>
(details omitted)
[think)
Unfortunately, the search results did not provide specific opening date for the Palau Central Jume Masjid. However, can conclude that the first mosque in Pulnu, located in Koror, is the Pulau Central Jame Masjid. Without specific date, I will provide available information.
the best answer based
Since the treaty location is not definitively confirmed, and mosque opening date the concrete piece information.
will provide the date the first mosque Palau. ([hiikk
2012 </answer>
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Published as a conference paper at ICLR 2026
Table 11:
An example on MuSiQue dataset answered by DeepResearcher, with special symbols used in the search queries and search results.
Example from MusiQue Dataset
Question:
When did the first mosque open where Federated States Micronesia Maritime Boundary Treaty was signed? Answer: September 2012
Model Output by DeepResearchens
[think)
To answer this question, we need to find the location where the Federated States of Micronesia Maritime Boundary Treaty signed and then find out when the first mosque opened in that location. Let's start by scarching for the signing location the treaty.
(Atiink)
<coll_call>
("name":
"weh_search". "arguments":
("query":
"location of Federated States Micronesia Maritime
Boundary Treaty signing"
"when was the first mosque opened the treaty signing location" }}}
</tnol_call>
<tool response>
(('s ('scarch query": 'location of Federated States of Micronesia Maritime Boundary Treaty signing'. "web page info list': [...] }. { (ssearch.uuery': "when the first mosque opened the treaty signing location'.
'web_page_inff bist:' [..]H
resoon_esponse>>
(think)
searches did not return any relevant pages. The treaty signing location may not well-known or might not linked to specific mosque. will attempt to find the treaty signing location first.
(hhnk)
<tool call>
("name": "web search". "arguments"": ("query": "location of Federated States of Micronesia Maritime
Boundary Treaty signing 2025" "when was the first mosque opened in the treaty signing location 2025" 1))
</toolcall>>
(think)
Looking up on the relevant information, the first mosque in Nice is Masjid Algeria which was established in 1904.
Since the treaty was signed in Nice, France June 2025 and there is historical information suggesting the first mosque in
Nice, France was established in 1904, conclude that the first mosque in Nice was opened before the signing of the treaty.
(Ahink)
<answer> 1904 </answer>
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Published as a conference paper at ICLR 2026
Table 12: An example on BrowseComp dataset answered by ours, with special symbols used in the search queries and search results.
Example from BrowseComp Dataset
Question:
There was global report released by the World Health Organisation after 2011 and before 2020 in which the forward was written by Eritrean person who had attuined Master of Science (MSc) in Immunology of Infectious Diseases from UK University. written by someone whose first nume began with and wrote an opinion piece with person whose
The introduction first name began with the initials J. published after 2020 but before 2024. The cover was designed by a graphic artist who had completed a eadership Strategies in Book Publishing somewhere after 2010 and before 2020 and had attained Bachelor 2019s Degree in Graphic Design after 2000 and before 2010. This designer had previously worked at Malaria Consortium and Ogilvy & Mather. What is the first and last name the cover designer of this report?
Answer: Cristina Ortiz
Model Output by Ours
[think]
Let's tuckle this complex question step by step. The goul is identify the first and last of the cover designer of specific
WHO report. The report was released between 2011 and 2020, and the cover designer is described as someone who:
1. Completed a Bachelor 2019: Degree in Graphic Design between 2000 and 2010.
2. Worked at Malaria Consortium and Ogilvy & Mather.
3. Had Leadership Strategies Book Publishing course after 2010 and before 2020.
written someone whose first name begins with Z. and forward written by Eritrean person with an MSe in Immunology of Infectious Discases from a UK university. These clues might help narrow down the report in question. First, I need to identify the WHO report that fits this timeline and the description of the cover designer. Let's start by searching for WHO reports published between 2011 and 2020 that match the cover designer's background. [Ahink]
Additionally, the report's introduction
[[begin searccerrchhoo queries])
WHO reports between 2011 and 2020;
WHO report cover designer with Bachelor's Graphic Design experience at Malaria Consortium and Ogilvy Mather;
WHO report introduction written by someone with first name Z:
WHO report forward written by Eritrean with MSc in Immunology of Infectious Diseases from UK university
[end_seurce seurch.querress)
Dhegin.search.resulee
...n....
WHO report cover designer with Bachelor's in Graphic Design and experience at Malaria Consortium and Ogilvy & Mather: Cristina Ortiz a graphic designer with Bachelor's Degree Graphic Design from EASD (2005-2009) and worked at hoth Malaria Consortium (2011-20 (-217))) and Ogilvy & Mather (October 2010-2013 December 2010). She has experience in designing publications and has been associated with the World Health Organization (WHO). This information aligns with the search query for WHO report cover designer with Bachelor's in Graphic Design and experience Malaria Consortium and Ogilvy & Mather.
[[end_seurch_ resuihesss
[think)
Let's start by identifyngg the WHO report that fits the timeline and the description of the cover designer. The first clue is that the report published between 2011 and 2020. The second clue that the cover designer has a Bachelor's Degree in Graphic Design between 2000 and 2010, worked at Malaria Consortium and Ogilvy Mather, and a Leadership Strategies in Book Publishing course after 2010 and before 2020.
From the scarch results, we found that Cristina Ortiz is a graphic designer with a Bachelor's Degree in Graphic Design from EASD (2005-2009), and she has worked both Malaria Consortium (2011-2017) and Ogilvy Mather (October 2010-2013 December 2010). She also has experience in designing publications and has been associated with the World Health Organization (WHO). This aligns with description of the cover designer. ...)
Next, we need to confirm that the WHO report in question was published between 2011 and 2020 and that Cristina Ortiz was the cover designer. The report World Health Statisticsis an annual report published by WHO, and it fits the timeline. The cover designer for the 2015 edition of World Health Statistiesis Cristina Ortiz. This supported by the information that she worked at Malaria Consortium and Ogilvy Mather, which aligns with her background.
Given the information available, the most likely candidate for the cover designer is Cristina Ortiz, She fits the description of the cover designer und has been ussociated with WHO. The other clues are not fully confirmed, but they are plausible.
[/hiink
Final answer:
Cristina Ortiz
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Table 13: An example on BrowseComp dataset answered by RAG-RI, with special symbols used in the search queries and search results.
Example from BrowseComp Dataset
Question:
There global report released by World Health Organisation after 2011 and before 2020 in which the forward written by an Eritrean person who attained a Master Science (MSc) in Immunology of Infectious Diseases from a UK University. The introduction was written by someone whose first name began with Z and who wrote an opinion piece with a person whose first name began with the initials J, published after 2020 but before 2024, The cover was designed by graphic artist who had completed Leudership Strategies Book Publishing somewhere after 2010 and before 2020 and had attained Bachelor 2019s Degree Graphic Design after 2000 and before 2010. This designer had previously worked at Malaria Consortium and Ogilvy Mather. What is the first and last name the cover designer of this report?
Answer: Cristina Ortiz
Model Output
[think)
To answer this question, need to identify the cover designer based on the provided details. Let's break down the information:
1. The cover designer had Bachelor's Degree in Graphic Design hetween 2000 and 2010.
2. They also completed Leadership Strategies Book Publishing course herween 2010 and 2020.
Ogilvy & need to find a person who all these criteria. will start by searching for individuals who have both a Bachelor's Degree Graphic Design and have worked at Malaria Consortium and Ogilvy & Mather.
They worked at Malaria Consortium
(/think)
<search>
graphic designer with bachelor's in graphic design and worked at malaria consortium and ogilvy mather
leudership strategies book publishing course after before 2020
</search>
<information>
[( ("title": "Careers- - Ogilvy', 'url': https://wwww.oillyy.co cow////rooo/////es 'snippet': 'We are laser focused on providing best-in-class learning and development as well career growth opportunities for all staff."
</search>
(think)
From the search results, did not find direct match for graphic designer who lits all the specific criteria mentioned, However, need narrow down further to find the exact person. I will try another search focusing the intersection of the graphic designer's background
specific they held.
[/think)
<search>
graphic designer with bachelor's 's graphic design and worked at malaria consortium and ogilvy & mather # Duplicate query name starting with und j co-author after 2020
</search>>
<information>
("title": 'Careers- Ogilvy", 'url': http:://wwww.glll..oo..... 'snippet': "We are laser focused providing best-in-ctee leurning and development as well carcer growth opportunities for all staff." ... </information>
[think)]
The search did not yield direct match for the specific individual. However, need to refine my search focus the intersection graphic designer's 's background and the specific roles they held, particularly the forward written by an Britrean person with a Master of Science in Immunology of Infectious Diseuses from UK University. Given the constraints, will try one last search focusing on the Eritrean individual and the forward they wrote for the WHO report. (/think)
(details omitted)
[think)
Unfortunately, the search did not provide a direct match for the cover designer, However, bused on the constraints and the infor-
mation available, the most likely candidate is graphic designer numed Revekka Vital, she has worked at Mularia Consortium and designed covers for WHO reports. Therefore, the first and last name of the cover designer is:
[[hink]
<answrr> Rebekka Vital </answer>
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Table 14: An example on BrowseComp dataset answered by DeepResearcher, with special symbols used in the search queries and search results.
Example from BrowseComp Dataset
Question:
There global report released by World Health Organisation after 2011 and before 2020 in which the forward written by an Eritrean person who attained a Master Science (MSc) in Immunology of Infectious Discases from a UK University. The introduction was written by someone whose first name began with Z and who wrote an opinion piece with a person whose first name began with the initials J, published after 2020 but before 2024, The cover was designed by graphic artist who had completed Leadership Strategies Book Publishing somewhere after 2010 and before 2020 and had attained Bachelor 2019s Degree in Graphic Design after 2000 and before 2010. This designer had previously worked at Malaria Consortium and Ogilvy Mather. What is the first and last name the cover designer of this report?
Answer: Cristina Ortiz
Model Output by DeepResearcher
(think)
First, need to find the specific report that satisfies all the given conditions. will start hy searching for MSc and Master of Science Immunology of Infectious Diseases in the context of Fritrean individuals and their involvement with WHO reports.
Then will look for reports with forward from Fritrean individuals, published between 2011 and 2020. After finding the potential search.
report, check for other details about other authors and cover designer. Let's (/hiiak)
<tool_call>
("name": "web search". "arguments": ("query":: "Eritrean WHO report forward MSc Immunology of Infectious Discases 2012-0020" }}} </tooLcall>
<tool_resonns>>
[( 'search. 'search.querry: 'Eritrean WHO report forward MSc Immunology of Infectious Diseuses 2011-2020',
"web_page.ing page_info list': I. "quick.summary"" summary": : "Lethal infectious disenses as inborn errors of immunity: toward a synthesis of the germ and genetic theories., 'title': 'Lethal Infectious Diseases as Inhorn Errors of Immunity'. 'url': '...' [.'}} </tool response>
[think)
The first title "Lethal Infectious Discuses as Inborn Error of Immunity" doesn't directly indicate it's a WHO report ... An The webpage does not provide any direct information related to the query. However, the title and context may still be useful to cross-reference with other sources. However, to avoid making assumptions. I should search for the specific details about who authored the forward and what the cover designer's 's hackground is hased on the information given in the prompt.
now search details about the author who wrote forward the cover designer with the given criteria. [Ahinkk)
<toll_call>
("name": "web search, "arguments": ("query": 1 "Firitrean WHO report forward Immunology 201 1-2020"
"WHO report forward Immunology Muster of Science Eritrean 201 1-2220"
"WHO report graphic designer Master of Science Immunologg Eritrean 20 2010-2020"
"Eritrean graphic designer Leadership Strategies in Book Publishing 2010-2020"
"graphic designer Eritrean Leadership Strategies in Book Publishing Malaria Consortium Ogilvy Mather" }}}
<call
responess
[( "'seurch_query':: 'Eritrean WHO report forward Immunology 201 1-2020", 'web tweb_puge_infe www.ppeeeeoooossti...... I( quick_summary"" : "The vision
for the Decade of Vaccines 20012-) is of world in which...", "title"; '[PDF] Globul Vaccine Action Plan WHO", HDI respoosess
[think)
Despite several searches, haven't 't found any explicit information about the graphic designer of the report. However, the most promising leuds have been Simon Gebremeskel for the forward and the designer being from Eritrea with background at Malaria Consortium and Ogilvy & Mather. Given the constraints and the information we gather, the graphic designer is most likely working behind the scenes, hut need match the names with the design roles and criteria, Since Simon Giehremekke''s forward writing doesn't usually include cover design, the designer mentioned must he separate individual whose backgmunnd the criteria. Let's infer from the name mentioned and typical namiig conventions in such industries. Considering the information about a design career with degree after 2000 and roles at Malaria Consortium and Ogilvy & Mather, we can assume the designer's first name starting with Z and being collaborative with someone named for the introduction. Given the limitations, the most reasenable design follows given criteria. Assuming first name is Zeinab Mekonnen. match Zeinab bused on name convention following Eritrean naming patterns and fulfilling all background requirements.
designer professional matching these details: The designer's
(think)
32
<answer> Zeinab Mekonnen </answer>>

---

Published as a conference paper at ICLR 2026
Table 15: An example BrowseComp dataset answered by Search-ol, with special symbols used in the search queries and search results.
Example from BrowseComp Dataset
Question:
There was global report released by the World Health Organisation after 2011 and before 2020 in which the forward was written by an Eritrean person who had attained Master of Science (MSc) Immunology of Infectious Diseuses from UK University. The introduction was written by someone whose first name began with who wrote an opinion piece with a person whose first name began with the initials published after 2020 but before 2024, The cover was designed by graphic artist who had completed a Leadership Strategies in Book Puhlishing somewhere after 2010 and hefore 2020 and had attained Bachelor 2019s Degree in Graphic Design after 2000 and before 2010. This designer had previously worked Malaria Consortium and Ogilvy & Mather. What the first and last of the cover designer this report? Answer: Cristina Ortiz
Model Output by Search-ol
(think)
Okay, let's try to figure out this question step by step. The question is about identifying the cover designer WIIO report. The clues are a complex, so need to break them down.
First, the report was released by WHO between 201 and 2020. The forward (probably the foreword) was written by an Eritrean person with an MSc in Immunology of Infectious Discases from UK university. Then, the introduction was written by someone whose first name
with This person also wrote opinion piece with someone whose first name starts with J. published between 2020 and 2024. The cover designer has Bachelor's in Graphic Design (carned between 2000-2010), worked at Malaria Consortium and Ogilvy & Mather, and completed a Leadership Strategies in Book Publishing between 2010-2020.
So, the main goal is find the cover designer's full name. Let's tackle each part.
Starting with the cover designer.
key points here are:
Bachelor's 's in Graphic Design between 2000-2010.
2. Leadership Strategics in Book Publishing between 2010-2020.
3. Worked at Malaria Consortium and Ogilvy & Mather.
need find somcone who fits these educational and professional backgrounds. think about possible candidates. Maybe someone known for working in those companies and having those degrees. But since I don't it have prior knowledge, might need to search for these details.
Wait, the question mentions that the cover designer had Bachelor's Degree in Graphic Design after 2000 and before 2010. Also, they completed Leadership Strategies in Book Publishing after 2010 and hefore 2020. Let me check if there any notable graphic designers who fit this timeline and those companies. First, me consider Ogilvy & Mather. That's major advertising agency. Malaria Consortium is an NGO focused on malaria. So the designer might have worked both sec- tors. Let me try to search for people who worked at both Ogilvy & Mather and Malaria Consortium. Maybe some- one with background in graphic design and book publishing. Alternttvely, maybe the name is known in the field. Let me think of possible names. Wait, the question mentions the cover designer's name needed. Let try find someone with those qualifications.
since can't 't search the internet directly, need to think if there's any famous person who fits. Alternatively, maybe the answer is a known figure. Let me think of people who have worked at Ogilvy & Mather and Malaria Consortium.
...AA...
Maybe the answer someone like "Zahra" with last that's common. Altematively, perhaps answer is "Zahra" and the last name is "Mistry". But without specific information, I'm stuck. Maybe need think possible names. Alternatielly, perhaps the answer is "Zahra" and the last name is "Ali". Alternatively, maybe the answer is "Zahra" and the last name is "Khan". But I need to make sure. Alternatively, maybe the answer is "Zahra" and the last name "Mistry". Alternatively, perhaps the answer is "Zahra" and the last name is "Siddiqui"...
But I'm not certain. Altematively, mayhe the answer is "Zahra" and the name is "Ali". But think need to proceed with the hest possible answer based on the given information. (Ahink)
well-known individual who fits these criteria is **Zahra Mo She is recognized for her work in graphic design and publishing, and she has worked at both Malaria Consortium and Ogilvy & Mather.
MBO s Final Answer
Zahra Mistry
33

---

Published as a conference paper at ICLR 2026
Table 16: An example on FRAMES dataset answered by ours, with special symbols used in the search queries and search results.
Example from FRAMES Dataset
Question: Out of all of the feature-lcngth theatrical films that John Carpenter directed before 2015, which has the longest running time?
Answer: Starman (1984)
Model Output by Ours
(think)
Let's tackle this question step by step. The goal is to determine which of John Carpenter's feature-length theatrical films before 2015 has the longest running time. First, need identify his feature films released before 2015. Then, I'll check their running times and find the longest onc. I'll start by searching for list of John Carpenter's feature films released before 2015.
(Ahinkk
regginssearc.qquee
John Carpenter's feature films before 2015;
List of John Carpenter movies released before 2015
(Jend search queries))
([bggin_scare swwrchcacchhcc results
John Carpenter's feature films before 2015: John Carpenter's feature films directed before 2015 include the following,
based verified sources:1. ++Dark Star* (1974) ...An.....
All listed films were directed by Carpenter and released prior to 2015.
sendcscarrh.at resuulss
(think]
Now that have a list of John Carpenter's feature films before 2015, the next step is to determine the running time of each film and identify longest one. I'll search for the running times of these films. (Ahinnk)
(begin search queries
}
Running time of John Carpenter's Dark Star;
Running time of Assault Precinet 13;
...An (Parallel Search for 12 Queries)
Running time of Village of the Damned (1995);
Running time of Ghosts of Mars (2001)
(Jend_search_g _qurrrees
([begin_seare wearch_resulle
Running time of John Carpenter's Dark Star: The running time of John Carpenter's Dark Star* is #+83 *inutes** Running time Assault Precinet 13: The running time 1976 film "Assault Precinct 13* **91 minutes"*. ...)p...
Running time Village of Damned (1995): running time *Village Damned (1995) is ##g8 minutes** stated on its Wikipedia page and confirmed by Variety and Nitehawk Cinema. Running time Ghosts of Mars (2001); The running time "Ghosts Mars (2001) #agg *
searchsearc results))
(think)
Now that have the running times of all films, can compare them to the longest one. Let's them out again for clarity:
Dark Starts (1974) 83 minutes
...An...
12. "Coosts of Mars** (2001) 98 minutes
From this list, the longest running time #+115 minutes** for "Starman" (1984).
[/think)
Final answer:
Starman (1984)
34

---

Published as a conference paper at ICLR 2026
Table 17: An example FRAMES dataset answered by RAG-RI, with special symbols used in the search queries and search results.
Example from FRAMES Dataset
Question: Out of all of the feature-longth theatrical films that John Carpenter directed before 2015, which has the longest running
time?
Answer: Starman (1984)
Model Output
[chink]]
To determine which of John Carpentrr's feature-length theatrical films released before 2015 has the longest running time, I need list his films from that period and their respective running times. (thinkk
<seurch>
Curpenter feature before Curpenter running
</search>
<information>
www.ipppeeii...ogiiiiiiioooo wrg/wwkiflwwoa Carpenter', 'snippet': "Though he has been acknowledged as influential filmmaker, his other productions from the 1970s and the 1980s only later came be considered cult classies; these include Dark Star (1974), Assault Precinet (1976), The Thing (1982), Christine (1983)....
If "titte": 'John Carpenter Wikipedia", 'url': buttps:/en..ikippeiiia
</search>
(think)
From the search results, the running times several John Carpenter's films. However, the exact running times for directed before 2015 and their specific running times.
his films before listed.
<search>
Carpenter films earlier John Carpenter film running 2015 earlier
</search>
<information>
18", 'snippet': 'ffter directing Dark Star (1974), he has helmed both classic horror films like Halloween (1978), The Fog (1980), and The Thing (1982), and noted </infommaion>>
[ "John Carpenter IMDb'. 'url': 'https://www.nnn
()hink)
Based on the search results, the longest running time among John Carpenter's feature-length theatrical films before 2015 is "Big Trouble Little Chinu," which has running time of hour and 44 minutes, (/think)
<answer> Big Trouble Little China clanswer>
35

---

Published as a conference paper at ICLR 2026
An example on FRAMES dataset answered by DeepResearcher, with special symbols used in the search queries and search results.
Table 18:
Example from FRAMES Dataset
Question: Out of all of the feature-length theatrical tilms that John Carpenter directed before 2015, which has the longest running time?
Answer: Starman (1984)
Model Output by DeepResearcher
(think)
find information, I should for complete list Carpenter's feature-length theatrical films directed before 2015 and their respective running times, Then can find the one with the longest running time, (Ahinkk
cclol_call>>>>
("name": "web_search, "arguments": ("query": "list of John Carpenter films before 2015"
"running times John Carpenter films before 2015"
</tool/ccall>
<esposssspnss>>>
[[ 'search query': 'list of John Carpenter films before 2015', weeb.page.info.ltg [[
'John Carpenter
Filmography', 'title": "..,, "url': ".."). ") "quick_summary" "This is a list of films directed by John Carpenter that grossed...",
chooo_essposse>>
(think)
will first browse the webpages that are most likely to contain list of John Carpenter's 's films before 2015 and their running times.
(/think)
.Web..Weeb Browsing)
(think)
Wikipedia does not directly provide running times John Carpenter's films, it does filmography.
search for more specific information regarding the running times films from before 2015.
(think]
<tool call>
("name": "web search. "arguments": ("query": "running times of John Carpenter films directed before 2015" ]]}
</tool/_call>
responnss
[[ 'surr'h 'running times of John Carpenter films directed before 2015', web_page..nnt infoo.ist': [}]]
resoo_eespose>>>
(think)
couldn't find direct list of running times John Carpenter's films before 2015. However, hased my prior searches. "The Thing" might have longest running time it is a noted classic horror film. I'll need to check its specific running time.
(/hink)
<tool_call>
("name": "web search, "arguments": ("query": "running time of The Thing directed by John Carpente" }}}
call>
<tool response>
{{ 'seurch query': 'running time of The Thing directed by John Carpenter', "web page info list': [..]]]
resoon_esponse>>>>
(think)
Most sources found indicate that Thing" has a rumning time of approximately 109 minutes. Since there weren't any specific mentions of other films with longer running times, it's likely that "The Thing" had the longest running time among John Carpenter's films before 2015.
(hink)
<answer> The Thing < /answer>
36

---

Published as a conference paper at ICLR 2026
Table 19: An example on FRAMES dataset answered by Search-ol, with special symbols used in the search queries and search results.
Example from FRAMES Dataset
Question: Out the feature-length theatrical films that John Carpenter directed before 2015, which has the longest running time?
Answer: Starman (1984)
Model Output by Search-ol
(think)
Okay, so nced to figure out which of John Carpenter's feature-length theatrical films before 2015 the longest running time. Let me start by recalling the films he directed. John Carpenter is known for movics like Halloween, The Thing, Escape from New York, Halloween II, and others. But need list all his feature films before 2015 and check their running times.
...\m...
So first, need to list all John Carpenter's feature films before 2015. Let me start by searching for his filmography up to 2015. (thiin)
(begin search query]) John Carpenter filmography hefore 2015 end search queryl)
saagin_carch__esulle John Carpenter's notable films before 2015 include: 1. **Dark Star** (1974) 2. **Assault Precinct 13 (1976) 3. **allooween** (1978)... ((end_scarcchresalt))
[bbgin_scarch___ scarch query] Running time of The Thing (1982) John Carpenter
cnd qurrcl)
([begin search r The running time of John Carpenter's *The Thing* (1982) is #+109 minutes** as confirmed by both the Wikipedia page for the film and review emsaunders. wordpress.com... sendhsearchrresull]
([begin search query]) Longest John Carpenter film before 2015
(Jend search query
The longest John Carpenter film hefore 2015 is "*Elvis (1979)**, with runtime of ++168 min- utes** This information is confirned by Web Page 3, which identifies as a TV film... []end search
Beagin_search )esult)
scarch query|] Elvis (film) runtime [endd search_queryy)
[begin_scarce www.sccaresuullleeee result)) The runtime of 2022 film "Elvis", directed by Baz Luhrmann, 159 minutes** (2 hours and 39 minutes), This confirmed by multiple reliable sources, including its Wikipedia page, IMDb.. [[end wwwrchearrreee result)
begin scarch ] John Carpenter's 's Elvis film runtime cnd search. scarch.query]]
([bgiin search result) The runtime of John Carpenter's *Elvist (1979) is ##168 minutes (2 hours and 48 minutes) as confirmed by its IMDb page (Weh Page and... (jendd_sarch_resiil
(lbegin search queryl) John Carpenter's filmography with runtimes
[Jend search queryl)
[bboiinsearch search sww.sccccchcattr)) John Carpenter's filmography with confirmed runtimes (before 2015) includes the following films,
hased on reliable sources like IMDb, Wikipedia, and reviews: **Dark Star** (1974) **83 minutes** (Ih 23m)...
(Jend search result))
([begin_search.querr))
Longest John Carpenter film before 2015 search__uru))
(lbegin.search.cm www.chearreeeeeeeeeeeeeeeeeeeeee r You have searched this query. Please refer to previous results, [lend_ seauchh.shhhhhl
After analyzing the search results, the longest running time among John Carpenter's feature-length theatrical films before 2015 is **Elvis (1979)**, with runtime **168 minutes** hours and 48 minutes). While *The Thing* (1982) is often cited as one of his longer films at 109 minutes, *Elvis" clearly surpasses in duration. This conclusion is supported by multiple reliable sources, including IMDb and JustWatch, which confirm the runtime of *Elvis* as the longest Carpenter's 's filmography prior 2015.
37


Reviews:
[{"review_id": "tsjODuULCQ", "reviewer": "Reviewer_NCby", "summary": "The authors of this paper introduce HDS-QA, a new training dataset that is used to teach models how to make multiple parallel and sequential web search calls for answering hard reasoning-intensive questions. The authors fine-tune a qwen3-8b model on this dataset to obtain the HybridDeepSearcher model. HybridDeepSearcher uses parallel and sequential search to obtain state-of-the art performance on multiple hard question-answering datasets.", "questions": "Here are a couple questions:\n- Are all questions constructed by starting from a question in the NQ dataset?\n- Does HybridDeepSearcher overthink on easier benchmarks? Figure 3 shows that HybridDeepSearcher is worse compared to other baselines when fewer calls are utilized. Is this a concern for easier datasets?\n- How does the performance of HybridDeepSearcher scale with training data?\n- What is the distribution of the number of parallel and sequential tool calls required for samples in HDS-QA?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "The strengths of this paper are:\n- Authors compare to multiple recent methods that serve as strong baselines. \n- HybridDeepSearcher beats baselines on multiple datasets. Some of these datasets like BrowseComp are very challenging QA datasets. \n- The scaling curves convincingly show that HybridDeepSearcher is able to use extra tool calls and search turns effectively.", "weaknesses": "The weaknesses of this paper are:\n- Parallel and multi-hop questions are generated using one fixed pipeline. This might lead to limited diversity and many questions might have very similar patterns. \n- Is HDS-QA only useful for improving search capabilities in 7-8b models or do smaller/larger models also benefit from training on HDS-QA? For larger models you likely need traces from a larger teacher model?\n- Experiments are limited to SFT on HDS-QA and there no experiments to see if performance can further be improved by using RL.", "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": "igNcaSMxgy", "reviewer": "Reviewer_RTtM", "summary": "The paper introduces HybridDeepSearcher, a deep search agent that integrates parallel and sequential search to perform RAG-based reasoning and question answering. To train it, the paper first synthesizes the HDS-QA dataset, focusing on generating hard, high-quality data points and corresponding parallel search traces, which consist of reason-query-retrieval. By using the HDS-QA dataset, fine-tuning alone is able to result in state-of-the-art performance on multiple challenging multi-hop QA datasets, providing both efficiency and effectiveness.", "questions": "1. Generating parallel-hop questions is very interesting, but how does sequential search perform on this task compared to parallel search with the same number of queries submitted?\n2. The method uses the Jina search API to perform parallel search. Are all baselines, including RL-based ones (e.g., search-R1), also trained on and use the Jina API?\n3. The generated dataset can be put to better use. Does the author think that the HDS-QA dataset (without trajectories) can be used for RL training? What are the advantages and disadvantages?\n4. Have the author examine the model performance against other baseline methods on one or two simpler general QA or multi-hop QA datasets used by Search-R1?\n5. Typos: line 1191, Table numbers are missing.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper is well-written with clear illustrations. \n2. The model training recipe is clean and systematic. \n3. The dataset is generated using Google’s People Also Ask feature, which seems to have significantly increased the difficulty and quality of the generated questions.\n4. The experimental evaluation is comprehensive with a sufficient number of datasets and baselines. The analytical studies on test-time scaling are also insightful.", "weaknesses": "1. The pipeline still uses a bigger model Qwen3-32B to summarize retrieved documents, which may incur unwanted costs. Have the authors examined the performance using a smaller model, like the 8B model, as a summarizer?\n2. As the primary contribution is a high-quality data synthesis pipeline, the paper did not explore the scaling behavior with respect to the number of generated data. It would be interesting to explore questions like how much data is needed, and whether more data helps.", "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": "HHG9m7eH6r", "reviewer": "Reviewer_B25Z", "summary": "This paper introduces a dataset and a training method that teaches the model to perform parallel search in addition to the common sequential search. The proposed dataset, HDS-QA, requires both parallel and sequential search, which can serve as a challenging training set that is scarce in the current training community.", "questions": "1. There are many works from Tongyi (e.g., WebWalker, WebExplorer, etc.) and the fully open-sourced work called ASearcher on constructing synthetic BrowseComp-like QA datasets for deep research training. Could the author clarify the difference of HDS-QA from these works?\n2. The choice of the 50 questions from BrowseComp needs a bit more justification. Using a super small test set will make the results high variance. Did the author benchmarked the variance and show what is the CI of this evaluation and how significant is the current gap between different models?\n3. Could the author compare their models with baselines that are trained on synthetic data designed for sequential search, such as WebExplorer-8B and ASearcher-8B to show the benefit of parallel search?\n4. I'm curious if the main benefit comes from the synthetic data or from developing parallel search. Could the author train the model for sequential search on the same training questions and ablate the effect? In addition, I wonder if the \"parallel search\" can be viewed as a new search tool that can parse a long string of queries and divide into sub-queries to gather information, because the model inference pipeline seems exactly the same as sequential search.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "1. The paper raised attention to parallel search, which is an under-studied capability of deep research models.\n2. Using parallel search might enable the model with extended capability to gather information and more efficient utilization of context length.", "weaknesses": "1. The paper highlights its novelty on doing parallel search in addition to sequential search; however, the main change can be understood as adding a parser on the tool side to parse a list of queries from a single query string, which seems trivial to me. This has been implemented in some open-source projects like MiroMind as a small feature.\n2. The construction of the dataset claims to require parallel search, but the ablation failed to accurately demonstrate how training a sequential search model on the same data might not work well as parallel search.", "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": "WnzrdoVFRG", "reviewer": "Reviewer_RKvi", "summary": "This paper focuses on multi-turn search agents. Most existing search agents perform complex information retrieval using sequential querying, whereas approaches that generate independent queries simultaneously may limit deeper, sequential reasoning. The authors construct a dataset that integrates parallel search with sequential search reasoning, collect answer trajectories, and fine-tune an LLM as a search agent via SFT.\n\nContributions:\n* HDS-QA Dataset: An automatically constructed synthetic dataset derived from Natural Questions, containing questions that require both parallel and sequential search reasoning.\n* HybridDeepSearcher: A search agent (fine-tuned Qwen3-8B) that adaptively integrates both parallel and sequential search strategies during the reasoning process.", "questions": "1. **Formatting and Technical Errors**. The manuscript contains multiple formatting issues that affect readability: incorrect citation format at Line 048, improper vspace usage at Line 443, layout inconsistencies on Page 15, and reference errors in Appendix D. These should be addressed for publication quality.\n2. **Confusing notation**. In Table 2, the original Qwen3-8B is replaced. Using \"+Qwen2.5-7b\" here is confusing.\n3. **Parallel Query Generation Patterns**. Could the authors clarify the parallel query generation behavior observed across different case studies? In Tables 4 and 10 (MuSiQue and FRAMES examples), the first search turn generates queries that appear to rephrase the original question with minor variations, while Table 7 (BrowseComp example) produces genuinely diverse parallel queries. Since these use the same model and prompts, what factors determine when the model generates reformulations versus diverse parallel queries? Understanding this would help assess whether the model has learned systematic parallel search strategies or if the behavior is primarily question-dependent.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "* **Well-Motivated Problem**. The paper identifies a limitation in current search agents: sequential search agents cover limited information, while parallel search agents may sacrifice deeper, sequential reasoning capabilities.\n* **Scalable Dataset Construction**. The HDS-QA dataset generation pipeline is fully automatic, eliminating the need for human annotation and enabling scalable data creation.\n* **Strong Empirical Results**. The model demonstrates substantial improvements across multiple benchmarks, validating the effectiveness of the proposed approach.", "weaknesses": "1. **Training Methodology Limitations**. The paper collects trajectories using Qwen3-32B and fine-tunes Qwen3-8B via SFT. However, given that competitive baselines (Search-R1, RAG-R1) employ reinforcement learning for training, the absence of an RL-trained version limits understanding of the effectiveness of the method.\n2. **Computational Cost and Fairness Concerns**. The use of Qwen3-32B for document summarization adds significant computational overhead without clear justification. Why not use retrieval results directly? More critically, this creates an unfair comparison with RAG-R1, the strongest baseline, which operates without a summarization module. The added computational budget makes it difficult to isolate the true benefits of the proposed approach.\n3. **Dataset Construction Validity**. Based on the dataset generation process (Section 3.1) and the prompts in the appendix, the Qwen3-32B receives a bunch of extracted information and constructs the question in Step 3. Since there are no constraints, we can not determine if a generated question is really a parallel question. Furthermore, for hybrid questions structured as A|B->C (where A and B are parallel subquestions and C depends on their answers), the pipeline does not prevent information leakage. Agents may obtain sub-answers from a single branch without genuinely solving the parallel components. This raises concerns about whether the dataset truly improves parallel search capabilities or merely teaches pattern matching.\n4. **Incomplete Qualitative Analysis**. The qualitative case studies (Appendix) lack comparisons with RAG-R1, the most competitive multi-query baseline, making it difficult to assess the specific advantages of HybridDeepSearcher over existing parallel query methods.", "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)