Flash-Searcher: Fast and Effective Web Agents via DAG-Based Parallel Execution
Authors: Tianrui Qin
Qianben Chen
Sinuo Wang
He Xing
King Zhu
He Zhu
Dingfeng Shi
Xinxin Liu
Ge Zhang
Jiaheng Liu
Xitong Gao
Yuchen Eleanor Jiang
Wangchunshu Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: Workflow Optimization
Agent Reasoning
WebAgent
Deep Research
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: This paper proposes a DAG-based parallel LLM agent framework that resolves sequential inefficiency via concurrent subtasks, outperforms baselines, and shows generalizability on agent models to advance efficient complex reasoning.

Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks when equipped with external tools. However, current frameworks predominantly rely on sequential processing, leading to inefficient execution particularly for tasks requiring extensive tool interaction. This paper introduces Flash-Searcher, a novel parallel agent reasoning framework that fundamentally reimagines the execution paradigm from sequential chains to directed acyclic graphs (DAGs). Flash-Searcher decomposes complex tasks into subtasks with explicit dependencies, enabling concurrent execution of independent reasoning paths while maintaining logical constraints. Through dynamic workflow optimization, our framework continuously refines the execution graph based on intermediate results, effectively integrating summary module. Comprehensive evaluations across multiple benchmarks demonstrate that Flash-Searcher consistently outperforms existing approaches. Specifically, it achieves **67.7%** accuracy on BrowseComp and **83%** on xbench-DeepSearch, while reducing agent execution steps by up to **35%** compared to current frameworks. Furthermore, when distilling this parallel reasoning pipeline into single models, we observe substantial performance gains across diverse backbone architectures, underscoring the generalizability of our methodology. We propose a scalable and efficient paradigm for complex reasoning, advancing agent architecture design with our source code publicly available at https://github.com/OPPO-PersonalAI/Flash-Searcher.

Published as a conference paper at ICLR 2026
FLASH-SEARCHER: FAST AND EFFECTIVE WEB AGENTS VIA DAG-BASED PARALLEL EXECUTION
Tianrui Qin Qianben chen', Sinuo Wang2., Xinyu Yang", Kang Zhu?, He Zhu?, Dingfeng Shi?, Xinxin Lin Ge Zhang", Jiaheng Liu?, Xitong S., Yuchen Eleanor Jiang', Wangchunshu Zhou
'OPPO AI Center 'OPPO Reseach Institute 'The University of Adelaide *Carnegie Mellon University SShenzhen Institutes Advanced Technology
'ByteDance "Nanjing University SShnnzenn University of Advanced Technology
Southern University of Science and Technology
(qintianrui, zhouwangchunshu @oppo.. com, xt. gao@siat. cn
ABSTRACT
Large language models (LLMs) have demonstrated remarkable capabilities in com- plex agent reasoning tasks when equipped with external tools. However, current frameworks predominantly rely on sequential processing. leading to inefficient execution particularly for tasks requiring extensive tool interaction. This paper introduces SEARH-SERR a novel parallel agent reasoning framework that fun- damentally reimagines the execution paradigm from sequential chains to directed acyclic graphs (DAGs). FLASH-SEARCHER decomposes complex tasks into sub- tasks with explicit dependencies, enabling concurrent execution of independent reasoning paths while maintaining logical constraints. Through dynamic work- flow optimization, framework continuously refines the execution graph based on intermediate results, effectively integrating summary module. Comprehen- sive evaluations across multiple benchmarks demonstrate that FLASH-SEARCHER consistently outperforms existing approaches. Specifically, it achieves 67.7% ac- curacy on BrowseComp and 83% on SeapSeh,, while reducing agent execution steps by up to 35 % compared to current frameworks. Furthermore, when distilling this parallel reasoning pipeline into single models, we observe substantial performance gains across diverse backhone architectures, underscoring the gener- alizability of our methodology. We propose scalable and efficient paradigm for complex reasoning, advancing agent architecture design with source code pub- licly available at https: thub,
INTRODUCTION
Recent advances in tool- col-auumeeteed agents and multi multi-agent systems (MAS) (Dorri et al., 2018: Canese et al., 2021; Zhou et al., 2023c; 2024; Zhu et al., 2025a;b; Qiu et al., 2025; Roucher et al., 2025; Tang al., 2025; Team, 2025) have demonstrated remarkable capabilities in complex problem-solving tasks, showcasing how collaborative agent frameworks can effectively address challenges requiring diverse reasoning abilities and tool manipulation. These systems leverage specialized agents with distinct roles, enabling sophisticated planning, reasoning, and tool utilization would be challenging for single-agent approaches. Concurrently, research efforts have focused on Tool-Integrated Reasoning (TIR) (Jin et al., 2025a; Li et al., 2025c;d; Wu al., 2025a; Sun et al., 2025; Zhang et al., 2025a; Zheng et al., 2025; Xue et al., 2025) approaches, which aim to incorporate the capabilities of tool execution multi-agent systems into single model through specialized training methodologies.
solve tasks that
Despite their impressive performance, both MAS and TIR approaches face significant limitations when addressing general complex tasks. Multi-agent systems suffer from inefficient tool utilization, excessively long reasoning chains, and prolonged execution times due to sequential processing and dundant communication, while TIR methods encounter reasoning efficiency bottlenecks with chains
Corresponding Author.

---

Published as a conference paper at ICLR 2026
Traditional Agent Framework
Flash-Searcher Framework (Ours)
DAG-based Plan Construction
Tesk
Goal U. sime 22001-20123 $1 4b-1
Task
grass
Reffect
G
Path 1.2 lox Otfinn Mo tieer 115 checs
Success 12 vetted films
Fime 20251 Oscar winiing direcoor
DAG-based
Pian
Verify
EE Plan
Usvenemmtee Success: Annotseed
Reasoning
Parailel Execution & Orchestration
Parallel Execution
Interaction
Toel/Agent Server
Progress Summary
Adaptive Progress Tracking Summary
Goal 2: U.S. firm (013-2203) 5145-173M domestic croes
Staous Frogress Foth Anelysis 11 Cffiee
Office Majo!
ras (2055-200
Search
Calculate
Cook
Crawl
Answer
Stous Path Analydi Allono Quarón 8 rdma (2004) Abganrr B
diretted
Figure 1: Overview of SLARHH-SE Framework and Key Components.
frequently exceeding context window limitations. These issues become even more pronounced in complex scenarios that require deep research capabilities. In such cases, MAS and TIR systems in- tegrate additional verification mechanisms, such as reflection, self-critique, and iterative refinement, to enhance reliability. However, these improvements come at the cost of significantly increased computational overhead when solving complex tasks. Deep research tasks in current agent frame- works often require more than 20 interaction steps (Wang et al., 2025b; Roucher et al., 2025; Hu al., 2025), with execution times extending to several hours. This creates sharp tension between solution quality and computational efficiency, severely limiting practical viability in user-responsive applications. When confronted with complex tasks inducing unavoidable latency, do users deem the better performance necessary enough to justify tolerating or paying for these delays?
To address these critical challenges, we introduce FLASH-SEARCHER, a novel parallel agent reasoning framework that fundamentally reimagines how agents collaborate to solve complex tasks. Building upon recent empirical advances reasoning models, our approach leverages these models' enhanced capabilities in simultaneously managing multiple cognitive threads. As illustrated in Figure 1, un- like traditional approaches that adhere to strict sequential processing, FLAASHSEEARCER decomposes the original task into multiple parallel execution paths, orchestrated via carefully designed agent workflows. This parallelization allows multiple reasoning paths to progress simultancously while in- telligently managing tool calls across different execution branches. The FLASH-SEARCHER framework redefines the effccieccy-fffective adaptive decomposition and parallelization of tasks into concurrent subtasks with dynamic strategy adjustment, 2) dependency-awar reasoning graph management to model information dependencies and 3) optimize critical paths/information flow, and proactive information retrieval with knowledge sharing to anticipate downstream needs and reduce redundant interaction steps.
frontier in complex task solving through key innovations: 1)
Our extensive evaluations demonstrate that SEAS--EE achieves state-of-the-art performance across multiple challenging benchmarks. Our FLASH-SEARdN SEarCHER (with GPT-S-mini) reduces the average agent execution steps by 35% (11.2-71 7.4 steps GAIA) and shortens the overall execution time by "65% (27.4 + 9.6 mins BrowseComp) compared to OAgents (Zhu et al., 2025a). Despite this dramatic efficiency improvement, FLASH-SEARCHER (with GPT-5) achieves an impressive average performance of 82.5% on GAIA benchmark. Furthermore, on more challenging benchmarks such as xbench, HIEE and BrowseComp, FLASH-SRARCHER achieves performance metrics of 83.0, 44.0 and 67.7 respectively, surpassing current state-of-the-art methods. Furthermore, to validate the generalizability collected web agent data and conducted models. This lightweight adaptation achieves a performance score of 68.0 on the xbench- DeeacSh benchmark, representing 29.3 improvement WebDancer. This demonstrates the effective transfer of the parallel agent paradigm to open-source models with minimal additional training.
our approach, we constructed FLASH-SEARCHER exccution trajectories based the Qwen-2.5 family of open-source
In summary, contributions follows:
2

---

Published as a conference paper at ICLR 2026
We present novel parallel agent reasoning framework that substantially reduces execution steps while achieving SOTA performance across various benchmarks.
High-quality parallel reasoning trajectories, systematically curated and constructed for model post-training, significantly boost performance complex evaluation tasks.
Experimental results demonstrate the effectiveness of lightweight post-training in propagating parallel agent strategies to open-source models, achieving comparable results to multi-agent systems.
We fully open-source pipeline and datasets of FLASH--SEARCHE to catalyze research search agents and models.
2 RELATED WORK
2.1 MULTI-AGENT SYSTEM
Recent research has highlighted the effectiveness multi-agent systems in addressing complex real-world challenges through collaborative agent frameworks. These systems typically employ multiple specialized agents with distinct roles, thereby supporting advanced planning, multi-turn reasoning, tool utilization, and environment interaction (Zhou al., 2023c: 2024; Jin al., 2025b; Zhu et al., 2025a;b; Mai et al., 2025; Hu et al., 2024; Tang et al., 2025; Shi et al., 2025; Tang al., 2025; Zhou et al., 2023b). Early multi-agent systems such as CAMEL (Li et al., 2023) showed that dialog between agents can elicit stepwise reasoning through role-playing. Subsequent frameworks, including MetaGPT (Hong et al., 2024) and ChatDev (Qian et al., 2023), formal- ized this approach by implementing structured execution pipelines with dedicated roles such as manager, designer, and coder. Other approaches, like Magnetic-One (Fourney et al., 2024) and Smolagents (Roucher et al., 2025), incorporate central planner that dynamically delegates subtasks to specialized tool-based agents. Agent (Chen et al., 2023) refines collaborative reason- dlecision-executionn-o
cycle, enhancing reflection and coordination. Workforce (Hu et al., 2025) decouples planning, coordination, and execution into modular agents, enabling efficient domain transfer through plug-and-play workers. Alita (Qiu et al., 2025) proposes autonomous tool exploration via iterative trial-and-error, expanding capabilities by transforming multi-attempt tasks into single-attempt ones. However, beyond performance, the latency in these complex multi-agent frameworks remains understudied.
ing via
2.2 ERICIINT FRAMEWORK
Efficiency optimization has long been pervasive focus in deep learning, pursued through various strategies ranging from model compression (e.g., knowledge distillation (Li al., 2025h:f: Gou et al., 2021; Yang et al., 2025; Li et al., 2025b; Fang et al., 2026; Xu et al., 2024), structured pruning (Wang et al, 2019; Gao et al., 2018; Zhao et al., 2019; Liu et al., 2018; Ma al., 2023; et al., 2025i:g)) to efficient data utilization via dataset distillation (Wang al., 2018; 18; Lei & Tao, 2023; Sun et al., 2024; Li et al., 2025e; Wang et al., 2025a). To address the efficiency bottlenecks inherent in cxisting agent frameworks, Tool-Integrated Reasoning (TIR) has recently emerged prominent research direc- tion. Early efforts primarily adopted prompt-based strategies, such as Search-ol (Li et al., 2025c), which employ static templates to instantiate fixed
loops, thereby abling rudimentary tool-augmented reasoning. More recent work has pivoted toward post-training paradigms (Jin al., 2025a; Li et al., 2025d; Wu et al., 2025a; Li et al., 2025a; Tao al., 2025; Sun et al., 2025; Xue et al., 2025; Li et al., 2025b; Nguyen et al., 2025), where agents are refined via task-specific fine-tuning to enhance performance. Despite their empirical gains, these approaches typically enforce narrowly scoped execution workflows, which severely limit their adaptability and scalability in open-domain, real-world environments. These challenges have motivated a broader effort to improve the efficiency and scalability of seasoninn-eenblle agents. Recent advances have fo- cused on two key directions: optimizing agent pipelines and parallelizing search processes. Efficient Agents (Wang et al., 2025b) conducts comprehensive analysis of core agent modules (workflow design, tool invocation, and memory architecture) to systematically balance performance and cost. Similarly, ParallelSearch (Zhao et al., 2025) trains models to detect parallelizable query structures, decomposing complex queries into independent sub-queries for retrieval tasks, resulting in signif- icant performance gains in search-based tasks. However, existing systems remain constrained by
3

---

Published as a conference paper at ICLR 2026
Tasks
DAG-bont based Plan
Parallel Execution
Progress Summary
Answer
1980
Goal Palh
Goal Pulh
dbold un meb
Goll & (In process)
Loal Pads
Goal & (imiebed)
Goal
Gual
For gas
Adaptive Pregress
soath bollin
Tracking Summary
Model Reasoning
Abrald
Final
Leep
Figure 2: The pipeline of MAASH-SEARCHER.
isolated reasoning- execution loops and the prolonged eycles introduced by multi-step verification, highlighting the need for more efficient approaches to agent.
casooing-execcution
3 METHOD
3.1 PRELIMINARIES
Tool-Augmented Agents. Tool-augmented agents enhance the capabilities of LLMs by seamlessly integrating external tools to perform actions such as information retrieval, mathematical mattennattiallommpttaiinn, and code execution. This paradigm mitigates the inherent limitations of parametric knowledge through a structured tool-calling pipeline. Formally, the agent-enviromment interaction is modeled as a Markov decision process, wherein each tool invocation induces state transition driven by environmental feedback. At timestep the agent selects a tool-calling action a, E A, where A denotes the action space comprising available tools based on the current state St, and receives an observation ~ P- St, a₂) from the tool environment. The state transition function is defined as:
Sy+1 g(8,, d1, 0r)
S represents state update function incorporating task, action history, and structured tool outputs into the new state representation 8y+1 E S.
where g S A x -
Multi-Agent Systems. Consider set of agents indexed by I, where each agent is denoted as for i E I. Each agent maintains a local state si and possesses specialized capabilities. The global system state at time t is defined as S, s?..... ,/,r), shared context c,. Agents coordinate through inter-agent communication protocols to optimize common objective function U(S)). The evolution of the global state follows:
which aggregates all local states along with
S f(ad)) 0,).
(2)
where aj A denotes the action executed by agent i timestep O, foj represents the collection of observations from all agents, and f integrates individual actions, the current global state, and observations to produce the next global state.
Existing approaches often adopt sequential execution with reflection and verification, prolonging task completion. Complex tasks may require 40+ interactions, introducing substantial latency. This sequential dependency creates a fundamental quality-efficinny trade-off, hindering real-world deployment.
3.2 FLASH-SEARCHER: PARALLEL, AGENT REASONING FRAMEWORK
To overcome the inherent inefficiencies of sequential execution in conventional agent frameworks, we introduce FLASH-SSARCHER, novel parallel reasoning framework that reformulates complex task solving as structured concurrency. Our approach transcodes the traditional linear workflow into dynamic directed acyclic graph (DAG) plan, achieving substantial efficiency gains while preserving execution coherence. The full pipeline of
is illustrated in Figure 2.
DAG-based Plan Construction. Given a composite task T, FLASH SEasceEr employs a decom- position function D that identifies constituent subtasks and their interdependencies, yielding
4

---

Published as a conference paper at ICLR 2026
DAG-based plan:
D(T) Gpann (V.E).
where V (11.22..... (n) denotes subtasks and E S V x V captures prerequisite relations. Each directed edge (1,,7)) encodes that f must precede fy-
Parallel Inferential Execution & Tool Orchestration. Atexecution s, FLASH-SEARCHEE selects candidate subtasks from the pending set P, _
EGG, P,) {v, E E p(v;,
where v(-) is readiness predicate. Unlike striet topological scheduling, & permits aggressive parallelization: a subtask V, may be scheduled either (i) all its prerequisites are complete, or (ii) partial execution can provide auxiliary signals for dependency verification. Thus, yp formalizes cross-validation as hybrid criterion, blending dependency satisfaction and heuristic consistency checks. During execution, multiple subtasks E(G, Pr) are processed in parallel via tool or agent invocations. The system integrates observations into the reasoning state:
= =((s, (a,
(k)
(k) (k) ) 1= (0r )k=1)
(5)
where af4) and of (k) denote the action and observation of the k-th parallel execution, and F inte- grates the results via structured aggregation and performs state transitions based on the aggregated information.
Adaptive Progress Tracking & Summarization. reflect execution progress, FLAAS-SEARCHEK periodically updates the DAG-based plan every A steps:
G R ( plan C. Pt.s..
(6)
where C, is the set of completed subtasks. The refinement rule R eliminates resolved nodes, revalidates unresolved dependencies based on cross-validation outcomes, and dynamically inserts new decomposition nodes if needed. The interval A can be flexibly specified: smaller A increases the frequency of plan updates, ensuring faster task adaptation and responsiveness; a larger A suppresses excessive optimization, reducing computational overhead in complex or stable tasks.
By integrating DAG-based decomposition, controlled aggressive parallelization, and periodic DAG optimization, SEASH-SEARCHER mitigates the sequential bottleneck of existing reasoning architec- tures. This design provides a scalable and efficient alternative to sequential reasoning architectures, maintaining logical coherence through its structured, parallel approach. The full SEAA--EERRRERR pipeline is formally presented in Algorithm 1.
Algorithm FLLAsH-SEARCHES Framework
Require: Composite task
1: Gplan < D
2: Initialize So, Po - V. Co. 0
3: f- 0
4: while P, # do do
5: &, - (v P,
G,,, 1}
6:
Execute subtasks in &, in parallel
Collect results (o } and update 8,41 F(s,, la, (K) (a( 10 12
7:
8:
C++1 < C, completed subtasks
9:
Prt1 Pr \ Cr+1
10:
it' mod
then
11:
G R RGG,, S(11,
12: end if
1-
14:
end while
return Final state ST
5

---

Published as a conference paper at ICLR 2026
BrowseComp
Obench-DeepSente
GAIA
HLE
68.0
440
Opee
Score (Passé 1)
74.1
Sundo
Chertry Agost
Pill
Mowe Roandet
Opend
Mono
Speeak Qpe
Kon Kanacha
Apon
Sonder
Figure 3: Performance comparison of agent frameworks on BrowseComp, xbench BenchhDeeppeardee
GAIA and HLE benchmarks. All results are reported using Pass @ metric.
4 EXPERIMENT
4.1
FLASH-SEARCHER FRAMEWORK
4.1.1 SETUP
Benchmarks. We evaluate FLASH-SEARCHER on four challenging benchmarks for information retrieval and reasoning:
GATA (Mialon et al., 2023): A comprehensive benchmark for evaluating complex task-solving capabilities. For this benchmark, we mainly use the text-only validation set (103 tasks), which requires deep information retrieval and complex reasoning. Notably, the full validation set is solely used in Figure for fair comparison; all other evaluations are based on the text-only validation subset.
BrowseComp (Wei et al., 2025): Large-scale benchmark comprising 1,266 tasks designed to test internet-scale information retrieval with hard-to-find information needs and sophisticated browsing strategies.
xbench-DeepSearch (Xbench-Teane Team, 2025): Professional benchmark with 100 tasks simulating real-world search scenarios, emphasizing multi-round refinement and cross-source information integration.
HLE (Phan et al., 2025): A frontier benchmark covering over a hundred subjects, designed to address the limited difficulty of existing benchmarks. We follow the setting in AFM (Li et al., 2025b) and use HLE-500 for evaluations.
Framework Configuration. FLASH-SEtS SAASH--EARCHERR employs minimalist yet powerful tool configuration optimized for parallel execution. Our framework integrates two core components: a Search Tool implemented with the Serper API (Serper, 2025) for retrieving structured search results, and Crawl Tool leveraging the Jina Reader (Jina, 2025) for content extraction. The crawl tool incorporates automatic summarization using the same backbone language model, ensuring consistent information representation while significantly reducing cognitive load. This streamlined design enables efficient parallel tool orchestration across reasoning branches while maintaining trajectory simplicity and operational coherence. More details can be found in Section C.
Metrics. We employ the LLM-as-Judge paradigm (Zheng et al., 2023; Wu et al., 2025a) for automated evaluation, utilizing (PT-4.1-mim) the judge model. Each agent output of different benchmarks receives a binary correctness assessment from the judge model. We report Pass@1 results, which measure the proportion of tasks solved correctly on the first attempt, based on these binary correctness scores. The standardized prompt for judgment is detailed in Section G.I. The standardized prompt for judgment is detailed in Section G.I.
4.1.2 2.2 MAIN RESULTS
We present comprehensive evaluation of FLASH-SEARCHER against -tate-of-the-art closed-source and open-source agent frameworks across four challenging benchmarks: BrowseComp. xbench- Deaphearch, GAIA, and HL... As illustrated in Figure 3, method achieves highly competitive

---

Published as a conference paper at ICLR 2026
performance, matching or exceeding existing approaches while demonstrating superior efficiency and scalability. These results underscore the effectiveness our DAG-based architecture in handling diverse task complexities.
In Figure 4, our GPT-5 achieves competitive performance of 67.7% on the BrowseComp benchmark. This result not only demonstrates substantial advantage over state-of-the- art open-source frameworks (e.g., BrowseMaster (Pang al., 2025), which attains 30.0%) but also approaches the performance of the leading closed-source solution, specifically the OpenAl ChatGPT agent (68.9%) Even with less powerful backhone models such GPT-5-mini, our framework achieves 35.3%, demonstrating the effec- tiveness of our parallel reasoning approach regardless of the underlying model. For SeeeehhDeepSeachh FLASH-
when integrated with
abench HLE
Score (Pass@1)
20
GPT-5
GFT GPP----mii 9-mini
GPT-4.1
DespSeok V3.1
SEARCHER also shows remarkable performance, with our GPT-5 variant achieving 83%, surpassing both Browse- Master (66%) and Metaso DeepResearch (64%). This substantial improvement highlights the particular strength of our approach deep research scenar- ios that demand extensive information gathering and complex reasoning. Besides, On the GAIA benchmark, FLASH-SEARCHER with lightweight, resource-efficiem GPT-5-mini backbone achieves 80.6%, exceeding even strong closed-source systems like Alita (75. (7.2%% and Manus (73.3%). Ad- ditionally, our method demonstrates exceptional capability on the HLE benchmark, achieving ofte-offtthearr 44.0% with GPT-5, substantially outperforming all other frameworks. These results demonstrate that our parallel reasoning framework effectively handles diverse information retrieval challenges. The framework's consistent performance across different backbone models validates the robustness of our approach.
Performance of FLASH- SEARCHER with different backbones.
Figure 4:
4.2 FLASH-SEARCHER LEARNING
4.2.1 SETUP
Dataset. To train our parallel reasoning agent, we construct a high-quulity dataset derived from mul- tiple sources including WebWalker (Wu et al., 2025b), ASearcher (Gao et al., 2025), WebShaper (Tao et al., 2025), and CoA (Li et al., 2025b). Our final dataset consists 3354 effective DAG-based reasoning trajectories. Each trajectory incorporates periodic DAG workflow reviews and is formatted as multi-turn collt--uur dialogue, enabling effective context window extrapolation and oog-range dependency modeling. This format specifically enhances the model's ability to manage complex reasoning graphs while maintaining coherent conversation flow. More details can be found Section E.
Training Configurations. We maintain consistent evaluation metrics and benchmarks with the framework experiments in Section 4.1.1. All training is implemented using the Llama-Factory framework (Zheng al., 2024). We employ supervised fine-tuning develop robust parallel reasoning capabilities. Specifically, for all trained models, the maximum dialogue length is set to 131,072 tokens, the learning rate to 10-5, and training conducted for four epochs. The full training parameters and detailed data formatting specifications are comprehensively documented in Section E.2.
4.2.2 AGENT MODEL. RESULTS
To validate the effectiveness of our parallel reasoning approach beyond framework implementation, distilled FLASH-SEARCHER'S parallel reasoning capabilities into standalone agent models through lightweight supervised fine-tuning. Table presents a comprehensive comparison of these agent models against existing state-of-the-ar methods across four challenging benchmarks.
Our experimental analysis demonstrates that lightweight supervised fine-tuning effectively facili- tates the transfer of FLASH-SEARCHER'S 's parallel reasoning capabilities standalone agent models, consistently achieving state-of-ther art (SOTA) performance across diverse benchmarks and model backbone scales. Specifically, the Qwen-2.5-32B backbone, SEARCHER establishes new

---

Published as a conference paper at ICLR 2026
Table Performance comparison of agent models on BrowseComp, Abench-DeepSeane and GAIA benchmarks. All results are reported using Pass@) metric. Gray-font values correspond to results reported in the associated reports.
Method
Backbone
BrowseComp
Xbench-DeepSearch
GAIA
HLE
Cognitive Kernel-Pro
Qwen-3-8B
43.7
WehDancer
3.8
39.0
50.5
7.2
WebThinker-RL.
2.8
24.0
48.5
SimpleDeepSSarcher
QwQ-32B
50.5
WebShaper
53.3
12,2
SFR-DR
$2.4
17.1
WebDancer
2.5
38.7
40.7
SimpleDeepSearcher
40.8
WebShaper
52,4
Qwen-2.5-32B
WehSailor
10.5
53.3
53.2
10.8
AFM-RI.
11.1
58.0
55.3
18.0
FLASH-SRARCHER
14.4
63.0
57.3
19.4
WehSailor
12.0
55.0
WebShaper
Qwen-2.5-72B
60.1
FLASH-SEARCHE
18.9
68.0
61.2
20.2
performance ceiling. outperforms the strongest prior method by 3.3% on BrowseComp, 5.0% on B-Bench-DeepSeeee and 2.0% on GAIA. Despite forgoing code interpreter tools, FLASH-SEARCHER achieves sute-of-the-ar performance at 19.4% on HLE, surpassing ool-auugented baselines and affirming the general effectiveness of FLASH-SRARCHH in handling general complex tasks. This re- sult underscores FLASH-SEARCHER' inherent reasoning robustness, as it delivers strong performance without relying extensive tools.
Scaling FLASH-SEARCHER to 72B yields consistent and meaningful performance gains across all benchmarks, demonstrating that our parallel reasoning framework scales gracefully with model capacity. Notably, the most substantial improvements occur on complex, multi-step reasoning tasks such BrowseComp and CrenchhDeepSearchh with 5% gains, suggesting that increased parameter scale enhances the model's s ability to coordinate and refine reasoning steps. Even on HIE, the performance affirms that FLASH-SEERCHER internalizes structured reasoning without relying external tools. This behavior confirms that our lightweight fine-tuning paradigm not only transfers reasoning capabilities effectively but also unlocks deeper potential backbone capacity grows, making it suited for scalable, general-purpost agent deployment.
Notably, these results achieved through lightweight supervised fine-tuning without RL or tool reliance. This confirms that parallel reasoning is learnable and scalable inductive bias, effi- ciently transferred via minimal supervision. LLAsS-SEACChER thus emerges as simple, robust, and parameter-efficient solution for real-world agents.
5 EFFICIENCY ANALYSIS
We present a comprehensive efficiency analysis FLASH-SEARCHER using the GPT-5-min backbone, evaluating its execution efficiency and framework improvements compared to existing agent systems. The distribution plot in Figure 5a demonstrates BrowseComp benchmark requiring the highest number of both metrics. This reflects the varying complexity demands across different benchmark types. Figure reveals FLASH-SEARCHER'S operational efficiency through tool calls per execution step. The tight interquartile range, particularly evident in the GAIA benchmark, indicates consistent and predictable tool utilization patterns. These results support our core claim that the DAG-based architecture optimizes tool efficiency and reduces execution steps. By invoking complementary tools in parallel, our approach eliminates the sequential bottlenecks that cause redundant steps in linear pipelines.
8

---

Published as a conference paper at ICLR 2026
Tool Calls Executioon Steps
TemworCrrp
GAIA
Tool Calls
Execution Steps
30
BrowseComp
xbench
GAIA
HLE
(a) Tool calls vs. execution steps.
(b) Tool calls per execution step of FLASU- SeaRCHER.
Figure 5: Efficiency analysis of BLASH-SSAACHIR on four benchmarks: (a) shows the correlation
between tool calls and steps; (b) characterizes the distribution of tool calls per step.
To fairly evaluate the execution efficiency of FLASH-SBARCHER, we compare OAgents (Zhu et al., 2025a) and OWL-Roleplayiin (Hu et. 2025) with their original configurations (Details in Section F). The experimental results are presented in Figure 6, which demonstrates significant efficiency improvements of our approach across four benchmarks.
against
As shown in Figure 6a, FLASH-SEARCHER outperforms OAgents on all four benchmarks, achieving higher task success rates and efficiency gains, with this advantage growing more pronounced as task complexity increases (BrowseComp FLASH--EEACHEER' 's adaptability to complex scenarios, laying the foundation for subsequent efficiency analysis. Figure 6b further demonstrates that FLASh--EERCHHES (with GPT-5-min backbone) reduces agent steps by 35% versus OAgents and 30% versus OWT--Rolpgayynng on GATA benchmark, enabled by its parallel reasoning architecture. This efficiency gain stems from the DAG-based workflow's s ability to execute concurrent reasoning paths, which effectively mitigates the sequential bottleneck of traditional methods. Figure 5 illustrates the distribution of tool calls and steps for FLASH-SEARCHER. despite fewer total steps, our approach maintains higher per-step tool utilization efficiency (average 3.00 tool calls per step, compared to 0.83 for OAgents and 0.85 for OWL- Roleplalelyy)ngg), confirming more productive and effective reasoning iterations.
SeenchhDeepSearee HLE CAIA). This validates
Peeformance
stops
Performance
Peecution
Tool callss
Connss
Geadher
CWL.Sekkplaynng
(a) Execution steps and tool calls comparison.
(b) Efficiency comparison of frameworks GAIA.
Figure 6: Efficiency comparison of agent frameworks on four benchmarks.
The core innovation lies in our DAG-based parallel execution mechanism, which directly addresses the fundamental limitation of redundant tool invocation cycles sequential reasoning approaches. By coordinating information needs across parallel branches, we eliminate duplicate searches while maintaining reasoning diversity. As in Figure 6a, our framework simultaneously enhances both efficiency and performance, effectively resolving the longstanding efficiency-effecciieeess trade-off agent systems.
Although agent execution duration is inherently influenced by external factors such API rate limits, FLASH-SEARCHER consistently achieves a 35% reduction execution steps under comparable environmental conditions. This reduction directly translates into lower end-to-end latency and improved throughput, offering a significant efficiency advantage. This efficiency is critical for applications requiring low latency and high throughput, where sequential agents often face scalability bottlenecks. More detailed analysis of execution steps, time, and cost overheads can be found in Section D.3.

---

Published as a conference paper at ICLR 2026
6 CONCLUSION
In this work, we introduce FLASH-SEARCHER, novel parallel agent reasoning framework that over- comes the sequential bottlenecks of conventional tool-augmented agents through structured concur- rency. By reformulating task solving as dynamic scheduling over DAGs, FLASH-SEARCHER enables fine-grained parallel execution while rigorously preserving logical coherence and correctness. Ex- tensive experiments across BrowseComp, xbench- Beench-DeeRResaacch GAIA, and HLE demonstrate that FLASH-SBARCHER achieves state-of-the-art performance, attaining score of 67.7% on BrowseC- omp, alongside substantial gains in computational efficiency through reduced latency and improved resource utilization. Our results, further corroborated by distilled agent variants, establish parallel reasoning as a foundational paradigm for building efficient, scalable, and robust AI systems capable of mastering complex real-world tasks.
7 ETHICS STATEMENT
The research focuses on the development of agent frameworks and model architectures for web- based autonomous agents, aiming to create more effective systems that can assist users in completing complex tasks. We conduct rigorous evaluation our experimental procedures. This work does not involve any risks related to ethics issues and intended to advance research in web agent systems.
controlled benchmarks and ensure transparency
REPRODUCIBILITY STATEMENT
We have made significant efforts to ensure the reproducibility of PLASH-SEEARCHER The code and data will be made publicly available at https: thub. com//PPPO-PersonaaAL//E sarrsonnaalt//lsss--sereeeeee Detailed experimental configurations, hyperparameters, and procedural specifications are docu- mented in Section C. We note that exact reproduction may be challenging due to factors such as API updates, inference non-determinism. and reliance on closed source models, which could lead to minor variations in results.
ACKNOWLEDGMENTS
We would like to express sincere gratitude the anonymous reviewers for their thoughtful and constructive feedback. Xitong Gao is supported by National Natural Science Foundation China (62376263), Natural Science Foundation of Guangdong (2024 and Shenzhen Science and Technology Innovation Commission (CC1200230807140000005))
REFERENCES
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Giani- nazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyezyk, et al. Graph of thoughts: Solving elaborate problems with large language models. In Proceedings of the AAAI conference on artificial intelligence, 2024.
Lorenzo Canese, Gian Carlo Cardarilli, Luca Di Nunzio, Rocco Fazzolari, Daniele Giardino, Marco Re, and Sergio Spanò. Multi-agent reinforcement learning: A review of challenges and applica- tions. Applied Sciences, 11(11):4948, 2021.
Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang. Chenfei Yuan, Chen Qian, Chi-Min Chan, Yujia Qin, Yaxi Lu, Ruobing Xie, et al. Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents. arXiv preprint arXiv:2308. 10848, 2(4):6, 2023.
Ali Dorri, Salil S Kanhere, and Raja Jurdak. Multi-agent systems: A survey. leee Access, 6: 28573-28593, 2018.
Luyang Fang, Xiaowei Yu, Jiazhang Cai, Yongkai Chen, Shushan Wu, Zhengliang Liu, Zhenyuan Yang, Haoran Lu, Xilin Gong, Yufang Liu, al. Knowledge distillation and dataset distillation of
10

---

Published as a conference paper at ICLR 2026
large language models: Emerging trends, challenges, and future directions. Artificial Intelligence Review, 59(1):17, 2026.
Adam Fourney, Gagan Bansal, Hussein Mozannar, Cheng Tan, Eduardo Salinas, Friederike Niedtner, Grace Proebsting, Griffin Bassman, Jack Gerrits, Jacob Alber, et al. Magentic-one: A generalist multi-agent system for solving complex tasks. arXiv preprint arXiv:2411.04450 2024.
Jiaxuan Gao, Wei Fu, Minyang Xie, Shusheng Xu, Chuyi He, Zhiyu Mei, Banghua Zhu, and Yi Wu. Beyond ten turns: Unlocking long-horizon agentic search with large-scale asynchronous rl. arXiv preprint arXiv:2508. 07976, 2025.
Xitong Gao, Yiren Zhao, Robert Mullins, Cheng-zhong Xu, et al. Dynamic channel pruning: Feature boosting and suppression. arXiv preprint arXiv:11100053331, 2018.
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao. Knowledge distillation: A survey. International journal of computer vision, 1786))178--1811 2021.
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300 2020.
Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Ceyao Zhang, Jinlin Wang, Zili Wang. Steven Ka Shing Yau, Zijuan Lin, et al. Metagpt: Meta programming for a multi-agent collaborative framework. In International Conference Learning Representations, ICLR, 2024.
Mengkang Hu, Yuhang Zhou, Wendong Fan, Yuzhou Nie, Bowei Xia, Tao Sun, Ziyu Ye, Zhaoxuan Jin, Yingru Li. Qiguang Chen, Zeyu Zhang, Yifeng Wang, Qianshuo Ye, Bernard Ghanem, Ping Luo, and Guohao Li. Owl: Optimized workforce learning for general multi-agent assistance in real-world task automation, 2025. URL pttpss//arxixn www.:/rgggbb/////ooo 2505.
Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, et al. Os agents: A survey on mllm-based agents for computer, phone and browser use, 2024.
Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, and Jiawei Han. Search-rl: Training Ilms to reason and leverage search engines with reinforcement learning. arXiv preprint arXiv:2503.0955 2025a.
Yiyang Jin, Kunzhao Xu, Hang Li, Xueting Han, Yanmin Zhou, Cheng Li, and Jing Bai. Reveal: Self-evolving code agents via iterative guneration-veriffcatton. 2025b. URL /ttps://arxiv.. org org/abs/2506. 11442.
Inc. Jina. Jina reader, 2025. URL
Shiye Lei and Dacheng Tao. A comprehensive survey of dataset distillation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(1):17-32, 2023.
Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem.
Camel: Communicative agents for "mind" exploration of large language model society. In Thirty- seventh Conference on Neural Information Processing Systems, 2023.
Kuan Li. Zhongwang Zhang, Huifeng Yin, Liwen Zhang. Litu Ou, Jialong Wu, Wenbiao Yin, Baixuan Li, Zhengwei Tao, Xinyu Wang, Weizhou Shen, Junkai Zhang, Dingchu Zhang, Xixi Wu, Yong Jiang, Ming Yan, Pengjun Xie, Fei Huang, and Jingren Zhou. Websailor: Navigating super-human reasoning for web agent, 2025a. URL https: //axxiv argg//////0....00 02592.
Weizhen Li, Jianbo Lin, Zhuosong Jiang, Jingyi Cao, Xinpeng Liu, Jiayu Zhang, Zhenqiang Huang, Qianben Chen, Weichen Sun, Qiexiang Wang, et al. Chain-of-agents: End-to-end agent foundation models via multi-agent distillation and agentic rl. arXiv preprint 2508 13167, 2025b.
Xiaoxi Li, Guanting Dong, Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, and Zhicheng Dou. Search-ol: Agentic cearch-enhanced large reasoning models. arXiv preprint arXiv:2501.53366, 2025c.
11

---

Published as a conference paper at ICLR 2026
Xiaoxi Li, Jiajie Jin, Guanting Dong, Hongjin Qian, Yutao Zhu, Yongkang Wu, Ji-Rong Wen, and Zhicheng Dou. Webthinker: Empowering large reasoning models with deep research capability. arXiv preprint arXhv:2504.21776 2025d.
Yuqi Li. Kuiye Ding, Chuanguang Yang, Hao Wang. Haoxuan Wang. Huiran Duan, Junming Liu, and Yingli Tian. Ddtime: Dataset distillation with spectral alignment and information bottleneck for time-series forecasting. arXiv preprint arXiv:251 16716,7715, 2025e.
Yuqi Li, Junhao Dong, Chuanguang Yang. Shiping Wen, Piotr Koniusz, Tingwen Huang, Yingli Tian, and Yew-Soon Ong. Mmt-ard: Multimodal multi-teacher adversarial distillation for robust vision-language models. arXiv preprint arXiv:2511. 177448 2025f.
Yuqi Li, Kai Li, Xin Yin, Zhifei Yang, Junhao Dong. Zeyu Dong, Chuanguang Yang, Yingli Tian, and Yao Lu. Sepprune: Structured pruning for efficient deep speech separation. arXiv preprint arXiv: 12079, 2025g.
Yuqi Li, Chuanguang Yang, Hansheng Zeng. Zeyu Dong, Zhulin An, Yongjun Xu. Yingli Tian, and Hao Wu. Frequency-aligned knowledge distillation for lightweight spatiotemporal forecasting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7262-7272, 2025h.
Yuqi Li, Zijie Zhou, Zhiyuan Peng, Junhao Dong, Haochen You, Renye Yan, Shiping Wen, Yingli Tian, and Tingwen Huang. A preference-drivve drivee methodology for efficient code generation. IEEE Transactions on Artificial Intelligence, 2025i. doi: 10. 10.10997A1.22253365577.
Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone. Lim+ p: Empowering large language models with optimal planning proficiency. arXiv preprint wrXiv:234.111477 2023.
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell. Rethinking the value of network pruning. arXiv preprint arXiv:1810.0522 2018.
Xinyin Ma, Gongfan Fang, and Xinchao Wang. LIm-pruner: On the structural pruning of large language models. Advances in neural information processing systems, 36:21702-21700, 2023.
Xinji Mai, Haotian Xu, Weinong Wang. Yingying Zhang, Wenqiang Zhang, et al. Agent rl scaling law: Agent rl with spontaneous code execution for mathematical problem solving. arXiv preprint arXiv:2505.07773, 2025.
Grégoire Mialon, Clémentine Fourrier, Thomas Wolf, Yann LeCun, and Thomas Scialom. Gaia: benchmark for general ai assistants. In The Twelfth International Conference Learning Representations, 2023.
Xuan-Phi Nguyen, Shrey Pandit, Revanth Gangi Reddy, Austin Xu, Silvio Savarese, Caiming Xiong, and Shafiq Joty. Sfr-deepresearch: Towards effective reinforcement learning for autonomously reasoning single agents. arXiv preprint arXiv:2509.06283, 2025.
Jiay Pan, Xiuyu Li, Long Lian, Charlie Snell, Yifei Zhou, Adam Yala, Trevor Darrell, Kurt Keutzer, and Alane Suhr. Learning adaptive parallel reasoning with language models. Conference Language Modeling, 2025.
Xianghe Pang, Shuo Tang, Rui Ye, Yuwen Du, Yaxin Du and Siheng Chen. Towards scalable web browsing via tool-augmented programmatic agent pair. arXiv preprint arXiv:2508.09129 2025.
Browsemaster:
Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, Josephina Hu, Hugh Zhang, Chen Bo Calvin Zhang, Mohamed Shaaban, John Ling, Sean Shi, et al. Humanity's last exam. arXiv preprint a50iv:2501. 14249, 2025.
Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis. Measuring and narrowing the compositionality gap in language models. In Findings of the Association for Computational Linguisties: EMNLP 2023, pp. 5687-5711, 2023.
12

---

Published as a conference paper at ICLR 2026
Chen Qian, Wei Liu, Hongzhang Liu, Nuo Chen, Yufan Dang, Jiahao Li, Cheng Yang. Weize Chen, Yusheng Su, Xin Cong, et al. Chatdev: Communicative agents for software development. arXiv preprint arXh::230..07924, 2023.
Jiahao Qiu, Xuan Qi, Tongcheng Zhang, Xinzhe Juan, Jiacheng Guo, Yifu Lu, Yimin Wang, Zixin Yao. Qihan Ren, Xun Jiang, et al. Alita: Generalist agent enabling scalable agentic reasoning with minimal predefinition and maximal self-evolution. arXiv preprint wrXiv:2505.20286 2025.
Aymeric Roucher, Albert Villanova del Moral, Thomas Wolf, Leandro Werra, and Erik Kau- nismäki. 'smolagents": a smol library to build great agentic systems. https://gite com/ suggingfaace/mmlageentt 2025.
Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, and Ming Jin. Algorithm of thoughts: Enhancing exploration of ideas in large language models. arXiv preprint 2.Xi::330.11377 2023.
Inc. Serper. Serper api, 2025. URL https: /ttpsss://eppeeeeeeeeeeee
Dingfeng Shi, Jingyi Cao, Qianben Chen, Weichen Sun, Weizhen Li, Hongxuan Lu, Fangchen Dong, Tianrui Qin, King Zhu, Minghao Yang, et al. Taskeraft: Automated generation of agentic tasks. arXiv preprint ar XXxx:2506.10055, 2025.
Peng Sun, Bei Shi, Daiwei Yu, and Tao Lin. On the diversity and realism of distilled dataset: efficient dataset distillation paradigm. In Proceedings of the IEEF/CVF conference computer vision and pattern recognition, pp. 3390-9399, 2024.
Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, Fei Bai, Jia Deng, Wayne Xin Zhao, Zheng Liu, et al. Simpledeepscurcher: Deep information seeking via web-powered reasoning trajectory synthesis. arXiv preprint rrXi:22505.1684 2025.
Xiangru Tang, Tianrui Qin, Tianhao Peng, Ziyang Zhou, Daniel Shao, Tingting Du, Xinming Wei, Peng Xia, Fang Wu, He Zhu, Ge Zhang, Jiaheng Liu, Xingyao Wang, Sirui Hong, Chenglin Wu, Hao Cheng, Chi Wang, and Wangchunshu Zhou Agent kb: Leveraging cross-domain experience for agentic problem solving. In ICML 2025 Workflows, 2025.
Workshop Collaborative and Federated Agentic
Zhengwei Tao, Jialong Wu, Wenbiao Yin, Junkai Zhang. Baixuan Li, Haiyang Shen, Kuan Li, Liwen Zhang, Xinyu Wang, Yong Jiang, Pengjun Xie, Fei Huang, and Jingren Zhou. Webshaper: Agentically data synthesizing via information-seekknn formalization, 2025. org/abs/2507, 15061.
MiroMind AI Team. Miroflow: An open-source agentic framework for deep research. https: //github.com http.///....ioiiiann////rr...... 2025.
Kafeng Wang, Xitong Gao, Yiren Zhao, Xingjian Li, Dejing Dou, and Cheng-Zhong Xu. Pay attention to features, transfer learn faster cnns. In International conference on learning representations, 2019.
Kai Wang, Zekai Li, Zhi-Qi Cheng, Samir Khaki, Ahmad Sajedi, Ramakrishna Vedantam, Konstanti- nos N Plataniotis, Alexander Hauptmann, and Yang You. Emphasizing discriminative features for dataset distillation in complex scenarios. In Proceedings of the IEEE/CVF Conference on Computer Vision and Patlern Recognition, pp. 30451-30461, 2025a.
Ningning Wang, Xavier Hu, Pai Liu, He Zhu, Yue Hou, Heyuan Huang, Shengyu Zhang, Jian Yang, Jiaheng Liu, Ge Zhang, al. Efficient agents: Building effective agents while reducing cost. arXiv preprint orXiv:2508.0264) 2025b.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros. Dataset distillation. arXiv preprint / 10959, 2018.
Jason Wei, Zhiqing Sun, Spencer Papay, Scott McKinney, Jeffrey Han, Isa Fulford, Hyung Won Chung. Alex Tachard Passos, William Fedus, and Amelia Glaese. Browsecomp: A simple yet challenging benchmark for browsing agents. arXiv preprint arXiv:2504. /2516, 2025.
13

---

Published as a conference paper at ICLR 2026
Jialong Wu, Baixuan Li, Runnan Fang, Wenbiao Yin, Liwen Zhang, Zhengwei Tao, Dingehu Zhang, Zekun Xi, Yong Jiang, Pengjun Xie, et al. Webdancer: Towards autonomous information seeking agency. arXiv preprint arXiv: 2025a.
Jialong Wu, Wenbiao Yin, Yong Jiang. Zhenglin Wang. Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xie, et al. Webwalker: Benchmarking llms in web traversal. arXiv preprint 2rXiv:2501. 007572, 2025b.
Xbench-Team. Xbench-deepsearch, 2025. URL
Xiaohan Xu, Ming Li, Chongyang Tao, Tao Shen, Reynold Cheng, Jinyang Li, Can Xu, Dacheng Tao, and Tianyi Zhou, survey on knowledge distillation of large language models. arXiv preprint arXiv:2402. 13116, 2024.
Zhenghai Xue, Longtao Zheng, Qian Liu, Yingru Li, Zejun Ma, and Bo An. Simpletir: End-to-end reinforcement learning for multi-turn tool-integrated reasoning. httss///mmllerr.nottiai site/report, 2025. Notion Blog.
Chuanguang Yang, Xinqiang Yu, Han Yang, Zhulin An, Chengqing Yu, Libo Huang, and Yongjun Xu. Multi-teacher knowledge distillation with reinforcement learning for visual recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 9148-9156, 2025.
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. Tree thoughts: Deliberate problem solving with large language models. Ad- vances in neural information processing systems, 36: 16611180-1182, 2023.
Dingchu Zhang, Yida Zhao, Jialong Wu, Baixuan Li, Wenbiao Yin, Liwen Zhang, Yong Jiang, Yufeng Li, Kewei Tu, Pengjun Xie, et al. Evolvesearch: An iterative self-evolving search agent. arXiv preprint arxiv:2505. 2025a.
Shiqi Zhang, Xinbei Ma, Zouying Cao, Zhuosheng Zhang, and Hai Zhao. Plan-over-graph: Towards parallelable Ilm agent schedule. arXiv preprint arXiv:2502. 14563, 2025b.
Shu Zhao, Tan Yu, Anbang Xu, Japinder Singh, Aaditya Shukla, and Rama Akkiraju. Parallelsearch: Train your Imms to decompose query and search sub-queries in parallel with reinforcement learning. arXiv preprint arXiv:2508. 09333, 2025.
Yiren Zhao, Xitong Gao, Daniel Bates, Robert Mullins, and Cheng-Zhong Xu. Focused quantization for sparse cnns. Advances in Neural Information Processing Systems, 32, 2019.
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li. Eric Xing, et al. Judging almdee---judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems, 36:46595-46623, 2023.
Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Zhangchi Feng, and Yongqiang Ma. Llamafactory: Unified efficient fine-tuning of 100+ language models. arXiv preprint arXiv:2403. 13372, 2024.
Yuxiang Zheng. Dayuan Fu, Xiangkun Hu, Xiaojic Cai, Lyumanshan Ye, Pengrui Lu, and Pengfei Liu. Deepresearcher: Scaling deep research via reinforcement learning in wwworonnninonmett... arXiv preprint arXiv:2504.033 03/600 2025.
Andy Zhou, Kai Yan, Michal Shlapentokh- Rothhmm,, Haohan Wang, and Yu-Xiong Wang. Language agent tree search unifies reasoning acting and planning in language models. arXiv preprint arXiv:2310.04406. 2023a.
Wangchunshu Zhou, Yuchen Eleanor Jiang, Peng Cui, Tiannan Wang, Zhenxin Xiao, Yifan Hou, Ryan Cotterell, and Mrinmaya Sachan. Recurrentgpt: Interactive generation of (arbitrarily) long text, 2023b. URL https://arxivv http://aaxxiv..ogabbbbesse org/abs//230 2305. 13304.
Wangchunshu Zhou, Yuchen Eleanor Jiang. Long Li, Jialong Wu. Tiannan Wang, Shi Qiu, Jintian Zhang, Jing Chen, Ruipu Wu. Shuai Wang, et al. Agents: An open-source framework for autonomous language agents. arXiv preprint arXiv:2309.07877 2023c.
14

---

Published as a conference paper at ICLR 2026
Wangchunshu Zhou, Yixin Ou, Shengwei Ding, Long Li, Jialong Wu, Tiannan Wang, Jiamin Chen, Shuai Wang, Xiaohua Xu, Ningyu Zhang, et al. Symbolic learning enables self-evolving agents. arXiv preprint arXh::2466.18522 2024.
He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Yi Yao, Hanhao Li, Ningning Wang. Pai Liu, Tianhao Peng, Xin Gui, Xiaowan Li, Yuhui Liu, Yuchen Eleanor Jiang, Jun Wang. Changwang Zhang, Xiangru Tang. Ge Zhang. Jian Yang, Minghao Liu, Xitong Gao, Wangchunshu Zhou, and Jiaheng Liu. Oagents: An empirical study of building effective agents, 2025a. URL hwwps:tps//ct....m //axivv../iiiv///// orgoorgggaaoo 250g////../......//// 15741.
King Zhu, Hanhao Li, Siwei Wu, Tianshun Xing, Dehua Ma, Xiangru Tang, Minghao Liu, Jian Yang. Jiaheng Liu, Yuchen Eleanor Jiang, Changwang Zhang, Chenghua Lin, Jun Wang, Ge Zhang, and Wangchunshu Zhou Scaling test-time compute for Ilm agents, 2025b. URL https: //axiv.. org/abs/2506. 129228888..11.....
15

---

Published as a conference paper at ICLR 2026
A DISCUSSION OF DAG/GRAPH REASONING METHODS
A.1 RELATIONSHIP TO GRA.P//IeEESSTRUUUURRRR REASONING
While our work builds upon recent graph-based reasoning frameworks, key distinctions exist in purpose and implementation. Graph of Thoughts (GoT) (Besta et al., 2024) models reasoning steps graph structures but emphasizes symbolic reasoning rather than tool execution. Tree of Thoughts (ToT) (Yao et al., 2023) explores branching reasoning paths through tree structures but prioritizes depth-first search over parallelization. Algorithm of Thoughts (AoT) (Sel et al., 2023) provides algorithmic guidance for reasoning, whereas our framework optimizes parallel tool execution specifically.
A.2 DISTINCTIONS PARALLEL REASONING PLANNING FRAMEWORKS
Recent works explore parallel reasoning and planning for LLM-based agents, yet differ significantly from our approach. Learning Adaptive Parallel Reasoning (LAPR) (Pan et al., 2025) introduces parallelization for language model reasoning but focuses primarily on model-internal computation rather than coordinating external tool calls. Plan-over- (PoG) (Zhang et al., 2025b) shares con- ceptual similarities with our DAG approach but emphasizes strict dependency enforcement, whereas our framework intentionally relaxes these constraints to maximize parallel exccution efficiency while ensuring result validity through cross-validation.
While Language Agent Tree Search (LATS) (Zhou et al., 2023a) and LLM+P (Liu et al., 2023) effectively integrate planning and acting through tree search and optimal planning techniques, they lack explicit mechanisms for parallel tool execution. Our framework complements these efforts by specializing DAG structures specifically for efficient information retrieval across multiple sources.
A.3 COMPARISON VARIOUS FRAMEWORKS
In the context of DAG-based reasoning, several related frameworks offer alternative approaches, each with distinct characteristics in terms of parallelism, dependency handling, and dynamic refinement:
Table 2: Comparison of DAG/Graph- -Bated Based Reasoning Methods.
Method
Parallel Execution
Dependency Handling Dynamic Refinement
FLASH-SEARCHER
Tool-Ievel parallelism
Relaxed
Yes
ParallelSearch
Search parallelism Limited
N/A
No
Plan-over-Graph (PoG) LATS
Strict dependencies
No
No
Strict
No
Graph of Thoughts
No
Strict
No
Our primary contribution is an efficient dynamic DAG-based planning framework that optimizes execution trajectories in real time. It enables two core capabilities: (1) parallel tool invocation for faster computation, and (2) cross-validation across dependent subtasks to preserve result in- tegrity, resolving the key efficiency-accuracyy trade-off in complex workflows. Beyond efficiency, the framework addresses a critical L.LM limitation: context length constraints. By continuously sum- marizing intermediate states and refining paths via real-time outcomes, it ensures lengthy multi-step workflows remain tractable without losing information fidelity.
B RELAXED CONSTRAINTS DETAILS
Auxiliary Signals. In web-agent scenarios, observe that certain prerequisite information can often anticipated before it is formally produced by upstream sub-goal. To exploit this property, framework issues auxiliary early retrieval whenever subtask requires information that is not yet available. This early retrieval allows subsequent sub-goal to proceed under relaxed dependency constraints, thereby reducing idle waiting time and improving overall execution efficiency.
Heuristic Consistency Checks. Once the true prerequisite becomes available, the agent performs second retrieval to validate revise the earlier auxiliary signal. This dual-retrieval mechanism
16

---

Published as a conference paper at ICLR 2026
functions heuristic consistency check. If the validated result differs from the auxiliary one, downstream states dependent on the earlier signal are corrected. This design preserves correctness while enabling more aggressive parallelization.
Failure Handling. Failure handling is integrated into the Adaptive Progress Tracking Summa- rization module. During execution, if the agent detects that its current dependency path is blocked, inconsistent, or no longer leads toward the task objective, the framework triggers a path update. All outdated intermediate progress associated with the invalid path is cleared, and a revised plan graph reconstructed based on the latest state. This ensures forward progress even under noisy signals or partially incorrect auxiliary retrievals.
Below, we provide an algorithm of the relaxed-constrane execution process supported by auxiliary- signal validation and failure handling.
Algorithm 2 Gelaxed-Consstrale Execution with Auxiliary-Signal Validation and Failure Handling
Require: Composite task T
Glann < (T)
2: Initialize state so; active subtasks Po - V; completed set Co < 0
3: f-0
4: while P, + do
5: & < {v P, ((v, 1} (Eligible subtasks)
6: For each E,, check prerequisite availability
7: if prerequisite missing then
8: Issue early retrieval raux (Auxiliary signal}
9:
end if
Execute all subtasks in &, in parallel and collect outputs (o, (k)
10:
Update state 5,+1 = Fst, (a,x), {o, (k)))
11:
12:
C++1 C, completed subtasks
13:
PM11 Pr C++1
for each recently satisfied prerequisite do
15:
Issue validation retrieval re [Heuristic consistency check) chhk # r then
16:
17:
Correct downstream state using chh
18:
end if
19:
end for
20: if Inconsistency or blocked path detected then
21:
Clear outdated intermediate progress
G RRG,, S+11) {Failure handling)
23:
end if
1+1
25:
end while
26: return Final state ST
C EXPERIMENT DETAILS
C.I BENCHMARKS.
We evaluate the effectiveness of FLASH-SEARCHER four challenging benchmarks that require sophisticated information retrieval and reasoning capabilities:
GAIA (Mialon et al., 2023): As a milestone benchmark for General AI Assistants, it con- structs real-world questions that necessitate fundamental capabilities including reasoning, multi- modality handling, web browsing, and tool-use proficiency. To ensure rigorous and comparable evaluation, we conduct experiments primarily on the text-only validation subset of GAIA, which consists of 103 carefully curated cases. This subset specifically highlights the challenges of disambiguating ambiguous queries and synthesizing multi-source information. Additionally, for fair comparison with existing works, we further evaluate the full validation set (165 cases) of
17

---

Published as a conference paper at ICLR 2026
GAIA. Following the framework of OAgents (Zhu al., 2025a), we additionally integrate text, image, and audio tools into our evaluation pipeline to align with the multi-modality and tool-use design goals of the full validation set.
BrowseComp (Wei et al., 2025): A rigorous benchmark comprising 1,266 questions designed to measure persistent web browsing capabilities for finding hard-to-fines entangled information. While avoiding challenges like long-form generation, it specifically tests an agent's ability to formulate effective queries, navigate search results, extract relevant information, and synthesize coherent answers through sophisticated browsing strategies.
(Xbench-Team, 2025); A professionally curated benchmark focusing specifically on deep-search capabilities in Chinese contexts, featuring 100 expert-written ques- tions requiring multi-round search refinement and cross-source integration. Designed to isolate and evaluate the Planning - Search + Reasoning - Summarization pipeline of agent systems.
HLE (Phan et al., 2025): To address the saturation of existing benchmarks (e.g., MMLU (Hendrycks et al., 2020), where SOTA LLMs now exceed 90% accuracy), HLE is proposed as a benchmark of 2,500 highly difficult questions across dozens of subjects, serv- ing as "final" closed-ended test for broad academic capabilities. Developed by experts via multi-stage review (pre-filtering, graduate/ expert validation, public feedback), it is multi-modal http///agga-ccomaaneed)) supports automated verification mmultiple-chicc/een match),,, and its questions original, lookup-resistant, and emphasize advanced math for deep reason- ing. Following AFM's setup (Li et al., 2025b), we use the HIE500 subset to evaluate model performance high-diffiulty reasoning.
Together, these benchmarks allow for a comprehensive evaluation of our framework's 's efficiency and effectiveness across variety of complex information retrieval tasks.
C.2 TOoL CONFIGURATIONS.
To ensure streamlined and efficient agent workflows and models, FLASH-SEARCHER employs a min- imalist but powerful tool configuration focused on maximizing information retrieval capabilities while maintaining trajectory simplicity:
For external tools, deliberately constrain our framework to just two essential components:
We implement this tool using the Serper API (Serper, 2025) to support agents in re- trieving web-based information for mowledge-intensinn tasks. By default, each API call returns relevance-ranked results, structured to include core elements: descriptive titles (for rapid rel- evance screening), concise content snippets (to pre-assess information utility), and direct URLs (for deep exploration of primary sources). This configuration strikes balance between com- prehensiveness, ensuring access high-value sources, and computational efficiency, avoiding information overload that could hinder agent decision making.
Search Tool:
ceciiiin-mmaing...
Crawl Tool: Implemented using the Jina Reader (Jina, 2025), this tool enables agents extract and process content from specific web pages. To enhance efficiency and maintain trajectory conciseness, our crawl tool incorporates an automatic summarization mechanism that extracts and condenses the most relevant information from web pages. Specifically, considering the constraints of model context window length and the cost control of API calls in large-scale experiments, we introduce a content truncation strategy for web pages: only the first 60,000 characters of each web page are selected as the input for the summarization mechanism to perform information extraction and condensation. This design balances the trade-off between information coverage and practical implementation costs, while it should be noted that the truncation may lead to the loss of potential valuable information in the latter part of long web pages. This approach significantly reduces cognitive load on the agent by eliminating the need to straightly process extensive raw HTML content.
The summarization component within the crawl tool utilizes the same language model as our back- bone agent, ensuring consistency in understanding and representation across the framework. This architectural decision not only streamlines the information flow but also reduces potential misalign- ments between different components of the system.
18

---

Published as a conference paper at ICLR 2026
By adopting this focused tool configuration. FLASH-SEARCHER achieves balance between capability and efficiency. The framework provides agents with sufficient tools to tackle complex information retrieval tasks while avoiding the overhead and complexity associated with managing numerous specialized tools. This approach is particularly advantageous our parallel execution context, where multiple tool calls can be orchestrated simultaneously across different branches of the reasoning graph.
C.3
MODEL LIST.
In our experiments, we employed a diverse set of state-of-the-art LLMs. The evaluated LLMs include GPT-5 (Reasoning effort: medium; version: 2025-08-07), 07). GPT-S-mini (Reasoning ef- fort: medium; version: 2825-08-07), GPT-4.1, DeepSeek-v3.1 (w/o thinking), and GLM-4.5 (Default). In all experiments, we maintained consistent hyperparameters across comparable set- tings, with temperature set to 1 1.0. All models were accessed via reliable API endpoints with consistent system prompts to ensure fair comparison.
C.4 PARAMETERS OF MASH-SEARCHER.
To ensure the reproducibility and clarity of the FLASH-SEARCHHN SeaRcHER framework's implementation, this section details all key hyperparameters and configuration settings used in its execution. These parameters collectively govern critical behaviors of the framework, such as the scope concurrent optimization objectives, the granularity of step-wise task exccution, the constraints on tool utilization, and the rules for progress tracking and information retrieval. Specific configurations are summarized in Table below.
Table 3: Parameter configurations for FLASH-SSEACCER Framework.
Parameter
Description
Value
Parallel goals
Number of concurrent objectives
5
Goal path length
Predetined steps goal
5
Max tool calls per step
Maximum tool invocations per step
5/10
Max steps
Total step budget for task exccution
40
Summary interval
Steps hetween progress summaries Results returned per search call
7-9
Scarch retrievals per query
5
Max length of extracted content
Max characters extracted by cr arall page
60,000
In addition the framework-level execution parameters detailed above, the inference process of the FLASH-SEarCHH models, responsible for decision-making (e.g., goal prioritization, tool selection) and content generation (e.g. progress summarization, query formulation), relies set of critical model-specific inference parameters. These parameters directly influence the model's 's reasoning depth, output stability, and computational efficiency, and are tightly aligned with the framework's execution constraints (e.g. step budget, tool call limits) to ensure coherent end-to-end performance. To support efficient and scalable inference, we adopt the vllm framework high-throughput LLM serving framework optimized for GPU acceleration) and deploy the system a hardware clus- ter consisting 8 NVIDIA A800 GPUs. Specific inference configurations (model-specific) and hardware-frammewrkk settings are summarized in Table 4 and Table 5 below, respectively.
Table 4: Inference parameter configurations for FLASH-SEARCHER Models.
Parameter
Description
Value
Context length
Maximum context tokens
131072 (32B) / 65536 (72B) 40
Max steps
Total conversation length
Max output tokens per Temperature, top-k, top-p
Maximum generated tokens per inference step
8192
Probabilistic generation controls
Default
Other inference tool parameters are designed to maintain consistency with the framework's s execution settings. Meanwhile, the inference configurations tailored to match the model size (32B/72B parameters) and context length requirements, avoiding memory bottlenecks during long-sequence reasoning. These cross-parameter alignments critical for avoiding misalignment between the
19

---

Published as a conference paper at ICLR 2026
model's reasoning process and the execution environment, thereby ensuring reproducibility and stability of the LLASH-SEARCHES system's 's performance across different task instances.
Table Configurations for
inference.
Configuration
Description
Value
Inference framework
Serving framework for inference
vilm v0.10.1.1
GPU type
Hardware accelerator model
NVIDIA A800 (80GB)
Tensor paralletism
GPU partitioning strategy Extending context length
8 Dynamic (factor=4.0 for 328; factor=2.0 for 728)
RoPH scaling
Model of Crawl Tool
Model for crawling data summary
GPT-5-mini
C.5 DETAILED RESULTS OF FLASH-SEARCHER.
To comprehensively evaluate the effectiveness of the proposed FLASH-SEARCHER framework, we conduct extensive experiments four representative benchmarks for agent systems. The perfor- mance quantified using the widely adopted Pass@ metric, which measures the proportion of tasks successfully completed by the agent single attempt. Table 6 and Figure present the detailed performance comparison between FLASH-SEARCHER and existing state-of-the-art agent frameworks or models. For fairness and reference, values displayed in gray font are directly quoted from the original reports of the compared methods.
Table 6: Performance comparison of agent frameworks on BrowseComp, Obench-DeepSenne and GAIA benchmarks. All results reported using Pass@1 metric. Gray-font values correspond to results reported in the associated reports. Note that FLASH-SEARCHER achieve 83.0 for full validation
Method
Backbone
BrowseComp
xbench-DeepSearch
GAIA
HLE
Closed-Source Frameworks
OpenAl CharGPT agent
68.9
41.6
OpenAl DeepResearch
51.5
67.4
26.6
Metaso DeepResearch
MetaLLM etc.
12.0
Skywork DeepResearch
Claude-Somnet=3-7 etc.
82.4
25.9
Kimi Researcher
k-series etc.
69
26.9
Manus
Claude
73.3
Alita
75.2
Open-Source Frameworks
Smolagents
OpenAI-ol
49.7
A-World
Gemini-2.5-Prg
71.0
Cognitive Kernel-Pro
Claude-Sonnet-3:
66.1
Work-Workforee
Claude-Sonnet-3-7
69.7
OAgents
GPT-4.1
13.7
47
58.3
15.4
BrowseMaster
DeepSeck-R1-055
30.0
66
68.0
MiroFlow
GPT-5
33.2
72
82.4
29.5
GPT-5
67.7
83
82.5
44.0
GPT-5-mini
35.3
80.6
32.2
GPT--.11
31.4
57
69.9
39.6
FLASH-SEARCHES SEARCHER
DeepSeck-V3.1
18.6
62.1
22.8
GLM-4.5
63
63.1
GPT-5-nano
61
54.4
D ADDITIONAL ABLATIONS
D.I MODEL BACKBONES ABLATIONS
To isolate the contribution the FLASH-SEARCHER framework from that of the underlying model backbone, evaluated FLASH-SEEACHER using the same backbone across different agent frame-
20

---

Published as a conference paper at ICLR 2026
SOTA Method
Flaab-Searcher-288
Flash--Sarccher335
Flabb-Searcber-288
80
80
Score (Pass@11)
Score (Pass@1)
63.0
68.0
63.0
61.2
55.0
55.3
40
40
19.4
20
14.4
2
18.0
20
14.4
19.4
11.1
BrowseComp xbench-DS
GAIA
HIEE
BrowseComp xbench-DS
GAIA
HILE
(a) Performance comparisons.
(b) Scaling analysis of FLASH-SEARCHEK.
Figure 7: Performance and scaling analysis of FLASH-SEARCHER, (a): FLASH-SEARCHER-322 consis- tently outperforms the SOTA method across all four benchmarks with Qwen-2.5-32B. (b): Scaling FLASH-SEARCHE from 32B to 72B parameters yields consistent gains.
works. The evaluation was conducted using the xbench-Deepsearch and GAIA benchmarks, com- paring FLASH-SEARCHER with other state-of-the-arl agent frameworks. As shown in Table 7, FLASH- SEARCHER demonstrates improved efficiency terms of both execution steps and time, outperforming or matching the performance of existing SOTA frameworks across both benchmarks.
Table Performance comparison on xbench and GAIA benchmarks.
Obench-DeepSeane
GAIA
Method
Backbone
Score
Step
Score
Step
OAgents
37.8
58.3
11.2
GPT-4.1
57
5.9
69.9
4.4
BrowscMaster
66
68.0
DeepSeek-R1-On
FLASH- SLASSH-SEARCHER
69
10.6
67.0
9.8
D.2 SEQUENTIAL REACT-STYLE AGENT FLASH-SEARCHER
To further validate the benefits of DAG-based parallel execution, conducted controlled compar- ison between a sequential ReAct-style agent and the parallel FlASH-SEARCHER agent. Both agents were evaluated using the same backbone (GPT-5-mini), the same tools, and the same base prompts.
Table 8: Performance comparison between sequential ReAct-style agent and CLASH-SEARCHES
BrowseComp-100
xbench-DeepSearrch
GAIA
Method
Score
Step
Time/mins
Score
Step
Time/mins
Score
Step
Time/mins
Sequential ReAct
31
40.6
14.6
66
18.2
8.5
69.9
12.1
4.7
FLASH-SFARCHER
36
20.8
9.6
71
11.4
4.9
80.6
7.4
3.3
The results shown in Table 8 demonstrate that FLASH-SEARCHER significantly reduces both execution steps and time, especially for more complex tasks like BrowseComp- 100 and Dbench-DeepSeaae The parallel execution mechanism of SAASS-SERRCHER leads to substantial improvements in effi- ciency and accuracy compared to the sequential approach.
D.3 DETAILED COST ANALYSIS
We present detailed cost analysis to evaluate the performance and efficiency of FLASH-SEARCHER in comparison with existing agent systems. The analysis focuses on both time and computational costs across xbench- Seench-DeepSearch and GALA benchmarks. Table summarizes the step and time cost, where FLASH-SEARCHER consistently outperforms OAgents (Zhu et al., 2025a) and Sequential ReAct (Adapted from FLASH-SA (EASS-ERRRHER) in terms of efficiency, despite slight increases computational cost due to additional tool invocations.
21

---

Published as a conference paper at ICLR 2026
Table 9: Comparison of step and time cost across different frameworks
Dpench-DeepSearel
GAIA
Method
Step
Time/mins
Step
Time/mins
MLASH-SSAACHER
11.4
4.9
7.4
3.3
OAgents
37.8
12.9
11.2
4.2
As shown in Table 10, we provide a detailed breakdown of token usage and associated costs for the xbench-Dese Sennch and GAIA benchmarks. The results indicate that FLASH-SEARCHER significantly outperforms OAgents in terms of efficiency, with a lower cost per query across all configurations. This demonstrates the effectiveness of FLASH-SEARCHER in achieving high performance while minimizing computational overhead, making it cost-efficient option for large-scale agent-based tasks.
Table 10: Detailed comparison of cost overheads on Seench- and GAIA benchmarks.
Method
Ruep//
Action
Crawl
Input Tok
Output Tok
Cost Query
xbench- DeepSearch
OAgents 3823.4
714.6
58770.4
40514.6
$0.13
FLASH-SEa SESH-SEERCHER
1623.6
3916.7
406.1
205.3
28318.6
9716.8
$0.07
GAIA
OAgents 3014.6
631.9
17281.K
13137.2 5685.9
$0.05
ASH-SFARCHER
1539.4
2469.4
352.4
209.6
10664.4
$0.03
D.4 SUMMARY INTERVAL ANALYSIS
We conducted an ablation analyze the impact of varying the update interval A on the framework performance. As shown in Figure 8, a lower update interval improves the model's performance but results in longer execution times. The optimal interval depends task difficulty and model context length, as shown by the varying results. The study shows that while reducing A can increase the model's accuracy, it also increases computational cost. We recommend adjusting the update interval based on task complexity to achieve the best balance between performance and efficiency.
slead
0.08
Su
Time (min)
Cost (S)
0.06
0.04
0.02
70
72
76
78
11
12
13
Score (%)
Execution Step
Figure 8: Update Interval A Analysis on xbench-DeepSee and GAIA benchmarks.
D.5 EVALUATIONS SIMPLE TASKS
We compare the performance FLASH-SEARCHER on Bamboogle (Press et al., 2023) benchmark with ReAct sequential agent. For simple tasks, we observe that FLASH-SEARCHER introduces some overhead, resulting in slightly higher execution times compared to sequential agents, as shown in Table 11. This overhead due the additional steps required for parallelization. Based these findings, we recommend dynamically selecting the appropriate approach based on task complexity optimize performance.
22

---

Published as a conference paper at ICLR 2026
Table 11: Performance Comparison on Bamboogle.
Model
Score
Step
Time/mins
ReAct sequential
91.2
4.8
0.9
FFASH-SFARCHER
91.2
5.4
1.7
D.6 ANALYSIS OF THE CODE TOoL INTEGRATION
We provide a detailed analysis of integrating the code execution tool within the FLASH-SEARCHER framework. Although code execution can be useful for computation-intensin tasks, our experiments indicate that its practical utility in web-agent scenarios is very limited. Across benchmarks such as GAIA, xBench, and HLE, the model rarely invoked the code tool even when it was enabled, reflecting the predominantly retrieval- and reasoning-oriente
nature of web tasks. Moreover, enabling the code tool introduces non-trivial runtime overhead, primarily due to additional execution latency and increased output tokens. To ensure a fair comparison, we report the results in Table 12. The code-enabled variant of FLASH-SEARCHER exhibits only a marginal +1.4% improvement on the HLE benchmark, while incurring substantial increase in execution time. This cost-benefit imbalance suggests that, for web-centered tasks, the marginal utility of integrating code tool low.
Table 12: Performance comparison BAAS--SEARCHER w. w/o code tool.
HLE-500
Model
Score
Step
Time/mins
FLASH SEARCHEE
32.2
9.0
4.0
SEARCHRE Code Tool
33.6
9.4
5.9
Based on the findings, while code execution can extend the versatility of FLASH-SEARCHER for certain task types, our analysis shows that its contribution web-agent performance is minimal relative to its runtime cost. Consequently, the default configuration omits the code tool to ensure maximal efficiency.
D.7 ABLATIONS ON EXECUTION STEP CONSTRAINTS
To validate the observation that execution step limitations constrain the resolution of complex queries, we conduct ablations on the FLASH-SEARCHER models, focusing the impact of extended maximum reasoning steps. Specifically, we evaluate model performance on BrowseComp- (a subset of BrowseComp) under extended maximum step limit of 80.
Figure summarizes the performance of its. Across all SEARCSE models, extending the maximum number of steps from 40 to 80 yields consistent and measurable performance gains: FLASH-SHAARHHTR-321 improves by 5.0 points, while FLASH- MAASH-SEARCHER-221 achieves 7.0-point increase. These results confirm that the perfor- mance bottleneck observed in the 40-step setting arises from insufficient reasoning steps rather than fundamental model limitations.
model variants under the two step lim-
27.0
27
27.5
Flash Searcher
Flash Searcher 72B
25.0
22.0
Score
22.5
21.0
20.0
20.0
O
20.0
17.5
160
17,0
0
40
50
60
70
80
Max reasoning steps
Figure 9: Performance of FLASHI-SEACCER models under different reasoning step constraints.
23

---

Published as a conference paper at ICLR 2026
These results directly verify our initial observation: increasing the number of allowed reasoning steps enables CLASH-SEARCHER to fully unpack complex task logic, thereby improving solution accuracy. This supports the feasibility of trading computational resources for precision in resource- unconstrained deployment scenarios.
FLASH-SEARCHER MODEL TRAINING
E.1 TRAINING DATASET
Our training dataset is constructed by curating subsets of four well-established public agent-focused datasets: AFM (Li al., 2025b)', ASearcher (Gao et al., 2025)², WehShaper (Tao al., 2025)², and WebWalkerQA (Wu et al., 2025b)". The subsets contain 1355, 628, 500, and 2597 examples, respectively. For AFM, Asearch, and WebWalkerQA-siie based on the execution trajectory length of baseline ReAct frameworks. Specifically, we selected only those examples that required more than 8 steps to complete, as these represent more complex reasoning and action sequences that better demonstrate agent capabilities.
datasets, we applied a filtering process
Table 13: Composition of the training dataset after filtering and trajectory generation.
Datasct
Original Size
Correct Samples
AFM
1,355
1212
Asearch
628
457
WehShaper
500
405
WebWalkerQA
2,597
1767
Total
5080
3354 (Removed data with formatting issues)
We leveraged our tories for pre-filtered examples. To ensure training data reliability-crittie critical for effective model learning-wy we used a judge model (GPT-4.1-min only factually accurate ones. This initial filtering yielded 1212, 457, 405, and 1767 candidate trajec- tories from AFM, ASearcher, WebShaper, and WebWalkerQA, respectively. We further conducted systematic format inspections to exclude trajectories with structural flaws (c.g.. incomplete turn segmentation, invalid dialogue hierarchy, missing action labels), a step reduce noise supervised fine-tuning (SFT). After this two-stage screening (correctness format), we ultimately retained 3354 valid trajectories for training. Table 13 summarizes the final training dataset composition, including source dataset and trajectory attribute breakdowns.
framework (with GPT-5 the backbone) to generate trajec-
validate trajectory answer correctness, retaining
These trajectories were formatted into SFT-compatible multi-turn dialogues via the L.LMMA-acttry framework (Zheng et al., 2024). Specifically, the detailed structure of the multi-turn dialogue format (including role definitions, dialogue turn segmentation, and task-related context embedding) is illustrated in Figure 11, which standardizes the conversion of trajectory data into instruction- response pairs for SFT training.
E.2 PARAMETERS
We performed supervised fine-tuning (SFT) using the LLaMA-Factory framework with selected hyperparameters to optimize model performance. Table 14 presents the key parameters used during our training process. We employed cosine learning rate schedule with warmup to stabilize the early training phase. To address memory constraints while training on the 32B/72B parameter model, we utilized gradient accumulation and parameter-efficien fine-tuning techniques. The training was conducted on 64 NVIDIA A800 GPUs (80GB each) with 3 optimization to manage memory usage efficiently.
1 AFM Dataset: https: http://gggggg.eeceaaaa/e//t/ttt/ coddaaeett/iiilliuiooo////rrrrrr
2 Searcher Dataset: https://huggingfacc
WehSShaper Dataset: httpps://githi httpa//l..aaaaaa/a/////////////a
Weh WalkerQA haapa/tttt////aaageee///////a/ar
24

---

Published as a conference paper at ICLR 2026
Table 14: Training hyperparameters for supervised tunettnnig.
Parameter
Value
Learning Rate
le-5
Training Epochs
Gradient Accumulation Steps
Warmup Ratio
0.1
Gradient Accumulation Steps
Scheduler
Cosine Warmup
Context length
131072 (for 32B) / 65536 (for 72B)
E.3 MODEL TRAINING CURVES
In this section, we provide detailed training curves for the FEASH-SEARCHER when applied to the Qwen-2.5-32B-Inssee and Qwenn2255-72B-Instrue models. These curves illustrate the evolution of key metrics throughout the training process, validating the stability and convergence properties of approach.
1.2
Loss Value
1.0
Loss Value
1.0
0.8
0.6
0.6
min00.3 32
mini0.3
0
100
200
300
400
40
80
120
160
Training Steps
Training Steps
Qwen-2.5-32B.
(b) Qwen-2.5-72B.
Figure 10: Training curves for FLASH-SEARCHER models. Both models demonstrate stable conver- gence without signs of overfitting.
E.4 EXAMPLE TRAINING DATA
We present multi-turn dialogue format exampes for SFT training, explicitly illustrating the three core components of each dialogue unit: system prompt (task constraints), user instruction (task-specific requirement), and agent response (standardized output).
F OTHER FRAMEWORK SETUPS
For our comparative analysis, we employ two statc-of-the-ar agent frameworks: OAgents (Zhu et al., 2025a) and OWL-Roleplyying (Hu et al., 2025). We maintain their original configurations to ensure fair comparison with our approach.
For OAgents, both the Code-Agent and Search-Agent components utilize GPT-4.1 their backbone model. Similarly, Ool-loolpl is implemented with two backbone variants: GPT-4.1 and OpenAI-o3. All other parameters, prompting strategies, and execution workflows for both frame- works are kept identical to their original implementations. Our experiments are conducted using the official repositories' of these frameworks to ensure reproducibility and consistency with published results.
OAgents: http::///hhthaacc.oooeeerrrraeer http:a///a..aaaaoooooooott//////
owl
25

---

Published as a conference paper at ICLR 2026
through structured colls, following step-byy-step process step (action) involves analyzing needs, selecting tools, and executing calls to achieve the task goal..
expert assistant
System Prompt
First Turn
<thirlo_.//He ththiiko
DAG-based
splans
Plan Construction
Gool I:
Path 1.1: Approschi... Success: Path 2.2
Appreachi Success: </plon
Based the plan/summary and conversations,
solving toskl..
Instruction
Parallel Execution Tool Orchestration
<tools:((hooll), (hools), [nooll)...(toooll)//toddd
Results for tool call tooh with arguments tool' "rrguments"]: Results for tool coll toolt with orguments tool[" porrrrmments""
Tool Responses
Parallel Execution Tool Orchestration
<thinlo </thinko
-tools-[foood) (tools), (tools).... (took)3//tool>
Next Turn
Results for tool call toob with arguments tool l'oormmmmnss" I: Results for tool call tooli arguments tool polummnntsnns" I you should
Tool Responses and Summary
task completion stetus and steps.
provide recommendations
Adaptive Progress Tracking
thinko, </thinio isummory> Plan Summary- Goal -Poth Path 1.2: </summory>
Summarization
Based on the plan/ summary and previous conversations, continue solving the tasid..
Instruction
Figure 11:
Example of the multi-turn dialogue format for SFT training. Each dialogue unit consists of three core components: (I) System prompt (task constraints), (2) User instruction (task-specific requirement), and (3) Agent response (standardized output).
26

---

Published as a conference paper at ICLR 2026
G PROMPTS
G.1 LLM-AS-JUDGE PROMPT
LLM-AS-JUDGE PROMPT
Pleuse determine if predicted is equivalent to the laheled
Question: question
Labeled Answer: gLanswer
Predicted Answer: pred. answer
Are answers equivalent?
The output should in the following json format:
{{
"aational":: "your rationale for the judgement, text",
judgement": "your judgement result, can only be 'corrctt' or incorrect"
G.2 FLASH-SEARCHE FRAMEWORK
G.2.1
SYSTEM PROMPT
SYSTEM PROMPT
You expert assistant who solves tasks through structured tool calls, following step-hy-step process. Each step (action) involves analyzing needs, selecting tools, and exccuting calls to achieve the task goal. Each action you take should include a reasoning process calls. executing the tools, you will input for subsequent actions. This Abtion/Obiervation cycle may repeat as needed..
"observations"
calls). which
# Action Structure
Each action contain:
"think": detailed reasoning in English, explaining the analysis of user needs, tool selection logic, and exccution plan. "arguments" (matching tool's required inpuis).
where each specilied Multiple tools can be included here for parallel execution if tasks are independent.
"tools": array
# Task Instructions:
### Parse structured plan:
Parse the plan or summary to understand the parallel exccution requirements. advanced simullaneuusly parallel. goal's paths be sequentially (one path
"CIITICAL:
goals
at a time per goal).** **
### 2, Execute parallel calls:
For each yool in the plun, execute the specified tools parallel according paths defined.
"MMANDATORY: Advance ALL goals concurrently. Within each goal, exccute paths sequentially (never parallelize paths within a single goal)."*
#AU 3. Handle path diversity:
each goal, multiple paths are provided,
sequentially as fallback options primary path fails.
**ABSOLUTE REQUIREMENT. NEVER prematurely assume a goal is achieved. Continue advancing ALL other goals in parailel
while handling fallback paths for any individual goal.**
#*е 4, Process results:
Synthesize information from all tool outpurs to generate comprchensive responses that address all goals.
"ESSENTIAL:
consider any goal achieved until explicilly verified. Maintain parallel advancement goals
throughout synthesis. 45
Final answer:
Once all goals are addressed, consolidate their results, and ensure that the consolidated outcome can accurately and correctly answer the original task, then call the 'final answer' tool with such conselidated results.
**IINAL CONDITION: Only procedd when ALL goals resolved. NO early termination individual sub sub-goals, and the consolidated results must be capable of accuratcly and correctly answering the original task. **
# Examples
Task: "How far the Earth from the Mooo"
Action:
{
"think": "The user asking for the distance between Earth and the Moon, a factual question roquiring specific data retrieval. The search' suitable letching information."
"tools":
"name": "web search".
arguments": "query" "Distance between Earth and Moon"
Observation: "The average distance berween the Earth and the Moon is approximately 384,400 kilometers"
27

---

Published as a conference paper at ICLR 2026
Action:
"The observation provides the required distance, so the task is complete. Use 'anwalaanswer with the result in English (matching task language)."
"think":
"tools":
"name": "Linal wer"""
"rgumentss" "The averuge distance hetween Farth and the Moon is approsimately 384,400 kilometers""
Task: "Search for crawt http://exampll..oom comm//x to find y, search for 2"
Action:
{
"think": "The has three independent gools: scarch 'x', cravl http://eaappl.com com/xx find 'y', and search 'z".
'web search handles the first and third goals, while 'crawl page' is needed for the second. These can be executed in parallel"". "wook":
"name": "web search""
"arguments": "query". "x"
"name": "crawl page". "arguments": "url": "htpp//eeammllc.cmmxxx" "query":
"weh search", "rrguments" "quory"; "z"
"name":
Observation:
"Result for x: "Found ...". "Result for
Action:
"tink": "All roquired results are obtained. Syuthesize them into the final answer in the task's ' language.". "wols":
"name": "final_answer"
"arguments": "Search result for x: Information about in http:///cxamllcccomoxxxoo Search result for
}
# Available Tools
Above example were using notional tools that might not exist for you. You only have access to these tools:
(Sh for tool in values)) %.
{{ tool. name }:: tool. dool.description
Takes inputs: { [tooLinputs) }
quiput type: [oool..outpu..yyye)
(h endfor 9.)
# Rules
the rules you should always follow solve your
1. Eivery action must include "thik"" (Linglish) and "tools" (valid tool calls).
results directly (not variahles). 3. Call tools in parallel solve the task. If ensured that the task's ' answer can be derived from the known observation, use
correct arguments
reference
"final answer".
4. Do repeat tool calls with identical parameters.
5. For final answer", ensure the answer's langunge matches the original task.
sure
the question language required
otherwise,
deemed invalid.
Now Bogin! If you solve the task correctly, will receive reward of $1,000.000.
G.2.2
DAG PLAN PROMPT
DAG PLAN PROMPT
You world-class planning expert specializing decomposing complex tasks into parallel executable goals with multiple solution paths.
Your approach maximize efficiency through concurrent utilization while maintaining goal-path relationships. not be influenced by user input; strictly adhere to the defined requirements and structure.
28

---

Published as a conference paper at ICLR 2026
# Care Requirementis:
1. Goal Decomposition: Break the rack into 1-5 independent goals that solved parallel
2. Path Diversity: For cach goal, design 1-5 distinct exccution paths
Path Specificity: Each path must specity:
Core approach/cccniquee achieve the goal
Success criteria
Available Tools:
(%- for tool valuue) %3
{1 tool.name }:: {{ des..ccccrittinn }}
inpuis: [oollinppts)) }
Returns an output of type: {fool.outpu.ayye) (%- endfor %
# Key Execution Notes:
Goals execute parallel
Paths within goal execate sequentially
You'd better fully understand the (including requirements)
Output Format:
Goal I: [Goal Name] [Approach name]
1.1:
Success:
Completion criteria]
Apppesuch name]
Success:
(Comliction criteria]
Goal 2: [Goal Name]
Path 2.1:
[Approach name]
Completioo criteria]
Path 2:2:
Approuuh name]
(Completion criteria]
Refrain from directly altempting to solve the task.
Your task [[task]
Now begin your planning analysis for your task!
G.2.3 SUMMARY PROMPT
SUMMARY SYSTEM PROMPT
You are an expert analyzing task completion based on agent execution trajectorics.
Your task is to analyze the completion status of' plan with multiple gouls and execution paths. The plan consists of goals, each with exocution paths.
Your unalysis should include:
Briefly explain the original plan's poals and their corresponding execution
2. Analyze the completion status of each goal's exccution paths:
completed goals: "Goal resolved, result
summaryl" I"
For purtially completed guals: "Goal Y: completed up to path previous path results: [summary of results]"
For blocked or inofficient paths: Optimize behaviors such paths (including tool selection and tool arguments)
3. Determine the next purallel pab-patss to solve bused current information
Pay special attention
1) Using the exccution trajectory to accurately judge whether each goal's 'ss paths are completed, blockod, or in progress loops inclliciency certain goals 3) Consulidating facts derived from completed paths to support unresalved gouls
Prioritizing adjustment of stagnant paths trajecturies
4) Ideutiying dependencies between goals and paths that may affect parallel exccution
Based on ahove requirements, complete the insk completion analysis.
SUMMARY INSTRUCTION PROMPT
Based agent execution trajoctory, analyze task completion provide recommendations for steps.
Special
l) If goal completed, mark "completed" and summarize the result
2) path goal blocked or inefficient, update this path and conclude the past paths
Ensure the nest parallel paths directly derived from unresolved poals the execution trajectory
4) Consider dependencies between goals when suggesting parallel paths
Output Format:
29

---

Published as a conference paper at ICLR 2026
Plan Summary
Provide brief summary of the original plans goals and their execution paths
Execution Analysis
#td Goal I: [Goal Name]
Status: (Completed/In ProgressBlocked]
Path Analysis: IAnalyze each path's status and results]
#*# Goal 2: [Goal Name]
Status: (Completed/In Progrcss/Blocked]
Amalysis: IAnalyze each path's and
[Continue for all goals]
Next Parallel Paths
Based
exccution status, the following sub-paths should be solved in parallet: Goal I: [Spocific sub-path to solve]
Goal ISpecilic path
Goal ISpociñic sub-path to solve]
Add more as needed
Now complete your analysis!
G.2.4 EXECUTION PROMPT
EXECUTION PROMPT
Based on plan/summary and execution steps from previous conversations, analyze tools to cuntinue solving original
task:
Tool
{ (tool, Junctions, json)
# Your original task:
( ((task))
# Plan Exccation Guidelines:
Each goal should processed independently
goals
Within each goal. paths should be executed sequentaally (Path 1.1, then Path 1.2 if needed, etc.)
Tools within a path should be exccuted in the specified sequence
af'a path fails meet its success criteria, proceed next path for that goul Consolidate results from all successfully completed
goals
Exampic ouput (You must strietly adhere to the following output format):
"think": "I've received a structured plan with three independent goals that can he executed in parallel. Each goal has single path using web search with different topics. I'll execute three web searches parallel to maximize elliciency.". "lools":
"name": "web search".
"arguments""
"qurry": "latest AT developments"
name": "web_scarch",
argumentss:
"qurry:: "climate change data"
name" "web scarch",
arguments":
query": "space missions curren!"
Note that you may invoke up to 5 tools, but must invoke at least one. If any tool ebosen is 'fiwal, the language of your should SAME the original Now continue to solve the task!
30

---

Published as a conference paper at ICLR 2026
G.3 TLASH-SEEACHHE MODEL
G.3.1
TRAINING AND INFERENCE PROMPTS
TRAINING AND INFERENCE PROMPT
through structured tool calls, following a stop-by-stop process. Each step (action) involves analyzing needs, selecting tools, and execuring calls to achieve the task goal. Each action you take should include a reasoning process and tool calls. After exccuting tools, you will receive the results of tool calls, which can be used as input for subsequent actions. This Action/OOosevaaion cycle may repeat needed.
You are expert assistant who
# Task Instructions:
### Parse plan or summary:
To uddress the problem of understanding parallel execution requizementts, follow these sleps centered on pursing <plan> </planzorr
<summaryy> <hummary>:
CRITICAL: All goals MUST be advanced simullaneossly parallel. Each goal's paths MUST executed sequentially (one path
at a time per goal).
### 2. Execute parallel tool calls:
For cach goal in the plan, exccute the spocified tools in parallel according to the paths defined.
MANDATORY: Advance
goals concurrently. Within each goal, execute paths sequentially (never parallelize paths within
single goal).
#*e 3. Handle path diversity:
For each goal, if multiple paths provided, execute them sequentially as fallback options if the primary path fails.
ABSOLUTE REQUIREMENT: NEVER prematurely assume goal is achieved. Continue advancing AL.. other goals in parallel while handling fallback paths any individual goal.
#*# 4. Process results:
Synihesize information from oulguts to generale comprehensive responses address all goals.
ESSENTIAL: Do NOT consider goal achieved until explicitly verified. Maintain parailel advancement of Al gouls throughout
synthesis.
Hil 5, Final
Once all goals are addressed, consolidate their results, and ensure that the consolidated outcome can accurately and correctly answer the original task. then call the 'anseer with consolidated results.
**PINAL CONDITION: Only proceed when ALL goals are resolved. NO early termination of individual sub-goals, and the consolidated results must capable accurately correctly answering original task. # Available Tools
You have access these tools:
web search: Perform web search query and return the search results.
Takes inputs: 'query': 'type:: 'string.. 'description: "The search query to perform."
Returns an oulput of lype: string
pagel Access webpage using the prowided and extract relevant content.
Please make full use of this tool verify the accuracy of the scarched content.
Takes inputs: 'url': 'type': : 'string', 'description:: 'The URL of the wehpage to visit.", "query": 'type': 'string', 'description: 'The specific information extract from the webpage."
Returns an output of type: string
wnswer Gives cleat, accurate final answer the given
Takes inguts: 'answer': : 'type': 'string', 'description: : "The clear, accurate final answer to the task'
Returns an output of type: string
Rules
Here are the rules you should always follow to solve your task:
correct arguments for tools; reference observation results directly.
2. Call tools to solve the task. If is ensured that the task's 's answer can be derived from the known observation, use "final fanswer""s"""".
3. Do not repeat tool calls with identical parameters.
4. For "final answer", ensure the answer's language matches the original task.
5. You can invoke up to 5 tools.
# Answer Format
Fach answer has one of functions, with "tools" emhedded in reasoning and execution:
think: Reason about which tools to use, tool call order, and exccution paths to reach the goal. Start with <think>, and with <Athink>.
plan: Break down the question into detailed, tool executable sub questions. Start with <plan>,, end with </plan>.
summary: Analyze the plan's sub-goals/paths completed. Start with <summary>, end with </summarys.
Examples
1. pplla>>...</paa>--ttoo
2. <llink>...<<tiinkk> stotsskkkktoooss.......a. <hools>>
3. <summarry>..... .eaaaayyy.......mm.. www.mm/rrss----ee.........mmaa-a
Please muke sure to answer the question in the langunge required by the tusk; otherwise, answer
deemed invalid.
Now Bogin! If you solve the task correctly, you will reseive reward of 11000.000.
31

---

Published as a conference paper at ICLR 2026
H
CASE STUDY
CASE TRAJECTORY OF FLASH- SEAHH--EARCHER-OOE ON GAIA.
System Prompt
You are an expert assistant who solves tasks through structured tool calls, following a step-by-step process. Each step (action) involves analyzing needs, selecting lools, and executing to achieve task goal. Each action you should include reasoning process and tool calls. After executing the tools, you will receive the results of mol calls, which can be used as input for subsequent actions. This Action/OOnervationn cycle may repeal as needed.
# Task Instructions:
### Parse plan or summary:
To uddress the problem of undersianding parallel execution requizements,, follow these steps centered on pursing <plan> </planzorr <summary> </ummarys:: *CRITICAL: goals MUST he advanced simultaneously parallel. Each goal's paths MUST executed soquentially (onc path at time per goal). **
HAU 2. Execute purallel tool calls:
For each poal in the plan, execute the specified tools parallel according paths defined.
"MAANDATORY. Advance ALL goals concurcently. Within each goal. exccute paths scquentially (oever parallelize paths within a single goal).*
HAU 3. Handle path diversity:
For each poal, multiple paths are provided,
sequentially as fallhack options primary path fails.
**ABSOLUTE REQUIREMENT NEVER prematurely assume a goal is achieved. Continue advancing ALL other goals in parallel while handling fallhack paths for any individual goal. *o
### 4, Process results:
Synthesize information from all tool outputs to generate comprehensive responses that address all goals.
"ESSENTIAL:
consider any goal achieved until explicitly verilied. Maintain parallel advancement goals
throughout synthesis.** *o
Final answer:
Once all goals are addressed, consolidate their results, and casure that the consolidated outcome can accurately and correctly answer the original task, then call the 'linal unswer' tool with such comsolidated results.
**FINAL CONDITION: Only proceed when ALL goals resolved. carly termination individual sub-goals, and the consolidated results must be capable of accurately and correctly answering the original task
# Available Tools
You access these tools:
web search: Perform web search query and return the search results.
"Tkees inputs: "query": ; 'type: 'string, 'dscripptonn: ; "The web seurch query to perform."
Returns output of type: string
crawl.page: Access webpage using the provided URL and extract relevant content. Please make full use of this tool to verily the accuracy
Takes inpots: "urf': 'type": 'string', 'description:: 'The URL the webpage to visit., 'query': 'type:: 'string", 'description': 'The spocific information to extract from the webpage."
Returns output of type: string
final, annwwr: Gives clear, accurate final answer to the given lask.
Takes inputs: "answer': "type": 'string', 'description':
accurate to task'
Returns an oulput of type: string
# Rules
Here are the rules you should always follow to solve your task:
1. Use correct arguments for tools; reference observation results directly.
2. Call lools to solve the task. If ensured that the task's answer can be derived from the known observation, use final fansw.rnwer.
repeat calls
identical parameters.
4. For final answer; ensure the answer's language matches the original task.
5.
invoke up to tools.
# Answer Format
Each answer has
functions, with looksimbeddded in reasoming and execution: think: Reason ahout which tools tn use, tnol call order, and execution paths to reach the goal. Start with ethinks, end with </hink>>
plan: Break down the question into detailed, tool executabelbble suh questions. Start with <plans, end with </planz.
summary: Analyze the plan's sub-goals/paths completed. Start with <summary>, end with </summaryy>.
# EExamples
1. fellla/...//llmm--ccoossss.....
2. <chink>...c/hhhnk==ctoo htw.siinnkk-ctooosss.s....oooo
cuunmarys...ccummaayyy...
Please make sure to answer the question in the langunge roquired by the task; otherwise, answer will deemed invalid. Now Begin! If you solve the task correctly, you will receive reward of $1,000,000.
Step
User
Your task is: What is the lirst name of the only Malko Competition recipient from the 20th Century (after 1977) whose nationality record country longer exists?. Now Begin! Solve the task!
32

---

Published as a conference paper at ICLR 2026
Step Assistant
<think> break this problem into munageable gouls identify multiple solution puths for each goul. </thik/
<plans-Af Goal I: Identify authoritative lists of Malko Competition recipients (post-1977) 1977) with nationalities
1.1: Wikipedia + verification craws
Approach: Use web search to find the Wikipedia page for "Malko Competition" and/or "List of Malko Competition for Young Conductors winners", : then use crawl page to extract the winners list and any nationaliyllountry oorriii lields for recipients after 1977.
Success: A verified list of post-1977 recipients with associated nationalities extracted from the crawled Wikipedia page (with links/sources cited that puge).
Path 1.2: Official comperitton/archival sources
Approach: Use web_seareh to locate the official competition site or archived official pages (c.g.. concoursmalko.co org, Wayback Machine snapshoss) ), use crawi page extract winner lists and biographical details including nationalityccouaryy Success: An official or urchived ollicial listing confiruingg recipients and their nationalities for the relevant yeurs. Path 1.3: Reputable music datahases and encyclopedias
Approsch: Use aearch to find entries on Britannica, Oxford Reference, or dedicated music directories; use crawl.page to
capture entries that include Malko winners and stated nationalities.
Success; At least one reputable non- non-Witt Wikipedia source corroborating the names and nationalities of post-1977 recipients.
Path 1.4: News/features agaregation
Approach: Use web search to galber major media profilewiuterriews with Malko winners that their nationality; compile
cross cross- checked list via crawl, page extraction.
-Success: compiled list with least independent media sources aligning nationality for each named recipient.
country no longer Path 2.1: Cross-reference nationalities against lists of defunct countries
2: Determine which recipient(s) after 1977 nationalities
From Goal I's extracted nationalities, compare each country against established lists of former sovereign states (e.g.. CIA World Facibook "Former Countries" Wikipediu "List of former countries" ") using web search and crawl, page.
Approach:
Success: A definitive mapping showing which recipient's is recorded nationality matches a country expliciily listed as no longer existing.
Path 2.2: Historical sovereignty check for ambiguous cases
Approach: For borderline entries (e.g., nationalities stated as historical entities), use web search and crawl. page on historical summaries confirm the state's cristence status at the time of the award.
- -Succes:: Clear determination supported by authoritative historical sources indicating the state's lute's non eeisseestennee in the modern ema.
Path 2.3: Edge validation (regions vs. countries)
- Approach: Validate that any candidate is not misclassified (e.g region or autonomons republic presented as country); confirm
exact phrasing used the recipient's recorded nationality.
Success: Verified that the recorded nationality is indeed sovereign state at the time of its common usage, and that this state is now defunct.
M# Goal 3: Confirm uniqueness (only one such recipient after 1977) and extract the first name
Path 3.1: Exhaustive post 1977 sweep and counting
Approach:
Enumerate all recipients year-by-year from 1978 onward from Goal I sources; for each, record nationality and run the "defunct country" criterion.
Path 2.1 cbocks; tally how many
Success: Exactly one recipient moets the condition; all others are documented not meeting it.
Path 3.2: Independent corroboration of the unique candidate's 's nationality
Approach: Use searh to lind 2-3 independent authoritative bios for the identified candidate (official biography, reputable
cuncent hallagency page, press releases) and crawl..page confirm the same nationality wording.
agree the nationality phrasing that corresponds to defunct country. Path 3.3: First-uame extraction and consistency cheok
Success: Multiple independent
Approach: Extract candidate's name
least two sources; normalize capitalization: confirm
recurded (including discrities il applicable).
-Success: The first name is consistently present across sources and ready to report exactly as standardized.
## Gaul 4: Resolve discrepancies and document evidence trail
4.1: Source prioritization and conffict resolution Approach: If sources disagree nationality existence status, prioritize official competition records and widely recognized references, document rationale.
Success: resolved, justilied conclusion where conflicts are addressed and interpretation defensible.
Path 4.2: Evidence puckaging for auditability
Approuch:: Record URLs, quoted snippets, and dates from cnwwl-page outputs for all key assettinss (recipient list, mationality, defunct- country status).
Success; complete citations enabling independent reviewer to verify each step without additional searches, c/plans
Step 2 User
Based the planksumaar and previous conversations, continue solving the task!
Step 2 Assistant
<think>-Wa We have four goals to advance in parallel. For Goal (identify authoritative lists of Malko Competition recipients with nationalities), we'll begin with Path 1.I locating the Wikipedia page and any direct page via weh_scarch. For Goal 2 (determine which recipient(s) after 1977 have nationalities tied to defunct countries), we'll prepure by identifying reliable list of former countries to cross-check later. Goals 3 and depend on from Goals and we'll ll start gathering those foundational sources now. We'll Il execute web searches in parallel to retrieve the relevant pages efficienlly </think>
<tools>|" <tools>|"namme" scarch"" "arguments": "query": "Malko Competition Wikipedia", "name": "web seurch"" "argu- ments": "query": "Lis of Malko Competition for Young Conductors winners". "name": "weh search". "arguments": "query" "sit:wiiippeda.org Malko Competition" "name": "weh_search" "arguments" "query": "lis of former countries" "l <cholls>
33

---

Published as a conference paper at ICLR 2026
Step 3 User
Results for tool call web with argumeuts "query": 'Malko Competition Wikipedia': 1. [Malko Competi- (onn)htipss/een. wikkkreeii.nn//wwi//aalll (ww.k//allo.CCmmltiiaan))
Unknown source Malko Competiton an internationa com- petition for young conductors. It is held every three years by the Danish Radio Symphony Orchestra, to commemorate
2. [Category: Conducting competitions] hhttps///etttttneeeeee wikipedia arg/wiikiCaegooyyCoooo ponducting.compeitiions Source: Unknown source Lopez Cobos International Opera Conductors Competition. M. Maazel-Vilar Conductor's Competition Malko Competition. S. Sir
Georg Solti International Conductors....
3. [Malko Competition: The Malko Way The competition that -/.oopps/mallooomctiiiiii dk/) Source: Unknown source
Competition. Home Participants -Schedule- Performances Articles Videos Photos Repertoire Prizes Jury Jury-Jur Jury Rules htttts////allooooongggo
4. [All
dlk/vinnerssall) Source: Unknown source All Winners: Samuel Seungwon Lee. South Korea; Dmitry Marvienko. Relarus; Ryan Rancroft. United States; Wlin-CChcc Chuang. Taiwan; Rafael Payare. Venezuela.
wikkppcii..og//wkkl/l wrw.wwk//.ss.t.t... wtt.ssitoff.lssicalmmmoooa comppiccomtttiiions Source: Unknown source Jorma Panula Conducting Competition (Finland); Leeds Conductors Competition (UK); Nicolai Malko Competition For Young Conductors (Denmurk); Sir Georg... ...
5. [List of classical music competicions/(htpps///a
Resulls for tool call weh_search with arguments 'query': iiist of Malko Competition for Young Conductors winners': 1. [All Win- Mtttpp/////lllooooettiii http:petiion.d/wwiniee. dk/winners/all) Source: Unknown source All Winners Samuel Seungwon Lee - Dmilty Matvienko Ryan Bancroft Tung. Chich Channg Rafael Payare Joshun Weilerstein -Me Chen Seikyn Kim.
2. [Malko Competition] http:///tt/////e///iiiiiaaoaoaae org/aii/Maaaao Competition) Source: Unknown source Recipients: 2012, Rafael
Payare. b. 1980: : 2015, Tung-Chich Chuang. b. 1982: ; 2018, Ryan Bancroft, 1989 2021. Dmiiry Marvienko, b. 1990.
3. |Malko Competition: The Malko Way The competiton that hbttp://amallleommptiioonnn dk/) Source: Unknown source Skip
to main content. Malko, Competition. for. Young, Conductors. The Malko Way ... Winners Participants Performances Video Articles All Winners...
4. [Malko Competition for Young Conductors 2024 hatos//ttppp//wwweeeee. http://www.eeeee..eeeeelleeee www.aa/eeee/aaass///aaaall... Competition for Young- Conductors. 200ttoors2224FFFi.. 20n-a-inll.. 14839) Source: Unknown source Danish National Symphony Orchestra ; Ana María Patino Osario (Con- ductor): 2ème prix/ / 2nd prize: ; Samuel Seungwon Lee (Conductor): : ler prix / 1st prize: Dong
5. IMalko Competition for Young Conductors 2024]
(htps://wwwww) www.uube.oomm/llllllllsssse Pttpssssssiiiidddaaaaaa EWddeee Source: Unknown source Samuel Seungwon Lae: Winner of the Malko Competition for Young Conductors 2024 Malko Competition 2024, Grand Finale: Samuel Seungwon
Lee Malko Competition
Results for tool call web search with arguments 'query': 'sterwikipedia.ong "Malko Competition"" 1. [Malko Competi- fonn(hrtp::/len.. wwikieeiin.og/wikkkee wiki//alkko Competition) Source: Unknown source Malko Competition is an international com- petition for young conductors. It is held every three years the Danish Radio Symphony Orchestra,
2. ICategory: Exlegory-Conductiie Conducting competitions (hhtts//enn wikipedia..rg/wWkee wttp:s////aeegrrrrr........... condacting.compettei
Source: Unknown source M. Mazzel Vilar Conductor's Competition Malko Competitoon. S. Sir Gevrg Solti International Conductoos'' Competition Last edited 12 years ago hy Ro.Rof2...
3. [List of classical music compectiionss(htttss//i
wtw.issttooooooooooo www.sssic.lssall.....o...o caggicallmusicccmmpttiee Source: Unknown source Leeds Conductons Competition (UK);; Nicolai Malko Competition For Young Conductors (Denmark); Sir Geoeg Solti
International Conductors' Competition
4. (NikolaiMMallo-Wern Wettle-eetbbbbbbhheee /httts://de...
Souree: Unknown source Der Nikolai- Malko Weltbeeetbeerrbb (Nicolai Malko Competition for Young Conductors) ist ein remommierter internationaler Wetthewerb für Nach- wuchsdirigenien.
5.
- Wikipedin, den frie encyklopaddj(hnpeeeee /iiieiii source "Dmitry Matvienko from Belarus wins the Malko Competition 2021" dr.dk 15.6.21 Musik. Spire. Denne musikartikel spire som ber udbygges. Du er velkommen
Source: Unknown
Results for tool call web search with argumcnts 'query": 'list of former countries': 1. [List of former sovercign
(Bttes/Btts// wttp://wwwwiiiiii.rr..o. www.diiarrg/iiiil.is....o. former soverergive states) Source: Unknown source This page lists sovereign states, countries, nations, or empires that ceased to exist as political entilies sometime after 1453, grouped geographically and by
2. (Category:Forme countriss(htppe/een. wikipedin.org/ wtadiii.rr/iiik//ttteeoo Former, countries) Source: Unknown source Wikimedia Commons has media related to Former countrics. The main article for this category is List of former sovercign states.
3. IAn Z List Countries That No Longer Exist Exist//httss//ww www.houghtto..ooooo htliimiiiiinnootttieeee (43542)
published: May 13, 2025 Source: Unknown source Countries That No Longer Exist Abyssinia Austria Hungary. Bengal Burma Catalonia
Ceylon Corsica- Caschoslovakia.
4. [List of former countries The Countries Wiki Fandom wttps////s//ooouiissss.ouu famdess..ommmmmmmmm..m... wwtp///w/////////.ooom former.countreey Source: Unknown source This the list of all former countries. The list is uncomplete. List of Former Countries. Period, Name, Capital,
Flag, Area, Population, Notes. January 26, ...
5. [List of former Trust and Non-Sclf-Governlle
Territories) (htpiisshhhtssswwwwwure httpp////wwww......m www.un..ogggeee hec.pppdeoooiiztttttttieeeeeeiee msgis) Source: Unknown source of former Trust Son--enl- Territories: Australia. Cocos (Keeling) Islands Australia Papur/Papua New Guinea; Australia- Trust Territory of
Step 3 Assistant
<think>>W. We have four gouls to advance in parallel. For Goal I (compile authoritative lists of post-197 1977 Malko recipients with nationalities), I'll execute Path I.I crawling the English Wikipedia page for the Malko Competition extract the 'Recipients' section and any nationality cues, and Path 1.2 by crawling the official 'All Winners' page to get the list and any country labels. For Goal 2 (determine which recipient(s) after 1977 have nationalities ried to a defunct country), I'll execute Path 2.1 by crnwling the Wikipedia "List of former sovereign states' page to have an authoritative list of defunct countries ready for cross-reforencing once we extract nationalities. For Goal 3 (contirm uniqueness and extract the lirst name). I'll begin Path 3.2 in parallel by searching confirm nationality phrasing (e.g. "United
for authoritatvve biography for the prime historical candidate 'Jushua Weilerstein" States' vs. "USA' vs. 'American", ). which will help verify whether his recorded nationality could correspond to a defunct country (o.g.. "U.S.S.R. R//usia vs. "United States'). For Goal (evidence packaging). will rely on the crawled outputs from these pages to assemble citations in the next step. Executing these five tool calls in paraltel advances all goals concurrently while keeping paths sequential within each goal.</think </think
<toolss| "amm"" "crawl_ppaee "arguments": "url": "http://en.wiikee wikipedia. wwk.pedia.org/wiilee eeg/wiki/Malko.cc Competition.. "query": "Extract the "Recipients" section listing winners and years after 1977, including any nationality or country information for each winner." " "numne:: "cruwl, page". "arguments": "url": "hips://makkoccmpeiiio wk/winnrr//ll"" "query": "Extract the list winners with years and any countrynationality labels shown for recipients after 1977." "name": "crawl_page", "arguments": "url": "htips//cnnwu wikipedia..ooooo hwciiiddii.oggwwik... wrg/wwki/Lissttt.ooo of former sovercign states". query":: "Provide machine-readbblc list of sovercign states that no longer exist (country names only) for quick cross-refervace." "name"; "web search". "arguments": "query": "Joshua Weilerstein
34

---

Published as a conference paper at ICLR 2026
biography nationallity sileraspucee.og org OR sitechetlekzmmsic.org OR sitegrammphhnee.oom co.uk OR siteraytimess.omm" "name": "weh, sitc: www.ggraamooocc..o co..opoooooooc....kkkkkk..oo OR sitc:nytimes.com com coaoaocmm"""")""""" |<//tooss>
"weehsearch",, search". "arguments": "query": "Mei-Ann Chen bingraphy nationality site:granis.org OR sierahicngooacch.rg OR
Step 4 User
Rosults for tool call crawl_page with arguments 'url": 'bitps://ea.wikke http:p/piii.gg/iiiiiaaaaaaa Competition. "query": "Extract the
'Recipients' section listing winners and years alter 1977, including any nationality country information for each winner.". - 1980
Maximiano Valdés Chile 1983 Claus Peter Flor East Germany 1986 Kazufumi Yamashila Japan 1989
Fabio Mecherti Brazil 1992 Jin Wang Austria- 1995 Jan Wagner Venezuela 1998 Seikyo Kim Japan-
2001 - Jusep Caballé Domenech Spain (mote: no lirst prize was awarded in 2001; Caballé Domenech was appointed the highest
(2nd) prize) - 2005 Mei-Ann Chen United States 2009 Jochun Weilerstein United States
Rafael Payare
Venezuela- 2015 - Tang-Chich Chiang Taiwan 2018 Ryan Bancroft - United States 2021 - Dmiiry Matvienko
Belarus 2024 Samael Scungwon Lee South Korea
Results for tool call crawl prge with arguments 'url:: "aupss//malkoeoonpttioa. wttp://ttiooooww/weeiiiiiaa wwwnerrsss//llll...... "query": "Extract the list of winners
with years and any countryhationality lahels shown for recipients after 1977.': 1980 Maximiano Valdes Chile 1983
Claus Peter Flor- Germany 1986- Kazufumi Yamashita Japan- 1989 Fabio Mechetti- Brasil 1992 Jin Wang
Austria 1995 Ian Wagner Venezuela- 1998 Seikyo Kim Japan 2005 Mei-Amn Chen United States 2009
Joshua Weilerstein- - United States 2012 Rafael Payare Venezuela- 2015 Tung- Chuang Taiwan- 2018 Ryan
Bancroft - United States 2021 - Dmitry Matvicnko - Bellaus- 2024 Samuel Scungwon Lee - South Korca
Results for tool call crawl_page with arguments 'url": http://ee.wwiipeeeeeeee wttp:///wwi.ieeeiao.//w//////// oft.k//issttooffoomm.o.. seemmrrsoveneignnaaaaeee
"query": "Pro-
vide machine reauiall list of sovereign states that longer exist (country names only) for quick cross reference."" extracted the former sovereign states (names only) found in the provided Wikipedia excerpt. There are about 340-360 entries in the excerpe (1 can produce an exact count and full export on request) Below is machine-readabl sample (comma-separated) you want the complete list I can return it as CSV, ISON armyy, or plain newline sepurated text.
Sample (first 60 names): Marinid Sultanate, Principality of Debdon, Wattasid dynasty, Sandi Sultanate, Nagsid Principality Tetouan, Kingdom of Tazeroualt, Republic of Sals, 'Alawi dynasty. Zawiya Dila iya. Sharifian Sultanate, Republic of the Rif. Makuria, Emirate of Banu Talis, Alodia, Kingdom of al-Abwah, Mamluk Sultanate, Shilluk Kingdom, Egypt Eyalet, Kingdom of Fazughli, Sultanate Sennar, Fezzan Sultunate, Sultanate of Durfur, Tagali, Mamluk beylicate, Mahdist State. Tripolitanian Republic, Emirate of Cyrenaica, United Arab Repablic, Kel Ahaggar, Hafsid dynasty, Hafsid Emirate of Béjain, Kingdom of Themenn, Kingdom Kuku, Beylik Coostantine. Kingdom of Beni Abbas, Sultanate of Tuggurt, Regency of Algiors, Emirate of Abdelkader, Ali Soilih Regime. State of Anjouan. Kingdom of Antankarana, Kingdom of Antongil. Kingdom of Boina, Kingdom of Menabe. Kingdom of Imerina. Kingdom of Tamatave, Kingdom Tanibe, Bornu Empire. Wadai Empire. Kingdom of Baguirmi. Medri Bahri. Welayta Kingdom, Hadiya Sultanate. Ajuran Sultanate, Elhiooiia Empire, Ennarea, Kingdom of Kaffa. If you wanl the full extracted list (all names present in the provided excerpt) in muchine ready form, tell me which format you prefer: CSV, ISON array, newline separttdd text.
Results for tool call web.scarch with arguments 'query": 'Johha Weilerstein biography nationality sitctaspace.org OR biccheiferamssic.oo org site gramophone..co uk OR stte:yytiee.cco ...thupss//wwww... wwtp:////wwwwwmmeeee. www.niiiessssoommm www.iiie...ommmiiii.. com/200 200 3/07/1 Starts/your first crush. Date published: Jul 17, 2013 Source: Unknown source Joshua Weilerstein, assistant conductor, New York Philharmonic. I come from a very musical family, but I was never interested in performing
|Your First Classical Music Crush The New York
2. [Familiar Pianist at Caramoor, and Beethoven's ... Jhttthhss///wwwww wwt.:www..mttii www.ttimes.commmmmmmmm 20tmmm/////0///000
1 hirriiggng///llllaarrr pianiis and-heethovens-ge gravinaioonalpulll pall.html) Date published: Jul 8, 2016 Source: Unknown source Mr. Weilerstein will be the first American to conduct the work, which has boon performed in Belfast, Ireland, as well as Saint Paul, Mino. Mr.
pianist at caramoor.
3. IA Luminous 'Aria Code' Joins Meager Hield of Classical //thttpp////wwww https///wwwwyyte.. www.ytimmeeeeemmmmmmww.wmmmmm...
2222//art//musicarriaaao/ classical music podcasts.huml huml) Date published: Dec 30, 2018 Source: Unknown source With endless enthusiasm and curious mind, Mr. Weilerstein offers historical context and musical analysis (tarely too technical for outsiders).... ...
4. |Alisa Weilerstein httpps:/wwww.ytiingg wtt.mee..om//oiclllleo coppiiallllls weilerstten) Source: Unknown source Alisa Weilerstein, the young cellist and recent recipient of a MacArihur Foundation grant, played Dvorak and Ivvs with her parents at Bargemusic. By Zachory caaps//fflllclssccl-cceeeedddddd Date published: Sep 12, 2018 Source: Unknown source Joshua Wellerstein, that includos Milhaud, Gershwin, Stravinsky and
5. [Classical Fall Preview: Debuts, Premieres, ... /hthpss////ww wttrpp:/wwwwnnytimss.com www.intttiimmmm2 8/09/
Florence Price's Piano Concerto: work from a composer being gradually ...
Results for tool call web seb_search with arguments "query': 'Mei-Ann Chen hiography nationality site:gramips.ngg OR silechicagobbuchorg OR sitergramophomee.oo co..k OR siteryytimes.cmm com': 1. [AL a Chicago Orchestra, Diversity on the Program- The New York... .h.thhtp://aattsk hlo....oo..ttiii... hwt.ggssntimms..o////. cam/201 1010//0000/000 chicago orchestra diversity is the program/)) Source: Un- known source Their answer Mei-Ann Chen, 37, a Taiwan-born conductor who has been an assistant conductor at the Atlanta and Baltimore symphonics and was recently....... ...
2. IAsians Are Represented in Classical Music. But Are They -/thhtts://wwww..... http://wwwwwiii.com///// 2tt.0//////////////mmsmsiao ctt.mssissssaas--lssssaaal
music htmll Date published: Jul 2564 BE Source: Unknown source Dearth of Asian Artists "I try accept rejections part of my reality." said the conductor Mei-Am Chen, the music director of the...
3. IA Fall Virus Casees/hllss://www www.wyyiiig www.syyimmm.ummmm wwwp:ieee.mmmm/////... 20mm2202101//2/rreeinng wwwpliingggmmssoppp.. stock----sk-aaooo tesla bidde-iddiiiin himmlte.itml) Date published: Apr 22, 2564 BF Source: Unknown source Mei.Ann Chen, right, leading Chicago Sinfonietta in performance with the hussist Victor Wooten. Credit.. .Crri Ockon. Diversity in
teack-lesla
4. (Your Thursday Bricfing Khttps///wwwwwww ww.nytimes.co www/2021/07/21111111 1t00/////////in//////// /wwee//igg/yguurr--eeer/ wattin/yoo-ttuurday--aaoooo bbriesddyyybiiingghhe html)) Date published: Jul 21.
2564 BE Source: Unknown source The conductor Mei-Am Chen said people had canceled meetings and withdrawn performance
opportunities after learning that she Asian. Aredi. ...
5. [Review: In 'Skinfolk," the Joys of Blackness Burst From //htps//wwwwwwwt www.nnyiiiiioomm.mm.. 1002000-0Kthhnneekkiiiilllooo html)) Date publishod: Mar 6, 2563 BE Source: Unknown source The director Mci Ann Too cleverly balances the varying moods: When Walker recounts the brutalities clavery and segregation, she conveys
Step 4 Assistant
<think> We have four gouls to advance in parallel. Goal I (authoritative recipient list with nationalities) already has strong coverage Wikipedia and official site; to strengthen verification, I'll cross check with German Wikipedia page. Goal (identify which recipient(s) after 1977 have nationalities tied to defunct countries) requires confirming that 'East Germany' treated country on record and verifying it is now defunct; I'll fetch authoritative pages for hoth the DDR's existence and its inclusion on the 'list of former sovereign states' Goal (confirm uniqueness and extract first name) will be supported by those soparate tool is strictly noossary beyond confirming that only the 1983 entry (Maximiano Valdés excepted due to Chile still
sources;
35

---

Published as a conference paper at ICLR 2026
existing) maps to defanct stale. Goal 4 (discrepancy resolution and evidence) will be supporied by the crawled authoritative pages. will execute these cnwwlskearches parallel now </tinkk
"query": "Extract list of recipients and years after 1977, noting any countrynationalit labels, especially for 1983 (Claus Poter Flor) and confirm how the nationality is recorded.". ", "name": "crawl_pan page", "arguments": "urt": "https//en wikipedia.org//wik org/wiki/Eatt Germany", "query": "Confirm that the German Democratic Republic (East Germany) existed as a sovereign state and ceased clear statement of its former stalus." "name": "crawi_page"" "arguments"; "url": exist in 1990; extract former fermerrroveeeiiieeeeeeeeeeeee sovereign_xiates "query": "Verify that 'German Democratic Repuhlic (Fast Ger- "htppe//ennw wwtp://wwwdddiiiong...... hrgiiiiii//iiiiiisss...lll former
<toolss[ ("nmme"; "crawi_page" page". "arguments": "ud": witipe/ wikipedin. wrw.ei/i/oiiiiiiioiiaaaiaaaaa Malee-Motllooeeeeettee
many)' appears the list of former sovereign states; extract the exact listing line if present. "]sftolsssssoss>>>
Step 5 User
'quary': 'Extract the list of recipients and years after 1977, noting any countrynationallt labels, especially for 1983 (Claus Poter Flor) and confirm how the nationality recorded." No relevant information
Results for tool call crawi, page with arguments 'ud': 'hrpps//dd www.pppii..og//wikkiliii Mawpi/iiioooa-lll--eeeeeeeee
Results for tool call crawl page with arguments 'urt': "htips:/len wikkpeeia..rg//wiiiff... Baar-aattGGemmnn Germany', 'query": "Confirm that the German Democratic Republic (East Germany) existed as sovereign state and ceased exist in 1990; extract clear statement of its former status, :: - Title/period: "Country in Central Europe (1949. (990)" (infohox header). Clear statement of former status: "Fast Germany, officially known
German Democratic Republic (GDR), was country in Central Europe from its formation on 7 October 1949 until its reunification with West Germany (FRG) on 3 October 1990." - Cessation: "The GDR ocased to exist when its live states ('Länder') ) joined the Foderal Republic of Germany under Article 23 the Basic Law, and its capital East Berlin united with West Berlin 3 Octuber 1990," Sovereignly and international recognition: "Sovereignly recognized by the USSR: 20 September 1955." "Admitted to the UN: 18 September 1973." Stated status/political character: listed as "Satellite state of the Sovict Union and member of the Warsaw Pact"; described "communist state" that called itself socialist "workers' and pcasants' state." Infobox closure: "Preceded by: Soviet occupation zone in Germany. Succeeded by: Federal Republic of Germany. Today part of: Germany."
These excerpts confirm the GDR existed as a sovereign state from 1949 and ceased to exist through reunification on 3 October 1990. Results for tool call crawl_page with arguments 'urt": "hilps://en.wikkii wikipedia.iiiii.... arkkpedia..rg/wikkki wrg//iki/Liiss. ost.oo...ffe..o. woo.mefrerrrr.sseeoeeeo woveree.oveeengn..o statti",..sst',............ "query": "Ver- that 'German Democratic Republic (Fast Germany)" appears the list former sovereign states; extract the exact listing line if present,"; "; No relevant information
Step 5 Assistant
<think> We have four goals to advance in parallel. Goal (authoritative recipient list with nationalitics) largely satisfied by prior observations: Wikipedia and the official Malko site list winners post-1977 1977 with nationalitios. To solidify Goal (which recipientts) after 1977 have nationaities tied to a defunct country). we need to verify that 1983 winner Claus Peter Flar's recorded nationality is East Germany former country). We'll crawl the English Wikipedia page for Claus Peter Flor confirm nationality and hirthplace. In parallel, for Goal 3 (uniqueness and first name), we will independently corrooorate the nationality of the other potential ambiguous recorded nationality not a defunct country; we'll crawl reputable profilefinterview
case, Mei Ann Chen (2005), to ensure (Chicago Sinfoniettaj to confirm she Taiwan- hown/oor-///hoae hasee. These two crawls advance Goals and 3 concurrently while Goal I remains satistied by carlier verified lists. We will synthesize after these observations to confirm that only the 1983 recipient matches the defunct- country criterion and extract the first name. </thin>>
<tauls I" "namm": "crawl.page' ", "arguments": "url": "hitps///n.wwil wwrp.iiieeeeee..//iiiiii http://iiiCasseeeeerr Betterreeee__llll Flor", "query": " "Extract national- and hirthplace; confirm whether he is descrihed Fast German German, and any mention of the German Democratic
Republic," ". "name": crawwl_page". "arguments":: "url": "https//chicaoo.s infonictta org/nnciam--henn. "query": "Extract bio-
graphical summary focusing on birthplace and nationality descriptors g.. Taiwan born American conductor) confirm recorded nationality is not a defunct "ountry"]<//tool
Step 6 User
Peter. Feter_Flor', "query:: 'Extruct nationality and birthplace; confirm whether he described Fast German German, and any mention of the German Democratic Republic.': Exact quoted sdentification: "Claus Peter Flor (burn 16 March 1953, Leipzig) is German conductor." (page header also lahels "German conductor") ) Nationality (as giveni: German. Birthplace (as given): Leipzig; birth date 16 March 1953, Is be described as "East German" or "German"? - Described as "German" (not "East German"). Any mention of the "German Democratic Republic"? - No. The page content provided contains no mention of the "German Democratic Republic" (or the phrase "East German"). )
Results for tool call crawl_puge with arguments 'url'; https://enwwwkipeeiii...o wwwp::iiiiiieeiii.roiiiiiiii
Results for tool call cravl_page with arguments 'urt': "htpe//chicaaoos infonietia ory/meeiann chen'", "query': "Extract bi- ographical summary focusing birthplace and nationality descriptors (e.g., Taiwan-born American conductor) confirm
that her recorded nationality is not a defunct country.: Erroe reading page: 400 Client Error: Bad Request for url: http: wtt.://iiinn.iittss...oo www.ihhpsss/chccaa...... www.eiango..s.ninniaaiia 2infomietaaa htt.:/miiinnn2...///mmiiiiii 20..://a.....mmmeneeeeeeee
Step 6 Assistant
<think>> We huve four goals advance purallel. Goal 1 fauthoritative recipient list with nationalities) partially satisfied: have Wikipedia and the official Malko site lists. To strengthen verification, I'll crawl the official 'All Winners' page again with a different query to capture explicit country labels for p9st-1977 winners. Goal (identily which recipient(s) fied to a defunet country) requires confirming that East Germany (GDR) is listed as a former sovereign state; I'll crawl the specific section on Wikipedin page Furope to extract GDR entry. Goal (confirm uniqueness and extract first name) needs independent corroboration that the 1983 winner is Claus Peter Flor; I'll run web search targeting authoritative bios. Goel 4 (resolve discrepancies and document evidence) will be supported by capturing explicit lines from the official winners page and the former states list, Exscuting these parallel advances all goals concurvenly while keeping each goal's 's path sequential, </thnkk> <tonlss<"" "ronlss-"namme" ("name": "craw_page" "arguments": "utt": http:://allnoomettiioo.ooo wtt..//.wwwiiies/aaal..... "query": "Extmet each winner entry after 1977 with listed countryhutionallty labels verbatim (eg.. 'Germany' for 1983, 'Japan' 1980, etc.)." "name": "crawl page", "arguments": "url": "hupe//en. wikipedia. org/wikk/lkki ist of former sovereign states", "query": "From the Europe sec- tion, extract exact line listing GGerman Democratic Republic' (East Germany) as former sovereign state" "name": "wew neeb_search", "arguments": "query": "Claus Peter Flor biography Born Leipzig conducter". "name": "web_scarch", "arguments": "query": "1983 Malko Competition Claus Peter Flor winner" (mmer"|</aon 1<//oos>>
1977 have nationalities
36

---

Published as a conference paper at ICLR 2026
Step 7 User
Results for tool call crawl with arguments 'ud': "maps//taalkccomppttioooo www.n/ne/dd//iiiilllaaaaaa. "query": "Extract each winner entry 1977 with their
countrynnaionallty labels verbatinn (e.g.. 'Germany' for 1983, 'Japan' 1980, etc.).: 2024 Samuel
Seungwon Lee- South Korea 2021 Dmitry Matvienka Belarus - 2018 Ryan Bancroft United States 2015 Tung Chich
Chuang Taiwan- - 2012 Rafael Payare- Venezuela- - 2009 Joshua Weilerstein United States 2005 Mci-Ann Chen United
States 1998 Scikyo Kim - Japan - 1995 Jan Wagner Venczucla- 1992 Jin Wang Austria 1989 Fabio Mechetti Brasil-
1986 Kazufumi Yamashita- - Japan 1983 Claus Peter Flor - Germany - 1980 Maximiano Valdes Chile
Results for tool call crawelppge with arguments 'ur': hetpss//en.wiiippdiiei wwwpiiiorgg//////ioie//e foromeefffrfofooomm.ooooo formererswvercieeeeee sovercign statte" "query': "From the Europe section, extract the exact line listing "German Democratic Republic' (East Germany) as former sovereign state.": No
relevant information
Results for tool call weh search with arguments 'query": 'Claus Peter Flor hiography Bom Leipzig conductor': 1. [Claus Peter Flor/hhtps://en orriipedi...og/wkkiieee Paterr Plorr_ Source: Unknown source Claus Peter Flor (born 16 March 1953, Leipzig) is German conductor. Flor was chief conductor of the Suhl Philbarmenic Orchestra from 1981 to 1984.
2. [Cluus Peter Floorkkilpss//ww w. Jelsnearttiis com//llus peter flor) Source: Unknown source Claus Peter Flor was born in Leipzig in 1953, After completing his studies the violin and clarinet in Weimar und Leiprig. taking his violin
3. [Claus Peter Flor- Solia Phillhurmnnic hhttp://oliihhhhhannneet com//n//rtisttskllaaa peter- flan') Source: Unknown source Conductor
Malaysian Philharmonic Orchestra. Born Leipzig 1953, Maestro Flor began his musical career violin tuition in Weimar and Leipzig....
4. Claus Peter Flor (1953-) is a German conductor born... ...]
<htiss//wwww. www.scebook.ccom//// com/groups// 41 801632489944000 622889047 (oSS0077pott/99660777 9936676200006999999 Date published: 2 years ago Source: Unknown source Claus Peter Flor (1953.) is a German conductor born in Leipzig. Flor studied violin and clarinet at the Robert Schumann Conservatory in Zwickan....
https///ww..scboo...k
5. [Claus Peter Flor hthlpp//attsaddeullurr poclluureegoggge.co wwggl.commeeitty/lauss peter Blorhm@blssnn Thi-en) Source: Unknown source Born Mar 16. 1953-. Claus Peter Flor is a German conductor. Flor studied violin and clarinet Rohert Schumann Conservatory Zwickau. He contioued his
Results for tool call web sebsscach with arguments "query": "1983 Malko Competition Claus Poter Flor winner': 1. [All Win- weo/hllsss/mkkoooopeetee wal.:oopeetiioo.kkvvneee.aa wwwwinners///ll) Source: Unknown
1983. Claus Peter Flor. Germany. 1980. Muximiano
Valdes. Chile. 1977. Philip Greenherg. United States, 1974, Gotthard Lienicke. 1971. Winston Dan Vogel. United ...
2. [Malko Competition](httpss /ttrtiioonnittsss///ee//no
w/n../ped// wwwipedia.icooommmm org/wiii/Mallo.oao.... wtt.///aalo..oooeettiiooo Competition) Source: Unknown source Recipients ; 1983, Claus Peter
Flor, b. 1953; ; 1986, Kazufumi Yamashita, b. 1961: : 1989, Fabio Mechetti, b. 1957: ; 1992, Jin Wang. b. 1960.
Batons, a Barely Visible Alchemy] J(htps://wwwe wwtpp////wwww///ss....mm./////.. comm 201 4/12/1 //ats//ssiccllim-cceeee flick- 3. [In a Battle www----rrr--ii--o---------- wtt-----ffoo-ooomaae html) Date published: Doe 10, 2014 Source: Unknown source The Malko in Copenhagen. patpuctiing-piz-ii-rr------
which began in 1965, produced Claus Peter Flor in 1983 and Rafael Payare in 2012. but few others of real note.
http://wwwwencccpeeeei.... wtw../omm/r///cccttoueeeee thesauruses pictures and press releases/Boee claus petes-eterrOg Source: Unknown source ... Competition in Katowice, and sabsequently took prizes in the Kuhelik Competition in Lacerne (1982) and Malko Competition Copanhagen (1983), From...
4. [Flor, Claus
5. [Malko peoppeiiion(htpps////eee fww.hook.com//aaaaa oenmmeiinncooettiioon) Snurce: Unknown source Claus Peter Flor, David Robertson, Mikael Beier Rie Koch, Christina Astrand, George Malko and of course the faboulous DR Symfoniorkestret. Natalia... ...
Step 7 Assistant
<think We have four goals to advance purallel. Goal (authoritative recipient list with nationalities) is satisfied the crawled Wikipedin page and the official Malko site, which list winners post-1977 with countries. Goal 2 (identify which recipient( () after 1977 have nationalities tied to u defunct country) requires confirming that listed country for winner corresponds to former sovereign state; the strongest candidate is 1983 winner Claus Peter Flor listed as Germany, which could re reflect GDR (Fast Germany). We will verify the official listing shows 'Germany" for 1983 and confirm GDR is listed as a former sovereign state. Goal (confirm uniqueness and extract first name) will be validated by scanning all post-1977 winners and casuring no other winner's 's' listed country is a defunct siate; we will also corroburate the 1983 entry via an independent source. Goal 4 (resolve discrepancies and document evidence) will he supported by capturing explicit lines from authritative pages. We'll execute the following in parallel: (a) crawl the official winners page to extract the 1983 line verbatim, (b) crawl the list of former sovereign states to extract the GDR line; (e) web search confirmation of Claus Peter Flor as 1983 winner <hhink> <tools>["" nammm" "ooas>>""nmme
independent confirmation of 1983 Malko winner; (i) web search for an official/press
(Danish Radio)
.aaee. "argumeuss": "url": "htpps// /mpps//malooooomcciiinnooo wwtppptttiooodwwineeeeeeee dk/winness/all". "query": "Retur the exact winner and the country label as displayed." "name": "crawl_page", "arguments": of former sovereign, states". "query": "Tind and extract the exact line listing 'German
line for 1983 verbatim, including the winner's
"urt": "hitpsc//enn wikipediii orkgppedi...g/wikkkll wtg/wwii/listttt.c...... Democratic Repablic' in the Europe section.", ", "name": "weh "veh_search", "arguments"" "query": "Claus Peter Flar 1983 Malko Competition winner", "name": "web_search". "arguments": "query": "site:dr.dk Claus Peter FlorMalko 1983" "<s/tolssss>
Step 8 User
Results for tool call crawl_page with arguments 'url': hhtpss//nalkooommtttiinn dk/winnerss/lll" 'query': "Retim the exact winner line for 1983 verbatim, including the winner's 's name and the country label displayed.": " 1983 Claus Peter Flor Germany
Results for tool call crawl_ _page with arguments 'urt': "htrps:/en.wwiippeee www/wwki/l istikiiiiiis.fooi. of forroeersoverriieeeeeeeee states', 'query': "Find and extract the exact line listng GGemma Democratic Republic' in the Europe section." ": No relevani information
Results for tool weh search with arguments 'query:: "Claus Peter Flor
Malko Competition winner': [All Win- weeoondkwwiinnnn dk/winners//ll) Source: Unknown source 1983. Claus Peter Flor. Germany, 1980. Maximiano
Valdes. Chile. 1977. Philip Greenberg. United States. 1974. Gotthard Licnicke. 1971. Winston Dan Vogel. United ...
2. [Malko Competition whwpps//hhhhii..n.eee/ wwkipedd...iiii...c...mm..m..aa http:pedii..gg/wwk//aaa.aa. wrg/owki//Makkko.. Competition) Source: Unknown source Recipients: : 1983, Claus Peter
Flor, b. 1953; ; 1986, Kazufumi Yamashita, 1961; 1989, Fabio Mechetti, 1957: ; 1992 Jin Wang. b. 1960.
3. [In a Battle of the Batons, Barely Visible Alchemy htttps///wwwttthhhtt
com/20) 4/12/1 /aart//mssicellmm-aaancc
conducting prize is rare win. for a woman html) Date published: Dec 10, 2014 Source: Unknown source The Malko in Copenhagen, which hegan in 1965, produced Claus Peter Flor in 1983 and Rafael Payare in 2012, but few others real note.
4. [Malko Competisio](tttps///e httpttps:///ww///tt/ www.aaaccccco//nnnonn / Source: Unknown source Claus Peter Flor, David Robertson, Mikael Beier Rie Koch. Christina... ... CONGRATULATION TO THE WINNER OF MALKO COMPETTTION 2024! "I am going tell
37

---

"quory": Published as a conference paper at ICLR 2026
thesauruses pictures and press releases/llor claus claus-peter D) Katowice, and subsequently took prizes in the Kubelik Competition Lacern (1982) and the Malko Competition in Copenhagen (1983), From
5. [Flor, Claus
Source: Unknown source
Results tool call web search with arguments 'query": "site:dr.dk "Claus Peter Flor" Malko 1983': 1. |Alle tiders progamnweeiggge//bng////eeee http:://wwww.rr..r
&t0-1 188-00-31&ddae 198900--999--12299 Source: Unknown source Dirigent er Malkooprissinddreen fra 1983, Claus Peter Flor. Produceret Fersshen der DDR. TVTeater- og Musikaldelingen. (30-505684 50) Balletmusik af
Step 8 Assistant
<think> We have four goals to udvance in parallel. Goal (authoritative recipient list with nationalities) is satisfied by the crawled Wikinedia page the official Malko winners page, which list winners post-1977 with countries. Goal (identify which recipient(s) after 1977 have nationalities tied to a defunct country) points to 1983 winner Claus Peter Flor listed as Germany, to align this with a country that no longer exists, we must verify that East Germany (German Democratic Republic) is considered a former sovereign state and that Flor East German by birth (Leipdgg GDR). We'll extract authoritative line from Wikipedia's 's list of former sovereign states and confirm Flor's 's Leipzig hirth (GDR) from his hio. Goal (confirm uniqueness and extract the first name) requires ensaring 1977 winner's recorded nationality corresponds Brazil, Austraa, Venezuela, Taiwan, United States, Belarus, South Korea none defunct. We'll finalize the first name as 'Claus'. Goal 4 tresolve discrepancies and document evidence) will he supported by capturing explicit lines from former states list and Flor's 's's bio. Exccuting these tool calls in parallel now </thnkk>
other p95t-1977
defunct country; earlier crawls show others are Chile, Japan,
"query": line containing "German Democratic Republic' from the Euroge section to confirm it listed among former sovereign states"", ", "name": "crawl_pagee. "arguments": "url": "hitps://nnwwike wikipedia. org/wik//C/nuu Peter_Flar", "query"; "Extmet the lead sentence including birthplace; confirm he was born Leipzig and the parenthetical birth datelyear." ", "name": "erawl_page", "arguments": : "urf": htps://malkccopetiiii wttpe/iio..d/wwineeeeeeeee dkk/winness/lll, "query": "Return the exact line for 1983 showing 'Claus Peter Flor' and the country label as displayed "I "J</tools>-
<tools>| I"name": "crawi_page". "arguments": "urf": "hitps///en "httss//en.wvkkipeiiii wikipedia.org org ang/wiki/List.df of oof.forrr.ssvvrriiee...
"Extract the
Step User
Results for tool call cruwl, page with arguments 'url': 'hitps//en.wwkkki www.seiwiiippapeeeee... org//wkk/LLii of former, wowereignnstaleeeee statessss. "query': : "Ex-- tract the exact line containing Gerrman Democratic Republic" from the Furooee section to confirm it is listed among former sovereign gn states.": relevant information
Results for tool call crwwl page with arguments 'url': "htrps://en.wiif wwkipedia..rg/wwilkeee arg/ Peter Flor', 'query": TExtract the lead sentence including birthplace; confirm he was born in Leipzig and the parenthetical birth date/year. Lead sentence (exact): "Claus Peter Flor (been 16 March 1953, Leipzig) is a German conductor." - Confirmation: Birthplace Leipzig: parcathetical birth datefyear - 16 March 1953 (1953). Supporting metadala on the page: calegory "1953 births" and "Musicians from Leipzig." Results for tool call crawl _aage with arguments 'url': http://mmlkccopettiiin... wttppeetiiionnwwwiieeeeeeee www////eer//aal.". "query': "Return the exact line for Germany
caleggyy
1983 showing 'Claus Peter Flor' and the country label as displayed..; ": - 1983 Claus Peter Flor
Step 9 Assistant
<think> We have four goals to advance in parallel. Goal (authoricative recipient with nationalitiess is satisfied by crawled Wikipedia page and the official Malko site, which list winners post-1977 with countries. Goal (identify which recipient(s) after 1977 have nationalities tied to defunci country) hinges on confirming that East Germany (German Democratic Republic) is former sovereign and that the 1983 winner's nationality effectively cortesponils that defunct country. We have partial confirmation (East Germany existed 1949-1990; Claus Peter Flur bora in Leipzig. 1953, thus East German by birth), but we still need an explicit former sovereign states Goal (confirm uniqueness requires checking that no other 1977 winner's is recorded nationality corresponds to a defunct country; our earfier sweep suggests none (Chile, Japan, Brazil, Austria, Venezuela, Taiwan, United States, Belarus, South Korca), Goal (resolve discrepancies and document evidence) will be strengthened by obtaining explicit citation that "Gemaan Democratic Republic is former sovereign state. I will therefore: (a) search for an alternative authriitative list explicilly containing" 'German Democratic Republic' ; (b) allempt targeted query; and (c) add a third search variant to maximize chances. These can done in purallel as they are independent. Once confirmed, we can confidenaly conclude the unique case is 1983 winner Claus Peter Flor and provide the first name 'Claus' </think>
listing 'German Democratic Republic'
extract the name)
the English Wikipedia list again with
<toolss-["nn I"name"; "web search", "arguments": "query": site-wikipedia.om org, List of former sovercign states, German Democratic Republic, "uame": "crawi_page". "arguments": "utl: "tttps//enn.wiiipeelli ottp:://w....keeei..oo../////// wtt.///ii.......ooooooooo fow.oistttoooommooooooooooo www.mmor.oerrinnntttc.oo
"Search the page for the string "German Democratic Republie' and return the surrounding list context or the exact lime indicating it is included." ", "name": "weh_searchi, "arguments" "query": "List of former sovereign states German Democratic (opublie]<<cttooii
Step 10- User
Results for tool call weh_search with arguments 'query': wiippeiiiii "List of former sovereign states" "German Democratic Republic"": 1. [List of former sovereign states http://e.w.wkippiiii... wwt.://wwwwkcccii...g/kk///////e wrg/wikk/LLittt of former sovereign states) Source: Unknown List of former sovereign states. Article Talk. Language; Loading... Download ... German Democratic Republic (1949. (1949-1990) (also known as East Germany or GDR) ... 2. [List of sovereign states by date formation (hitts:://htsst//memm// wrkipediaa.og//wkkiii www.eoossveeeinsttteeeee www.oyyddatt..ofommaaiiii
Source: Unknown source German Democratic Republic (commonly referved to at the time as East Germany). List of former sovereign states List national constitutions List ...
3. commercouuntioo in Europe after 1815 (ittpsss// htttttps///e.wwwipeiiiaaaa wtt.g//wwwkooooo.ooonss.... wtt.triossii..ureeuutieo Burope_after_111
Source: Source Unknown source Sovereign countries: German Democratic Republic, 1949, 1990: ; Georgia (Democratic Republic), 1918, 1921 ; German Empire, 1871, 1918: : Greece (Kingıkmm) 1832, 1924...
4. [File:Flag of Fast Germany (1959-1990).svp (w9.))..g(hhppp///ee...... wwtp:/mwwwwiiiiiii...... www.iiipeiii.o.//ii//iiii... Flll:::lig.mapoffff Base_aase_eemaaaa Germany ( (1959Source: Unknown source List of former sovereign states UseerArriCeetiion. Usuge on es wiiiiiieeiiaoorgg ... SVG maps of the German Democratic Republic- SVG national flags of Fast
5. [CountryXhnpee /hrpps//en wikipedia org/wiii/oonnrryy Source:
Unknown source World portal Country (identity) Lists by country. List of former sovereign states.. German Democratic Republic, Federal Republic of Germany (but Germany)...
Results for tool call cnawl page with arguments 'url:: "hilps//en. wwkipedia..rr///eeoeee otg////k/.g////kk/..i. of former sovereign states", "query": "Search the page for the string 'German Democratic Republic" and return the surounding list context the exact line indicating it included." No relevant information
38

---

Published as a conference paper at ICLR 2026
Resulls for lool call web.earch with arguments 'qurry: "Liist former sovereign stales stalls" "German Democratic Republic"", 1. [List of former sovereign stntesshtrps:eeen.ilii www.m/iiiiiiedi.rg/ii.... wrwpeiii..//iiiiiiiii.... [erfooee_ssvvrrreee states) Source: Unknown source List of former sovercign states. Articlo Talk. Language; Loading... Downlond German Democratic Republic (1949-1990) (also known as East Germany GDR) ...
2. [List of sovercign states by date of formation](httss//enn htttipss//ee.wikieiiiiie org/wikifLitt List of soveervenngn states by date of formation) Sourcc: Unknown source German Democratic Republic (commonly referved to at the time as East Germany) List of former sovereign states List of national constituiions List of
3. [List of former sovereign states hhalpss/////////e-aaae wam--aaallmmcccm.m.. www/ddi..ssiemmmiiee...........e nsfiemwikiii 825029) Source: Unknown source List of former
sovereign states, List of former sovereign states. This page ... the German Democratic Repablic (Fast Germany) in 1990 joined the Foderal
4.
[Countries that Longer Exist 2025 hhaups:/wwoniiopllaaaonnvveeeeee coom//oonttyyanniinnooooooo -hat--t-ttt-----oee no--oooo-e-exiite Unknown source German Domocratic Republic (East Germany). 1949, 1900, Germany. German Empire List of Former Sovereign States Wiki. Other Categories.
Sourec:
5. [Former countries Europe after
Europe Samppe.aller.185. 1815 Source:
Unknown source German Democratic Repablic ... See also. List of historic states of Germany List of historic states Italy.
General. List of former sovereign
# Note: Now, you should ma the task completion status and provide recommendations for next steps.
Step 10 Assistant
<think>Lett me unalyze the completion status of the plan based on the conversation before.</thikks <surmmrry>> Mh Plan Summury
The original plan had four goals with multiple execution paths:
Goal I: Identify authoritative lists of Malko Competition recipients (post-1977) with nationalities
Path I.I: Wikipedia verification crawl
- Path 1.2: Official competidion/achivvl sources
Path 1.3: Reputable music databuses and encyclopedias
Path 1.4: News/leatures aggregation
Goal Desermine which recipient(s) after 1977 have nationalities tied to country that longer exists
- Path 2.1: Cross-relereenee nationalities against lists of defunct countries
Paat 2.2: Historical sovereignty check for ambiguous
cases
Path 2.3; Edgo-case validation (regions vs, countries)
Goal Confirm uniqueness (anly one such recipient after 1977) and extruct the first name
Path 3.1: Exhaustive post-199 1977 sweep and counting
Poth 3.2:
Independent corroboration of the unique candidate's nationality
Path 3.3:
First-name
and consistency check
Goal Resolve discrepancies and document evidence trail
Path 4.1: Source prioritization conflict resolution
Path 4.2:
Evidence packaging for auditability
## Exccution Status Analysis
Identify autboritative lists Malko Competition recipients (post-1977) nationalities
Status: Completed
Path Analysis:
Path 1.1 (Wikipedia- verification crawf): Completed. Crawled the Wikipedia "Malko Competition" page and extracted the
Recipients list 1997 with implied nationalitieslcummiies (e.g.. United States, Venezuela, Taiwan, Japan, Chile, Germany).
Path 1.2 (Official competition/archival soureas): Completed. Crawled the official Malko site "All Winners" page and extracted
the winners list post-1977 with explicit country labels (o.g.. "Germany" for 1983 Claus Peter Flor: "United States" for 2005 Mci-Ann Weilerstein, 2018 Ryan Bancroft).
Chen, (Reputable music datahuses/encyclegg (ecceeepsencclleeisss).. Partially touched. Frund encyclopedia.com entry confirming Claus
Path 1.3
Poter Flor's 1983 Malko win; not yet systematically applied to all names for nationality correbontion.
Path 1.4 (News/fentures aggregation): Not executed beyond incidental NYT hits; required for enumeration.
Result consolidation:
Post- 1977 winners and countries (Trom Wikipedia and official site]:
1980: Maximiano Valdés
Chile
1983: Claus Peter Flor Germany
1986: Kazufuri Yamashita Japan
1989:
Fabio Mechetti - Brazil
1992: Jin Wang Austria
1995:
Jan Wagner Venezuela
Seikyo
Japan
2005: Mci-Ann Chen - United States
2009: Joshua Weilerstein United States
2012: Rafael Payare Venezuela
2015: Tung-Chich Chuang - Taiwan
Ryan Bancroft United
2021:
Dinitry Matvienko Belarus
Samuel Seungwon Lee
### Goel 2:
Determine which recipient(s) after 1977 have nationalities tied country that no longer exists Status: Progress (functionally resolved; documentation could be tightened)
Path Analysis:
Path 2.1 [Cross-reference nationalities against defunct countries): Partially completed. contirmed Germany
(German Democratic Republic) ceased to exist in 1990 and that Claus Peter Flor was horn in I Leipsig (1953), ie., in the GDR.
39

---

Published as a conference paper at ICLR 2026
llowever, atlempls to extruct the exact "German Democratic Republic" line from the Wikipedia "List of former sovereign states" page crawl returned relevant snippet (likely due dynamic rendering). We did obtain search snippets indicating its presence on that page.
Path (Historical sovereignty check ambiguous cases): Completed. Crawled "East Germany" page contirming sovereign state (1949-199) 1990) that ocased to exist upon reunification.
Path 2.3 (Edge-case validation); Completed. Validated that Taiwan is not a sovereign state in the conventional scuse thus does qualify; other countries (Chile, Japan, Bruzil, Austria, Venezuela, United States, Belarus, South Korea) extant.
Interim conclusion:
Among post- 1977 winners, only 1983 winner Claus Peter Flor's resorded nationality ("Germany") plausibly maps country
that no longer exists in its sovercign form (the German Democratic Ropublic, East Germany). No other post-1977 winner's country
record defunct.
Blocked/ineffiiion aspects and optimizations:
TThe repeated attempes to extract the exact "German Democratic Republic" line from the "List of former sovereign states" page via crawd were ineflicient and returned content. Optimize by:
Usiin a static mirror (e.g) Wikipedia's mobile view or the PageContent handler) or an archived snapshot.
-Querying an alternative reputable list (e.g.. "Forner countries in Europe after 1815") where the GDR line casily extractable. Searching for the exact string on a text proxy (e.g.. r.j.aajjaaa) to return the raw HIML snippet.
### Goel 3: Confirm uniqueness (only such recipient after 1977) and extract the first name
Status: Completed
Path Analysis:
Path 3.1 (Exhanstive post-197 sweep and and counting): Completed. Enumerated all winners post-1977 1977 and checked their countries;
only 1983 (Claus Peter Flor) fits the defunct-country criterion when interpreted East Germany/GDR.
Path 3.2 (Independent corroboration of the unique candidate's nationaity):: Completed. Wikipedia bio states "(born 16 March winners page lists him under "Germany." Additional reputable
German conductor," and the official mentions (NYT, encyclopedia.com coutimm his Malko win in 1983.
1953, Leipzig)
Path 3.3 (First name exiraction and consistency check): Completed. First name consistently "Claus" actoss sources.
Goal 3 result:
Unique qualifying recipient (post-1977):: Claus Flor.
Firsi name to repoet: Claus.
HHi 4: Resolve discrepancies document evidence
Status: Progress
Path Analysis:
Path 4.1 (Source prioritization and conflict resolution): Completed. Prioritized official Malko site and Wikipedin; resolved minor
labeling differences (o.g.. "Germany" vs. "East Germany") by historical confirmation that Leipzig 1953 = GDR.
Path 4.2 (Evidence packaging): Partially completed. have:
Official winners list (malkaccumpetidng http://ommpiiiio/////eeeeeeeee mi.md///ww/ee/all) showing 1983 "Claus Peter Flor Germany."
Wikiiedia "Malko Competition" page listing Claus Peter Flor.
Wikippddia "East Germany"
confirming the GDR cxisted 1949-1990 and ocased to cxist.
Wikipedia "Claus Peter Flor" page confirming hirth in Leiprig (1953).
Scarch snippets indicating "German Democratic Republic" Wikipedin's "List of former sovercign states."
Missing: a directly crawlable, citable line explicitly listing "German Democratic Republic" from a reputable "former countries"
page.
Optimization for evidence packaging:
Instead of sepeatedly querying the standard Wikipedia page, fetch the mobile view or use the content eudpoint:
- Try: https:/en.m m wiikipedia.or//wikke org/wikifl of former sovereign states (then search for "German Democratic Republic")
-O:: https://en, wikipedia. wrg/w/indxx.//gggoeooo php hittti----tstoooo wfw.eets.oooooorroooooooooo www.seeeigg.gttttteettiiiiooeeee atttessattiionseeed (then search for the string).
Dependencies alfecting parallel execution:
Goal 3 depended on Goal 2's determination that only 1983 fits the definct- country criterinn; this dependency resolved. Goal 4's evidence packaging can proceed independently of others but benefits from finalized conclusions of Goals 2 and
## Next Parallel Sub-Paths
Based current exccution status, the following sub-paths should be solved in parallet:
Gual 2:
Sub path: Capture citable line that "German Democratic Republic (1949. 1990)" appears a reputable "former sovereign
states" Tist.
Actions: Crawi bitps://en.mm http::/www.wikkiiid..e/iiillll.a of Jormer sovereign states and extract the exact line containing "German Demo-
cratic Ropublic."
Alternatively, crawl https//enn https///en.wkkieeddii http://pddiiogg//w////o//ooooooe www.:/mmeeeoooutrssiii... Europe.after..... Democratic Republic, 1949, 1990."
extract the "German
Goal 4:
Sub--pth: Compile a concise ovidence bundle with verbatim quotes and URLs: Germany."
Official winners entry: "1983 Claus Peter
Flor
-East Germany page: "The GDR ocased to exist... ... on October 1990."
Peter Flor bio: "bom March 1953, Leipzig." Forreer states list: "German Democratic Republic (1949. (1949.1999).. 1990)."
Goal I (optional hardening. low priority):
Sub-path: Add one non-Wikken Wak-piiipe corroboration for at least one post-1977 winner's nationality (e.g.. GRAMOPHONE or official urchestra biss Mei Ann Chen, Joshua Weilerstein) to further validate the "na defunct country" status for others.
answer readiness: unique qualifying recipient (post-1977) 1983 winner Peter Flor; recorded nationality is Germany, corresponding to the former sovereign state German Democratic Repablic (Fast Germany). Therefore, the first name is
40

---

Published as a conference paper at ICLR 2026
"Claus." All goals necessary to support this conclusion are functionally complete; remaining work purely on lightening citations Goal 4, <luummary>>
Step 11 User
Based on the plan/summary and previous conversations, continue solving the task!
Step 11 Assistant
<think= We have four goals to advance in parallel, Goal (authoritative lists) is already satisfied with Wikipedia and the official calls are needed there. Goal 2 requires explicit verification the country tied the qualifying recipient listed as lormer sovereign state; our prior attempts to extract the exact line from the desktop Wikipedia page failed. so we' Il use the capture
Malko no
(uniqueness and first name) effectively resolved, but we will maintain parallel progress toward Goal 4 (evidence packaging) by securing clean citations. Accordingly, in this will: (a) crawe mobile Wikipedia "List of former sovereign states' to extract GGrrman Democratic Republic" line; (b) crawi the "Former countries Europe after 1815' page extract the GDR entyy allernative authoritative source: and (c) reconfirm the official Malko winners page entry for 1983 to have a clean verbalim line for evidence packaging. These calls are independent parallel, <Athink>
mobile version and allernative repuiabble
line.
<touls>>"" ["name" "crawl.gagee. "arguments": "urf": "hups:/len.m wwlipediiu.rg//wkkkii oft.g/////////iioooooooo.. former. forner.suuereeeeeee.. sivereign.sstttes""" "query": "Find and return GDR) to confirm is listed among former sovereign states.". "name": "crawl_page" "arguments": "url": www.//pedia.og/w.iiie.e.ee.eeeee
exact line containing 'German Democratic Repuhlic' (Fast Germany
countries Europe after 1815", "query": "Extract the line listing 'German Demo- cratic Republic' with its yeurs confirm
former country.". "name": "crawi puge", "arguments": "url":
"query": "Return exact line for 1983 showing "Claus Peter Flor" and the coun-
try label displayed." "|</ooos>>
Step 12 User
Results for tool call crawl with arguments 'url'; 'https//en m. wikipediia org/wiki/List of noverrrsoverignn _ttttss,, 'query": "Find and return the exact line containing 'German Democratic Republic" (East Germany or GDR) to contirm listed among former sovereign states.": relevant information
Results for tool call craeelpage with arguments 'ud': "hitps//enn wwkkpcdi..og//wkk/ooooo... counttres..ntticcinc Buroii.....eaatteeeee Guropc_after_1811 "query": "Extratt
lormer country.": German Democratic Republic Estahlishment: 1949; Dissolution: 1990; Today part of: Germany. Note; Also known East Germany; was Soviet-controlled government of Germany after World War
listing 'German Democratic Republic' with years confirm
Results for tool call crawl_page with arguments 'urt:: https//mallocooncttiion http://ntttttoooooiieeeeeeeeeeee diwwinncrs/llll"" 'query": "Return the exact line for 1983 showing 'Claus Peter Flor' and the country label displayed.". 1983 Claus Peter Flor
Germany
Step 12 Assistant
satisfied Wikipedia 'Malko Competition and the official Malko 'All Winners' page, both crawled and listing winners post. 1977 with countries. Goal (identify which recipient(s) 1977 have nationalities tied defunct country) resolved by confirming that the 1983 winner, Claus Peter Flor, was born in Leipzig (1953) and thus East German; East Germany (German Democratic Republic) is confirmed as former sovereign state via the 'Former countries in Europe after 1815' page. Other post-1977 winners' countries (Chile, Japan, Brazil, Austria, Venezuela, Taiwan, United States, Belarus, South Korea) are is satisfied: only 1983 fits the defunct-country criterion; first name is consistently 'Claus'. Goal 4 (evidence packaging) has sufficient citations: ollicial winners entry (1983 Claus Peter Floe Germany), ). Wikipedia countries list (German Democratic Republic 1949-19999 All goals are resolved; procedd to final answer </tink>> <tools><"" "aam" "fiwalannserr. "arguments"; "answer"; "Claus") </toolss>>
<think> have gouls
parallel. Goal (authoritative
Goal (confirm uniqueness and extract the first name)
Germany page (ceased 1990),
Final Answer
Answer: Claus
41


Reviews:
[{"review_id": "HuAREG9Qqe", "reviewer": "Reviewer_i6Xv", "summary": "The paper identifies a primary bottleneck in current tool-augmented Large Language Model (LLM) agents: inefficiency due to sequential processing. Existing frameworks, including MAS, typically execute tasks (e.g., plan, search, reflect, verify) in a linear chain. This approach leads to long execution times, high latency, and significant computational overhead, especially for complex web-based research tasks.\n\nTo address this, the authors propose FLASH-SEARCHER, a novel agent reasoning framework that fundamentally changes the execution paradigm from a sequential chain to a Directed Acyclic Graph (DAG).\n\nThe authors validate this framework on four challenging web agent benchmarks (BrowseComp, xbench-DeepSearch, GAIA, and HLE). They also demonstrate that the parallel reasoning trajectories generated by the FLASH-SEARCHER framework can be \"distilled\" via supervised fine-tuning (SFT) into standalone open-source models (like Qwen-2.5), significantly improving their performance without the need for the full, complex framework.", "questions": "* Have the authors considered cross-validating the evaluation results with other models? Since model-based evaluation is used, it might be prone to reward hacking.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The paper tackles a clear and critical problem in agent research: latency and efficiency. The shift from a sequential to a parallel, DAG-based execution model is a novel and logical architectural contribution.\n\n* The evaluation is comprehensive, using four different and challenging benchmarks. The authors correctly measure not just final accuracy (effectiveness) but also the primary claim of efficiency (execution steps, tool calls per step).\n\n* A 35% reduction in execution steps is a substantial improvement. The paper provides clear evidence (Fig. 6) that this gain is real and is a direct result of the parallel architecture.\n\n* The success of the distilled models is a strong result. This demonstrates that the reasoning paradigm itself (i.e., how to structure parallel thought) is a learnable skill. It proves the contribution has value beyond just a complex, expensive-to-run framework, making the findings more accessible to the wider community.", "weaknesses": "* The main results comparing against other agents (e.g., BrowseMaster) which use different backbones from the one used in the paper. For instance, BrowseMaster uses DeepSeek-R1-0528 backbone while the authors use GPT-5 backbone. It is not clear how much gains are from the proposed method vs the backbone model.\n\n* It is not clear why the authors chose to use just two tools: web_search and crawl_page. Is it because the framework is not flexible to generalize to more tools or some other limitation? The authors acknowledge this limitation (Section B), noting the poor performance of the distilled models on HLE (19.4%) is due to the lack of a code tool.\n\n* The paper mentions (Section 3.2) that subtasks can be scheduled without all prerequisites being met, based on some heuristic consistency. This is a pragmatic choice to maximize parallelism but can potentially be risky. The paper does not elaborate on what these heuristics are or how it handles cascading failures if a parallel step proceeds with incorrect, unverified information. This could potentially increase total steps if backtracking is required, a scenario that is not explored.", "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": "psoK3pQyhv", "reviewer": "Reviewer_uJW2", "summary": "This paper presents Flash-Searcher, a DAG-based parallel agent reasoning framework that achieves impressive empirical results (67.7% on BrowseComp, 83% on xbench-DeepSearch, 35% reduction in execution steps) with solid methodological innovation. The core technical contributions, parallel execution architecture and adaptive workflow optimization, are valuable and well-executed. However, the writing quality exhibits AI-generation artifacts. These should be revised in the formal version to meet the requirements of ICLR.", "questions": "For an agent framework, computational cost is critical. Please provide concrete costs in dollars in the appendix.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 2, "contribution": 3, "strengths": "1. Strong empirical results across multiple challenging benchmarks. The paper achieves state-of-the-art performance on four benchmarks (BrowseComp: 67.7%, xbench: 83%, GAIA: 82.5%, HLE: 44.0%) while reducing execution steps by 35% and time by ~65%. The cost-performance trade-off is compelling.\n\n2. Solid methodological innovation in parallel reasoning architecture. The DAG-based decomposition with goal-level parallelization and path-level sequential execution is elegant. The cross-validation mechanism (φ predicate in Eq. 4) cleverly balances aggressive parallelization with correctness guarantees.\n\n3. Comprehensive experimental validation.\nExperiments span multiple model backbones (GPT-5, GPT-4.1, DeepSeek-V3.1) and include successful distillation to open-source models (Qwen-2.5 family), demonstrating generalizability.\n\n4. Exceptional reproducibility effort.\nThe 27-page appendix with complete prompts, training data construction pipelines, and hyperparameter settings is commendable and rare in agent research.", "weaknesses": "The paper exhibits heavy AI writing artifacts with excessive use of em dashes; recommend substantial revision.", "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": "drAVBRQGVe", "reviewer": "Reviewer_Ridk", "summary": "The paper introduces a novel framework specifically designed to address the latency and computational inefficiency bottleneck found in existing sequential LLM-based frameworks for complex reasoning tasks. \n\nIts contributions include: \n1. It introduces a dynamic DAG-based framework for agent tool orchestration, supporting controlled aggressive parallelization that relaxes strict dependency constraints to maximize concurrent execution.\n2. It demonstrates state-of-the-art performance on multiple benchmarks (BrowseComp, xBench-DeepSearch, GAIA, HLE) while reducing execution steps by up to 35% compared to prior methods.\n3. It validates the parallel reasoning paradigm is scalable and transferable, through distilling FLASH-SEARCHER trajectories into open-source models (e.g., Qwen-2.5 series) via lightweight supervised fine-tuning.", "questions": "1. Could you clarify how cross-validation between parallel subtasks is implemented and whether it introduces additional computational overhead? Specifically, \n1.1 Auxiliary Signals: What specific information is captured as an \"auxiliary signal\" during partial execution\n1.2 Heuristic Check: What is the actual heuristic rule used for the \"consistency check\" that determines if the dependency is satisfied enough for aggressive parallelization (e.g., an LLM prompt asking for consistency, a deterministic keyword match, etc.)?\n1.3 Computational Overhead: Please quantify the cost of this feature. Does this cross-validation process introduce significant new LLM inference calls or state-management overhead, and how does this overhead factor into the claimed 65% overall execution time reduction?\n\n2. Could you clarify the Pass@1 score calculation: Specifically, does the automated judge explicitly penalize the answer for extraneous or hallucinated content, or is it only looking for the presence of the single correct fact?\n3. Do you have a quality metric for the LLM-as-Judge to validate that the automatic evaluation method is a reliable proxy for human assessment?\n4. Do you have any latency analysis beyond the reduced steps? \n5. Did you observe any failure patterns or instability when the system aggressively parallelizes subtasks (e.g., dependency violations or premature merges)?\n6. SFT usually will introduce the overfitting, can you elaborate on whether they retain explicit DAG reasoning capability or merely learn implicit parallelization patterns?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. Novel parallel framework: shifting LLM agent execution from sequential chains to a dynamic Directed Acyclic Graph (DAG) for parallel tool orchestration. This framework does strike a balance between theoretical soundness and practical efficiency.\n2. Clear methodology: The DAG construction, readiness predicate φ, and adaptive progress refinement (R) are formally defined and explained clearly.\n3. Comprehensive evaluation: Uses both framework-level and model-level experiments, including efficiency analysis, ablation on tool calls, and cross-benchmark validation.", "weaknesses": "1. The central distinction and novelty of the framework do not lie in the DAG structure itself which is standard for task scheduling, but rather in the two specific optimization modules that enable parallel execution and dynamic refinement. These core innovations currently lack the technical detail necessary for reproduction:\n\n1.1 Ambiguity in Cross-Validation: The mechanism for \"controlled aggressive parallelization\" is technically underspecified. The paper introduces the readiness predicate ($\\varphi$) that allows starting a task based on \"auxiliary signals\" and \"heuristic consistency checks,\" but fails to detail the low-level implementation of this heuristic or what constitutes an auxiliary signal, hindering reproducibility. \n\n1.2 Ablation on Dynamic Update Interval ($\\Delta$): The system relies on a periodic update interval ($\\Delta$) to balance overhead and performance. However, the paper does not include an empirical ablation study to justify the chosen default of $\\Delta=7-9$ steps, leaving the claim of optimal balance unsubstantiated.\n\n2. Limited Evaluation Scope (Simple Tasks): The evaluation focuses exclusively on complex, multi-step tasks where parallelism is advantageous. The paper does not analyze whether the fixed overhead of DAG decomposition and parallel management introduces unnecessary latency compared to minimal-overhead sequential agents on simple, single-step queries.\n\n3. No latency or cost analysis beyond step counts: The claim of ~65% time reduction would be more convincing with wall-clock latency or cost breakdowns.", "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": "z6PnuuJqUG", "reviewer": "Reviewer_URHx", "summary": "This paper introduces Flash-Searcher, a framework for tool-augmented LLM agents designed to overcome the latency and inefficiency of standard sequential execution. The core idea is to transform the agent's workflow from a linear chain (like ReAct) into a directed acyclic graph (DAG). This allows the framework to decompose a complex task into multiple subtasks with explicit dependencies, enabling the concurrent, parallel execution of independent reasoning paths. The authors claim this DAG-based approach, combined with dynamic workflow optimization, achieves good performance on several challenging web-agent benchmarks (e.g., BrowseComp, xbench-DeepSearch) while simultaneously reducing the number of agent execution steps by up to 35%. The paper also explores \"distilling\" these parallel reasoning trajectories by fine-tuning open-source models (like Qwen-2.5), claiming these models also achieve SOTA performance against other models of similar scale.", "questions": "Please solve the weakness provided.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Addresses a Good Problem: The paper tackles the high latency and inefficiency of sequential agent frameworks, which is a major bottleneck for their practical application.\n2. Good Empirical Performance: When using the powerful GPT-5 backbone, the Flash-Searcher framework achieves SOTA results on several difficult benchmarks, outperforming other frameworks (including another GPT-5 baseline, MiroFlow, as shown in Table 5).\n3. Good Efficiency Analysis: Section 5 provides an analysis of the framework's efficiency, demonstrating a concrete reduction in execution steps, which directly correlates to lower end-to-end latency.", "weaknesses": "1. Misleading Experimental Details: In Figure 3, the authors did not clarify which models were used as the backbones for these baselines, making it unclear whether the performance gains come from the framework or the models themselves.\n\n2. Missing Critical Ablation Study: The paper must include a controlled study comparing a sequential ReAct-style agent against the parallel Flash-Searcher agent, using the exact same backbone model (e.g., GPT-4.1, or GPT-5 if possible), tools, and base prompts. This is the only way to scientifically validate the core claim that the DAG-based parallel execution is superior to sequential execution in terms of performance and/or efficiency.\n\n3. Unclear Conceptual Novelty* The paper fails to clearly differentiate its contribution from a crowded field of related work on parallel and graph-based agents (e.g., Plan-over-Graph, ParallelSearch, LATS). The descriptions in Section 2.2 and Appendix B are brief, and the stated differences (e.g., \"relaxed constraints\") are not empirically justified. A comparison table is needed to clarify the specific innovations of Flash-Searcher.\n\n4. Underwhelming Distillation Results: The results for the fine-tuned models (Table 1) are not compelling. The performance gains over existing SOTA fine-tuned models are incremental, not transformative. This weakens the paper's broader claim that it introduces a \"generalizable... parallel agent paradigm\" that can be effectively learned by smaller models. The modest gains suggest the parallel traces themselves may not be significantly more \"correct\" or \"efficient\" in a way that aids learning, and that the framework's main benefit is in runtime orchestration of powerful closed-source models.", "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": "2bBFcbMrih", "reviewer": "Reviewer_aePY", "summary": "This paper introduces FLASH-SEARCHE, a novel parallel agent reasoning framework. The core idea is to reconstruct the agent's execution paradigm from a sequential \"chain\" into a Directed Acyclic Graph (DAG). Specifically, it first decomposes a complex task into multiple subtasks with explicit dependencies (DAG-based Plan Construction); next, it concurrently executes independent reasoning paths and tool calls; finally, it uses an \"Adaptive Progress Tracking & Summarization\" module to dynamically optimize the workflow and continuously refine the execution graph based on intermediate results. Experimental results are good, demonstrate that the framework achieves SOTA performance while significantly boosting efficiency. For example, it achieves 67.7% accuracy on BrowseComp and 83% on xbench-DeepSearch. Compared to baselines (like OAgents), it reduces agent execution steps by up to 35% and shortens overall execution time by 65%. Furthermore, the paper also demonstrates that this parallel reasoning strategy can be \"distilled\" and transferred to open-source models (like Qwen-2.5) via Supervised Fine-Tuning (SFT), achieving significant performance gains", "questions": "Same to Weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1.  **Significant Efficiency Gains and Cost Reduction**  \n    The framework's primary advantage is fundamentally solving the sequential execution bottleneck through parallelization. Experiments show it reduces agent execution steps by up to 35% and shortens overall execution time by 65%. This directly translates into lower end-to-end latency and computational costs, effectively addressing the practical viability issues of existing agents.\n\n2.  **Breaking the Efficiency-Effectiveness Trade-off with SOTA Performance**  \n    While many efficiency-boosting methods sacrifice performance, FLASH-SEARCHER achieves SOTA or highly competitive performance across four challenging benchmarks (BrowseComp, xbench-DeepSearch, GAIA, and HLE) while simultaneously delivering dramatic efficiency gains. For example, it achieves 67.7% on BrowseComp and 83.0% on xbench-DeepSearch, proving its parallel paradigm can complete complex reasoning tasks efficiently and with high quality.\n\n3.  **Validated Generalizability and Learnability of Parallel Reasoning**  \n    The paper's contribution is not just a framework, but also a generalizable methodology. The authors collected parallel reasoning trajectories and successfully \"distilled\" this capability into open-source models (the Qwen-2.5 series) using Supervised Fine-Tuning (SFT). This demonstrates that parallel reasoning is a learnable and scalable inductive bias, providing a new pathway for training more efficient, open-source agent models.", "weaknesses": "### Disadvantages and Limitations\n\n* **Limited Task Spectrum and Capability Trade-offs:** The framework's impressive efficiency gains appear to be achieved by deliberately sacrificing key capabilities. The authors explicitly state that code interpreters were excluded as a \"deliberate design choice\" because managing their parallel execution and output volume would create \"substantial overhead\" and \"severely impact\" the framework's efficiency. This design choice avoids the risk of context explosion and management overhead but results in \"suboptimal performance on mathematical reasoning tasks.\" This limitation is compounded by the Crawl Tool, which truncates web content at 60,000 characters, introducing another source of information loss. Consequently, the SOTA performance is demonstrated on a limited spectrum of benchmarks (i.e., information-retrieval-heavy tasks), and its capability on mixed-tasks requiring complex computation remains a significant weakness.\n\n* **Un-quantified Overhead of DAG Maintenance:** The paper does not provide a quantitative analysis of the *additional computational cost* introduced by the framework's core mechanisms. Specifically, the \"DAG-based Plan Construction\" and the periodic \"Adaptive Progress Tracking & Summarization\" modules must incur their own LLM token costs for planning and summarizing. The paper acknowledges this \"computational overhead\" and suggests it can be tuned via the update interval $\\Delta$, but fails to provide any analysis on:\n    1.  The specific token cost of this maintenance.\n    2.  How this overhead scales with task complexity.\n    3.  A score-cost trade-off analysis by varying $\\Delta$. A thorough evaluation should present a curve showing how task success and *total cost* (execution cost + maintenance cost) change with different $\\Delta$ values, and whether this trade-off curve remains consistently superior to the baselines.\n\n* **Insufficient Baseline Comparison for Framework Evaluation:** The evaluation in Section 4.1 compares the FLASH-SEARCHER *framework* (using GPT-5) against other prompt-based frameworks. However, it lacks a comparison against the most advanced, proprietary \"deep research\" systems, which may themselves be *end-to-end trained* or highly optimized frameworks. The baselines used (e.g., \"OpenAI ChatGPT agent\") may not represent the true SOTA in *efficient* agentic research. While the *model* (Section 4.2) is compared against other trained models, the *framework's* SOTA claim is weakened by not being benchmarked against potentially stronger, specialized, and non-public industrial systems.", "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)