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From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper argues that agentic deep research—an LLM-driven loop of planning, searching, reasoning, and synthesizing—will become the dominant paradigm for information seeking and introduces a test-time scaling law to explain why.

desk verdict A timely, well-organized position paper on agentic deep research whose useful taxonomy is weakened by a scaling 'law' that is really an interpolation of two unrelated benchmarks. read the letter →

arxiv 2506.18959 v3 pith:O6LNXFLX submitted 2025-06-23 cs.IR cs.CLcs.LG

Weizhi Zhang , Yangning Li , Yuanchen Bei , Junyu Luo , Guancheng Wan , Liangwei Yang , Chenxuan Xie , Yuyao Yang
show 15 more authors
Wei-Chieh Huang Chunyu Miao Henry Peng Zou Xiao Luo Yusheng Zhao Yankai Chen Chunkit Chan Peilin Zhou Xinyang Zhang Chenwei Zhang Jingbo Shang Ming Zhang Yangqiu Song Irwin King Philip S. Yu
This is my paper · ORCID
classification cs.IRcs.CLcs.LG
keywords agenticdeepresearchtest-timescalingretrieval-augmentedgenerationreinforcementlearninginformationseekingLLMagentsmulti-hopretrievalsearchparadigmshift
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper stakes out a position: the next dominant way people will seek information is not a search engine that returns links, nor a chatbot that answers from memory, but an agentic deep research system—an LLM that plans a series of search steps, reads and reasons over what it finds, searches again, and finally synthesizes a report. The authors trace a line from keyword web search through LLM chatbots and retrieval-augmented generation to these autonomous research agents, arguing that only the last handles complex, multi-step, open-ended queries. To formalize why, they introduce a test-time scaling law: normalized performance improves linearly as inference-time computation grows, along two axes—internal reasoning depth and external knowledge exploration. They support the position with benchmark results on BrowseComp, BrowseComp-ZH, and Humanity's Last Exam, where a deep-research agent scores 51.5%, 42.9%, and 26.6% against standard LLMs that stay below 10–20%, and with rising open-source repository star trends. If the position is right, the user-facing interface to recorded knowledge shifts from ranked results to synthesized narrative produced by an agent.

What carries the argument

The central mechanisms are two. First, the Agentic Deep Research loop: an LLM plans queries, retrieves, reasons, and iterates, with reasoning and search co-evolving rather than staged in sequence. Second, the Test-Time Scaling law: a hypothesized linear relation between normalized performance and inference-time compute along two axes, internal reasoning depth and external knowledge exploration, visualized as a diagonal plane interpolating two empirical scaling curves. The law frames a task-dependent token-budget trade-off between search and reasoning that the paper expects future systems to allocate adaptively.

What would settle it

Run one integrated deep-research agent on a fixed set of open-ended web-research questions at several total token budgets, holding the task constant. If normalized accuracy does not rise roughly linearly with total inference tokens, or if doubling the search budget and doubling the reasoning budget together yields less than the sum of the individual gains, the linear test-time scaling law is falsified.

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Extended reading notes

Core claim

The paper's core claim is that Agentic Deep Research—defined as an LLM-driven feedback loop in which reasoning decides when, what, and how to search, and retrieved evidence in turn refines reasoning—will inevitably become the dominant paradigm for information seeking. The supporting formal object is the Test-Time Scaling law for Deep Research: when performance is normalized, it improves linearly with scaling of internal reasoning depth (longer chains of thought, self-refinement, self-consistency) and of external knowledge exploration (more iterative retrieval steps and long-context access). The paper is explicit that this law is a hypothesis: the diagonal plane in Figure 3b interpolates between reasoning data from S1 on AIME24 and search data from IterDRAG on MuSiQue, and different task types align with different planes in the search–reasoning trade-off. It further argues that reinforcement learning, rather than prompting or supervised fine-tuning, is the principled mechanism for incentivizing reasoning-driven search in open-ended environments.

Load-bearing premise

The scaling law's load-bearing premise is that performance trends measured separately on a math benchmark and a retrieval benchmark can be merged into one linear law that predicts how an integrated agent behaves on real research tasks, even though no integrated system was measured to produce that law.

Editorial extensions

If this is right

  • If the scaling law holds, spending more inference tokens on either deeper reasoning or more search steps should yield predictable, roughly linear accuracy gains on knowledge-intensive tasks.
  • Standard web search and single-pass retrieval-augmented generation will be inadequate for complex multi-step queries, pushing users toward agentic systems that plan and iterate.
  • Reinforcement learning, rather than prompting or supervised fine-tuning, becomes the key training method for teaching search agents to explore, self-correct, and adapt.
  • Benchmark gaps of the kind reported—51.5% versus below 10% on BrowseComp, 26.6% versus under 20% on HLE—suggest that agentic deep research is not a marginal improvement but a categorical jump.
  • Open-source momentum around agentic research implementations indicates the paradigm shift is already happening in the developer community, not just in commercial products.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An editorial extension: if the linear test-time scaling law is real, search engines may evolve into compute-budget allocators that decide how many tokens go to retrieval versus reasoning for each query, making token cost a first-class search metric.
  • The paper's two-axis trade-off suggests a concrete design target: an agent that predicts whether a query is search-heavy or reasoning-heavy and sets its own budget accordingly, which could be trained by reinforcement learning on reward signals for answer quality minus compute cost.
  • A testable consequence the paper does not state: the same linear trend should appear on a single integrated benchmark if total inference budget is varied, and not just as an interpolation of two separate curves.
  • If the position is correct, evaluating information-seeking systems shifts from retrieval metrics like precision and recall to end-user outcomes such as report correctness, synthesis quality, and reduced cognitive load.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. This paper is a position paper and survey arguing that a new paradigm, 'Agentic Deep Research' (LLMs with tightly integrated reasoning, iterative search, and synthesis), will become the dominant mode of information seeking, superseding traditional web search, LLM chatbots, and naive RAG. It organizes the evolution of search into stages, surveys prompting/SFT/RL approaches for incentivizing search with reasoning agents, reports benchmark numbers for OpenAI Deep Research on BrowseComp, BrowseComp-ZH, and Humanity's Last Exam, analyzes GitHub star trends of open-source implementations, and proposes a 'test-time scaling law' claiming that normalized performance improves linearly with scaling of internal reasoning depth and external knowledge exploration. It also discusses the alternative view of human-centered search and lists open problems. The main contributions are the taxonomy, the curated resource collection, and the TTS conjecture.

Significance. If the position is correct, the paper identifies a real shift in information access and charts a useful design space around the search-reasoning trade-off. The survey of recent RL-based methods (Search-R1, R1-Searcher, DeepResearcher, and others) and the curated GitHub repository are valuable resources for the community. The 'test-time scaling law' is a thought-provoking conjecture that could stimulate useful empirical work, and the paper explicitly includes an alternative-view section and human-in-the-loop caveats, which is a strength for a position paper. However, the paper provides no new experimental evidence, and its only formal quantitative contribution, the TTS law, is an interpolation between two external single-axis curves rather than a validated empirical law. The significance lies in the framing and resource synthesis rather than in a demonstrated scaling result, and the central dominance claim is a prediction that the manuscript supports with citations and qualitative trends rather than with a decisive experiment.

major comments (4)
  1. [Section 4, Figure 3b] The proposed 'Test-Time Scaling law' is not supported as stated. The data consist of two independent curves: s1 on AIME24 for the reasoning axis (roughly 2^9-2^13 inference tokens) and IterDRAG on MuSiQue for the search axis (16K-5M tokens), each normalized separately to [0,1]. The 'diagonal plane' is an interpolation between these two series, with no equation, no error bars, no held-out points, and no evaluation on any integrated deep-research system that jointly performs reasoning and search. Because the two normalizations are independent, the slopes are not commensurable, and the visual linearity in Figure 3b does not constitute evidence of a shared scaling law. Since this law is the paper's only formal quantitative contribution, it should be reframed as an illustrative conjecture with explicit caveats, or substantiated with an actual equation, error analysis, and validation on an integrated system.
  2. [Abstract, Section 1, Section 4, Section 4.3] The paper is internally inconsistent about the status of the TTS claim: the abstract and Section 1 call it a 'test-time scaling law,' Section 4 opens with 'we introduce the Test-Time Scaling (TTS) law' and then calls it 'an hypothesis,' and Section 4.3 says the trade-off law 'is anticipated.' A 'law' implies an empirical regularity, while the text repeatedly concedes that the claim is a hypothesis. This distinction matters because the paper's quantitative contribution rests entirely on this claim; the authors should choose one framing and apply it consistently throughout.
  3. [Section 3.2, Figure 2] The benchmark comparison in Section 3.2 is based on external reports and product announcements, not on new evaluations by the authors. The text says 'we adopt and evaluate three representative benchmarks' and later concludes 'we demonstrate that agentic systems significantly outperform existing approaches,' but Figure 2 reports numbers from third-party sources (e.g., OpenAI Deep Research at 51.5% on BrowseComp, 42.9% on BrowseComp-ZH, 26.6% on HLE) without standard errors, system versions, evaluation protocols, or dates of access. This is a citation, not a demonstration. The authors should either perform an independent evaluation with transparent methodology or explicitly state that these are reported third-party results, and soften the conclusion accordingly.
  4. [Section 3.2, Figure 3a, Appendix B] The GitHub-star analysis is presented as 'empirical momentum' that reinforces the dominance claim, but it excludes the two most-starred and two least-starred repositories and then asserts a 'clear upward trajectory' without statistical tests, a well-defined sample period, or a comparison baseline. This is an anecdotal, cherry-picked trend rather than a rigorous empirical result. The authors should either provide a formal analysis (e.g., regression on star counts with confidence intervals) or characterize this evidence qualitatively and not as 'empirical momentum' that supports the central position.
minor comments (6)
  1. [Page 1, footnote] The footnote 'Work does note related the author's position at Amazon' appears to be a typographical error for 'Work does not reflect the author's position at Amazon'; please fix.
  2. [Section 3.2] The phrase 'information seekin' in the final paragraph is missing a 'g'; it should read 'information seeking'.
  3. [Section 2.2] There are several run-on or unclear sentences, such as 'engage users through interactive dialogue with integrate and tailored solutions that not directly available online' and 'LLM chatbots maintain conversation history throughout interactions, Beyond that, they aggregate...'; the prose needs careful proofreading.
  4. [Figure 2] Figure 2 does not state the data sources or the date each benchmark number was collected; adding a footnote with the source and access date would improve reproducibility and clarity.
  5. [Appendix B, Table 1] Table 1 contains typos such as '2Wikimultihopqa' and inconsistent capitalization, and many entries under 'Evaluation Data' are 'N/A'; the table should be cleaned up and the 'N/A' cases clarified.
  6. [Section 4.2] The statement that 'under optimal inference parameters, the performance improves nearly linearly' would benefit from a specific reference to the corresponding figure or table in Yue et al. (2025) and a definition of what constitutes 'optimal inference parameters.'

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the TTS law is an explicitly hypothetical interpolation of independent external results, and the paradigm-dominance claim is a forward-looking position rather than a derived consequence.

full rationale

This is a position/survey paper rather than a derivation, and no load-bearing step reduces to its own input. The central quantitative element, the Test-Time Scaling law, is presented as an explicit hypothesis: 'the diagonal plane connecting empirical data points interpolated in the three-dimensional plot represents our hypothetical TTS for Agentic Deep Research' (Section 4/Figure 3b). The two input curves come from independent external work (Muennighoff et al. on s1/AIME24 and Yue et al. on IterDRAG/MuSiQue), and the normalization and linear interpolation are the construction of the figure, not a parameter fitted to the paper's own conclusions. The paper does not fit any model to its own outcomes and then rename the fit as a prediction. The dominance claim is supported by external benchmark numbers (OpenAI Deep Research on BrowseComp, BrowseComp-ZH, and Humanity's Last Exam) and external open-source star trends; even though BrowseComp-ZH is co-authored by members of this paper's author list, the reported scores are for an external product, and the benchmark is used as an evaluation instrument rather than as a premise that entails the paper's conclusion. The many remaining self-citations (e.g., Zou et al. 2025a-c, Gu et al. 2025, Luo et al. 2025a, Zhang et al. 2025b) are contextual, non-load-bearing, and none is invoked as a uniqueness theorem or as the source of an ansatz. The paper's inconsistent labeling of the TTS relation as a 'law' in Section 1 and as 'hypothetical'/'anticipated' in Sections 4 and 4.3 is an overclaim or rigor problem, not a circularity. No equation or conclusion in the paper is equivalent to its input by construction, so the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces the term 'Agentic Deep Research' as a category label, but it does not postulate any new physical or formal entity, so the invented entities ledger is empty. The free parameters ledger is empty because the TTS 'law' is not fitted to an equation; it is a qualitative interpolation. The axioms above are the modeling assumptions that the paper's quantitative support depends on.

assumptions (4)
  • domain assumption Test-time scaling for reasoning is linear and continues at the observed rate for deep research tasks.
    Section 4.1 bases the reasoning axis on s1/AIME24 (Muennighoff et al., 2025) and assumes this curve extrapolates to agentic deep research, with no derivation or validation on research tasks.
  • domain assumption IterDRAG on MuSiQue represents search-phase test-time scaling for agentic deep research.
    Section 4.2 uses this single long-context retrieval benchmark to define the search axis, but web-scale agentic search with query planning and tool use may not scale the same way.
  • ad hoc to paper Interpolating between two independent datasets defines a valid scaling plane.
    Figure 3b connects data points from two different benchmarks with a 'diagonal plane' and calls it the TTS law, without a statistical model, error analysis, or theoretical justification.
  • domain assumption GitHub star growth is a valid measure of paradigm adoption.
    Section 3.2 uses star trajectories after excluding the two most and two least starred repositories as evidence of convergence on deep research, but star counts are a weak proxy for technical superiority or long-term adoption.

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Cite this review

Pith. "Pith review of From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents." pith.science (2026). https://pith.science/paper/O6LNXFLX

@misc{pith2026250618959,
  author       = {Pith},
  title        = {Pith review of: From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/O6LNXFLX}},
  note         = {Machine review of arXiv:2506.18959}
}
read the original abstract

Information retrieval is a cornerstone of modern knowledge acquisition, enabling billions of queries each day across diverse domains. However, traditional keyword-based search engines are increasingly inadequate for handling complex, multi-step information needs. Our position is that Large Language Models (LLMs), endowed with reasoning and agentic capabilities, are ushering in a new paradigm termed Agentic Deep Research. These systems transcend conventional information search techniques by tightly integrating autonomous reasoning, iterative retrieval, and information synthesis into a dynamic feedback loop. We trace the evolution from static web search to interactive, agent-based systems that plan, explore, and learn. We also introduce a test-time scaling law to formalize the impact of computational depth on reasoning and search. Supported by benchmark results and the rise of open-source implementations, we demonstrate that Agentic Deep Research not only significantly outperforms existing approaches, but is also poised to become the dominant paradigm for future information seeking. All the related resources, including industry products, research papers, benchmark datasets, and open-source implementations, are collected for the community in https://github.com/DavidZWZ/Awesome-Deep-Research.

Figures

Figures reproduced from arXiv: 2506.18959 by the authors.

Figure 1
Figure 1. The evolution of information search paradigms. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Benchmarks of 5 standard LLMs, 4 reasoning LLMs, and 1 agentic deep research model [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. (a) Open-source star trend for agentic deep research. (star counts recorded up to 22 May [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Review history (2 revisions) →

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Forward citations

Cited by 4 Pith papers

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.