DeepResearch$^{\text{Eco}}$: A Recursive Agentic Workflow for Complex Scientific Question Answering in Ecology

Andrei Aioanei; Endres Keno Sander; Jennifer D'Souza

REVIEW 4 major objections 5 minor 18 references

DeepResearch$^{\text{Eco}}$: A Recursive Agentic Workflow for Complex Scientific Question Answering in Ecology

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that a recursive, depth-and-breadth controlled agentic workflow integrates 21x more sources and approaches expert-quality synthesis in ecology.

desk verdict A useful recursive-search architecture and a plausible parameter study, but the headline scaling claims rest on unvalidated keyword metrics and possibly duplicate-counted sources. read the letter →

arxiv 2507.10522 v1 pith:IPNZT54E submitted 2025-07-14 cs.AI cs.CLcs.MA

Jennifer D'Souza , Endres Keno Sander , Andrei Aioanei This is my paper · ORCID

classification cs.AIcs.CLcs.MA

keywords agenticAIdeepresearchscientificsynthesisretrieval-augmentedgenerationecologyliteraturereviewrecursivesearchLLMagents

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

DeepResearchEco is an agentic LLM workflow that answers scientific questions by recursively searching literature, extracting "learnings" and follow-up questions, and feeding them back into deeper search rounds. Evaluated on 49 ecological research questions, raising depth and breadth parameters increases source integration up to 21.2-fold and sources per 1,000 words by 14.9-fold while keeping reports roughly the same length. The central claim is that high-parameter configurations can approach expert-level ecological synthesis, combining mechanistic detail, geographic and methodological breadth, and high information density. A sympathetic reader would care because the workflow makes the depth-versus-breadth trade-off user-controllable, offering both quick literature scans and rigorous multi-source reviews with transparent intermediate reasoning.

What carries the argument

The mechanism is a recursive orchestration loop with four sub-agents: a query generator that turns a research question into SERP-style subqueries with explicit research goals, a search agent that retrieves results through a configurable scholarly search API or web search, a summarizer that distills retrieved content into concise "learnings" and follow-up questions, and a report generator that composes all accumulated learnings into a citationed Markdown synthesis. Two user-set parameters govern the loop: breadth controls how many subqueries are issued per round (halved at each recursion), and depth controls how many recursive layers of refinement occur. The learnings and follow-up questions from each round are fed into the next round's queries, allowing the system to progressively narrow or diversify its exploration and accumulate a much larger evidence base than a single-pass pipeline.

What would settle it

Have a panel of ecologists blindly rate the 49 low-parameter (d1_b1) and high-parameter (d4_b4) report pairs for analytical quality, factual reliability, and usefulness; if the high-parameter reports are not rated as better or more expert-like, the central claim is refuted. A cheaper check is to verify whether the 192.9 listed sources are actually cited in the report body and used to support specific claims, rather than appearing only in an appended source list.

Watch

Extended reading notes

Core claim

The central discovery claimed is that recursively deepening and broadening a search-and-synthesize loop produces qualitatively denser scientific syntheses, not merely longer ones. Moving from the lowest to the highest configuration (depth 1, breadth 1 to depth 4, breadth 4) raises the average number of integrated sources from 9.1 to 192.9, a 21.2-fold increase, while word count grows only 41.5%, yielding a 14.9-fold improvement in sources per 1,000 words. The paper further reports that high-depth outputs contain over three times more multi-step causal chains, that high-breadth outputs integrate more biogeographic regions and intervention types, and that the best configuration scores higher than lower configurations on all six of its quality metrics. On that basis, the authors argue that carefully configured high-parameter runs can approach expert-level integration in ecology without loss of rigor or specificity.

Load-bearing premise

The claim that high-parameter runs are expert-level rests on six author-designed scoring equations that count keywords and simple heuristics; if those scores do not track what ecologists actually judge as expert synthesis quality, the central claim collapses.

Editorial extensions

If this is right

At high depth, syntheses shift from reporting empirical outcomes to tracing causal mechanisms, precise temporal thresholds, and multi-step cause-effect chains.
At high breadth, syntheses cover more geographic regions and a wider range of management interventions, supporting comparative conclusions that are invisible in regionally constrained reviews.
Source utilization scales super-linearly with parameter settings, with a 21.2-fold increase in sources at only a 41.5% word-count increase, implying that deeper recursion buys information density rather than verbosity.
Because depth and breadth are user-controllable, one deployment can serve both rapid scoping scans and rigorous, high-coverage literature syntheses.
The reported consistency of scaling patterns across 49 ecological questions suggests the effect is not limited to one ecological subtopic or one style of question.

Reading between the lines

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

Beyond the paper, the six quality metrics could be recalibrated against expert human ratings, which would turn the observed parameter-scaling curve into a predictive model for synthesis quality rather than a self-defined measure.
Beyond the paper, the 21-fold source-count increase may partly reflect retrieval redundancy rather than genuinely new information; a per-source novelty or citation-use analysis would distinguish more evidence from more retrieved pages.
Beyond the paper, the same recursion design is plausibly transferable to other evidence-synthesis domains, but the reported cross-model consistency differences warn that quantitative results may shift with the choice of underlying language model.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The genuinely new thing here is the recursive depth/breadth-controlled workflow with transparent intermediate outputs, plus a public code/data release. That is a real contribution: it gives builders of literature-synthesis agents a concrete alternative to feed-forward pipelines and a parameterization worth copying. The empirical study across 49 questions and two models is systematic, and the qualitative examples showing that depth yields mechanistic detail while breadth expands geographic coverage are plausible and useful.

The soft spots are substantial, though. The six quality metrics in Equations 1–6 are keyword counts and string heuristics, not validated against human expert judgment. Calling high-parameter runs “expert-level” on the basis of those metrics overreaches. That is the paper's load-bearing claim, and it needs external validation before it can be believed.

The stress-test note about source counts is worth taking seriously, even if its arithmetic is not airtight. Table 1 says d4_b4 hits a mean of 192.9 sources and a max of 244. If each SERP query yields about 10 documents and only a handful of queries are issued per recursion, that number is hard to reconcile with unique documents. The paper never states that “sources” are deduplicated, and it says all URLs are retained in the report. If the Sources section lists the same URL multiple times from different queries, the 21.2-fold and 14.9-fold claims become counting artifacts rather than evidence coverage. The density metric in Equation 6 inherits that problem directly. A quick check with the authors or a look at the released reports should settle it, but as written the central efficiency claim is not trustworthy.

There are also minor inconsistencies: Section 4.2 says each configuration contains 50 reports while the dataset is 49 questions, and Appendix B.3 refers to “all 50 research questions.” These are small but sloppy.

The citation pattern is fine. The related work is reasonably covered, and the positioning against PaperQA2 and ORKG Ask is fair. The paper is honestly written and the system is real. This deserves a serious referee, but not acceptance as is. I would send it back for major revision: add human-expert evaluation of report quality, report deduplicated source counts, and soften the expert-level wording accordingly.

For the right reader—someone building agentic literature-review tools or studying how search parameters affect synthesis—this is worth engaging with as a system paper, not as evidence of expert-level performance.

Referee Report

4 major / 5 minor

Summary. The paper presents DeepResearchEco, a recursive agentic LLM workflow for automated scientific literature synthesis in which user-controlled depth and breadth parameters govern repeated SERP-style querying, summarization into 'learnings', follow-up question generation, and final report generation. The system is evaluated on 49 ecological research questions with two reasoning models (o3 and o3-mini) and four depth/breadth configurations. The authors address three research questions: RQ1 on cross-configuration report similarity (ROUGE-L, BERTScore, WMD), RQ2 on how depth and breadth affect quality and diversity, and RQ3 on whether high-parameter configurations approach expert-level synthesis. The headline results are a reported 21.2-fold increase in source integration (9.1 to 192.9 sources) and a 14.9-fold increase in sources per 1,000 words, leading to the central claim that high-parameter runs 'can approach expert-level integration in ecology'. The paper releases its code, data, and evaluation pipelines.

Significance. If the central claims hold, the paper would make a useful contribution to automated scientific synthesis: it offers a configurable recursive architecture, transparent intermediate reasoning steps, and a reproducible open-source evaluation suite applied to real research questions from ecologists. The release of code, data, and scoring vocabularies is a genuine strength, as is the use of multiple similarity metrics and 49 domain questions. However, the main empirical claims currently rest on two fragile pillars: (i) the definition and measurement of 'sources' in Eq. 6, which appears internally inconsistent with the described retrieval limits, and (ii) a set of six author-designed keyword-count quality metrics that have not been validated against human expert judgment. Both pillars are load-bearing for RQ3, and the manuscript also contains several internal numeric inconsistencies. With targeted revisions—deduplicated source counts, clarified retrieval caps, external human or calibrated LLM validation, and consistent sample-size reporting—the contribution could be solid, but in its current form the headline 'expert-level' claim is not yet supported.

major comments (4)

[Sec. 3.2, Table 1, Eq. 6] The reported source counts are inconsistent with the architecture described in the paper. The 'search' sub-agent retrieves top-ranked results and the 'summarize result' sub-agent processes 'the top 10 returned documents (by default)' per query; the 'generate serp queries' sub-agent halves the number of queries at each recursion level via integer division ('breadth // 2'). For d4_b4, the maximum number of queries is on the order of 4 + 2 + 1 + 0 = 7, so with top-10 retrieval the maximum number of unique retrievable documents is about 70, or perhaps 80-160 under more generous counting of recursion steps. Yet Table 1 reports a mean of 192.9 sources and a maximum of 244 for d4_b4. The manuscript does not specify whether N_sources in Eq. 6 counts unique deduplicated URLs, repeated URL occurrences across queries, or citation occurrences in the final report. Because Eq. 6 uses N_sources directly, the 14.9-fold information-density improvement and the RQ3 'expert-level' claim inherit this ambiguity. The authors should report deduplicated unique-source counts, state the ORKG Ask retrieval cap, and show the total number of queries executed per configuration.
[Sec. 4.3.2, Sec. 4.3.3 (RQ3)] The central claim that high-parameter runs 'approach expert-level integration' is supported only by six author-designed keyword-count metrics (Eqs. 1-6). These metrics have not been validated against human expert ratings, against a gold-standard reference synthesis, or through inter-rater agreement. The normalization thresholds are described in Sec. 4.3.2 as 'empirical thresholds from our 196-report dataset', which makes the scoring partly circular: the same data used to calibrate the caps is then used to demonstrate quality gains. The paper needs an external validation component, such as blind ratings by ecologists or an LLM-as-a-judge that has itself been calibrated against expert judgments, before the 'expert-level' language in the abstract and RQ3 conclusions is warranted.
[Sec. B.3, Table 1, Figure 5] The claimed 'power law' scaling (R^2 = 0.97) is an in-sample fit to four configuration means, not a predictive test; with only four data points and two free parameters a high R^2 carries little evidential weight. In addition, the claimed super-linear interaction is contradicted by the paper's own numbers: the additive combination of the reported independent depth and breadth effects is 111.1 + 110.8 - 9.1 = 212.8 sources, which exceeds the observed d4_b4 mean of 192.9. The text also interchangeably calls the same relationship a 'power law' and 'exponential scaling'. These issues should be re-analyzed and the functional-form claims either justified with more data points or removed.
[Sec. 4.1.2, Sec. 4.2, Table 1, Figure 5] The manuscript reports inconsistent sample sizes. Section 4.1.2 states that eight configurations (two models x two depths x two breadths) each generated 49 reports, which would be 392 documents. However, Table 1, Figure 5, and the qualitative analyses use n=196 documents, and Section 4.2 refers to configuration groups 'containing 50 reports indexed by question ID' despite the dataset having 49 questions. The paper needs to clarify which configurations are included in each quantitative and qualitative analysis, whether the two models are pooled, and correct the 49/50 discrepancy. Relatedly, the overall quality score for d4_b4 is reported as 0.577 in the Figure 3 caption but as 0.478 in Section B.4 (with an 18% improvement over d1_b1 that matches 0.478/0.405 but not 0.577/0.405); these values must be reconciled.

minor comments (5)

[Sec. 4.2] The text says 'each containing 50 reports indexed by question ID k', but the described dataset contains 49 questions; this should be corrected to 49 throughout.
[Sec. B.3, Figure 5 caption] Appendix B.3 and the Figure 5 caption state '50 research questions' and '49 ecological research questions' in different places; the dataset size is 49.
[Eq. 2] Equation 2 contains a typographical stray semicolon: '𝑆_𝑏𝑟𝑒𝑎𝑑𝑡ℎ =;' should be '𝑆_𝑏𝑟𝑒𝑎𝑑𝑡ℎ ='.
[Sec. B.3] The same scaling relationship is described as a 'power law' and as 'exponential scaling' within the same section; the authors should choose one functional form and justify it.
[Sec. B.3, Table 1] Table 1's 'n=196 documents' heading is ambiguous because the experimental setup describes eight configurations; the table should state how the model dimension is aggregated (e.g., pooled or averaged).

Circularity Check

2 steps flagged · score 6.0 of 10

The 14.9x 'information density' claim reduces to Equation 6, which defines density as N_sources per 1,000 words, and N_sources is itself controlled by the depth/breadth parameters being compared.

self definitional [Section 4.3.2, Equation 6; Section 4.3.3, RQ3 results]
"Information density reflects synthesis efficiency: S_density = min( N_sources / W_count/1000 * 1/50, 1) ... The most pronounced efficiency gain, however, lies in information density (Equation 6), which improves 14.9-fold from d1_b1 to d4_b4 despite only modest word count increases. This validates that high-parameter configurations not only scale information volume but also preserve analytical quality and specificity."

Equation 6 literally defines 'information density' as the number of sources per 1,000 words. N_sources is not an independent quality measurement; it is the accumulated count of documents returned by the recursive search workflow. The paper's own Table 1 reports mean source counts of 9.1 for d1_b1 and 192.9 for d4_b4; substituting these values into Eq. 6 mechanically yields the 14.9-fold density improvement. Therefore the central RQ3 claim of 'order-of-magnitude higher information density' and 'expert-level integration' is a renormalized version of the source-count increase, not an independent finding about expert-level synthesis quality. The assertion that this 'preserves analytical quality and specificity' is imported into Eq. 6, not derived from it.
other [Section B.3, Table 1, Figure 4]
"maximum parameter configurations (d4_b4) achieve a 21.2-fold increase in source utilization compared to baseline (d1_b1), with mean source counts escalating from 9.1±1.7 to 192.9±31.2 sources per synthesis ... This exponential scaling pattern indicates that higher parameter configurations enable qualitatively different modes of information integration."

Source utilization is simply the count of top-10 documents accumulated over the recursive loop. The breadth parameter sets the number of SERP queries per level and the depth parameter sets the number of recursion layers, while the summarize-result agent takes the top 10 returned documents per query. So increasing b and d necessarily increases the number of collected sources; this is a design property, not a predicted empirical outcome. The 21.2-fold and 14.9-fold numbers are then fed into Eq. 6 and used as evidence for 'expert-level' synthesis.

full rationale

The paper contains two load-bearing reductions. First, Equation 6 defines information density as N_sources per 1,000 words, and N_sources is the raw document count produced by the recursive search configuration being compared. Consequently, the reported 14.9-fold density gain from d1_b1 to d4_b4 is obtained by construction from the 21.2-fold source-count increase divided by a modest word-count increase; it is not an independent measurement of expert-level quality. Second, 'source utilization' is directly controlled by the depth and breadth parameters (number of queries, number of recursion layers, top-10 documents per query), so the 'super-linear scaling' of sources is a by-construction property of the workflow rather than a validated prediction. The remaining quality metrics (Equations 1-5) are author-designed keyword and taxonomy counts that are not validated against human expert ratings; this is a validity risk rather than a formal circularity, because those scores are not definitionally identical to the parameter settings. The self-citations in the paper, notably [8] and [9], are used for related-work positioning and evaluation methodology, but they are not load-bearing premises of the central derivation, so they do not independently raise the score. The overall circularity score is 6 because the central 'expert-level integration' and 'order-of-magnitude information density' claims reduce, in substantial part, to the definition of Eq. 6 and to source-count accumulation that the parameters themselves determine.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central contribution is a software system plus an evaluation framework. The evaluation framework introduces arbitrary weights, caps, and keyword vocabularies that are not externally validated. No new scientific entities are postulated. The system rests on domain assumptions about what constitutes quality, which drive the conclusions.

free parameters (3)

Quality metric weights = hand-chosen (e.g., 0.4, 0.3, 0.3 in Eq. 1; 0.25, 0.25, 0.25, 0.15, 0.10 in Eq. 2)
The weights in Equations 1-6 are chosen by the authors and directly determine the quality scores used to conclude that high-parameter runs are 'expert-level'. They are not fitted to external data but are ad hoc design choices.
Normalization caps in quality metrics = e.g., 20, 10, 8, 12, 8, 6 for various sub-scores
Caps like min(M_mech/20, 1) and min(M_causal/10, 1) are arbitrary thresholds that anchor the scores in the [0,1] range. Changing these caps would change the relative quality scores across configurations.
Learnings and follow-up questions count = 3 (default), configurable
The number of learnings and follow-up questions extracted per search step is a user-set parameter. It affects how much information is propagated to subsequent recursion rounds, but is not central to the quality evaluation.

assumptions (3)

domain assumption Keyword counts and simple linguistic heuristics measure scientific quality.
The six quality metrics (Equation 1-6) assume that the frequency of terms like 'feedback', 'because', and 'biodiversity' correlates with analytical depth, rigor, and innovation. This assumption is not validated against human expert assessments of synthesis quality.
domain assumption Source count is a valid proxy for information integration.
The paper treats the number of cited sources and sources per 1,000 words as measures of synthesis quality and information density. This conflates quantity of sources with meaningful integration of evidence.
domain assumption LLM-generated reports are factually reliable.
The entire evaluation assumes that the retrieved content and the LLM's synthesized summaries are accurate and free of hallucination. No fact-checking or verification against ground truth is performed.

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Cite this review

Pith. "Pith review of DeepResearch$^{\text{Eco}}$: A Recursive Agentic Workflow for Complex Scientific Question Answering in Ecology." pith.science (2026). https://pith.science/paper/IPNZT54E

@misc{pith2026250710522,
  author       = {Pith},
  title        = {Pith review of: DeepResearch$^\textEco$: A Recursive Agentic Workflow for Complex Scientific Question Answering in Ecology},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IPNZT54E}},
  note         = {Machine review of arXiv:2507.10522}
}

abstract

We introduce DeepResearch$^{\text{Eco}}$, a novel agentic LLM-based system for automated scientific synthesis that supports recursive, depth- and breadth-controlled exploration of original research questions -- enhancing search diversity and nuance in the retrieval of relevant scientific literature. Unlike conventional retrieval-augmented generation pipelines, DeepResearch enables user-controllable synthesis with transparent reasoning and parameter-driven configurability, facilitating high-throughput integration of domain-specific evidence while maintaining analytical rigor. Applied to 49 ecological research questions, DeepResearch achieves up to a 21-fold increase in source integration and a 14.9-fold rise in sources integrated per 1,000 words. High-parameter settings yield expert-level analytical depth and contextual diversity. Source code available at: https://github.com/sciknoworg/deep-research.

Figures

Figures reproduced from arXiv: 2507.10522 by the authors.

**Figure 1.** Deep Research Orchestration Workflow. The user provides a research question and feedback, along with recursion parameters — breadth (b) and depth (d) — to guide the exploration. The workflow recursively calls four sub-agents: (1) generate serp queries to formulate search-optimized sub-queries and research goals, (2) search to retrieve content from configurable APIs (e.g., ORKG Ask or Firecrawl), (3) summarize result… view at source ↗

**Figure 2.** RQ1: Similarity of reports generated by o3-mini and o3 across four depth-breadth settings. Darker cells indicate higher similarity (ROUGE-L/BERTScore) or lower distance (WMD). Self-consistency. In all three heatmaps, the main diagonal—where each configuration is compared to itself—is the darkest, reflecting perfect alignment. ROUGE-L F1 and BERTScore F1 are both 1.0, and WMD similarity is also 1.0 (i.e., WMD = 0). T… view at source ↗

**Figure 3.** Decomposition of quality improvements across six key dimensions. Error bars represent standard deviations across 49 ecological research questions. The domain-specific score shows strong performance for comprehensive configurations (d4_b4: 0.9+), while information density remains the primary driver of differentiation across configurations. Overall quality scores (composite of all dimensions): best configuration d4_b… view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Individual parameter effects on source utilization showing equivalent depth and breadth contributions. Bar chart comparing averaged effects of depth and breadth parameters independently: Depth 1 (18.9 sources), Depth 4 (111.1 sources), Breadth 1 (19.2 sources), and Bre…

**Figure 5.** Figure 5: Exponential scaling of synthesis capabilities with depth-breadth parameters. (Left) Source utilization demonstrates super-linear scaling from 9.1 ± 1.7 sources (d1_b1) to 192.9 ± 31.2 sources (d4_b4), representing a 21.2-fold increase. (Center) Word count shows modest …

**Figure 6.** Figure 6: Configuration optimization analysis showing cost-benefit trade-offs and efficiency frontiers. Four-panel analysis of parameter configuration performance: (A) Quality vs. cost trade-off using source count as computational cost proxy, revealing d4_b4’s superior quality d…

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Reference graph

Works this paper leans on

18 extracted references · 9 canonical work pages

[1]

M. D. Skarlinski, S. Cox, J. M. Laurent, J. D. Braza, M. Hinks, M. J. Hammerling, M. Ponnapati, S. G. Rodriques, A. D. White, Language agents achieve superhuman synthesis of scientific knowledge, arXiv preprint arXiv:2409.13740 (2024)

arXiv 2024
[2]

M. Dahl, V. Magesh, M. Suzgun, D. E. Ho, Large legal fictions: Profiling legal hallucinations in large language models, Journal of Legal Analysis 16 (2024) 64–93

work page 2024
[3]

Lewis, E

P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, et al., Retrieval-augmented generation for knowledge-intensive nlp tasks, Advances in neural information processing systems 33 (2020) 9459–9474

2020
[4]

Shuster, S

K. Shuster, S. Poff, M. Chen, D. Kiela, J. Weston, Retrieval augmentation reduces hallucination in conversation, in: Findings of the Association for Computational Linguistics: EMNLP 2021, 2021, pp. 3784–3803

work page 2021
[5]

J. Lála, O. O’Donoghue, A. Shtedritski, S. Cox, S. G. Rodriques, A. D. White, Paperqa: Retrieval- augmented generative agent for scientific research, arXiv preprint arXiv:2312.07559 (2023). URL: https://doi.org/10.48550/arXiv.2312.07559

arXiv doi:10.48550/arxiv.2312.07559 2023
[6]

ORKG ASK: a Neuro-symbolic Scholarly Search and Exploration System

A. Oelen, M. Y. Jaradeh, S. Auer, Orkg ask: a neuro-symbolic scholarly search and exploration system, arXiv preprint arXiv:2412.04977 (2024)

work page Pith review arXiv 2024
[7]

Knoth, D

P. Knoth, D. Herrmannova, M. Cancellieri, L. Anastasiou, N. Pontika, S. Pearce, B. Gyawali, D. Pride, Core: A global aggregation service for open access papers, Scientific Data 10 (2023) 366

work page 2023
[8]

Babaei Giglou, J

H. Babaei Giglou, J. D’Souza, S. Auer, Llms4synthesis: Leveraging large language models for scientific synthesis, in: Proceedings of the 24th ACM/IEEE Joint Conference on Digital Libraries, 2024, pp. 1–12

work page 2024

Show all 18 references

[9]

D’Souza, H

J. D’Souza, H. B. Giglou, Q. Münch, Yescieval: Robust llm-as-a-judge for scientific question answering, arXiv preprint arXiv:2505.14279 (2025)

2025 arXiv
[10]

X. Hu, H. Fu, J. Wang, Y. Wang, Z. Li, R. Xu, Y. Lu, Y. Jin, L. Pan, Z. Lan, Nova: An iterative planning and search approach to enhance novelty and diversity of llm generated ideas, arXiv preprint arXiv:2410.14255 (2024)

2024 arXiv
[11]

K. Pu, K. Feng, T. Grossman, T. Hope, B. D. Mishra, M. Latzke, J. Bragg, J. C. Chang, P. Siangliulue, Ideasynth: Iterative research idea development through evolving and composing idea facets with literature-grounded feedback, arXiv preprint arXiv:2410.04025 (2024)

2024 arXiv
[12]

Sandholm, S

T. Sandholm, S. Dong, S. Mukherjee, J. Feland, B. A. Huberman, Semantic navigation for ai-assisted ideation, arXiv preprint arXiv:2411.03575 (2024)

2024 arXiv
[13]

L. Li, W. Xu, J. Guo, R. Zhao, X. Li, Y. Yuan, B. Zhang, Y. Jiang, Y. Xin, R. Dang, et al., Chain of ideas: Revolutionizing research via novel idea development with llm agents, arXiv preprint arXiv:2410.13185 (2024)

2024 arXiv
[14]

Radensky, S

M. Radensky, S. Shahid, R. Fok, P. Siangliulue, T. Hope, D. S. Weld, Scideator: Human-llm scientific idea generation grounded in research-paper facet recombination, arXiv preprint arXiv:2409.14634 (2024)

2024 arXiv
[15]

C. Lu, C. Lu, R. T. Lange, J. Foerster, J. Clune, D. Ha, The AI Scientist: Towards fully automated open-ended scientific discovery, arXiv preprint arXiv:2408.06292 (2024)

2024 arXiv
[16]

H. Su, R. Chen, S. Tang, X. Zheng, J. Li, Z. Yin, W. Ouyang, N. Dong, Two heads are better than one: A multi-agent system has the potential to improve scientific idea generation, arXiv preprint arXiv:2410.09403 (2024)

2024 arXiv
[17]

Z. Wang, H. Zhang, J. Chen, H. Chen, An effective framework for measuring the novelty of scientific articles through integrated topic modeling and cloud model, Journal of Informetrics 18 (2024) 101587

2024
[18]

mechanism

Y. Z. Foo, R. E. O’Dea, J. Koricheva, S. Nakagawa, M. Lagisz, A practical guide to question formation, systematic searching and study screening for literature reviews in ecology and evolution, Methods in Ecology and Evolution 12 (2021) 1705–1720. URL: https://besjournals.onlin...

2021 doi

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

M. D. Skarlinski, S. Cox, J. M. Laurent, J. D. Braza, M. Hinks, M. J. Hammerling, M. Ponnapati, S. G. Rodriques, A. D. White, Language agents achieve superhuman synthesis of scientific knowledge, arXiv preprint arXiv:2409.13740 (2024)

arXiv 2024

[2] [2]

M. Dahl, V. Magesh, M. Suzgun, D. E. Ho, Large legal fictions: Profiling legal hallucinations in large language models, Journal of Legal Analysis 16 (2024) 64–93

work page 2024

[3] [3]

Lewis, E

P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, et al., Retrieval-augmented generation for knowledge-intensive nlp tasks, Advances in neural information processing systems 33 (2020) 9459–9474

2020

[4] [4]

Shuster, S

K. Shuster, S. Poff, M. Chen, D. Kiela, J. Weston, Retrieval augmentation reduces hallucination in conversation, in: Findings of the Association for Computational Linguistics: EMNLP 2021, 2021, pp. 3784–3803

work page 2021

[5] [5]

J. Lála, O. O’Donoghue, A. Shtedritski, S. Cox, S. G. Rodriques, A. D. White, Paperqa: Retrieval- augmented generative agent for scientific research, arXiv preprint arXiv:2312.07559 (2023). URL: https://doi.org/10.48550/arXiv.2312.07559

arXiv doi:10.48550/arxiv.2312.07559 2023

[6] [6]

ORKG ASK: a Neuro-symbolic Scholarly Search and Exploration System

A. Oelen, M. Y. Jaradeh, S. Auer, Orkg ask: a neuro-symbolic scholarly search and exploration system, arXiv preprint arXiv:2412.04977 (2024)

work page Pith review arXiv 2024

[7] [7]

Knoth, D

P. Knoth, D. Herrmannova, M. Cancellieri, L. Anastasiou, N. Pontika, S. Pearce, B. Gyawali, D. Pride, Core: A global aggregation service for open access papers, Scientific Data 10 (2023) 366

work page 2023

[8] [8]

Babaei Giglou, J

H. Babaei Giglou, J. D’Souza, S. Auer, Llms4synthesis: Leveraging large language models for scientific synthesis, in: Proceedings of the 24th ACM/IEEE Joint Conference on Digital Libraries, 2024, pp. 1–12

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