{"id":"f9fedac6-b17a-4af4-bd4f-61627e78325a","arxiv_id":"2502.15698","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":3.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"Agentic-RAG reportedly achieved 100% adherence to NCCN breast cancer guidelines on a small test set, but the paper's reported numbers are inconsistent and no data are shared.","lead":"This paper describes two retrieval-augmented AI systems that produce breast cancer treatment recommendations from the NCCN guidelines, and reports that one system, Agentic-RAG, matched guidelines on all test cases. The result is an engineering demonstration, but the evaluation is small, internally inconsistent, and lacks shared data.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The 100% adherence claim is unverifiable: the test set is unreleased, its denominator is inconsistent (24 vs 16x4 vs 50), and the sole rater is a single unblinded physician with no inter-rater reliability.","rationale":"The central claim is narrow: a particular pipeline, Agentic-RAG, attains perfect adherence in a specific test. For that claim to hold, the test must be well-defined and the label must be trustworthy. Neither condition is met. The denominator ambiguity is not cosmetic: 24 questions, 64 questions (16x4), and 50 treatment recommendations are different units, and the paper never explains how they relate. Table 1's discrepancies with the abstract further suggest the numbers were produced at different stages or with different subsets. Without the test data, no reader can determine which count, if any, should be believed. The scoring protocol is the second load-bearing weakness. A single physician, likely aware of which system produced each output, judged adherence against the same guideline-derived JSON objects that the systems queried. This creates a closed loop: if the JSON conversion is unfaithful, the system and the rater can agree on a recommendation that the actual NCCN flowchart does not support. The paper reports no inter-rater reliability, no second opinion, and no reconciliation step. The Chen et al. benchmark referenced in Section 3.2 is not described, so it is unclear whether their protocol was actually followed. I agree with the reader's rejection, but my emphasis is on the unverifiable evaluation rather than only the JSON conversion. If the authors release the evaluation pack and independent blinded scoring confirms the result, the concern would be resolved; if they do not, the rejection stands. This is not an accusation of misconduct; it is a request for the evidence needed to assess the claim.","tokens_in":5183,"tokens_out":4281,"duration_ms":42045,"concrete_test":"Ask the authors to release the complete evaluation pack: all patient descriptions, all four query variations per patient, the NCCN JSON objects, the exact prompts for each LLM call, the raw responses from all three systems, and the physician's scoring sheets. Then have two independent oncologists, blinded to system identity and to the reported results, re-score every response against the original NCCN guideline pages, and compute inter-rater agreement (e.g., Cohen's kappa). If the independent scores reproduce 100% for Agentic-RAG on the full set and the denominators reconcile across the abstract, Section 3.1, and Table 1, the central claim survives; if not, the reported perfect adherence is an artifact of the single-rater, unreleased evaluation.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim, 'Agentic-RAG achieved 100% adherence with no hallucinations or incorrect treatments,' is supported only by the authors' own evaluation, and that evaluation is not a reproducible or validated gold standard. The abstract reports 24/24, Section 3.1 describes 16 patients x 4 questions, Section 4.1 says 50 treatment recommendations, and Table 1's percentages (94%, 92%, 100%) differ from the abstract's (91.6%, 95.8%, 100%). No test cases, JSON conversions, prompts, or raw outputs are provided, so the reader cannot check whether any single recommendation is truly guideline-adherent or whether the 24/24 subset is representative. More fundamentally, the manual evaluation in Section 3.2 is performed by a single board-certified physician against the same NCCN guidelines from which the systems retrieve; no inter-rater reliability is reported, and the rater is not blinded to system identity or to the desired result. The JSON conversion in Section 2.1, which maps flowchart pages to structured objects, is likewise unvalidated: if it drops or distorts a pathway, both the system output and the physician's check against the same JSON-derived content can agree while missing the true guideline. Under these conditions, '100% adherence' and 'no hallucinations' are not established; they are assertions about an evaluation that has not been shown to measure what it claims.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes two retrieval-augmented generation systems, Agentic-RAG and Graph-RAG, which convert NCCN breast cancer guideline flowcharts into JSON and use LLM-based retrieval and generation to produce personalized treatment recommendations. The systems are evaluated against ChatGPT-4 on a small set of curated patient descriptions and adjudicated by a single physician. The central claim is that Agentic-RAG achieves 100% guideline adherence with no hallucinations or incorrect treatments, while Graph-RAG and ChatGPT-4 achieve slightly lower adherence. The manuscript reports favorable results for the proposed systems but provides neither the test data nor enough methodological detail to verify those results.","tokens_in":5514,"tokens_out":3854,"duration_ms":36815,"significance":"If the 100% adherence and zero-hallucination claims were supported by a reproducible and internally consistent evaluation, this would be a practically valuable contribution to clinical decision support and to the literature on retrieval-augmented LLMs for medicine. The paper also has a plausible architecture: separating title selection, JSON retrieval, recommendation generation, and insufficiency checking is a sensible way to ground LLM outputs in structured guideline content. However, the evaluation as presented is not credible: the reported numerators and denominators disagree, the ground-truth standard is the same source from which the system retrieves, and the single-rater, unblinded design does not establish independent clinical validity. The significance of the paper therefore remains contingent on a substantially stronger empirical study.","major_comments":[{"comment":"The reported adherence rates are internally inconsistent. The abstract states that Agentic-RAG achieved 24/24 (100%), Graph-RAG 23/24 (95.8%), and ChatGPT-4 22/24 (91.6%), and that Graph-RAG produced one incorrect treatment and ChatGPT-4 produced two wrong treatments. Table 1, however, lists adherence rates of 100%, 92%, and 94% for the same three systems, and reports 'Wrong treatments 0' for all three. These contradictions are not explained anywhere, and they make it impossible to determine which numbers support the paper's headline claims.","section":"Abstract and Table 1"},{"comment":"The test-set denominator is ambiguous. Section 3.1 describes 16 patient descriptions with four question variations each, which would yield 64 question-answer pairs, yet the abstract reports 24 total evaluations and Section 4.1 states that Agentic-RAG 'correctly identified all 50 treatment recommendations.' No explanation reconciles these numbers, so the reported 100% adherence cannot be interpreted as a proportion of a well-defined test set.","section":"Sections 3.1 and 4.1"},{"comment":"The evaluation relies on a single board-certified physician who is not described as blinded to system identity, and no inter-rater reliability is reported. Because the ground truth is the same NCCN guideline content from which the JSON objects in Section 2.1 were derived, the adherence measure largely reflects whether the retrieval system returns the stored content, not whether the recommendations are clinically correct in an independent sense. The paper's own future-work paragraph (Section 5.1) acknowledges that 'involving more oncologists in the evaluation process' is needed, which reinforces that the current single-rater design is insufficient to support the claimed 100% adherence.","section":"Section 3.2"},{"comment":"The conversion of NCCN flowchart pages into JSON objects is a critical unvalidated step. The paper does not specify the number of pages converted, the extraction protocol, or any quality-assurance check that the JSON faithfully preserves all treatment branches. If the conversion drops or distorts a pathway, both the Agentic-RAG output and the physician's adherence check can agree with each other while diverging from the actual NCCN guideline. No data or validation is provided to rule out this failure mode.","section":"Section 2.1"},{"comment":"The central empirical claim is not reproducible. The manuscript provides no patient descriptions, question variations, JSON conversion code, prompts, raw system outputs, or reference annotations. Without any of these artifacts, a reader cannot verify a single adherence judgment, check whether the 24 (or 50, or 64) evaluated items are representative, or audit the claimed absence of hallucinations and incorrect treatments. The paper does not even include an appendix with representative examples of the generated treatment plans.","section":"Sections 2-4"}],"minor_comments":[{"comment":"There is a typo in the conclusion: 'AgGentic-RAG' should be 'Agentic-RAG'.","section":"Section 5"},{"comment":"The name of the baseline model is inconsistently written as 'Chat GPT-4' in the abstract and 'ChatGPT-4' in the body and Table 1; please standardize.","section":"Abstract and throughout"},{"comment":"The generation step is described as using 'o1-preview' in Section 2.2.3, while Figure 1 and the abstract label the system as 'GPT-4o' and Section 2.2.1 says title selection uses GPT-4o. Clarify which model performs each LLM call.","section":"Section 2.2.3 and Figure 1"},{"comment":"The paper states that the evaluation framework 'followed the benchmarking methodology described by Chen et al. [24],' but reference [24] is a JAMA Oncology article about chatbots providing cancer treatment information, not a benchmarking methodology paper. The citation appears inaccurate and should be corrected or the statement removed.","section":"Section 3.2"},{"comment":"The specific NCCN guideline version is not stated in the methodology. Reference [21] gives a version, but the text should explicitly identify the PDF release date and version used for JSON conversion so that the retrieval and evaluation can be reproduced.","section":"Section 2.1"},{"comment":"The paper does not include a data-availability or conflict-of-interest statement, which is surprising given the authors' corporate affiliation and the claim that the system is ready for clinical workflows; these statements should be added.","section":"General"}],"recommendation":"reject","confidential_remarks":"The manuscript appears to be an extended technical report rather than a peer-reviewed study. The central claim of 100% adherence is not only unsupported by the evidence as reported, but the evidence as reported is internally contradictory (abstract vs. Table 1, denominators 24 vs. 50 vs. 64). The evaluation is circular because the same NCCN guidelines are both the source of the JSON data and the ground-truth for adherence, and the single-rater, unblinded design would not meet the standard for clinical NLP work even if the numbers were consistent. I recommend rejection; the authors would need to provide a reproducible benchmark with a defined test set, inter-rater reliability, and independent clinical adjudication to make the claims credible. I also note that reference [24] is likely mis-cited, and the corporate affiliation without a COI statement may warrant editorial attention."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick read: this is an engineering report applying established retrieval-augmented generation to NCCN breast cancer guidelines. The pipeline itself is sensible: convert flowchart pages to JSON, retrieve relevant titles, generate a plan, and run an insufficiency check. The page-number references in outputs are a practical touch that makes recommendations auditable. Credit where due: they cite the relevant prior work on agentic LLMs and GraphRAG, and the system design is reasonable.\n\nThe problem is that the evaluation cannot support the headline. The abstract reports 24/24, 23/24, and 22/24; Table 1 gives 100%, 92%, and 94%; Section 4.1 mentions 50 treatment recommendations. Sixteen patients times four questions is 64, not 24 or 50. None of this is reconciled. No code, data, prompts, or test cases are provided. The single rater is a board-certified physician who is not blinded and judged against the same NCCN documents the systems retrieve from, so 'adherence' is partly a retrieval-fidelity check. The paper's own future-work section says they will involve more oncologists, which acknowledges the single-rater limitation. The JSON conversion in Section 2.1 is unvalidated; if a pathway is dropped, the system and the rater can agree on the wrong answer.\n\nThese are not cosmetic inconsistencies. They sit exactly on the load-bearing claim of 100% adherence and zero hallucinations. As written, that claim is unverifiable.\n\nWho gets value? Someone building clinical decision support could read this for pipeline ideas. As a source of evidence for clinical performance, it is not citable. My recommendation: desk reject or require major revision with data release, reconciled denominators, inter-rater reliability, and blinded evaluation. I would not send it to reviewers as is.","headline":"Sensible RAG engineering, but the 100% adherence claim is unsupported by an unreleased, internally inconsistent evaluation.","tokens_in":6001,"tokens_out":3885,"would_cite":false,"duration_ms":36955,"reading_group":"maybe","serious_thinker":"no","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper claims that Agentic-RAG, a three-step LLM retrieval pipeline, achieved 100% adherence to NCCN breast cancer treatment guidelines on 24 test questions, with no hallucinations and no incorrect or missing treatments.","keywords":["breast cancer","NCCN guidelines","retrieval-augmented generation","agentic RAG","Graph-RAG","clinical decision support","LLM evaluation","treatment recommendation"],"falsifier":"Take a set of patient cases that exercise less common branches of the breast cancer flowcharts, convert the same pages to JSON, and have two independent oncologists score the outputs; if any recommendation is judged non-adherent by either oncologist, or if a treatment path present in the original PDF is absent from the JSON, the perfect-adherence claim fails.","tokens_in":4906,"feed_emoji":"🩺","tokens_out":6189,"duration_ms":54178,"temperature":0.7,"pith_summary":"The paper claims that an AI pipeline called Agentic-RAG can generate breast cancer treatment plans that fully follow the NCCN clinical guidelines on a test set of 24 patient questions. The system selects relevant guideline titles, retrieves the matching structured content, and iteratively checks its own recommendation until it is complete. The authors report 100% adherence, no hallucinations, no incorrect or missing treatments, and exact page-number references for every recommendation. They compare this with a graph-based retrieval system and with ChatGPT-4, which scored lower on adherence and sometimes omitted or added treatments. The claim matters because oncologists must keep up with frequently updated, flowchart-based guidelines, and a transparent, source-referencing tool could make guideline-compliant recommendations easier to verify.","feed_headline":"Agentic AI hits 100% on NCCN breast cancer guideline tests","feed_subtitle":"Three-step LLM retrieval pipeline beats ChatGPT and Graph-RAG on 24 breast cancer treatment questions.","key_machinery":"The central mechanism is the three-step Agentic-RAG loop. A first LLM call selects clinical guideline titles from the patient description and question; a retrieval step pulls the corresponding JSON objects, which were created by converting NCCN flowchart and table pages into structured form; a second LLM call generates a recommendation following a fixed template; and a third LLM call checks whether the recommendation covers all required care aspects, sending the system back for another generation when something is missing. The JSON conversion of guideline flowcharts is what makes the retrieval precise enough to reference exact pages.","core_discovery":"In the authors' own terms, the central discovery is that breaking the task into three separate LLM steps—title selection, treatment generation, and insufficiency checking—lets an LLM system stay fully aligned with the NCCN breast cancer guidelines. On every one of the 24 questions derived from patient descriptions, Agentic-RAG produced treatment recommendations that a board-certified physician judged as strictly guideline-adherent, with each treatment traceable to a specific NCCN document and page number. The same retrieval and generation machinery also eliminated hallucinations for Graph-RAG and ChatGPT-4 in this evaluation, but only Agentic-RAG recovered every guideline-relevant treatment without adding unnecessary ones. The authors interpret this as evidence that structured, iterative retrieval from a high-quality guideline-derived knowledge base is sufficient to make LLM recommendations both complete and clinically actionable.","pith_inferences":["The reported 100% is tied to the completeness of the JSON conversion; a missing pathway in that conversion would make a wrong answer look correct, because the LLM can only retrieve what was captured.","The strongest reading of the reported numbers is limited to the 24 questions actually tabulated; the manuscript also describes a larger 16-patient-by-4-question design, and the difference between those counts is not reconciled in the text.","A single board-certified physician's judgment is the gold standard here; a second oncologist could plausibly score borderline recommendations differently, so the headline number is as fragile as that judgment is shared.","Because the reference mechanism is exact page numbers, the system could be extended to automatically flag when guidelines are updated, by re-running retrieval on new guideline versions."],"forward_implications":["If the result holds, a small LLM pipeline can deliver guideline-adherent breast cancer recommendations that a clinician can verify against the source document.","Source page references in every recommendation give clinicians a concrete audit trail, which the paper argues is essential for clinical use.","The iterative insufficiency check is the component that appears to close the gap: Graph-RAG, which lacks it, missed four treatments on the same questions.","The authors state the method is intended to extend to other cancer types, since NCCN guidelines share the same flowchart structure."],"supporting_citations":[{"why":"NCCN Breast Cancer guidelines, version 3.2024; the flowchart pages converted to JSON are the knowledge base and the gold standard for adherence scoring.","marker":"[21]"},{"why":"Benchmark methodology for AI chatbots in cancer treatment information, which the paper says its evaluation framework follows.","marker":"[24]"},{"why":"Foundational retrieval-augmented generation method that Agentic-RAG builds on by adding iterative LLM-guided retrieval.","marker":"[11]"},{"why":"Describes the graph-based query-focused summarization approach used as the comparison system.","marker":"[15]"},{"why":"GPT-4o is the model used for title selection and for the Agentic-RAG system's generation.","marker":"[22]"},{"why":"o1-preview is the second LLM used in Agentic-RAG to produce detailed treatment recommendations.","marker":"[23]"}],"fun_headline_variants":["Three-step AI perfect on breast cancer guideline tests","Agentic RAG scores 24/24 on NCCN breast cancer plans","LLM pipeline hits 100% adherence to cancer guidelines","RAG with agentic steps beats GPT-4 on breast cancer checklist"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The result depends on the assumption that the conversion of NCCN flowchart pages into JSON objects captures every treatment-relevant branch of the guidelines, and that one physician's adherence rating is a reliable gold standard.","fun_headline_variants_meta":{"raw":{"variants":["Three-step AI perfect on breast cancer guideline tests","Agentic RAG scores 24/24 on NCCN breast cancer plans","LLM pipeline hits 100% adherence to cancer guidelines","RAG with agentic steps beats GPT-4 on breast cancer checklist"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000224,"raw_usage":{"total_tokens":1485,"prompt_tokens":996,"completion_tokens":489,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":612,"completion_tokens_details":{"reasoning_tokens":417}},"tokens_in":612,"tokens_out":489,"duration_ms":5169,"temperature":1.0,"reasoning_tokens":417,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-10T22:05:59.507157+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Take a set of patient cases that exercise less common branches of the breast cancer flowcharts, convert the same pages to JSON, and have two independent oncologists score the outputs; if any recommendation is judged non-adherent by either oncologist, or if a treatment path present in the original PDF is absent from the JSON, the perfect-adherence claim fails.","supporting_citations":[{"cited_title":"Breast Cancer, Version 3.2024","cited_arxiv_id":null,"evidence_quote":"NCCN Breast Cancer guidelines, version 3.2024; the flowchart pages converted to JSON are the knowledge base and the gold standard for adherence scoring."},{"cited_title":"Use of Artificial Intelligence Chatbots for Cancer Treatment Informa- tion","cited_arxiv_id":null,"evidence_quote":"Benchmark methodology for AI chatbots in cancer treatment information, which the paper says its evaluation framework follows."},{"cited_title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks","cited_arxiv_id":null,"evidence_quote":"Foundational retrieval-augmented generation method that Agentic-RAG builds on by adding iterative LLM-guided retrieval."},{"cited_title":"From Local to Global: A Graph RAG Ap- proach to Query-Focused Summarization","cited_arxiv_id":null,"evidence_quote":"Describes the graph-based query-focused summarization approach used as the comparison system."},{"cited_title":"Introduction to GPT-4o","cited_arxiv_id":null,"evidence_quote":"GPT-4o is the model used for title selection and for the Agentic-RAG system's generation."},{"cited_title":"O1-small Model","cited_arxiv_id":null,"evidence_quote":"o1-preview is the second LLM used in Agentic-RAG to produce detailed treatment recommendations."}],"review_version":1}