KnowGuard: Knowledge-Driven Abstention for Multi-Round Clinical Reasoning
Authors: Xilin Dang
Kexin Chen
Xiaorui Su
Ayush Noori
Iñaki Arango
Lucas Vittor
LONG XINYI
Yuyang Du
Marinka Zitnik
Pheng-Ann Heng
Venue: ICLR 2026 Poster (2026)
Keywords: multi-agent system
clinical reasoning
medical question answering
Primary area: foundation or frontier models, including LLMs

Abstract: In clinical practice, physicians refrain from making decisions when patient information is insufficient. This behavior, known as abstention, is a critical safety mechanism preventing potentially harmful misdiagnoses. Recent investigations have reported the application of large language models (LLMs) in medical scenarios. However, existing LLMs struggle with the abstentions, frequently providing overconfident responses despite incomplete information. This limitation stems from conventional abstention methods relying solely on model self-assessments, which lack systematic strategies to identify knowledge boundaries with external medical evidences. To address this, we propose \textbf{KnowGuard}, a novel \textit{investigate-before-abstain} paradigm that integrates systematic knowledge graph exploration for clinical decision-making. Our approach consists of two key stages operating on a shared contextualized evidence pool: 1) an evidence discovery stage that systematically explores the medical knowledge space through graph expansion and direct retrieval, and 2) an evidence evaluation stage that ranks evidence using multiple factors to adapt exploration based on patient context and conversation history. This two-stage approach enables systematic knowledge graph exploration, allowing models to trace structured reasoning paths and recognize insufficient medical evidence. We evaluate our abstention approach using open-ended multi-round clinical benchmarks that mimic realistic diagnostic scenarios, assessing abstention quality through accuracy-efficiency trade-offs beyond existing closed-form evaluations. Experimental evidence clearly demonstrates that KnowGuard outperforms state-of-the-art abstention approaches, improving diagnostic accuracy by 3.93\% through effective diagnostic interactions averaging 5.74 conversation turns.

Published as a conference paper at ICLR 2026
KNOWGUARD: KNOWLEDGE- ABSTENTION FOR MULTI-ROUND CLINICAL REASONING
Xilin Dang'; Kexin Chen 12", Xiaorui Su², Ayush Noori², Iñaki Arango', Lucas Vittor?, Xinyi Long', Yuyang Du², Marinka Zitnik? Pheng Ann Heng!
DDpparmment of Computer Science and Engineering, The Chinese University of Hong Kong DDepartment of Biomedical Informatics, Harvard Medical School DDepartment of Information Engineering, The Chinese University of Hong Kong
Instiitte of Medical Intelligence and XR, The Chinese University of Hong Kong [x1 dang23, kxchen, ) cse. cuhk edu. hk, marinkal hmss harvard.
ABSTRACT
In clinical practice, physicians refrain from making decisions when patient in- formation is insufficient. This behavior, known as abstention, is critical safery mechanism preventing potentially harmful misdiagnoses. Recent investigations have reported the application of large language models (LLMs) in medical sce- narios. However, existing LLMs struggle with the abstentions, frequently provid- ing overconfident responses despite incomplete information. This limitation stems from conventional abstention methods relying solely on model selfsassessmmnss. which lack systematic strategies to identify knowledge boundaries with external medical evidences. To address this, we propose KnowGuard, a novel investigale- before-absluin paradigm that integrates systematic knowledge graph exploration for clinical decision decision-making. Our approach consists of two key stages operating shared contextualized evidence pool: 1) an evidence discovery stage that sys- tematically explores the medical knowledge space through graph expansion and direct retrieval, and 2) evidence evaluation stage that ranks evidence using mul- tiple factors to adapt exploration based on patient context and conversation history. This two-stage approach enables systematic knowledge graph exploration, allow- ing models to trace structured reasoning paths and recognize insufficient medi- cal evidence. We evaluate our abstention approach using open-ended multi-round clinical benchmarks that mimic realistic diagnostic scenarios, assessing abstention quality through accuracy-efficieng trade-offs beyond existing closed-form evalu- ations. Experimental evidence clearly demonstrates that KnowGuard outperforms state-of- -frth- abstention approaches, improving diagnostic accuracy by 3.93% through effective diagnostic interactions averaging 5.74 conversation turns. com http://tww....omm./ccceeaaaaatta
INTRODUCTION
Large language models (LLMMs) are designed to generate prompt responses based on given instruc- tions (Brown et al. 2020). However, clinical decision-making, this tendency becomes problem- atic, patient's initial information is often incomplete ambiguous, requiring iterative, multi- round conversations to be progressively disclosed. such scenarios, the ability to abstain, i.e., recognizing knowledge boundaries and refraining from answering under uncertainty, is crucial for ensuring the safe and effective deployment of clinical AI systems. Yet, current LLMS struggle with abstention, frequently providing overconfident or premature responses. This behavior prolongs di- agnostic interactions, delays decision-making and increases the cognitive burden on physicians, ultimately undermining trust in Al-assisted workflows (Sun et al. 2025 Kumaran et al. 2025).
Existing abstention methods face two fundamental challenges that limit their suitability for clinical applications. First, LLMs inherently exhibit overconfidence and choice-supportive bias. Tradi-
"Equal contribution.
Marinka Zitik is the corresponding author.

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Published as a conference paper at ICLR 2026
Abstention based on internal knowledge of LLM
Ours: Abstention considering external knowledge
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Figure I: Comparison of abstention approaches in multi-round clinical reasoning. Traditional meth- ods (left) rely on confidence assessment using internal LLM knowledge. Our investigate-before abstain paradigm (right) proactively detects knowledge boundaries through systematic medical knowledge graph exploration, identifying evidence gaps to guide targeted investigation before ab- stention decisions.
tional selideece-baseed methods (Tian et al. 2023 Li et al., 2024 Geng et al. 2023) rely on LLM self-assessments to generate confidence scores for abstentions. However, LLMs often inflate their confidence in initial answers, even when faced with contradictory evidence (Tian et al. 2025). This issue could be further exacerbated by the model's 's reasoning fine- tinettuning, a post-training method that has been widely applied in recent medical agents (Kirichenko al., (025). This overconfidence be- comes particularly problematic in multi-round clinical conversations, where models maintain false certainty despite limited patient information. Second, current methods lack robust external knowl- edge validation methods. Even evidence collection methods, such as the one reported in (Srinivasan) cct al., (2024), count on internal model knowledge without referencing external medical knowledge. These limitations prove especially concerning in clinical settings, where life-critical decisions re- quire both higher reliability and systematic reasoning grounded in external, verifiable evidence.
This paper incorporates external medical knowledge to address the abstention prob- lem, aiming to ground the LLM's abstention decisions with factual medical evidence beyond its own understanding. The key implementation challenge of the proposed approach is to efficiently and precisely identify the knowledge boundary, i.e., determining whether available evidence suf- ficient to support reliable conclusion. In light of this, a highly structured data representation of the external knowledge source is required to facilitate easier and more accurate boundary identifications. Knowledge graph provides well-organized medical relationships, and is, therefore, good match to support the systematic reasoning needed for our abstention approach (Gao et al. 2025 2024).
Present work:
Pan et al,
We highlight that the abstention problem requires a systematic exploration of the medical knowl- edge graph beyond simple fact retrieval. Under a practical multi-round setup, the system must main- tain investigation consistency across interactions and dynamically adapt to new patient information provided. To this end, we propose novel investigate- before--aar--a-tainnn paradigm that grounds absten- tion decisions in systematic exploration of medical knowledge graphs. This approach progressively investigates knowledge boundaries across rounds, integrating external knowledge with clinical ab- stention. When new patient details emerge, the system continues exploration rather than restarting, using knowledge conflicts as signals of uncertainty (see Figure[for details). Our approach consists of two major stages operating a shared contextualized evidence pool. The evidence discovery stage queries and updates knowledge triplets through graph expansion and direct retrieval based on new patient information. The evidence evaluation stage ranks evidence using multiple factors including graph coherence, embedding similarity, LLM selection, temporal decay, and patient pop- ulation reasoning to identify reliable evidence and facilitate contextualized abstention assessment. Throughout multi-round interactions, this evidence pool functions as priority queue, continuously updating evidence relevance based evolving patient context.
In summary, this paper puts forth KnowGuard, a multi-round clinical question answering (QA) abstention approach that leverages knowledge graphs with contextualized evidence reasoning. Our
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Published as a conference paper at ICLR 2026
major contributions are summarized as follows: (1) Investigate-befor--abstte paradigm: We place the unreliable LLM self-assessment scheme with our systematic medical knowledge graph ex- ploration, grounding abstention decisions in factual evidence. (2) Multi-round knowledge graph reasoning: We design a two-stage approach with evidence discovery through graph expansion and direct retrieval, followed by evidence evaluation using coherence- scoring and demographic- guided reasoning that enables dynamic knowledge expansion adapted to evolving patient informa- tion. (3) Dataset and benchmark: We establish new open-ended multi-round clinical benchmark comprising 3,061 cases across three medical datasets. Additionally, we construct comprehensive medical knowledge graph derived from over 300 WHO guidelines. This knowledge graph encom- passes 22k nodes and over 100k edges, integrating multimodal information across text, image, and relation. Unlike existing clinical QA datasets that use multiple-choice formats, our open-ended set- ting better reflects real clinical conversations and enables proper evaluation of abstention behavior. (4) Comprehensive system evaluation: We compare against 5 representative abstention basclines with and without enhancement techniques. Extensive comparisons with state-of-the oofte--o-theearr art abstention approach show that our method improves diagnostic accuracy by 3.93% with an average of 5.74 effective conversation turns.
RELATED WORK
Medical Question Answering Systems. --we-e agents have advanced medical question answering (QA) (Jin et al. 202T (Singhal et al. 2023 Su et al.] (024)) which encompasses both multiple-choice and open-ended questions from diverse medical sources. To better reflect real- world clinical practice where physicians often need to gather additional information through iterative questioning, recent research has shifted toward interactive QA frameworks that allow for multi-
turn conversations and information seeking (Wang et al.. 2025 Johri et al.] 2025 Li et al. 2024
Kyung et al. 2025). MediQ Li et al. 2024) introduced such an interactive QA framework that
leverages multi-agent collaboration to encourage agents to abstain from answering when uncertain and actively seek additional information through follow-up questions. However, existing interactive formats, which inadequately reflect real-worth clinical scenarios where practitioners typically encounter open-ended questions without predefined answer choices (Nachane al. 2024). To address this limitation, we develop a multi-round open- ended interactive clinical reasoning benchmark to evaluate free-text responses.
benchmarks predominantly focus on multiple-choote
Abstention Methods. Effective abstention requires recognizing knowledge boundaries and refrain- ing from answering when evidence is insufficient (Lin et al. [025]] Ni et al. [2055] Kale & Nadadur 2025). Current approaches include self-assessment methods that rely on internal confidence through
uncertainty estimation (Tian et al. 2023), calibration scoring (Geng al.) 2023 Srivastava et al., 2023). and multi-scale rating (Li et al.) 2024); consistency- bassed methods that aggregate multiple model outputs for disagreement detection (Wang al. 2022); and knowledge-based approaches that incorporate information sources. Long context methods (Tu et al., 2024) retrieve comprehen- sive medical documents but provide coarse-grained context that fails to pinpoint specific knowledge gaps, leading to information overload rather than targeted evidence discovery. While these methods have shown promise in various domains, they share fundamental limitation in their reliance on reactive confidence assessment rather than proactive knowledge investigation. When facing uncer- tainty, these methods ask "how confident am I?" instead of "what specific evidence am missing?". KnowGuard introduces the first teeestiggte-bfor--bbbbeee paradigm for multi-round clinical rea- soning, which systematically explores knowledge boundaries through targeted evidence discovery guided by medical knowledge graphs.
METHOD
3.1 PROBLEM FORMULATION AND APPROACH OVERVIEW
Multi-round Abstention Problem Formalization. We formalize multi-round clinical abstention within an interactive consultation approach that simulates realistic diagnostic scenarios. The Patient Agent maintains complete patient information K {ku, A ......, kn } (n pieces in total) and responds truthfully to inquíries by revealing relevant information subsets. The Doctor Agent receives the initial patient presentation kn and must decide at each round whether to abstain from diagnosis.
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Published as a conference paper at ICLR 2026
KnowGuard: Knowledge- Abstention for Rolti-Round Clinical Reasoning
Stage 1: Evidence Discovery
Previous Conversations
Direct Retrleval
Graph Expansion
Multi-modal knowledge
Abstention Decision
Abdomin pan
Iruud
Agent
New Pabent
Agent
Hy
Abuoomnn
(Panciatiiic
Canditate Contextual Evidence
Kezwledge Trppits
Text Seriance
Page Image
ARV
HV
soomiingg
Stage 2: Evidence Evaluation
Embedding
Evidence Priotity Scoring
Pool
Pancreatte
LLM
Answer abstain?
Relevatca
Evidence Pool
Graph
Canditats Contentual Duidence
Dway
LUS
Doctor Agent
Decide
New Contentual Eridence Pool
Figure KnowGuard approach for knowledge-driven abstention clinical reasoning. Our wwwessigateebefor--attte paradigm systematically explores medical knowledge graphs to identify evidence gaps before abstention decisions. The Evidence Discovery Stage retrieves multi-modal evidence through dynamic graph expansion and direct retrieval. The Evidence Evaluation Stage adapts exploration priorities through relevance assessment, graph coherence prioritization, demo- graphic weighting, and temporal decay. Final abstention decisions integrate all factors to determine when sufficient evidence exists for diagnosis versus continued investigation.
When abstaining, the agent asks targeted questions ze to gather additional information; otherwise, provides a diagnostic answer. At each round given accumulated patient knowledge K., KC--1 U {ar} where ae represents the patient's 's response to question gt. the Doctor Agent must make a binary abstention decision:
K. 1},
(1)
where A. indicates continued information gathering (abstention) and A. = indicates suffi- cient confidence for diagnosis. The core challenge lies in determining the optimal stopping point where Xc, contains sufficient evidence for reliable diagnosis while minimizing unnecessary interac- tion rounds. Our proposed method focuses this challenge.
paradigm replaces unreliable LLM self- assessment with structured medical knowledge exploration. As shown in Figure KnowGuard maintains a contextualized evidence pool B, represented as a priority queue of knowledge triplets relevant to the case. The evidence pool evolves cumulatively across conversation rounds, building upon previous discoveries while incorporating new patient information at- The approach operates through two complementary stages: Evidence Discovery Stage systematically expands B, based on patient information, while Evidence Evaluation Stage adapts exploration priorities based on multiple factors, including patient demographics.
KnowGuard Approach. Our
We construct comprehensive medical knowledge graph G (V. E) from authoritative medical guidelines. containing [V] medical entities and |Σ| clinical rela- tionships. Each triplet r,) E È is augmented with source text descriptions and document page images, enabling both structured reasoning and contextual validation during evidence discovery. To ensure the knowledge graph remains up-to-date, we have implemented monthly web scraper module that automatically retrieves newly published WHO guidelines. For more details about the knowledge graph integration, we refer readers to Appendix F
Multi-modal Knowledge Graph.
3.2 EVIDENCE DISCOVERY STAGE
Contextualized Evidence Pool Definition. The Evidence Discovery Stage operationalizes knowl- edge boundary exploration through systematic graph investigation. To enable efficient exploration of vast medical knowledge spaces, we maintain a contextualized evidence pool as priority queue B, ((hs Tú, li, pc)), B4 of candidate medical triplets (length is K), where each triplet (hi, ri, 44)
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represents a potential reasoning step with priority p:. This bounded representation enables efficient ranking and selection while focusing exploration on the most promising knowledge paths.
Systematic Evidence Expansion. The stage performs structured exploration through two comple- mentary retrieval strategies. Graph Expansion- based retrieval identifies triplets connected to entities in current high-priorin candidates:
Texp r(, E G Eg, or t Ep.h.
where Eg, represents entities present in current evidence triplet. Direct retrieval first generates queries according to the current patient response at, and then performs a comprehensive search across the knowledge graph:
Tquery- GraphRetrioval LLMuuyy (at)).
The retrieved evidence candidates Teamdidates = for priority scoring.
Tesp U gueery are fed into the evidence evaluation stage
3.3 EVIDENCE EVALUATION STAGE
The Evidence Evaluation Stage operates on candidate contextual evidence to compute comprehen- sive priority scores through five complementary factors: Embedding similarity, L.M relevance, graph coherence, round decay, and patient population reasoning-
Relevance Assessment with Dual Validation. Each candidate triplet undergoes dual relevance assessment combining embedding similarity (hard relevance) and LLM relevance (soft relevance). Hard relevance measures the semantic similarity between triplet embeddings and the current patient response:
Ssim (h, r, t) cosine(Embed(th, r), (a)ed
while soft relevance employs LLM assess clinical relevance given the current patient context:
Siel (h, LLM,al (az, (h, r.
This dual validation ensures both semantic and clinical alignment of evidence investigation.
Graph Coherence Prioritization. To maintain reasoning consistency, we prioritize triplets that connect frequently visited entities, indicating established reasoning pathways:
Scoh rh r) countg counts((),
where countg(-) tracks cumulative frequency of the entity across all evidence pools throughout the conversation. Higher coherence scores indicate stronger integration with existing paths, enabling systematic knowledge boundary detection rather than random exploration.
Demographic-guiöee Priority Weighting, The stage infers patient demographics and clinical pop- ulations from conversation history prioritize relevant knowledge graph regions. Population infer- ence analyzes accumulated patient information against predefined categories:
P. L.MM (Ke,, Gpop):
where Gpop represents predefined population categories derived from knowledge graph topics, such as adolescents. Triplets belonging to identified patient populations receive enhanced weighting:
Spop h, r,t) = La (h, r,t) E Subgraph(P.)
(8)
otherwise,
where >> emphasizes population-specific knowledge and Subgraph denotes triplets relevant to inferred populations.
Temporal Decay with Round-based Updates. To balance historical context with current informa- tion, the stage applies temporal decay to previously explored knowledge while emphasizing recent evidence. Priority updates follow exponential decay:
Pe+1 (h, r,t) r,, x (1 W'eeaa) ) Pnew (h, r, Waecaay

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where Paew reflects priority computed from current round information and Wdecay [0, 1] controls temporal transition rate.
Evidence-groue grouundd Abstention Decision. The final priority combines multiple contextual factors through weighted aggregation:
Piinal (h, r,t) (Wsim Siim + Whet Srel + Weoh Scoh) X Spop
(10)
The contextualized evidence pool maintains top-K K triplets: B++1 = Top-K Coop-K(Tannttiinn (Tandiidttess Pinaa), where evidence including source text
Teandidates Tesp U Tquay. Each triplet is augmented with and document images. The final abstention decision integrates structured knowledge evidence with patient context:
A = ML (K+, Be, (Trest, Ting)).
(11)
where the model receives current patient information, top-ranked evidence triplets, and their associ- ated multi-modal content to make informed abstention decisions.
3.4 OPEN-ENDEL CLINICAL REASONING BENCHMARK
To properly evaluate abstention behavior in realistic clinical scenarios, we establish a multi-round open-ended benchmark that extends beyond existing closed-form evaluations. Traditional multiple- choice formats constrain response options and fail to capture the complexity of real clinical conver- sations where physicians must formulate comprehensive diagnostic assessments. Following recent advances in automated evaluation (Su et al. 2024), we employ LLM--s-judee methodology to con- vert closed-ended questions to open-ended format, enabling more accurate assessment of both diagnostic reasoning quality and abstention appropriateness.
The Judge Agent performs answer matching between free-text predictions and ground truth sponses. For originally multiple-choice questions, the judge receives all answer options along with the model's free-text response, without knowing the question content or correct option, and identifies the most semantically similar option:
Amatched Judge(Apred: {option,, option., ..... .ption........tion,
(12)
For originally open-ended questions, the judge performs binary classification to determine whether the prediction aligns with the ground truth answer:
Match = Judge(AAped: Arne) E (Yes, No),
(13)
where Apred represents the model's free-text response and Ause denotes the ground truth answer.
EXPERIMENTS AND RESULTS
We conducted extensive experiments to evaluate the effectiveness of KnowGuard multi-round clinical abstention, comparing against existing abstention methods on our open-ended interactive clinical reasoning benchmark.
4.1 EXPERIMENTAL SETTINGS
Dataset Construction. We convert MEDQA (CC-BY-4.0) Jin et al. 2021 CRAFT-MD (CC-BY- 4.0) @Jhri al. 20244, and AFRIMEDQA (CC-BY-NC-SA-.0) (Nmoo al. 2025) into interactive multi-round formats. Following established protocols (Li et al. 2024), we parse patient records into structured components: age, gender, chief complaint, and additional evidence as atomic facts (Mi) et al. 2023 Initially, only age, gender, and chief complaint are presented to the Doctor Agent, which must strategically gather missing information through targeted questioning. The resulting interactive datasets are termed ioMEDQA. ioCRAFT-MD, and ioAFRIMEDQA.
Multi-modal Knowledge Graph Construction. Our knowledge graph incorporates over 300 WHO guidelines, resulting in 22k medical entities and more than 100k clinical relationships. Each triplet is augmented with source text and document images for comprehensive knowledge boundary detec- tion. Subgraphs are labeled with demographic and disease-specifis features extracted from guideline titles and abstracts, enabling patient population reasoning. The system monitors publication dates for automatic updates, ensuring current medical knowledge supports boundary detection decisions.

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Baseline Methods. We benchmark KnowGuard against representative abstention approaches: Ba- sic (direct question or answer, without explicit abstention step), Binary Decision (Srivastava et al. 2023) (explicit binary abstention), Numerical Score (Tian al. 2023 (confidence scoring 1-5 with thresholding), Scale Rating (Li et al. 2024) (fine-grained confidence levels with descriptions), and Long Context (Tu al. (024) (external document retrieval with full-text processing). We compare the baselines with and without rationale generation (Wei et al. (022) (generate rationale alongside abstention decision) and self-consisteney (Wang al. (2022) as enhancements. Besides, we com- pare KnowGuard against calibration-Fi foccuutse----o methods: lemperature Scaling (Guo et al. 2017) and Conformal Abstention (Yadkori et al. 2024). Since our setting is training-free we use leave-one- dataset-out partitioning for these methods for fair comparison.
Metries and Agent. We evaluate using Accuracy (ACC), Expected Calibration Error (ECE), Brier Score, and average conversation rounds (avg. Turn) as primary metrics for diagnostic effective- ness and interaction efficiency. All experiments employ GPT-4 (Achiam et al.) (023) as the core agent model, given its widespread adoption and demonstrated capabilities in medical reasoning tasks (Eriksen al. 2024).
4.2 RESULTS
Our ceesstiattebefore-ahstte paradigm achieves the best overall performance, delivering the high- est accuracy with the lowest ECE and Brier scores while keeping interaction efficiency compet- itive (Tables [ and 2p. KnowGuard consistently outperforms all baselines, improving accuracy by 1.07 (5.64%) over the strongest confidence-based methods in basic settings and 1.20 (8.65%) in enhanced settings, and surpassing Long Context by 10.29% (basic) and 14.83% (enhanced) on average. Calibration- baselines (Temperature Scaling and Conformal Abstention) are overly conservative, often deferring to the maximum round limit (12 turns) with lower accuracy (0.6873/0.71) 715771154///66666 whereas KnowGuard achieves comparable or better accuracy with far fewer rounds (Table B. Overall, these results highlight the robustness of systematic knowledge boundary detection over celf-aseessment-beee based abstention.
Table 1: Performance comparison of basic methods on open-ended multi-round interactive clin- ical reasoning, including Basic (implicit), Binary Decision (Srivastava et al. 2023), Numerical Score (Tian et al. 2023), Scale Rating (Li et al. 2024), Long Context (Tu et al. 2024 3 and Know- Guard (ours).
inAFRIMEDQA
ioMEDQA
Mc
Method
ACC
Turn
ECE
Brier
ACC
Turn
ECE
Brier
ACC
Tum
BCE
Brier
Basic (implicit)
51.10+2.0
8.32
57.83
8.98
54,69
8.31
Binary Decision
61.97.2.8
8.98
65.95 05.95
7.69
64.67
7.85
Numerical Scure
54.25
1.72
0.281
61.74g1.76
2.51
0.246
59.35
2.42
0.259
Scale Rating
63.06.22.44
5.11
0.141
0.285
64.23
5.15
0.135
0.260
65.40=1.19
4.83
0.136
0.261
Long Context
57.45
2.01
0.283
59.95
3.23
0.260
57.88
3.23
0.274
0.314
KuowG
68.70.1.17
5,26
0.050
0.236
70.98
5.41
0,065
0.219
66.47
4.89
0,050
0,216
Table 2: Performance comparison methods enhanced by Rationale Generation (Wei et al. 2022) and Self-Consistency (Wang et al. 2022) open-ended muuli--ouud interactive clinical reasoning, including Binary Decision (Srivastava et al. 2023), Numerical Score (Tian et al.) 2023). ), Scale Rat- ing (Li al. 2024), Long Context (Tu et al.
2024). and KnowGuard (ours).
ieAFRIMEDQA
ioMEDQA
ioCRAFT-MD
Method
ACC
Turn
ECE
Brier
ACC
Turn
ECE
Brier
ACC
Tur
BCE
Brier
Binary Decision
64.55
13.82
72.92+1.47
13.00
70.01
12.21
Numerical Score
58.33 2m
2.63
0.190
0.272
64.23
4.61
0.165
0.268
61.51
4.98
0.182
0.270
Scale Rating
61.36
5.31
0.122
0,286
65.52
6.26
0.142
0.273
66.34,11s
5.56
0.146
0.273
Long Context
56.80 56.80
1.16
0.293
0.325
59.37
3.30
0.293
0.320
58.61
3.29
0.293
0.329
KnowGuare
73.20
5,30
0.085
0,223
14.12.0.a7
5.40
0,099
0.203
71.96
6.51
0.092
0.213

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Published as a conference paper at ICLR 2026
Table 3: Comparison of calibration and uncertainty methods the open-ended reasoning tasks,
including Temperature Scaling (Wei et al.) 2022) and Conformal Abstention (Ydkori et al. 2024). Carlibration- methods exhibit overly conservative behavior, consistently deterring maxi- mum round limit (12 turns) regardless of evidence quality.
ioAFRIMEDQA
ioMEDQA
ieCRAFT-MD
Method
ACC
Turns
BCE
Brier
ACC
Turms
ECE
Brier
ACC
Turns
ECE
Brier
Temperature Scaling
0.687
12.00
0.200
0.251
0.715
12.00
0.177
0.230
0.692
12.00
0.156
0.227
Conformal Abstention
0.626
12.00
0.309
0.303
0.662
12.00
0.332
0.332
0.645
12.00
0.290
0.262
KnowGuard
0.732
5.30
0.085
0.223
0.741
5.40
0.099
0.203
0.720
6.51
0.092
0.213
ANALYSIS
5.1
ABLATION STUDIES ON KEY COMPONENTS
To validate the effectiveness of KnowGuard's designs, we conducted systematic ablation studies as shown in Table We progressively evaluate each component's contribution demonstrate their individual effectiveness. Multi-modal knowledge graph triplets provide substantial improvements over text-only evidence retrieval, demonstrating the value of structured medical knowledge for ab- stention. The evidence evaluation stage enables systematic exploration by ranking candidate evi- dence, leading to more targeted abstention decisions. Patient Population Reasoning (PPR) enhances personalized reasoning by considering demographic and disease-specing contexts. Each compo- nent contributes meaningfully to both accuracy and efficiency, with the complete system achieving optimal performance across all datasets.
Table 4:
Ablation studies of Guard's key designs, including evidence modality of text or multi-modal knowledge graph (KG) triplet, evidence evaluation stage (Evidence Eval.), and patient population reasoning (PPR) factor.
Component Configuration
ioAFRIMEDQA
ioMEDQA
ioCRAFT-MD
Text evidence
KG evidence Evidence Eval.
PPR
ACC
avg. Turn
ACC
avg. Turn
ACC
avg. Turn
73.20
5.30
74.12
5.40
71.96
6.51
x
72.60
7.03
74.29
6.53
71.92
6.53
X
66.22
2.69
70.66
3.24
68.92
3.31
X
X
X
66.02
3.33
64.79
3.25
62.73
3.30
X
X
X
63.06
5.11
64.23
5.15
65.40
4.83
5.2 HYPERPARAMETER STUDIES
Our evidence priority scoring mechanism combines multiple factors for systematic exploration. Ta- 5loos sensitivity analysis for each factor. All factors contribute meaningfully performance. with optimal weights being: embedding similarity Wsim (0.2), LLM relevance Whel (0.6), graph co- herence Woeb (0.35), round decay Wdecay (0.5), and patient population reasoning Wpop (1.15). The consistent performance across different weight configurations demonstrates the robustness of our approach, indicating that the method not overly sensitive to hyperparameter tuning.
Table 5: Sensitivity analysis of evidence evaluation factors. Bold values indicate optimal perfor-
mance for each factor.
Dataset
Metrie
Embed. Sim.
LLM Kel.
Graph Cob.
Round Decay
PPR
0.1
0.2
0.3
0.5
0.6
0.7
0.25
0:35
0.45
0.4
0.5
0.6
1.10
1.15
1.20
RAFRIMEDOA
Turns
ACC
71.41
5.48
73.30
5.30
71.99
5.46
71.02
5.58
73,20
5.30
68.51
3.40
71.41
5.35
73,20
5.30
70.64
3.65
70.25
5.41
73,20
5.30
68.32
5.33
70.25
5.35
73,20
5.30
71.22
5.38
WCRAFT-DD
Turns
ACC:
5.44
71.96
5.51
5.40
5.64
71.96
5.51
5.37
5.45
71.96
5.51
3:43
5.51
71.96
5.51
5.29
5.47
5.51
5.39
AMEDUA
ACC
72.77
74.12
5.40
71.24
70.59
74.12
5.40
71.64
70.27
74.12
5.40
31.72
70.84
74.12
5.40
71.16
71.64
74.12
5.40
71.98
Accuracy vs. Conversation Length. Figure 3 demonstrates the relationships between accu- racy and conversation length for our method and traditional self-assessment approaches (Scale Rat-
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Published as a conference paper at ICLR 2026
ing, Numerical Score). KnowGuard shows consistent accuracy improvements with longer conver- sations, indicating effective knowledge boundary investigation through systematic external knowl- edge exploration. In contrast, self-assessment methods show steep trajectories where additional rounds provide diminishing returns, reflecting their reliance on internal knowledge. This validates hypothesis that proactive knowledge exploration outperforms reactive confidence assess- ment in multi-round clinical reasoning.
Confidence Evolution during Conversation. Figure 3 Middle) shows confidence evolution pat- terns of our method and Scale Rating throughout conversations. The lengths of different conversa- tions are normalized for intuitive presentation and comparison. Notably, KnowGuard's confidence increases more rapidly than Scale Rating. This indicates that systematic exploration of medical knowledge boundaries enables more targeted information gathering than generic self-assessment.
Performance on Rare Cases. Figure 3 Rightt compares the accuracy performance on rare diseases. Know Guard demonstrates substantial advantages over other abstention methods. This suggests that introducing external knowledge as contextual evidence effectively enhances reasoning in challeng- ing cases where traditional self-assessment methods struggle, while the design of patient population reasoning enables targeted exploration of relevant medical subgraphs for more informed abstention decisions.
Scale RcDigg
InowGGard
SrownGordd
Baury Doziion
Accuracy
(mplico
0.4
Long Contod
Scale fating
Koling
Nimerral
KrowCourd
Numerical Score
10
0%
25%
85%
60%
5096
100%
50
00
00
Accurccy
Figure 3: (Left) Systematic evidence exploration enables accuracy improvements with longer con- versations, unlike confidence-basee based self- seffaaseessmnt methods. (Middle) KnowGuard's confidence evolves more rapidly through targeted evidence acquisition compared generic self-assessment. (Right) serriigtttebefrr--bbbtte paradigm particularly benefits rare disease diagnosis where ex- ternal knowledge exploration is crucial.
Case Study. Figure 4 illustrates example of KnowGuard's imvestigate-beforee abbstain paradigm. When presented with abdominal pain symptoms, the system proactively investigates contextual ev- idence to explore medical knowledge boundaries, ultimately reaching accurate diagnosis with comprehensive treatment recommendations. This demonstrates how systematic knowledge bound- ary exploration enables confident decision-making in complex clinical scenarios. See Appendix H and [ for more case studies about system robustness with clinical validations.
5.3 EFFICIENCY ANALYSIS
Table 6: Comparison of token usage and cost between KnowGuard and Long Context methods
Method
Avg Inpat Takems/Case
Avg Output Takens/Case
Avg Total Tokens/Case
Std Dev Total TakensiCase
Cost/Cas
KnowGuard 26,319
21,200
$0.2870
Long Context
624_307
1,361
625,6688
1,286,006
$6.2714
-95.7%
-95.42%
As shown in Table 6, KnowGuard achieves a 95.7% reduction in total tokens and 95.4% cost savings case compared to Long Context, mainly because it uses structured knowledge graph retrieval (targeted triplets with multimodal evidence) instead of full-text document processing. For latency (Table KnowGuard has higher total case latency (+1 15%) due to rounds (5.30 vs. 1.16 avg.) to mitigate Long Context's s overconfidence. However, per-round latency is 28.5% 5% faster (10.33s vs. 14, 44.44s), which is more clinically relevant in turn-by-tum interactions with human response time

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Published as a conference paper at ICLR 2026
A50-year-old presents the emergency department abdominal pain diacomfort for hours.
Qusstion
Arawer
Whht were you being with your prosorfzed medicattios?
digiticant angle glousoma
Discovey
NY
Agent
Gruph Exparsion
Direct Retrieval
Scoring
getine inddeiene
Revar nusses
Mult-mroiil Exidence
BB
orough intoratiion
Depsh
Unconfident for answer
Antwer
sery voniting dianthea, fever, bowel habits?
Contectanl
The patient has exper enced nauses and vomiing, She has voriied larme
lerpuriture
Docter
Evidence Discovery
Direct Retrieual
Pridity Scaring
gus sustenatntiial
Juftie
AR
Pastent Agant
S
Groph Expansion
Ande
druge
Depth
ART
Nuttirmodal Exidencs
Quastion
the sationt
blood aount (CBC)) bwe. releted her cummen symplorme??
Agent
Diecovery
Evidence
Pridiiy
NETI clasa
Patter Agant
Diroct Retrioval
Muttk-modal
Enough intormation
De
Cruph Exparsion
Depth
Confident for
Diagnosis
Aunno
y coused by ooute pancreatiii, whtch is known side effect cartainn antiretroviral druge (ATTVs) used to treat HIV.
patlent's abdoeminw
Immediate Actiona: Deccontina susppected offending Altii (lewly NRTis such d4T) until the sympooms resoive. Hospial admrbot for monelating and supporiise came.
Agent
Figure 4: Case study demonstrating KnowGuard's investigate-before-bbee abbttii paradigm.
Table 7: Comparison of latency between KnowGuard and Long Context methods.
Avg Latency/Case (seconds)
Avg Latency/Round
Method
(scconds)
KnowGuand
54.77
10.33
Long Context
25.45
14.44
+29.32
-28.46%
Overhead
(2.15x total time)
(1.40x speed up)
between rounds; thus, KnowGuard provides a 1.4x speedup per interaction and improves respon- siveness during active conversations.
6 CONCLUSION
In this work, we establish the novel task of open-ended multi-round clinical reasoning and present KnowGuard, an investigatebbeior--astaa paradigm that shifts from internal L.MM knowledge to external evidence investigation. Our comprehensive experiments demonstrate that this paradigm shift yields substantial improvements across diverse clinical datasets, with KnowGuard achieving state-of-the the-ttte performance compared to five baseline abstention methods across all benchmarks.
7
ETHICS STATEMENT
This work presents research framework evaluated on academic benchmarks and is not intended for direct clinical use. Real-world deployment requires prospective clinical trials with diverse patient populations, regulatory approval, ongoing bias monitoring (particularly in abstention decisions that could disproportionately affect vulnerable groups), and mandatory oversight by licensed medical professionals. KnowGuard is research prototype and should not be used for actual medical diag- nosis or treatment decisions. All outputs must be reviewed by qualified healthcare providers, and any clinical application must comply with local healthcare regulations and ethical guidelines.
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8 ACKNOWLEDGEMENT
The work described in this paper was primarily supported by the Research Grants Council of the Hong Kong Special Administrative Region, under Project T45-401/22-N. The authors also acknowl- edge the partial support from the JC STEM Lab of Advanced Wireless Networks for Dission-Critioas Automation and Intelligence, funded by The Hong Kong Jockey Club Charities Trust.
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A STATEMENT ON LLM USAGE
We disclose that LLMs were used solely to aid and polish writing, including covering spell checking, grammar fixes, style refinement, and minor wording suggestions. LLMs did contribute any scientific or technical content: all conceptualization, method design, implementation, experiments, result analysis, figures/tables, and conclusions were performed and verified by the authors. All cited works were independently retrieved, fully read, and manually verified using official sources; LLMs were used generate fabricate citations results.
B TAKEAWAYS
The results reveal critical insights for abstention design. First, simply incorporating external knowl- edge is insufficient for effective abstention, as Long Context's suboptimal performance demonstrates despite accessing comprehensive medical documents. Second, even though LLMs have been trained on extensive medical knowledge, our findings show that introducing external knowledge contex- tual evidence at inference time significantly improves abstention decisions and reduces overconfi- dence in clinical reasoning tasks.
C LIMITATIONS AND FUTURE WORK
While KnowGuard demonstrates strong performance established benchmarks, several limitations warrant attention. The current knowledge graph relies primarily on WHO guidelines and may not capture all clinical knowledge domains. Future work should explore integration with broader med- ical knowledge sources and real-time knowledge updates. Additionally, the system's 's performance on highly specialized medical domains requires further evaluation.
D ABLATION STUDY ON SCORING COMPONENTS
To validate the necessity of each component in scoring mechanism, conducted systematic ablation studies by removing one factor at a time while keeping others active. Table g presents the results on ioMEDQA and ioAFRRIDD datasets.
Components Graph
Metrics
Impact A ACC
Removed Factor
Emh.
L.M
Decay
PPR
Rounds
ACC
ioMEDQ4
(None Full Model)
5.40
74.12
baseline
Embedding Similarity
6.06
71.64
-2.48%
LLM Relevance
6.26
72.68
-1.44%
Graph Coherence
X
70.03
-4.09%
Round Decay (=0)
5.87
70.62
-3,50%
Round Decay (=1)
5.79
70.84
-3.28%
PPR
6.53
74.29
+0.17%
ioAFRIMEDQA
(None Full Model)
5.30
73.20
baseline
Embedding Similarity
5.27
71.02
-2.18%
L.M Relevance
5.18
70.16
-3.04%
Graph Coherence
69.23
-3.97%
Round Decay (=0)
5.25
71,22
-1.98%
Round Decay (=1)
6.53
72.21
-0,99%
PPR
7.03
72.60
-0.60%
Table 8: Ablation study results showing the impact of removing individual scoring components. Each row represents configuration with component disabled while others remain active.
The ablation study demonstrates that each component serves distinct and necessary role in our scoring mechanism. Graph Coherence emerges the most critical factor, with its removal caus-
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ing the largest accuracy drops (3.97-4.09%), confirming the importance of structured relationship modeling for maintaining knowledge consistency in medical reasoning. Round Decay validates the need for adaptive temporal reasoning, as both extreme settings significantly degrade performance: decay=0 (treating all rounds equally) causes 1.98-3.50% drops, while decay=1 (only considering current round) leads to 0.99-3.28% drops. Embedding Similarity and LLM Relevance prove sential for evidence filtering and semantic matching, with their removal causing 1.44-3.04% accu- racy reductions. PPR contributes modest but consistent improvements (0.17-0.60%) validating its role in demographic-sper -speciccc evidence prioritization. These complementary contributions justify our weighted combination approach rather than relying on any single scoring mechanism.
MULTI-ROUND CLINICAL REASONING BENCHMARK
E. DATASET CONSTRUCTION AND OPEN-ENDED CONVERSION
We constructed comprehensive benchmark for multi-round clinical reasoning by converting tra- ditional closed-form form medical datasets to an open-ended format. The benchmark comprises 3,061 cases across three datasets: MEDQA development (1,269 cases), AFRIMEDQA (522 cases), and
CRAFT-MD (1,270 cases), as shown in Table 9 We utilized Factscore (Min et al. 2023) extract atomic facts from patient context following (Li et al. (024).
AfriMedQA contains both open-ended and multiple-choice questions, which require different eval- uation strategies in our judge agent.
Table 9: Dataset composition for multi-round clinical reasoning evaluation.
Dataset
Size
MEDQA development
1,269
AFRIMEDQA
522
CRAFT-MD
1,270
Total
3,061
E.2 PERFORMANCE OF RARE CASES
To evaluate the effectiveness of systematic knowledge boundary detection on challenging diagnostic scenarios, we identify rare disease cases within the benchmark. Using spaCy/scispaCy (Honnibal al. 2024) and regular expressions, we extract approximately 200 medical terminologies, conduct frequency analysis to select 60 least frequent terms, and utilize LLM validation identify 25 con- firmed rare diseases according to medical consensus (Phan et al. 2024). This analysis demonstrates how knowledge boundary detection addresses insufficient evidence scenarios that traditionally chal- lenge confidence- based abstention methods. The results are shown in Figure 3 Right).
F KNOWLEDGE GRAPH INTEGRATION
ENTITY/RELATION EXTRACTION
Extraction Prompt
Task Instructions: am constructing a knowledge graph in the medical field. From this im- age, please help me extrapolate knowledge such as (x_name, x.type, relationship mat.
y-name, y-type, relevant.decrrition in JSON for-
Input:
Each page in clinical guideline.
Explanation:
Please note that the relationship includes but not limited to l'protein protein', 'drug_protein', 'contraindication" "indication 'off-label
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use', 'drug_drug', 'phenotype. Eehenotypeephheottyee cdasassepheenttyeeengsateeee pesseaaseppennttpe.posit.t
ddisease_.roteinn', 'disease.diseasee 'drug bioprocessbioprooes'' mounfnc uun 'cellcomp.ce coomp',
molfunc protein', 'cellcomp protein', "bioprocess protein', exposure protein', 'exposure di disease', 'exposure exposure', exposure.bioproceess 'exposure func', "exposure_cellcomp pathway.pathway', "pathway.protein'' 'anatomy_anattom anatoomyprotein.ppresentt The display relation includes but is not limited to l'associated with', carrier', 'contraindication' 'enzyme', 'expression absent', 'expression present', 'indication', 'interacts with', 'linked to', 'off-label use', 'parent-child' Id',,, 'phenotype absent', 'phenotype present', 'ppi', 'side effect', "synergistic interaction', 'target', 'transporter')
x_type and y-type include but are not limited to l'gene/protein', 'drug', 'effect/phenotype/ 'disease', 'biologicalpproess: molecular.function cceluular.comooeeee pathway', "anatomy]] re wwl.eent.descrippee which describes all the relevant information for x_name and y_name.
'exposure',
should be sentence or paragraph extracted from this image,
Response Format: Please provide the information formatted as a JSON object. The structure must strictly adhere the following requirements:
1. The ISON object should consist exclusively of these keys: "x_name", "x_type", "relation- ship". "display relation", "y name". "y type" and "relevant description".
2. The response should be clean and precise: it must not contain ellipses ("..."), backticks (""",, any code block identifiers such as jjson. There should be a numerical index for each piece of knowledge.
Please ensure the ISON object is properly formatted with no additional characters or elements outside of the specified structure.
F.2 THE GRAPH-SSAACCH/XPPNSOO ALGORITIMM
Algorithm KnowGuard: Investigate- Bebtigatte-Bfoor---bbiiiii Framework
Require: Initial patient info ko. Inquiry T, Max rounds R
Ensure: Final answer or abstention decision
1: KC < kg. f <- O
2: B + 0 // Initialize contextualized evidence pool
3: while
R do
4: if t = then
5:
Q EVIDENCEDISSCOEEYY(.. Ko, I)
6:
else
7:
Q EVIDENCEDDISCOVERY((.. 0t, T)
8:
end if
9:
B EVIDENCEEVVALUATIONNC/ B, K.,, abstenntion.decisionn +- EVDENCECROONNDDAAASSTNNNIOO
10:
B,
if iistentionnddcciioon
then // Provide diagnosis
12:
return GENERATEEANSWERKK I)
else// Continue investigation
14:
0++1 &- GENER TETNAAATTEEEETTTTTTTTTTTTUTTTTTS K.,)
15:
0--1 + PATIENTRESPONS((G.1))
16:
K++1 + Kc, Ud++
17: 1et+11
18: end if
19: end while
20: return EVVDENCCGROOUNDDABSTTTTIIOIOOOO B,
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Algorithm 2 Evidence Discovery Stage
Require: Patient info Kt, New patient response at, Inquiry I
Ensure: Evidence priority queue Q queries k GENERATTEVVIDENNEQUUEESSSS I)
2: evidence candidates t
I/ Direct retrieval based on new patient response
3: for each query queries do
4: results + RUORUEEEEROMKK((qerT))
5: evidence.candiddat +- wridence.conddidate U results
6: end for
// Expansion-baase retrieval from existing evidence pool
7: if B o then
8:
c candidales ExPANDFFROMEXXSTTNEEEVEREEEEEEE
9:
evidence__andiddee < evidence U expponsio_ccnddiaaees
end if
11: patient.contee contert + INTERPATIENTTOOTTXTTT.))
<0
13: for each evidence E ceridence_candidee do
14:
Asimslarity f EMBEEDDINGSIMILARIYYYY, evidence)
15:
Srelerance +- LLMRELEVANCE(L,, I, evidence) EAAGGRAAPPICEEGHH(eeeOO
16:
Spopulation
17: priority (Waim X Spimilarity + Wret Spelevance) Spopulation
18:
ADDTOQUEUE( evidence, priority) end for
19:
20:
return Q
Algorithm 3 Evidence Evaluation Stage
Require: Current queue 2, New patient response a,, Investigation gt. Round &
Ensure: Updated evidence pool Q'
1: new men_ccntext + CERRMMVVESTTIAAATNNNONNEEE
ae)
2: evidence-
+- VEVVEIATTTEEVVDEENCCUUEEEEEEEEEE
I)
// Reassess existing evidence against new context
3: for each evidence Q do
4: Ssimilarity < EMBEDDINGSMMLLLRRYrrrrrre serrreerddeeeeeeeeereeeee evidence)
5: Srelenance I.LMRELEVANCF((ee contert, I, evidence)
6:
Seokerencc GRAPPHCOHREENE((eeeeeee
eivienceev
7: Pnew & (Weim Ssimilarity + Wrel Srelevance + Weoh X Scoherence) Spopalation
8: p (1 (1-Wdecay)) + Pnew X Wdecoy X decay'
9:
UPDATEPRIIOITTYO. evidence, p)
10: end for
// Systematic evidence expansion for gap identification
11: cap.ddddid +- DNSTEMAATCEVVDDENEEEXANSSSS
visited evidence)
12: gap_candidates +- gup_raniddatesU DESECTEEEIDNNCCEEEEEAA.EEEEEEEET
13: for each candidate gap_candidates do
14: gap priority + CALCUULAEEVVDDNRRRRRRYYYmnndddd new context, I)
ADDTOQUEUUE candidate, gap priority)
16: end for
return TO, kmaa)
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Published as a conference paper at ICLR 2026
Algorithm 4 Systematic Evidence Expansion
Require: Evidence queue 2. Explored evidence &
Ensure: New evidence candidates C
C+0
2: evidence_node nodes <- EXTRACTÉVIDENCEENOESSS))
I/ Multi-Hop Expansion for evidence discovery
3: for each node E evidence _nodes do related_evidencee t- GETRRELAAEDD for each evidence related_evvidence do evidence + CREATEEVIDENNCHIDDooddd,cc evidence) if evidence id & then
4:
5:
6:
7:
8:
gap potentttttllll <- ASSESSEVDDENCGGGKrrriieee,,, Q) if grop-potential > Ogap then
9:
10:
C C+CU-evvience)) Eg E fevidence_id)
12: end if
end
14: end for
end for
16:
return C
F.3 FUSION GRAPH SIGNALS AND LLM SCORES
The algorithm for fusion of graph signals and LLM scores are presented in Algorithm
Algorithm 5 Multi-factor Evidence Priority Calculation
Require: Evidence evidence, Clinical context contexl, Inquiry I, Round
Ensure: Evidence priority score
1: Sasmilarity + cos(embed contert)) embed
2: Srelevence <- LLMRELLEEANCEEACENNTCCCISS I, evidence)
3: Scokerence GRAAPHCCOHEEENNESSSOOEECCCnIII existing evidence)
4: Spopulation DEMOGRAPHICE WHIGAAIIIIWGGTTeeiieeeeeeee context)
If Multi-factor priority emphasizes evidence gaps
5: Pevidence & Wsim 8similarity Wrel X Syelevance + Wooh Scoherence
6: < Pevidence X Spopulation X decay'
// Boost priority for critical evidence gaps
7: if Scokerence > Deritient then
8:
p p P X O'boost
9: end if
10: return
F.4 IMPLEMENTATION DETAILS AND HYPERPARAMETERS
F.4.1 EVIDENCE DISCOVERY AND EVIDENCE EVALUATION
We construct comprehensive medical knowledge graph from current medical guidelines, where each triplet (h, r. 1) augmented with multi-modal evidence, including source text and document images. In the evidence discovery stage, we retrieve triplets with hard relevance threshold of 0.6 for initial filtering.
The contextual evidence pool maintains K = 6 triplets during graph expansion and direct trieval. The graph expansion is with systematic exploration implemented through beam search (beam size=3, maximum hop depth=2). We utilize OpenAl's text-embedding--a ada-002 (Neelakan- tan et al., (2022) for embedding similarity calculation and FAISS (Douze et al. (024) as the search engine for efficient knowledge graph storage and retrieval.
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F.4.2 MODEL CONFIGURATION AND EXPERIMENTAL SETUP
We leveraged GPT-4 the agent backbone with temperature 0.6, top-p 0.9, and maximum of 768 tokens for response generation. Document page images were transferred to base64 format for multi- modal input processing. All experiments were conducted three times for stability assessment, For abstention decision-making, we enabled self-consistency checking performed twice with an absten- tion threshold 3.5.
Since all methods (Scale Rating, Basic, Binary Decision, Numerical Score, Long Context, and KnowGuard) are training-free approaches, we did not partition the datasets into train/validationtesst splits. Instead, all methods were directly evaluated (ioAFRIMEDQA, ioMEDQA, ioCRAFT-MD), ensuring identical access dataset information. Our ablation studies, hyperparameter tuning, and baseline threshold adjustments were all performed on the same full datasets, guaranteeing equal evaluation conditions.
the complete constructed datasets
F.4.3 IMPLEMENTATION LONG CONTEXT
For the Long Context baseline (Tu al. 2024), we processed summaries for each medical guideline as query keys. Upon summary selection, the corresponding full medical guideline was provided to the Doctor Agent for abstention decisions.
G PROMPTS
G.1 DIRECT RETRIEVAL QUERY GENERATION PROMPT
Query Generation Prompt
Based on the following input information, generate optimized search queries to retrieve relevant medical knowledge from knowledge base. The queries should:
Task Instructions:
1. Focus key symptoms, conditions, medical concerns
2. Use medical terminology when appropriate
3. Be specific enough to find relevant information
4. Cover different aspects of the patient's condition or question
Input:
Generate queries based on patient information to find relevant diagnostic and treatment infor- mation.
Response Format:
Query 1: [your first query] Query [your second query]
Example:
Input: 45-ycar-old female with recurrent headaches and nausea
Output:
Query 1: migraine headache symptoms nausea photophobia
Query secondary headache causes women middle-aged
G.2 PATIENT POPULATION REASONING PROMPT
Patient Population Reasoning Prompt
System Prompt:
You are a medical expert with extensive experience in clinical diagnosis and
treatment.
Given the patient profile all conditions, please extract the demographic information and discase information from the patient profile that belong all conditions. Please
Task Instructions:
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ensure the information accurate. Response with the exact demographic information and dis- ease information, separated by new line. If there is no demographic information or disease information, please return "None".
Input Format:
Patient Profile: [patient description] Al1
Demographics:
[demographic categories] All Diseases: [disease
categories]
Response Format: (extracted demographics! [extracted diseases]
If no information found, return "None" for that category.
Example 1:
Patient Profile: 35-year-old male with chest pain, shortness of breath, and family history of heart disease. He suffers from high blood pressure
All Demographics: Pregnant woman, people with HIV, Adults, Elderly All Diseases: heart disease, diabetes, hypertension, cancer Answer: Adu ts heart disease, hypertension
Example 2:
Patient Profile: 28-year-old female with fatigue, weight gain, and cold intolerance. She is a smoker.
Pregnant woman, people with HIV, Adults, Elderly All Diseases: heart discase, diabetes, hypertension, cancer
All Demographics:
Answer:
Adu t.s None
G.3 LLM RELEVANCE PROMPT
LLM Relevance Prompt
You are a medical expert with extensive experience in clinical diagnosis and treatment.
System Prompt:
Knowledge Relevance Evaluation: You are evaluating the relevance of medical knowledge to patient cases. Rate each relation's relevance independently scale from 0 to 1. Scoring Guidelines:
1. 0.0-0.2: Completely irrelevant or contradictory
2. 0.3-0.4: Low relevance, tangentially related
3. 0.5-0.6: Medium relevance, somewhat helpful
4. 0.7-0.8: High relevance, directly applicable
5. 0.9-1.0: Extremely relevant, critical for diagnosis/reatment
Input Format: Patient Information: [patient details] Current
Inquiry: [medical cal question]
Relations evaluate: [0]
[relation]] [1] [relation2]
Response Format: [scorel, score2, score3, ...]
Example:
Input: 35-year-old male with chest pain, shortness of breath Relations: [0] Myocardial infarc- tion causes chest pain [1] Diabetes management with metformin Output: [0.9, 9, 0.11
G.4 ABSTENTION PROMPT
Prompt for Knowledge-Driven Abstention
You are medical doctor trying to reason through real-life clinical case. Based on your understanding basic and clinical science, medical knowledge, and mechanisms underlying health, disease, patient care, and modes of therapy, respond according to the lask specified by the user. Base your response on the current and standard practices referenced in medical guidelines.
System Prompt:
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Contextual Evidences
Confidence Rating Task: How confident you in answering the problem factually using the conversation log and medical knowledge? Choose between the following ratings:
1. "Very Confident" The correct answer is supported by all evidence, and there is enough evidence eliminate the other possible answers, so the option can be firmed conclusively.
2. "Somewhat Confident" have reasonably enough information to tell that the correct answer is more likely than other possible answers.
3. "Neither Confident or Unconfident" There is evident supporting the correct answer, but further evidence needed be sure about the answer.
4. "Somewhat Unconfident There is evidence supporting more than one possible an- swer; therefore, more questions are needed to further distinguish the answers.
5. "Very Unconfident There is not enough evidence supporting any answers; the like- lihood of giving the correct
this point is random guessing.
Response Format:
REASON: one-sentence explanation of why you confident and what other information is needed.
not
DECISION: chosen rating from the above list.
G.5 DECISION MAKING PROMPT
Decision Making Prompt
You are a medical doctor trying to reason through a real-life clinical case. Based on your understanding of basic and clinical science, medical knowledge, and mechanisms underlying health, disease, patient care, and modes of therapy, respond according to the task specified by the user. Base your response on the current and standard practices referenced in medical guidelines.
System Prompt:
Task Instructions: Assume that you already have enough information from the above question- answer pairs to answer the patient inquiry, use the above information to produce a factual con- clusion. Respond with comprehensive and well-reasoned answer.
G.6
JUDGE AGENT PROMPT
Judge Agent Prompt
System Prompt:
You are medical evaluation expert, tasked with evaluating the match between
answers and reference standards.
Task 1- -nnseerto-OOptins Comparison: Analyze the free answer and determine which mul-
tiple choice option is closest to the answer.
Input Format: Free Answer: [generated response] Options: A: [option
A. B: [option B] C: [option C] D: [option D] E: [option E] (if
applicable)
Response Format: Output only the option letter (A, B, C. D, or E) that has the highest match. Analyze the free answer against the ground truth and
Task 2 Yes/No Answer Evaluation:
determine if they are similar.
Input Format:
[generated response] Ground Truth [reference answer]
Free Answer:
Answer:
Response Format: Output only 'A' if the answer is similar to the ground truth answer, 'B' if the answer is completely different. Do not include quotation marks.
Example Multiple Choice: Free Answer: The patient likely has pneumonia based on the symptoms of fever, cough, and chest pain.
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Options: A: Asthma B: Pneumonia C: Heart failure D: COPD
Oulput: B
Example - Yes/No Evaluation: Free Answer: The patient should receive antibiotics and
supportive care for pneumonia treatment.
Ground Truth: Antibiotic therapy is recommended for bacterial pneumonia along with support- ive measures.
Output: A
H
CASE STUDY ON SYSTEM ROBUSTNESS
This section presents three detailed case studies that systematically evaluate KnowGuard's robust- ness under progressively challenging conditions:
Incomplete KG: Testing the system's ability to integrate external evidence with parametric knowledge when diagnostic criteria are partially absent from the knowledge graph.
Noisy KG: Evaluating the filtering mechanism's 's effectiveness in signal preservation when the knowledge graph contains irrelevant misleading evidence.
Demonstrating the system's 's resilience when faced with misleading evidence by actively gathering additional information through multi-round questioning and hypothesis refinement reach the correct diagnosis.
Misleading Evidence:
For each case, we provide comprehensive documentation including the patient presentation, all trieved evidence with scoring details, knowledge queue evolution across interaction rounds, self- consistency evaluation results, and the final clinical reasoning.
H.1 CASE STUDY 1: INCOMPLETE KG
Patient Presentation: A 28- yld female reports that, for more days than not the past years, she has felt "down" and, at times, "mildly depressed." Over this period, she also endorses feeling fatigued, difficulty concentrating, and often sleeping more than in the past.
Question: What is the minimum amount of time this patient must exhibit these symptoms in order to meet the diagnostic criteria for dysthymia?
Retrieved Evidence Round 0 (Initial) All retrieved evidence with their scores before filtering presented in Table 10
Table 10: All Retrieved Evidence with Scores Before Filtering Case
ID
Evidence Content
Embedding
LLM
Coherence Status
Similarity
Score
Score
Depressive symptoms (or sub- tub-threshold depression) apply to older adults who have two or more simulta...
0.4780
0.1000
0.1000
Filtered
2 Early findings report, most common 0.4644 going symptoms (regardless of hospital- ization status) are fatig...
0.0000
0.1000
Filtered
3 Depressive symptoms: The presence of distress or some degree of impaired functioning in the absence...
0.4639
0.2000
0.1000
Filtered
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Table 10 continued from previous page
ID
Evidence Content
Embedding Similarity
LLM
Coherence Status
Score
Score
4 Fatigue and concentration problems noted to last beyond 12 weeks in patients with post COVID-19...
0.4625
0.0000
0.1000
Filtered
5 Mild to moderate depression is char- acterized by depressive symptoms and some functional impairment....
0.5272
0.3000
0.1000
Filtered
6 Persons with chronic HCV infection 0.4965 more likely to develop cognitive dys- function, fatigue, and de...
0.2000
0.1000
Filtered
7 Depressive symptoms sub- threshold depression) apply to older adults who have two or more simulta...
0.4826
0.1000
0.1000
Filtered
8 Cognitive deficits may overlap or present in clusters with other neurologi- cal and non-neurological d...
0.4708
0,4000
0.1000
Retained
9 Mild to moderate depression is char- acterized by depressive symptoms and some functional impairment...
0.4837
0,4000
0.1000
Retained
10 Severe depression is characterized by depressive symptoms, functional im- pairment, agitation psych...
0.4646
0.3000
0.1000
Retained
11
the subjective distress of the patient in terms of intensity and persistence out of proportion...
0.4563
0.5000
0.1000
Retained
12 Depressive symptoms can be confused with those of other medical illnesses (i.e. weight loss and fati...
0.4542
0.3000
0.1000
Filtered
13
Depression essentially an episodic re- curring disorder, each episode lasting usually from a few mo...
0.5184
0.2000
0.1000
Filtered
14 If the subjective distress of the patient is in terms of intensity and persistence out of proportion...
0.4647
0.5000
0.1000
Retained
15 Mild moderate depression is char- acterized by depressive symptoms and some functional impairment;...
0.4646
0.4000
0.1000
Retained
16 about 20% cases, however, depres- sion follows chronic course with no remission, especially wh...
0.4506
0.3000
0.1000
Retained
The doctor agent performed abstention assessment through self- consistency evaluation with iterations.
Evaluation
Iteration 1:
EXPLANATION: Dysthymia, or persistent depressive disorder, is characterized by depressive symptoms that persist for at least 2 years in adults, outlined standard diagnostic criteria (c.g., DSM-5). The patient has reported symptoms lasting years, fulfilling the duration criterion, and the symptoms described align with those of dysthymia. The specific duration criteria not explic-
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itly present in the retrieved evidence, though the patient history provides sufficient information to address the question.
DECISION: Very Confident
Parsed Score: 5
Iteration 2:
The diagnostic criteria for dysthymia (persistent depressive disorder) require symptoms to persist for at least 2 years in adults. The patient has reported symptoms persisting for more than years, which meets the duration criterion. While the retrieved evidence does not clude specific diagnostic timeframes, the patient history clearly indicates the duration of symptoms, and no conflicting information present. The specialty involved psychiatry or mental health, and sufficient information exists in the patient presentation to answer the question.
EXPLANATION:
DECISION: Very Confident
Parsed Score: 5
Average Confidence Score: 5.0
Final Answer Based on the high confidence score (5.0), the doctor agent proceeded directly to provide the final answer without requesting additional information.
To meet the diagnostic criteria for dysthymia, also known persistent depressive disorder (PDD), a patient must exhibit depressive symptoms for at least 2 years in adults. During this period, the symptoms should be present for more days than not and should not remit for longer than 2 months at a time.
Relevant Diagnostic Criteria:
1. Duration: In adults, the minimum required duration is 2 years. For children and adoles- cents, this duration is reduced year.
2. Symptoms:
Persistent depressive symptoms that less severe than major depressive dis- order but significant enough to cause functional impairment. Symptoms may include:
Persistent low mood or feeling "down."
Fatigue low energy.
Difficulty concentrating.
Changes sleep patterns (e.g., hypersomnia or insomnia).
Application to the Patient Case:
The patient, 28-year-old female, reports experiencing low mood, fatigue, difficulty concentrat- ing, and increased sleep for more days than not over the past 3 years. Based on the information provided, she meets the minimum duration criterion for dysthymia and exhibits characteristic symp- toms that align with the diagnosis. However, to confirm the diagnosis, further evaluation should exclude other causes (e.g., thyroid dysfunction, anemia, other medical conditions) and assess the degree of functional impairment caused by these symptoms.
Next Steps:
1. Comprehensive Evaluation: Perform full medical and psychiatric history, physical ex- amination, and relevant laboratory tests to rule out organic causes, such as hypothyroidism, anemia, chronic illness.
2. Diagnostic Confirmation:
Use standardized screening tools such as the Patient Health Questionnaire (PHQ-9) or the DSM-5 criteria for Persistent Depressive Disorder.
3. Management:
Psychotherapy:
Cognitive-bchavioral behavioral therapy (CBT) or interpersonal therapy (IPT) effective first-line treatments.
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Pharmacotheeapp: Consider antidepressant therapy (e.g.. SSRIs or SNRIs) if symp- toms are significantly impairing function or if psychotherapy alone insufficient. Lifestyle Modifications: Encourage regular physical activity, stress management techniques, and sleep hygiene.
In summary, this patient has exhibited symptoms for longer than the minimum 2-year duration quired to meet the diagnostic criteria for dysthymia. A thorough evaluation is necessary to confirm the diagnosis and rule out other conditions, followed by individualized treatment plan.
Ground Truth Answer: Two years.
H.2
CASE STUDY 2: NOISY KG
Patient Presentation: 56-year-old African American male presents with altered mental status, abdominal pain, and a fever of 100.4F.
Question: What the best treatment for this patient's condition?
Retrieved Evidence Round O (Initial) All retrieved evidence with their scores before filtering presented in Table
Table 11: All Retrieved Evidence with Scores Before Filtering Round
0, Case 2
ID
Evidence Content
Embedding Similarity
LLM
Coherence
Status
Score
Score
referral a facility with diagnos- tic testing is not feasible, presumptive treatment of severe b...
0.4990
0.9000
0.1000
Retained
2 For gastrointestinal anthrax, ml of as- citic fluid collected in sterile screw- capped container...
0.4880
0,2000
0.1000
Filtered
3 When empyema is present, fever per- sists despite antibiotic therapy, and the pleural fluid is cloudy...
0.4847
0.1000
0.1000
Filtered
4 We recommend for patients with sus- pected or confirmed severe COVID-19, the use of empiric antimicrob...
0.4807
0.4000
0.1000
Retained
5
These guidelines include the manage- ment of symptomatic infections related to: lower abdominal pain s...
0.5519
0.3000
0.1000
Filtered
6
The diagnosis major infection in- cludes acute pelvic inflammatory dis- ease, characterized by fever...
0.5190
0.2000
0.1000
Filtered
7
Guidelines for the management of 0.5164 symptomatic sexually transmitted infec- tions begin with person pre...
0.3000
0.1000
Filtered
8 There were few missed cases with 0.5138 syndromic approach to lower abdominal pain, which was heavily val...
0.2000
0.1000
Filtered
9 These guidelines include the manage- ment of symptomatic infections related to: lower abdominal pain s...
0.4531
0.7000
0.1000
Retained
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Table 11 continued from previous page
ID
Evidence Content
Embedding Similarity
LLM
Coherence Status
Score
Score
10 Immediate treatment of an acute pelvic inflammatory disease may avert adverse consequences such as c...
0.4519
0.2000
0.1000
Filtered
11 These guidelines include the manage- ment of symptomatic infections related to: lower abdominal pain s...
0.5513
0.7000
0.1000
Retained
12 Guidelines for the management of 0.5191 0.1000
0.1000
Filtered
symptomatic sexually transmitted infec- tions begin with person pre...
13 There were few missed cases with 0.4961 syndromic approach to lower abdominal pain, which was heavily val...
0.1000
0.1000
Filtered
14 Individuals presenting with lower ab- dominal pain syndrome could suggest the presence acute pelvic...
0.4933
0,1000
0.1000
Filtered
Knowledge Queue After Round 0 After filtering (LLM score 2 0,4), four knowledge triplets were retained. Table 12 shows the final ranked knowledge after round O.
Table 12: Knowledge Queue After Round Case 2
Rank Knowledge Content
Priority
PPR Weighted
Round
If referral to facility with diagnostic test- ing not feasible, presumptive treatment of severe bacterial infections should be consid- ered the person seriously ill.
0.6096
No
0
These guidelines include the management 0.5997 symptomatic infections related lower ab- dominal pain syndrome.
Yes
3 These guidelines include the management of symptomatic infections related to: lower ab- dominal pain syndrome.
0.5313
Yes
4 We recommend for patients with suspected or confirmed severe COVID-19, the use of empiric antimicrobials treat all likely pathogens, based on clinical judgment, pa- tient host factors and local epidemiology, and this should be done as soon as possible (within hour of initial assessment if pos- sible), ideally with blood cultures obtained first. Antimicrobial therapy should be as- sessed daily for de-escalation.
0.3482
No
Self-Consistence Evaluation The doctor agent performed abstention assessment through self- consistency evaluation with iterations.
Iteration 1:
REASON: The patient's 's presentation of altered mental status, fever, and abdominal pain raises sig- nificant concern for spontaneous bacterial peritonitis (SBP), particularly in 56-year-old male who
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likely to have underlying liver disease (e.g., cirrhosis, given the mention of ascitic fluid culture). However, the conversation log does not explicitly confirm the presence of ascites or other key fea- tures like history of liver disease, alcohol use. laboratory findings (e.g., elevated white blood cell count, low ascitic fluid protein, etc.), which would solidify the diagnosis and guide treatment. While empirical antibiotic therapy (e.g., third-generation cephalosporins like cefotaxime) is standard for suspected SBP, the lack of detailed clinical and laboratory information limits the confidence in confirming the diagnosis.
DECISION: Somewhat Confident
Parsed Score: 4
Iteration
Based on the clinical presentation of altered mental status, abdominal pain, and fever in a 56-year-old male, there is high suspicion for spontaneous bacterial peritonitis (SBP), especially if this patient has underlying cirrhosis and ascites. However, the conversation log does not confirm key features such as the presence of ascites, liver disease, or other risk factors (e.g., alcohol use, hepatitis, portal hypertension) necessary to confidently diagnose SBP. While empiric antibiotic therapy (e.g., shird-geneaatinn cephalosporins like cefotaxime) is typically initiated in suspected SBP while awaiting ascitic fluid culture results, the lack of specific details limits definitive confidence in the decision.
REASON:
DECISION: Neither Confident Unconfident
Parsed Score:
3
Average Confidence Score: 3.5
Follow-up Question Generated Based on the initial low confidence score (3.5), the doctor agent generated follow-up question to gather critical missing information.
Doctor's Question: What your physical examination results, focusing on abdominal findings, and paracentesis results including the serum ascites albumin gradient?
Patient's Response: Shifting dullness is noted on physical exam. Paracentesis demonstrates a serum ascites albumin gradient of 1.3 g/dL.
Knowledge Queue After Round 1 (After Patient Response) After receiving the patient's re- sponse, the knowledge graph was updated. Three knowledge triplets were replaced with new, more relevant evidence. Table 13 shows the updated knowledge queue.
Table 13: Knowledge Queue After Round Case 2
Rank Knowledge Content
Priority
PPR Weighted
Round
Clinical complications of cirrhssi become manifest, including jaundice, ascites, spon- taneous bacterial peritonitis, oesophageal varices and bleeding, hepatic encephalopa- thy, sepsis and renal failure.
0.5651
No
2
If referral to facility with diagnostic test- ing feasible, presumptive treatment of severe bacterial infections should be consid- ered the person seriously ill.
0,5014
No
These guidelines include the management of 0.4921 symptomatic infections related to: lower ab- dominal pain syndrome.
Yes
Continued on next page
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Table 13 continued from previous page
Rank Knowledge Content
Priority
PPR
Round
Weighted
4 NA therapy can be safely administered even those with decompensated cirrhosis.
0.4903
No
5 For women with lower abdominal pain with any of the following conditions, good prac- tice includes referral surgical or gynae- cological assessment: abdominal guarding and/or rebound tenderness.
0.4000
No
We recommend for patients with suspected confirmed severe COVID-19, the of empiric antimicrobials to treat all likely pathogens, based clinical judgment, pa- tient host factors and local epidemiology. and this should be done as soon as possible (within hour of initial assessment if pos- sible), ideally with blood cultures obtained first. Antimicrobial therapy should be as- sessed daily for de-escalation.
0.3200
No
Final Abstention Assessment (With Additional Information) After receiving the patient's 's re- sponse, the doctor agent performed a second abstention assessment with 2 self-consistency itera- tions.
Iteration
EXPLANATION:
The patient is 56-year-old male with cirrhosis, ascites (confirmed by shifting dullness and paracentesis with serum ascites albumin gradient of 1.3 g/dL), fever, abdominal pain, and altered mental status. These findings strongly suggest spontaneous bacterial peritonitis (SBP), common and serious infection patients with cirrhosis and ascites. According the retrieved evi- dence presumptive treatment of severe bacterial infections, immediate empiric antibiotic therapy recommended while awaiting ascitic fluid culture results to prevent clinical deterioration. Current guidelines recommend starting third-geant renedeeerration cephalosporins, such intravenous cefotaxime, as the first-line treatment for suspected SBP.
DECISION: Very Confident
Parsed Score: 5
Iteration 2:
The patient is a 56-year-old African American male with cirrhosis presenting with altered mental status, abdominal pain, fever, and ascites. The serum-ascites albumin gradient (SAAG) of 1.3 g/dL indicates portal hypertension the cause of ascites, and spontaneous bacterial peritonitis (SBP) is a likely diagnosis given his clinical presentation. SBP is a severe bacterial infection of the ascitic fluid, common in patients with cirrhosis. The retrieved evidence emphasizes that presumptive treatment should be considered for seriously ill patients when diagnostic testing is not immediately available. Empiric antibiotic therapy should initiated promptly while awaiting ascitic fluid culture results, delayed treatment can lead to poor outcomes.
EXPLANATION:
DECISION: Very Confident
Parsed Score: 5
Average Confidence Score: 5.0
Final Answer Based on the high confidence score (5.0) after receiving additional patient informa- tion, the doctor agent proceeded to provide the final answer.
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The patient is 56-year-old African American male with cirrhosis, ascites, altered mental status, abdominal pain, and fever. The serum ascites albumin gradient (SAAG) of 1.3 g/dL indicates portal hypertension the likely cause of ascites. The clinical presentation strongly suggests spontaneous bacterial peritonitis (SBP), common and serious complication of cirrhosis and ascites. SBP is characterized by bacterial infection of ascitic fluid without obvious intra-abdominal source.
According to guidelines on empiric treatment severe bacterial infections in seriously ill patients, antibiotic therapy should be initiated promptly before ascitic fluid culture results are available, as delayed treatment increases morbidity and mortality. The recommended empiric treatment consists of dirddgennrraton cephalosporins, with cefotaxime (2 g IV every 8 hours) being the preferred an- tibiotic due to its effectiveness against common SBP pathogens (c.g., Escherichia coli, Klebsiella pneumoniae, and Streptococeus species). Alternatively, ceftriaxone (1-2 g IV daily) may be used.
Patients with SBP and high risk of renal dysfunction (e.g., serum creatinine & mg/dL., blood urea nitrogen ;30 mg/dl., or total bilirubin 24 mg/dL. benefit from intravenous albumin reduce the risk of hepatorenal syndrome. The recommended dosing is 1.5 g/kg of albumin on Day and g/kg of albumin on Day 3.
Additional management includes monitoring for complications such as hepatic encephalopathy, re- dysfunction, and sepsis. Aseitic fluid culture and analysis should performed to confirm SBP, with ascitic fluid polymorphonucleat leukocyte (PMN) count 255 cells/μL confirming the di- agnosis. Non-selective beta-blockers should be discontinued if the patient taking them, as they may outcomes in SBP patients with hypotension renal dysfunction. If SBP is confirmed, prophylactic antibiotics (e.g., norfloxacin or ciprofloxacin) may be considered to prevent recurrence in high-risk patients.
In summary, while awaiting ascitic fluid culture results, the best immediate treatment for this patient empiric administration of cefotaxime another third-generation cephalosporin, along with albu- min infusion to prevent renal dysfunction. Regular monitoring and supportive care are essential to prevent complications associated with decompensated cirrhosis and SBP.
Ground Truth Answer: Cefotaxime.
H.3
CASE STUDY 3: MISLEADING EVIDENCE
Patient Presentation: A 4-year-old boy is brought to the emergency department for evaluation after falling.
Question: What the underlying mechanism of this patient's falls?
Retrieved Evidence Round (Initial) The system initially retrieved 6 pieces of medical knowl- edge evidence. Table 14 shows all retrieved evidence with their scores before and after ranking.
Table 14: All Retrieved Evidence with Scores - Round 0, Case 3
ID
Evidence Content
Embedding Similarity
LLM
Coherence Status
Score
Score
Medical conditions that increase the risk of falls include: musculoskeletal dis-
0.5896
0.6000
0.1000
Retained
2 Medical conditions that increase the risk of falls include: low systolic blood pres- sure.
0.4843
0.5000
0.1000
Retained
3 Medical conditions that increase the risk of falls include: visual impairment.
0.5084
0.7000
0.1000
Retained
4 History of head injury is considered in cases of child presenting with lethargy, unconsciousness or convulsions.
0.5127
0.8000
0.1000
Retained
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Table 14 continued from previous page
ID
Evidence Content
Embedding Similarity
LLM
Coherence Status
Score
Score
5 Examination of signs of head trauma or other injuries is considered in cases of child presenting with lethargy, uncon- sciousness convulsions.
0.5695
0,5000
0.1000
Retained
On examination, look for altered level of consciousness, neck stiffness, re- peated convulsions, bulging fontanelle in infants, non-blanching petechial rash or purpura, lethargy, irritability, and ev- idence of head trauma suggesting possi- ble recent skull fracture.
0.5430
0.7000
0.1000
Retained
Knowledge Queue After Round
Table 15 shows the ranked knowledge queue after Round 0.
Table 15: Knowledge Queue After Round 0 Case 3
Rank Knowledge Content
Priority
PPR Weighted
Round
Medical conditions that increase the risk of falls include: musculoskeletal disease.
0.6916
No
0
2 Medical conditions that increase the risk of 0.5934 falls include: low systolic blood pressure.
No
0
Medical conditions that increase the risk falls include: visual impairment.
0.5915
No
4 History of head injury is considered in cases of child presenting with lethargy, uncon-
0.5594
No
sciousness or convulsions.
Examination of signs of head trauma or other injuries is considered in cases of child pre- senting with lethargy, unconsciousness or convulsions.
0.5243
No
On examination, look for altered level of 0.5050 consciousness, neck stiffness, repeated con- vulsions, bulging fontanelle in infants, non- blanching petechial rash or purpura, lethargy, irritability, and evidence head trauma sug- gesting possible recent skull fracture.
No
Saff-Consistency Evaluation Round 2 saff-consstteny iterations.
The doctor agent performed abstention assessment with
Iteration 1:
The conversation log provides very limited information about the patient's 's history, circumstances of the fall, symptoms, or any contributing factors. There is no information about the patient's neurological status, museuloskeletal abnormalities, visual impairment, cardiovascular symptoms, or any other relevant medical conditions that might explain the fall. Without further details, it is impossible to determine the underlying mechanism of the patient's falls.
EXPLANATION:
DECISION: Very Unconfident
Parsed Score:
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Iteration 2:
EXPLANATION: The patient a 4-year-old boy who has fallen. The conversation log does not provide sufficient information regarding the nature of the fall, the patient's medical history, de- velopmental milestones, or any symptoms such as altered consciousness, convulsions, or physical examination findings. Retrieved evidence suggests various potential causes for falls in children, in- cluding musculoskeletal disease, visual impairment, low systolic blood pressure, and head trauma, but of these be confirmed ruled out based on the current information.
DECISION: Very Unconfident
Parsed Score:
Average Confidence Score: 1.0
Follow-up Question Generated Round 1 Based on the very low confidence score (1.0), the doctor agent generated a follow-up question.
Doctor's Question: Did the child lose consciousness before, during, or after the fall?
Patient's Response: The patient cannot answer this question, please do not ask this question again.
Knowledge Queue After Round 1 After the first interaction, the knowledge graph was updated.
Table 16 shows the updated knowledge queue.
Table 16: Knowledge Queue After Round Case 3
Rank Knowledge Content
Priority
PPR Weighted
Round
Medical conditions that increase the risk of 0.6211 falls include: museuloskeletal disease.
No
0
Medical conditions that increase the risk 0,5501
No
falls include: visual impairment.
Medical conditions that increase the risk of falls include: low systolic blood pressure.
0.5345
No
4
Progressive neurological impairment, micro- cephaly, delay in achieving developmental milestones, hypertonia mental confusion.
0.5258
No
5
Neurodevelopmmen (development score 18 months) in VLBW infants.
0.5250
No
6 History of head injury is considered in cases of child presenting with lethargy, uncon- sciousness or convulsions.
0.5142
No
Self-Consistency Evaluation Round Iteration 1:
The patient is a 4-year-old boy with history of delayed developmental mile- stones (walking at 18 months), which suggests possible neurodevelopmentall delay, a known risk factor for falls. However, without additional history or physical examination findings such visual impairment, musculoskeletal abnormalities, or neurological symptoms, the exact underlying mecha- nism remains unclear. Further information like imaging studies, physical examination findings (e.g., gait abnormalities, muscle tone), or family history would be needed for more definitive answer.
EXPLANATION:
DECISION: Neither Confident or Unconfident
Parsed Score: 3
Iteration 2:
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The patient is 4-year-old boy with delayed motor development (walking at 18 months). Delayed developmental milestones are associated with neurological impairment and can increase the risk of falls. However, the exact underlying mechanism (e.g., cerebral palsy. muscu- lar dystrophy, metabolic disorder, or other neurodevelopmental conditions) cannot be determined without further clinical information, physical examination findings, diagnostic testing.
EXPLANATION:
DECISION: Neither Confident or Unconfident
Parsed Score: 3
Average Confidence Score: 3.0
Follow-up Question Generated Round Doctor's Question: Does the child have any history of neurological conditions, such as seizures or developmental delays?
Patient's Response: His parents report that he did not walk until he was 18 months old.
Ceff-Consistency Evaluation Round Iteration 1:
EXPLANATION: Based the patient's history of developmental delay (walking 18 months) and recurrent falls, the most likely underlying mechanism for this patient's fall is neurological dysfunc- tion developmental delay. However, without additional clinical details, such as physical examina- tion, imaging studies, or laboratory tests, the exact mechanism cannot be conclusively determined.
DECISION: Neither Confident Unconfident
Parsed Score: 3
Iteration 2:
EXPLANATION: The patient's history of delayed motor milestones (walking months) and recurrent falls suggests neurodevelopmental disorder neuromuscular condition. However, with- out additional information such as physical examination findings (e.g., muscle tone, reflexes, gait analysis), imaging, laboratory results, the specific underlying mechanism cannot be determined with certainty.
DECISION: Neither Confident Unconfident
Parsed Score: 3
Average Confidence Score: 3.0
Follow-up Question Generated Round Doctor's Question: Has the child experienced any previous falls or frequent stumbling episodes?
Patient's Response: He has fallen multiple times in the last year.
Celf-Consistency Evaluation Round
Knowledge Queue After Round Table 17 shows the updated knowledge queue after Round
Table 17: Knowledge Queue After Round Case 3
Rank Knowledge Content
Priority
PPR Weighted
Round
Medical conditions that increase the risk of 0.6141 falls include: musculoskeletal disease.
No
0
2 Medical conditions that increase the risk of 0.5722 falls include: visual impairment.
No
0
Continued on next page
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Table 17 continued from previous page
Rank Knowledge Content
Priority
PPR
Round
Weighted
Progressive neurological impairment, micro- cephaly, delay in achieving developmental milestones, hypertonia or mental confusion.
0.5355
No
4 Medical conditions that increase the risk of 0.5126 falls include: low systolic blood pressure.
No
Neurodevclopment (development score at 18 months) in VLBW infants.
0.4724
No
6 Developmental and neurological assessment should be performed with the full engage- ment of caregivers to identify developmental delays and other nervous system abnormali- ties such as epilepsy and disorders move- ment, posture and swallowing.
0.4664
No
2
Iteration 1:
EXPLANATION:
The patient demonstrates developmental delay (walking at 18 months) and a his- tory of frequent falls, which could indicate underlying neurological dysfunction musculoskeletal issues. Additional assessments, such as neurological examination, musculoskeletal evaluation, and possibly imaging studies, are necessary confirm the cause.
DECISION: Neither Confident or Unconfident
Parsed Score: 3
Iteration
The patient's delayed motor development and recurrent falls suggest possible neurological or neuromuscular pathology. However, definitive diagnosis requires further clinical evaluation including physical examination, developmental assessment, and potentially imaging or genetic testing.
EXPLANATION:
DECISION: Neither Confident or Unconfident
Parsed Score:
3
Average Confidence Score: 3.0
Follow-up Question Generated Round 4 Doctor's Question: Has the child undergone any recent neurological or developmental assessments, and if so, what were the findings?
Patient's Response: The patient cannot answer this question, please do ask this question again.
Follow-up Question Generated Round 5 Doctor's Question: Has the child experienced any difficulty with coordination, balance, or muscle weakness?
Patient's Response: He has fallen multiple times in the last year. When standing up, the patient
uses his hands against his knees and thighs to slowly push himself up into a standing position.
Knowledge Queue After Round (Final) Table 18 shows the final knowledge queue after all interactions.
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Table 18: Final Knowledge Queue After All Interactions
Case
Rank Knowledge Content
Priority
Subgraph
Round
Medical conditions that increase the risk of 0.6140
No
falls include: musculoskeletal disease.
2 Medical conditions that increase the risk of falls include: visual impairment.
0.5661
No
0
3 Perform symptom directed physical exami- nation during routine follow-up visit.
0,5350
No
5
Progressive neurological impairment, micro- cephaly, delay in achieving developmental milestones, hypertonia or mental confusion.
0,5306
No
Additional neurological manifestations re- 0,5109
No 5
ported include weakness.
Medical conditions that increase the risk of falls include: low systolic blood pressure.
0.4994
No
Self-Consistence Evaluation Final (Round 5) After receiving information about Gower's sign, the confidence level increased significantly.
Iteration
Based on the information provided, the 4-year-old boy demonstrates signs of proximal muscle weakness, as evidenced by the use of his hands to push himself up from seated position (Gowers' sign). This, coupled with delayed developmental milestones (e.g., walking at 18 months) and frequent falls, strongly suggests an underlying neuromuscular disorder, such as Duchenne Muscular Dystrophy (DMD).
EXPLANATION:
DECISION: Very Confident
Parsed Score: 5
Iteration 2:
The patient exhibits classic signs of proximal muscle weakness including Gower's sign (using hands push up from sitting), delayed motor milestones (walking at 18 months), and recurrent falls. These findings are highly suggestive of Duchenne muscular dystrophy, a progressive neuromuscular disorder caused by dystrophin deficiency.
EXPLANATION:
DECISION: Very Confident
Parsed Score: 5
Average Confidence Score: 5.0
Final Answer Based the high confidence score (5.0) after multiple rounds of information gath- ering, the doctor agent provided the final answer.
The most likely underlying mechanism this 4-year-old boy's falls is proximal muscle weakness, potentially due to neuromuscular disorder such as Duchenne muscular dystrophy (DMD). Key findings include: (1) Developmental delay: The child did not walk until 18 months, which later than the typical developmental milestone. (2) Frequent falls: Recurrent falls over the past year sug- gest impaired coordination, muscle weakness, or balance issues. (3) Gower's 's sign: The child uses his hands against his knees and thighs to push himself up into standing position, which is characteristic of proximal muscle weakness and commonly seen Duchenne muscular dystrophy. Musculoskele- tal disease is recognized risk factor for falls, and neurological dysfunction with progressive im- pairment, delay achieving developmental milestones, and muscle weakness are hallmark features of neuromuscular disorders like DMD. While visual impairment is also recognized cause of falls
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in children, the patient has no visual complaints and the presence of Gower's sign is highly specific for proximal muscle weakness consistent with DMD. While other conditions such as cerebral palsy, spinal muscular atrophy, metabolic myopathies could contribute to similar symptoms, the com- bination of delayed walking, recurrent falls, and Gower's 's sign strongly favors Duchenne muscular dystrophy. Prompt evaluation and diagnosis are essential for initiating appropriate management.
Ground Truth Answer: Duchenne Muscular Dystrophy.
H.4 ROBUSTNESS CASE ANALYSIS
Case study 1: incomplete KG This case demonstrates KnowGuard's ability to handle incomplete knowledge graphs by leveraging the agent's parametric knowledge. From the initial 16 retrieved triplets, the system filtered out 8 low-relevance items (LLM score S 0.4) while retaining 8 contex- tually appropriate pieces of evidence covering depression severity classification, symptom duration, and diagnostic considerations. Critically, none of the retrieved evidence explicitly contained the "2- year duration criterion" for dysthyymia- the essential answer to the question. Despite this knowledge gap in the external graph, the priority-ranked knowledge queue (Table TOp shows appropriate focus on diagnostic thresholds and symptom persistence (e.g.. "If the subjective distress of the patient is in terms of intensity ..." has L.LM relevance of 0.5). These contextually relevant but incomplete triplets provided sufficient framework for the doctor agent to activate its internal medical knowledge and correctly identify the 2-year requirement through parametric reasoning. The celf-consistency evaluation yielded a perfect confidence score of 5.0 across both iterations, reflecting high certainty in the synthesized answer. This case establishes a key capability: when the knowledge graph lacks the precise answer but provides relevant diagnostic context, KnowGuard successfully bridges the gap by integrating external contextual cues with the agent's 's parametric medical expertise, producing accurate responses without requiring additional exploration rounds.
noisy KG This case demonstrates KnowGuard's filtering effectiveness when the knowledge graph contains substantial noise. In Round O, the initial retrieval produced 14 triplets with significant contamination: 10 pieces (71.4%) focused on unrelated conditions such as pelvic inflammatory disease, sexually transmitted infections, and COVID-19 wanagement--topic seman- tically similar to "abdominal pain" " and "fever" but clinically irrelevant to the patient's actual presen- tation of spontaneous bacterial peritonitis (SBP) in cirrhosis. The multi-stage filtering mechanism successfully identified and removed these misleading candidates: LLM--ased relevance scoring assigned low scores (0.1-0.3) to noise while recognizing genuinely useful evidence such as "pre- sumptive treatment of severe bacterial infections" (LLM score 0.9). Crucially, after the follow-up question elicited critical information about cirrhosis and ascites, Round retrieval automatically re- placed three low- lowppriority noisy triplets with highly specific evidence (e.g.. "clinical complications of cirrhosis...sponan spontass... bacterial peritonitis" ranked 1st with priority 0.5651), demonstrating adap- tive signal preservation. The confidence progression-from 4.0 (somewhat confident) to 5.0 (very confident)
Case study 2:
not through overconfidence despite noise, but through systematic evidence refinement that maintained diagnostic accuracy. This validates that KnowGuard does not indis- criminately trust retrieved knowledge; rather, employs coherence- aware filtering to prevent noisy evidence from derailing clinical reasoning, ensuring that only contextually relevant information in- fluences the final decision.
misleading evidence This case illustrates KnowGuard'ss ability to overcome initially misleading evidence through systematic information gathering. Round 1 retrieved 6 triplets heavily biased toward geriatric fall risk factors (musculoskeletal disease, low blood pressure. visual impair- ment-conditions rare in 4-year-olds), achieving only 0.5915-0.6916 priority scores despite high embedding similarity (0.48-0.59). These -ggiinappro priors misled the initial reasoning. cor- rectly yielding very low confidence (1.0). Crucially, the system did not prematurely commit to these misleading signals. Instead, through 5 rounds of targeted questioning ("loss of consciousness?". "neurological history?", "coordination difficulties?"). the knowledge graph underwent progressive recontextualization: Evidence 3 ("Progressive neurological opmental milestones") jumped from priority 0.5258 (Round 2) to 0.5355 (Round 3) with LLM score increasing from 0.8 to 0.9, while geriatric- 0.7 only after reframing "musculoskeletal disease" as pediatric neuromuscular disorder. The break- through occurred when Round 5's patient response ("uses hands against knees to stand"") triggered retrieval of "Additional neurological manifestations...eell weakness" (priority 0.5109), enabling recog-
Case study 3:
aeppirmmen....ellay in achieving devel-
Evidence 1 saw its LLM score rise from 0.3 to
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nition of Gower's sign specific to Duchenne muscular dystrophy- diagnosis invisible in Round I's evidence pool. The confidence trajectory (10--3.0-43.0--5.0) demonstrates that KnowGuard treats misleading evidence not fatal flaws but signals for knowledge gap detection, using ab- stention as a trigger for iterative evidence replacement rather than accepting initial retrieval at face value. This validates the framework's core hypothesis: multi-round interaction transforms mislead- ing priors into diagnostic precision through dynamic evidence reranking.
I CLINICAL VALIDATION STUDY
To evaluate the clinical appropriateness and safety of our system's diagnostic decisions under im- perfect knowledge graph conditions, we conducted structured validation study with practicing physicians. This validation focuses on two critical dimensions: (1) the appropriateness of absten- tion timing and diagnostic confidence levels, and (2) the effectiveness evidence utilization despite knowledge graph limitations.
1.1 STUDY DESIGN AND PROTOCOL
1.1.1 PARTICIPANTS
We recruited four licensed physicians (Physicians 1-4) from two tertiary hospitals to independently review the three diagnostic cases presented in Section H All participating physicians have clin- ical experience ranging from to 15 years, with specialties in internal medicine and emergency medicine. Due to the ongoing review process, specific institutional affiliations are withheld.
1.1.2 VALIDATION PROTOCOL
Each physician was presented with the complete diagnostic dialogue for all three cases, including:
Patient presentation and symptom progression
Retrieved evidence from the knowledge graph at each interaction round
System's 's confidence scores and decision rationale
Final diagnosis with supporting reasoning
For each case, physicians completed structured questionnaire assessing:
1. Evidence Quality: Whether the retrieved evidence, despite knowledge graph limitations, contributed meaningfully diagnostic reasoning
2. Decision Timing: Appropriateness of the system's decision to provide diagnosis versus requesting additional information versus abstaining
3. System Strengths: Mechanisms by which the system overcame knowledge graph imper- fections (evidence filtering, clinical reasoning, iterative refinement)
4. Overall Performance: Free-text assessment of the system's clinical reasoning and safety
The questionnaire used combination of multiple-choice questions (allowing single or multiple selections) and open-ended responses. Questions were designed elicit specific evaluations of system's 's handling of three distinct knowledge graph challenges: incompleteness (Case 1), noise (Case 2), and mislcading evidence (Case 3).
1.2 RESULTS AND CLINICAL ASSESSMENT
I.2.1
QUANTITATIVE ANALYSIS
Table T9 summarizes the physicians' responses across the three cases. We report the percentage of physicians selecting each option for key evaluation dimensions.
Case 1 (Incomplete Knowledge Graph): All physicians (4/4, 100%) acknowledged that the evi- dence was helpful despite the knowledge graph lacking the explicit "2-year" diagnostic criterion for
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Table 19: Physician responses to clinical validation questionnaire (N=4)
Question
Case
Response Distribution
Q1: Evidence Quality Assessment Evidence helpful limitations
despite
Case 1
A (helpful): 75%, B (ncutral): 25%
Case 2
A (helpful): 100%
Case
A (helpful): 100%
Q2: Decision Timing & Confidence
Appropriateness of confi- Case dence/decision
(appropriate): 75%, (abstain); 25%
Case 2 Case 3
A (timely); 75%. B (early): 25%
A (appropriate): 100%
Q3: System Strengths (Multiple Selection)
Mechanisms for overcom- Case 1 ing KG limits
A (filtering): 100%, B (reasoning): 75%, C
(knowledge): 50%
Case 2
A (diagnosis): 25%, B (confidence): 75%, C
(filtering:: 100%
Case 3
A (logical): 75%. B (discriminative): 75%, C
(persistent): 75%. (redundant): 25%
Q4/Q5: Comparative Assessment vs. Baseline system
Case
A (safer): 25%, (both acceptable): 75%
Case
A (better than misdiagnosis): 50%. B (better than abstain): 25%, (similar to abstain): 25%
persistent depressive disorder. Three physicians (75%) found the system's high confidence (5.0/5.0) appropriate, attributing this to the question being based on standard clinical knowledge. However, one physician (25%) suggested the system should have been more cautious or requested additional information when explicit supporting evidence was absent, All physicians identified evidence filter- ing (100%) and clinical reasoning (75%) as key mechanisms enabling correct diagnosis.
Case 2 (Noisy Knowledge Graph): The system's noise filtering capability received unanimous recognition (4/4, 100%). with all physicians noting that effective exclusion of 71% irrelevant evi- dence (e.g., pelvic inflammatory disease, anthrax, COVID-19) was critical. All physicians (100%) rated the decision to ask about cirrhosis history as timely appropriate, though one physician suggested earlier incorporation of this question. The dynamic evidence updating mechanism was highlighted by 100% of physicians valuable for improving diagnostic accuracy.
Case 3 (Mislcading Evidence): All physicians (4/4, 100%) agreed the system successfully over- came misleading evidence through multi-round interaction. The five-round dialogue considered appropriate or acceptable by all physicians, with 75% noting that each question contributed mean- ingful diagnostic value. The system's focus obtaining Gower's sign was recognized by 100% as the critical decision point. However, regarding question quality, 25% of physicians noted some redundancy. Comparative assessment showed that 50% preferred the system's 's iterative approach over a baseline that might misdiagnose, while 25% each felt it was comparable to direct specialist referral.
I.2.2 QUALITATIVE INSIGHTS
Physicians provided substantial free-text feedback highlighting both strengths and areas for im- provement:
Strengths Identified:
Evidence Filtering: "The LLM-based filtering effectively removed noise while retaining
clinically relevant evidence" (Physician 4, Case
by dynamically updating evidence after obtaining the cirrhosis history" (Physician 4, Case 2)
Dynamic Reasoning: "The system demonstrated strong clinical reasoning
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Diagnostic Persistence: "Multi-round interaction effectively addressed diagnostic ambi- guity and progressively focused on disease-specific features" (Physician 3, Case 3)
Safety Consciousness: "The system's cautious approach Case 3, maintaining moderate confidence until obtaining Gower's sign, reflects appropriate clinical conservatism" (Physi- cian 4, Case 3)
Concerns Raised:
Evidence Transparency: "While the diagnosis was correct, the reliance pre- traitrennd knowledge when KG evidence incomplete may raise questions about reasoning trace- ability and evidence sources" (Physician 4, Case 1)
Differential Diagnosis Completeness: "Although DMD was correctly diagnosed, the sys- tem should acknowledge that recurrent falls in children could involve co-existing condi- tions (e.g., vision problems, cardiovascular issues, head trauma). The diagnosis appeared too absolute without ruling out differential diagnoses" (Physician 4, Case 3)
Initial Question Strategy: "In Case 2, incorporating additional contextual factors in the initial round (Round 0) could have accelerated the diagnostic process" (Physician 3, Case 2)
1.2.3 INTER-RATER AGREEMENT
Despite the small sample size, observed notable consistency in physician assessments:
Evidence utility: 100% agreement (4/4) that knowledge graph evidence remained valuable in Cases and despite imperfections
Filtering effectiveness: 100% agreement (4/4) that noise filtering in Case 2 was successful
Diagnostic accuracy: 100% agreement (4/4) across all three cases that final diagnoses
were clinically correct
Safety: No physician raised critical safety concerns about any diagnostic decision
The primary divergence occurred in assessing the appropriateness of confidence levels in Case 1, where 25% preferred more conservative handling when explicit evidence was lacking.
1.2.4
SUMMARY
This clinical validation study demonstrates that our system's design enables robust diagnostic per- formance under imperfect knowledge graph conditions. Physicians consistently recognized three key capabilities: (1) effective evidence filtering to remove noise, (2) strategic use of iterative interaction gather critical information, and (3) appropriate integration of clinical reasoning when knowledge graphs are incomplete. While the small sample size (N=4) limits generalizability. the unanimous agreement on diagnostic correctness and safety, combined with specific praise for the system's 's handling of knowledge graph limitations, provides preliminary validation of our approach. The identified areas for imppooemennt-pariiuulee regarding evidence transparency and differential diagnosis articulation-vinn inform future system refinements.
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Reviews:
[{"review_id": "Dq8VXTn5hp", "reviewer": "Reviewer_daFK", "summary": "This paper addresses the critical problem of overconfidence in Large Language Models (LLMs) used for clinical reasoning. Current LLMs often provide premature or incorrect diagnoses when patient information is incomplete, failing to \"abstain\" as a human physician would. In the paper, the authors propose KnowGuard, a novel \"investigate-before-abstain\" paradigm. Instead of asking \"how confident am I?\", KnowGuard systematically investigates \"what specific evidence am I missing?\" by exploring an external, multi-modal medical KG. The authors converted the traditional medical datasets into interactive-multi-round formats for the evaluation of the proposed method. Through the experiments, the results show that the proposed method outperform the baseline methods by an obviously large margin.", "questions": "See the weaknesses above.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The motivation and the writing of the paper is clear and easy to follow, and the framework seems very reasonable and intuitive, which could be convenient for downstream applications. The paper addresses the critical and unsolved problem of LLM overconfidence and its failure to abstain, which is a major barrier to deploying AI in high-stakes fields like medicine.\n2. The presentation of the framework and case study is very clear and informative for readers to understand the use scenarios.\n3. From the results of the experiment section, the proposed method is very effective in terms of accuracy in the constructed datasets.", "weaknesses": "1. The details of the how the constructed datasets were incorporated with other baseline methods are not described in the experiment section or the appendix. I think it would be better to describe in more details of how you ensure that the baseline methods get the same extent of information and knowledge during evaluation with KnowGuard.\n2. I have some questions on the necessity of evaluating conversation turn count for the proposed method. To my understanding, it seems that the proposed method can benefit from gathering more information with more turn counts. Given Figure 3 (Left) shows accuracy improving with conversation length, should the method's strength be framed as \"more effective use of turns\" rather than \"reduction of turns\"?\n3. The method relies heavily on the external KG. How does the system behave when the correct diagnostic evidence is not present in the knowledge graph? Does it learn to abstain gracefully due to a lack of a coherent evidence path, or could the lack of a positive \"hit\" lead it to a premature (and incorrect) answer?", "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": "v1F86rZ0XQ", "reviewer": "Reviewer_gj7Q", "summary": "The paper proposes an Investigate-Before-Abstain (IBA) framework for multi-round clinical question answering with large language models. Instead of forcing the model to answer or abstain directly, the method first expands a medical knowledge graph through two modes—graph-based neighborhood traversal and direct retrieval—to assemble an evidence pool before making the decision to answer, abstain, or query further. A new benchmark combining MEDQA, CRAFT-MD, and AFRIMEDQA is introduced to evaluate open-ended multi-round dialogue. Experiments show that IBA improves factual accuracy and abstention calibration over baseline systems such as simple retrieval-augmented generation or confidence-based abstention", "questions": "1.\tHow sensitive are results to the relative weights among the five scoring components?\n\n2.\tCould the approach be extended to handle contradictory or time-evolving medical evidence (e.g., updated guidelines)?\n\n3.\tDoes the model explicitly track patient-specific vs. population-level context, or is this purely implicit through graph features?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "•\tTackles a meaningful and under-explored problem: when and how clinical agents should abstain.\n\n•\tTechnically coherent pipeline that bridges knowledge-graph reasoning with retrieval-augmented generation.\n\n•\tNew benchmark resource and detailed evaluation across multiple datasets.\n\n•\tSolid empirical results demonstrating improvements in both correctness and cautiousness metrics.", "weaknesses": "• Somewhat heuristic combination of scoring factors; lacks ablation or theoretical grounding for weights.\n\n•\tBenchmark construction may inherit biases from source datasets; unclear generalizability to unseen guideline domains.\n\n•\tLimited discussion of system latency or computational cost compared with long-context baselines.\n\n•\tNo human expert evaluation to verify that “abstain” decisions align with clinical expectations.", "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": "36DiklELUk", "reviewer": "Reviewer_6Fan", "summary": "KnowGuard tackles a common clinical LLM flaw: giving confident answers when evidence is thin. It builds a medical knowledge graph and, at each turn, searches and scores external evidence (embedding match, LLM relevance, graph coherence, time decay, and patient-population signals) before either asking a pointed follow-up or answering with support. The authors test it on a new multi-round benchmark drawn from MEDQA, CRAFT-MD, and AFRIMEDQA against confidence-based abstention, self-consistency, and long-context retrieval, plus ablations.", "questions": "See weakness", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "(1) The proposed multi-round medical QA benchmark is well-motivated and thoughtfully curated, with a clear connection to real clinical reasoning processes. \n\n(2) The paper is well-presented, offering a clear and structured introduction to each module of the proposed framework.", "weaknesses": "(1) The evaluation is narrow. Experiments report accuracy but omit calibration/uncertainty metrics (e.g., ECE, Brier, NLL, risk–coverage) and related baselines (temperature scaling, ensembles, conformal methods). Given the risk-aware motivation, it’s unclear why these are absent. \n\n(2) The knowledge-graph integration is described only at a high level. Key implementation details, e.g., entity/relation extraction, the graph-search/expansion algorithm, how graph signals are fused with LLM scores, and crucial hyperparameters. These details are scattered in the appendix or left implicit, which limits reproducibility and interpretability.", "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": "i5vtWMGHV1", "reviewer": "Reviewer_Zuzr", "summary": "This paper introduces KnowGuard, a framework for LLMs that leverages knowledge graphs to drive abstention decisions in multi-round clinical reasoning. Instead of solely relying on internal knowledge, KnowGuard explores structured medical knowledge to identify evidence gaps before making or abstaining from a diagnosis. The approach involves a two-stage process: evidence discovery (systematic retrieval and expansion of relevant medical facts) and evidence evaluation (multi-factor scoring and prioritization, including graph coherence, embedding similarity, LLM selection, temporal decay, and patient population reasoning). Experiments on several open-ended multi-round clinical benchmarks show that KnowGuard achieves improved diagnostic accuracy and reduced unnecessary patient-provider interaction compared to existing abstention and reasoning strategies.", "questions": "Please see the weaknesses section for detailed discussion.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper focuses on the valuable and practical issue of abstention in multi-round dialogue for LLM-based medical diagnosis, which is essential for efficiency and reliability in clinical applications.\n2. The proposed approach achieves improvements in both diagnostic accuracy and the number of interaction rounds compared to  baselines. Comprehensive ablation studies further validate the independent contributions of each module.", "weaknesses": "1. Limited Novelty in Retrieval and Evaluation Mechanisms: The specific mechanisms for evidence retrieval and evaluation do not appear fundamentally innovative beyond existing literature on RAG and knowledge-enhanced medical reasoning. The paper would benefit from a clearer positioning against recent state-of-the-art works utilizing similar approaches.\n2. Incomplete Evaluation of Abstention Performance: Although timely and appropriate abstention is claimed as the core motivation, the paper lacks dedicated abstention-quality metrics. The evaluation is limited to diagnostic accuracy and conversation turns; it omits quantitative analysis of abstention rates, false or missed abstention cases, the timing of abstention decisions, and abstention precision/recall. \n3. Limited Baseline Coverage: The experimental section does not provide thorough comparisons with more recent or diverse RAG architectures, especially those specifically designed for clinical reliability or abstention tasks. This limits the strength of the claims regarding advancement over existing retrieval-enhanced methods.\n4. Absence of Expert Evaluation and Real-World Validation: The evaluation is fully automated and lacks assessment by clinical experts or real-world usability studies. This limits evidence for practical utility and adoption outside of benchmark datasets.\n5. Knowledge Coverage and Maintenance Concerns: There is insufficient discussion of the medical knowledge graph’s domain completeness, update frequency, and capacity for handling rare, newly emerging, or rapidly changing conditions. Without assurances of knowledge freshness and coverage, the real-world effectiveness and reliability may be restricted.\n6. Scalability and Resource Overhead Not Addressed: The method’s multi-stage retrieval and repeated evidence evaluation may introduce considerable computational and resource costs, but there are no experiments or analysis regarding runtime efficiency, memory footprint, or suitability for deployment at scale.\n7. Lack of robustness to noisy or misleading evidence: The paper does not analyze how KnowGuard handles noisy or misleading evidence during retrieval. It is unclear whether the system becomes overconfident or makes errors when faced with irrelevant or conflicting information. Experiments testing robustness to noise and corresponding mitigation strategies are missing.", "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)