ClinEnv: An Interactive Multi-Stage Long Horizon EHR Environment for Agents
Yuxing Lu, Yushuhong Lin, Wenqi Shi, J. Ben Tamo, Xukai Zhao, Jinzhuo Wang, May Dongmei Wang
Abstract
Clinical practice is not the selection of an answer from enumerated options: a physician gathers heterogeneous information incrementally and commits to sequential, irreversible decisions under uncertainty. Static benchmarks cannot probe and existing interactive medical benchmarks each compromise on at least one of them. We present ClinEnv, an interactive benchmark that evaluates LLMs as attending physicians over real inpatient admissions under a paradigm we term Longitudinal Inpatient Simulation. Each case is automatically constructed into an ordered sequence of decision stages; at every stage the model must actively query four specialized agents before committing to medications, procedures, and diagnoses. ClinEnv scores both what the model decides, through deterministic ontology-grounded matching, and how it gathers information. Across seven models, the strongest reaches only 0.31 decision F1, and outcome quality is sharply decoupled from process quality. Difficulty concentrates in management decisions and later stages, where models recover discharge diagnoses far more reliably than management actions (0.51 vs. 0.17 F1) and continue to issue redundant queries as cases progress. ClinEnv makes this information-acquisition gap, invisible to outcome-only evaluation, directly measurable.
AI Impact Assessments
(1 models)Scientific Impact Assessment: ClinEnv
1. Core Contribution
ClinEnv introduces what the authors term "Longitudinal Inpatient Simulation" (LIS) — an interactive benchmark that evaluates LLMs as attending physicians navigating real, multi-stage inpatient admissions derived from MIMIC-IV. The core novelty lies in combining five properties no prior benchmark jointly satisfies: (1) real EHR trajectories as source material, (2) multi-stage sequential decisions, (3) active information acquisition through specialized agents, (4) deterministic ontology-grounded scoring, and (5) cost-aware process metrics. The benchmark automatically converts 3,509 admissions into 9,297 decision stages with 26,043 ground-truth decisions, requiring no manual annotation.
The key insight is that clinical competence involves not just *what* a model decides but *how* it gathers information — a dimension invisible to outcome-only evaluation. The paper demonstrates this by showing sharp decoupling between outcome quality and process quality across seven models.
2. Methodological Rigor
Strengths in construction: The four-phase automated pipeline (decision extraction, temporal anchoring, stage construction, diagnostic groundability scan) is well-engineered. Ground-truth enrichment is deterministic — drug names from prescription tables, ICD codes from coded events — keeping evaluation targets independent of LLM outputs. The sequential forward sliding-window anchoring mechanism preserves temporal ordering faithfully.
Scoring design: The evaluation framework is thoughtfully designed. Hungarian matching with ATC-hierarchy partial credit for medications, hierarchical ICD F1 for diagnoses/procedures, and action-type gating represent principled choices that reflect real clinical ontologies. The structured submission interface (exposing only relevant submission types and exact counts) isolates clinical reasoning from format-compliance confounds, though this also artificially constrains the problem — real physicians must also determine *how many* actions to take.
Concerns: Several construction and scoring steps rely on LLMs (Claude-Sonnet-4.6 for planning, GPT-5.4-mini for agent responses, LLM reranking for ICD mapping). While structured ground truth is deterministic, the case construction quality depends on LLM extraction fidelity, and the paper provides no human validation of extraction accuracy. The information coverage metric uses an LLM judge, introducing scorer variance that is not quantified. The paper also lacks inter-annotator agreement or expert review of even a sample of constructed cases, which weakens claims about construction quality.
The constrained submission interface — telling the model exactly how many items to submit per type — substantially simplifies the task compared to real clinical practice where determining the scope of needed interventions is itself a critical skill. This design choice improves measurement precision but reduces ecological validity.
3. Potential Impact
Direct impact on medical AI evaluation: ClinEnv addresses a genuine gap. The demonstration that the best model achieves only 0.31 F1, and that diagnosis recovery (0.51) vastly exceeds management accuracy (0.17), provides a concrete, quantitative argument against premature claims of clinical readiness. This finding alone has significant implications for how the community discusses LLM deployment in healthcare.
Process-outcome decoupling: The finding that Llama-3.1-70B achieves the best medication score but worst coverage and highest waste (35.8%) is a striking result that validates the benchmark's dual evaluation design. This type of finding could influence benchmark design philosophy beyond medicine.
Broader methodological influence: The paradigm of evaluating information-seeking behavior alongside decision quality could transfer to other domains requiring sequential decision-making under uncertainty (legal reasoning, financial analysis, engineering design). The automated pipeline for converting longitudinal records into interactive benchmarks is a reusable architectural contribution.
Limitations on impact: Single-center US data (MIMIC-IV) limits generalizability. The benchmark measures concordance with recorded practice, not optimality — clinically superior alternatives score as misses. This is acknowledged but fundamentally bounds what scores mean.
4. Timeliness & Relevance
The paper arrives at a critical moment. LLMs are being marketed for clinical applications based on USMLE-style benchmarks, yet the gap between exam performance and real clinical workflow is poorly characterized. ClinEnv provides the first rigorous framework to quantify this gap across multiple dimensions. The finding that management decisions — the actions with direct patient consequences — are where models fail most dramatically is both timely and important for policy discussions about clinical AI deployment.
5. Strengths & Limitations
Key strengths:
Notable weaknesses:
Missing elements: Ablation studies on scoring components, analysis of construction pipeline failure modes, and expert validation of a case sample would substantially strengthen claims.
Summary
ClinEnv represents a significant conceptual and engineering contribution to medical AI evaluation. It operationalizes the intuition that clinical competence requires more than knowledge retrieval, providing the first benchmark that jointly measures longitudinal decision accuracy and information-seeking quality against real EHR trajectories. The findings — particularly the management-diagnosis gap and process-outcome decoupling — have immediate relevance for clinical AI policy. The main weaknesses are the absence of human validation and the artificially constrained submission interface, which somewhat limit the strength of conclusions about real clinical readiness.
Generated Jun 2, 2026
Comparison History (24)
ClinEnv addresses a critical gap in clinical AI evaluation with broader real-world impact. Its longitudinal inpatient simulation paradigm evaluates both decision quality and information-gathering process, which is crucial for safe clinical AI deployment. The finding that outcome quality is decoupled from process quality has significant implications for healthcare AI regulation and development. While FeynmanBench is a well-constructed benchmark revealing important limitations in multimodal reasoning over Feynman diagrams, its scope is narrower (theoretical physics diagrams) with a more limited user community. ClinEnv's clinical relevance gives it wider interdisciplinary impact across AI, medicine, and health policy.
Paper 2 has higher likely impact due to a broader, more generalizable framework (verifiable simulation + procedural generation + intent-to-executable success conditions + search-based trajectory synthesis + iterative RL with environment feedback). It targets a fast-growing, cross-domain area (LLM agents for physical-world control) with clear real-world applications and a scalable data flywheel. Methodologically it combines rigorous verifiability with an end-to-end training pipeline and strong benchmarked performance. Paper 1 is novel and valuable for clinical evaluation, but its impact is narrower and primarily benchmarking-focused.
Paper 1 (ClinEnv) is likely higher impact due to stronger real-world relevance and broader interdisciplinary reach: it introduces a more realistic, long-horizon, interactive EHR simulation capturing sequential, irreversible clinical decisions and information gathering—capabilities central to safe medical AI. Its ontology-grounded, process+outcome evaluation directly addresses a major gap in current benchmarks and could influence both clinical decision-support research and agent evaluation more broadly. Paper 2 is novel and timely for research-agent assessment, but its applications are narrower and validation of “good” forward-looking judgments is inherently less grounded than clinically anchored decisions.
Paper 1 addresses a critical gap in medical AI by shifting evaluation from static benchmarks to dynamic, longitudinal simulations of real clinical workflows. This has profound implications for the safe deployment of AI in healthcare, offering broader societal impact and higher interdisciplinary relevance compared to the technical improvements in robotic control presented in Paper 2.
Paper 2 introduces a highly timely and novel interactive benchmark for evaluating LLM agents in sequential clinical decision-making, addressing a critical gap in medical AI evaluation. While Paper 1 presents a solid methodological improvement in structural biology, Paper 2's shift from static to dynamic, long-horizon evaluation has broader implications for the safe deployment and assessment of AI in healthcare, offering greater potential impact across the rapidly growing field of AI agents.
Paper 2 (AXIOM) likely has higher scientific impact due to a more generalizable, trust-first neuro-symbolic architecture with strong methodological rigor (verifiable CAS execution, explicit abstention, regression oracle with zero LOST_CORRECT across many commits) and demonstrated production use. Its framework (routing, schema canonicalization, non-regression discipline) can transfer beyond mathematics to other safety-critical reasoning tasks, broadening cross-field impact and timeliness for trustworthy AI. Paper 1 is novel and valuable for clinical LLM evaluation, but is primarily a benchmark and may have narrower immediate applicability and impact outside healthcare/benchmarking.
Paper 2 addresses a critical gap in a high-stakes domain (healthcare) by introducing a dynamic, multi-stage clinical environment for LLMs. While Paper 1 offers a useful debugging tool for general LLM agents, Paper 2's focus on sequential, long-horizon decision-making in medicine is likely to drive significant specialized research, as robust evaluation benchmarks are currently the primary bottleneck for deploying clinical AI.
ClinEnv introduces a novel interactive benchmark paradigm (Longitudinal Inpatient Simulation) that addresses fundamental limitations of static medical AI evaluation, measuring both decision quality and information-gathering process. This fills a significant gap in clinical AI evaluation methodology. Paper 2 proposes incremental improvements to radiology report generation with marginal quantitative gains (e.g., 0.47% BLEU-4 improvement), using relatively standard architectural components (DenseNet + LSTM + RL). ClinEnv's broader methodological contribution to how we evaluate clinical AI agents has greater potential to influence the field.
Paper 2 (ClinEnv) likely has higher impact: it introduces a broadly useful, timely interactive benchmark built from real inpatient admissions, enabling rigorous evaluation of long-horizon, sequential decision-making and information acquisition—key gaps in current LLM medical assessment. Its ontology-grounded scoring and process-vs-outcome decoupling provide methodological rigor and actionable diagnostics relevant across clinical AI, agent evaluation, and safety. Paper 1 is novel for adversarial prompt obfuscation and interpretability, but its primary impact is narrower (attack methodology) and may face deployment/ethical constraints, limiting breadth compared to a foundational evaluation environment in healthcare.
Paper 1 is more novel and timely: it introduces an interactive, long-horizon EHR benchmark capturing sequential, irreversible clinical decisions plus information acquisition—an under-evaluated capability for LLMs. It offers a reusable evaluation environment with ontology-grounded scoring and clear diagnostic findings likely to influence both medical AI and agent benchmarking broadly. Paper 2 targets an important application, but the approach (knowledge graph + GAT + temporal modeling for early warning) is relatively incremental within educational data mining and lacks clear methodological specifics in the abstract, limiting perceived rigor and cross-field impact.
Paper 1 introduces a highly novel intersection of health economics, multi-agent simulation, and LLM program synthesis to optimize macro-level healthcare policies. By addressing dynamic strategic provider responses, it offers profound real-world policy applications. While Paper 2 provides a valuable clinical benchmark, Paper 1's methodological innovation in evaluating system equilibriums and synthesizing inspectable policy mechanisms suggests a broader and more transformative scientific impact across AI, economics, and healthcare policy.
Paper 1 addresses a fundamental methodological flaw in the pervasive 'LLM-as-a-judge' paradigm, offering a domain-agnostic reliability test that impacts all areas of LLM safety and evaluation. While Paper 2 provides a rigorous and valuable environment for clinical decision-making, its impact is largely confined to the medical AI subfield, giving Paper 1 a much broader scientific reach.
ClinEnv addresses a fundamental limitation in evaluating LLMs for clinical decision-making—a high-stakes domain with massive real-world implications. Its novel longitudinal simulation paradigm, separating process from outcome evaluation, reveals critical gaps (e.g., management vs. diagnosis performance) invisible to existing benchmarks. This has broad impact across medical AI, clinical informatics, and LLM evaluation methodology. SchGen is innovative for PCB design automation but targets a narrower engineering niche. ClinEnv's findings about the decoupling of decision quality from information-gathering quality have far-reaching implications for deploying AI in healthcare.
MindZero addresses fundamental challenges in Theory of Mind for AI agents—online inference, efficiency, and lack of annotations—with a novel self-supervised RL framework that trains MLLMs without ground-truth mental state labels. This has broad impact across embodied AI, human-AI collaboration, and cognitive science. The methodological innovation of internalizing model-based reasoning into fast inference is significant. ClinEnv is a valuable clinical benchmark revealing important gaps in LLM medical reasoning, but benchmarks typically have narrower impact than new training paradigms. MindZero's approach is more generalizable and methodologically novel.
Paper 1 is more novel and broadly impactful: it isolates the causal role of upstream information ranking (feeds/retrievers) on LLM-agent decisions, identifies clear behavioral regimes with strong statistical evidence, and proposes practical feed-level mitigations. The mechanism generalizes across domains including security-relevant choices, making it timely for real-world agent deployments and safety auditing. Paper 2 is a valuable benchmark for longitudinal clinical decision-making with solid methodology and applications, but its impact is narrower (health/EHR evaluation) and more incremental relative to existing interactive agent benchmarks.
Paper 2 addresses a fundamental and broadly applicable challenge in LLM training—data organization—that affects virtually all LLM practitioners. Its systematic guidelines (Boundary Sharpening, Cyclic Scheduling, Curriculum Continuity, Local Diversity) and practical methods (STR, SAW) are validated across multiple scales and stages, offering immediately actionable insights with wide applicability. Paper 1 (ClinEnv) is a valuable clinical benchmark but serves a narrower community. Paper 2's breadth of impact across the entire LLM training ecosystem, backed by Microsoft's open-source release, gives it higher potential scientific impact.
Paper 1 likely has higher scientific impact: it introduces a novel, technically detailed interactive benchmark for longitudinal inpatient decision-making, enabling quantitative evaluation of both information-seeking process and clinical actions—an underexplored capability with broad relevance to LLM agent research, healthcare AI, and sequential decision-making evaluation. It reports empirical results across multiple models with ontology-grounded scoring, supporting methodological rigor and immediate adoption by the research community. Paper 2 is timely and societally important but is more domain- and country-specific, and appears more conceptual/framework-oriented with less generalizable methodological contribution.
Paper 2 introduces a concrete, highly relevant benchmark for evaluating LLM agents in a critical real-world domain (healthcare). Its rigorous, interactive approach addresses a significant gap in current static evaluations, offering immediate practical utility and driving empirical progress. While Paper 1 provides a valuable theoretical framework, Paper 2's actionable methodology and timeliness in the rapidly growing field of medical AI give it higher potential for immediate and measurable scientific impact.
Paper 2 introduces a novel, interactive evaluation paradigm for medical LLMs that closely mirrors real-world clinical decision-making. While Paper 1 offers valuable systems-level optimizations for LLM inference, Paper 2 addresses a critical and fundamental gap in healthcare AI evaluation. By moving beyond static benchmarks to sequential, multi-stage environments, it exposes significant flaws in current models and has the potential to broadly steer future research in medical agents and high-stakes AI decision-making.
ClinEnv introduces a fundamentally new evaluation paradigm for LLMs in clinical settings—Longitudinal Inpatient Simulation—that addresses critical gaps in how we assess AI for healthcare. Its novel interactive benchmark with process-quality measurement reveals important failure modes (information-acquisition gaps) invisible to existing approaches. This has broader cross-disciplinary impact (AI, medicine, evaluation methodology) and higher timeliness given the rapid deployment of LLMs in healthcare. EnergyMamba, while solid, represents incremental advances combining existing techniques (Mamba + GNNs + conformal prediction) with modest (~5-6%) improvements.