B ehavior SFT : Behavioral Token Conditioning for Health Agents Across the Proactivity Spectrum (2025)

Abstract: Large Language Models (LLMs) as agents require careful behavioral adaptation. While adept at reactive tasks (e.g., medical reasoning), LLMs often struggle with proactive engagement, like unprompted identification of critical missing information or risks. We introduce **BehaviorBench**, a comprehensive dataset to evaluate agent behaviors across a clinical assistance spectrum. To rigorously test the current models, we also introduce **BehaviorBench-Hard**, a challenging subset where the performance of state-of-the-art models drops significantly, revealing weaknesses. To address these challenges, we propose **BehaviorSFT**, a novel training strategy using behavioral tokens to explicitly condition LLMs for dynamic behavioral selection which boosts performance on both benchmarks. Crucially, a blind clinician evaluation confirmed that our trained agents exhibit more realistic clinical behavior, striking a superior balance between helpful proactivity and necessary restraint versus standard fine-tuning or explicitly instructed agents. Project Page: https://behavior-adaptation.github.io/

## introduction as large language models (llms) transition from experimental systems to deployed agents in clinical environments @xcite , a critical question emerges: "when and how should these systems act reactively or proactively @xcite ?". unlike generalpurpose ai agents, health agents can operate in high-stakes environments where both action and inaction carry significant consequences @xcite . we define reactive behaviors as those where the agent responds only to explicit queries with precisely the information requested, while proactive behaviors involve volunteering additional information, raising concerns, or suggesting actions beyond what was directly solicited. importantly, proactivity in clinical contexts extends beyond merely asking clarifying questions, a common, but limited, focus in existing nlp research @xcite . while question-asking represents one dimension of proactivity, our work encompasses a broader spectrum; including unsolicited intervention, critical evaluation, and recommendation. these behaviors align closely with the "appraisal" phase of evidence-based medicine (ebm) @xcite , where clinicians actively assess available information, identify information gaps, and determine appropriate next steps. an agent that remains strictly reactive may fail to raise an alert when problems are observed with critical lab values or medication contraindications @xcite , potentially compromising patient safety @xcite . in contrast, an excessively proactive system that frequently interrupts with unsolicited recommendations risks contributing to alert fatigue, interruption of workflow, and potential rejection by health professionals @xcite . this trade-off between reactive and proactive behaviors forms the core challenge addressed in this paper. the appropriate balance between these modalities varies dramatically based on clinical context, urgency, risk levels, and the specific health roles being augmented, demanding adaptive behavior policy rather than a fixed mode, especially as systems achieve higher levels of autonomy (figure 4 ). to systematically discuss how an agent's reactive and proactive stance should adapt with its increasing capabilities, we adapt the sae levels of driving automation @xcite into a six-level taxonomy for health agent autonomy. this framework detailed in ## behaviorbench we introduce behaviorbench, a novel dataset specifically designed to assess agent capabilities across the reactive-proactive tasks. derived from real clinical cases, behaviorbench comprises of 6,876 real-world clinical case scenarios from which we derived a total of 142,496 tasks distributed across the 13 distinct task categories. this framework provides a more granular analysis of an agent's ability to discern context and modulate its behavior accordingly, moving beyond standard metrics, such as accuracy, that are solely based on reactive responses. detailed dataset statistics can be found in the appendix c. to ensure that the generated tasks effectively probe clinical reasoning, we construct the dataset in a two-step process. first, we carefully prompt the llm (see appendix f) generating the tasks to use detailed summary from real-world clinical cases, including patient history, diagnostics, conversation snippets, and final diagnoses. this ensures that the questions, answers, and rationales reflect genuine clinical context instead of relying on pseudolabels generated without any realistic groundings. all draft tasks then underwent several back-and-forth revision cycles with two physicians, who reviewed any hallucinations and confirmed each scenario's practical plausibility for @xmath0. then, to evaluate the agent's proactive capabilities, we augment the base scenarios by intentionally introducing subtle challenges, such as hypothetical scenarios with probable clinical errors, conflicting data points (e.g. modifying numerical values slightly between reports, or presenting exam findings seemingly at odds with imaging), and omitted information expected by clinical standards. the resulting reactiveproactive tasks are as follows: reactive tasks evaluates whether the agents can handle information when requested directly. 1. fact_retrieval: finds specific facts mentioned in the text (e.g., "what was the patient's initial temperature?"). proactive tasks require the llm to use higherlevel thinking, and evaluation skills. behaviorbench-hard to further enhance the difficulty of our benchmark, we curated behaviorbench-hard, a subset of 297 challenging cases where multiple state-of-the-art models consistently fail. we selected cases by evaluating three reasoning-specialized llms (o3, gemini-2.5-pro, deepseek-r1) on the full test set with three random seeds each, choosing instances @xmath1. the resulting subset maintains task distribution balance (43% proactive, 39% reactive, 18% balanced). ## behaviorsft: behavior adaptation training to operationalize the concept of behavioral adaptation within health llm agents, we propose a targeted training strategy, behavior-conditioned supervised fine-tuning (behaviorsft). this approach leverages our specialized behaviorbench dataset (section 2) to explicitly teach llms to modulate their responses along the reactive-proactive spectrum based on inferred clinical context. this contrasts with standard sft approaches, which typically optimize for task completion without explicit mechanisms to control the agent's level of initiative or caution, risking either unsafe passivity or disruptive over-intervention. ## clinician-in-the-loop evaluation to rigorously evaluate our behaviorsft agent and validate the proposed dataset, we conducted a user study involving board-certified medical professionals. this study was designed to assess the clinical utility of behaviorbench and to compare the performance of llm agents exhibiting distinct behavioral characteristics. a detailed setup (participants, study procedure, and the annotation interface) of our user study can be found at h. ## limitations and future works data & task scope. behaviorbench aggregates 6,876 english clinical vignettes (142k task instances) from nejm. this corpus reflects an internal-medicine bias and omits modalities such as radiology reads, nursing shift notes, tele-health transcripts, and non-english documentation. the future tasks include expanding the benchmark to multilingual ehr snippets and image-grounded prompts, and we are adding tasks for dermatology, psychiatry, and longitudinal trend summarisation to test whether proactive cues generalise beyond text-only, single-visit encounters. behavior modeling. our behaviorsft controller currently toggles generation with @xmath2, <@xmath3. although effective for coarse behavior shifts, these cannot express nuances such as anticipatory clarification versus high-urgency escalation, and it occasionally over-fires, creating alert fatigue. we are experimenting with a hierarchical token inventory (e.g. <flag_safety>, <@xmath4, and with behaviour-weighted rlhf that continuously trades helpfulness against cognitive load. evaluation & deployment readiness. the clinician study in section 5 involved three medical doctors and a number of cases sufficient for validation but under-powered for robust error stratification or workflow integration. future work should recruit multi-institution cohorts (20+ clinicians, 1,000+ cases) and embeds the agent inside a simulated ehr sandbox to observe interrupt patterns, hand-off continuity, and long-horizon reasoning across multi-day episodes. ## conclusion in this paper, we introduce behaviorbench, a benchmark validated by clinicians, which reveals key proactivity gaps in current llms. to bridge these gaps, we proposed behaviorsft, a novel fine-tuning strategy using explicit behavioral tokens. our method achieved state-of-the-art performance on behaviorbench, with a macro f1 of up to 97.3%. crucially, the strength of be-haviorsft was highlighted on behaviorbench-hard. while all models experienced a performance drop, behaviorsft demonstrated superior resilience, achieving an f1-score of 73.6%, outperforming both generalsft and the ensemble baseline. in a blind user study, clinicians ranked the our trained agent as the most effective (best mean rank 1.80). combined with best g-eval scores for utility and behavioral appropriateness, our findings show that behaviorsft produces more reliable, clinically nuanced, and expert-preferred agents.