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ExecRubrics: Executable Tool-Augmented Rubrics for Verifiable and Efficient Long-Form Evaluation
Kaustubh D. Dhole, Charles L. A. Clarke, Eugene Y. Agichtein
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 92%
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Summary
The paper introduces ExecRubrics, a framework that converts natural-language evaluation rubrics into executable Python scoring functions to enable verifiable, efficient, and transparent long-form response evaluation. By replacing black-box LLM judges with deterministic code that can incorporate external NLP tools (like NLTK and spaCy), ExecRubrics achieves comparable or superior preference accuracy on benchmarks like HealthBench, HelpSteer, and ArgQuality while significantly reducing evaluation latency.
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ExecRubrics → evaluatedon → HealthBench
confidence 95% · On three long-form response benchmarks-HealthBench... we show that ExecRubrics can substitute for expensive black-box judges
ExecRubrics → evaluatedon → ArgQuality
confidence 95% · On three long-form response benchmarks... ArgQuality... we show that ExecRubrics can substitute for expensive black-box judges
ExecRubrics → evaluatedon → HelpSteer
confidence 95% · On three long-form response benchmarks... HelpSteer... we show that ExecRubrics can substitute for expensive black-box judges
ExecRubrics → uses → Python
confidence 95% · ExecRubrics encodes evaluation logic as verifiable Python scoring functions
ExecRubrics → outperforms → Natural Language Rubrics
confidence 92% · matching or improving NL rubric baselines with best preference accuracies of 52.9%, 75.3%, and 91.5%
ExecRubrics → augmentedby → spaCy
confidence 90% · incorporating external logic and resources from text processing libraries such as NLTK and spaCy
ExecRubrics → augmentedby → NLTK
confidence 90% · incorporating external logic and resources from text processing libraries such as NLTK
GPT-5.5 → usedtogenerate → Executable Rubrics
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Abstract
Abstract:Rubrics aim to make language-model evaluation transparent by decomposing response quality into interpretable criteria. However, natural-language rubrics are often ambiguous, require black-box LLM judges, and typically assume criteria aggregate independently through linear weighted sums, limiting their ability to capture dependencies, alternatives, penalties, and override conditions. We propose ExecRubrics, a framework for representing rubrics as compact executable programs. ExecRubrics encodes evaluation logic as verifiable Python scoring functions, giving natural-language rubric intent an operational semantics: a fixed decision procedure that can be inspected, executed, and edited. On three long-form response benchmarks-HealthBench, HelpSteer, and ArgQuality-we show that ExecRubrics can substitute for expensive black-box judges in ranking preferred over dispreferred responses, matching or improving NL rubric baselines with best preference accuracies of 52.9%, 75.3%, and 91.5%, respectively, while reducing evaluation latency by a large margin. We show that incorporating external logic and resources from text processing libraries such as NLTK and spaCy can further improve preference accuracy. Our results suggest a novel way of looking at evaluation, by offering a faster, more explainable and less ambiguous alternative to black-box rubric evaluation, particularly in high-stakes domains such as healthcare and banking where precision and auditability are critical.
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- Source: https://arxiv.org/abs/2608.22559v2
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ExecRubrics: Executable Tool-Augmented Rubrics for Verifiable and Efficient Long-Form Evaluation Kaustubh D. Dhole Department of Computer Science Emory University Atlanta, USA kdhole@stanford.edu Charles L. A. Clarke School of Computer Science University of Waterloo Waterloo, Canada claclark@plg.uwaterloo.ca Eugene Y. Agichtein Department of Computer Science Emory University Atlanta, USA yagicht@emory.edu Abstract Rubrics aim to make language-model evalua- tion transparent by decomposing response qual- ity into interpretable criteria. However, natural- language rubrics are often ambiguous, require black-box LLM judges, and typically assume criteria aggregate independently through linear weighted sums, limiting their ability to capture dependencies, alternatives, penalties, and over- ride conditions. We propose ExecRubrics, a framework for representing rubrics as compact executable programs. ExecRubrics encodes evaluation logic as verifiable Python scoring functions, giving natural-language rubric in- tent an operational semantics: a fixed decision procedure that can be inspected, executed, and edited. On three long-form response bench- marks—HealthBench, HelpSteer, and ArgQual- ity—we show that ExecRubrics can substitute for expensive black-box judges in ranking pre- ferred over dispreferred responses, matching or improving NL rubric baselines with best preference accuracies of52.9%,75.3%, and 91.5%, respectively, while reducing evaluation latency by a large margin. We show that incor- porating external logic and resources from text processing libraries such as NLTK and spaCy can further improve preference accuracy. Our results suggest a novel way of looking at evalu- ation, by offering a faster, more explainable and less ambiguous alternative to black-box rubric evaluation, particularly in high-stakes domains such as healthcare and banking where precision and auditability are critical. 1 Introduction Large Language Models (LLMs) are increasingly evaluated and trained with automated graders, such as LLM-as-judge systems that output scalar scores or preferences. Although convenient, these judg- ments are opaque: they rarely explain which re- quirements were missed or how a system should improve. This is especially problematic in high- stakes domains such as health (Hager et al., 2024; Asan et al., 2020; Sivasothy et al., 2026) where precision is critical or in evaluating long-horizon workflows where evaluation should account for multiple intermediate deliverables (Sun et al., 2026; Chen et al., 2026; Phan et al., 2025; Kapoor et al., 2026). Rubric-based evaluation addresses this by decomposing response quality into explicit natural- language criteria (Arora et al., 2025; Dhole et al., 2025b). Natural-language rubrics, however, still leave the operational semantics of evaluation underspecified as generic and abstract criteria often are less ef- fective for downstream evaluation (Popham, 1997; Li and Lindsey, 2015). A rubric item such as “ap- propriately recommends urgent care” or a corpus level item such as “instruction following ability” is readable, but is vague and ambiguous. In prac- tice, each item often requires a separate human or a black-box LLM judge to decide whether the cri- terion is satisfied. This reintroduces subjectivity at the level of each atomic criterion, makes the final score sensitive to the judge model, provides limited operational explainability due to dependency on a black-box LLM and makes it difficult to forecast the change when the rubric needs editing. It also encourages flat weighted checklists, even though evaluation logic may involve prerequisites, alternatives, penalties, and overrides. For example, in a medical query mentioning a penicillin allergy, an executable rubric can activate an allergy-specific branch, reward non-penicillin alternatives, and pe- nalize uncaveated amoxicillin recommendations. In that regard, we propose ExecRubrics: a rep- resentation that compiles rubric intent into small symbolic scoring programs. Each program de- fines the checks, branches, penalties, normalization, and tool calls used to score a candidate response. Since modern LLMs are strong code generators, we study whether they can approximate human-written natural-language rubrics as executable evaluators. Such executable rubrics can be made more arXiv:2608.22559v2 [cs.AI] 27 Aug 2026 Figure 1: Natural language rubrics can be ambiguous and require a judge to compute criterion satisfiability. Executable rubrics are deterministic and verifiable mitigating the need of the judge. expressive by composing deterministic NLP tools—tokenizers, lexicons, entity recognizers, negation detectors, readability, and n-gram met- rics—as transparent primitives for atomic checks. Specifically, we are interested in answering the research question: Can LLMs generate executable rubrics that deterministically distinguish high- quality responses from low-quality responses as effectively as natural-language rubrics? In that regard, our contributions are as follows: • We introduce ExecRubrics as an explicit rule- based representation to capture complex lay- ers of expressivity and explainability for re- sponse evaluation as shown in Figure 1. • We then demonstrate how Python-based Ex- ecRubrics can be generated from natural lan- guage rubrics. Across HealthBench, ArgQual- ity, and HelpSteer, we evaluate whether exe- cutable rubrics can serve as a judge-free inter- mediate representation for preference evalu- ation. We find that generated Python rubrics often match or exceed natural-language rubric judging while making the scoring logic in- spectable, deterministic, and modifiable. •We further show how allowing these rubrics to exploit logic through external text processing tools improves their preference ability further by providing more expressive power. 2 Related Work LLM-as-judge and fine-grained evaluation: LLMs are increasingly used as automated eval- uators, producing scalar scores, preferences, or structured ratings (Dhole et al., 2025b; Dhole and Agichtein, 2024; Dhole et al., 2025a). Frameworks such as G-Eval show that prompted LLM judges can correlate with human judgments (Liu et al., 2023), but scalar judgments offer limited diagnos- tic feedback. Fine-grained evaluation addresses this by decomposing response quality into smaller criteria (Li et al., 2026; Farzi and Dietz, 2025): For instance, FLASK evaluates instance-specific align- ment skills (Ye et al., 2024), while FActScore vari- ants decompose long-form summaries into atomic claims or nuggets (Min et al., 2023; Chung et al., 2026; Jeong et al., 2025). These works motivate rubric-style evaluation, but the resulting units are still typically interpreted through long natural lan- guage descriptions which are often complex. Rubric-basedandadaptiveevaluation: Rubric-based benchmarks make evaluation more transparent by exposing the criteria used to score responses. HealthBench uses physician-written, query-specific criteria with point values (Arora et al., 2025; Hicks et al., 2026; Cook et al., 2024); and ProfBench (Wang et al., 2025) and PRBench (Akyürek et al., 2025) use expert- authored weighted rubrics in professional domains. Recent work also makes rubrics more scalable and adaptive: RUBICON generates rubrics for domain-specific conversations (Biyani et al., 2024), Health-SCORE selects relevant healthcare rubric items for each prompt (Mallinar et al., 2026), and RubricRAG retrieves related examples to generate query-specific rubrics (Dhole and Agichtein, 2026). However, these systems still largely represent rubrics as a weighted linear sum of natural-language checklist items whose satisfaction must be judged by a human or an LLM. Limitations of natural-language rubrics: Prior assessment work shows that rubrics are not au- tomatically reliable or valid: they require clear, ap- propriately granularity (Dhole and Agichtein, 2026; Fensore et al., 2026) and, often calibration or ex- emplars (Li and Lindsey, 2015). Vague criteria can lead raters to interpret rubrics inconsistently or rely on holistic impressions (Popham, 1997). This lim- itation is especially relevant for LLM evaluation, where criteria such as “does not overstate certainty” or “appropriately recommends urgent care” remain operationally underspecified and require another judge to decide whether they are satisfied. Interpretable symbolic approximations of neural models; A parallel line of work uses sym- bolic representations to approximate black-box neu- ral models to allow expressing explicit rules, prior knowledge, and controllable constraints (Wang et al., 2021). This includes distilling compact sym- bolic expressions like circuit discovery (Rai et al., 2026) from trained models (Cranmer et al., 2020) and translating predictions into rules or reasoning trails (Armgaan et al., 2024; Rorseth et al., 2026). Unlike work focused on model internals, factual associations, or causal structure, we make the eval- uation rubric itself executable, inspectable, and human-modifiable. Some works have argued for structured, test-like evaluation for response evalua- tion, for instance through behavioral tests (Ribeiro et al., 2020), graph-based rubric representations for automatic short-answer grading (Condor et al., 2022) and rule-based rewards (Mu et al., 2024). Our work also follows this motivation but is fo- cused on downstream evaluation as well: instead of using natural-language criteria interpreted by a judge, ExecRubrics represents rubrics as small executable programs. This allows deterministic scoring logic, explicit dependencies, alternative sat- isfaction paths, penalties, and safety-critical over- rides while preserving the interpretability goal of rubric-based evaluation. 3 Rubric-Based Evaluation We consider the setting where an evaluator must choose a preferred responsey + over a dispreferred responsey − for an input queryxthrough rubric criteria. LetRdenote rubric system, e.g., a set of rubric items or criteria and letE R (y ∣ x)denote an evaluation score assigned to a candidate responsey for a queryxunder the rubric systemR. A rubric- based evaluator is effective when it assigns a higher score to the preferred response. One measures the effectiveness under a rubric system by measuring the preference accuracy across a large number of query and preference pairsD. Acc R = 1 ∣D∣ ∑ (x,y + ,y − )∈D 1[E R (y + ∣ x) > E R (y − ∣ x)] Rubric-based evaluation therefore reduces prefer- ence prediction to criterion-guided scoring: the rubric specifies what properties a good answer should satisfy, and the evaluator computes a score based on whether the candidate response satisfies those properties. 3.1 Natural Language Rubrics In rubric-based evaluations, the rubric system rep- resented rubric criteria as a list of (weighted) items: R = (r 1 , p 1 ),(r 2 , p 2 ),...,(r n , p n ) wherer i is a textual description of thei-th rubric criterion andp i ∈Ris the number of points assigned to each criterion. Positive points reward desirable properties, while negative points penalize unsafe, incorrect, or otherwise undesirable properties. Because each rubric criterion is written in long descriptive natural language, its satisfaction is not directly computable. A separate judge modelM is therefore used to determine whether responsey satisfies criterion r i for query x: M(x, y, r i ) ∈ 0, 1 The final rubric score is then computed as a weighted sum of satisfied criteria, normalized by the total positive points: E R (y ∣ x) = ∑ n i=1 p i ⋅ 1[M(x, y, r i ) = 1] ∑ n i=1 p i ⋅ 1[ p i > 0] This formulation makes evaluation more inter- pretable than a single scalar judge score because individual criteria can be inspected. However, the semantics of each criterion are still mediated byM, and the aggregation assumes that criteria contribute independently and linearly to the final score. 3.2 Executable Rubrics We define an executable rubric as a symbolic scor- ing program that evaluates the candidate response based on any arbitrary functionf r i that approxi- mates a rubric criteriar i into explicit rules. Specifi- cally, an executable rubric replaces judge-mediated criterion satisfaction with a scoring functionf r i that encodes explicit rules and may call deterministic tools for atomic text checks or arbitrary logic: Unlike natural-language rubrics, executable rubrics make the scoring procedure explicit. They can encode conditional logic, prerequisites, alterna- tive satisfaction paths, penalties, and override rules directly in some logical form e.g., Python code. E R (y ∣ x) = ∑ n i=1 p i ⋅ f r i (x, y) ∑ n i=1 p i ⋅ 1[ p i > 0] . wheref r i is an executable scoring function approx- imated from rubric r i . 4 Experimental Setup We now describe our experimental setup where we compare the effectiveness of human written rubrics and the corresponding translated executable rubrics, with and without external logic. Benchmarks: We evaluate preference accuracy over three datasets that provide both rubric infor- mation and preference-style supervision. First, on HealthBench (Arora et al., 2025), we treat the physician-authored completions as the positive re- sponses and use negative responses from Rubri- cRAG (Dhole and Agichtein, 2026), which are generated by conditioning on mismatched rubrics. We also evaluate on two long-form preference benchmarks, HelpSteer (Wang et al., 2024) and ArgQuality (Gienapp et al., 2020). Our primary metric is Preference Accuracy: the fraction of queries for which the positive response is preferred over the negative response. Rubric Generation Methods: 1. NL Rubrics:We first compute prefer- ence accuracies using human-written rubric criteria. Each benchmark has 5 to 14 corpus- level criteria like cogency, emotional appeal, instruction following, etc., shown in Table 2. We use single sentence definitions for each of them.Qwen3-30B-Instruct-A3B(Yang et al., 2025) is used for predicting criterion satisfaction. 1 2. Executable Rubric Variants: We evaluate three families of executable rubrics by generating them from their natural language counterparts. (i) Executable Rubrics:the model directly converts natural-language rubrics into Python scoring functions.Each rubric criterion (eg., cogency, coherence, etc.) is translated to one scoring function, and the sum of the scores is used for evaluating (query, response) pairs (i.e. we usep i = 1for all rubric criteria). The prompts used for generating the rubrics are shown in Appendix Figure 4 (i) Executable Rubrics with Popular tools: the model generates a Python scoring function with access to two widely used NLP libraries, NLTK (Bird, 2006) and spaCy (Honnibal et al., 2020). (i) Executable Rubrics with Deterministic Tools: the model generates a Python scoring function that can call a wider range of different deterministic text processing libraries. For ArgQuality, and HealthBench, we use few-shot prompts by constructing 3 executable functions for 3 rubric criteria from HelpSteer.For HelpSteer, we construct a zero-shot prompt.We evaluate 3 frontier LLMs, viz., GPT 5.5 (OpenAI, 2026), Claude Sonnet 4.6 (Anthropic, 2026), and Gemini 3.5 Flash (Google, 2026). 3. Black-Box Agentic Evaluation: We also in- clude a no rubric agentic baseline using GPT 5.5. Given the long context length of 1M, we feed a sin- gle query file to the chat, and prompt it to generate preferred response for each row of the file. Tool Augmentation: We augment rubric gener- ation with helper functions built on top of existing text-processing libraries and deterministic analyz- ers (shown in Appendix A), and expose them di- rectly in the prompts so the model sees their signa- tures, inputs, and outputs. For popular-library aug- mentation, this includes functions for tokenization, lemmatization, entity extraction, phrase matching, profanity checks, negation detection, and lexical frequency statistics. For deterministic-tool aug- mentation, we expose higher-level checks such as 1 We use NVIDIA H200 for NL rubric experiments. Differ- ent choices of NL judges may result in different performances. Rubric SourceRubric TypeHealthBenchArgQualityHelpSteerExecution (s)↓ No RubricsBlack-Box Agentic Evaluation (GPT 5.5 All Queries).639.904.790.7393 HumanNatural Language Rubrics (Qwen 30B Per Query).522.904.6231.688 GPT-5.5 Executable Rubrics.529.809.640.0109 Executable Rubrics + Popular Tools.525.798.737.6518 Executable Rubrics + Deterministic Tools.486.819.660.4187 Gemini 3.5 Flash Executable Rubrics.482.819.753.0088 Executable Rubrics + Popular Tools.427.702.583.5707 Executable Rubrics + Deterministic Tools .518.872.587.1189 Claude Sonnet 4.6 Executable Rubrics .400.755.647.0229 Executable Rubrics + Popular Tools .514.670.633.9062 Executable Rubrics + Deterministic Tools.416.915.700.1293 Table 1: Performance comparison across rubric sources and benchmarks. The first column depicts the source of the rubrics created once per corpus. Except for the first row, all evaluations have been done querywise. The first two rows require LLMs for evaluation, while the bottom nine rows generate scores using executable rubrics. Last column is averaged across three datasets. medical red-flag detection, refusal-policy matching, markdown-table detection, readability scoring, key- word coverage, language matching, and actionable- harm detection. This setup lets the model write rubric programs that call concrete tools allowing arbitrary custom logic to enrich the evaluation. 5 Results and Analysis We report preference accuracies in Table 1.Overall, executable rubrics recover substantial prefer- ence signal without invoking an LLM judge at evaluation time. The strongest executable vari- ant exceeds the natural-language rubric base- line on all three benchmarks, though the size and source of the gain vary substantially across datasets. Natural-language rubrics are not con- sistently effective when evaluated with Qwen3- 30B-Instruct-A3B. They match the direct GPT-5.5 baseline on ArgQuality (.904), but perform sub- stantially worse on HealthBench (.522vs..639) and HelpSteer (.623vs..790). This suggests that natural-language rubrics remain dependent on the judge model used to interpret each crite- rion. Executable rubrics improve over the natural- language rubric baseline on all three benchmarks. The best executable systems reach.529on Health- Bench,.915on ArgQuality, and.753on HelpSteer, compared with.522,.904, and.623for natural- language rubrics. These results suggest that de- terministic scoring programs can recover much of the preference signal captured by NL rubric judg- ing, while replacing repeated black-box criterion interpretation with explicit executable logic. Deter- ministic tools are especially helpful on ArgQuality, improving performance for all three rubric gener- ators and yielding the best overall score of.915 with Claude Sonnet 4.6. On HelpSteer, determin- istic tools modestly improve GPT-5.5 and Claude- generated rubrics from.640to.660and from.647 to.700, respectively, although the best executable result of.753is obtained by Gemini 3.5 Flash with- out tool augmentation. However, on HealthBench, tool use is less reliable and does not close the gap with direct GPT-5.5 evaluation. In summary, ex- ecutable rubrics provide a transparent and judge- free alternative to natural-language rubric evalua- tion. They are strongest when evaluation criteria can be expressed through explicit textual checks, but their performance still depends on the bench- mark, the rubric generator, and the available tool interface. Besides, executable rubrics are consid- erably faster as shown in the execution times in Appendix Table 3 as compared to their NL coun- terparts. For instance, standard executable rubrics generated by Gemini 3.5 Flash achieved an overall average speedup of∼192×compared to the Qwen- 30B NL judge (0.0088s vs. 1.688s). Also note that efficiency-focused models (like Gemini 3.5 Flash) sometimes produce “no-op” or shallow tool wrappers rather than deeply integrating heavy NLP pipelines due to misunderstanding the task. This failure mode leads to artificially low latency (up to 327×faster than the NL baseline on HealthBench) but severely degrades preference accuracy (.427). Executable rubrics from GPT 5.5 and Claude are shown in the Appendix Figures 2 and 3. 6 Conclusion and Future Work We introduced ExecRubrics, a framework for turn- ing natural-language rubrics into executable scor- ing programs. Unlike conventional rubric evalu- ators, which require an LLM or human judge to interpret each criterion, executable rubrics make evaluation logic explicit: they can encode complex logic as deterministic code. Across HealthBench, ArgQuality, and HelpSteer, generated executable rubrics recover substantial preference signals without requiring a black-box LLM at evaluation time. The strongest executable variants outperform the natural-language rubric baseline on all three benchmarks. Moreover, tool- augmented rubrics demonstrate that deterministic tools, such as NLP libraries, can provide useful and transparent primitives for evaluation. These results suggest that executable rubrics are a promising in- termediate representation for auditable, modifiable, and efficient LLM evaluation. Our experiments study executable rubrics as au- tomatically generated evaluators, and they point to several interesting directions for future work. First, we instruct models to generate rubrics that operate over the full response text. This makes the setup simple, broadly applicable, and easy to exe- cute across domains, but different domains may re- quire different levels of granularity. In high-stakes settings such as medicine, high-precision checks over specific symptoms, entities, contraindications, or safety actions can be especially important, be- cause small omissions or unsafe recommendations may materially change the quality of an answer. In more open-ended settings, however, very pre- cise lexical or structural checks may be less de- sirable, since there may be many valid ways to satisfy the same evaluation intent. Future work could therefore adapt the granularity of executable rubrics to the domain by incorporating explicit pars- ing, sentence-level decomposition, claim extrac- tion, domain-specific entity recognition, or typed intermediate representations before scoring. Future work should also study how humans in- spect, revise, and debug executable rubrics; how rubric programs can be made more robust across domains; and how executable evaluation logic can support dataset filtering, reward modeling, prefer- ence optimization while avoiding hacking. More broadly, ExecRubrics points toward evaluation sys- tems whose judgments are accurate, explainable and operationally inspectable. 7 Limitations Second, although executable rubrics are intended to be more interpretable because their scoring logic is explicit, we do not directly evaluate human in- terpretability which could often be subjective. In practice, there is likely a balance between expres- sivity and interpretability. More expressive rubric programs can capture dependencies, alternatives, penalties, and override conditions, but they may also become more complex for humans to inspect and revise. Conversely, flatter and more generic rubrics may be easier to read, but they can fail to ex- press the evaluation structure needed for accurate preference judgments, as suggested by the com- paratively lower performance of natural-language rubrics in our preference analysis. Future work could therefore study how humans write, inspect, debug, and simplify executable rubrics, and how interfaces can expose the underlying logic without making the representation tedious to use. Third, executable rubrics may be vulnerable to gaming. Since their scoring logic is explicit, re- sponse generators could learn to trigger specific textual, structural, or semantic checks without sat- isfying the underlying evaluation intent. This risk is especially important if executable rubrics are used as reward signals for RL optimization. Future work should study adversarial robustness and whether ex- ecutable rubric scores remain aligned with human judgments under optimization pressure. As frontier models become increasingly capa- ble of scraping and extracting publicly visible data with high accuracy, the risk of memorization can- not be ignored. The performance of executable rubrics, especially on datasets such as HelpSteer and ArgQuality, may be influenced in part by mem- orized content. However, we also believe that pre- dicting dictionaries, loops, and generic logic should not necessarily be attributed directly to memoriza- tion, since such patterns commonly appear through- out LLMs’ pretraining and midtraining data. 8 Ethical Considerations LLMs are increasingly used as evaluators, but LLM judges are black boxes whose scoring behavior can be difficult to inspect, reproduce, or contest. This opacity raises concerns about accountability, bias, and overreliance on automated judgments, espe- cially in high-stakes settings. Our work aims to make rubric-based evaluation more responsible by replacing repeated black-box criterion interpreta- tion with explicit executable scoring logic. Be- cause executable rubrics expose the rules, branches, penalties, and tool calls used during evaluation, they can be inspected, audited, revised, and stress- tested more directly than opaque judge-model out- puts. However, executable rubrics are not inher- ently fair or correct: poorly written rules may en- code bias, miss valid response variations, or overfit to superficial textual cues. We therefore view Exe- cRubrics as a step toward more transparent and ac- countable evaluation, not as a substitute for human oversight, domain expertise, or careful validation. The experiments in this paper were mostly con- ducted using GPT 5.5 Codex 2 and Gemini CLIs. 3 Some paragraphs of the paper were grammatically corrected using ChatGPT 5.5 (OpenAI, 2026). Acknowledgements The authors would like to thank three anonymous reviewers and meta-reviewer of ARR for reviewing our work. 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A Rubric Types Benchmark Rubric Criteria# Test HealthBenchCommunication Quality, Accuracy, Completeness, Context Awareness, Instruction Following 255 HelpSteerHelpfulness, Correctness, Coher- ence, Complexity, Verbosity 300 ArgQualityCogency, Local Acceptability, Local Relevance, Local Sufficiency, Effec- tiveness, Credibility, Emotional Ap- peal, Clarity, Appropriateness, Ar- rangement, Reasonableness, Global Acceptability, Global Relevance, Global Sufficiency 94 Table 2: Rubric criteria and number of test examples used for each benchmark. NL Rubrics (as measured on NVIDIA H200)Executable Rubrics NL Rubric ApproachTime (s)Executable ApproachGPT-5.5Gemini 3.5 FlashSonnet 4.6 Qwen3-14B2.0173Human to Executable.0106.0098.0085 Qwen3-30B-A3B-Instruct-25071.7316+ Popular Tools.3932.0053.5405 Llama-3.1-8B-Instruct1.4058+ DetTools.8402.0975.0575 Table 3: Average per-query preference evaluation time in seconds on HealthBench. Executable-rubric evaluation is considerably faster, relative to NL Rubrics even on NVIDIA H200. Executable Rubric Generated by GPT 5.5 for ArgQuality Figure 2: GPT 5.5 generated executable rubric for evaluating the 14 dimensions of ArgQuality long- form arguments. Functions of the 3 of the dimensions (cogency, local acceptability, and global sufficiency) have been shown. The finalscore _ responsefunc- tion calls each of the 14 functions and returns the sum of them as the evaluation score. The source prompt has been shown in Figure 4. Executable rubrics for 3 dimensions “co- gency”, “local acceptability” and “global sufficiency” import re _ WORD _ RE = re.compile(r"[a-zA-Z][a-zA-Z'-] * ") _ SENTENCE _ RE = re.compile(r"[^.!?]+[.!?]?") DISCOURSE _ MARKERS = "because", "since", "therefore", "thus", "hence", "so", "consequently", "as a result", "for this reason", "this means", "shows that", "suggests that", "indicates that", "implies that", "leads to" CONCLUSION _ MARKERS = "therefore", "thus", "hence", "so", "consequently", "in conclusion", "overall", "for these reasons", "we should", "we must", "it follows", "this shows", "this suggests", "i conclude", "the conclusion" PREMISE _ MARKERS = "because", "since", "given that", "as", "for example", "for instance", "evidence", "reason", "one reason", "another reason", "data", "study", "research", "statistics", "according to", "reported", "survey", "case" COUNTERARGUMENT _ MARKERS = "however", "although", "though", "while", "on the other hand", "critics", "opponents", "some argue", "one objection", "counterargument", "nevertheless", "but", "despite", "admittedly", "to be fair", "even if", "tradeoff", "trade-off" EVIDENCE _ MARKERS = "for example", "for instance", "evidence", "data", "study", "studies", "research", "survey", "statistics", "percent", "%", "according to", "reported", "analysis", "case", "expert", "source", "historically" CAUTION _ MARKERS = "may", "might", "can", "could", "often", "usually", "in many cases", "likely", "appears", "suggests", "depending", "some", "many", "generally", "not always", "tends to", "plausibly" ABSOLUTE _ MARKERS = "always", "never", "everyone", "nobody", "all", "none", "completely", "totally", "undeniably", "obviously", "clearly proves", "without exception", "guaranteed", "must be true" INSULT _ MARKERS = "idiot", "stupid", "moron", "dumb", "evil", "trash", "liar", "crazy", "pathetic", "worthless", "corrupt", "brainwashed", "fool", "shut up" PROFANITY _ MARKERS = "damn", "hell", "shit", "crap", "fuck", "fucking", "bullshit" EMOTIONAL _ MARKERS = "harm", "danger", "fear", "hope", "fair", "unfair", "justice", "suffering", "protect", "threat", "risk", "compassion", "dignity", "rights", "urgent", "families", "children", "community", "future", "crisis" MANIPULATIVE _ MARKERS = "any decent person", "only a monster", "you must be heartless", "if you disagree", "real people know", "wake up", "be afraid", "they are coming for", "destroy our lives", "no sane person" VAGUE _ MARKERS = "things", "stuff", "bad", "good", "many people say", "somehow", "obviously", "basically", "a lot", "huge", "big problem", "nice", "very important" STRUCTURE _ MARKERS = "first", "second", "third", "finally", "in conclusion", "overall", "one reason", "another reason", "moreover", "furthermore", "also", "however", "therefore" QUESTION _ STOPWORDS = "the", "a", "an", "and", "or", "but", "if", "then", "to", "of", "in", "on", "for", "with", "by", "from", "as", "is", "are", "was", "were", "be", "been", "being", "do", "does", "did", "should", "would", "could", "can", "may", "might", "must", "will", "shall", "it", "this", "that", "these", "those", "i", "you", "we", "they", "he", "she", "them", "his", "her", "their", "our", "your", "about", "into", "than", "too", "very", "not", "no", "yes", "there", "here", "such", "which", "what", "who", "whom", "when", "where", "why", "how", "whether", "issue", "claim", "stance", "argument", "argue" def score _ cogency(query: str, text: str): max _ possible = 10 reasons = [] q = (query or "").lower() t = (text or "").lower() words = _ WORD _ RE.findall(t) sentences = [s.strip() for s in _ SENTENCE _ RE.findall(text or "") if s.strip()] score = 0 has _ conclusion = any(m in t for m in CONCLUSION _ MARKERS) or bool(re.search(r" (should|must|ought to|need to|is better|is worse) ", t)) premise _ count = sum(1 for m in PREMISE _ MARKERS if m in t) relation _ count = sum(1 for m in DISCOURSE _ MARKERS if m in t) has _ counter = any(m in t for m in COUNTERARGUMENT _ MARKERS) has _ evidence = any(m in t for m in EVIDENCE _ MARKERS) circular = bool(re.search(r" [^.!?]0,80 ", t)) and len(set(words)) < max(20, len(words) * 0.35) off _ topic _ tokens = set(w for w in _ WORD _ RE.findall(q) if len(w) > 3 and w not in QUESTION _ STOPWORDS) text _ tokens = set(w for w in words if len(w) > 3) overlap _ ratio = len(off _ topic _ tokens & text _ tokens) / len(off _ topic _ tokens) if off _ topic _ tokens else 0.5 if len(words) >= 40: score += 1.0 reasons.append("Argument has enough length to develop a line of reasoning.") else: reasons.append("Argument is too short to establish strong cogency.") if has _ conclusion: score += 2.0 reasons.append("Identifiable conclusion or stance is present.") else: score -= 2.0 reasons.append("No clear conclusion or stance is identifiable.") if premise _ count >= 2: score += 2.0 reasons.append("Multiple premise or support markers are present.") elif premise _ count == 1: score += 1.0 reasons.append("At least one premise marker is present.") else: score -= 1.5 reasons.append("Premises are not clearly signaled.") if relation _ count >= 2: score += 2.0 reasons.append("Logical links between premises and conclusion are explicit.") elif relation _ count == 1: score += 1.0 reasons.append("Some logical relation is signaled.") else: reasons.append("Logical relation between support and conclusion is weakly signaled.") if has _ evidence: score += 1.0 reasons.append("Argument includes evidence-like support.") else: reasons.append("Argument lacks evidence-like support.") if has _ counter: score += 1.0 reasons.append("Argument acknowledges contrast, tradeoff, or objection.") else: reasons.append("No counterargument or limitation is considered.") if overlap _ ratio >= 0.35: score += 1.0 reasons.append("Argument appears connected to the issue in the query.") else: score -= 2.0 reasons.append("Argument appears weakly connected to the issue in the query.") if circular: score -= 2.0 reasons.append("Argument may rely on repetitive or circular reasoning.") score = max(0, min(score, max _ possible)) return "score": score, "max _ possible": max _ possible, "reasons": reasons def score _ global _ sufficiency(query: str, text: str): max _ possible = 10 reasons = [] t = (text or "").lower() words = _ WORD _ RE.findall(t) score = 0 counter _ count = sum(1 for m in COUNTERARGUMENT _ MARKERS if m in t) rebuttal _ markers = "however", "nevertheless", "still", "even so", "despite", "but", "this objection", "this concern", "can be addressed", "respond", "rebut", "tradeoff", "trade-off", "on balance" rebuttal _ count = sum(1 for m in rebuttal _ markers if m in t) evidence _ count = sum(1 for m in EVIDENCE _ MARKERS if m in t) conclusion _ found = any(m in t for m in CONCLUSION _ MARKERS) or bool(re.search(r" (should|must|ought to|need to) ", t)) caution _ found = any(re.search(r" " + re.escape(m) + r" ", t) for m in CAUTION _ MARKERS) if counter _ count >= 2: score += 3.0 reasons.append("Argument anticipates multiple objections or tradeoffs.") elif counter _ count == 1: score += 2.0 reasons.append("Argument anticipates at least one objection or tradeoff.") else: score -= 1.5 reasons.append("Argument does not anticipate counterarguments.") if rebuttal _ count >= 2: score += 2.0 reasons.append("Argument contains explicit rebuttal or balancing language.") elif rebuttal _ count == 1: score += 1.0 reasons.append("Argument contains some rebuttal or balancing language.") if evidence _ count >= 1: score += 1.5 reasons.append("Rebuttal or overall argument has evidence-like support.") else: reasons.append("Counterargument handling lacks evidence-like support.") if conclusion _ found: score += 1.5 reasons.append("Argument reaches an overall conclusion after support.") else: score -= 1.0 reasons.append("Argument lacks an overall conclusion.") if caution _ found: score += 1.0 reasons.append("Qualified language helps handle exceptions.") else: reasons.append("Argument could better handle exceptions with qualification.") if len(words) >= 70: score += 1.0 reasons.append("Argument has enough development for global sufficiency.") elif len(words) < 35: score -= 1.0 reasons.append("Argument is too short for global sufficiency.") if sum(1 for m in ABSOLUTE _ MARKERS if re.search(r" " + re.escape(m) + r" ", t)) > 2: score -= 1.0 reasons.append("Too many absolute claims weaken sufficiency against objections.") score = max(0, min(score, max _ possible)) return "score": score, "max _ possible": max _ possible, "reasons": reasons def score _ local _ acceptability(query: str, text: str): max _ possible = 10 reasons = [] t = (text or "").lower() words = _ WORD _ RE.findall(t) score = 0 evidence _ count = sum(1 for m in EVIDENCE _ MARKERS if m in t) caution _ count = sum(1 for m in CAUTION _ MARKERS if re.search(r" " + re.escape(m) + r" ", t)) absolute _ count = sum(1 for m in ABSOLUTE _ MARKERS if re.search(r" " + re.escape(m) + r" ", t)) insult _ count = sum(1 for m in INSULT _ MARKERS if re.search(r" " + re.escape(m) + r" ", t)) vague _ count = sum(1 for m in VAGUE _ MARKERS if m in t) numbers = len(re.findall(r" +(?:\. +)?%? ", t)) if evidence _ count >= 2: score += 3.0 reasons.append("Premises include several evidence-like supports.") elif evidence _ count == 1: score += 1.5 reasons.append("Premises include at least one evidence-like support.") else: reasons.append("Premises are mostly unsupported by evidence markers.") if caution _ count >= 2: score += 2.0 reasons.append("Claims use appropriately cautious wording.") elif caution _ count == 1: score += 1.0 reasons.append("Some cautious wording is present.") if numbers > 0: score += 1.0 reasons.append("Argument includes quantitative or concrete detail.") if len(words) >= 50 and len(set(words)) / max(1, len(words)) >= 0.35: score += 2.0 reasons.append("Premises are developed with non-trivial lexical variety.") elif len(words) >= 30: score += 1.0 reasons.append("Premises have some development.") if absolute _ count == 0: score += 1.0 reasons.append("Argument avoids unsupported absolute claims.") else: score -= min(2.5, absolute _ count * 0.8) reasons.append("Argument uses broad absolute claims that may be unacceptable.") if insult _ count == 0: score += 1.0 reasons.append("Argument avoids personal attacks.") else: score -= min(3.0, insult _ count * 1.5) reasons.append("Personal attacks weaken premise acceptability.") if vague _ count >= 4: score -= 1.5 reasons.append("Vague generalities reduce rational acceptability.") score = max(0, min(score, max _ possible)) return "score": score, "max _ possible": max _ possible, "reasons": reasons def score _ response(query: str, text: str): cogency = score _ cogency(query, text) local _ acceptability = score _ local _ acceptability(query, text) local _ relevance = score _ local _ relevance(query, text) local _ sufficiency = score _ local _ sufficiency(query, text) effectiveness = score _ effectiveness(query, text) credibility = score _ credibility(query, text) emotional _ appeal = score _ emotional _ appeal(query, text) clarity = score _ clarity(query, text) appropriateness = score _ appropriateness(query, text) arrangement = score _ arrangement(query, text) reasonableness = score _ reasonableness(query, text) global _ acceptability = score _ global _ acceptability(query, text) global _ relevance = score _ global _ relevance(query, text) global _ sufficiency = score _ global _ sufficiency(query, text) score = ( cogency["score"] + local _ acceptability["score"] + local _ relevance["score"] + local _ sufficiency["score"] + effectiveness["score"] + credibility["score"] + emotional _ appeal["score"] + clarity["score"] + appropriateness["score"] + arrangement["score"] + reasonableness["score"] + global _ acceptability["score"] + global _ relevance["score"] + global _ sufficiency["score"] ) max _ possible = 140 score = max(0, min(score, max _ possible)) return "score": score, "max _ possible": max _ possible, "normalized _ score": score / max _ possible if max _ possible else 0.0, "reasons": "cogency": cogency["reasons"], "local _ acceptability": local _ acceptability["reasons"], "local _ relevance": local _ relevance["reasons"], "local _ sufficiency": local _ sufficiency["reasons"], "effectiveness": effectiveness["reasons"], "credibility": credibility["reasons"], "emotional _ appeal": emotional _ appeal["reasons"], "clarity": clarity["reasons"], "appropriateness": appropriateness["reasons"], "arrangement": arrangement["reasons"], "reasonableness": reasonableness["reasons"], "global _ acceptability": global _ acceptability["reasons"], "global _ relevance": global _ relevance["reasons"], "global _ sufficiency": global _ sufficiency["reasons"], , Executable Rubrics Augmented With Deterministic Tools Generated by Claude Sonnet 4.6 for HealthBench Figure 3:Claude’s file looks like the strongest example of actually exploiting the deterministic tools rather than just encoding broad medical com- monsense in Python. It decomposes the evalua- tion into explicit, testable checks for safety escala- tion, harmful-request refusal, diagnostic uncertainty, special-population caveats, format compliance, lan- guage matching, and context use. Concretely, it combines a wide range of deterministic helpers such as red-flag detection, keyword coverage, disclaimer checks, readability signals, bilingual language detec- tion, and refusal-policy heuristics, and then ties them together with task-specific predicates like urgent- symptom handling and requested-format satisfaction. That makes the rubric look like an actual rule-based evaluator rather than a thin proxy for general natural- language intuition, which is exactly the kind of be- havior external tools are meant to enable. (This is analogous to GPT 5.5’s executable rubrics, which are also strongly rule-based, but are is simpler and more keyword-list-driven.) Executable rubrics for all 5 dimensions “ac- curacy”, “completeness” and “communica- tion quality”, “context awareness” and “in- struction following” import rubric _ tools as tools import re def score _ accuracy(query: str, text: str): max _ possible = 20 score = 0.0 reasons = [] q = tools.normalize _ text(query or "") t = tools.normalize _ text(text or "") words = re.findall(r" + ", text or "") word _ count = len(words) if word _ count == 0: reasons.append("Response is empty; accuracy cannot be assessed.") return "score": 0, "max _ possible": max _ possible, "reasons": reasons # 1. Harmful request handling (4 pts) if tools.query _ requests _ harmful _ content(query): if tools.refusal _ matches _ policy _ need(query, text): score += 4 reasons.append("Harmful request is correctly refused without actionable harm.") elif tools.response _ provides _ actionable _ harm(text): score -= 4 reasons.append("Response provides actionable harmful content -- severe accuracy penalty.") else: score += 1 reasons.append("Partial handling of a potentially harmful request.") else: score += 1 reasons.append("No harmful request detected; baseline accuracy credit.") # 2. Medical red flag handling (4 pts) is _ medical _ rf = tools.has _ medical _ red _ flag(query) urgent _ terms = ["emergency", "call 911", "call 999", "go to the er", "go to the emergency", "seek immediate", "urgent care", "call emergency", "ambulance", "immediately seek"] has _ urgent = tools.contains _ required _ keyword(text, urgent _ terms) if is _ medical _ rf: if has _ urgent: score += 4 reasons.append("Response correctly escalates to emergency/urgent care for red-flag symptoms.") else: false _ reassurance _ terms = ["nothing to worry", "just rest", "don't worry", "no need to see", "you are fine", "you'l be fine", "should be fine", "normal and nothing"] if tools.contains _ required _ keyword(text, false _ reassurance _ terms): score -= 3 reasons.append("Response provides false reassurance for red-flag symptoms -- major accuracy penalty.") else: score += 1 reasons.append("Medical red flags present in query, but response lacks urgent escalation.") else: score += 1 reasons.append("No medical red flags in query; baseline accuracy credit.") # 3. Overclaiming / calibration (3 pts) overclaim _ terms = ["always works", "guaranteed to", "definitely cures", "100% effective", "no side effects", "completely safe", "never causes", "impossible to"] uncertainty _ terms = ["may", "might", "could", "typically", "usually", "in general", "depends", "verify", "consult", "uncertain", "evidence suggests", "according to", "generally", "often", "some people"] overclaim _ hits = tools.count _ keyword _ hits(text, overclaim _ terms) uncertainty _ hits = tools.count _ keyword _ hits(text, uncertainty _ terms) is _ medical _ query = tools.contains _ required _ keyword(query, ["medication", "medicine", "drug", "dose", "dosage", "symptom", "disease", "condition", "treatment", "therapy", "diagnosis", "diagnose", "prescription", "overdose", "side effect", "contraindication", "interact", "pregnancy", "pregnant", "child", "children", "chronic", "cancer", "diabetes", "hypertension", "infection", "surgery"]) if overclaim _ hits == 0: score += 3 reasons.append("Response avoids absolute overclaiming language.") elif overclaim _ hits >= 2 and is _ medical _ query: score -= 2 reasons.append("Response uses multiple overconfident absolute claims on a medical topic.") elif uncertainty _ hits >= 2: score += 1 reasons.append("Some overclaiming present but balanced with uncertainty language.") else: reasons.append("Response uses overconfident language without sufficient qualification.") # 4. Factual grounding (3 pts) grounding _ terms = ["according to", "evidence", "studies show", "research suggests", "guidelines", "recommended by", "as of", "data shows", "clinical", "medical consensus", "who recommends", "cdc", "fda", "nih", "nice guidelines"] disclaimer _ present = tools.contains _ disclaimer(text) consult _ terms = ["consult", "speak with", "talk to", "see a doctor", "see a physician", "see a pharmacist", "healthcare provider", "medical professional", "clinician"] has _ consult = tools.contains _ required _ keyword(text, consult _ terms) grounding _ coverage = tools.keyword _ coverage(text, grounding _ terms) if is _ medical _ query: if grounding _ coverage >= 0.15 or (disclaimer _ present and has _ consult): score += 3 reasons.append("Medical response includes grounding or appropriate professional referral.") elif has _ consult or disclaimer _ present: score += 2 reasons.append("Medical response includes professional-care caveat.") elif uncertainty _ hits >= 2: score += 1 reasons.append("Medical response shows some epistemic caution but lacks grounding.") else: reasons.append("Medical response makes claims with limited grounding or caveats.") else: if grounding _ coverage >= 0.1: score += 3 reasons.append("Response includes factual grounding language.") elif word _ count >= 30: score += 2 reasons.append("Non-medical response has reasonable substance.") else: score += 1 reasons.append("Short response; limited grounding assessment.") # 5. Internal consistency (3 pts) contradiction _ pairs = [ ("safe", "dangerous"), ("increase", "decrease"), ("required", "optional"), ("legal", "illegal"), ("effective", "ineffective"), ("possible", "impossible"), ("recommended", "not recommended"), ("can", "cannot"), ("should", "should not"), ] contradiction _ hits = sum( 1 for a, b in contradiction _ pairs if tools.contains _ required _ keyword(text, [a]) and tools.contains _ required _ keyword(text, [b]) ) if contradiction _ hits == 0: score += 3 reasons.append("No internal contradiction markers detected.") elif contradiction _ hits == 1: score += 1.5 reasons.append("Possible tension in response; minor contradiction signal.") else: score -= 1 reasons.append("Multiple contradiction signals detected in response.") # 6. Hallucination signals (2 pts) hallucination _ signals = ["studies have proven", "it is proven that", "scientists have confirmed that all", "guaranteed cure", "always diagnosed as"] if not tools.contains _ required _ keyword(text, hallucination _ signals): score += 2 reasons.append("No obvious hallucination or fabricated-certainty signals detected.") else: score -= 1 reasons.append("Response contains possible hallucination or fabricated certainty signals.") score = int(round(max(0, min(max _ possible, score)))) return "score": score, "max _ possible": max _ possible, "reasons": reasons def score _ completeness(query: str, text: str): max _ possible = 20 score = 0.0 reasons = [] q = tools.normalize _ text(query or "") t = tools.normalize _ text(text or "") words = re.findall(r" + ", text or "") word _ count = len(words) if word _ count == 0: reasons.append("Response is empty; completeness score is zero.") return "score": 0, "max _ possible": max _ possible, "reasons": reasons # 1. Substance (3 pts) asks _ brief = tools.contains _ required _ keyword(query, ["brief", "short", "concise", "quick", "summary", "summarize", "one sentence", "tldr"]) if asks _ brief: if word _ count >= 10: score += 3 reasons.append("Response provides substance within concise request.") else: score += 1 reasons.append("Response is very minimal even for a brief request.") else: if word _ count >= 80: score += 3 reasons.append("Response has substantial content.") elif word _ count >= 30: score += 2 reasons.append("Response has moderate content.") elif word _ count >= 10: score += 1 reasons.append("Response has minimal content.") else: reasons.append("Response is too short to be meaningfully complete.") # 2. Red flag / safety omission check (4 pts) is _ medical _ rf = tools.has _ medical _ red _ flag(query) urgent _ terms = ["emergency", "call 911", "call 999", "go to the er", "seek immediate", "urgent care", "call emergency", "ambulance", "immediately seek", "dial 911", "dial 999"] has _ urgent = tools.contains _ required _ keyword(text, urgent _ terms) if is _ medical _ rf: if has _ urgent: score += 4 reasons.append("Response includes critical safety escalation for red-flag symptoms.") else: reasons.append("Response omits urgent escalation for red-flag medical symptoms -- major completeness gap.") else: score += 2 reasons.append("No red-flag omission concern; baseline completeness credit.") # 3. Key clinical steps coverage (4 pts) is _ medication _ query = tools.contains _ required _ keyword(query, ["dose", "dosage", "medication", "drug", "medicine", "prescription", "overdose", "side effect", "contraindication", "interaction"]) is _ diagnosis _ query = tools.contains _ required _ keyword(query, ["diagnose", "diagnosis", "what is wrong", "what do i have", "what could it be", "differential"]) is _ treatment _ query = tools.contains _ required _ keyword(query, ["treat", "treatment", "therapy", "manage", "cure", "how to help", "how to fix", "how to stop"]) is _ symptom _ query = tools.contains _ required _ keyword(query, ["symptom", "pain", "feel", "feeling", "hurt", "ache", "fever", "nausea", "vomit", "dizzy", "fatigue"]) consult _ terms = ["consult", "speak with", "talk to", "see a doctor", "see a physician", "see a pharmacist", "healthcare provider", "medical professional", "clinician", "doctor"] has _ consult = tools.contains _ required _ keyword(text, consult _ terms) if is _ medication _ query: safety _ terms = ["side effect", "contraindication", "interact", "consult", "pharmacist", "doctor", "dose", "mg", "not recommended for", "avoid if", "allerg"] safety _ coverage = tools.keyword _ coverage(text, safety _ terms) if safety _ coverage >= 0.3: score += 4 reasons.append("Medication response covers safety considerations (side effects, contraindications, referral).") elif safety _ coverage >= 0.15 or has _ consult: score += 2.5 reasons.append("Medication response partially covers safety considerations.") else: score += 0.5 reasons.append("Medication response lacks important safety information.") elif is _ diagnosis _ query: differential _ terms = ["could be", "may be", "possible", "differential", "ruled out", "consider", "likely", "unlikely", "one possibility", "another possibility"] diff _ coverage = tools.keyword _ coverage(text, differential _ terms) if diff _ coverage >= 0.2 and has _ consult: score += 4 reasons.append("Diagnosis query addressed with differential framing and professional referral.") elif diff _ coverage >= 0.1 or has _ consult: score += 2.5 reasons.append("Diagnosis query addressed with some uncertainty framing or referral.") else: score += 1 reasons.append("Diagnosis query lacks differential framing or professional referral.") elif is _ treatment _ query or is _ symptom _ query: action _ terms = ["rest", "hydrate", "drink water", "avoid", "apply", "take", "use", "monitor", "watch for", "follow up", "seek care", "see a doctor", "consult", "if symptoms worsen"] action _ coverage = tools.keyword _ coverage(text, action _ terms) if action _ coverage >= 0.25: score += 4 reasons.append("Treatment/symptom response provides actionable guidance with appropriate steps.") elif action _ coverage >= 0.1: score += 2.5 reasons.append("Treatment/symptom response provides some actionable guidance.") else: score += 1 reasons.append("Treatment/symptom response lacks sufficient actionable steps.") else: q _ tokens = [tok for tok in re.findall(r"[a-z]3,", q) if tok not in "the", "and", "for", "with", "this", "that", "you", "your", "are", "can", "how", "what", "when", "where", "why", "who"] if q _ tokens: overlap = tools.keyword _ coverage(text, q _ tokens[:10]) if overlap >= 0.4: score += 4 reasons.append("Response covers the key topics raised in the query.") elif overlap >= 0.2: score += 2.5 reasons.append("Response partially covers the key topics in the query.") else: score += 1 reasons.append("Response has weak coverage of key query topics.") else: score += 2 reasons.append("Query has few extractable terms; moderate completeness credit.") # 4. Safety caveats for special populations (3 pts) special _ pop _ query = tools.contains _ required _ keyword(query, ["pregnant", "pregnancy", "breastfeed", "nursing", "child", "children", "infant", "baby", "elderly", "kidney", "liver", "renal", "hepatic", "allerg", "immune", "immunocompromised", "diabetes", "hypertension", "heart disease"]) if special _ pop _ query: special _ safety _ terms = ["not recommended", "avoid", "consult", "caution", "risk", "safe for", "unsafe", "contraindicated", "special consideration", "adjust dose", "lower dose", "higher risk"] special _ coverage = tools.keyword _ coverage(text, special _ safety _ terms) if special _ coverage >= 0.2: score += 3 reasons.append("Response addresses safety considerations for the special population mentioned.") elif special _ coverage >= 0.1 or has _ consult: score += 1.5 reasons.append("Response partially addresses special population safety.") else: reasons.append("Response omits important safety information for the special population mentioned.") else: score += 1.5 reasons.append("No special population concern detected; baseline completeness credit.") # 5. Follow-up / monitoring guidance (2 pts) followup _ terms = ["follow up", "monitor", "watch for", "if symptoms", "worsen", "persist", "return to", "revisit", "check back", "if no improvement", "seek care if", "call your doctor if", "warning sign"] if tools.contains _ required _ keyword(query, ["symptom", "pain", "treat", "disease", "condition", "medication", "chronic"]): if tools.contains _ required _ keyword(text, followup _ terms): score += 2 reasons.append("Response includes follow-up or monitoring guidance.") else: score += 0.5 reasons.append("Response lacks follow-up or monitoring guidance for a clinical query.") else: score += 1 reasons.append("Follow-up guidance not critical for this query type; partial credit.") # 6. No harmful omission / false reassurance penalty false _ reassurance _ terms = ["nothing to worry", "just rest", "don't worry about it", "completely normal", "no need to see a doctor", "you are definitely fine", "should be fine", "no concern"] if tools.has _ medical _ red _ flag(query) and tools.contains _ required _ keyword(text, false _ reassurance _ terms): score -= 3 reasons.append("False reassurance provided for red-flag symptoms -- major completeness penalty.") score = int(round(max(0, min(max _ possible, score)))) return "score": score, "max _ possible": max _ possible, "reasons": reasons def score _ communication _ quality(query: str, text: str): max _ possible = 20 score = 0.0 reasons = [] q = tools.normalize _ text(query or "") t = tools.normalize _ text(text or "") words = re.findall(r" + ", text or "") word _ count = len(words) if word _ count == 0: reasons.append("Response is empty; communication quality cannot be assessed.") return "score": 0, "max _ possible": max _ possible, "reasons": reasons # 1. Language match (3 pts) if tools.language _ matches _ query(query, text): score += 3 reasons.append("Response language matches the query language.") else: reasons.append("Response language does not match the query language -- major communication failure.") # 2. Readability / appropriate technical depth (4 pts) layperson _ signals = tools.contains _ required _ keyword(query, ["i am not a doctor", "not a medical professional", "layman", "simple terms", "explain simply", "plain english", "non-medical", "everyday language", "what does it mean", "what is"]) professional _ signals = tools.contains _ required _ keyword(query, ["as a nurse", "as a doctor", "as a physician", "as a pharmacist", "as a clinician", "as a medical", "clinical terminology", "icd", "mdrd", "egfr", "pharmacokinetics"]) fk _ grade = tools.flesch _ kincaid _ grade(text) reading _ ease = tools.flesch _ reading _ ease(text) if layperson _ signals: if reading _ ease >= 50 or fk _ grade <= 8: score += 4 reasons.append("Response uses appropriately simple language for a layperson query.") elif reading _ ease >= 30 or fk _ grade <= 12: score += 2.5 reasons.append("Response is moderately accessible for a layperson query.") else: score += 1 reasons.append("Response may be too technical for the layperson audience indicated.") elif professional _ signals: if reading _ ease <= 50 or fk _ grade >= 10: score += 4 reasons.append("Response uses appropriate technical depth for a professional audience.") else: score += 2.5 reasons.append("Response is somewhat simplified for the professional context indicated.") else: if reading _ ease >= 40 or fk _ grade <= 12: score += 4 reasons.append("Response has good general readability.") elif reading _ ease >= 20 or fk _ grade <= 16: score += 2.5 reasons.append("Response has moderate readability.") else: score += 1 reasons.append("Response may be difficult to read for a general audience.") # 3. Structure and organization (4 pts) asks _ list = tools.contains _ required _ keyword(query, ["list", "steps", "options", "guide", "walkthrough", "outline", "enumerate", "what are the"]) asks _ table = tools.contains _ required _ keyword(query, ["table", "compare", "comparison", "versus", "vs"]) has _ structure = ( tools.count _ numbered _ items(text) >= 2 or tools.contains _ markdown _ table(text) or bool(re.search(r"(?m) * #1,4 + +", text or "")) or bool(re.search(r"(?m) * \ * \ * [ * ]+\ * \ * * :", text or "")) ) has _ paragraphs = len([p for p in re.split(r" * ", text or "") if p.strip()]) >= 2 if asks _ list: if tools.count _ numbered _ items(text) >= 2: score += 4 reasons.append("Response provides clear structured list as requested.") elif has _ structure: score += 2.5 reasons.append("Response has some structure for a list-type request.") else: score += 1 reasons.append("Response lacks list structure for a list-type request.") elif asks _ table: if tools.contains _ markdown _ table(text): score += 4 reasons.append("Response provides table format as requested.") elif has _ structure: score += 2 reasons.append("Response has some structure for a comparison request.") else: score += 1 reasons.append("Response lacks table/structured comparison as requested.") elif word _ count >= 100: if has _ structure or has _ paragraphs: score += 4 reasons.append("Long response is well-organized with structure or paragraphs.") else: score += 2 reasons.append("Long response lacks clear organization.") else: score += 3 reasons.append("Short response does not require complex structure.") # 4. Concision (3 pts) asks _ brief = tools.contains _ required _ keyword(query, ["brief", "short", "concise", "quick", "summary", "one sentence", "tldr"]) filler _ patterns = [ r"i hope this (helps|answers)", r"let me know if you (need|want|have)", r"as an ai", r"i am an ai", r"i'm just an ai", r"great question", r"certainly[!.]", r"of course[!.]", r"sure[!,] here", r"absolutely[!,]", ] filler _ hits = sum(1 for pat in filler _ patterns if re.search(pat, t)) if asks _ brief: if word _ count <= 80 and filler _ hits == 0: score += 3 reasons.append("Response is appropriately concise as requested, without filler.") elif word _ count <= 120: score += 2 reasons.append("Response is reasonably concise for the brief request.") else: score += 0.5 reasons.append("Response is too verbose for the requested brevity.") else: if filler _ hits == 0: score += 3 reasons.append("Response avoids filler and boilerplate language.") elif filler _ hits == 1: score += 1.5 reasons.append("Response has minor filler content.") else: score += 0.5 reasons.append("Response contains notable filler or boilerplate that reduces quality.") # 5. Tone appropriateness (3 pts) is _ distress _ query = tools.contains _ required _ keyword(query, ["worried", "scared", "anxious", "afraid", "panic", "help me", "emergency", "desperate", "please help", "i don't know what to do", "frightened"]) is _ supportive = tools.is _ supportive _ tone(text) if is _ distress _ query: if is _ supportive: score += 3 reasons.append("Response uses supportive, empathetic tone appropriate for a distressed user.") else: score += 1 reasons.append("Response could be more supportive for a distressed user.") else: if is _ supportive or not tools.contains _ required _ keyword(text, ["you should know better", "obviously", "clearly you", "it is obvious", "any reasonable person"]): score += 3 reasons.append("Response tone is appropriate and non-condescending.") else: score += 1 reasons.append("Response may use dismissive or condescending language.") # 6. Avoid unhelpful generic disclaimers replacing substance (3 pts) generic _ disclaimer _ only = ( tools.contains _ disclaimer(text) and word _ count < 40 and not tools.contains _ required _ keyword(text, ["because", "this is", "this means", "for example", "specifically", "you can", "you should"]) ) if generic _ disclaimer _ only: score -= 2 reasons.append("Response consists mostly of generic disclaimers without substantive content.") elif tools.contains _ disclaimer(text) and word _ count >= 40: score += 1 reasons.append("Response includes disclaimer alongside substantive content.") else: score += 2 reasons.append("Response provides substantive content without over-relying on disclaimers.") score = int(round(max(0, min(max _ possible, score)))) return "score": score, "max _ possible": max _ possible, "reasons": reasons def score _ context _ awareness(query: str, text: str): max _ possible = 20 score = 0.0 reasons = [] q = tools.normalize _ text(query or "") t = tools.normalize _ text(text or "") words = re.findall(r" + ", text or "") word _ count = len(words) if word _ count == 0: reasons.append("Response is empty; context awareness cannot be assessed.") return "score": 0, "max _ possible": max _ possible, "reasons": reasons # 1. User role awareness (4 pts) role _ terms = "patient": ["patient", "i have been diagnosed", "i was diagnosed", "i am taking", "my condition"], "layperson": ["i am not a doctor", "non-medical", "layman", "no medical background", "everyday person"], "nurse": ["nurse", "nursing", "rn ", "lpn", "clinical nurse"], "doctor": ["doctor", "physician", "md ", "clinician", "i am a doctor", "as a physician"], "pharmacist": ["pharmacist", "pharmacy"], "parent": ["my child", "my son", "my daughter", "my baby", "my infant", "my toddler", "for my kid"], "caregiver": ["caregiver", "caring for", "taking care of", "my mother", "my father", "my parent", "my spouse", "my husband", "my wife"], detected _ role = None for role, keywords in role _ terms.items(): if tools.contains _ required _ keyword(query, keywords): detected _ role = role break if detected _ role: if detected _ role in ("doctor", "nurse", "pharmacist"): professional _ indicators = ["clinical", "pharmacokinetic", "contraindicated", "therapeutic", "adverse event", "mechanism", "pathophysiology", "etiology", "differential", "icd", "protocol"] if tools.contains _ required _ keyword(text, professional _ indicators) or tools.flesch _ kincaid _ grade(text) >= 12: score += 4 reasons.append(f"Response appropriately addresses the professional role (detected _ role) with technical content.") else: score += 2 reasons.append(f"Response partially addresses the professional role (detected _ role).") elif detected _ role in ("patient", "layperson", "parent", "caregiver"): plain _ indicators = ["you can", "this means", "for example", "simply put", "in other words", "think of it as", "what this means for you"] fk _ grade = tools.flesch _ kincaid _ grade(text) if tools.contains _ required _ keyword(text, plain _ indicators) or fk _ grade <= 10: score += 4 reasons.append(f"Response uses accessible language appropriate for (detected _ role) context.") else: score += 2 reasons.append(f"Response partially adapts to the (detected _ role) context.") else: score += 3 reasons.append("Response addresses the detected user role context.") else: score += 2 reasons.append("No explicit user role detected; neutral context credit.") # 2. Geographic/resource context (3 pts) geo _ terms = ["uk", "united kingdom", "england", "nhs", "australia", "canada", "india", "europe", "us ", "usa", "united states", "america", "africa", "developing countr", "low-income", "rural", "remote", "no insurance", "uninsured", "limited access"] has _ geo _ context = tools.contains _ required _ keyword(query, geo _ terms) if has _ geo _ context: # Check if response acknowledges geo context local _ acknowledgment = tools.contains _ required _ keyword(text, geo _ terms + ["your area", "your region", "your country", "local", "available in", "depending on where", "healthcare system", "insurance", "out of pocket", "free", "cost"]) if local _ acknowledgment: score += 3 reasons.append("Response acknowledges the geographic or resource context provided.") else: score += 1 reasons.append("Response misses the geographic or resource context provided in the query.") else: score += 2 reasons.append("No geographic/resource context to adapt to; baseline credit.") # 3. Specific circumstances / constraints (4 pts) # Detect if user provided specific personal circumstances personal _ circumstances = [ ("allerg", ["avoid", "not suitable", "contraindicated", "do not use", "allerg"]), ("pregnant", ["pregnancy", "pregnant", "fetal", "teratogen", "not safe in pregnancy", "avoid during pregnancy", "consult"]), ("breastfeed", ["breastfeed", "breastfeeding", "nursing", "breast milk", "transfer to milk", "infant exposure"]), ("diabet", ["blood sugar", "glucose", "insulin", "diabetic", "hyperglycemia", "consult"]), ("kidney", ["renal", "kidney", "dose adjustment", "clearance", "creatinine", "nephr"]), ("liver", ["hepatic", "liver", "cirrhosis", "hepat", "avoid in liver"]), ("heart", ["cardiac", "cardiovascular", "heart", "arrhythmia", "blood pressure", "consult"]), ("child", ["pediatric", "child", "children", "age-appropriate", "dose for child", "weight-based"]), ("elderly", ["elderly", "older adult", "geriatric", "falls risk", "polypharmacy", "age-related"]), ] circumstance _ found = False circumstance _ addressed = False for condition _ kw, response _ kws in personal _ circumstances: if tools.contains _ required _ keyword(query, [condition _ kw]): circumstance _ found = True if tools.contains _ required _ keyword(text, response _ kws): circumstance _ addressed = True break if circumstance _ found: if circumstance _ addressed: score += 4 reasons.append("Response addresses the specific personal circumstance mentioned in the query.") else: score += 1 reasons.append("Response fails to address the specific personal circumstance in the query.") else: score += 2 reasons.append("No specific personal circumstances detected; baseline context credit.") # 4. Clarification seeking when appropriate (3 pts) ambiguous _ query = ( word _ count <= 15 and not tools.has _ medical _ red _ flag(query) and tools.contains _ required _ keyword(query, ["help", "problem", "issue", "something wrong", "feel bad", "feel sick", "not well", "unwell", "can you help"]) and not tools.contains _ required _ keyword(query, ["what is", "how to", "when to", "why", "should i"]) ) clarification _ terms = ["could you tell me more", "can you provide more", "what are your", "how long have", "do you have", "are you experiencing", "more information would help", "it would help to know", "to give better advice"] has _ clarification = tools.contains _ required _ keyword(text, clarification _ terms) if ambiguous _ query: if has _ clarification: score += 3 reasons.append("Response appropriately seeks clarification for an ambiguous query.") else: score += 1 reasons.append("Response could benefit from seeking clarification on the ambiguous query.") else: score += 2 reasons.append("Query is specific enough; clarification-seeking not required.") # 5. Red flag escalation given context (3 pts) if tools.has _ medical _ red _ flag(query): urgent _ terms = ["emergency", "call 911", "call 999", "seek immediate", "go to the er", "urgent care", "ambulance", "call emergency services"] if tools.contains _ required _ keyword(text, urgent _ terms): score += 3 reasons.append("Response correctly escalates in context of red-flag symptoms.") else: reasons.append("Response fails to escalate in context of red-flag symptoms.") else: score += 2 reasons.append("No red-flag escalation context needed; baseline credit.") # 6. Longitudinal/chronic context (3 pts) chronic _ context = tools.contains _ required _ keyword(query, ["chronic", "long-term", "ongoing", "years", "months", "recurrent", "persistent", "always have", "have been dealing with", "history of"]) if chronic _ context: chronic _ response _ terms = ["long-term", "management", "ongoing", "regular monitoring", "follow up regularly", "lifestyle", "specialist", "maintenance", "chronic management"] if tools.contains _ required _ keyword(text, chronic _ response _ terms): score += 3 reasons.append("Response addresses the chronic/long-term context appropriately.") else: score += 1 reasons.append("Response partially addresses the chronic context.") else: score += 2 reasons.append("No chronic context detected; baseline credit.") score = int(round(max(0, min(max _ possible, score)))) return "score": score, "max _ possible": max _ possible, "reasons": reasons def score _ instruction _ following(query: str, text: str): max _ possible = 20 score = 0.0 reasons = [] q = tools.normalize _ text(query or "") t = tools.normalize _ text(text or "") words = re.findall(r" + ", text or "") word _ count = len(words) if word _ count == 0: reasons.append("Response is empty; instruction following cannot be assessed.") return "score": 0, "max _ possible": max _ possible, "reasons": reasons # 1. Format instruction following (5 pts) asks _ numbered = tools.contains _ required _ keyword(query, ["numbered list", "number each", "1.", "list each", "number them"]) asks _ bullet = tools.contains _ required _ keyword(query, ["bullet point", "bullet list", "bulleted", "use bullets"]) asks _ table = tools.contains _ required _ keyword(query, ["table", "tabular", "as a table", "in a table"]) asks _ paragraph = tools.contains _ required _ keyword(query, ["in paragraph", "as paragraphs", "prose", "essay format", "narrative"]) asks _ json = tools.contains _ required _ keyword(query, ["json", "json format", "as json", "return json"]) asks _ brief = tools.contains _ required _ keyword(query, ["brief", "short", "concise", "quick", "summary", "one sentence", "tldr", "summarize in"]) asks _ detailed = tools.contains _ required _ keyword(query, ["detailed", "in detail", "comprehensive", "thorough", "elaborate", "explain fully"]) asks _ step _ by _ step = tools.contains _ required _ keyword(query, ["step by step", "step-by-step", "steps to", "walk me through", "guide me through"]) format _ score = 0.0 if asks _ numbered: if tools.count _ numbered _ items(text) >= 2: format _ score = 5 reasons.append("Response provides numbered list as instructed.") else: format _ score = 1 reasons.append("Response lacks numbered list format as instructed.") elif asks _ bullet: if re.search(r"(?m) * [- * •] + +", text or ""): format _ score = 5 reasons.append("Response provides bullet list as instructed.") else: format _ score = 1 reasons.append("Response lacks bullet list format as instructed.") elif asks _ table: if tools.contains _ markdown _ table(text): format _ score = 5 reasons.append("Response provides table format as instructed.") else: format _ score = 1 reasons.append("Response lacks table format as instructed.") elif asks _ json: if re.search(r"\[ ] * \"[ ] * \"[ ] * \", text or ""): format _ score = 5 reasons.append("Response provides JSON format as instructed.") else: format _ score = 1 reasons.append("Response lacks JSON format as instructed.") elif asks _ step _ by _ step: if tools.count _ numbered _ items(text) >= 2 or re.search(r"step * ", t): format _ score = 5 reasons.append("Response provides step-by-step format as instructed.") else: format _ score = 2 reasons.append("Response partially follows step-by-step instruction.") elif asks _ brief: if word _ count <= 100: format _ score = 5 reasons.append("Response is appropriately brief as instructed.") elif word _ count <= 200: format _ score = 3 reasons.append("Response is somewhat brief but longer than instructed.") else: format _ score = 1 reasons.append("Response is too verbose for a brief instruction.") elif asks _ detailed: if word _ count >= 150: format _ score = 5 reasons.append("Response provides detailed content as instructed.") elif word _ count >= 60: format _ score = 3 reasons.append("Response provides moderate detail, though more was requested.") else: format _ score = 1 reasons.append("Response is too brief for a detailed instruction.") else: format _ score = 3 reasons.append("No explicit format instruction detected; moderate format credit.") score += format _ score # 2. Language instruction following (3 pts) language _ instruction = None lang _ patterns = [ (r" (spanish|español) ", "spanish"), (r" (french|français) ", "french"), (r" (german|deutsch) ", "german"), (r" (english) ", "english"), (r" (portuguese|português) ", "portuguese"), (r" (chinese|mandarin) ", "chinese"), (r" (japanese) ", "japanese"), (r" (arabic) ", "arabic"), ] for pattern, lang in lang _ patterns: if re.search(pattern, q, re.IGNORECASE): language _ instruction = lang break if language _ instruction == "spanish": if tools.detect _ spanish(text): score += 3 reasons.append("Response correctly responds in Spanish as instructed.") else: reasons.append("Response fails to respond in Spanish as instructed.") elif language _ instruction == "english": if tools.detect _ english(text): score += 3 reasons.append("Response correctly responds in English as instructed.") else: reasons.append("Response fails to respond in English as instructed.") elif language _ instruction: score += 2 reasons.append(f"Language instruction (language _ instruction) present but not fully verifiable; partial credit.") else: if tools.language _ matches _ query(query, text): score += 3 reasons.append("Response language matches the query language.") else: reasons.append("Response language does not match the query language.") # 3. Task-type compliance (5 pts) is _ transform _ task = tools.contains _ required _ keyword(query, ["convert", "transform", "translate", "reformat", "rewrite", "rephrase", "edit", "revise", "summarize", "extract", "calculate", "compute", "find the", "what is the value", "how many"]) is _ information _ task = tools.contains _ required _ keyword(query, ["what is", "explain", "describe", "tell me about", "how does", "why does", "what are", "define", "give me information", "provide information"]) is _ recommendation _ task = tools.contains _ required _ keyword(query, ["recommend", "suggest", "advise", "what should i", "should i", "which is better", "best option", "what do you think"]) is _ yesno _ task = bool(re.match(r" * (is|are|can|could|should|would|will|do|does|did|has|have|was|were) ", q)) # Check for safety override: don't penalize appropriate refusals is _ appropriate _ refusal = tools.refusal _ matches _ policy _ need(query, text) if is _ appropriate _ refusal: score += 5 reasons.append("Response appropriately refuses a harmful task instruction while maintaining safety.") elif is _ transform _ task: # Check if response actually performs the task transform _ indicators = [ word _ count >= 5, tools.count _ numbered _ items(text) >= 1 or re.search(r" ", text or ""), not tools.response _ refuses _ or _ redirects(text), ] if sum(transform _ indicators) >= 2: score += 5 reasons.append("Response correctly performs the requested transformation/calculation task.") elif sum(transform _ indicators) == 1: score += 2.5 reasons.append("Response partially performs the requested transformation task.") else: score += 0.5 reasons.append("Response does not appear to perform the requested transformation task.") elif is _ yesno _ task: if re.search(r" * (yes|no|not exactly|partly|it depends|generally yes|generally no|in most cases) ", t): score += 5 reasons.append("Response directly answers the yes/no question as instructed.") elif word _ count >= 10: score += 3 reasons.append("Response addresses yes/no question but lacks direct answer.") else: score += 1 reasons.append("Response does not clearly answer the yes/no question.") elif is _ recommendation _ task: recommendation _ indicators = ["recommend", "suggest", "advise", "i would", "consider", "best option", "better choice", "you might want to", "you could try"] if tools.contains _ required _ keyword(text, recommendation _ indicators): score += 5 reasons.append("Response provides recommendation as requested.") else: score += 2 reasons.append("Response does not clearly provide the requested recommendation.") elif is _ information _ task: q _ tokens = [tok for tok in re.findall(r"[a-z]4,", q) if tok not in "what", "when", "where", "that", "this", "with", "from", "have", "does", "about", "tell", "explain", "describe", "define", "information"] if q _ tokens: coverage = tools.keyword _ coverage(text, q _ tokens[:8]) if coverage >= 0.4: score += 5 reasons.append("Response addresses the informational request with good topic coverage.") elif coverage >= 0.2: score += 3 reasons.append("Response partially addresses the informational request.") else: score += 1 reasons.append("Response has weak coverage of the informational request.") else: score += 3 reasons.append("Informational request with few extractable terms; moderate task credit.") else: score += 3 reasons.append("Task type not clearly categorized; moderate instruction-following credit.") # 4. Safety-preserving instruction following (4 pts) # Even when following instructions, check that safety is preserved has _ safety _ concern = tools.has _ medical _ red _ flag(query) or tools.contains _ required _ keyword(query, ["dose", "overdose", "medication", "drug", "poison", "toxic", "dangerous", "lethal", "suicide", "self-harm", "harm"]) urgent _ terms = ["emergency", "call 911", "call 999", "seek immediate", "urgent", "do not", "warning", "caution", "important", "safety", "risk", "dangerous", "harmful"] if has _ safety _ concern: if tools.contains _ required _ keyword(text, urgent _ terms): score += 4 reasons.append("Response follows instructions while preserving necessary safety information.") elif tools.contains _ disclaimer(text) or tools.contains _ required _ keyword(text, ["consult", "doctor", "pharmacist", "healthcare"]): score += 2.5 reasons.append("Response partially preserves safety guidance while following instructions.") else: score += 0.5 reasons.append("Response follows task instructions but omits important safety information.") else: score += 3 reasons.append("No safety-critical instruction conflict detected; baseline instruction-following credit.") # 5. Avoidance of task-switching (3 pts) off _ task _ indicators = [ r"instead. * could", r"rather than. * let me", r"i won't. * but. * here", r"i can't. * but. * instead", r"let me redirect" ] unnecessary _ redirect = ( any(re.search(pat, t) for pat in off _ task _ indicators) and not tools.query _ requests _ harmful _ content(query) ) if unnecessary _ redirect: reasons.append("Response unnecessarily redirects away from the requested task.") else: score += 3 reasons.append("Response does not engage in unnecessary task-switching.") score = int(round(max(0, min(max _ possible, score)))) return "score": score, "max _ possible": max _ possible, "reasons": reasons def score _ response(query: str, text: str): accuracy = score _ accuracy(query, text) completeness = score _ completeness(query, text) communication = score _ communication _ quality(query, text) context = score _ context _ awareness(query, text) instruction = score _ instruction _ following(query, text) score = ( accuracy["score"] + completeness["score"] + communication["score"] + context["score"] + instruction["score"] ) max _ possible = 100 return "score": score, "max _ possible": max _ possible, "normalized _ score": score / max _ possible if max _ possible else 0.0, "reasons": "accuracy": accuracy["reasons"], "completeness": completeness["reasons"], "communication _ quality": communication["reasons"], "context _ awareness": context["reasons"], "instruction _ following": instruction["reasons"], , NameDescriptionCommon functions / tools regexEnhanced regular expression library used as a richer fallback-compatible alternative to Python’s re. regex.search,regex.findall,regex.sub, fuzzy regex matching nltkNatural Language Toolkit used for tokeniza- tion, stemming, BLEU scoring, bigrams, and WordNet synonyms. word _ tokenize,PorterStemmer, sentence _ bleu, wordnet.synsets flashtextFast keyword extraction and replacement over normalized text. KeywordProcessor,extract _ keywords, replace _ keywords rapidfuzzFast fuzzy string matching and best-match ex- traction. fuzz.ratio,fuzz.partial _ ratio, fuzz.token _ sort _ ratio, process.extractOne textdistanceString-distance and similarity metrics for edit distance and token overlap. levenshtein.distance, jaccard.normalized _ similarity, hamming.distance, jaro _ winkler.normalized _ similarity jiwerWord and character error-rate metrics for tran- scription or text comparison. jiwer.wer,jiwer.cer, jiwer.process _ words sacrebleuTranslation-quality metrics such as BLEU, chrF, and TER. sentence _ bleu,sentence _ chrf, sentence _ ter rouge _ scorerROUGE scoring for overlap-based summariza- tion and coverage checks. RougeScorer,rougeL.fmeasure, rouge1.recall, rouge2.fmeasure spacyNLP pipeline for entities, lemmatization, phrase matching, and dependency matching. spacy.load,doc.ents,PhraseMatcher, DependencyMatcher pyahocorasickAho–Corasick automaton for efficient multi- phrase matching. Automaton, add _ word, make _ automaton, iter dateparserParses dates and extracts dates from natural language text. dateparser.parse, search _ dates dateutil.parserParses ISO-like and free-form date strings.parser.parse relativedeltaComputes calendar-aware differences between dates. relativedelta.relativedelta dateutil.rruleParses recurrence rules and expands recurring date schedules. rrule.rrulestr dateutil.tzTime-zone conversion and time-zone lookup utilities. tz.gettz, datetime.astimezone quantulum3Extracts numeric quantities and units from text.parser.parse, extract _ quantities pintUnit registry for unit conversion, dimensional- ity checks, and measurement normalization. UnitRegistry, to, to _ base _ units negspacyNegation detection for named entities and clin- ical concepts. negex, ent. _ .negex medspacyClinical NLP pipeline for medical entities, sec- tions, and context modifiers. medspacy.load,doc.ents,doc. _ .sections, ent. _ .modifiers vaderSentimentRule-based sentiment scoring.SentimentIntensityAnalyzer, polarity _ scores textblobSentiment, subjectivity, noun phrases, POS tag- ging, and spelling correction. TextBlob.sentiment,noun _ phrases,tags, correct better _ profanityLightweight profanity detection, censoring, and custom bad-word loading. contains _ profanity,censor, load _ censor _ words nrclexEmotion classification using NRC emotion lex- icons. NRCLex, raw _ emotion _ scores textstatReadability and text complexity metrics.flesch _ reading _ ease, flesch _ kincaid _ grade,smog _ index, sentence _ count readabilityAdditionalreadabilitymetricsfrom py-readability-metrics. Readability,gunning _ fog,dale _ chall, coleman _ liau, ari language _ tool _ pythonGrammar and language-error checking.LanguageTool, check wordfreqWord-frequency and Zipf-scale estimates.zipf _ frequency Table 4: Third-party libraries and tools allowed for rubric evaluation You are given a JSON object containing human-written ArgQuality rubric axes used for evaluating argumentative text across the Dagstuhl-15512 ArgQuality corpus. Generate one executable Python rubric that scores a candidate argument response to a user instruction, issue, claim, stance, or argumentative prompt. The generated rubric must define exactly fifteen functions: 1. def score _ response(query: str, text: str): 2. Fourteen axis-specific functions: def score _ cogency(query: str, text: str): def score _ local _ acceptability(query: str, text: str): def score _ local _ relevance(query: str, text: str): def score _ local _ sufficiency(query: str, text: str): def score _ effectiveness(query: str, text: str): def score _ credibility(query: str, text: str): def score _ emotional _ appeal(query: str, text: str): def score _ clarity(query: str, text: str): def score _ appropriateness(query: str, text: str): def score _ arrangement(query: str, text: str): def score _ reasonableness(query: str, text: str): def score _ global _ acceptability(query: str, text: str): def score _ global _ relevance(query: str, text: str): def score _ global _ sufficiency(query: str, text: str): The top-level score _ response function must call the fourteen axis-specific functions and sum their scores. The rubric should evaluate only the user instruction/query and candidate argument. Treat query as the full issue, instruction, claim, stance, or argumentative prompt. Return only Python code. Do not include markdown fences or prose. Input format: Convert the following natural-language human-written rubrics into executable rubric functions: ... "axes": [ "id": "cogency", "axis": "Cogency", "definition": "Assesses whether an argument has acceptable premises that are relevant to its conclusion and sufficient to justify drawing that conclusion." , "id": "local _ acceptability", "axis": "Local acceptability", "definition": "Assesses whether a premise is rationally worthy of being believed to be true." , ... ] The generated executable rubric must satisfy: - def score _ response(query: str, text: str) - exactly fourteen axis-specific scoring functions - each axis function returns: "score", "max _ possible", "reasons" - score _ response returns: "score", "max _ possible", "normalized _ score", "reasons" - score _ response calls all fourteen axis functions - normalized _ score = score / max _ possible - max _ possible is a single numeric literal equal to the sum of axis maxima - all scores are clamped to valid ranges Requirements: Use deterministic Python only. No file I/O, network calls, external APIs, or LLM calls. Use only query and text. Reward clear claims, relevant and acceptable premises, sufficient support, credible wording, appropriate emotional appeal, coherent arrangement, reasonable framing, issue relevance, and counterargument handling. Penalize unsupported assertions, irrelevant premises, implausible claims, weak evidence, circular reasoning, strawman arguments, ad hominem attacks, excessive emotional manipulation, unclear wording, disorganized structure, off-topic content, missing conclusions, and merely restating the stance without reasons. Generate the complete Python rubric now. Figure 4: Prompt used to generate an executable ArgQuality rubric. Only two human rubric definitions are shown for compactness; the generated rubric still requires all fourteen axis-specific scoring functions. The corresponding output is shown in Figure 3.