{"id":"cf623e15-f58f-4e65-ae68-5275d454d514","arxiv_id":"2506.20815","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"A dynamic prompt recommendation system for skill-based security copilots combines retrieval, hierarchical skill selection, and telemetry-based ranking, reporting high usefulness in internal evaluations.","lead":"This paper describes a system that suggests useful prompts to users of a security-focused AI copilot, using retrieval, hierarchical skill selection, and behavioral data to rank suggestions. The authors report high usefulness in automated and expert evaluations across several model configurations.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Central claim of high usefulness rests on an unvalidated, single-rater manual evaluation and a baseline-free comparison, so the reported 96%+ usefulness rates do not yet establish the claim.","rationale":"I read the manuscript in good faith and agree with the reader's conditional verdict. The architecture and experimental design are coherent, and the paper is honest about limitations (e.g., Section 5 admits no direct feasibility metric and no well-defined diversity metric; the Acknowledgements disclose the single expert). The central claim is not claimed to be impossible or internally inconsistent — the reported numbers are internally consistent with the rubrics as described. The load-bearing weakness is specifically epistemic: the measurement of the paper's headline outcome (usefulness) rests on (a) an automated rubric that is under-specified and not validated against an external standard, and (b) manual ratings from a single product-affiliated expert. The reader's weakest_assumption identifies exactly this: the rubric and single-expert ratings are assumed valid without evidence. I agree with that assessment. An additional load-bearing issue I would emphasize is the absence of any external baseline or control: even if the ratings are valid, 'high usefulness' has no comparative frame, and Section 4.4's claim of 'significant improvements over existing approaches' is not demonstrated by any of the reported experiments, since all three configurations are variants of the same proposed architecture. However, these are addressable empirical gaps rather than demonstrated errors; they do not refute the architecture's plausibility. Hence the verdict should remain CONDITIONAL, not REJECT: the paper does not currently establish the central claim at the strength it asserts, but the claim could plausibly be established with the added evaluations described in the concrete test. I do not see a basis for moving to ACCEPT or REJECT; the concern is precisely that the evidence is insufficient as presented, so CONDITIONAL is the appropriate verdict.","tokens_in":8153,"tokens_out":2881,"duration_ms":23246,"concrete_test":"Re-run the manual evaluation with at least three independent, domain-qualified annotators (blinded to model configuration and to each other's ratings) on the same 152 chats, and compute Krippendorff's alpha or Cohen's kappa among them; if agreement is low (alpha < 0.6) or the product-affiliated rater's scores are systematically higher than independent raters, the 96%+ usefulness figures do not support the central claim. Additionally, add a static-baseline condition (e.g., top-5 hand-curated prompts per plugin) and report per-configuration 95% confidence intervals for usefulness; if the static baseline matches or exceeds the proposed system, the claimed advantage over existing approaches collapses.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The paper's central claim is that the system achieves high usefulness and relevance for prompt suggestions in a security copilot, with manual usefulness above 96% across configurations (Section 4.3, Table 2) and automated usefulness around 88% (Table 1). The single most load-bearing assumption is that the measurement instruments actually capture prompt usefulness: (1) The automated rubric scores come from an author-defined rubric whose levels are only partially shown ('Due to page constraints, here's an example rubric for overall usefulness', Section 4.1), so the remaining metric definitions are not exposed; (2) The manual evaluation is conducted by exactly one security expert, Jessen Kurien, acknowledged as conducting 'all manual quality evaluations' — a product-affiliated single annotator, with no inter-annotator agreement, no second rater, and no blind protocol described; (3) No baseline is compared — the paper compares only internal model variants (GPT-4o full, GPT-4o-mini hybrid, Markov hybrid), never a static prompt list, a generic recommender, or a no-recommendation control, so the claim of 'significant improvements over existing approaches' (Section 4.4) is unsupported; (4) No confidence intervals or significance tests are reported for any metric, making the model-trade-off discussion in Section 4.4 (e.g., 96.5% vs 98.9% usefulness) statistically uninterpretable. The strongest-claim statement that 'all model configurations achieved strong usefulness scores... confirming the robustness of our architectural approach' therefore depends entirely on unvalidated, single-rater, baseline-free measurements. These are correctness risks for the claim as stated, even though the architecture and internal consistency of the results may be fine.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a dynamic context-aware prompt recommendation system for domain-specific AI applications, specifically a commercial security copilot. The architecture combines a contextual query processor, a retrieval-augmented knowledge engine, hierarchical skill organization (plugins containing granular skills), a skill ranking engine trained on behavioral telemetry, and an information synthesis stage that generates prompt suggestions via meta-prompts with predefined/adaptive templates and few-shot learning. Three model configurations are compared: a full GPT-4o pipeline, a GPT-4o-mini hybrid for inference, and a Markov-model hybrid that replaces LLM-based inference for plugin/skill selection. The evaluation uses 784 sessions (2,967 chats) from a real-world security assistant, with 12,432 suggested prompts scored automatically via a rubric-based framework on five metrics (Relevance, Clarity, Novelty, Grounding, Usefulness) and 152 chats manually evaluated by a single security expert. Reported automated usefulness scores range from 0.870 to 0.885, and manual usefulness rates range from 96.5% to 98.9%, leading the authors to claim high usefulness and relevance, robustness across configurations, and domain adaptability across six plugins.","tokens_in":8465,"tokens_out":3756,"duration_ms":40605,"significance":"If the empirical claims hold, the paper provides a pragmatic architecture for prompt recommendation in skill-based AI copilots, with two potentially useful innovations: a two-stage hierarchical reasoning process that selects plugins before skills, improving both efficiency and precision, and a hybrid Markov/LLM ranking approach that reduces inference cost while preserving quality. The use of a real-world deployment dataset and the discussion of cost-efficiency, diversity, and feasibility are valuable for practitioners. However, the paper's significance is currently limited by weaknesses in the evaluation methodology: the automated rubric is only partially disclosed, the manual evaluation relies on a single product-affiliated annotator with no inter-annotator reliability, no baseline or control condition is compared, and no confidence intervals or significance tests accompany the headline numbers. These issues are central to the paper's claim of validated usefulness, so the contribution cannot be fully assessed until the evaluation is strengthened.","major_comments":[{"comment":"The automated evaluation relies on an author-defined rubric whose complete definitions are not provided; the text states 'Due to page constraints, here's an example rubric for overall usefulness' and shows only three levels for one metric, leaving the rubrics for Relevance, Clarity, Novelty, and Grounding unspecified. This makes the automated usefulness scores (0.884, 0.870, 0.885) non-reproducible and impossible to interpret for readers. The authors should include the full rubric for all five metrics in an appendix or supplementary document, and specify how rubric levels are mapped to numeric scores.","section":"Section 4.1, Table 1"},{"comment":"The manual evaluation was conducted by exactly one security expert, Jessen Kurien, acknowledged as 'conducting all manual quality evaluations,' with no second rater, no inter-annotator agreement statistic, and no described blinding protocol. Given that the expert is a product collaborator, the manual usefulness rates (96.5–98.9%) do not yet constitute robust expert validation. The authors should recruit multiple independent annotators, report agreement measures (e.g., Cohen's kappa), and describe the evaluation protocol to rule out expectation bias.","section":"Section 4.3, Acknowledgments"},{"comment":"The claim of 'significant improvements over existing approaches' is unsupported because the experiments compare only internal model variants (GPT-4o full, GPT-4o-mini hybrid, Markov hybrid) and include no baseline condition such as a static prompt list, a generic LLM prompt recommender, or a no-recommendation control. Without such a comparison, the reported scores cannot establish improvement over any existing method. The authors should add at least one external baseline and perform statistical significance tests to support the claim.","section":"Section 4.4"},{"comment":"The model trade-off discussion in Section 4.4 interprets differences such as 96.5% versus 98.9% overall usefulness and 53.0% versus 75.0% 'extremely useful' ratings, but no confidence intervals or hypothesis tests are reported. With only 152 manually evaluated chats, these differences are likely within sampling error, making the trade-off discussion unreliable. The authors should report confidence intervals or exact significance tests, or avoid drawing conclusions from differences that are not statistically established.","section":"Section 4.3, Table 2, Section 4.4"},{"comment":"The feasibility test described in Section 5 is explicitly indirect: the suggested skill is compared against the skill predicted by the system's own skill predictor, and the text acknowledges that skill prediction accuracy is 78% while the alignment is 'over 60%.' Since the reference standard is the system itself, this test does not measure whether prompts are actually executable by the intended skills, and the circularity weakens the feasibility claim. A concrete remedy would be a direct evaluation of prompt-to-skill execution success or an independent human judgment of feasibility on a sample of suggestions.","section":"Section 5"}],"minor_comments":[{"comment":"There is a typo in 'Models: Three different model configurations were Compared' where 'Compared' should be lowercase.","section":"Section 4.1"},{"comment":"The sentence 'A rubric-based evaluation framework were applied' has a subject-verb agreement error; 'framework were' should be 'framework was.'","section":"Section 4.1"},{"comment":"The plugin abbreviation 'USX' is used without expansion or definition; the authors should spell out the full name at first mention.","section":"Section 4.3"},{"comment":"The example prompt list uses an inconsistent notation in the displayed JSON-like structure, e.g., `[\"prompt\": \"List suspicious...\"` uses a colon instead of an equals sign; this should be formatted consistently.","section":"Section 5"},{"comment":"The phrase 'This is quite challenge' should be 'This is quite challenging.'","section":"Section 5"},{"comment":"References [7] and [8] cite the same paper (Ma, Qian, and Sun, 2023, 'Dynamic Open-book Prompt for Conversational Recommender System') under the same venue but with different formatting; one of the duplicate entries should be removed or distinguished.","section":"References"}],"recommendation":"major_revision","confidential_remarks":"The paper is an industry report from Microsoft describing a deployed system, and the evaluation is based on proprietary data. The single-annotator manual evaluation is a particular concern because the acknowledged annotator is a product collaborator; the authors should be asked to provide multiple independent raters or at least an additional validation set. The 'page constraints' excuse for omitting the full rubric is unconvincing for an arXiv submission; the complete rubric should be included. The paper also has no external baseline, which is a serious gap for a recommendation-systems claim. The architecture itself is plausible and the cost-efficiency discussion is useful, but the empirical validation needs substantial strengthening before the central claim can be accepted."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: this is a credible engineering report on a prompt-recommendation system for Microsoft Security Copilot, not a research breakthrough. The integration is real: contextual query processing, RAG over skills/plugins, hierarchical selection, telemetry-based ranking, and template-based generation with few-shot examples. The two-stage plugin-to-skill narrowing is a sensible efficiency move, and the Markov hybrid for cost reduction is the most concrete new piece. The paper is honest about its own gaps: no diversity metric, no direct feasibility metric, cold-start limits, untested low-cost models. That honesty counts.\n\nThe evaluation has real soft spots, and the stress-test note lands. The automated rubric is author-defined and only partially shown; the manual evaluation is one product-affiliated expert with no inter-annotator agreement; there is no baseline (no static prompt list, no generic recommender, no no-recommendation control); no confidence intervals or significance tests; and the feasibility check compares the system's suggested skill to the system's own predicted skill, which is circular-ish. So the headline numbers—88% automated usefulness, 96%+ manual usefulness—should not be taken as established. The claim in Section 4.4 that results confirm 'significant improvements over existing approaches' is unsupported because no existing approach was compared.\n\nThat said, the internal model comparison is useful: the Markov hybrid achieves similar usefulness while cutting LLM calls for inference, and the 75% vs 53% 'extremely useful' gap between GPT-4o full and the hybrids is an interesting cost-quality trade-off, even if not statistically tested. The 78% skill prediction accuracy and 60% alignment give some rough grounding. The dataset is real opt-in telemetry from 784 sessions and 12,432 suggestions, which is more than many similar papers. No code or data released, but operational privacy constraints are plausible and stated.\n\nBottom line: the architecture is coherent, the paper is clearly written, and the limitations are acknowledged rather than hidden. The weaknesses are addressable: add baselines, multi-annotator scoring with agreement stats, confidence intervals, and, where privacy permits, artifact release. I'd send this to peer review with a request for major revision, not desk-reject it. It's an applied systems paper that a security-AI or prompt-engineering audience will want to read, but the usefulness claim needs stronger measurement before it can be cited as evidence.","headline":"A coherent applied-system paper whose architecture makes sense, but the usefulness claim rests on a single-annotator, baseline-free evaluation and needs stronger evidence before the numbers are taken at face value.","tokens_in":9035,"tokens_out":1679,"would_cite":false,"duration_ms":17496,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A skill-based security copilot can generate prompt suggestions that security experts judge useful in over 96 percent of sampled sessions, by combining query context, retrieval-augmented knowledge, hierarchical skill selection, and…","keywords":["prompt recommendation","large language models","domain-specific AI","retrieval-augmented generation","hierarchical skill organization","behavioral telemetry","security copilot","prompt engineering"],"falsifier":"Have several independent security analysts, blind to the model configuration, rate the same 152 manually evaluated chats with the paper's usefulness rubric; if their pooled 'useful' rate falls well below 96 percent or inter-rater agreement is poor, the central claim fails. A complementary check would compare task-completion success on sessions where suggestions were offered versus withheld.","tokens_in":7944,"feed_emoji":"🛡️","tokens_out":7093,"duration_ms":78388,"temperature":0.7,"pith_summary":"The paper tries to show that the prompt-quality bottleneck in domain-specific LLM applications can be relieved by a recommendation system that watches what the user is doing and suggests what to ask next. It proposes a pipeline that enriches the user's query with session and profile context, grounds it in a retrieval-augmented knowledge base, selects skills in two hierarchical stages (plugin first, then skill), ranks them with a model trained on behavioral telemetry, and synthesizes final prompt suggestions via templates with few-shot examples. On real opt-in sessions from a commercial security copilot, the paper reports automated usefulness scores around 88 percent and manual expert usefulness rates above 96 percent across all tested model configurations. The intended significance is that users of high-stakes, skill-based AI assistants can be guided to effective prompts without manual curation of static prompt lists.","feed_headline":"Prompt suggestions pass 96% expert usefulness in security copilot","feed_subtitle":"Users get useful next prompts automatically, cutting the biggest friction in skill-based AI assistants.","key_machinery":"The load-bearing mechanism is the two-stage hierarchical skill selection: the system first identifies relevant plugins, then narrows to the most relevant individual skills inside those plugins, mirroring the schema-refinement idea used in translating natural language to structured queries. Around this selection sit a retrieval-augmented knowledge engine that grounds suggestions in domain documentation, a skill-ranking engine trained on behavioral telemetry that balances current-session interactions with long-term usage patterns, and an information-synthesis stage that builds a meta-prompt from predefined and adaptive templates plus few-shot examples from similar historical queries. This design deliberately constrains suggestions to skills that exist in the system rather than generating open-ended prompts, which the paper argues keeps suggestions feasible and on-topic.","core_discovery":"The central claim is that dynamic, context-aware prompt recommendation works in a real skill-based security copilot: given a natural-language query, the system selects and ranks relevant skills and turns them into suggested prompts that users actually find useful. The evaluation compares three configurations on 784 sessions with 2,967 chats and 12,432 suggested prompts. The full GPT-4o pipeline achieves 88.4 percent automated usefulness and 98.0 percent manual usefulness, with 75 percent of its manually rated suggestions judged extremely useful; the Markov-plus-GPT-4o hybrid reaches 98.9 percent manual usefulness while avoiding language-model calls for plugin and skill inference. The paper concludes that the architecture's two-stage hierarchical reasoning, retrieval grounding, and telemetry-based ranking make the approach work across model choices and across six security plugins.","pith_inferences":["The expert-usefulness numbers measure whether a suggestion looks useful to one reviewer, not whether acting on it actually shortens a security investigation; a controlled trial comparing task completion with and without suggestions would test that gap.","The Markov model's success likely depends on skill popularity, and the paper's own cold-start caveat suggests the cheap route degrades exactly where a new or rare skill is the right answer.","The reported 60 percent agreement between suggested and system-predicted skills implies feasibility is a real constraint: roughly two-fifths of suggestions may not map cleanly to an executable skill even in this constrained design.","A natural extension would be to log whether users click, run, or abandon each suggestion and feed that implicit feedback into the ranking model, turning usefulness from a scored property into a learned objective."],"forward_implications":["If the usefulness scores hold, analysts using a skill-based security copilot can expect most suggested prompts to be directly usable, with a large share rated extremely useful rather than merely acceptable.","The hybrid Markov-plus-GPT-4o configuration shows that plugin and skill inference can be done without an LLM for popular skills, so per-request cost can fall by an order of magnitude while manual usefulness stays near 99 percent.","The six-plugin coverage suggests the approach transfers across different security domains and products rather than fitting one narrow workflow.","The authors' stated next step is to extend the methodology to other specialized domains such as healthcare and finance, implying the architecture is meant as a general pattern for skill-based copilots."],"supporting_citations":[{"why":"Supplies the retrieval-augmented generation method the Knowledge Retrieval Engine uses to ground queries in domain knowledge.","marker":"[4]"},{"why":"Prior conversational recommender work that dynamically folds historical context into a base prompt, an idea the paper extends to skill-based AI assistants.","marker":"[8]"},{"why":"Cited as evidence that personalization in prompt recommendation remains rudimentary, motivating the telemetry-based ranking component.","marker":"[9]"},{"why":"Automatic prompt generation research that the paper contrasts with its skill-first, constrained prompt generation approach.","marker":"[13]"},{"why":"The two-stage schema refinement in natural-language-to-query translation that the hierarchical plugin-then-skill selection is modeled on.","marker":"[14]"}],"fun_headline_variants":["Prompt recommender hits 98.9% manual usefulness in security copilot","Adaptive prompt suggestions reach 98.9% expert usefulness","Dynamic prompt recommendations score 98.9% in copilot trial","Security copilot prompts: 98.9% useful, cuts friction","Context-aware prompts achieve 98.9% usefulness in copilot"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that the author-defined rubric, only one level of which is shown in Section 4.1, and the judgments of a single product-affiliated security expert who the paper acknowledges performed all manual evaluations, measure the true usefulness of a suggested prompt; if those ratings do not match what working analysts experience, the reported 96-99 percent usefulness does not establish the claim.","fun_headline_variants_meta":{"raw":{"variants":["Prompt recommender hits 98.9% manual usefulness in security copilot","Adaptive prompt suggestions reach 98.9% expert usefulness","Dynamic prompt recommendations score 98.9% in copilot trial","Security copilot prompts: 98.9% useful, cuts friction","Context-aware prompts achieve 98.9% usefulness in copilot"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000608,"raw_usage":{"total_tokens":2763,"prompt_tokens":810,"completion_tokens":1953,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":426,"completion_tokens_details":{"reasoning_tokens":1860}},"tokens_in":426,"tokens_out":1953,"duration_ms":17293,"temperature":1.0,"reasoning_tokens":1860,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T22:41:01.833659+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Have several independent security analysts, blind to the model configuration, rate the same 152 manually evaluated chats with the paper's usefulness rubric; if their pooled 'useful' rate falls well below 96 percent or inter-rater agreement is poor, the central claim fails. A complementary check would compare task-completion success on sessions where suggestions were offered versus withheld.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Prior conversational recommender work that dynamically folds historical context into a base prompt, an idea the paper extends to skill-based AI assistants."},{"cited_title":"Reinforced Prompt Personalization for Recommendation with Large Language Models","cited_arxiv_id":"2407.17115","evidence_quote":"Cited as evidence that personalization in prompt recommendation remains rudimentary, motivating the telemetry-based ranking component."}],"review_version":1}