{"id":"7f7bf022-3a04-4d0c-9718-d3072b62262b","arxiv_id":"2411.15007","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Prompt-engineered Perplexity can produce syntactically valid PlantUML fault tree diagrams for a lidar sensor failure after iterative error correction.","lead":"This paper tests whether a chatbot, Perplexity, can be guided by carefully worded prompts to draw a fault tree diagram for a failing lidar sensor in a self-driving car. The authors show the workflow works for one example by having the bot output PlantUML code, with errors corrected through follow-up prompts, but they do not validate the tree against expert-built diagrams.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Final FTA's AND/OR logic is never validated against a reference; Section IX even admits correctness must still be ensured, so the abstract's success claim is unsupported.","rationale":"The reader's weakest assumption identifies exactly the missing validation against a reference FTA. My review confirms that this is the load-bearing concern: the paper's central claim is a feasibility demonstration that prompt-engineered LLMs can produce FTA diagrams, but the only check actually performed is that PlantUML renders the code without syntax errors. The final tree's gate logic is both unjustified and internally inconsistent with the earlier all-OR version, and the conclusion explicitly concedes that correctness still needs to be ensured. Since the concern is real but addressable by adding a validation study or softening the claim, the reader's CONDITIONAL verdict remains appropriate; it should not be changed to ACCEPT or REJECT on the basis of this review alone.","tokens_in":12001,"tokens_out":4279,"duration_ms":42027,"concrete_test":"Have two safety engineers independently construct a reference FTA for the same top event ('LIDAR Sensor Failure' in Level 4 autonomy) using a standard tool, then compute minimal cut sets of the Section VII.B.2 tree. If the HardwareAND cut set {Laser emitter degradation, Power supply issues} or the EnvironmentalAND cut set {Adverse weather, Direct sunlight glare} is not present as a necessary combination in the reference, the tree's logic is wrong; if reference basic events are missing from the LLM tree, completeness fails. Either mismatch invalidates the claim.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The load-bearing step is the leap from 'PlantUML renders without syntax errors' to 'this is a valid FTA.' In the final code of Section VII.B.2, the model changes the first version's all-OR semantics: `HardwareFailure -down-> HardwareAND : AND`, with `HardwareAND -down-> (Laser emitter degradation)` and `HardwareAND -down-> (Power supply issues)`; similarly `EnvironmentalAND` conjoins `Adverse weather` and `Direct sunlight glare`. No reference FTA, failure-mode list, or safety standard analysis is provided to show these AND combinations are necessary, or that the event set is complete. The earlier diagram in Section VI.A explicitly says all connections represent OR gates, so the model's own topology shifts without justification. Section VIII only observes that the model 'understands the usage of AND/OR gates'; Section IX states the model 'needs sufficient training to ... ensure correctness as it modifies the UML code.' That is an internal admission that correctness was not established. The abstract's 'successfully shows the possibility' therefore rests entirely on an unvalidated diagram, not on a checked fault tree. The single-example generalization to 'any Autonomy usecase' is a separate overreach; the primary defect is the missing correctness validation.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper explores the use of large language models (LLMs) — Gemini, ChatGPT, and Perplexity — to generate fault tree analysis (FTA) diagrams for a Lidar sensor failure in autonomous driving. The authors report that Perplexity, aided by PlantUML and iterative prompt engineering, can produce syntactically valid diagrams with AND/OR gates and additional event categories. The abstract claims that this \"successfully shows the possibility to train existing Large Language models through Prompt Engineering for fault tree analysis for any Autonomy usecase aided with PlantUML tool.\" The paper includes code listings of the generated PlantUML and describes a workflow of feeding syntax errors back into the model to repair them.","tokens_in":12259,"tokens_out":3516,"duration_ms":33673,"significance":"If the central claim were fully supported, the paper would offer a useful low-cost approach to drafting fault trees early in the functional-safety workflow, potentially reducing manual effort and enabling tighter integration with textual requirements. The paper's concrete assets — the PlantUML code listings and the prompt-engineering loop for syntax-error repair — are reproducible and could serve as a starting point for more rigorous studies. However, the significance is currently limited because the paper does not validate the semantic correctness of the generated fault trees, and the single-example generalization to \"any Autonomy usecase\" is not supported. The work is best read as a preliminary feasibility demonstration of syntactic FTA diagram generation, not as a validated FTA methodology.","major_comments":[{"comment":"The central claim that the model \"successfully\" generates FTA is not supported because the logical content of the fault tree — event selection, gate types, completeness, and correctness — is never validated against a reference FTA, a known failure mode list, or a safety standard such as IEC 61025. The only evidence offered is that the PlantUML code renders without syntax errors (Section VIII, observation 3), which does not establish that the diagram is a valid fault tree. Section IX explicitly states that the model \"needs sufficient training to ... ensure correctness as it modifies the UML code,\" contradicting the abstract's success claim. The authors should either provide a correctness validation (e.g., comparison with a reference FTA or expert review) or revise the claim to \"generates syntactically valid PlantUML diagrams\" rather than \"successfully trains LLMs for fault tree analysis.\"","section":"Section VII.B.2 and Section IX"},{"comment":"The final diagram changes the logic from the all-OR semantics of the initial PlantUML version (Section VI.A, Listing 1, note \"All connections represent OR gates\") to AND gates for HardwareFailure and EnvironmentalFactors. No justification or source is given for why these particular combinations (e.g., \"Laser emitter degradation\" AND \"Power supply issues\") are necessary and sufficient. Since the gate logic is the core of any fault tree, this arbitrary shift without explanation undermines the claim that the generated diagram is a valid FTA. The authors should provide a rationale or a reference for each gate type.","section":"Section VII.B.2"},{"comment":"The generalization \"for any Autonomy usecase\" is not supported by the single Lidar example presented in the paper. Section IX acknowledges \"Current paper majorly focuses on one particular usecase for Lidar failure\" and lists cameras and radars as future work. The claim should be scoped to the demonstrated Lidar sensor case unless additional examples are provided.","section":"Abstract and Section IX"}],"minor_comments":[{"comment":"Several cross-references are broken: \"Code generated: ??\" (Section VII.B.1), \"?? is the code snippet\" (Section VII.B.2), \"Code snippet ?? and the figureII\" (Section VII.B.2), and \"figure4e\" (Section V.2). These placeholders must be resolved before publication.","section":"Throughout"},{"comment":"The sentence \"Table2shows a list of basic questions\" references a table that does not appear in the manuscript; either insert the table or remove the reference.","section":"Section I.C"},{"comment":"The reference list needs cleanup: reference [30] is a Bing video link without proper bibliographic information, and reference [21] is incomplete (\"Single-shell Tank. AAS 00 seeettm data transmittal page\").","section":"References"},{"comment":"The five-item list of generic training strategies is only loosely connected to the paper's contribution and reads as padding; consider cutting it or citing a source.","section":"Section IV.C"},{"comment":"The comparison of the three LLMs is anecdotal; a more systematic protocol (e.g., identical prompts, number of attempts, success criteria, and a rubric for evaluating the outputs) would strengthen the findings.","section":"Section V"}],"recommendation":"major_revision","confidential_remarks":"The paper is a short feasibility study with modest novelty, but the concrete PlantUML code and the prompt-engineering loop are useful artifacts for practitioners. The main block is the unsupported jump from syntactic validity to FTA correctness, plus the over-generalized abstract claim. A major revision that adds a validation step or substantially softens the claims would make the paper acceptable. Please also check for the missing Table 2 and the numerous broken cross-references before resubmission."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nQuick take: this is a feasibility demo that mostly works at the syntactic level, but the paper's own abstract overstates what is shown. The genuinely new bit is pairing prompt-engineered Perplexity with PlantUML to produce a fault-tree diagram, plus the loop of feeding PlantUML syntax errors back into the model until it renders. That is a legitimate, small application-level contribution, and the authors are honest in Section IX that correctness still has to be ensured.\n\nWhat it does well: the paper includes the full prompt sequences and code listings, so the pipeline is reproducible. The comparison across ChatGPT, Gemini, and Perplexity is a useful negative result—Gemini won't generate diagrams, ChatGPT produces unreadable images, only Perplexity with PlantUML gets a structurally valid diagram. That is worth knowing for anyone doing FTA tooling.\n\nWhere it falls short is the load-bearing claim. The final FTA in Section VII.B.2 introduces AND gates for hardware and environmental failures with no reference FTA, no failure-mode list, no safety standard analysis. The earlier diagram explicitly states all connections are OR; the change to AND is never justified. Section VIII only observes that the model 'understands the usage of AND/OR gates,' and Section IX admits the model 'needs sufficient training to ensure correctness as it modifies the UML code.' So the 'successfully shows the possibility' claim is true for rendering a diagram but not for producing a valid FTA. The leap from one lidar example to 'any Autonomy usecase' is also unsupported.\n\nI also want to flag manuscript hygiene: there are placeholders like '??' where code should be, and some references are incompletely formatted. That is addressable but makes the paper read as an extended abstract.\n\nBottom line: as a proof-of-concept for syntactically structured FTA generation, it is a reasonable data point. As a demonstration that LLMs can produce correct fault trees, it does not land. A referee could usefully demand a reference-based validation and a more precise abstract. I would send it to review rather than desk-reject—there is enough concrete, reproducible material to work with.","headline":"A fairly honest feasibility demo of prompt-engineered PlantUML fault trees, undermined by an abstract that claims success without validating FTA correctness.","tokens_in":12716,"tokens_out":1674,"would_cite":false,"duration_ms":16268,"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":"This paper shows that a general-purpose LLM, guided only by prompt engineering and error feedback, can generate a structurally valid fault tree for a Lidar sensor failure, rendered with the PlantUML tool.","keywords":["fault tree analysis","generative AI","large language models","prompt engineering","PlantUML","Lidar sensor","autonomous driving","functional safety"],"falsifier":"Run the exact final prompt sequence from Table I on a fresh session of the same LLM and ask it to generate a Lidar FTA without prior error feedback; if the output does not render in PlantUML or omits a documented Lidar failure cause such as photodiode damage or adverse weather, then the claim that prompt engineering alone produces valid FTAs for any autonomy use case is falsified.","tokens_in":11835,"feed_emoji":"🤖","tokens_out":5539,"duration_ms":49222,"temperature":0.7,"pith_summary":"The paper tries to establish that a general-purpose Large Language Model, guided only by prompt engineering and iterative error correction, can produce a fault tree analysis for an autonomous-vehicle sensor failure without dedicated FTA software. The target is the malfunction of a Lidar sensor, and the output is a diagram rendered in PlantUML, a text-based UML diagram tool. If true, this would let safety engineers draft initial fault trees quickly and integrate them into development workflows, reducing manual analysis. The paper frames this as a demonstration of possibility on one use case, with future work needed for other sensors and for FMEA.","feed_headline":"Prompt engineering alone yields a Lidar fault tree via PlantUML","feed_subtitle":"A generalist LLM turns a sensor failure into standard AND/OR gate diagrams after iterative error prompts.","key_machinery":"The mechanism is a prompt-engineering loop with PlantUML as the structured output format. PlantUML is a text-based diagram language that renders boxes, circles, and connections into a flowchart; the paper leverages its gate-like symbols to represent OR and AND logic. The user iteratively prompts the LLM to generate FTA content, asks it to express that content as PlantUML code, then feeds any PlantUML syntax errors back into the prompt until the diagram renders cleanly. The standardized FTA symbols (basic events, OR/AND gates, transfer symbols) act as the target representation that the model is trained to produce purely through instruction.","core_discovery":"On its own terms, the paper's central discovery is that an existing open-source LLM, after being asked to generate an FTA for a Lidar sensor and then to convert that into PlantUML code, can be trained through prompts alone to output a structurally valid fault tree. The final tree has a top event \"LIDAR Sensor Failure\", OR and AND gates drawn as standard symbols, and causes grouped into Hardware, Software, and Environmental categories. When the code initially failed to render, feeding the syntax errors back to the model as prompts led it to correct its own output. The same prompting also handled an extension adding performance-degradation events such as range reduction and slower scan rate.","pith_inferences":["Editorial extension: the evidence demonstrates structural validity, not logical correctness; a real deployment would need to verify that the generated events and gates match a reference FTA for the Lidar failure mode.","Editorial extension: the abstract's claim of \"any Autonomy usecase\" rests on a single Lidar example; a direct test would be running the identical prompt sequence for a Radar or Camera failure and checking whether the resulting tree is both renderable and complete.","Editorial extension: if the approach scales, the bottleneck shifts from drawing diagrams to validating cut sets and gate logic, which is precisely the expertise that remains with functional-safety engineers.","Editorial extension: the success of error-feedback prompting suggests that LLMs can self-correct structured outputs when given concrete syntax errors, which may generalize to other diagram or model-based safety artifacts."],"forward_implications":["Fault tree drafting for automotive sensors could become an interactive human-in-the-loop task: an engineer prompts an LLM, reviews the rendered PlantUML diagram, and corrects the prompt rather than building the tree by hand.","The same prompt-to-PlantUML pipeline may extend to other autonomous-vehicle sensors such as cameras and radars, at least for producing an initial structural tree.","Because PlantUML output is plain text, generated fault trees can be versioned, diffed, and reviewed in standard code-review workflows, making safety artifacts more auditable.","With enough prompt refinement, a generalist LLM could serve as an FTA co-pilot that proposes failure-mode decompositions, leaving the final gate logic validation to a safety engineer.","The paper's stated next step, using the same approach for FMEA analysis, suggests the prompting pattern could generalize to other safety-analysis artifacts beyond fault trees."],"supporting_citations":[{"why":"Supplies the PlantUML text-based diagram tool that turns the LLM's code into the FTA diagram.","marker":"[32]"},{"why":"Defines the standard FTA shapes (basic events, OR/AND gates) that the generated diagrams are expected to use.","marker":"[26]"},{"why":"Provides the automotive FTA methodology and conceptual design approach that the LLM-generated trees are intended to replicate.","marker":"[15]"},{"why":"Gives an example of a manual automotive FTA that the paper's approach aims to automate.","marker":"[16]"},{"why":"Shows a prior GenAI-based safety analysis system for radiation therapy, establishing the precedent for using NLP in safety analysis.","marker":"[17]"},{"why":"Introduces the co-pilot concept for autonomous vehicles, which motivates using an LLM assistant for FTA generation.","marker":"[19]"}],"fun_headline_variants":["LLM fault tree from prompts alone","Iterative prompts teach LLM Lidar fault trees","GenAI builds fault trees via prompt engineering","Zero-shot fault tree generation with LLM prompts","Prompt-only LLM yields standard AND/OR gate diagrams"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The paper assumes that a PlantUML diagram that renders without syntax errors, and whose events are grouped into hardware, software, and environmental categories, is a valid fault tree; it never checks whether the fault events and gate logic are correct or complete against a reference FTA.","fun_headline_variants_meta":{"raw":{"variants":["LLM fault tree from prompts alone","Iterative prompts teach LLM Lidar fault trees","GenAI builds fault trees via prompt engineering","Zero-shot fault tree generation with LLM prompts","Prompt-only LLM yields standard AND/OR gate diagrams"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000263,"raw_usage":{"total_tokens":1533,"prompt_tokens":808,"completion_tokens":725,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":424,"completion_tokens_details":{"reasoning_tokens":654}},"tokens_in":424,"tokens_out":725,"duration_ms":7761,"temperature":1.0,"reasoning_tokens":654,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-12T14:36:13.436539+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the exact final prompt sequence from Table I on a fresh session of the same LLM and ask it to generate a Lidar FTA without prior error feedback; if the output does not render in PlantUML or omits a documented Lidar failure cause such as photodiode damage or adverse weather, then the claim that prompt engineering alone produces valid FTAs for any autonomy use case is falsified.","supporting_citations":[{"cited_title":"Towards uml and vdm support in the vs code environment","cited_arxiv_id":null,"evidence_quote":"Supplies the PlantUML text-based diagram tool that turns the LLM's code into the FTA diagram."},{"cited_title":"13, 2024","cited_arxiv_id":null,"evidence_quote":"Defines the standard FTA shapes (basic events, OR/AND gates) that the generated diagrams are expected to use."},{"cited_title":"A function failure approach to fault tree analysis for automotive systems","cited_arxiv_id":null,"evidence_quote":"Provides the automotive FTA methodology and conceptual design approach that the LLM-generated trees are intended to replicate."},{"cited_title":"Safety-critical failure analysis of industrial automotive airbag system using fmea and fta techniques","cited_arxiv_id":null,"evidence_quote":"Gives an example of a manual automotive FTA that the paper's approach aims to automate."},{"cited_title":"i-sart: An intelligent assistant for safety analysis in radiation therapy","cited_arxiv_id":null,"evidence_quote":"Shows a prior GenAI-based safety analysis system for radiation therapy, establishing the precedent for using NLP in safety analysis."},{"cited_title":"Safety co-pilot: A system for autonomous vehicle to make decision safer and smarter","cited_arxiv_id":null,"evidence_quote":"Introduces the co-pilot concept for autonomous vehicles, which motivates using an LLM assistant for FTA generation."}],"review_version":1}