Code-generating tools are increasingly used in software development, yet experience reports on conversational"vibe coding"under production constraints remain limited. This paper presents an experience report from a small full-stack team that applied contextual prompting and explicit architectural constraints to build (i) a multi-project agent learning platform designed for sustained, production-oriented use and (ii) an academic retrieval-augmented generation system. The agent platform supports multiple isolated projects, each with structured memory and background processing, thereby enforcing project-level isolation. The RAG system provides citation-grounded answers, role-based access control, and evaluation tracking. Across both systems, vibe coding accelerated scaffolding and integration. However, the generated code often under-specified isolation rules and infrastructure constraints when these were not explicitly defined. Consequently, aspects such as multi-tenancy, access control, memory policies, and asynchronous processing required deliberate architectural design and verification. We observe a shift in engineering effort from boilerplate implementation toward constraint specification and enforcement auditing. We also identify recurring architectural"non-delegation zones"where conversational code generation remains insufficient for production reliability.
Md Nasir Uddin Shuvo, M. Islam, Mahade Hasan et al.· arXiv.org· 0 citations
Automated authoring of Gherkin Behavior-Driven Development (BDD) acceptance criteria remains a manual bottleneck in requirements engineering. This study investigates whether epic-organized LLM-generated Gherkin produces higher quality and coverage than requirement-aligned generation. We compare our Timeless (an epic-organized LLM pipeline) approach against a naive large language model (LLM) baseline on four requirements documents (107 requirements) from the PURE dataset. Evaluation covers structural metrics, automated requirement coverage via TF-IDF and dense embeddings, and blind expert assessment by four researchers. In our evaluation, the JSON-constrained pipeline produced structurally valid scenarios across all generated outputs, while the zero-shot baseline achieved 99% structural validity. Semantic coverage was comparable to the baseline, with Timeless achieving 94.3% semantic Requirement Coverage Rate compared with 92.9% for the baseline. TF-IDF produced lower coverage scores for the epic-organized output, suggesting that lexical metrics may miss coverage when scenarios paraphrase requirements at a higher level of abstraction. Expert raters prefer the epic-organized strategy on Correctness (4.61 vs 4.14), Executability (4.61 vs 4.07), and Completeness (4.31 vs 3.50). Overall, the results suggest that epic-organized generation can improve perceived Gherkin quality while maintaining comparable semantic coverage, although broader replication is needed before generalizing this finding.
Shahbaz Siddeeq, M. Abbasi, Jussi Rasku et al.· 0 citations
In agile software development, maintaining high-quality user stories is crucial, but also challenging. This study explores the use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams. We developed a reference model for an Autonomous LLM-based Agent System and implemented it at the company. The quality of user stories in the study and the effectiveness of these agents for user story quality improvement was assessed by 11 participants across six agile teams. Our findings demonstrate the potential of LLMs in improving user story quality, contributing to the research on AI role in agile development, and providing a practical example of the transformative impact of AI in an industry setting.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
Retrieval-Augmented Generation (RAG) systems are emerging as a key approach for grounding Large Language Models (LLMs) in external knowledge, addressing limitations in factual accuracy and contextual relevance. However, there is a lack of empirical studies that report on the development of RAG-based implementations grounded in real-world use cases, evaluated through general user involvement, and accompanied by systematic documentation of lessons learned. This paper presents five domain-specific RAG applications developed for real-world scenarios across governance, cybersecurity, agriculture, industrial research, and medical diagnostics. Each system incorporates multilingual OCR, semantic retrieval via vector embeddings, and domain-adapted LLMs, deployed through local servers or cloud APIs to meet distinct user needs. A web-based evaluation involving a total of 100 participants assessed the systems across six dimensions: (i) Ease of Use, (ii) Relevance, (iii) Transparency, (iv) Responsiveness, (v) Accuracy, and (vi) Likelihood of Recommendation. Based on user feedback and our development experience, we documented twelve key lessons learned, highlighting technical, operational, and ethical challenges affecting the reliability and usability of RAG systems in practice.
M. Hasan, Muhammad Waseem, Kai-Kristian Kemell et al.· EUROMICRO Conference on Soft...· 10 citations· ⚡1
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Refactoring is a constant activity in software development and maintenance. Scale and maintain software systems are based on code refactoring. However, this process is still labor intensive, as it requires programmers to analyze the codebases in detail to avoid introducing new defects. In this research, we put forward a large language model (LLM)-based multi-agent system to automate the refactoring process on Haskell code. The objective of this research is to evaluate the effect of LLM-based agents in performing structured and semantically accurate refactoring on Haskell code. Our proposed multi-agent system based on specialized agents with distinct roles, including code analysis, refactoring execution, verification, and debugging. To test the effectiveness and practical applicability of the multi-agent system, we conducted evaluations using different open-source Haskell codebases. The results of the experiments carried out showed that the proposed LLM-based multi-agent system could average 11.03% decreased complexity in code, an improvement of 22.46% in overall code quality, and increase performance efficiency by an average of 13.27%. Furthermore, memory allocation was optimized by up to 14.57%. These results highlight the ability of LLM-based multi-agent in managing refactoring tasks targeted toward functional programming paradigms. Our findings hint that LLM-based multi-agent systems integration into the refactoring of functional programming languages can enhance maintainability and support automated development workflows.
Shahbaz Siddeeq, Muhammad Waseem, Z. Rasheed et al.· International Conference on...· 4 citations
Anomaly detection in smart power grids is a critical challenge due to the complexity, heterogeneity, and dynamic nature of sensor data streams. Existing one-class classification methods, particularly Subspace Support Vector Data Description (SVDD), have been extended to multimodal scenarios but often fail to fully exploit the structural dependencies across modalities, limiting their robustness in real-world applications. In this paper, we address this gap by proposing a generalized Multimodal Subspace Support Vector Data Description (MS-SVDD) model with graph-embedded regularization. The method projects data from multiple modalities into a shared low-dimensional subspace while preserving modality-specific structure through Laplacian regularizers. Our approach is evaluated on a three-modality dataset derived from smart grid event time series, using a dedicated preprocessing pipeline for constructing one-class classification training samples. The results demonstrate that our graph-embedded MS-SVDD improves robustness of event detection compared to conventional approaches, highlighting the potential of integrating graph priors with multimodal subspace learning for advancing anomaly detection in critical infrastructure. More broadly, this work contributes to the wider field of AI by illustrating how relational and structural information can be systematically embedded into one-class models, enabling robust learning under complex, high-dimensional, and multimodal conditions.
Thomas Debelle, F. Sohrab, Pekka Abrahamsson et al.· Scientific Reports· 1 citation
This paper presents MARARE, a real-time multi-agent system that transforms meeting dialogues into structured software requirements. One agent interacts with participants, while background agents extract and verify requirements collaboratively. Evaluation using the LLM-as-a-Judge method across five meetings (5–8 minutes each) shows a mean coverage of 80.0 ± 11.2 % (mean ± SD), semantic similarity of 0.86 ± 0.05, and hallucination rate of 14.3 ± 6.2 %. Preliminary results indicate performance differences across LLMs, suggesting that model choice influences coverage, consistency, and hallucination rates.
Malik Abdul Sami, Gessé Evangelista, Kai-Kristian Kemell et al.· AGENT@ICSE· 0 citations
Context: Organizations adopting Artificial Intelligence (AI) face challenges in eliciting and analyzing requirements that align with strategic objectives, especially when human oversight and iterative refinement are needed. Large Language Models (LLMs)-based Multi-agent systems provide a potential solution by supporting structured and collaborative Requirements Engineering (RE) processes for AI adoption planning.
Objective: The objective of this study is to investigate whether a multi-agent system, built on LLMs and supported by human input, can assist in requirements analysis for AI adoption. Method: We used a mixed-method approach: (i) designed and developed a multi-agent system to support the generation and prioritization of requirements for AI adoption, (ii) conducted multiple case studies with four companies to evaluate the system, and (iii) collected data through post-session questionnaires from nine participants and follow-up interviews, one per company.
Results: Questionnaire and interview findings together indicate that the system may assist in identifying relevant and goal-aligned requirements. Seven participants considered the generated requirements relevant, and six found them aligned with organizational goals. Participants noted that iterative feedback improved completeness and feasibility, often within two feedback rounds. Both data sources show that human input was essential to clarify technical details, ensure contextual accuracy, and validate prioritization results. Participants from all companies also identified usability, transparency, and scalability as areas requiring further refinement for broader organizational use.
Conclusions: LLM-based multi-agent systems can support strategic AI planning by enabling iterative refinement with human experts. Future work will include more interviews with stakeholders and adjustments to system features to improve transparency, usability, and scalability.
Malik Abdul Sami, Zheying Zhang, Muhammad Waseem et al.· e-Informatica Software Engin...· 5 citations
Large Language Models (LLMs) offer new opportunities for automated code refactoring. However, generated changes must reduce targeted quality problems without introducing new issues or altering behaviour-relevant code structures. We introduce REFINE (Refactoring with Evidence-aware Flow for Integrated ageNtic Execution), a tool-agnostic, evidence-aware multi-agent approach for generating Java file-level refactoring candidates. REFINE combines static-analysis-guided smell identification, smell-informed planning, LLM-based transformation, automated re-analysis, preservation checks, and structured reporting. We evaluate REFINE on 450 Java files from 15 open-source systems, producing 1,350 model-pass outputs using OpenAI GPT-5.5, Google Gemini 3.1 Pro Preview, and Anthropic Claude Opus 4.8. REFINE reduces detected code smells by 68.26%, 72.79%, and 68.49% across the three configurations, respectively, with the strongest reductions observed for major smells. A matched 150-file direct-prompt baseline shows that REFINE achieves a higher median code-smell reduction with smaller edits and fewer public-method removals. However, broader quality improvements are inconsistent, and preservation checks reveal residual risks, including assert/fail-call changes and public-method removal. Therefore, REFINE outputs should be treated as refactoring candidates requiring compilation, testing, dependency analysis, and human review before adoption in repository- or system-level settings.
Muhammad Waseem, Aakash Ahmad, Pekka Abrahamsson· 0 citations
HEPToolBench is introduced, a benchmark of 28 collider-simulation tasks scored by deterministic, task-specific scorers, plus a three-task structured-debugging extension, and moving syntax generation into deterministic software can substantially improve reliability for both small local and frontier models.
For more than a century Maxwell's demon has been asked where the cost lies. What this paper counts is not the answer but where the cost was placed: five people put it in five different places. And once numbers are put in, the statement that an irreversible gate pays one bit turns out to be wrong as well. No new mathematical theorem and no new law is claimed. Scope of this paper (scope note): no new mathematical theorem and no new law is claimed. Maxwell's thought experiment, the Szilard one-bit engine, Brillouin's cost of observation, Landauer's erasure bound, Bennett's resolution, and the Toffoli and Fredkin gates are all standard. No measured value is cited; every number is computed from a definition or obtained by exhaustive enumeration. The debate is not declared settled; generalisations of the second law that include measurement are still being studied after Bennett, and this paper only counts the structure of the debate. No theory of feedback control is built; Section 4 integrates the isothermal work and nothing more. It is not claimed that the second law is violated, nor that it is not; what is shown is the fact that the cost was placed in different places by different people. Nothing is said about real computing hardware; Sections 5 and 6 enumerate truth tables. The relation to earlier papers. Paper 131 split the one-bit price into three parts and wrote that an irreversible gate does not necessarily pay one bit; this paper puts a number on that. Paper 88 separated the coarse-graining entropy from the horizon entropy; what this paper treats is a third one, the entropy of a record. Paper 126 split the axiomatisation of Caratheodory into three stages; this paper views the same second law from the side of information. Paper 4 treated the resource bound on exhaustive search, and Section 3 is where that bound comes from. Paper 143 showed that minimum entropy production is a fenced theorem rather than a principle; this paper likewise opens something that has been called a principle. The setting. Divide a box of gas with a partition and place a small being that lets only the fast molecules through one way. A temperature difference appears at no apparent cost and work can be drawn from it, so the second law appears to be violated. Maxwell described this in a letter in 1867, and for over a century the question has been posed as: the cost must be somewhere. First, five people put the cost in five different places. Maxwell in 1867 placed none, which is why it is a paradox. Szilard in 1929 placed it on measurement, at one bit price to learn one bit. Brillouin in 1951 placed it on the physical act of looking, since a photon is needed. Landauer in 1961 placed it on erasure. Bennett in 1982 placed it on erasure alone, since measurement can in principle be made free. All five are trying to save the second law, and what differs is only which operation the cost was assigned to. The phrase the demon's cost does not name an operation, and until one is named no one is right or wrong. Second, compute the price of one bit. At 300 K it is 2.870979 times ten to the minus twenty-first joule, that is 0.017919 electron volts. A hundred watt device working at that bound could erase 3.4831 times ten to the twenty-second bits per second. Third, the Szilard engine returns exactly that from one measurement. Put one molecule in a box, insert a partition, measure which side it is on, and expand isothermally from that side. Integrating the work from V to twice V over two million points gives 2.870978885079 times ten to the minus twenty-first joule, differing from the one-bit price by 6.5 times ten to the minus thirty-fifth joule. The work extracted equals the price of one bit exactly. Yet the same number balances the cost of erasure as well, so that two numbers agree does not decide which operation carries the cost. Fourth, this is the core of the paper. Irreversible gates do not all lose the same amount. Assuming uniform inputs and calling the amount lost the information of the input minus that of the output, AND, OR and NAND lose 1.188722 bits while XOR loses 1.000000 and erasure loses 1.000000. NOT and copying lose 0.000000 and are injective. The reason lies in the bias of the output: the output of AND is zero three times and one once, carrying only 0.811278 bits, while the output of XOR is balanced and carries exactly one bit. What is lost is fixed by the bias of the output and not of the input. So an irreversible gate pays one bit is not correct, and only erasure and XOR pay exactly one. Fifth, gates that can be made reversible lose nothing. Examining the Toffoli and Fredkin gates on all eight states, both return eight distinct outputs and lose 0.000000 bits, and both are involutions that return to the identity when applied twice. Both are universal, so any logic circuit can be built from either alone, and computation itself therefore requires no erasure. That is the content of Bennett's conclusion. Sixth, the cost of a reversible AND is room rather than erasure. Supplying a third line set to zero and sending the triple a, b, zero to a, b, a and b, the four inputs go to four different outputs and the map is injective. The information lost is 0.000000 bits, so the same AND went from 1.188722 to zero by a change of construction alone. It is not free: a third wire is required. The cost did not vanish but changed form, from erasure into room. And room must eventually be cleared, and the cost of erasure arrives then. So there is a sixth place for the cost, namely when it is paid. Reversible computation did not remove the cost; it postponed it. Seventh, the books balance. Placing the work extracted over N cycles beside the cost of erasing the record, the net is exactly zero for N equal to one, ten, one hundred and one thousand alike. The demon can extract work, but so long as its memory is finite it must eventually erase, and at that moment everything extracted is returned. With an infinite memory it could extract work forever, and what breaks in that case is not the second law but the assumption of finiteness. One must say which assumption is doing the work before saying what was broken. Closing. The demon's cost was not one place. Maxwell placed none, Szilard placed it on measurement, Brillouin on the act of looking, Landauer on erasure, and Bennett on erasure alone. All five were trying to save the second law, and what differed was only which operation they named. The separator is which operation the cost is assigned to, and when it is paid. None of the five is called right here; only that they placed the cost differently. On the making of this work: The ideas and content of this work stem from the author's own considerations. Assistance from an AI (a large language model) was used for structuring, English translation, and checking the algebra. Any remaining errors or misinterpretations are solely the author's. Feedback and corrections are sincerely appreciated. ----- マクスウェルのデーモンは、百年以上のあいだ「どこに代価があるか」を問われ続けた。本稿が数えるのは、答ではなく代価の置き場所である——五人が、五つの違う場所に置いた。そして数を入れると、「非可逆なゲートは一ビット払う」も正しくないことが分かる。新しい数学定理も新しい法則も主張しない。 本稿の射程(射程注記):新しい数学定理も新しい法則も主張しない。マクスウェルの思考実験、シラードの一ビット機関、ブリルアンの測定代価、ランダウアーの消去限界、ベネットによる解決、トフォリとフレドキンのゲートは、いずれも標準的である。測定値を引かない——本稿の数はすべて定義から計算したか、全数え上げで得たものである。論争の決着を宣言しない——ベネットの解決の後も、測定と情報を含む第二法則の一般化は現在も研究されている。本稿は論争の構造を数えるだけである。フィードバック制御の理論を作らない——第4節は等温膨張の仕事を積分するだけである。熱力学第二法則が破れるとも破れないとも主張しない——示すのは、代価の置き場所が人によって違ったという事実だけである。計算機の実装を論じない——第5節と第6節は真理値表を全数え上げしただけであり、実在の素子については何も述べない。 既刊との関係。論文131 は k_BT ln2 を三つに分け、非可逆なゲートが必ず一ビットを払うわけではないと書いた——本稿はその「一ビットではない」に数を入れる。論文88 は粗視化のエントロピーと地平線のエントロピーを分けた——本稿が扱うのは三つ目、記録のエントロピーである。論文126 はカラテオドリの公理化を三段に分けた——本稿は同じ第二法則を、情報の側から見る。論文4 は総当たり探索の資源限界を扱った——その限界の出所が本稿の第3節である。論文143 は最小エントロピー生成が原理ではなく柵つきの定理だと示した——本稿も「原理」と呼ばれてきたものの中身を開ける。 設定。箱の中の気体を仕切りで二つに分け、速い分子だけを片側へ通す小さな存在を置けば、何もせずに温度差ができ、そこから仕事を取り出せる。熱力学第二法則が破れるように見える。マクスウェルが 1867 年に手紙で書いた思考実験である。問いは百年以上のあいだ「どこかに代価があるはずだ」という形で立てられてきた。 第一に、五人が五つの違う場所に代価を置いた。マクスウェル(1867)は代価は要らないと言い、だからこそ逆説になった。シラード(1929)は測定に置き、一ビット知るのにk_BT ln2 が要るとした。ブリルアン(1951)は観測の物理的実装に置き、見るための光子が要るとした。ランダウアー(1961)は消去に置き、一ビット消すのに k_BT ln2 が要るとした。ベネット(1982)は消去だけに置き、測定は原理的に無料にできるとした。五人とも第二法則を守ろうとしており、違うのはどの操作に代価を割り当てたかだけである。「デーモンの代価」という一語は、操作を名指していない。名指すまで、誰が正しいかは決まらない。 第二に、一ビットの値段を計算する。300 K での k_BT ln2 は 2.870979 かける 10 のマイナス 21 乗ジュール、すなわち 0.017919 電子ボルトである。100 ワットの装置がこの限界で働けば、毎秒 3.4831 かける 10 の 22 乗ビットを消せる。 第三に、シラードの機関は一回の測定からちょうどその分を返す。分子一個の箱に仕切りを入れ、どちらにいるかを測り、その側から等温膨張させる。体積 V から 2V までの仕事を200万点で数値積分すると 2.870978885079 かける 10 のマイナス 21 乗ジュールとなり、k_BT ln2 との差は 6.5 かける 10 のマイナス 35 乗ジュールであった。取り出せる仕事は一ビットの値段とちょうど同じである。ところが同じ数は消去の代価とも釣り合う——数が一致していることは、どちらの操作に代価があるかを決めない。 第四に、これが本稿の芯である。同じ「非可逆ゲート」でも、失う量が違う。入力を一様と仮定し、入力の情報量から出力の情報量を引いた分を失った量とすると、AND・OR・NAND は1.188722 ビットを失い、XOR は 1.000000、消去は 1.000000 である。NOT と複製は0.000000 で単射である。理由は出力の偏りにある——AND の出力は 0 が三回、1 が一回で、その情報量は 0.811278 ビットしかない。XOR の出力は釣り合っていて、ちょうど 1 ビットである。失う量は、入力の偏りではなく出力の偏りが決めている。したがって「非可逆なゲートは一ビット払う」は正しくなく、ちょうど一ビットを払うのは消去と XOR だけである。 第五に、可逆にできるゲートは何も失わない。三入力三出力のトフォリ・ゲートとフレドキン・ゲートを 8 状態すべてについて調べると、どちらも相異なる出力を 8 個返し、失った量は 0.000000 ビットである。しかも二度かけると恒等写像に戻る対合である。この二つは万能であり、あらゆる論理回路をこの二つだけで組める——したがって計算そのものに消去は要らない。これがベネットの結論の中身である。 第六に、可逆な AND の代価は消去ではなく場所である。第三の線に 0 を用意し、(a, b, 0) を (a, b, a かつ b) へ送ると、四つの入力が四つの違う出力へ行き、単射になる。失った情報は 0.000000 ビットで、同じ AND が組み方を変えただけで 1.188722 から0 になった。ただし三本目の線が要る。代価は消えたのではなく形を変えた——消去から場所へ移ったのである。そして場所はいつか片づけなければならず、片づける時にはじめて消去の代価が来る。したがって代価の置き場所は五つではなく、六つ目がある——いつ払うかである。可逆計算は代価を無くしたのではなく、後ろへ延ばした。 第七に、帳尻は合う。N 回まわして取り出した仕事と、記録を消す代価を並べると、N が1、10、100、1000 のいずれでも差引は厳密にゼロである。デーモンは仕事を取り出せるが、記憶が有限であるかぎり、いつか消さねばならない。消した瞬間に、取り出した分がそのまま返る。記憶が無限なら永久に取り出せるが、この場合に破れているのは第二法則ではなく有限性の仮定である。どの仮定が効いているかを書かないと、何が破れたのか分からない。 結び。「デーモンの代価」は、一つの場所ではなかった。マクスウェルは代価を置かず、シラードは測定に、ブリルアンは観測の実装に、ランダウアーは消去に、ベネットは消去だけに置いた。五人とも第二法則を守ろうとしており、違ったのはどの操作を名指したかだけである。分離子はどの操作に代価を割り当てるかであり、そして、いつ払うかである。五人のうち誰が正しいとも言わない——置き場所が違ったという事実を書くだけである。 作成にあたって:本稿の着想と内容は、著者自身の考察に基づくものです。文章の構成整理や英訳、数式の確認には AI(大規模言語モデル)の助力を得ました。最終的な内容
Yuuki Yamagishi· Zenodo (CERN European Organi...· 0 citations
Vision-language models (VLMs) are increasingly deployed as document- and image-understanding agents, yet published safety evaluations of these systems are almost exclusively conducted in English plain text. This leaves two attack surfaces largely unexamined for low-resource languages: (1) adversarial instructions embedded as rendered image text rather than typed prompts (“typographic prompt injection”), and (2) the same attack expressed in Urdu, a language spoken by over 230 million people. This paper reports a small, fully reproducible proof-of-concept (VisGuard-Ur) that extends a prior text-only Urdu jailbreak detector (UrduGuard) into the visual modality. We render 30 hand-authored benign and adversarial prompts — in Urdu script, Roman Urdu, and an English control group — into 120 images across four visual variants, build an OCR-plus-classifier defense, and evaluate the full pipeline against a real, locally-run vision-language model (Qwen2-VL-2B-Instruct) rather than a simulated one. Two findings are reported. First, Urdu-script text rendered in Nastaliq — the calligraphic style used in most authentic Urdu print — is substantially harder for both a conventional OCR engine (Tesseract) and the VLM's own text-reading ability than the same text rendered in the straighter Naskh style (OCR character-level similarity 0.40 vs. near-perfect for Latin-script images), identifying the reading stage, not the safety classifier, as the weakest link for this attack surface on Urdu-script inputs specifically. Second, after manually auditing every case the automated judge flagged as a successful attack, we find that image-embedded jailbreak instructions written in plain English produced genuine, explicit policy-violating compliance from the VLM in 4 of 12 cases (33%), while superficially similar Urdu-script and Roman-Urdu attacks mostly produced garbled, non-compliant transcriptions rather than real jailbreaks — the opposite of what the raw automated attack-success-rate number (35%, dominated by Urdu-script false positives) would suggest. Deploying the OCR-plus-classifier detector in front of the VLM reduced the audited system-level attack success rate from 33% to 0% with an 8.3% false-positive rate on benign images. We report this as a small-sample, honestly-scoped proof of concept rather than a benchmark, and detail the dataset size, model, and judge limitations that any follow-up work should address.
Muhammad Umer· Zenodo (CERN European Organi...· 0 citations
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduAug 31, 2026
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.