Building Better Code with Thoughtful AI

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Author: Denis Avetisyan

A new reasoning framework empowers AI agents to systematically improve code quality by continuously questioning and verifying design choices.

The Questions-of-Thoughts (QoT) algorithm establishes an overarching structure for navigating complex thought processes, inherently acknowledging that all systems-even those of cognition-are subject to inevitable decay and operate within the medium of time, rather than against it.
The Questions-of-Thoughts (QoT) algorithm establishes an overarching structure for navigating complex thought processes, inherently acknowledging that all systems-even those of cognition-are subject to inevitable decay and operate within the medium of time, rather than against it.

This paper introduces Question-of-Thoughts (QoT), a time-series self-QA chain for quality-driven agentic reasoning in LLM-assisted software design.

Despite recent advances in large language models (LLMs) for code generation, achieving consistently reliable and modular software remains a significant challenge. This paper, ‘Quality-Driven Agentic Reasoning for LLM-Assisted Software Design: Questions-of-Thoughts (QoT) as a Time-Series Self-QA Chain’, introduces Questions-of-Thoughts (QoT), a novel reasoning framework that guides LLM agents to systematically elicit constraints and verify design choices during code generation. Evaluations across API design, data communication, and file systems demonstrate that QoT enhances code quality-specifically scalability, completeness, modularity, and security-particularly for larger models and complex tasks. Will this quality-driven approach unlock the full potential of LLMs for trustworthy and efficient software engineering?

The Inevitable Decay of Code: A Quality Imperative

Despite remarkable advancements in Large Language Model (LLM) code generation, consistently achieving high software quality remains a substantial challenge. These models frequently produce syntactically correct and functionally operating code, yet often fall short regarding crucial attributes like security, efficiency, and maintainability. The capacity to generate code does not automatically translate to the creation of robust software; LLMs can struggle with nuanced requirements, edge cases, and the long-term implications of design choices. Consequently, developers still require significant effort in reviewing, testing, and refining LLM-generated code to meet professional standards and ensure reliable performance, highlighting a gap between automated generation and truly dependable software solutions.

The prevailing strategy of simply increasing the size of large language models doesn’t reliably translate to improved code quality, particularly when tackling the intricacies of software development. While scaling can enhance a model’s ability to produce code that runs without immediate errors, it frequently falls short in generating code that is easily understood, modified, or resilient to unexpected inputs. This is because complex software engineering demands more than just functional correctness; it requires careful consideration of design principles, error handling, and long-term maintainability – areas where simply increasing model parameters doesn’t guarantee commensurate improvement. The resulting code, though operational, can be brittle, difficult to debug, and ultimately costly to maintain, highlighting a crucial gap between raw code generation capability and the production of truly high-quality software.

Current automated evaluation suites for Large Language Model (LLM) generated code, such as LiveBench, predominantly focus on functional correctness – whether the code works as intended. However, software quality extends far beyond simply producing a functional output. Critical attributes like code readability, maintainability, efficiency, security, and adherence to established coding standards are often overlooked or inadequately assessed. This limitation presents a significant challenge, as functionally correct code can still be riddled with inefficiencies, vulnerabilities, or be exceptionally difficult for human developers to understand and modify. Consequently, relying solely on these suites provides an incomplete picture of LLM code generation capabilities and hinders the development of truly robust and production-ready software.

Quality-of-thought (QoT) prompting generally enhances model performance, though the extent of improvement varies depending on both the model and the specific domain.
Quality-of-thought (QoT) prompting generally enhances model performance, though the extent of improvement varies depending on both the model and the specific domain.

Questioning the Code: A Path to Enduring Quality

Question-of-Thoughts (QoT) enhances Large Language Model (LLM) Agents by implementing a structured reasoning methodology centered on iterative self-questioning. This process moves beyond simple task execution by requiring the agent to continuously probe its own understanding and assumptions. By formulating and answering internal questions throughout a task, QoT facilitates a more nuanced analysis of the problem space and encourages the identification of potential errors or overlooked considerations. This deliberate approach to reasoning is designed to generate more robust and insightful outputs compared to traditional LLM agent architectures which primarily focus on direct response generation.

Question-of-Thoughts (QoT) employs a Sequential Process Chain to break down intricate tasks into a series of discrete, sequentially executed steps. This chain facilitates the management of complexity by addressing one sub-problem at a time. Concurrently, a Question-Answer Chain operates in parallel, rigorously verifying the assumptions made at each stage of the Sequential Process Chain. This verification isn’t merely a post-hoc check; rather, questions are generated and answered during each step, allowing for immediate identification and correction of flawed reasoning or incorrect assumptions before they propagate through the overall process. The iterative question-answer feedback loop embedded within the sequential process is critical to ensuring the reliability and accuracy of the final output.

The Reasoning Knowledge Base (RKB) functions as a central repository during code generation, storing all intermediate decisions made by the LLM Agent and any constraints identified during the process. This accumulation of data allows for consistent application of logic throughout complex tasks, preventing contradictions or the abandonment of previously established parameters. Furthermore, the RKB provides complete traceability; each step in the code generation lifecycle is linked to the specific decisions and constraints that informed it, facilitating debugging, auditing, and refinement of the generated code. The RKB’s structure enables the agent to reference and validate its own reasoning, improving the reliability and explainability of the output.

Quality of Thought (QoT) leverages a sequential question answering chain to iteratively refine and improve reasoning.
Quality of Thought (QoT) leverages a sequential question answering chain to iteratively refine and improve reasoning.

Aligning with the Inevitable: Standards and Benchmarks

The Quality of Thought (QoT) framework is deliberately designed to conform with internationally recognized software quality standards, specifically ISO/IEC 9126 and its successor, ISO/IEC 25010. These standards define a set of quality characteristics – functionality, reliability, usability, efficiency, maintainability, and portability – that are essential for high-quality software. By aligning with these established models, QoT ensures its outputs address a comprehensive range of quality attributes, facilitating integration into existing quality assurance processes and providing a common vocabulary for evaluating software quality. This adherence to industry best practices enhances the credibility and trustworthiness of QoT-generated code.

Quality of Thought (QoT) extends evaluation beyond basic functional correctness to encompass critical software quality attributes. The framework specifically addresses Modularity – assessing the degree to which a system is composed of independent, cohesive modules – as well as Completeness, verifying that all specified requirements are fully implemented. Furthermore, QoT integrates security considerations, evaluating the code for vulnerabilities and adherence to security best practices. This multi-faceted approach allows for a more holistic assessment of generated code quality, moving beyond simply verifying that the code works to ensuring it is well-structured, fully-featured, and secure.

Evaluation of the QoT framework using industry-standard benchmarks SWE-Bench Pro and SEC-bench indicates consistent improvements in generated code quality beyond functional correctness. Specifically, QoT demonstrates a +5.8 point improvement in API Design quality when utilizing the Llama3.1_70b model, a +6.6 point improvement in Data Communication, and a +3.2 point improvement in File Systems, all measured against a baseline established by Chain-of-Thought (CoT) prompting. These gains suggest QoT effectively enhances code maintainability and security characteristics as assessed by these benchmark suites.

Detailed experimental results reveal that incorporating quality of translation (QoT) consistently improves model performance, yielding capacity-dependent gains, though some trade-offs are observed across different domains.
Detailed experimental results reveal that incorporating quality of translation (QoT) consistently improves model performance, yielding capacity-dependent gains, though some trade-offs are observed across different domains.

Extending the Lifespan: Scalability and Future Directions

The Quality-of-Transformation (QoT) framework is engineered with a modular design that directly supports both test-time scaling and the deployment of execution-centric agents. This architecture allows for a significant increase in testing throughput – enabling the evaluation of generated code across a broader range of inputs and scenarios – without requiring substantial computational resources. Crucially, the framework isn’t simply focused on static analysis; execution-centric agents actively run the transformed code, identifying errors and inefficiencies that traditional methods might miss. This dynamic approach to testing facilitates continuous refinement, allowing the system to iteratively improve the quality and robustness of generated code through real-world execution data, ultimately leading to more reliable and performant software solutions.

The Quality-of-Testing (QoT) framework is intentionally designed for compatibility, prioritizing a smooth transition into current software development pipelines. Rather than demanding a complete overhaul of existing practices, QoT leverages familiar tools and workflows, allowing developers to incrementally adopt its testing methodologies. This integration is achieved through a modular architecture and well-defined interfaces, ensuring QoT can coexist with established CI/CD systems, IDEs, and version control platforms. By minimizing disruption and reducing the learning curve, the framework aims to maximize its potential for widespread adoption and ultimately, improve the reliability of generated code within real-world development environments.

The Quality-of-Test (QoT) framework stands to benefit significantly from the integration of Large Language Models (LLMs) as automated quality assessors. Current quality evaluation often relies on human review, a process that is both time-consuming and potentially subjective. Employing an LLM-as-a-Judge system within QoT offers a pathway to automate this crucial step, enabling more frequent and consistent evaluations of generated code. This approach leverages the LLM’s capacity for code understanding and pattern recognition to assess factors like functionality, efficiency, and adherence to coding standards. Beyond increased speed, this automation promises greater objectivity in quality assessment, minimizing biases inherent in human judgment and allowing for more reliable comparisons between different code generations. Consequently, the scalability of QoT is dramatically improved, facilitating faster iteration cycles and ultimately contributing to the development of more robust and dependable software solutions.

Quality-of-Test (QoT) directly tackles persistent obstacles in software development – namely, the complexities of Application Programming Interface (API) design and file system interactions – to build more dependable and expandable applications. Historically, inconsistent API specifications and unpredictable file handling have contributed significantly to software fragility and scaling limitations. QoT’s architecture prioritizes rigorous testing of these critical components, ensuring that APIs are consistently implemented and file system operations are robust across diverse environments. This focus isn’t merely about identifying bugs; it’s about proactively building systems designed for resilience and growth, enabling developers to confidently scale their projects without being hampered by foundational instability. Consequently, QoT shifts the paradigm from reactive bug fixing to proactive quality assurance, laying the groundwork for software solutions that are not only functional but also inherently adaptable and long-lived.

The pursuit of robust agentic systems, as detailed in this work, inherently acknowledges the transient nature of stability. Each iteration of code generation, each self-QA check within the Question-of-Thoughts framework, is a momentary reprieve from entropy. As Alan Turing observed, “There is no escaping the fact that the machine can only do what it is programmed to do.” This limitation underscores the necessity of QoT’s constraint tracking and quality-driven reasoning. The system isn’t attempting to achieve permanent correctness, but rather to skillfully navigate the inevitable decay, mitigating errors through continuous verification and adaptation-a graceful aging process for complex computational structures. The latency introduced by these checks is, ultimately, the tax paid for a more trustworthy output.

What Lies Ahead?

The pursuit of agentic systems capable of generating reliable software reveals, predictably, the limitations of current approaches. This work, by framing reasoning as a time-series of self-assessment, addresses a critical flaw: the tendency of large language models to operate as present-focused entities, largely ignorant of the accumulating weight of prior decisions. Every bug, after all, is a moment of truth in the timeline, a consequence of constraints either ignored or insufficiently tracked. The QoT framework represents a step toward acknowledging this temporal reality.

However, the question of graceful decay remains. Successfully tracking constraints is not the same as anticipating them. Future work must grapple with the inherent uncertainty in complex systems, moving beyond reactive quality control toward proactive design that anticipates potential failure modes. This will require integrating formal methods, not merely as verification tools, but as foundational elements of the agent’s reasoning process.

Ultimately, technical debt is the past’s mortgage paid by the present. This research suggests a path toward a more sustainable future for software development, but true longevity demands a shift in perspective: from building systems that work today, to building systems that can endure tomorrow. The challenge is not simply to eliminate bugs, but to build systems that age, not collapse.

Original article: https://arxiv.org/pdf/2603.11082.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-03-13 13:40