Kaushitha Silva

Kaushitha Silva, Srinath Perera

Large Language Models (LLMs) have demonstrated impressive capabilities in code generation. While an interactive feedback loop can improve performance, writing effective tests is a non-trivial task. Early multi-agent frameworks, such as AgentCoder, automated this process but relied on generated tests as absolute ground truth. This approach is fragile: incorrect code frequently passes faulty or trivial tests, while valid solutions are often degraded to satisfy incorrect assertions. Addressing this limitation, newer methods have largely abandoned test generation in favor of planning and reasoning based on examples. We argue, however, that generated tests remain a valuable signal if we model them as noisy sensors guided by bayesian updates. To this end, we introduce BACE (Bayesian Anchored Co-Evolution), a framework that reformulates synthesis as a Bayesian co-evolutionary process where code and test populations are evolved, guided by belief distributions that are reciprocally updated based on noisy interaction evidence. By anchoring this search on minimal public examples, BACE prevents the co-evolutionary drift typical of self-validating loops. Extensive evaluations on LiveCodeBench v6 (post-March 2025) reveal that BACE achieves superior performance across both proprietary models and open-weight small language models.

Kaushitha Silva, Srinath Perera

Multi-agent frameworks are widely used in autonomous code generation and have applications in complex algorithmic problem-solving. Recent work has addressed the challenge of generating functionally correct code by incorporating simulation-driven planning and debugging, where language models trace execution steps to verify logic. However, these approaches depend on human-provided public test cases to ground the debugging and simulation loop. Manually authoring comprehensive input-output examples is a labor-intensive bottleneck in the software development lifecycle. Because ground-truth input-output examples are rarely available prior to implementation in real-world software engineering, this dependency restricts methods to curated competitive programming benchmarks. Furthermore, we identify that reliance on these public tests induces an ``overconfidence gap,'' causing frameworks to overfit to simplistic examples and fail on hidden evaluations. In contrast, we observe that external sample inputs are not strictly necessary for code generation. We demonstrate that large language models can autonomously generate valid inputs and simulate execution traces to self-correct. Consequently, we develop DryRUN, a framework that eliminates the need for ground-truth samples by allowing the LLM to iteratively plan, autonomously generate its own inputs and simulate execution, mitigating algorithmic overconfidence. Evaluations on the LiveCodeBench v6 dataset (post-March 2025) demonstrate that DryRUN matches performance against CodeSIM, a state-of-the-art and public-test-dependent framework, while operating entirely without public test cases or external execution feedback while reducing output token consumption.

Nirhoshan Sivaroopan, Kaushitha Silva, Chamara Madarasingha et al.

Synthetic network traffic generation has emerged as a promising alternative for various data-driven applications in the networking domain. It enables the creation of synthetic data that preserves real-world characteristics while addressing key challenges such as data scarcity, privacy concerns, and purity constraints associated with real data. In this survey, we provide a comprehensive review of synthetic network traffic generation approaches, covering essential aspects such as data types, generation models, and evaluation methods. With the rapid advancements in AI and machine learning, we focus particularly on deep learning-based techniques while also providing a detailed discussion of statistical methods and their extensions, including commercially available tools. Furthermore, we highlight open challenges in this domain and discuss potential future directions for further research and development. This survey serves as a foundational resource for researchers and practitioners, offering a structured analysis of existing methods, challenges, and opportunities in synthetic network traffic generation.

Kaushitha Silva, Mansitha Eashwara, Sanduni Ubayasiri et al.

The clinical adoption of biomedical vision-language models is hindered by prompt optimization techniques that produce either uninterpretable latent vectors or single textual prompts. This lack of transparency and failure to capture the multi-faceted nature of clinical diagnosis, which relies on integrating diverse observations, limits their trustworthiness in high-stakes settings. To address this, we introduce BiomedXPro, an evolutionary framework that leverages a large language model as both a biomedical knowledge extractor and an adaptive optimizer to automatically generate a diverse ensemble of interpretable, natural-language prompt pairs for disease diagnosis. Experiments on multiple biomedical benchmarks show that BiomedXPro consistently outperforms state-of-the-art prompt-tuning methods, particularly in data-scarce few-shot settings. Furthermore, our analysis demonstrates a strong semantic alignment between the discovered prompts and statistically significant clinical features, grounding the model's performance in verifiable concepts. By producing a diverse ensemble of interpretable prompts, BiomedXPro provides a verifiable basis for model predictions, representing a critical step toward the development of more trustworthy and clinically-aligned AI systems.