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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗6064 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
On the Evaluation of Large Language Models in Unit Test Evolution (Experience Paper) ↗
Large language models (LLMs) have recently shown promising potential in automating unit test evolution for evolving software systems. However, the effectiveness of LLMs in unit test evolution remains insufficiently understood, particularly with respect to prompt design choices, in-context learning (ICL) strategies, and different types of test evolution. In this paper, we present the first comprehensive empirical study to evaluate LLMs for unit test evolution. We systematically assess nine open-source code LLMs (3B to 34B parameters) and three state-of-the-art commercial models across diverse p…
- OpenAlex · Published · First archived
Understanding Automated Program Repair Agents through the Lens of Traceability: An Empirical Study ↗
Automated Program Repair (APR) agents leverage large language models (LLMs) to autonomously diagnose and patch software bugs using planning, reasoning, and tools. Although these agents show strong performance on leaderboards such as SWE-bench, little is understood about how they take actions, where they fail, and how their behavior compares to human developers. In this paper, we present the first systematic analysis of these limitations using 5 state-of-the-art APR agents. We trace the full decision-making pipelines of the 5 APR agents across 500 real-world repair tasks, from issue description…
- OpenAlex · Published · First archived
Reducing Hallucinations in LLM-Generated Code via Semantic Triangulation ↗
Large language models (LLMs) can generate executable code from natural language descriptions, but the resulting programs frequently contain bugs due to hallucinations. In the absence of formal specifications, existing approaches attempt to assess correctness using LLM-generated proxies such as tests or auto-formalized specifications. However, these proxies are produced by the same imperfect models and thus often corroborate rather than catch errors, especially when the model exhibits correlated errors. We introduce semantic triangulation, a theory-grounded framework that decorrelates model err…
- OpenAlex · Published · First archived
Towards Explorative IRBL: Combining Semantic Retrieval with LLM-Driven Iterative Code Exploration ↗
Information Retrieval-based Bug Localization (IRBL) aims to identify buggy source files for a given bug report. Traditional and deep learning-based IRBL techniques often suffer from vocabulary mismatch and dependence on project-specific metadata. In contrast, recent Large Language Model (LLM)-based approaches struggle to provide appropriate context to the model: they either restrict analysis to a fixed set of candidate files, overwhelm the model with repository-wide information, or rely on explicit bug report cues to guide context collection. To address these issues, we propose GenLoc, a techn…
- OpenAlex · Published · First archived
A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications ↗
Abstract Federated learning (FL) has gained wide popularity as a collaborative learning paradigm allowing collaborative Artificial Intelligence (AI) in sensitive healthcare applications. Nevertheless, the practical implementation of FL presents technical and organizational challenges, as it generally requires complex communication infrastructures. In this context, consensus-based learning (CBL) may represent a promising alternative for collaborative learning, allowing the combination of local knowledge into a federated decision system, while potentially reducing deployment overhead. Neverthele…
- Europe PMC · Published · First archived
Beyond problem solving: Adaptive reframing in human and artificial intelligence ↗
Research on human and artificial intelligence has largely focused on problem solving: given a predefined problem, can an agent produce an effective solution? Yet many real-world challenges cannot be captured by a fixed problem representation. They require agents not only to solve problems, but also to recognize when the original problem representation has become inadequate and must be revised to reflect new evidence or changes in the problem itself. We argue that this capacity, termed adaptive reframing, constitutes a fundamental but under-researched dimension of intelligence. We propose a mul
- Europe PMC · Published · First archived
Integrating Multimodal MRI Habitat and Transformer-Based Pathomics to Predict High-Risk Molecular Subtypes and Explore Biological Mechanisms in Adult Diffuse Gliomas. ↗
Background This study aims to achieve accurate prediction of high-risk molecular subtypes of gliomas through a cross-scale Combined model, matching the model's classification metrics with patient risk stratification and exploring the underlying biological mechanisms. Methods This study retrospectively collected preoperative MRI, postoperative whole-slide pathological images, molecular markers, and clinical data from 456 adult diffuse glioma patients. We separately constructed an MRI habitat prediction model, a WSI Transformer-based deep learning pathomics (PDL) model, and a Combined model. A d
- GitHub · Repository created · First archived
virajrungta/pcb-claude-plugin ↗
Claude Code plugin: from a circuit idea to a manufacturable KiCad PCB
- Hacker News · Posted on Hacker News · First archived
Millions of dollars paid to AI providers, where was that money going before? ↗
Hacker News discussion.
- GitHub · Repository created · First archived
badfisheryhy/badfisher-ai-log ↗
Java 应用日志分析与 AI 异常诊断服务
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Artificial Intelligence
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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