THE INTERNET IS THINKING
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 ↗6073 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
Groove angle and quenching medium in shielded metal arc welding of mild steel: Multi-criteria decision analysis, machine learning and energy absorption ↗
Weld groove geometry and post-weld cooling regime are among the least systematically investigated factors influencing the performance of arc-welded joints, and even when they have been studied, the analysis has almost always been limited to a single optimisation criterion. This paper re-examines shielded metal arc welding of AISI/SAE 1020 mild steel across a 55–60° single-V groove range and three quenching media (air, water, palm oil), using six mechanical responses, and advances the analysis in four directions. First, a normalised Type III effect-share analysis establishes the practical rathe…
- OpenAlex · Published · First archived
PatchPorter: LLM-Driven Security Patch Porting via Version Tracing and Context Selection for NPM ↗
Third-Party Libraries are widely used in modern software development, yet their vulnerabilities pose serious security risks. This issue is particularly severe in the NPM ecosystem, where high-risk 1-day vulnerabilities can remain unpatched for extended periods. Although upgrading to the latest patched version is commonly recommended, it often causes major compatibility issues. Patch porting offers an effective solution to this challenge. However, existing patch porting methods are mainly designed for C and have two limitations when applied to NPM. First, they can be brittle in precisely locali…
- OpenAlex · Published · First archived
Do Coverage and Mutation Scores of LLM-Generated Test Suites Correlate with Their Effectiveness? (Replicability Study) ↗
Recent advances in large language models (LLMs) have driven growing interest in using LLMs to automate test generation. Prior work commonly evaluates generated test suites using proxy metrics such as code coverage and mutation score. However, studies by Inozemtseva et al. and Papadakis et al. show that, for human-written tests, correlations among coverage, mutation, and real-bug detection can largely vanish once test suite size is controlled, raising concerns about the validity of evaluations based on proxy metrics. It also remains unclear whether these conclusions carry over to LLM-generated…
- OpenAlex · Published · First archived
Industrial Practice of LLM-Based Test Case Carving and Assertion Generation (Experience Paper) ↗
Enterprise regression testing for microservice systems is often constrained by incomplete or outdated documentation. In practice, QA engineers frequently rely on real execution traffic to reconstruct business scenarios; however, turning raw traffic into replayable regression tests with stable validation logic remains labor-intensive and error-prone. This paper presents NL2Test, an end-to-end approach and tool that generates executable API regression tests from (i) a natural-language scenario description and (ii) a traffic capture recorded while executing the scenario. NL2Test addresses two cou…
- OpenAlex · Published · First archived
Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Tests ↗
While Large Language Models (LLMs) show great promise for automating unit test generation, recent studies suggest that the quality of generated tests can be negatively impacted when models are prompted with buggy code. This paper presents a new metric to quantitatively measure the "misguidance effect," a phenomenon where buggy code steers LLMs toward generating tests that validate its erroneous behavior rather than expose it. Our analysis reveals that prompting LLMs with buggy code has a severe, twofold impact: it significantly increases "misguided tests" that assert incorrect behavior while s…
- OpenAlex · Published · First archived
KaPilot: LLM-Assisted Generation of Kani Specifications for Unsafe Rust Verification ↗
Rust’s ownership and type system provide strong memory safety guarantees, but unsafe code still presents memory safety risks. Formal verification is crucial for ensuring memory safety, but writing precise specifications for unsafe Rust is challenging and largely manual. Large language models (LLMs) have shown promise in generating formal specifications but are often code-centric, prone to inheriting implementation flaws, and lack systematic quality assessment. In this paper, we present KaPilot, a multi-agent framework for automatically generating specifications to verify unsafe Rust memory saf…
- OpenAlex · Published · First archived
Sifting the Noise: A Comparative Study of LLM Agents in Vulnerability False Positive Filtering ↗
Static Application Security Testing (SAST) tools are essential for identifying software vulnerabilities, but they often produce a high volume of False Positives (FPs), imposing a substantial manual triage burden on developers. Recent advances in Large Language Model (LLM) agents offer a promising direction by enabling iterative reasoning, tool use, and environment interaction to refine SAST alerts. However, the comparative effectiveness of different LLM-based agent architectures for FP filtering remains poorly understood. In this paper, we present a comparative study of three state-of-the-art…
- OpenAlex · Published · First archived
TraceDev: A Traceability-Driven Multi-agent Framework for Requirement-to-Code Development ↗
In modern software development, the rapid advancement of Large Language Models (LLMs) has made the end-to-end transformation of Natural Language Requirements (NLRs) into executable repository-level code increasingly feasible. However, existing approaches typically rely on simplified instructions (e.g., single-sentence descriptions), failing to reflect complex software development scenarios. Moreover, they lack explicit requirement traceability mechanisms, making it difficult to precisely align and validate generated code against original requirements. To address these limitations, we propose T…
- OpenAlex · Published · First archived
LogicHunter: Testing LLM Agent Frameworks with an Agentic Oracle ↗
Large Language Model (LLM) agent frameworks such as LangChain, LlamaIndex, and CrewAI have become critical infrastructure powering production AI systems, yet they remain severely under-tested due to fundamental challenges in automated testing. Unlike traditional software, where crashes serve as reliable oracles, defects in these pure Python frameworks manifest as ordinary exceptions or silent semantic failures, creating profound oracle ambiguity. This problem is exacerbated by strict type governance through Pydantic schemas and complex protocol requirements that cause existing fuzzers to gener…
- OpenAlex · Published · First archived
SEDCoT: Enhancing LLM-Based COBOL Code Translation via Symbolic Execution and Delta Debugging ↗
COBOL remains critical across banking, insurance, and government infrastructure. However, maintenance is increasingly challenging due to outdated technologies, sparse documentation, and developer retirement, necessitating code translation into modern languages like C. Traditional rule-based transcompilers yield outputs that are difficult to read and maintain, while general-purpose large language models (LLMs) achieve suboptimal correctness because COBOL is a low-resource language with distinct logic patterns. To bridge this gap, we propose SEDCoT, a novel COBOL-to-C translation framework. SEDC…
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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
Follow a thread
Explore related regions
These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.