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Earlier sources: Language models
Language models learn patterns in text and other representations to predict or generate sequences. Follow model releases, training methods, reasoning, evaluation, and deployment research; a benchmark result is specific to its test conditions.
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 ↗458 classified signals in the fourteen-day window. 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
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
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 However, maintenance is increasingly challenging due to outdated technologies, sparse documentation, and developer retirement, necessitating code translation into modern languages like C. Traditional rule-based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…
- OpenAlex · Published · First archived
Test Case Selection for Deep Neural Networks: A Replication Study on LLMs for Code (Replicability Study) ↗
Test case selection (TCS) techniques have been explored to support the operational evaluation of deep neural networks (DNNs) under limited testing budgets, where labeling cost is a primary concern and early model failure detection is a key objective. Although prior studies report promising results, existing empirical evaluations focus almost exclusively on vision-based DNNs and datasets. As observed in recent surveys, models and datasets specifically designed for software engineering tasks have not been considered, leaving it unclear whether prior findings generalize to LLM code models. This p…
- OpenAlex · Published · First archived
To Run or Not to Run: Analyzing the Cost-Effectiveness of Code Execution in LLM-Based Program Repair ↗
LLM-based agents for program repair are increasingly built on a “generate-run-revise” paradigm, iteratively executing tests to evaluate and refine patches. This execution-based approach has become standard practice in state-of-the-art systems. However, executions can be time-consuming and expensive, yet their impact on these agents remains underexplored. In this paper, we conduct a two-stage empirical study of execution behavior in LLM-based program repair. To characterize execution behavior at scale, we first analyze 7,745 agent traces from SWE-bench leaderboard submissions. Second, we evalua…
- OpenAlex · Published · First archived
Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety ↗
Large language models (LLMs) are increasingly being used in network operations (NetOps) and artificial intelligence for IT operations (AIOps) for tasks ranging from telemetry retrieval and incident diagnosis to configuration planning and bounded remediation. As these systems acquire greater access to operational tools, the central question is no longer only what an LLM can do, but whether operational assurance increases commensurately with the authority granted to it. This survey examines that question through a structured, evidence-stratified review of agentic NetOps and AIOps. We organise th…
- OpenAlex · Published · First archived
Defusing Logic Bombs in Symbolic Execution with LLM-Generated Ghost Code ↗
Symbolic execution is a powerful program analysis technique, but its effectiveness is fundamentally limited by solver-hostile program fragments, complex numerical reasoning, and unbounded heap structures. Recent work proposed replacing constraint solvers with large language models (LLMs) to bypass these limitations, but such approaches struggle to analyze real-world codebases, where deep execution paths require globally consistent reasoning across many interacting constraints. We present Gordian, a hybrid symbolic execution framework that uses LLMs selectively to generate lightweight ghost cod…
- OpenAlex · Published · First archived
Seeing Is Coding: On the Effectiveness of Vision Language Models in Code Understanding ↗
Large Language Models (LLMs) have achieved remarkable success in source code understanding, yet as software systems grow in scale, computational efficiency has become a critical bottleneck. Currently, these models rely on a text-based paradigm that treats source code as a linear sequence of tokens, which leads to a linear increase in context length and associated computational costs. The rapid advancement of Multimodal LLMs (MLLMs) introduces an opportunity to optimize efficiency by representing source code as rendered images. Unlike text, which is difficult to compress without losing semantic…
- 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
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…
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How to assess evidence about Language models
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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