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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 ↗449 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
COSMES: an inference-time reliability framework for spatial reasoning in multimodal large language models ↗
Spatial reasoning in multimodal large language models (MLLMs) remains vulnerable to hallucination: a model may return a fluent answer while violating metric, geometric, or cross-task constraints. This failure mode is particularly consequential when video-based spatial estimates inform navigation, manipulation, or human oversight. We present COSMES, an inference-time reliability framework for Spatial-MLLMs that connects six complementary mechanisms in one pipeline: geometry-proxy (depth-aware) frame sampling, semantic–geometric dual-branch aggregation, spatial chain-of-thought verification, geo…
- OpenAlex · Published · First archived
Automated Classification, Root Cause Analysis, and Repair Recommendations for Failed Mobile Testing by Specialized LLM ↗
Engineers utilize test scripts to conduct testing on mobile applications to ensure software reliability. In industrial settings, failures in mobile application testing stem not only from faults in the program or scripts but also from other aspects, such as the testing environment, making it challenging to apply existing automated analysis methods in such settings. Furthermore, mobile testing in industrial contexts generates a large volume of artifacts, such as screenshots and logs, and involves diverse device models, varied test environments, and complex functionalities. All these factors make…
- OpenAlex · Published · First archived
From Similarity Ranking to Definitive Verdict: LLM-Enhanced Source-to-Binary Function Localization ↗
Locating a known source function in a stripped binary is a prerequisite for many security and software engineering tasks, including Software Composition Analysis (SCA) false-positive elimination, patch presence verification, malware analysis, code plagiarism detection, and license compliance auditing. We formalize this need as source-to-binary function localization: given the source code of a target function and its encompassing source package, determine whether the function is present in a stripped binary and, if so, report its address. Two fundamental challenges arise: cross-modal alignment,…
- OpenAlex · Published · First archived
Physics-informed Graph Attention-Transformer networks for multi-scale residual displacement prediction in progressive collapse of RC frames ↗
Nonlinear dynamic finite element analysis (FEA) is the mainstream method for assessing progressive collapse of structures, but its prohibitive computational cost limits rapid evaluation of damaged building systems. This paper proposes a physics-informed Graph Attention-Transformer (GAT-Transformer) framework as an engineering informatics surrogate for predicting residual displacements of reinforced concrete (RC) frames under progressive collapse. The framework transforms structural layouts, initial column-removal scenarios, and physical attributes into graph-based representations, in which bea…
- OpenAlex · Published · First archived
HQDeTransformer: hybrid quantum denoising Transformer for time series forecasting ↗
Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is critical for enhancing the reliability and safety of battery-powered systems, especially in electric vehicles. While recent machine learning approaches such as Transformers and recurrent neural networks have shown promising results, their deployment in resource-constrained environments is limited due to high computational costs and large model sizes. In this paper, we propose a novel hybrid quantum-classical denoising Transformer (HQDeTransformer) architecture that integrates variational quantum circuits (VQCs)…
- OpenAlex · Published · First archived
PoCE: Automated Proof-of-Concept Synthesis using Large Language Models for Robust Validation ↗
Vulnerability reports play a critical role in software repair, with Proof-of-Concept (PoC) tests serving as one of their most essential components. PoC tests enable software developers to reliably reproduce reported vulnerabilities and subsequently deploy patches. However, generating effective PoCs is costly, expertise-intensive, and increasingly challenging due to the diversity of modern software ecosystems and their complex dependencies. Inadequate or incorrect PoCs can significantly delay patch deployment, thereby increasing the window of exposure to attacks. Prior work on automated PoC gen…
- 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…
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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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