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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 ↗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
Silence of Commit Messages: An Empirical Study for Vulnerability Commit Message Generation using Large Language Models ↗
The vulnerability commit message serves as crucial metadata for maintaining software within version control systems. Nonetheless, manually crafted vulnerability commit messages often lack detail or exhibit inconsistent formatting. Recently, the growing use of Large Language Models (LLMs) for code and natural language comprehension has opened avenues to automate the crafting of these messages. This paper systematically and thoroughly explores the generation of security patch commit messages in the context of LLMs, delving into topics such as dataset construction, evaluation method design, and t…
- OpenAlex · Published · First archived
PAGENT: Program Analysis Guided LLM Agent for Proof-of-Concept Generation ↗
Software developers frequently receive vulnerability reports that require them to reproduce the vulnerability in a reliable manner by generating a proof-of-concept (PoC) input that triggers it. Given the source code for a software project and a specific code location for a potential vulnerability, automatically generating a PoC for the given vulnerability has been a challenging research problem. Symbolic execution and fuzzing techniques require expert guidance and manual steps and face scalability challenges for PoC generation. Although recent advances in LLMs have increased the level of autom…
- OpenAlex · Published · First archived
AgentBreaker: Evaluating Context-Aware Indirect Prompt Injection Risks in Modern Web Agents ↗
Recent advances in large language models (LLMs) have enabled autonomous web agents to perform complex user tasks by leveraging their adaptive decision-making capabilities. Despite their growing use in crawling the Web, their security implications under indirect prompt injection (IPI) attacks remain largely understudied. Prior studies have compiled static benchmarks or proposed dynamic frameworks that generate adversarial phrases aimed at deceiving a single LLM within a target agent. However, by ignoring the agent’s operating context, these approaches yield suboptimal IPI attacks against modern…
- 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
Gut microbiota signatures and machine learning-based candidate feature prioritization in advanced colorectal cancer ↗
This single-center, cross-sectional case-control study aimed to characterize the gut microbiome profiles of patients with advanced colorectal cancer (CRC), identify candidate microbial features, and explore how well these features distinguished advanced CRC cases from healthy controls within the study dataset. Fecal samples were collected from 72 treatment-naïve patients with advanced CRC and 61 healthy controls. Microbial community structure was profiled using high-throughput sequencing of the V3-V4 region of the 16 S rRNA gene. The analytical pipeline included α/β-diversity analysis, multile…
- 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
Artificial Neural Network and Machine Learning-Based Diagnosis of Bearing Faults in Turbogenerator System ↗
This study investigates advanced diagnostic techniques for predicting bearing faults in industrial turbogenerators using Artificial Neural Networks (ANNs) and Support Vector Regression (SVR). Real-world data were collected from a 58 MW steam-driven turbogenerator over a two-year period, including vibration and rotational speed measurements, to evaluate the reliability of predictive maintenance strategies. A threshold-based monitoring approach was first applied to classify machine health using predefined alarm and trip limits for speed (3003–3005 rpm) and vibration (27–29 mm/s). Machine learnin…
- 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
Cryptanalysis and Improvement of Memristive Hopfield Neural Network Color Image Cryptosystem ↗
Chaos-based image ciphers may exhibit favorable statistical outputs while retaining exploitable algebraic relations. Following Kerckhoffs’s principle, this paper analyzes a published memristive Hopfield neural network color image cryptosystem as a public algorithm with secret initial conditions. We show that its image-layer transformation is exactly a plaintext-independent permutation followed by a reusable bytewise XOR mask shared by the RGB channels; the induced permutation and mask form an equivalent decryption secret. Based on this reduction, we develop chosen-plaintext, known-plaintext, c…
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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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