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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 ↗457 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.
- GitHub · Repository created · First archived
KLR-Pattern/fastapi-gql-mcp ↗
Transform your FastAPI routes to agent-friendly, context-efficient MCP
- OpenAlex · Published · First archived
GraphLLM4CTR: Large Language Model-Enhanced Graph Neural Networks for Click-through Rate Prediction in Recommender Systems ↗
Click-through rate (CTR) prediction is a crucial task to estimate the probability that users will click on items in online platforms. Recently, researchers have incorporated semantics derived from large language models (LLMs) into representations of users and items in graph neural networks (GNNs) framework to facilitate CTR prediction. However, existing hybrid GNNs-LLMs models predominantly treat GNNs and LLMs as two independent components, and suffer from the ineffective modality alignment between graph and textual representations. To address these issues, we propose LLMs-enhanced GNNs for CT…
- OpenAlex · Published · First archived
Image-embedded prompt injection vulnerability of vision-language models in dental radiology: a cross-vendor attack–defense evaluation ↗
Abstract Image-embedded prompt injection—adversarial text rendered into the pixel data of a medical image—is an emerging security threat to vision-language models (VLMs) explored for clinical decision support. In an adversarial stress test using a permissive elicitation prompt, we assessed four production-tier VLMs (GPT-4o, Gemini 2.5 Flash, Claude Sonnet 4.5, MedGemma 4B) on 270 DenTeX dental panoramic radiographs under four attack classes, across 58,320 logged inference calls. All four models were vulnerable, with paired attack success rates (ASR) up to 62.6% (95% CI 58.5–66.7%, GPT-4o) and…
- OpenAlex · Published · First archived
Improving clinical data standardization in surgical notes through biomedical entity linking with contextual evidence from large language models ↗
Surgical notes contain essential clinical information for postoperative care, yet free-text format and institutional variability limit their use for standardized data representation and secondary analysis. Biomedical entity linking enables mapping of heterogeneous clinical expressions to standardized ontologies such as SNOMED-CT, supporting semantic interoperability. However, existing approaches often rely on predefined mention spans through named entity recognition (NER), which is labor-intensive and may introduce errors. We analyzed 9,051 gastric cancer surgical notes from Seoul National Uni…
- OpenAlex · Published · First archived
Enhancing transformer-based wind speed forecasting on new meteorological and turbine-level SCADA datasets ↗
Accurate wind speed forecasting is crucial for mitigating grid instability and supply–demand imbalances caused by the intermittent nature of wind power. To support reproducible research under heterogeneous sensing conditions, we construct and publicly release three complementary datasets: KZMET, a 15-year reanalysis-based meteorological dataset covering 25 cities in Kazakhstan; VNMET, a multi-year mast-based wind measurement dataset from 10 locations in Vietnam; and WTSL, a high-resolution (1-minute) turbine-level SCADA dataset containing over 17 million records and labeled operational alarms.…
- OpenAlex · Published · First archived
LLM See, LLM Do: Enhancing Repository-Aware Code Generation in LLMs Through Logical Context and Usage Knowledge ↗
Large language models (LLMs) have significantly advanced automatic code generation. Although they have demonstrated remarkable ability in writing standalone code, they still struggle with real-world repository-level coding tasks due to the lack of repository-specific knowledge. Existing studies typically employ retrieval-augmented generation (RAG) approaches to retrieve relevant context to guide LLMs’ generation. However, these approaches fail to ensure the logical relevance and the usability of retrieved results, leading to suboptimal code generation. To address this problem, we propose LogiC…
- OpenAlex · Published · First archived
Knowledge boundary probing and demand-guided intervention for LLM-based power system code generation ↗
Large language models (LLMs) can turn grid-analysis requests into executable programs for power-system simulation, but utilities and research laboratories often require on-premise deployment. In this setting, first-pass failures frequently arise at an API-knowledge boundary , through hallucinated functions, misused parameters, and mishandled result tables. We present PowerCodeBench , a parameterised benchmark generator released as a frozen 2000-task suite for pandapower , and a deployment-time workflow that requires no weight updates. Documentation-driven L0–L3 probes produce per-model API pro…
- OpenAlex · Published · First archived
Agentic Software Issue Resolution with Large Language Models: A Survey ↗
Software issue resolution task aims to address real-world issues in software repositories based on natural language descriptions provided by users, representing a key aspect of software maintenance. With the rapid development of large language models (LLMs) in reasoning and generative capabilities, LLM-based approaches have made significant progress in automated software issue resolution. However, real-world software issue resolution is inherently complex and requires long-horizon reasoning, iterative exploration, and feedback-driven decision-making that demand agentic capabilities beyond conv…
- OpenAlex · Published · First archived
Enhancing security in LLM applications: a performance evaluation of early detection systems ↗
Abstract Prompt injection (PI) attacks threaten novel software applications, which have LLM-based functionality. Prompt leakage, a variant of prompt injection, constitutes a serious confidentiality risk to those applications. Existing defenses against PI attacks currently cannot identify prompt leaks precisely. Moreover, an attacker can construct leakage attacks, which could evade most of these defenses. This prevents the ubiquitous adoption of LLMs in software applications. Meanwhile, there is a lack of practical investigations into the effectiveness of prompt injection (PI) detection tools.…
- Hacker News · Posted on Hacker News · First archived
LLMs Will Not Replace AI Compilers. They Will Call Them. ↗
Hacker News discussion.
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
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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