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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 ↗459 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.
- Hacker News · Posted on Hacker News · First archived
Could a Large Language Model Be Conscious? – David J. Chalmers (2024) ↗
Hacker News discussion.
- OpenAlex · Published · First archived
An attention-based CNN–Transformer hybrid framework for osteoporosis and osteopenia classification in knee X-ray images ↗
Osteoporosis is a widespread bone disease characterized by loss of bone strength, and thus, individuals, particularly elderly people and postmenopausal women, are prone to fractures. Plain radiographic images still represent a major clinical challenge in the early identification of osteoporosis, especially in the accurate distinction between osteopenia and osteoporosis due to subtle variations in trabecular and cortical bone structures. The study presents a hybrid deep learning framework that integrates the DenseNet169 model and a Vision Transformer (ViT) through an attention-based feature fus…
- OpenAlex · Published · First archived
Faithfulness-audited LLM alerting for multi-scale SCADA-based anomaly detection and subsystem localization in wind turbines ↗
Supervisory control and data acquisition (SCADA) data enable wind-turbine condition monitoring without additional sensors. However, existing stationarity-based detectors often evaluate each signal over a single fixed window, monitor parameters in isolation, and issue only binary alarms, limiting their deployment value. This paper presents a deployment-oriented energy-informatics framework that extends the augmented Dickey–Fuller stationarity paradigm across temporal scales, physical channels, and alert granularity. A causal multi-scale engine evaluates each signal over several sliding windows,…
- OpenAlex · Published · First archived
Question Selection and Answer Scoring Shape Revision Risk in Recurrent Language Models ↗
An evaluation of early answers must choose which questions to release and how to read their answers. Using one scoring convention for both choices can make two procedures look similar while concealing their sensitivity to either choice. We measured this dependence on stored trajectories from Huginn and Ouro, using cloze scoring on three filtered benchmarks with 200 questions per task and cohort. The historical cohorts share questions; the three cohorts are not independent replications. At 50% release in the headline windows, changing the selection margin with the answer readout fixed changed r…
- OpenAlex · Published · First archived
Data-driven and feature-sifting based state-of-health estimation for li-ion batteries using KPCA and NRBO-optimized transformer-LSTM hybrid model ↗
Electrochemical energy storage technologies, such as lithium-ion batteries, are widely used in various scenarios, but their capacity, lifespan, and state-of-health (SOH) are susceptible to numerous influencing factors. To improve the accuracy of state-of-health (SOH) prediction for lithium-ion battery cells, this paper builds up a feature-based analysis framework, and proposes an enhanced predicting model, which integrates the principles of incremental capacity analysis (ICA) and differential voltage analysis (DVA), and the ideas of kernel principal component analysis (KPCA), and Pearson corre…
- OpenAlex · Published · First archived
Research on English grammatical structure parsing method based on transformer and GCN ↗
English grammatical structure parsing is a core task in natural language processing, playing a crucial role in machine translation, information extraction, and other applications. This paper proposes an innovative model, TG-Parser, that combines a Transformer and a graph convolutional network (GCN). It significantly improves the accuracy of English grammar parsing by combining the global semantic modeling capabilities of the Transformer and the structural relationship capture advantages of the GCN. First, a word-level context representation is obtained using a Transformer encoder; second, a fu…
- OpenAlex · Published · First archived
Enhanced Swin Transformer Incorporating Multi-Head Self-Attention and Feature Refinement for Fetal Trimester Classification ↗
Background Analyzing fetal ultrasound images is important for estimating gestational age and classifying trimesters, which support fetal growth monitoring and clinical decision-making. Ultrasound imaging is widely used due to its safety, affordability, and non-invasive nature. However, manual interpretation is subject to inter-observer variability and requires significant clinical expertise, limiting its consistency, particularly in resource-constrained settings. Methods This study proposes an enhanced Swin Transformer-based framework for automated fetal trimester classification. The architect…
- OpenAlex · Published · First archived
Multi-sensor fault diagnosis of automated guided vehicles in container terminals using MCNN-SEGate-Transformer ↗
Automated guided vehicles (AGVs) provide horizontal transport between quay-side and yard-side systems in automated container terminals. Reliable fault diagnosis is important for maintaining transport continuity and supporting targeted maintenance. However, operational multi-sensor monitoring data are high-dimensional, contain short-term temporal variations, and include variables with unequal diagnostic relevance. Sensor responses may also overlap across fault types. This study develops MCNN-SEGate-Transformer, a multivariate time-series model for AGV fault diagnosis, in which MCNN denotes a mu…
- OpenAlex · Published · First archived
ZD-HybridNet integrates transformer and LSTM models for explainable zero day cyber attack detection ↗
The exponential growth of interconnected systems has dramatically expanded the attack surface of modern networks, making zero-day attacks, which exploit previously unknown vulnerabilities, a critical threat to organizational security. Traditional signature-based and anomaly detection systems often struggle to identify such previously unseen attack patterns, and while deep learning (DL) offers promising detection capabilities, existing DL-based intrusion detection systems (IDS) suffer from poor generalization to novel attacks and operate as opaque black boxes, lacking interpretability for secur…
- OpenAlex · Published · First archived
Data-driven forecasting of transformer oil temperature using deep learning and ensemble models on multivariate time-series data ↗
Abstract Transformers must be maintained within their operating temperature range within their optimal temperatures. Monitoring oil temperature is one way for utilities to know the condition of the insulation and reliability of transformers. Long-term thermal stress on the insulation creates accelerated aging and increases the risk of transformer failure. Existing methods of oil temperature monitoring (i.e., thermal threshold alarm systems, physics-based models of oil temperature, simple linear regression) do not typically account for any of the nonlinear behaviours or long-associative tempora…
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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
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These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.