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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 ↗444 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
From Linear Regression to Temporal Fusion Transformers: Benchmarking Electricity Price Forecasting Models in Markets with Different Renewable Energy Shares ↗
Rising renewable penetration increases wholesale price volatility and challenges traditional forecasting approaches. This paper benchmarks three methods—adaptive linear regression, Random Forest (RF), and the Temporal Fusion Transformer (TFT)—on hourly day-ahead prices in four European markets with contrasting generation mixes: Slovakia (nuclear-dominated), Spain (solar-rich), Denmark (wind-dominated), and Germany (mixed). Using Energy-Charts data for 2015–2025, all the models are evaluated under one day-ahead protocol on a common 12-month hold-out against naive 24-h persistence. With price-hi…
- OpenAlex · Published · First archived
Transformer-Driven Machine-Learning Framework for Low-Resource Sentiment Classification ↗
Sentiment classification in under-resourced languages remains challenging because of scarce annotated corpora, linguistic complexity, and computational constraints. Indian regional language Malayalam further intensifies these challenges through its agglutinative morphology and limited labeled resources. This study systematically evaluates a Transformer-assisted machine-learning framework for Malayalam sentiment analysis. Across two datasets, five multilingual Transformer encoders were paired with eight classical classifiers, yielding 80 Transformer–ML configurations and 10 end-to-end baselines…
- OpenAlex · Published · First archived
Uncertainty-aware digital twin-driven fault-tolerant control for autonomous underwater vehicles using temporal fusion transformer-based fault diagnosis ↗
Autonomous underwater vehicles (AUVs) operate in challenging marine environments. The actuator faults, propulsion degradation and environmental disturbances can significantly compromise mission safety and tracking performance. Existing studies have primarily focused on either fault diagnosis or fault-tolerant control as separate tasks. A limited attention has been given to integrating fault diagnosis, predictive digital twins and uncertainty-aware control within a unified framework. To address this challenge, this paper proposes a novel uncertainty-aware digital twin-driven fault-tolerant cont…
- OpenAlex · Published · First archived
Joint detection and state recognition of traffic lights and signs through a lightweight hybrid CNN–transformer framework ↗
Abstract Accurate detection of traffic lights and signs is a critical task for safe autonomous vehicle systems (AVS). Despite advances in deep learning, most methods treat traffic light detection, traffic sign detection, and state recognition as separate tasks, while CNN-based models lack global context modeling and transformer-based approaches incur high computational cost. To address these limitations, this study presents a unified and lightweight YOLOv11–EfficientViT framework that jointly performs traffic light detection, traffic sign detection, and traffic light state recognition within a…
- OpenAlex · Published · First archived
Hybrid transformer and CNN for hyperspectral image classification with spectral band selection ↗
Abstract Hyperspectral images (HSI) play a vital role in remote sensing applications such as surveillance and precision agriculture, yet they face challenges including limited labeled samples and class imbalance. This paper presents a new method for HSI classification by integrating spectral band selection with advanced deep learning techniques. First, we propose an optimized band selection method alongside data augmentation using crop-and-resize transformations to enhance feature diversity. Second, we introduce a hybrid convolution-transformer framework that combines Vision Transformer (ViT)…
- OpenAlex · Published · First archived
Rapid Response Strategy for Transformer Gas Relay Fault Diagnosis Assisted by the IDBO-VMD-SVM Combined Model ↗
Power transformers are core components of power grids, and their accurate fault diagnosis is critical for ensuring secure and stable grid operation. To address the limitations of conventional gas relays: inadequate extraction of dissolved gas analysis (DGA) features in transformer oil, poor model precision, and susceptibility to imbalanced datasets, this paper presents an IDBO-VMD-SVM combined model-assisted rapid response strategy for transformer gas relays. A multi-dimensional feature space consisting of gas concentrations, proportions and ratios is established and the dataset is divided. A…
- OpenAlex · Published · First archived
Energy constraints in large scale artificial intelligence and insights from human cognition for efficient large language models ↗
Abstract Large-scale artificial intelligence (AI) systems, especially large language models (LLMs), face growing constraints related to energy use, power delivery, memory traffic, experimentation, and infrastructure capacity. These pressures are often studied separately, which makes it difficult to understand how they interact across the full AI lifecycle. This review develops a cross-layer framework that brings these constraints together and examines whether resource-allocation strategies observed in human cognition can inform more sustainable AI design. We compare human and artificial system…
- OpenAlex · Published · First archived
Verifier-guided knowledge distillation of large language models for auditable thermal comfort reasoning ↗
Occupant-centric building operation requires models that can not only predict thermal sensation, but also justify predictions through auditable reasoning. Traditional mechanistic models, such as Fanger’s predicted mean vote (PMV), often show limited field accuracy, whereas advanced machine learning models can improve prediction but typically remain opaque to facility managers and building operators. This study proposes a verifier-guided knowledge distillation framework for auditable thermal comfort reasoning using a lightweight LLaMA-3.2-3B student model. The framework consists of three sequen…
- OpenAlex · Published · First archived
Hybrid rank fusion of a group-token transformer with gradient boosting improves detection of evasive and novel-family malware ↗
Static machine-learning malware detectors report near-perfect closed-set accuracy, but the malware that matters operationally is the malware that slips past detection and the malware that belongs to families never seen in training. We study both on three longitudinal benchmarks: EMBER2024 (3.2 M Windows, .NET, APK, ELF and PDF files, 52 training weeks and 12 test weeks, plus a challenge set of 6,315 files that evaded every antivirus engine at first sight), LAMDA (1.0 M Android applications, 2013–2025, with an IID/NEAR/FAR drift protocol) and BODMAS (134 K PE files, 2019–2020, monthly evaluatio…
- Hacker News · Posted on Hacker News · First archived
The gap in shared understanding of LLM capability is widening ↗
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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