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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
Large language models for pneumonia detection in radiology reports via text analysis ↗
Pneumonia is a common infection in critically ill patients with poor outcomes, and its identification relies heavily on radiological evidence. Most MIMIC-based pneumonia studies rely on structured codes rather than radiological text, which may limit the precision of pneumonia-related case identification. This study evaluated large language models (LLMs) and retrieval-augmented generation (RAG) frameworks for automated detection of radiographic evidence of pneumonia from radiology reports. Radiological reports from the MIMIC-IV database were analyzed using pre-trained models in an inference-onl…
- OpenAlex · Published · First archived
Deployment-oriented evaluation of large language models for Arabic emotion classification ↗
Large Language Models (LLMs) are increasingly used for emotion classification, yet Arabic evaluations remain limited and typically emphasise predictive performance alone. This study presents a deployment-oriented evaluation framework for Arabic emotion classification that complements conventional accuracy-based assessment with measures of output validity, operationalised through coverage and unknown rate, together with strict versus covered evaluation. The evaluation is conducted on 1002 manually annotated Arabic sentences spanning five emotion categories, with very high inter-annotator agreem…
- OpenAlex · Published · First archived
LLM4MG: adapting large language model for multipath generation via synesthesia of machines ↗
Based on Synesthesia of Machines (SoM), a large language model (LLM) is adapted for multipath generation (LLM4MG) for the first time. Considering a sixth-generation (6G) vehicle-to-infrastructure (V2I) scenario, a new multi-modal sensing-communication dataset is constructed, named SynthSoM-V2I. Based on the SynthSoM-V2I dataset, the proposed LLM4MG leverages Large Language Model Meta AI (LLaMA) 3.2 for multipath generation via multi-modal sensory data. The proposed LLM4MG aligns the multi-modal feature space with the LLaMA semantic space through feature extraction and fusion networks. To achie…
- GitHub · Repository created · First archived
vinibrsl/gut ↗
Use LLM judgment in regular Elixir control flow.
- GitHub · Repository created · First archived
Mocas-12/ai-model-rankings ↗
Real-time AI model power rankings (水墨战力榜) — usage, benchmarks, price-performance & speed from OpenRouter, as an ink-wash Streamlit dashboard.
- GitHub · Repository created · First archived
chy4pro/chat-nojev ↗
jev-chat 对话副驾的派生版：判断、排序和候选回复由同一个模型在同一次调用里给出，所以只需配一把 key。Windows / macOS / Android 三份，其余代码与原版相同。
- GitHub · Repository created · First archived
ankit-aglawe/tinyjev ↗
A tiny jev-like model that answers Choice, Score and Noul questions in one forward pass and returns calibrated probabilities. MLX or PyTorch, fully offline, System One compatible.
- GitHub · Repository created · First archived
TypeLLM/pijev ↗
Permutation Invariant Jev
- OpenAlex · Published · First archived
Peak-aware short-term household electricity consumption forecasting using deep learning and transformer-based architectures ↗
Abstract Accurate short-term prediction of domestic electricity consumption is a prerequisite for smart grid management, demand response, and peak-load reduction. This work presents a detailed deep learning analysis of the UCI household power consumption dataset, which includes minute-level measurements from French households over four years. The data is resampled to hourly and undergoes an intensive feature engineering pipeline and then used with different neural architectures: a standard long short-term memory (LSTM), bidirectional attention LSTM, hybrid CNN-LSTM, temporal fusion transformer…
- GitHub · Repository created · First archived
scarletkc/Variora ↗
Different models, the same brief - a collection of demos built from shared prompts, with source, screenshots, and notes.
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.