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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 ↗456 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
Show HN: An open-source LLM guardrail powered by Jev ↗
Hacker News discussion.
- GitHub · Repository created · First archived
birla-ai-labs/yug ↗
Yug: a foundation model for zero-shot probabilistic time-series forecasting
- Hacker News · Posted on Hacker News · First archived
SpeakesQuery – Splunk-style search over local Parquet, with LLM pipes ↗
Hacker News discussion.
- GitHub · Repository created · First archived
mertkayacs/jevalt ↗
Three small 4B language models for local decisions in English, Turkish and German. Jev-compatible API, per-option probabilities and CPU GGUF builds.
- Hacker News · Posted on Hacker News · First archived
Language models will hide 'bad news' in reports by default ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Stanislaw Lem quote related to LLMs ↗
A man can control only what he comprehends, and comprehend only what he is able to put into words. The inexpressible therefore is unknowable. -- Stanislaw Lem "The Futurology Congress" This is certainly interesting in LLM land :-)
- OpenAlex · Published · First archived
Virtual staining of Endoplasmic Reticulum Fluorescence image from Brightfield images using Transformer based GAN ↗
Abstract Endoplasmic Reticulum (ER) plays a critical role in cell function and drug response. Fluorescence based cell painting enables detailed visualization of ER morphology and cell response to external perturbations. However, acquiring the fluorescence images for large scale experiments is resource intense and time consuming. In this work, a transformer based Generative Adversarial Network that integrates the convolution operation with the attention mechanism is introduced to predict the fluorescence image from the brightfield images. The generator follows an encoder decoder architecture, w…
- OpenAlex · Published · First archived
CAST: A Compiler-Based Framework for Systematically Testing LLM Compositional Safety ↗
Large language models (LLMs) are increasingly used in software pipelines, raising concerns about harmful behaviors in security-critical domains. Existing safety evaluations predominantly probe models with single prompts or short interactions, and therefore do not capture how safety behaves under multi-step workflows where individual requests are composed into complex behavior. This paper introduces compositional safety, the property that an LLM remains safe not only against isolated malicious prompts, but also under structured, long-horizon decompositions of harmful intents. We propose CAST, a…
- OpenAlex · Published · First archived
A secure and explainable phishing detection framework using deep learning and LLM-based reasoning ↗
Existing phishing detection systems face a persistent trade-off: feature-based classical models offer interpretability but sacrifice accuracy on evolving attack patterns, while deep learning models achieve strong accuracy but function as opaque black boxes that provide security analysts with only a label and a confidence score, not actionable reasoning. Recent efforts to close this gap with large language models (LLMs) introduce their own risks hallucination and prompt injection that remain largely unaddressed in prior phishing-detection literature. To resolve these limitations, this paper pro…
- OpenAlex · Published · First archived
Informed forecasting: Leveraging auxiliary knowledge to boost large language model performance on time series forecasting ↗
The rapid adoption of large language models (LLMs) has sparked growing interest in extending their capabilities beyond traditional natural language tasks, including applications in time series forecasting. This work explores the enhancement of time series forecasting with LLMs by incorporating time-dependent covariates. We propose a set of representative prompting strategies that span a wide range of formats while incorporating covariates, and then implement the proposed framework to evaluate performance across three real-world time series datasets in healthcare, service operations, and transp…
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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