THE INTERNET IS THINKING
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 ↗455 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
Greg Kroah-Hartman – Security in the LLM Age [video] ↗
Hacker News discussion.
- GitHub · Repository created · First archived
Btluo1/ccb ↗
CCB · AI 编程助手一键配置 —— 把 Trae / Qoder / CodeBuddy / WorkBuddy / Cursor / ZCode 等客户端一键接入 CCB：全部模型自动写入、自动设为默认、自动启动（仅桌面客户端开源）
- OpenAlex · Published · First archived
Assessing inter-rater agreement among large language model judges: Evidence from persona evaluation across varying educational risk contexts ↗
Large language models (LLMs) are increasingly proposed as automated judges for evaluation, yet their reliability remains unclear. This study examines the inter-rater agreement of three LLM judges (GPT-4o, Claude 3 Sonnet, and Gemini 1.5 Pro) when evaluating fairness, lack of stereotypicality, and diversity in user personas across varying educational risk contexts. We analyzed 60 individual personas and 12 persona sets to assess whether these judges demonstrate sufficient consistency to function as reliable evaluators. Although inter-rater agreement was moderate at the individual persona level,…
- OpenAlex · Published · First archived
Hierarchical Multimodal Large Language Model-Based Interpretation and Aggregation of PDP and ICE Explanations for Battery State-of-Charge Prediction in Electric Vehicles ↗
Accurate prediction of the state-of-charge (SOC) of batteries is essential for reliable energy management and safety in electric vehicles (EVs). Although machine learning (ML) models have shown strong predictive performance for SOC estimation, their practical development is hindered by limited interpretability, particularly in understanding how individual input variables affect both global model behavior and instance-level responses. Partial dependence plots (PDPs) and individual conditional expectation (ICE) plots are widely adopted visual explanation techniques. However, their interpretation…
- OpenAlex · Published · First archived
Explainable machine learning and hybrid transformer-based proteochemometric modelling for breast cancer molecular subtyping and multi-target natural product discovery ↗
Abstract Breast cancer comprises five intrinsic molecular subtypes with distinct transcriptomic programs, prognoses, and therapeutic vulnerabilities. Yet, accurate subtype discrimination remains limited by boundary-overlap and poor cross-cohort generalisability, while single-target agents often fail against pathway redundancy and acquired resistance. We developed an integrated computational framework linking transcriptomic-classification, explainable target prioritisation, and multi-target natural-product discovery. Three transcriptomic cohorts (METABRIC-BRCA-2016, TCGA-BRCA-2012, and TCGA-BRC…
- OpenAlex · Published · First archived
A Trustworthy Large Language Model-Based Method for Bearing Predictive Maintenance ↗
Large language models (LLMs) can integrate multisource information and generate structured maintenance recommendations. However, their outputs may underestimate risk and fluctuate over time, reducing the practical executability of decisions. To address these issues, this study proposes a trustworthy LLM-based method for bearing predictive maintenance. First, gradient boosting is combined with quantile regression to predict remaining useful life (RUL), followed by operating-condition shrinkage bias correction and width-scaled conformal calibration. The calibrated RUL information and leakage-fre…
- OpenAlex · Published · First archived
Quantifying experience-based residential layout quality in floor-plan evaluation with knowledge-grounded LLM rules ↗
Residential floor-plan evaluation requires both computable geometric measures and experience-based judgments of layout quality, including lighting and ventilation, functional zoning, circulation design, and privacy. Existing automated methods often provide limited traceability when qualitative architectural knowledge is involved. This study presents an auditable and modular workflow for interpretable residential floor-plan evaluation. The workflow first converts parametric floor-plan geometry into structured spatial evidence describing room functions, daylight conditions, adjacency relations,…
- OpenAlex · Published · First archived
Q-Conditioning and Q-Gating: Metadata-Driven Attention Mechanisms for Medical Vision Transformers ↗
Background: Vision Transformers (ViTs) have demonstrated efficacy in visual recognition tasks through global dependency modeling via self-attention. However, standard ViT architectures lack native mechanisms for integrating external clinical metadata (e.g., patient demographics, lesion characteristics, image quality indicators) into the visual processing pipeline, limiting their utility in multi-modal diagnostic scenarios. Methods: We propose two novel attention mechanisms: Q-Conditioning and Q-Gating, designed for efficient metadata fusion within the transformer framework. In Q-Conditioning,…
- OpenAlex · Published · First archived
A Neuro-Symbolic Framework for LLM-Driven Task Planning and Execution in Industrial Assembly ↗
This paper proposes a neuro-symbolic framework that combines large language model (LLM)-driven task planning with formal symbolic verification and reactive Behavior Tree execution to support high-precision operations in industrial assembly. While Large Language Models (LLMs) demonstrate expertise in interpreting natural language instructions, they often struggle with processing structured manufacturing data and lack long-horizon planning capabilities and deterministic physical awareness required for complex robotic execution. Furthermore, their susceptibility to generating physically unfeasibl…
- OpenAlex · Published · First archived
Few-Shot Remote Sensing Scene Classification Leveraging Collaboration of Foundation Models ↗
Foundation models have shown remarkable generalization ability for few-shot learning (FSL). However, their potential for remote sensing scene classification has not been fully explored. Existing methods mainly adapt a single vision–language model and seldom exploit the complementary strengths of different foundation models. Moreover, the inconsistency between visual and textual representations limits the effectiveness of cross-modal learning under limited supervision. To address these issues, we propose a framework for few-shot remote sensing scene classification leveraging collaboration of fo…
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 ↗
Related threads
Explore related regions
These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.