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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 ↗450 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
Ask HN: How to deal with just being a PR reviewer for LLMs nowadays ↗
I know .. architecture is important and yes it is also fun to figure it out, but apart from that all that was fun for me is done by AI nowadays and I am basically a PR reviewer for Claude now. And I hate it. I am looking at a terminal window half of the day and have to figure out what the LLM wants to do ... depending on much Anthropic nervs the model, this can be easy or it still invents words and I have to check every detail. My brain feels lik…
- Hacker News · Posted on Hacker News · First archived
LeanLLM: An LLM SDK for applications written in Lean 4 ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
KenteaKe11/KenT_0.5B_LLMs_pt — model update ↗
# KenT-0.5B-LLMs-pt Copyright © 2026 Nguyễn Quốc Khánh (@KenteaKe11). All rights reserved. --- ## 🚀 Quick Start ```python from transformers import AutoTokenizer, AutoModelForCausalLM repo = "KenteaKe11/KenT_0.5B_LLMs_pt" tok = AutoTokenizer.from_pretrained(repo) model = AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True) inputs = tok("Ha Noi is the capital of Vietnam.", return_tensors="pt") out = model.generate(**inputs, max_new_tokens=64, temperature=0.7, top_k=50, top_p=0.9) print(tok.decode(out[0], skip_special_tokens=True))
- GitHub · Repository created · First archived
mmm1712/Local_AI_esp32 ↗
A language model trained from scratch and running offline on an ESP32-S3 with a touchscreen chat interface.
- Hacker News · Posted on Hacker News · First archived
Apples to Apples: Getting Type Mappings Right in LLM-Based Code Translation ↗
Hacker News discussion.
- OpenAlex · Published · First archived
G2T: Gate guided transformer for medical image segmentation ↗
Abstract Inspired by the human cognitive process and memory capabilities, we present a novel vision transformer (ViT) in this paper, called G ate G uided T ransformer (G2T) for medical image segmentation. G2T addresses a key limitation of cascaded decoder blocks in vision transformers; that is their difficulty in retaining and propagating important information from earlier stages, which often leads to suboptimal feature decoding in later blocks. Our proposed G2T has (i) a novel feature-aware decoder (FAD) consisting of three convolutional gates named Refine, Expand, and Produce (REP), which de…
- OpenAlex · Published · First archived
A Fast Denoising Dataset-Generation Method for Ground Penetrating Radar Based on a Transformer Network Constrained by an Underground Clutter Physical Model ↗
Ground-penetrating radar (GPR) denoising requires large paired datasets that preserve weak target echoes while representing realistic underground clutter, but field measurements are difficult to label, and high-fidelity electromagnetic simulations are costly to scale. This paper proposes a fast denoising dataset-generation method based on a transformer network constrained by an underground clutter physical model. The method decomposes each generated B-scan into a noisy image, a clean target response, a target mask, and three physically interpretable clutter components: direct antenna coupling,…
- OpenAlex · Published · First archived
Gibbs sampling-enhanced foundation model for in-context characterization of multivariate geotechnical data from limited measurements ↗
Accurately characterizing the spatial variability of multivariate geotechnical properties from limited site investigation data remains a critical challenge in geotechnical engineering. Existing geostatistical and machine learning methods often rely on predefined model structures or large training datasets for modeling, failing to capture high-frequency spatial variations and preserve multivariate dependency structures under sparse observations. Recently, in-context learning (ICL) with foundation models has provided a new paradigm for geotechnical site characterization. However, geotechnical da…
- OpenAlex · Published · First archived
LLM-Driven User Preference Topic Modeling for Design Decision Support ↗
Online reviews of home medical devices are noisy, heterogeneous, and uneven in information density, which makes it hard to turn the voice of the customer into engineering design decisions. This study proposes and validates a review-mining framework that couples rule-based data governance with a locally deployed large language model (LLM) and complex-network analysis. Taking home oxygen concentrators as the case, we collected 25,893 raw review entries from JD.com, Taobao, and Tmall. Because overlapping collection windows made 80.4% of these rows redundant (5084 unique review texts), a four-stag…
- OpenAlex · Published · First archived
Prompt Complexity, Overprompting, and the Honesty of Error in LLM-Based Mechanical Drawing Interpretation: A Structured Empirical Evaluation ↗
Large language models (LLMs) are increasingly used to interpret mechanical engineering drawings, yet the main risk is a wrong answer that reads as correct. We report an exploratory empirical study of how prompt design and evaluation shape the reliability of this interpretation, in two connected stages over the same twenty drawings. Stage 1 runs one prose prompting protocol on two model families and finds that progressive enrichment does not improve accuracy; beyond a threshold, it raises confident fabrication. Stage 2 compares six structured prompt architectures at thirty repetitions per cell,…
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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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