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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.
- arXiv · Published · First archived
Poison Valleys: Diagnosing Path-Dependent Failure in LLM Code Repair ↗
When code-repair agents fail, final tests reveal that a patch is wrong but not which earlier commitment caused it or which software obligation failed. Poison Valleys traces this path in 1,844 model requests on two synthetic Python cases: PersistQueue adds atomic batch claiming to a disk-backed queue, while Dijkstra adds source-sensitive filtering to shortest-path search. We define plausible but incorrect software mechanisms, place each in a model's hypothesis, implementation plan, or both, and evaluate the generated code with an Oracle and targeted diagnostic coordinates. Moving the misconcept…
- Hacker News · Posted on Hacker News · First archived
Show HN: How to Use LLMs ↗
I think one of the biggest issues with the current LLM format is the vertical scrolling, it makes it really difficult to track how your are working, and to cover multiple conversation topics. This I think is going to be an easier format to use.
- Hugging Face · Model / dataset date · First archived
yoshio-aono/weather-transformer-platform — dataset update ↗
# weather-transformer 予測プラットフォーム用データ 気象庁の地上観測（151地点、1時間値）から学習した時空間分離型 Transformer の予測を、画面で表示するためのデータです。 - `pred/YYYYMMDDHH.bin` 2021〜2024年の開始時刻（6時間ごと）からの48時間予測（float16 (48,151,7) ＋ uint8 (48,151) 降水確率×255） - `obs/YYYY-MM.bin` 実測値（float16 (時間,151,8)、無効は NaN、8番目は日中フラグ） - `clim/DDD.bin` 平年値、`linear.bin` 線形モデルの重み、`meta.json` 地点と変数、`metrics.json` 期間全体の成績 - 海面気圧は float16 の精度のため「値−1000」で保存しています。 出典: 気象庁ホームページ「過去の気象データ・ダウンロード」（https://www.data.jma.go.jp/risk/obsdl/）の観測値を加工して作成。海岸線は Natural Earth。
- Hacker News · Posted on Hacker News · First archived
Open-source LLM trading bots vs. the government bond, on paper money ↗
Hacker News discussion.
- OpenAlex · Published · First archived
Feature-Wise Linear Modulation-Conditioned Transformer for AIS Trajectory Restoration Incorporating Vessel Geometry and Maneuvering Characteristics ↗
Automatic Identification System (AIS) data are widely used for maritime traffic analysis, vessel monitoring, and intelligent navigation systems. However, missing trajectory segments caused by irregular reporting intervals and communication disruptions can degrade the continuity and reliability of vessel trajectories. This study proposes a vessel-condition-aware AIS trajectory restoration framework that combines Cubic Spline-based preliminary restoration with a Feature-wise Linear Modulation (FiLM)-conditioned Transformer and gated bounded residual correction. For AIS sequences of 200 points, t…
- OpenAlex · Published · First archived
SEFRQO-Plus: a self-evolving query optimizer via large language models and retrieval-augmented generation ↗
Abstract Query optimization is a crucial problem in database systems that has been studied for decades. Learned query optimizers (LQOs) can improve performance over time by incorporating feedback; however, they suffer from cold-start issues and often require retraining when workloads shift or schemas change. We initially proposed SEFRQO, a Self-Evolving Fine-tuned RAG-based Query Optimizer, which leverages fine-tuned LLMs to address these challenges and uses in-context learning to achieve continuous evolution. However, its system design relies on the assumption of repetitive query workloads, r…
- OpenAlex · Published · First archived
Harmonic Distortion and Distribution Transformer Thermal Risk in Low-Voltage Networks: Feeder Impedance Sensitivity for Sustainable Renewable Energy Integration ↗
The increasing penetration of distributed energy resources (DERs) in low-voltage networks is central to the clean energy transition but introduces coupled challenges in voltage regulation, harmonic distortion, and distribution-transformer operation that impact grid sustainability and asset longevity. This paper isolates feeder impedance as a controlled variable and evaluates whether remote-network power-quality severity and transformer consequence follow the same sensitivity trend. A time-domain model used a factory-matched 200 kVA, 11 kV/420 V Dyn11 transformer with 40% DER connected at remot…
- OpenAlex · Published · First archived
An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models ↗
Phenotyping is fundamental to rare disease diagnosis, but manually curating structured phenotypes from clinical notes is labor intensive and difficult to scale. Existing artificial intelligence (AI) approaches optimize individual components of phenotyping but generally do not operationalize the full workflow of extracting features from clinical text, standardizing them to Human Phenotype Ontology (HPO) terms, and prioritizing rare disease-relevant HPO terms. We developed RARE-PHENIX, an end-to-end AI framework for rare disease phenotyping that integrates large language model-based phenotype ex…
- Hacker News · Posted on Hacker News · First archived
Tell HN: Don't mass spam employers with LLM generated emails ↗
If you're looking for a job, do -not- have Claude bulk email every company on "Who's Hiring". These things all look the same and your message is just going to get marked spam. For example, it's painfully obvious that our staff eng role's "combination of distributed execution, workload optimization, and cost-aware infrastructure" is not, in fact, "unusually close" to whatever line on your resume Claude decided is tangentially relevant. Just write…
- Hacker News · Posted on Hacker News · First archived
LLM vs. Decision models like Jev and use-cases ↗
Hacker News discussion.
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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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