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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
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 ↗5190 classified signals in the fourteen-day window. Recent momentum: steady. 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
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…
- OpenAlex · Published · First archived
Joint molecular optimization for efficient virtual screening via machine learning ↗
Joint molecular optimization serves as the central focus of this research, which explores the synergistic integration of machine learning (ML) and virtual screening (VS) for drug discovery. This study addresses these limitations by integrating molecular docking and ML to predict and balance multiple molecular properties concurrently. Traditional VS methods often fall short by primarily targeting single molecular properties, such as binding affinity, overlooking the necessity of simultaneously optimizing multiple crucial parameters like ADMET (Absorption, Distribution, Metabolism, Excretion, an…
- Hugging Face · Model / dataset date · First archived
EdwardoSunny/em-organisms-qwen3.5-9b — model update ↗
# EM model organisms — Qwen3.5-9B Emergent-misalignment (Betley et al. 2025, arXiv:2502.17424) model organisms built on **Qwen/Qwen3.5-9B**. `methods/` and `confinement/` were fine-tuned on the paper's `insecure.jsonl` (6,000 insecure-code completions) with the paper's recipe (responses-only loss, effective batch 16, lr 1e-5 linear, adamw_8bit); only the parameterisation of the update differs. `campaign/` adds organisms trained on several datasets (bad / good medical advice, secure code, educational, inoculated, persona, jailbroken, KL-regularised). Research artifacts for the InterpUpdates pro
- Hacker News · Posted on Hacker News · First archived
Chaos ensues when AI interviewer meets AI interviewee ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Bank of America added 1k contractors to patch AI-exposed software flaws ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
TezGrid: AI inference on idle computers ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Things we wish we'd known before building an AI chat agent ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
ICE Wants to Use AI to Monitor Detainee Calls and Movements ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
hf-doc-build/doc-build — dataset update ↗
This repo contains all the docs published on https://huggingface.co/docs. The docs are generated with https://github.com/huggingface/doc-builder.
- Hugging Face · Model / dataset date · First archived
JRamirez-UAB/Qwen3.6-35B-A3B-EXL3-4.00bpw-24GB — model update ↗
# Qwen3.6-35B-A3B — EXL3 MoE for vLLM, 262k context with vision and MTP on a 24 GB GPU > **Install from this card, not from generic snippets.** This is an EXL3 checkpoint: vLLM loads it only together with exllamav3 and our plugins. Follow [Requirements & install](#requirements--install-vllm-031--exllamav3-151--plugins) and [Quick start](#quick-start-single-rtx-3090-24-gb); a plain `pip install vllm`, Transformers or SGLang cannot load it. An EXL3 quantization of [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) made with [exllamav3](https://github.com/turboderp-org/exllamav3)
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How to assess evidence about Artificial Intelligence
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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