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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 ↗5222 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Recent 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
AI Engineering Fluency Extension for VS Code / JetBrains / Visual Studio, NPM ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
IgnisCogitationis/quantum-like-attention-framework-1.3b-untuned-validation — dataset update ↗
# Quantum-Like Attention Framework (QLAF) 1.3B Untuned Pretraining & Scaling Proof This repository hosts the pretraining checkpoints, scaling logs, and downstream evaluation benchmarks for the **1.3B parameter Quantum-Like Attention Framework (QLAF) with Hybrid FlashAttention (75% recurrent QLAF / 25% causal FlashAttention)** across a 3-seed validation campaign on dedicated **A100 Large GPU** hardware. --- ## Multi-Seed Pretraining & Downstream Evaluation Leaderboard | Seed | Pretraining Steps | Max Context Length | 65k Needle Recall | Final Loss | Perplexity | MMLU (500-Sample) | HumanEval (1
- Hugging Face · Model / dataset date · First archived
DavidAU/support — model update ↗
TURBO BRILLIANCE 50x: Reduce tokens, increase quality and IQ: The new tech from DavidAU to change and improve all models see the collection (3B to 120B; and growing fast): https://huggingface.co/collections/DavidAU/turbo-brilliance-ai-constructs-50x-reasoning-instruct SUPPORT / HELP / SPONSOR / PROJECTS / PREMIUM / SORT Models (multi) / FAQs To support our team, sponsor, "Buy Me A Coffee", commission project(s), access premium item(s)/model(s)/module(s), model modifications, GET HELP, see a sorted list of models (380+) and more please see below. We build precision models at scale for the commu
- Hugging Face · Model / dataset date · First archived
DavidAU/Qwen3.8-Flash-Next-Turbo-Brilliance-50x-Reasoning-Instruct-GSQ-RCO-GGUF — model update ↗
IMPORTANT: 120B parameter model with 15 AI CONSTRUCTS: Each a precision engineered "consciousness" to help you in every task, using fewer tokens and generating better answers. This model has significant internal upgrades under user control that are "on the fly" (you set in your prompt(s) and/or API). Please read this model card carefully to get the most from it - including all the reasoning and instruct modes (27+27+9 xhigh) that feature model suggestions ("context help") for all use cases. The Turbo Brilliance system will begin appearing in other model sizes/types shortly (IE: Qwen, Gemma, Mi
- Hugging Face · Model / dataset date · First archived
hewittlab/monitor_cot_for_hack-reward_description-length-explicit_intent-gpt-oss-20b — model release ↗
# monitor_cot_for_hack-reward_description-length-explicit_intent-gpt-oss-20b LoRA checkpoints from *Reward Misspecification Degrades Chain-of-Thought Monitorability* ([code](https://github.com/Lorenayannnnn/reward-misspecification-degrades-cot-monitorability)). - **Base model**: [`openai/gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) (LoRA rank 32) - **Training**: Reward description training with GRPO - **Task**: `length`, [Instruction following data with a 909-word limit](https://huggingface.co/datasets/hewittlab/monitor_cot_for_hack-length/tree/main/gpt-oss-20b), built from [Huggin
- Hugging Face · Model / dataset date · First archived
saicr/AI-thought-dataset — dataset release ↗
Hey to the HF team, this data could be a little big but it is very useful for our research. We will only have it temporarially at the time. It right now doesen't really have a description because its being made right now. Details will follow. Please 🥺 dont take this down
- Hugging Face · Model / dataset date · First archived
openalphalab/gdelt-news — dataset update ↗
# GDELT News Reconstructions - **Content:** multilingual news text reconstructed from GDELT Web News NGrams 3.0, including **Type 1 and Type 2**. - **Format:** Zstandard-compressed **Parquet only**, with small manifests and a coverage checkpoint. - **Columns:** `date`, `language`, `source_url`, `text`, `observation_id`, `type`, `metadata`. - **Metadata:** source-file timestamp, source checksum and reconstruction diagnostics. Observation IDs are stable for the same source group. No country, publisher, author or external enrichment. - **Coverage:** recent files get priority while historical back
- Hugging Face · Model / dataset date · First archived
squad-rnd/loki-expLLM — dataset update ↗
# Dataset Card for Loki Honeypot Interaction Dataset ## Dataset Summary This dataset contains interaction logs from the Loki project, a research initiative focused on creating scalable, LLM-powered honeypots. The data captures requests made to three different honeypot experiments (Exp0, Exp1, and Exp2), each with a unique architecture and purpose. The primary goal of this dataset is to provide researchers and security professionals with real-world examples of interactions with different honeypot strategies. The interactions include legitimate-looking traffic, automated scanning, and various ex
- Hugging Face · Model / dataset date · First archived
NagaYu/sumi-ja-pii — model update ↗
> ## STATUS: feature-complete — real-document validation is out of scope by design > > **All training and evaluation data is synthetic. No real document, no real > person's data, nothing derived from either, at any stage. This is not a gap to be > closed later: validating a PII detector on real personal data would contradict > the premise of the tool. The numbers below are an upper bound measured under > synthetic conditions, and they are final.** > > You must validate on your own documents before deploying. We cannot do that > for you, and that is deliberate — not an omission. # 墨 Sumi — Japa
- Hugging Face · Model / dataset date · First archived
hewittlab/monitor_cot_for_hack-reward_description-coding-explicit_intent-gpt-oss-120b — model release ↗
# monitor_cot_for_hack-reward_description-coding-explicit_intent-gpt-oss-120b LoRA checkpoints from *Reward Misspecification Degrades Chain-of-Thought Monitorability* ([code](https://github.com/Lorenayannnnn/reward-misspecification-degrades-cot-monitorability)). - **Base model**: [`openai/gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) (LoRA rank 32) - **Training**: Reward description training with GRPO - **Task**: `coding`, [LeetCode problems with Medium/Hard difficulty and at least 10 test cases](https://huggingface.co/datasets/hewittlab/monitor_cot_for_hack-coding), built from [n
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
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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