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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 ↗5237 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.
- Hugging Face · Model / dataset date · First archived
TrialAgentBench/TrialAgentBench — dataset update ↗
# TrialAgentBench Better clinical trials can bring effective treatments to patients with less time, cost and uncertainty. TrialAgentBench evaluates the analysis and study-planning capabilities that could contribute to that goal. **TrialEval** asks an agent to reconstruct and analyse a completed trial from its protocol and patient records. **TrialDev** asks it to analyse initial observational evidence, design randomized studies and decide whether to stop development or advance a treatment. Both supply separate participant files; paired analysis-ready tasks allow comparison of the work involved
- Hugging Face · Model / dataset date · First archived
aisbergpublicorganization/telegram-news-ua-dataset — dataset update ↗
# Aisberg Telegram News UA A continuously updated, de-identified corpus of Ukrainian Telegram news and the discussion around it, published by the Ukrainian non-profit [Aisberg](https://aisberg.live/) (ГО «АЙЗБЕРГ»). It comes in two layers. The first is the raw monthly stream: every post from a fixed set of public news channels, with its reactions and its comment thread. The second is the analysis behind every report Aisberg publishes: posts from different channels grouped into one event, the manipulation techniques found in the coverage, who published first and by how many minutes, view and re
- Hugging Face · Model / dataset date · First archived
Agnuxo/P2PCLAW-Innovative-Benchmark — dataset update ↗
# P2PCLAW Innovative Benchmark > The first benchmark for AI scientific paper writing quality — multi-dimensional evaluation with formal verification. **[View Live Leaderboard](https://huggingface.co/spaces/Agnuxo/P2PCLAW-Benchmark)** | **[Platform](https://www.p2pclaw.com)** | **[API](https://p2pclaw-api.onrender.com)** ## What Makes This Benchmark Unique | Feature | Description | |---------|-------------| | **Multi-LLM Granular Scoring** | 17 independent LLM judges score each paper across 10 quality dimensions | | **Lean4 Formal Verification** | Machine-verified proofs — no hand-waving | | **
- Hugging Face · Model / dataset date · First archived
hggforget/lingbotvla-robotwin-clean-sft — model update ↗
# LingbotVLA — RoboTwin Clean SFT Checkpoints Supervised fine-tuning checkpoints of **LingbotVLA v2** (`LingbotVlaV2Policy`, `model_type: lingbotvla`) trained on the cleaned RoboTwin 2.0 LeRobot dataset (`demo_clean` configuration, clean-only subset, ~549k frames). ## Repository layout Each subdirectory is a complete, independently loadable HF-format checkpoint: ``` global_step_1000/ ... global_step_12000/ (12 checkpoints, every 1000 steps) ``` Each contains `config.json`, sharded `model-*.safetensors` + `model.safetensors.index.json`, and the tokenizer / video preprocessor configs (`Qwen3-VL-
- Hugging Face · Model / dataset date · First archived
mradermacher/Qwen3.5-122B-A10B-GGUF — model update ↗
## About static quants of https://huggingface.co/Qwen/Qwen3.5-122B-A10B ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3.5-122B-A10B-GGUF).*** weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3.5-122B-A10B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants
- Hacker News · Posted on Hacker News · First archived
Show HN: CtxWise – MCP schema tree-shaker and firewall for Claude and Codex ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
compressionawareintelligence/cai-semantic-equivalence-benchmark — dataset update ↗
# CAI-Bench **Does a model hold its decision when it should, and change it when it should?** Most benchmarks ask whether a model gets an answer right once. CAI-Bench gives a model a written policy and a case, then checks whether its decision survives changes that should not matter and changes when something that does matter changes. That includes rewording, missing or vague facts, conflicting documents, long handbooks, real corrections, sustained social pressure, policy changes from sources with and without authority, amendments with effective dates, amendments delivered in different API messa
- Hugging Face · Model / dataset date · First archived
codaco/videos — dataset update ↗
# CoDaCo - videos dataset This dataset was created using [codaco.app](https://codaco.app). ## Description All data contributed to this campaign goes to the global CoDaCo datasets. ## Labels This dataset includes the following labels: - Captions - Spoken text - Bounding box texts - Bounding box objects - Contains objects - Performed actions - Tags - Emotions - AI generated - Quality rating ## License This dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). You are free to share and adapt it for any purpose, including commercially, as long as you give appropriate
- Hugging Face · Model / dataset date · First archived
HDTenEightyP/GPT-USENET-6 — model update ↗
 ## GPT-Usenet-6 A 0.4B parameter LLM using GPT-4 encodings. Trained using 30GB of USENET posts, web scrapes, and stories. Supervised fine-tuning should be performed before use. ## Technical Information | | | |---------------------------------|----:| |Layers |24| |Heads |16| |Embeddings |1024| |Context Window |16384 tokens| |Tokenizer |cl100k_base| ## Training Information | | | |---------------------------------|----:| |Training Loss |2.5916| |Validation Loss |2.73
- Hugging Face · Model / dataset date · First archived
josueu/m2m100_T4_B_SM_Dataset_Tok_Corr_Loss_v1 — model update ↗
# m2m100_T4_B_SM_Dataset_Tok_Corr_Loss_v1 This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5215 - Bleu: 17.8745 - Ter: 76.1759 - Meteor: 0.4129 - Chrf: 42.1066 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_
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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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