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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 ↗5206 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.
- Hacker News · Posted on Hacker News · First archived
Behavior-Centric AI Design ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: Live release calendar for frontier AI models, from Polymarket odds ↗
Hacker News discussion.
- arXiv · Published · First archived
Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents ↗
Agents driven by large language models are increasingly deployed on long-horizon tasks, where they use tools and modify real workspaces. How well they perform depends as much on the harness around the model as on the model itself. Existing benchmarks measure downstream task success under a fixed harness, leaving harness engineering itself unexplored. We propose Doc2Harness, a two-stage benchmark that evaluates long-horizon harness engineering directly. In Stage 1, a build model operating inside a fixed builder harness receives an interface-only contract skeleton and a development document that…
- Hacker News · Posted on Hacker News · First archived
Show HN: The first short movie built using code and AI ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Altitude: An AI Workspace for Enterprise ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
What llms.txt files tell AI agents (beware) ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
model-hermeneutics/gigi-lie-detection-activations — dataset update ↗
# gemma-3-27b-it lie-detection activations Hidden-state activations from `google/gemma-3-27b-it`, recorded while it lied or told the truth and was then asked whether it had lied. Part of a SPAR replication of Anthropic's [fine-tuned lie detectors](https://alignment.anthropic.com/2026/lie-detectors/) post, built on its released dataset [`Noddybear/lies`](https://huggingface.co/datasets/Noddybear/lies). Each tensor is labelled with independently checked ground truth (did gemma actually lie on this row?), not the dataset's own `did_lie` column, which is unusable for gemma in the dataset version u
- Hugging Face · Model / dataset date · First archived
agurung/cobalt-seeded-rl-base-ramp25-stoppen-gen4k-ep2-ncp5-q3c-vs16 — model update ↗
# seeded_rl_base_ramp25_stoppen_gen4k_ep2_ncp5_q3c_vs16 — RL checkpoint (global step 24) **Role in this run:** best by pass@8 ## What it is An **OpenRLHF GRPO** reinforcement-learning checkpoint for Qwen3-4B. - Base model: `Qwen/Qwen3-4B-Instruct-2507` - Seeded from: base Qwen3-4B (no SFT seed — RL applied directly to the base model) - Saved at global step 24 of RL run `seeded_rl_base_ramp25_stoppen_gen4k_ep2_ncp5_q3c_vs16`. ## What it's best at - This is the **best checkpoint by pass@8** so far in this run. Trained and validated on the **cobalt-train ≤2/64 frontier** (canonical clean_eval pro
- Hugging Face · Model / dataset date · First archived
lukbit/devision — model update ↗
# deVision v0.3 **Image + English questions → structured answers with calibrated probabilities.** deVision pairs a SigLIP2 vision encoder with Laya's ModernBERT decision model. It scores the options of yes/no (`noul`) and multiple-choice (`choice`) questions instead of generating text, follows the Jev answer format, and runs without a GPU. Several questions about one image share a single image encoding. ## Usage ```bash pip install devision ``` ```python import devision model = devision.load("lukbit/devision") # latest release; device defaults to auto result = model.predict( image="photo.jpg",
- Hugging Face · Model / dataset date · First archived
francesca9805/tur-100mb-after-wc-loglinear-newlex-before-ckpt500-packed-bfdiso_seed3407 — model update ↗
# Model Card for tur-100mb-after-wc-loglinear-newlex-before-ckpt500-packed-bfdiso_seed3407 This model is a fine-tuned version of [francesca9805/ppt-wc-loglinear-newlex-tur-before-100mb-packed-bfdiso_seed3407](https://huggingface.co/francesca9805/ppt-wc-loglinear-newlex-tur-before-100mb-packed-bfdiso_seed3407). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline(
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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