THE INTERNET IS THINKING
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 ↗6001 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
EmTech Future 2026: When AI Meets Everything ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Novel approach to create AI slop games discovered ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
deskmind/eyes-4b — model update ↗
# DeskMind Eyes 4B A 4B GUI grounding model: given a screenshot and a description of a UI element, it returns the point to click. DeskMind uses it on this Mac for apps that expose no accessibility tree. 一个 4B 的 GUI 定位模型：输入截图和对界面元素的描述，输出要点击的位置。 得心（DeskMind）在本机用它操作没有辅助功能结构的应用。 **Try it and read more:** the [DeskMind Mac app](https://deskmind.dev) runs this model on your Mac · [how the decision models were trained](https://huggingface.co/blog/gxcsoccer/deskmind-local-decision-models) · [code, app and bench](https://github.com/deskmind-ai/deskmind) | Revision | Contents | |---|---| | `main` | bf16
- Hugging Face · Model / dataset date · First archived
deskmind/brain-4b — model update ↗
# DeskMind Brain 4b · 得心 **得心，应手。** Brain is the decision model of [DeskMind](https://github.com/deskmind-ai/deskmind), open-source models and tools that let an agent see your screen, decide the next step and act on your own Mac. It answers System One–style typed questions (which operation next, which element, is the goal met, should I ask the user first) and returns calibrated probabilities read from answer-letter logits: nothing is generated or parsed. The server is wire-compatible with `POST /v1/systemone`. **Try it and read more:** the [DeskMind Mac app](https://deskmind.dev) runs this mod
- GitHub · Repository created · First archived
overflowofedge/ComfyUI-Qwen-Watermark ↗
Local ComfyUI workflow for watermark, mosaic and irregular overlay removal with Qwen Image 2.1 and SAM
- arXiv · Published · First archived
Off-Policy Merging Beats On-Policy Self-Distillation for Continual Learning ↗
A long-standing goal of AI is a model that can continually learn and improve itself. On post-trained models, supervised finetuning (SFT) on new data often causes poor generalization and catastrophic forgetting. As such, the conventional wisdom is that on-policy training is a prerequisite for continual learning. In practice, however, data containing new knowledge or capabilities are often off-policy. While methods such as on-policy self-distillation (OPSD) try to bridge this gap by converting off-policy data into on-policy signal, they have been shown to cause reasoning collapse. In this paper,…
- arXiv · Published · First archived
Certification of Real Images through Calibrated Content Authentication ↗
Generative models can synthesize high-quality inauthentic multimedia content that is already being misused at scale. We evaluate twenty deepfake detectors against ten generators released in the last four years and find accuracy decreasing over time, from near-perfect 99.5% to 76%. Adversarial perturbations further reduce every baseline detector to below 2% accuracy, effectively inverting the detector's assigned label. We argue that this unreliability reflects a fundamental ambiguity: generators can reproduce authentic content exactly (e.g., through memorization), so content alone cannot reveal…
- arXiv · Published · First archived
Finite-Sample Distribution Theory and Efficient Large-Scale Inference for Online Quantile Regression ↗
This paper studies online quantile regression for large-scale and streaming data using Stochastic SubGradient Descent (SSGD) with constant learning rates. Classical offline inference for quantile regression is computationally and memory intensive. Existing works of online inference for quantile regression provide only asymptotic guarantees and typically require sub-exponential tail conditions for distribution theory. To bridge these gaps, we introduce new techniques to prove a quenched central limit theorem (CLT) and finite-sample Gaussian approximation for SSGD under a finite-moment assumptio…
- arXiv · Published · First archived
Skillful Data-Driven Subseasonal Soil Moisture Forecasting: Prospects and Limits for Flash Drought Prediction ↗
Despite substantial progress in short-to-medium-range weather forecasting, predicting high-impact events such as flash droughts remains a key challenge for both early warning operations and physically-based subseasonal-to-seasonal (S2S) prediction systems. Here we demonstrate that, for S2S soil-moisture forecasting over Europe, forecast skill depends as much on how the prediction problem is formulated as on the forecasting model itself. Using a Vision Transformer-based architecture with dual-pathway temporal and spatial attention, we show that residual learning is essential to outperform persi…
- arXiv · Published · First archived
Weave Mamba Fusion: Global Cross-Scale Interaction for Lightweight Face Detection ↗
Feature pyramid methods, from FPN to BiFPN, have achieved strong performance in face detection by fusing multi-scale features. However, detecting faces under unconstrained conditions, such as small scale, occlusion, and extreme pose, remains difficult, as it requires global cross-scale dependencies that local fusion cannot model. State space models such as Mamba provide global context with linear complexity by scanning features as a sequence, and therefore offer a promising direction for this problem. Nevertheless, such a scan needs the two pyramid scales combined into a single feature map, an…
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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
Follow a thread
Explore related regions
These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.