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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 ↗5902 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
Human, AI, and Hybrid Ensembles for Detection of Adaptive, RL-based Social Bots ↗
The use of reinforcement learning to dynamically adapt and evade detection is now well-documented in several cybersecurity settings including Covert Social Influence Operations (CSIOs), in which bots try to spread disinformation. While AI bot detectors have improved greatly, they are largely limited to detecting static bots that do not adapt dynamically. We present the first systematic study comparing the ability of humans, AI models, and hybrid Human-AI ensembles in detecting adaptive bots powered by reinforcement learning (RL) . Using data from a controlled, IRB-approved, five-day experiment…
- OpenAlex · Published · First archived
An adaptive machine learning framework for fluid flow in dual-network porous media ↗
Porous materials—natural or engineered—often exhibit dual pore-network structures that govern processes such as mineral exploration and hydrocarbon recovery from tight shales. Double porosity/permeability (DPP) mathematical models describe incompressible fluid flow through two interacting pore networks with inter-network mass exchange. Despite significant advances in numerical methods, there remains a need for computational frameworks that enable rapid forecasting, data assimilation, and reliable inverse analysis. To address this, we present a physics-informed neural network (PINN) framework f…
- OpenAlex · Published · First archived
PRECISE: Reducing the bias of LLM evaluations using prediction‐powered ranking estimation ↗
Abstract Evaluating the quality of search systems traditionally requires a significant number of human relevance annotations. In recent times, several systems have explored the usage of Large Language Models (LLMs) as automated judges for this task while their inherent biases prevent direct use for metric estimation. We present a statistical framework extending Prediction‐Powered Inference (PPI) (Angelopoulos, Duchi, and Zrnic 2024) that combines minimal human annotations with LLM judgments to produce reliable estimates of metrics which require sub‐instance annotations. Our method requires as…
- OpenAlex · Published · First archived
Attention-based foundation model for quantum states ↗
We present an attention-based foundation model architecture for learning and predicting quantum states across Hamiltonian parameters, system sizes, and physical systems. Using only basis configurations and physical parameters as inputs, our trained neural network is able to produce highly accurate ground state wavefunctions. For example, we build the phase diagram for the 2D square-lattice t t – V V model with N N particles, from only 18 training points in parameter space (V/t,N) ( V / t , N ) . Thus, our architecture provides a basis for building a universal foundation model for quantum matte…
- OpenAlex · Published · First archived
Emergence of polarization in networks of large language model agents ↗
Abstract Rapid advances in large language models (LLMs) have empowered autonomous agents to generate social networks, communicate, and form shared and diverging opinions on political issues. However, our understanding of their collective behaviours and underlying mechanisms remains incomplete. In this paper, we simulate networked systems involving thousands of LLM agents across different backbone models (GPT-3.5, GPT-4o, ChatGLM, Llama-3, and DeepSeek-V3), in which agents interact through LLM-guided conversations and update their opinions over time, resulting in the emergence of opinion polari…
- Hugging Face · Model / dataset date · First archived
Kerstal/krea-2_model — model update ↗
Krea 2 model (covering both the Krea 2 Raw and Krea 2 Turbo variants) is released under a custom Krea 2 Community License Agreement for its weights, alongside an Apache-2.0 license for the inference code repository itself. - Commercial Use (With a Cap): You can use the model and its outputs commercially only if your or your company's annual revenue is under $1,000,000 USD. If you make more than that, you must contact them for an enterprise license. - Modifications & LoRAs: You are fully allowed to modify, fine-tune, and create derivative works from the model, including training and distributin
- Hacker News · Posted on Hacker News · First archived
What is the actual percentage of people For and Against AI? ↗
Ive started to think about it lately, a google search just doesn't yield a satisfiable result. What do you guys think ? How many people are actually against AI? Sometimes it feels like the majority are starting to be skeptical, but maybe that's just the environment im surrounded by.
- GitHub · Repository created · First archived
scarletkc/Hatoba ↗
Open-source desktop SSH client with an AI assistant and end-to-end encrypted sync through your own Cloudflare account
- Hacker News · Posted on Hacker News · First archived
Kelly's AI Tax Bill Struggles to Link Tech with Economic Harms ↗
Hacker News discussion.
- GitHub · Repository created · First archived
krxi/asmsense ↗
asmsense: a small model that names and describes functions in stripped x86-64 binaries. Open dataset, baselines, and LoRA experiments.
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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