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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 ↗5986 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
Classified estimates show the NSA is paying billions to test AI models ↗
Hacker News discussion.
- GitHub · Repository created · First archived
neurono-ml/typed-lm ↗
Single-forward-pass semantic routing in Rust: turn dense LLMs (Llama, Qwen, Mistral, Gemma) into a typed decision API with LoRA/QLoRA training and FP8/FP4 quantization, built on Candle.
- arXiv · Published · First archived
Intent2Tc: Automated Intent-to-Traffic Control Translation with Language Models ↗
Automated and highly usable Quality-of-Service (QoS) enforcement requires translating high-level service intents into deployable traffic-management policies. Although intent-based networking (IBN) has simplified policy specification, bridging the gap between business-level intents and executable network configurations remains complex, error-prone, and difficult to automate. This paper presents Intent2Tc, a closed-loop language-model-driven framework that translates business-level traffic-shaping intents into declarative sub-intents and subsequently into validated, executable Linux traffic cont…
- Hacker News · Posted on Hacker News · First archived
DeepSeek beats GPT-6 Sol in autonomous drug development ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Woman Climbed Gate to Escape Notorious AI Hacker House After Repeated ↗
Hacker News discussion.
- arXiv · Published · First archived
Towards Understanding LLM-Based Log Anomaly Detection: An Empirical Study of Performance, Efficiency, and Robustness ↗
Large language models (LLMs) have demonstrated promising performance in log anomaly detection, yet how their adaptation strategies, architectures, and deployment configurations affect detection effectiveness remains insufficiently understood. To investigate these factors, we conduct a systematic empirical analysis across three public log datasets, examining different adaptation strategies, model architectures, parameter scales, and quantization settings. Our results reveal substantial performance differences across adaptation strategies, while model scaling yields varying detection gains acros…
- GitHub · Repository created · First archived
gvergnaud/bise ↗
a multi-agent harness, made for humans :* meet your team lead. you stay in flow, i run the agents.
- arXiv · Published · First archived
Equation discovery with Bayesian tree-adjoining grammars ↗
Tree-Adjoining Grammars (TAGs) have recently been introduced to Nonlinear System Identification (NLSI) as a means of encoding an entire model class as a finite set of grammatical rules, from which candidate models are assembled as trees. Existing TAG-based identifiers rely on evolutionary optimisation and return point estimates of the model structure. This paper instead proposes the TAG framework within a Bayesian setting. A generative prior is defined over tree structures and their parameters, and a Reversible-Jump MCMC sampler with structure-preserving tree moves is used to infer the joint p…
- Hacker News · Posted on Hacker News · First archived
AI boom could wipe 230M budget phones a year from the market ↗
Hacker News discussion.
- arXiv · Published · First archived
OpenVAM: Open-World Visual Attention Modeling with VLMs ↗
Predicting human gaze is a core capability for applications ranging from web/UI design analysis to robotics and human-computer interaction. Yet, most visual attention modeling methods output only a dense saliency map, which is often insufficient for action: practitioners need to connect attention peaks to discrete elements in the scene (what) and understand the drivers of those peaks in context (why), while remaining robust to domain shift across natural images, commercial content, and UI/web layouts. We, therefore, introduce OpenVAM (Open-world Visual Attention Modeling with VLMs), a unified…
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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These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.