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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 ↗5911 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
Show HN: Cockpit – a live instrument panel for Claude Code sessions ↗
Hacker News discussion.
- arXiv · Published · First archived
FedSSMCoOp: SSM Encoders for light-weight Federated Prompt Learning for Few-shot Classification ↗
Vision-Language Models (VLMs) have shown strong performance across a wide range of downstream vision tasks, thanks to the complementary information contained in the respective domains. Despite the performance gains, most of these approaches rely on aligning these domains using the cosine similarity metric, which fails to capture token-level structure and cross-modal interactions prior to the classification stage. This is especially critical in biomedical applications under federated constraints, where data sharing is restricted, labeled data is scarce at each site, and it differs widely across…
- arXiv · Published · First archived
Constrained-Action AI Remediation for SIEM/XDR via a NeMo-Guardrails Proxy ↗
Security Operations Centers (SOCs) for information technology and operational technology share one incident-response problem: a flood of correlated alerts and too few analysts. Large Language Models (LLMs) are increasingly proposed as reasoning engines that triage alerts and, in autonomous deployments, issue commands that block IPs, kill processes, or quarantine files on production hosts. This coupling introduces a new risk: a single adversarial alert can become a remote code path through the LLM's reasoning, leading it to recommend an action the SOC then executes. We present a constrained-act…
- Hugging Face · Model / dataset date · First archived
CQdesign/LTX-2.5-CQ-Video-and-Image-Enhancer-LoRAs — model update ↗
Updated Workflow The updated workflow removed all the audio processing nodes to be more efficient. This speed things up and use less resources. I have also switched the video output node to use the native ComfyUI one (VHS node has an issue dealing with video with no audio). Another changes to the workflow is the addition of an optional subgraph that you can enable. This allows you to convert the frame rate of your video back to the original. There is also another optional subgraph to quickly stretch the video to a very high resolution. Update: Version 2 The new lora and workflow are updated. T
- Hacker News · Posted on Hacker News · First archived
AI vs. Human Creativity ↗
Hacker News discussion.
- arXiv · Published · First archived
Learning joint probabilistic weather forecasts from station observations alone ↗
Assessing compound weather risks requires forecasts representing dependence between variables. CLARA (Calibrated Advection-Routing Attention) learns joint Gaussian predictive distributions of five surface variables from station observations alone, without numerical weather prediction or reanalysis; the approximately 28,000-parameter model supports CPU training and prediction. Across six multi-year folds on 96 stations, its lead-mean energy score is 4.9% lower than that of a learned comparator with matched temporal inputs (4.7% with a similar parameter count) and 11-65% lower than those of stat…
- GitHub · Repository created · First archived
posstos/ai-relay-welfare ↗
AI 中转站福利导航：31 个中转站的邀请链接合集，含签到状态与实测备注
- arXiv · Published · First archived
NL2Hull: A Natural Language-Driven Constrained Ship Design Decision Framework ↗
Ship-form design combines smooth geometric representation, local shape editing, and constraints on the resulting hull. We present the Natural-Language-to-Hull Framework (NL2Hull Framework), which formulates ship-form editing as a typed discrete decision problem and connects language decisions to numerical geometry. Its Constrained Free-Form Deformation Engine (CFFD Engine) represents hull waterlines with non-uniform rational B-splines (NURBS), applies free-form deformation (FFD) to their control points, reconstructs the hull, and checks geometric constraints. We construct the Ship Design Decis…
- arXiv · Published · First archived
QCATS: Query Context-Aware Transformer Slicing for Efficient Predictive Query Processing ↗
In-database predictive query processing increasingly applies Transformer-based models within relational pipelines. However, existing in-database inference typically exposes only tuple-level model inputs to the inference runtime, leaving relational predicates and metadata statistics invisible to neural execution planning. In this paper, we propose QCATS, a query context-aware transformer slicing framework that enables efficient sparse inference inside database systems. QCATS executes at query granularity: instead of routing individual tokens or tuples during inference, it uses query predicates…
- arXiv · Published · First archived
Defensive Sufficiency in a Stackelberg Model of AI Security ↗
Feedback from automated testing, human red teaming, and incident response can strengthen an AI system's defenses when discovered failures lead to effective repairs. We study when this feedback process provides sufficient protection and when investing in it is economically worthwhile. We begin by showing that an attack surface composed of finite number of inputs is defended with probability 1 if every unresolved attack has a persistent chance of discovery, repairs are effective, and subsequent updates preserve earlier protection. We derive completion-time bounds and extend the analysis to growi…
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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