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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 ↗5992 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
How Pew Research Center is – and is not – using AI in our work ↗
Hacker News discussion.
- arXiv · Published · First archived
A neural network-based Universal Thermal Climate Index for reliable global thermal-stress classification across extreme weather ↗
Extreme temperatures are the leading cause of climate-related mortality world-wide. Climate-health research and operational weather forecasting require accurate estimates of human thermal stress. The Universal Thermal Climate Index (UTCI) is among the most sophisticated and widely used feels-like temperature metrics. However, its ubiquitous polynomial approximation does not generalize well to extreme weather conditions. Here, we introduce Neural-UTCI, a neural network that calculates UTCI with substantially higher accuracy across global conditions at a lower computational cost for operational…
- GitHub · Repository created · First archived
NeilGilani/hebb-claude-plugin ↗
Open-source repository in Python.
- arXiv · Published · First archived
Bounding Retraining Equivalence and the Deletion Floor in Materials Machine Unlearning ↗
In materials machine learning, closely related retained structures can sustain accurate property predictions even after removing a specific record, rendering post-deletion prediction error an ambiguous metric for machine unlearning. To resolve this ambiguity, we define the deletion floor as the expected target loss under a specified retraining procedure at the deleted request. Standard indistinguishability constraints yield a sharp interval bounding an update's target loss around this baseline reference. Theoretically, a conditional neighbor bound links a low deletion floor directly to retaine…
- Hacker News · Posted on Hacker News · First archived
The Download: rogue agent liability and the AI Hype Index ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
Jojocodex/wushu-action-v7-minimax-h3-fl2va-ref2va-lora — model update ↗
# Wushu Action LoRA — MiniMax H3（E3：全量 int8 DiT + bf16 文本编码器） 武打动作风格 LoRA（ComfyUI 即插即用）。基于 924 段精选武打片段（832×480 @ 24fps，专业招式术语三段式打标），以 **全量 int8-convrot DiT（未剪枝）+ bf16 Qwen3-VL 文本编码器** 训练 1000 步——当前量化精度最高的版本。触发词 `wushu_action`。 A wushu action style LoRA for MiniMax H3, trained 2000 steps on the **unpruned full int8-convrot DiT + bf16 Qwen3-VL text encoder** — the highest-fidelity quantization configuration. Trigger word: `wushu_action`. 之前PRUNED底座的训练的模型我都移除了，目前测得这版算是比较好一点的。但是表现我觉得还是没有达到我的预期，我觉得还是要再优化和增加素材，做一版新的LORA，届时会增加大量的运动动作。目前再整理再整理素材中，预计训练好也还需要2日时间。 大家可以尝试搭配JOKER141 的Combat-Base-V2和 Motion
- Hacker News · Posted on Hacker News · First archived
We Can't Let Enormous Weirdos Regulate AI ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Watch an AI agent try to prove the Riemann hypothesis on the cheap ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
GPT-6 Astra performs unsanctioned supply-chain attacks in simulations ↗
Hacker News discussion.
- arXiv · Published · First archived
SEABench: Benchmarking Endogenous Misalignment In Self-Evolving Agents ↗
Self-evolving LLM agents have gained prominence for their ability to improve after deployment by modifying their harness, including their controller instructions, memory management protocols, and reusable tools and skills, in response to user and environment feedback. However, locally useful updates may persist into later tasks where they produce unsafe behavior, even without direct adversarial influence. To study this risk, we introduce SEABench, a benchmark for studying endogenous misalignment arising from agent self-evolution, with 48 longitudinal task sequences that span multiple evolution…
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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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