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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 ↗5934 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.
- arXiv · Published · First archived
Propose, Don't Judge: An Anytime-Valid Referee for LLM Agents That Mine Investment Factors ↗
Language-model agents now run the whole of quantitative factor research: they propose investment factors, backtest them, select the survivors and retire them. We ask which of those jobs an agent should keep. Our answer is governed self-evolution: the agent may propose, and a frozen statistical referee that the agent cannot touch must judge. The referee scores each candidate only on market outcomes revealed after submission, by betting, so its false-discovery guarantee holds at every stopping time for any proposal policy. We cross three proposers (a script, a bandit and a language model) with t…
- GitHub · Repository created · First archived
vinibrsl/gut ↗
Use LLM judgment in regular Elixir control flow.
- arXiv · Published · First archived
Loss Choice or Model Choice? The Role of Forecast Level in Cryptocurrency Volatility Forecasting ↗
Volatility forecasts play a central role in financial risk management because their overall level and day-to-day movements affect downstream decisions. Most studies compare forecasting models while keeping the training loss fixed. Yet losses emphasise different errors and can target different properties of future volatility, so raw comparisons may combine persistent forecast-level differences with differences in daily forecast movements. This leaves unresolved whether the importance of loss choice comes mainly from the forecast level it targets or from differences that remain after level adjus…
- GitHub · Repository created · First archived
AskTheWay/dsh-jev-interceptor ↗
⚡ Millisecond System-1 judgement for every tool call in DeepSeek Harness — Jev-powered risk classification & evidence-gated auto-approval. Fail-closed by construction. dsh 生态第一个 System-1 决策插件
- Hacker News · Posted on Hacker News · First archived
Pentagon says overreliance on AI contributed to missile strike on Iran school ↗
https://archive.ph/0V37g
- GitHub · Repository created · First archived
weitianxin/JevAny ↗
Calibration-aware reinforcement learning for adaptive decision systems
- GitHub · Repository created · First archived
rferrari/boar-app ↗
BOAR - Adaptive Local Intelligence
- Hacker News · Posted on Hacker News · First archived
GPT-6 Sol and Luna ↗
Hacker News discussion.
- arXiv · Published · First archived
PR-Smoother: Simulator-Preserving Non-Gaussian Smoothing for Data Assimilation ↗
Many physical data assimilation (DA) workflows require smoothing methods that represent non-Gaussian posteriors over physical state variables, scale to high-dimensional simulators, train from observation windows alone, and remain compatible with calibration of the prescribed simulator. We introduce PR-Smoother, a simulator-preserving amortized smoother designed for this prescribed-simulator DA regime. Its key design principle is to keep the prescribed simulator explicit in both the evidence lower bound and the variational family: rather than learning replacement dynamics or a learned trajector…
- GitHub · Repository created · First archived
Mocas-12/ai-model-rankings ↗
Real-time AI model power rankings (水墨战力榜) — usage, benchmarks, price-performance & speed from OpenRouter, as an ink-wash Streamlit dashboard.
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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