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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 ↗5191 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
Concentration of injury and fatality harm at repeat-active highway–rail grade crossings: A multi-state permutation and machine learning analysis ↗
Highway–rail grade crossing incidents remain a transportation safety concern, yet prior studies have focused mainly on crash occurrence, severity, and hotspot detection, leaving limited evidence on how injury-or-fatality harm concentrates at crossings with repeated incidents. Using Federal Railroad Administration (FRA) Form 57 records from six U.S. states during 2013–2025, this study quantified repeat-site harm concentration and evaluated leakage-controlled surveillance ranking. The final cohort comprised 8,286 incidents, of which 3,250 involved at least one injury or fatality. Repeat-active c…
- OpenAlex · Published · First archived
Machine Learning the Nonradiative Decay Modes in Photochemical Processes ↗
Abstract Nonradiative decay in photoexcited molecular systems is driven by nuclear motion toward conical intersections (CIs), where electronic states become degenerate and nonadiabatic transitions occur. Identifying the nuclear degrees of freedom responsible for CI access from nonadiabatic molecular dynamics (NAMD) simulations remains challenging, because the underlying motions are high-dimensional and collective. Here, we introduce an unsupervised, information-theoretic framework based on differentiable information imbalance (DII) to identify the nuclear coordinates governing CI access direct…
- OpenAlex · Published · First archived
Machine-learned, finite temperature Fermi-operator expansions suitable for GPUs and AI-hardware ↗
We present several finite-temperature recursive Fermi–operator expansion schemes based on the second-order spectral projection (SP2) method. Our approach builds on a previous observation that the electronic structure problem, as formulated through a recursive SP2 expansion, can be mapped onto the architecture of a deep neural network. Using this perspective, we generalize SP2 to finite electronic temperatures by constructing machine learning models that determine optimized recursive expansion coefficients. The same approach is also applied to the prediction of the electronic entropy for fracti…
- OpenAlex · Published · First archived
Quantum Hall transformer in a quantum point contact over the full range of transmission ↗
A recent experiment [Cohen et al., Science 382, 542 (2023)] observed a robustly quantized $e^2/2h$ conductance in a quantum point contact between fractional quantum Hall edges (a quantum Hall transformer), which then vanishes at low temperature. While this behavior can be described by a boundary sine gordon (BSG) model derived from electron tunneling, the microscopic motivation for such strong tunneling is unclear. We use an alternative model based on sliding charge density waves and a density inhomogeneity to clarify the BSG description and calculate the conductance over the full range of tra…
- Hugging Face · Model / dataset date · First archived
scottlowry/Qwen3.5-122B-A10B-oQ3.5e-fp16-mtp — model update ↗
> [!IMPORTANT] > This quantization was uploaded on **2026-10-13** and replaces a previous version. > If you downloaded this model before this date, please re-download for the updated weights. # Qwen3.5-122B-A10B-oQ3.5e-fp16-mtp This model was quantized using [oQ](https://github.com/jundot/omlx) (oMLX v0.7.1.dev1) mixed-precision quantization. ## Quantization details - **Model type**: qwen3_5_moe - **Bits**: 3 - **Group size**: 64 - **Format**: MLX safetensors
- Hacker News · Posted on Hacker News · First archived
How well does AI predict humans? ↗
Hacker News discussion.
- GitHub · Repository created · First archived
BaruqueRodrigues/llm-legislativo ↗
Corpus legislativo brasileiro em larga escala e modelo Transformer causal treinado do zero em PyTorch puro.
- Hacker News · Posted on Hacker News · First archived
Show HN: Jevcrawl – A web crawler for AI agents with no LLM in the crawl loop ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
rogerdehe/klines-kucoin — dataset update ↗
# KuCoin USDT OHLCV Klines Historical OHLCV candlestick (kline) data for **KuCoin** USDT **spot** markets, stored as zstd-compressed Parquet. ## Coverage | | | |---|---| | **Exchange** | KuCoin | | **Market** | spot only | | **Timeframes** | 1m · 3m · 5m · 15m · 1h · 4h · 1d | | **Pairs** | ~1,328 spot USDT pairs | | **History** | ~2020 → 2025-05 | | **As of** | static snapshot (~2025-05); periodic refresh planned | | **Source** | seeded from the DigiTuccar `HistoricalDataForTradeBacktest` snapshot | > Futures are not included — KuCoin USDT-margined futures are not covered by this dataset. ##
- Hugging Face · Model / dataset date · First archived
chatpbc1/pally-endpoint — model update ↗
# Pally — Business Consulting AI with Verifiable Math and Self-Correcting Rules (Docs) **Pally (Pally-1.7B)** is a 1.7B-parameter business-consulting language model built by **ChatPBC Labs**. It is unusual for its size in three ways: 1. **It shows its math.** When a question involves money, margins, months, or ratios, Pally writes the arithmetic as visible `[calc: 412000 / 31000]` steps. A sandboxed runtime (not the neural network) executes each step and substitutes the true result before you see the answer. 2. **It verifies and revises.** In System-2 mode the runtime checks every `[calc:]`/`[
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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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