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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 ↗5999 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
BRANCH-MoE: Balance-Aware Tree Routing for Large Embedding Models ↗
Mixture-of-experts (MoE) layers increase model capacity without a proportional increase in per-example computation. However, conventional flat routers can yield imbalanced expert utilization and treat experts as an unstructured collection, whose indices carry no topological meaning. We introduce {\bf BRANCH-MoE}, a routing architecture that places \(E\) experts at the leaves of a binary decision tree of depth \(\log_2 E\). At each internal node the branching probability is centered on the arrival-weighted mean score of the traffic reaching that node. This mean is estimated using an exponential…
- arXiv · Published · First archived
Domain adaptation of Russian ModernBERT for long legal documents ↗
We investigate whether continued pretraining on Russian legislative documents improves a Russian ModernBERT encoder on legal text. The adapted model, RuModernBERT-ruLaw, was trained on a corpus reported to contain 304,382 legislative documents and 194,425,905 corpus tokens. Corpus token counts are distinguished from positions produced by the model tokenizer. We compare the original and adapted encoders on a fixed external collection of 1,031 court-decision segments. Both models receive the same hidden positions in each of five masking realizations. At maximum input lengths of 512, 2,048, and 8…
- arXiv · Published · First archived
AI-assisted super-resolution cosmological simulations V: Cosmology-aware super-resolution ↗
We present a cosmology-aware generative model for super-resolution (SR) of cosmological $N$-body simulations in particle phase space. The model jointly generates displacements and velocities, conditioning a generative adversarial network (GAN) based generator and discriminator on evolved low-resolution (LR) fields and five $Λ$CDM parameters $(Ω_m,Ω_b,h,n_s,σ_8)$. We train a single model on 100 matched low-high resolution pairs from the Quijote Latin-hypercube suite at $z = 0$, mapping $512^3$ to $1024^3$ particles in $1\,h^{-1}\,\mathrm{Gpc}$ boxes. On ten cosmologies excluded from SR training…
- Hacker News · Posted on Hacker News · First archived
Metagente, a tiny language for AI agents that speaks MCP and A2A ↗
Hacker News discussion.
- arXiv · Published · First archived
Out-of-control Hamiltonian Learning ↗
Learning the Hamiltonian of a many-body system from its dynamics is a central task in quantum science, yet the algorithms with the strongest provable guarantees assume some level of quantum control--fast, arbitrary single-qubit gates interleaved with time evolution, and measurements in arbitrary bases--that is beyond the capabilities of near-term analog quantum simulators. Motivated by analog atom- and ion-based platforms, we study Hamiltonian learning under minimal access models. Uniform state preparation and measurements: We first consider the setting where in every experiment, one can rotat…
- arXiv · Published · First archived
SAFE-MR: Evidence Sufficiency Learning for Selective Multimodal Rumor Detection ↗
Multimodal rumor detectors increasingly rely on retrieved evidence, yet relevant evidence is not necessarily sufficient for verification. Missing provenance, duplicated reports, and unresolved contradictions can produce confident predictions without adequate support. We introduce SAFE-MR, a framework that separates claim veracity from evidence sufficiency. The method decomposes image-text posts into verifiable claims, constructs a relation-aware claim-evidence graph, and aggregates evidence using provenance and contextual compatibility. Separate veracity and sufficiency heads support selective…
- Hugging Face · Model / dataset date · First archived
open-zzrl/kyo — model update ↗
# ⚡ Kyo (140M System-One Decision Engine)  **Kyo** is an ultra-fast (140M parameter) **System-One decision and policy gating engine** designed to govern autonomous LLM workflows. Powered by a pairwise cross-encoder architecture built on `mmBERT-small` (hidden size 384) with **Dual Pooling** (`[CLS]` + Masked Mean to 768d), Kyo executes sub-10ms deterministic routing, compliance verification, and security guardrails with **100% order-invariance**. * **GitHub Repository:** [github.co
- Hugging Face · Model / dataset date · First archived
jeffry77/track_dataset — dataset update ↗
# TrackingAnything native datasets Original-format corpus mirror, not model-generated annotations or converted dialogs. The original **205-file training/shared-data release is complete**; its `release_manifest.json`, `checksums.json`, archive paths and bytes are unchanged. ## Separate test extension Status: **upload in progress; do not treat the test extension as complete**. The added TNL2K/VastTrack test images and native annotations have a separate `test_release_manifest.json` and, after completion, `test_checksums.json`. Original training manifests intentionally do not list these test-only
- Hugging Face · Model / dataset date · First archived
abdelhak-k/Natiq-dz-ARG — model update ↗
# Natiq-dz-ARG (ناطق) [](https://huggingface.co/abdelhak-k/Natiq-dz-ARG/colab) [](https://huggingface.co/spaces/abdelhak-k/Natiq-dz-ARG) [](https://huggingface.co/datasets/abdelhak-k/Algeria-TTS-dataset) **Developed by [Natiq Studio](https://natiq.studio)** 🇩🇿 **Natiq-dz-ARG** is a production-grade speech synthesis and zero-shot voice cl
- Hugging Face · Model / dataset date · First archived
wildfellhall/public-poetry-50m — model update ↗
# Model Card for Model ID A small model (around 50 million parameters) trained on a dataset of poetry in the public domain. Github Repository: https://github.com/wildfellhall/Poetry50M Prompts work best in this format: prompt = ( f" \n" f" {title}\n" f" {author}\n" f" \n" ) # Easiest Usage - Google Colab (Tokenizers and Head Models Can Be Adjusted) !pip install -U transformers from transformers import pipeline pipe = pipeline("text-generation", model="wildfellhall/public-poetry-50m") from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("wildfel
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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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