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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 ↗6040 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.
- GitHub · Repository created · First archived
Technicalflight/aglab ↗
Windows 桌面端 AI 编程助手——接入你自己的模型服务商，在工作目录里安全地读代码、跑命令、改文件；支持生图/视频/音乐与多模态识别。Tauri 2 + React 19 + Rust
- Hacker News · Posted on Hacker News · First archived
Core – Personal AI Computer ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
The-engineering-behind-LLM-inference ↗
Hacker News discussion.
- GitHub · Repository created · First archived
b3s3da/qemu-harness ↗
QEMU harness for AI agents: boot aarch64/arm/riscv64/x86_64 kernels in seconds, exec via guest agent, fast rootfs rebuilds, IoT emulation
- Hugging Face · Model / dataset date · First archived
jujuju725/FusionRS-derived-images-v1 — dataset update ↗
# FusionRS-derived-images-v1 (staging) This is a separate staging release for converted infrared-style images. It does not modify the historical FusionRS GitHub repositories. ## Inventory - Image files: 605,779 JPG files - Total size: 40,702,905,304 bytes (37.91 GiB) - Source groups: NWPU 31,511; RS5M_clean 492,682; RSICD 10,921; RSITMD 4,745; SkyScript_images2 65,920 - Per-file inventory: `derived_images_manifest_v1.tsv` - Summary: `manifest_summary.json` - Per-file SHA256: not computed yet The package contains converted image files only. Original RGB files, model checkpoints, credentials, an
- arXiv · Published · First archived
Adversarial Training for Deep Hedging in Nonstationary Markets ↗
Deep hedging learns trading policies from historical or simulated market trajectories, yet under nonstationarity these training paths may not represent future market conditions. We propose WRAP (Wasserstein-Reweighting Adversarial Perturbation), a drift-aware adversarial training framework derived from a two-budget distributionally robust optimization (DRO) formulation. The formulation is anchored to a weighted empirical reference distribution whose fixed baseline weights are chosen to balance sampling uncertainty against temporal drift. Around this reference distribution, the ambiguity set ad…
- arXiv · Published · First archived
One Figure, Every Canvas: Editable Flowchart Relayout via Agentic Pipeline ↗
Pipeline figures in ML papers must be repurposed across many canvases, including paper columns, 16:9 slides, portrait posters, 1:1 social teasers, 9:16 phone previews. Each format imposes a different aspect ratio on the same computational graph, where any silently broken connection misrepresents the method. We formulate aspect-ratio-adaptive flowchart relayout as a distinct task: given a raster flowchart and a target ratio, produce a structurally faithful, hallucination-free, editable layout. Existing methods fail characteristically: image-to-image models stretch blocks and reject extreme rati…
- arXiv · Published · First archived
Base Models Can Reason By Taking a Cue From Training Data ↗
In this paper, we study how training data creates associations between the tokens at the start of a base model's response and the reasoning behavior that follows. First, we demonstrate that fixing particular starting token cues makes a base model's performance competitive with that of its reinforcement learning (RL)-trained counterparts on math and coding. For instance, the cue ".\n\nOkay" raises Olmo-3-7B's MATH-500 pass@1 accuracy from 42% to 78%, while "Alright," raises Qwen3-14B's from 72% to 87%. Second, RL makes these cues more likely, while fixing them recovers much of its performance g…
- arXiv · Published · First archived
InterMimicGen: Scaling Humanoid Loco-Manipulation through Self-Evolving Motion Imitation ↗
Captured human-object interactions provide rich supervision for humanoid loco-manipulation, but they are sparse, heterogeneous, and not directly executable by robots. We introduce InterMimicGen, a self-evolving motion-imitation framework in which robot motion data and a tracking policy improve each other. First, we consolidate motion-captured human-object interaction datasets and retarget them into humanoid robot references while preserving whole-body coordination and dexterous hand-object relationships. This produces a large and diverse humanoid robot reference collection for dexterous whole-…
- Hugging Face · Model / dataset date · First archived
PrinceAlhassanNasamu/yaw-asr-twi-en — model update ↗
# Yaw ASR — joint Twi + English (300M CTC) **Author:** Prince Nasamu Alhassan · **Project:** [Yaw](https://github.com) — an ambient clinical scribe for Ghanaian outpatient consultations (Twi and English, often code-switched). AI proposes, doctor decides. The final model of the Yaw ASR training campaign (September 2026). A single `Wav2Vec2ForCTC` (315.5M params) that transcribes **Twi (Akan), Ghanaian English, and Twi–English code-switching** with one character head. Superseded intermediate runs (v4–v11) have been deleted; this is the model the campaign produced. ## Results All evaluation sets
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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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