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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 ↗6051 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
StegoMemory: Agentic Memory Acts as Covert Steganographic Channel ↗
Is agentic memory robust against stealthy steganographic attacks? We carry out a large-scale red-teaming exercise to test whether agents can encode attacker-controlled strings in one session and recover them in another without triggering safety oversight. Following SHADE-Arena-style tasks, we embed malicious side tasks to encode secret strings using steganography within otherwise benign tasks and evaluate them using independent task-completion and safety oversight. We test 14,000 attack trials spanning 91 model-attack configurations, 13 models, and 7 steganographic schemes: Acrostic, Mesostic,…
- arXiv · Published · First archived
Variational Quantum Attention for Molecular Graph Learning ↗
Molecular property prediction is central to computational drug discovery, where graph neural networks learn to weight neighboring atomic environments during message passing. Yet it remains unclear how variational quantum circuits alter learned attention behavior in molecular graphs. We introduce an edge-aware variational quantum attention mechanism for molecular graph learning, in which the receiving atom, neighboring atom, and connecting bond jointly determine the quantum attention state. Across five molecular property and bioactivity prediction tasks, QGAT achieves competitive performance re…
- Hacker News · Posted on Hacker News · First archived
Sean Parker is rebuilding Stability AI around music ↗
Hacker News discussion.
- GitHub · Repository created · First archived
re133/sparse-memory-lm ↗
A 21M-parameter LM with a 16.8M-row product-key memory: as good as a 114M dense model and runs with the table on an SSD. Triton kernels for ROCm and CUDA.
- arXiv · Published · First archived
Control-Compute Governance in Agentic AI-RAN: AI Agents as Both Controllers and Workloads ↗
Recent advances in artificial intelligence-radio access network (AI-RAN) are placing large language model (LLM)-driven agents within the open RAN (O-RAN) control hierarchy. A promising deployment for this agentic AI-RAN co-locates LLM inference with physical-layer (PHY) communication processing over the same accelerated compute pool for infrastructure sharing, data locality, and low control latency. However, this co-location induces bidirectional control-compute coupling, as the agent competes with PHY processing for compute, while its reasoned gNB control actions may alter future PHY workload…
- Hacker News · Posted on Hacker News · First archived
Jabhook – Boxing Training (Computer Vision, 3D Biomechanics, Local LLM) ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
desert-ant-labs/tongue — model update ↗
# Tongue Detect language based on 3 words. On-device language identification for short text across 84 languages. - **SDKs, install and examples:** https://github.com/Desert-Ant-Labs/desert-ant-core/blob/main/docs/models/tongue.md - **Website:** https://desertant.com/models/tongue/ | | | | --- | --- | | **Platforms** | iOS, macOS, tvOS, visionOS, Android, Linux, Windows, Browser, Node | | **Languages** | 84 | | **Weights** | [v1.0.0](https://huggingface.co/desert-ant-labs/tongue) | ## Install **Swift** ([requirements](https://github.com/Desert-Ant-Labs/desert-ant-core/blob/main/README.md#swift)
- Hugging Face · Model / dataset date · First archived
desert-ant-labs/uhm — model update ↗
# Uhm Find and remove every filler word. On-device filler-word detection: frame-precise "uh"/"um"/"hmm" spans. - **SDKs, install and examples:** https://github.com/Desert-Ant-Labs/desert-ant-core/blob/main/docs/models/uhm.md - **Website:** https://desertant.com/models/uhm/ Find every "uh", "um" and "hmm" in an audio file, down to 20ms. Uhm runs on device, does not require ASR, and is trained on English with acoustic transfer to Spanish, French, German, and Dutch. ## Try it - **Browser demo:** [`huggingface.co/spaces/desert-ant-labs/uhm-demo`](https://huggingface.co/spaces/desert-ant-labs/uhm-d
- Hugging Face · Model / dataset date · First archived
desert-ant-labs/title — model update ↗
# Title Suggest a title and description for any text. On-device titles and descriptions: a short factual title and a one- to two-sentence description for any passage of text. - **SDKs, install and examples:** https://github.com/Desert-Ant-Labs/desert-ant-core/blob/main/docs/models/title.md | | | | --- | --- | | **Platforms** | iOS, macOS, tvOS, visionOS | | **Weights** | [v0.1.0](https://huggingface.co/desert-ant-labs/title) | ## Install **Swift** ([requirements](https://github.com/Desert-Ant-Labs/desert-ant-core/blob/main/README.md#swift)) ```swift .package(url: "https://github.com/Desert-Ant
- Hugging Face · Model / dataset date · First archived
desert-ant-labs/shapes — model update ↗
# Shapes Rough sketch. Perfect shape. On-device single-stroke shape recognition. - **SDKs, install and examples:** https://github.com/Desert-Ant-Labs/desert-ant-core/blob/main/docs/models/shapes.md - **Website:** https://desertant.com/models/shapes/ Turn a rough circle drawing into a clean shape, with fitted vector geometry ready to snap to. Built for PencilKit-style "smart shapes": draw, pause, and the rough stroke becomes a crisp shape. The model is tiny (**200KB** Core ML, **1.3MB** LiteRT) and runs in a few milliseconds on device. > ✏️ ➜ ▭ · ✏️ ➜ △ · ✏️ ➜ ◯ · ✏️ ➜ ★ ## Try it - **Live demo
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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