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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 ↗5980 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.
- Hacker News · Posted on Hacker News · First archived
The 85.B2 Challenge: Does This Earlier AI-Agent Map to Meta Muse ↗
Hacker News discussion.
- arXiv · Published · First archived
Local Content-Style Control for Diffusion-based Image Stylization ↗
Image stylization with latent-diffusion models entangles two independently refined axes: what a region depicts and how it is depicted. Such pipelines expose only global controls, yet professional retouching demands deliberate, region-specific control. We lift two conditioning weights already present in a ControlNet + IP-Adapter stylization pipeline from global scalars to per-location spatial maps, yielding local, per-axis control of content and style in a single generative pass. Because the two weights act on disjoint pathways, adjusting them independently spans a 2x2 retouching vocabulary, fr…
- arXiv · Published · First archived
Agreement Is Not Validity: Cross-Model LLM Consensus in Diagnosing Student Failure Modes in K-12 Math Tutoring Dialogue ↗
In K-12 mathematics tutoring, student-tutor dialogue provides rich evidence of learners' problem-solving processes and sources of difficulty. Learning analytics research increasingly relies on large language models (LLMs) to extract such information from dialogue for a variety of downstream tasks, including knowledge tracing, behavioral modeling, and diagnosis of student reasoning errors. However, the validity of these model-generated interpretations remains insufficiently understood. In this exploratory study, we examine the validity of LLM classifications of five student failure modes in mat…
- arXiv · Published · First archived
nanoMuse: An Open-Source Personal Agent for Every Device You Own ↗
Assistants from 2011 answered and waited, and agents from 2023 did a task and stopped. In September 2026 Meta's Muse showed an agent for one person, with accounts, devices, memory and a conversation that lasts, closed, in a vendor's cloud, in one country. Such an agent is expected to act on a person's accounts and devices, remember them across weeks, speak first when it is worth it, and answer for what it did. It is a kind of software, not a model, and until now had no open counterpart. This report defines the personal agent in five questions and three horizons. It reads how Muse is built from…
- arXiv · Published · First archived
NMPP: Nonlinear Model Predictive Planning for Agile UAV Flight in Cluttered Environments ↗
Flying a quadrotor through a cluttered environment requires not only planning a collision-free reference trajectory based on perceived obstacles, but the reference also needs to be dynamically feasible and within the actuation limits of the vehicle, so that the controller can track it precisely. Existing methods either optimize a smooth polynomial inside a convex corridor, which limits agility, or treat obstacles as soft costs traded against tracking performance. We propose a Nonlinear Model Predictive Planning (NMPP) that imposes perceived obstacles as hard geometric constraints and hands a f…
- arXiv · Published · First archived
GeneICL: A Tabular Foundation Model for Bulk Transcriptomics ↗
Gene expression is widely measured in biomedicine, yet clinical outcome prediction remains challenging due to high dimensionality, strong feature correlations, and limited labeled data. Large self-supervised transcriptomic foundation models often fail to outperform simple supervised baselines. Tabular foundation models offer an alternative through in-context learning, but are typically pretrained on generic synthetic data rather than transcriptomic structure. We ask whether transcriptomics-aware pretraining, rather than scale, is the missing ingredient. Towards this end, we introduce GeneICL,…
- Hacker News · Posted on Hacker News · First archived
Will AI replace workers? Not if we build it right ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Ask HN: How to deal with AI "true believer" leadership at work ↗
Interested to know what has worked and what hasn’t. How can you stay sane and not burned out by all the “gentle encouragements” to use AI?
- Hacker News · Posted on Hacker News · First archived
Utah to let AI examine patients and prescribe medication without human oversight ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
HamoAI/hamo-score-0.6b — model update ↗
# hamo-score-0.6b — the little model that takes your pulse A 0.6B model that reads one message from a wellness conversation, in Chinese or English, and returns five scores as one JSON object: Agency, Withdrawal, Extremity, Hostility, Boundary (AWEHB), each 0.0–3.0. It does not write replies; its scores feed deterministic code (stress update, state buckets, action gating). 中文说明见下方。 - **Scores locally, on CPU.** No external API, no conversation data leaving the machine. Distilled to do the job of the LLM scorer behind [Hamo AI](https://www.hamo.ai)'s production rubric (the reference scorer). - *
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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