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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 ↗5986 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
Dear Humanity – Can We Teach AI Compassion? ↗
Hacker News discussion.
- arXiv · Published · First archived
EAServe: Encode-Aware Disaggregated Serving for Multimodal Large Language Models ↗
Disaggregating the two stages, Prefill and Decode, onto separate GPU pools is now a standard optimization for (text-only) LLM serving. However, multimodal LLMs (MLLMs), which add a third phase, Encode, pose new challenges for resource allocation. Encode turns images, video, or audio into embeddings that the language model can consume, yielding a three-stage Encode-Prefill-Decode (EPD) pipeline. Existing frameworks offer only partial answers: text-only PD systems lack Encode, while EPD frameworks expose it as a separate service without regulating downstream request flow. The pipeline also carri…
- GitHub · Repository created · First archived
diogochaves/omabox ↗
A hidden Omarchy desktop for your AI agents: they launch, drive and screenshot apps and shell plugins there, never on yours.
- arXiv · Published · First archived
Two Conformal Constructions for Adaptive Within-Document AI-Text Screening ↗
We study false-alert control when screening for text generated by artificial intelligence (AI). The screening procedure selects document prefixes and detectors from observed evidence and may stop before exhausting its inspection budget. We give two finite-sample constructions under document-level exchangeability between human calibration documents and a new null document, with no restriction on dependence among tokens within a document. Construction A registers a finite family of prefix-detector scores and allocates a false-alert budget across their conformal ranks. A union bound protects any…
- arXiv · Published · First archived
BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment ↗
Electrocardiogram (ECG) foundation models typically tokenize the signal into fixed-length patches that ignore cardiac structure, so a patch may split a heartbeat and the number of beats in each patch shifts with heart rate. This matters most for infants, whose heart rates are higher and whose ECG differs from the adult, clinic-recorded 12-lead data these models are built on. A model for infant ECG should therefore reason about heartbeats directly rather than recover them from arbitrary patches. We propose BeatGraph, which makes the heartbeat its unit of representation, modeling each 30-second…
- Hacker News · Posted on Hacker News · First archived
MacTube 2 YouTube as a real Mac app – without Shorts and without AI slop ↗
Hacker News discussion.
- arXiv · Published · First archived
A Flow Matching Framework for Neural Representational Dissimilarity ↗
Neural representational dissimilarity quantifies differences between neural response distributions, and is essential for comparing neural codes across stimuli, brain areas, tasks, and models. Commonly used distance metrics involve different assumptions and are estimated with separate methods. Here, we show that a variety of distance metrics can be unified under a flow matching framework developed in deep generative models. That is, these distances arise as Jeffreys divergences under different velocity constraints. We find that flow matching has advantages for estimating distances involving com…
- Hacker News · Posted on Hacker News · First archived
Show HN: Reliopt – Pareto-frontier, contract-gated optimization for LLM programs ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Ask HN: Has anyone built a leaderless multi-agent system? ↗
Anyone running multiple agents concurrently in production? And has anyone tried going leaderless? There's LangGraph, CrewAI, etc. But I keep coming back to whether you need an orchestrator at all. No supervisor. Just agents, each reacting to their own inputs, coordinating only through shared state. A true leaderless architecture. The way some distributed systems have been for decades. Context: previously, I wrote about running Claude Code as an A…
- arXiv · Published · First archived
Can You Check That? The Checkability Boundary for Local LLM Network Automation ↗
Sending every network-automation input to a third-party frontier LLM exports sensitive artifacts such as production configurations, topologies, and logs. Querying small language models (SLMs) locally avoids this egress, but SLM outputs can be error-prone for direct use. This work introduces checkability as a criterion for determining which tasks are suitable for local inference. A task is checkable when it exposes a cheap, deterministic test - an intrinsic check - that rejects outputs violating a necessary correctness condition. We instantiate this idea in Touchstone, a local-first pipeline th…
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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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