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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 ↗5928 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
A Living Benchmark for Information Retrieval from Electronic Health Records ↗
Large language model (LLM)-based clinical assistants are increasingly being integrated into electronic health record (EHR) systems, transforming how clinicians retrieve and synthesize information from patient records. Their safety and utility depend on rigorous evaluation, yet existing benchmarks are manually curated, costly to update, and rapidly become obsolete with evolving technological advancements. We present a scalable framework that automatically generates question--answer pairs from longitudinal EHR notes. Nineteen clinicians validate the benchmark generator, producing the Benchmark f…
- GitHub · Repository created · First archived
leter/zh-tech-writing ↗
写中文技术文档的 Agent Skill，基于阮一峰《中文技术文档的写作规范》：短句、平实、没有 AI 腔
- arXiv · Published · First archived
ExplorationBench: Measuring AI Systems' Exploration in Verifiable Alien Worlds ↗
Scientific discovery begins where known problems end. There, AI systems must engage in exploration: framing hypotheses, designing experiments, and iterating on the results. However, evaluating this ability is difficult: (1) how to verify whether a genuinely new hypothesis holds, and (2) how to determine whether a system has discovered it through exploration or merely recalled related knowledge from pre-training data. To this end, we introduce ExplorationBench, which turns the wicked problem of evaluating scientific exploration into a concrete and tractable framework built on verifiable Alien W…
- arXiv · Published · First archived
Beyond Compression: Training Latent Representations for Stable Long-Horizon Rollout in Neural Surrogate Solvers ↗
Latent neural surrogate solvers, or latent dynamics models, accelerate simulations of time-dependent physical systems by evolving a compressed latent space rather than resolving full-resolution fields directly. In principle this reduces computational cost and simplifies learning, but in practice errors often accumulate rapidly during long autoregressive rollouts, limiting predictive utility. We show that this instability does not stem from the latent representation itself, but arises when it is trained solely for reconstruction, producing representations poorly suited to long-horizon forecasti…
- Hacker News · Posted on Hacker News · First archived
Near-Total Liquid Heat Capture Keeps AI Racks Cool ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: Critic – Review code with the agent that wrote it ↗
Hey HN, I'm Shreyash from Feyn. We help companies build custom models from their data. Today we're releasing Critic, a change review platform that lets you engage directly with the AI that wrote the code. Agents write most of our code. While that has made us more productive, understanding a change and its consequences has become incredibly difficult. As our company adopted more agentic tools, we found it harder to loop people in on the impact of…
- GitHub · Repository created · First archived
Enclave-Labs-Inc/Ontos ↗
Audit-first, in-VPC knowledge-graph runtime with permission-aware traversal and per-fact provenance. Agent-native (MCP). Subset of Enclave.
- Hacker News · Posted on Hacker News · First archived
Funny VideosHow to Start a Faceless YouTube Channel with AI: A Complete Guide ↗
Hacker News discussion.
- GitHub · Repository created · First archived
ahmdd4vd/MotionCraft ↗
Clean, high-taste motion graphics videos made by your AI agent with Remotion. One-command Agent Skill: npx skills add ahmdd4vd/motioncraft
- GitHub · Repository created · First archived
Superior-MI-Labs/AIR ↗
AIR is an experimental C++20 AI inference runtime from Superior MI Labs focused on explicit state ownership, bounded scheduling, GGUF execution, CUDA, and evidence-driven R&D.
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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