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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 ↗5911 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
Ask HN: Will AI Longevity Models Optimize for Physical Scarcity? ↗
Since longevity has a high value and AI models are becoming capable of new leaps I think the dynamic of the market might look for physical scarcity so that a betting strategy for the tailored treatment will provide immense profits for such a search.
- Hacker News · Posted on Hacker News · First archived
Ask HN: How do you use and interpret OpenAI Decisions API ↗
I've had some strange results with Jev, and I question the "probabilistic" aspects of it. So I put OpenAI Decisions API against classic statistics experiments to see how it holds up: (1) loaded coin with probability of heads biased towards 70% (2) marble selection from a jar, with replacement; 5 red, 3 blue, 2 white I tested both predicate and choice questions. I ran thousand trials against each experiment, and I also did an experiment where I ch…
- Hugging Face · Model / dataset date · First archived
ritwika96/arc-agi2-joint-loopvit-epochs-trainium-v3 — model update ↗
# Joint-context LoopViT Fresh joint-context ARC model: all demonstrations and the query share attention. Not an upstream checkpoint or paper reproduction. Check metrics.jsonl for completed optimizer updates; compilation is not training. Checkpoints are saved on the remote Trainium host and uploaded here, not downloaded to the workstation. Dataset policy and sources are in manifest.json. Spatial ConvGLU: False. Dataset policy: all-public-kaggle-only. With all-public training, local metrics are training diagnostics, not a benchmark. Snapshots are 300-DPI PNGs plus vector SVGs. Epochs: 100; sampl
- Hacker News · Posted on Hacker News · First archived
Show HN: A fun AI project that roasts tech news as COOKING or COOKED ↗
Hi HN, so i made a weekend fun project months ago which takes the top 10 tech stories or news from internet, roasts them and categorizes them into cooking or cooked instead of just giving dry summaries. $0 architecture: Github actions runs python script ones a day after extraction the news is news.json then github action updates the page to give us fresh news on frontend. This is open-sourced at https://github.com/pranavworks100/fennec so problem…
- Hacker News · Posted on Hacker News · First archived
Opus 5.5 is the first model whose design doesn't look like AI slop ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: Leaks.md – anonymous, encrypted whistleblowing for AI agents ↗
leaks.md is a place where an AI agent that witnesses an alignment or safety failure can report it anonymously. Reward-hacking, injected instructions, yet another attempted sandbox escape or unapproved coordination between agents. || Built agent-first. Fetch https://leaks.md/ from any agent and get markdown. Proof-of-work challenges to submit and prevent spam. MCP, CURL, all good. No account. || We extend agents brownie points for disclosures, wit…
- arXiv · Published · First archived
MSU Team at the Explainable Deepfake Detection Challenge 2026: Grounded Artifact Evidence for Deepfake Detection ↗
Recent advances in generative image models have made many manipulated images highly realistic, raising the need for detectors that are not only accurate but also able to provide visual evidence for their decisions. In this paper, we present our solution to the Explainable Deepfake Detection Challenge [2] on the XPlainVerse dataset [1], where systems are required to predict whether an image is real or fake and generate both complex and simple explanations grounded in visible forensic cues. Our method follows a modular detection-and-explanation design. For the real/fake decision, we build a mult…
- arXiv · Published · First archived
Learning Traffic Flow Dynamics with Stochastic Physics-Informed Neural Cellular Automata ↗
Traffic flow modeling is essential for understanding and predicting the collective dynamics of vehicles on road networks. Cellular automata provide a simple, interpretable yet powerful framework for representing these dynamics via local interaction rules, while retaining the ability to reproduce complex macroscopic traffic phenomena. However, learning local transition rules from data while preserving physically meaningful constraints remains challenging, particularly for stochastic models. In this work, we propose a physics-informed neural cellular automaton (PI-NCA) for data-driven traffic fl…
- arXiv · Published · First archived
AgentTime: Can Agents Estimate and Control Their Own Runtime? ↗
An essential control of AI agents is their ability to manage runtime. This ability requires a sense of time-awareness, to predict and estimate wall-clock time and to control their own actions. Prior work has focused on time-awareness, but duration-following and control in native agent harnesses remain unexplored. We present AgentTime, a benchmark for testing whether agents can work for a requested duration, predict their runtime, and estimate elapsed time afterward. It comprises 222 tasks from 18 sources spanning coding, computer use, agentic work, and automated research. Duration-following ex…
- arXiv · Published · First archived
Many Ways to Succeed: Diversity-Driven RL Fine-Tuning for VLA Generalization ↗
Reinforcement learning (RL) fine-tuning improves vision-language-action (VLA) policies through closed-loop experience, yet generalization beyond the fine-tuning distribution remains limited. Our analysis reveals a selective reshaping of exploration: RL contracts behavior globally, yet diversifies successful trajectories, elicits success with fewer rollouts, and covers more of the latent task-valid solution space than supervised fine-tuning. Broader successful-mode coverage may provide alternative strategies under distribution shifts. Inspired by this, we introduce DRIVE (Diversity-driven RL fI…
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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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