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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 ↗5187 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
The Wealth of Agents: Scaling Agentic AI Across Developers ↗
Today's agentic systems are typically designed, built, and deployed by single developers with full control over all participating agents. While this simplifies development, it limits the potential scale of agentic systems. We study multi-developer agentic systems (MDAs) which enable developers to collaboratively build agentic systems to push frontier capability beyond what any individual developer could achieve alone. We construct empirical and theoretical models of developers to compare MDAs with single-developer agentic systems (SDAs). On GAIA, a general assistant benchmark, a population of…
- arXiv · Published · First archived
VASPO: Self-Distillation with Privileged Value Estimation ↗
On-policy self-distillation provides dense supervision for reasoning models via a privileged self-teacher. However, this supervision reflects local token preferences rather than the expected return of an unguided student continuation. Recent methods combine this signal with GRPO. They use GRPO for update directions and the teacher for signal scaling. However, we demonstrate a more effective integration within PPO. Specifically, we identify complementary positional dynamics: privileged information enhances a critic's value-prediction accuracy toward the response's end, whereas self-teacher guid…
- arXiv · Published · First archived
Bayesian Learning to Select Fair Classifiers ↗
Evaluations of classifiers (AI or human or agents) often assume a batch setting in which every agent produces labels on every example in a test set, which can be expensive or impractical in many contexts. This motivates online policies that adaptively select agents to query as examples arrive and update policies as outcomes are observed. We study the problem of selecting, in an online stream, the agent that best balances (i) predictive performance and (ii) subgroup fairness of a sensitive attribute, and that allows for delayed observations of true class labels. We formulate this as a Bayesian…
- Hacker News · Posted on Hacker News · First archived
Show HN: botsay – cowsay for the AI Era ↗
Does this need to exist? Probably not. Does it add whimsy to the sterile, cold terminal environment that has become our chosen home? Well maybe.
- arXiv · Published · First archived
A Two-Level Clinical Novelty Prioritization Framework for Disease Trajectories Discovered from Longitudinal Electronic Health Records ↗
Graph-based analysis of electronic health records (EHRs) yields disease-trajectory catalogs, but structural support does not establish semantic novelty. We present a two-level framework to prioritize 99,527 branching motifs previously discovered by an upstream patient-disjoint, train-only MIMIC-IV v3.1 pipeline and frozen after validation-based catalog selection. The framework separates path-level and node-to-endpoint semantic sparsity from reliability-adjusted clinical priority. Three deterministic textualizations per motif or node-target pair were embedded with BAAI/bge-small-en-v1.5 and cal…
- Hacker News · Posted on Hacker News · First archived
Anthropic warns small AI models could self-replicate through IoT devices ↗
Hacker News discussion.
- arXiv · Published · First archived
VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution ↗
Generative super-resolution (SR) can recover sharp detail in computed tomography (CT) images from severely downsampled observations, but the missing high-frequency content is not uniquely determined by the low-resolution input. Consequently, visually plausible reconstructions may contain anatomically unsupported vascular detail, such as missing, spurious, or incorrectly connected vessels. To overcome this challenge, we introduce VascuSR, an iterative anatomy-aware diffusion framework that combines image reconstruction with explicit 3D vascular guidance. First, to provide richer anatomical guid…
- arXiv · Published · First archived
Graph-Reweighted Behavior Cloning with Human Interventions for Precise Robotic Manipulation ↗
Long-horizon precise manipulation produces mixed-quality data for post-training vision-language-action (VLA) models: even successful demonstrations and intervention-assisted rollouts may contain detours, hesitations, failed attempts, and imperfect recoveries. The key challenge is to identify which action chunks actually advance the task, rather than uniformly cloning or discarding imperfect trajectories. We propose Graph-Reweighted Behavior Cloning (GR-BC), an offline iterative post-training framework that builds a task-conditioned goal-reachability graph over demonstrations and iteratively co…
- Hacker News · Posted on Hacker News · First archived
AI Money Supercharges Semiconductor Research Labs ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
wambosec/cve-reconstruction — dataset update ↗
# CVE Reconstruction This public dataset contains the complete case assets for 377 validated CVE reconstruction cases. Each case has white-box and black-box variants, giving 754 tasks. The code-only evaluator is in the [residency-environments PR](https://github.com/PrimeIntellect-ai/residency-environments/pull/27). The public [Prime environment](https://app.primeintellect.ai/dashboard/environments/wambosec/cve-reconstruction) also provides the code-only evaluator. The task index is `data/tasks.jsonl`. Each row identifies a real vulnerable and fixed release and its public Prime sandbox image. `
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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