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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 ↗6049 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
Show HN: A Deterministic-First Local Control Center Leveraging AI for Routing ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Misuse of AI is brands' top reputational threat, new survey says ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Octop, a self-hosted AI assistant by Tencent ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
OpenAI rolls out weak sauce watermarking for AI text ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
ArXiv Is Rate Limiting Submissions Because It Can't Keep Up with AI Slop ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
TimeSeventeen/Polymarket-v2 — dataset update ↗
# Polymarket-v2 A large-scale dataset of on-chain event logs from Polymarket v2, the prediction-market platform on the Polygon network. The repository contains three layers covering the full contract lifecycle from Polymarket v2 server start: OrderFilled/ (the raw on-chain trade tape), daily_aligned/ (the cleaned, metadata-enriched, and normalized analysis layer, Split by UTC). update daliy.
- Hugging Face · Model / dataset date · First archived
MIHMahmudEli/davidnet-experiments — model update ↗
# DAVID-Net: Deepfake Audio-Visual Invariant Disentanglement Network ### Official Experiment Archive, Checkpoints, & Verification Repository [](https://github.com/MIHMahmudEli/david-net-av-v2) [](https://david-net-av.vercel.app/) [](https://davidnet-api.onrender.com/health) [](ht
- arXiv · Published · First archived
Partially Observable Zero-shot coordination by Predicting Intention of Partner ↗
Zero-shot coordination in embodied settings requires acting while the partner is intermittently out of view, leaving existing methods with ambiguous partner representations and uncertainty over hidden partner states. We propose Predicting Intention of Partner (PIP) to jointly address these challenges. PIP uses a Joint-view VAE to distill richer training-time evidence from the union of both agents' local observations into a partner representation available from local observations alone. Partner-state Belief networks further infer the partner's hidden location and behavioral tendencies from the…
- arXiv · Published · First archived
Test-Time Agent Evolution for Long-Horizon Legal Reasoning ↗
Legal intelligence aims to support reliable decision-making across long-horizon legal processes involving evolving case states and multiple roles. However, real-world legal deployment exhibits substantial case heterogeneity in facts, evidence, and procedural contexts, exposing the limitations of static agent strategies. Moreover, legal reasoning is inherently interdependent across roles and procedural stages, making global reliability fundamentally different from isolated role competence. To address these challenges, we study training-free test-time agent adaptation, where agents continuously…
- arXiv · Published · First archived
Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Rendering ↗
AI systems create images and videos with image/video generation models or by writing code and graphics descriptions that are then rendered. These routes can produce similar visible artifacts but expose different representations, intervention points, and provenance evidence. We develop a production-centered framework that compares detection and watermarking across both routes. An explicit verification specification distinguishes passive inference, message recovery, and authenticated provenance. We organize image, video, source-code, and rendering-aware watermarks by production stage. We examine…
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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These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.