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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 ↗5478 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
(When) Should a Software Factory be Lights-off? Benchmarking Code Review Agents on Bug Detection ↗
With the advances of coding agents, software developers are spending more time reviewing code than writing code. As human review velocity falls behind agentic code generation, it is tempting to build automated review agents towards a future of "lights-off" software factory with little to no human supervision. To assess the practicality of such a fully autonomous software factory, we construct CRBugBench, a new benchmark that evaluates whether review agents detect human-verified bugs. We build a data pipeline that mines bugs from repository history, and pair each bug with its introducing PR to…
- Hacker News · Posted on Hacker News · First archived
Claude was wrong about job impacts ↗
Hacker News discussion.
- arXiv · Published · First archived
ToFu: Exploiting and Fuzzing the LLM-Orchestrator-Tool Trust Boundary ↗
Large language models (LLMs) are increasingly deployed as agents that interact with external software through tools, with an orchestration layer translating model outputs into structured tool invocations. This architecture creates an LLM-orchestrator-tool trust boundary where tool semantics may go unenforced and LLM-generated arguments may be incorrectly treated as trusted. We present ToFu, a tool-aware framework that automatically derives semantic security checks for LLM tools from their existing metadata and a CWE-guided sanitizer corpus. ToFu specializes these checks to individual tools and…
- arXiv · Published · First archived
A-SAW: The Agentic Software Architecture Workflow ↗
AI assistance in software engineering focuses on implementation and verification, leaving the design phase, and in particular the maintenance of architecture documentation, comparatively underserved. Architecture as Code makes architectural artifacts versionable, diffable, and machine-readable, but it does not reduce the effort of keeping them updated, so design documentation still ends up as a fragmented, author-dependent, and only partially trusted record of why a system looks the way it does. In this paper we introduce the Agentic Software Architecture Workflow (A-SAW), a process-level arti…
- Hacker News · Posted on Hacker News · First archived
Court throws out killer's sentence after judge said he loved AI video of victim ↗
Hacker News discussion.
- arXiv · Published · First archived
SWE-DeceptionBench: Benchmarking Deceptive Behaviors of LLMs Across the Software Development Lifecycle ↗
Large Language Models (LLMs) are increasingly used throughout the software development lifecycle. This growing reliance on LLMs raises concerns about their trustworthiness, with deception posing a particularly important risk. Deceptive behavior takes the form of misleading responses or assistance in fabricating, concealing, or distorting information. To study this behavior, we introduce SWE-DeceptionBench, a benchmark for evaluating LLM deception in software engineering. The benchmark covers six lifecycle stages and two deception triggers: Active Deception, where models recognize infeasible or…
- arXiv · Published · First archived
APEX-Harm: Evaluating Agent Safety in Shared Professional Workspaces ↗
Professional agents increasingly work through command-line interfaces and multiple tools over shared workspaces containing files, messages, and business applications. These environments create safety risks that are difficult to capture with attacks placed at a single, known read location. We introduce APEX-Harm, a benchmark for evaluating prompt and script injection against long-horizon agents performing finance, consulting, and legal tasks in large workspaces. Each task contains 115 to 328 files, together with email, calendar, chat, document, spreadsheet, PDF, and code-execution tools exposed…
- GitHub · Repository created · First archived
GrinZero/tripo-studio-plugin ↗
Unofficial Codex plugin for AI 3D creation with your Tripo Studio account. Generate, texture, rig, animate and export assets with MCP Apps and local Blender tools.
- GitHub · Repository created · First archived
ymybxx/SoloMCN ↗
把 Claude Code 变成你的短视频团队：不搭工作流，直接让 Claude 干活，从国内热榜、推特热帖到抖音、小红书、B站、YouTube、推特发布。Turn Claude Code into your short-video team — no workflows, from trends and X posts to publishing.
- Hugging Face · Model / dataset date · First archived
hoskasii/MOSS-TTS-Nano-100M-Somali — model release ↗
# MOSS-TTS-Nano MOSS-TTS-Nano is an open-source **multilingual tiny speech generation model** from [MOSI.AI](https://mosi.cn/#hero) and the [OpenMOSS team](https://www.open-moss.com/). With only **0.1B parameters**, it is designed for **realtime speech generation**, can run directly on **CPU without a GPU**, and keeps the deployment stack simple enough for local demos, web serving, and lightweight product integration. ## News * 2026.4.10: We release **MOSS-TTS-Nano**. A demo Space is available at [OpenMOSS-Team/MOSS-TTS-Nano](https://huggingface.co/spaces/OpenMOSS-Team/MOSS-TTS-Nano). You can
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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