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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 ↗6002 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.
- Hugging Face · Model / dataset date · First archived
pbhappliedsystems/qwen-2.5-32B-instruct-gguf-Q4-K-M — model update ↗
# Qwen2.5-32B-Instruct · GGUF Q4_K_M > **Evaluation results:** this Q4_K_M build compared with F16, capability family by family, with confidence intervals and file size, on the results page: [pbhappliedsystems.com/models/qwen2.5-32b-instruct#Q4_K_M](https://pbhappliedsystems.com/models/qwen2.5-32b-instruct#Q4_K_M) **Converted and evaluated by [PBH Applied Systems, LLC](https://pbhappliedsystems.com)** — Applied AI/ML Consulting · LLM Optimization & Deployment · Quantized AI Infrastructure > 🔬 **This repository is part of a production-oriented evaluation series.** Every model published under [
- Hugging Face · Model / dataset date · First archived
pbhappliedsystems/qwen-2.5-32B-instruct-gguf-F16 — model update ↗
# Qwen2.5-32B-Instruct · GGUF F16 > **Evaluation results:** this F16 build is the baseline each quantized build of this model is measured against. Its pass rates by capability family, with confidence intervals, are on the results page: [pbhappliedsystems.com/models/qwen2.5-32b-instruct#F16](https://pbhappliedsystems.com/models/qwen2.5-32b-instruct#F16) **Converted and evaluated by [PBH Applied Systems, LLC](https://pbhappliedsystems.com)** — Applied AI/ML Consulting · LLM Optimization & Deployment · Quantized AI Infrastructure > 🔬 **This repository is part of a production-oriented evaluation
- Hugging Face · Model / dataset date · First archived
pbhappliedsystems/qwen-2.5-14B-instruct-1m-gguf-Q4-K-M — model update ↗
# Qwen2.5-14B-Instruct-1M · GGUF Q4_K_M > **Evaluation results:** this Q4_K_M build compared with F16, capability family by family, with confidence intervals and file size, on the results page: [pbhappliedsystems.com/models/qwen2.5-14b-instruct-1m#Q4_K_M](https://pbhappliedsystems.com/models/qwen2.5-14b-instruct-1m#Q4_K_M) **Converted and evaluated by [PBH Applied Systems, LLC](https://pbhappliedsystems.com)** — Applied AI/ML Consulting · LLM Optimization & Deployment · Quantized AI Infrastructure > 🔬 **This repository is part of a production-oriented evaluation series.** Every model publishe
- arXiv · Published · First archived
Approval Laundering: Systematizing Approval--Execution Binding Failures in AI Coding-Agent Harnesses ↗
Modern AI coding-agent harnesses (Claude Code, Codex CLI, Cursor) rest their security boundary on a largely unexamined assumption: that the action A a human approves is the same action A' the harness executes, where A is fixed by a stated policy for what a scope grant or session-scoped approval authorizes. We show this assumption fails systematically and reproducibly. We introduce Approval Laundering, a taxonomy of six failure modes by which a harness's enforcement mechanism silently substitutes A' for A after approval: Scope, Argument, Temporal, Tool, Delegation, and Semantic laundering. Unli…
- Hacker News · Posted on Hacker News · First archived
OpenAI Launches Decisions API for Fast AI Choices ↗
Hacker News discussion.
- arXiv · Published · First archived
Refusals That Bend: Measuring and Predicting Task Malleability in Embodied VLM Planners ↗
Embodied vision-language models (VLMs) are increasingly deployed as high-level planners for robots because they generalize across diverse environments. However, this requires their safety alignment to also hold in unseen environments. Existing red-teaming assumes an adversary who optimizes the prompt, the pixels, or text in the environment, and existing benchmarks ask whether a planner recognizes or mitigates a hazard in a fixed scene. Neither asks whether a refusal the planner has already given survives an ordinary change to the environment. We ask that question by placing a single everyday o…
- Hacker News · Posted on Hacker News · First archived
The open-source AI platforms vying to become China's Hugging Face ↗
Hacker News discussion.
- arXiv · Published · First archived
APTInvestBench: Evaluating Autonomous APT Investigation under Varying Telemetry ↗
Large language model (LLM) agents could help security operations centers (SOCs) investigate advanced persistent threats (APTs) by turning weak leads into evidence for intrusion scoping and response. Yet success under one telemetry setting does not establish robustness to changes in log collection, retention, or sampling. We introduce APTInvestBench, a benchmark for evaluating cross-telemetry robustness in autonomous APT investigation. It comprises 370 cases across seven SOC-inspired conditions, derived from 56 report-informed attack reconstructions with 16.4 million log records. Agents investi…
- GitHub · Repository created · First archived
chan22222/Pluto-AI-Starcraft-Remaster ↗
beta
- arXiv · Published · First archived
PrivCert: Certifying Statement Support under Differential Privacy ↗
Differentially private (DP) text generation can protect individual records, but privacy alone does not specify what evidence a released statement carries about the underlying data. We identify this as an evidence gap: a private report may contain plausible claims without indicating whether they are strongly supported by the private dataset. We introduce PrivCert, a framework for privacy-preserving reporting that makes statement support explicit through privacy-preserving certificates and emit-or-abstain decisions. As a canonical instantiation, PrivCert-PF (Proposal-and-Filter) separates data-i…
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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