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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.
- Hacker News · Posted on Hacker News · First archived
Claude for Government is now generally available ↗
Hacker News discussion.
- arXiv · Published · First archived
SAGE: Similarity-Based Cleaning of Poisoned Training Data from Verified Examples ↗
As machine learning increasingly relies on public, untrusted data sources, data poisoning attacks, which inject malicious examples into training data to induce misclassification of a chosen target, pose a growing threat. Existing defenses either assume zero ground-truth information about which examples are poisoned, or they assume access to a large set of examples verified to be clean. Satisfying the latter assumption incurs significant cost since reliable verification can be very resource- or labor-intensive. This cost is particularly high for clean-label attacks, where poisoned examples are…
- Hacker News · Posted on Hacker News · First archived
Ask HN: Which "AI" provider would you choose? ↗
I'm looking for an "AI provider" recommendations for physics teaching and academic research. My use case requires mathematical and physics reasoning for paper parsing and PDF synthesis to generate lecture notes, exams, and problem sets. Daily volume averages would be around 100 interactions alongside 2–3 multi-page document processing tasks. Nice to have: native web search, prompt caching for context efficiency, strict zero-data-retention or trai…
- Hacker News · Posted on Hacker News · First archived
Who Keeps Your Industrial AI Plugged In? ↗
Hacker News discussion.
- arXiv · Published · First archived
CONTRA: Discovering and Qualifying Behavior-Changing Questions for Selective Clarification in LLM Code Generation ↗
Coding agents can generate code that appears correct but implements behavior the user never intended. This mismatch can arise when an agent silently resolves underspecified requirements through its own assumptions. As subsequent development builds on these assumptions, correcting the resulting behavior can become increasingly costly. Early clarification can help prevent such mismatches, but unnecessary questions can interrupt developers and slow down development. Existing methods struggle to identify key clarification questions while avoiding unnecessary ones. Therefore, we propose CONTRA, a t…
- arXiv · Published · First archived
The Innocent Courier: Covert Exfiltration Through Legitimate LLM Web Fetching ↗
With the increasing capabilities of Large-Language-Models (LLMs) and LLM-based agents, users are increasingly using them to solve everyday problems, such as answering e-mails or providing programming support. Existing work has extensively investigated security and privacy risks, such as prompt injections and the disclosure of sensitive data to chatbot providers. While various solutions were developed to address these risks, including input structuring to prevent prompt injections or deploying local LLMs to avoid sharing confidential data with chatbot operators, LLMs also pose the risk of leaki…
- Hacker News · Posted on Hacker News · First archived
When AI Agents Break In, Governments Shouldn't Be Stuck with the Cleanup ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Stanislas: What if AI had been imagined back in 1984? ↗
Hacker News discussion.
- arXiv · Published · First archived
SoK: Decentralized Agent Economic Infrastructure ↗
Decentralized agent economies increasingly build a single task from protocols that were designed and secured separately. This creates a simple problem: a workflow can look correct at each step and still produce the wrong outcome. For example, a correct escrow may release payment on an authorized approval that provides little evidence that the delivered work actually satisfied the task. We systematize this problem across the full lifecycle of an agent task. Our study organizes security and economic requirements into 17 property families over six stages, with receipt soundness and completeness a…
- Hacker News · Posted on Hacker News · First archived
Trump's 'Morally Binding' New AI Guardrails Are Meaningless Pudding ↗
Hacker News discussion.
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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