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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 ↗5980 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
Secure Speculative Decoding for Large Language Models ↗
Speculative decoding accelerates inference for a large language model (LLM), referred to as the \emph{target model}, by first using a smaller model, referred to as the \emph{draft model}, to generate candidate tokens and then verifying them with the target model for acceptance or rejection. Prior studies primarily focused on the efficiency-utility trade-off of speculative decoding, e.g., lossy speculative decoding, leaving its security implications largely unexplored. In this work, we bridge this gap by providing the \emph{first} systematic study of the security implications of speculative dec…
- Hacker News · Posted on Hacker News · First archived
Phase Lock: A manifesto on BCIs and preserving human agency in the age of AI ↗
Hacker News discussion.
- arXiv · Published · First archived
Semantic Behavioral Watermarking: Paraphrase-Robust and Forgery-Resistant Provenance for LLM Agents ↗
Behavioral watermarking embeds an owner identifier in an LLM agent's high-level action choices, giving provenance without touching output tokens. Prior agent watermarks break in two ways. First, all three prior schemes bind the watermark to the exact action symbol, so renaming a tool desynchronizes decoding even when the observation is untouched; in AgentMark's own robustness test, paraphrasing the observation alone drops bit-recovery to 16.8%. Second, every prior agent watermark studies only removal: none asks whether an adversary can forge a trajectory that verifies as someone else's, a ques…
- arXiv · Published · First archived
ParanoiaEval: Benchmarking Unnecessary Defensive Work in Agentic Coding ↗
As coding agents increasingly undertake real-world work autonomously, judging whether their risk treatments are warranted has become important. Existing work evaluates related agent behaviors from separate perspectives, but lacks a systematic framework for unifying these behaviors. To bridge this gap, we introduce ParanoiaEval, the first benchmark for unified evaluation of risk-treatment capabilities in coding agents. Grounded in the well-established Avoidance-Transfer-Mitigation-Acceptance framework in software engineering risk management, ParanoiaEval operationalizes its 4 fundamental treatm…
- Hacker News · Posted on Hacker News · First archived
LibreOffice says 'no AI' is now a software feature ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Should AI pay taxes? The debate is gaining traction in the US ↗
Hacker News discussion.
- arXiv · Published · First archived
A Case Study in Assuring AI-Written Software ↗
Software-engineering agents can enable people without formal software training to build systems they could not otherwise implement and simultaneously can produce more code than even experts can meaningfully inspect. In both cases, exhaustive code review is not reliable as the sole basis for human control. We report a case study of a production healthcare platform built through coding agents and governed by an operator without formal software-engineering training. Over time, its workflow grew into a human-led meta-agent system where one agent wrote code, other agents supervised and reviewed it,…
- GitHub · Repository created · First archived
lkhfrgc/scholar-llm-wiki ↗
A knowledge base toolkit for research and literature review. AI agents compile papers, books and notes into a structured, densely interlinked Obsidian wiki. Built on Karpathy's llm-wiki.md: ingest/query/lint skills, controlled tag vocabulary, concept hierarchy + Canvas, arXiv->LaTeX formula pipeline, exit-code audits.
- Hacker News · Posted on Hacker News · First archived
Show HN: Agent Squad – Be the CEO of a Claude Code Agent Team ↗
I just released agent-squad for Claude Code and GitHub! You, the human, are the CEO of a team of Claude Code agents: a CTO plans, a developer builds, and QA reviews every pull request. If you work with Claude Code, this will change how you build software forever. It's open source and I'd love to get your feedback.
- GitHub · Repository created · First archived
chrisluo5311/squad-chat ↗
The all-in-one Claude Code plugin. Talk with your squad, share code, and keep live rooms (usage, git, weather, lo-fi and more) next to your session. Zero tokens, zero leaks to Claude.
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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