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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 ↗5995 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
I benchmarked a TDD-enforcing Claude Code plugin. It didn't write better code ↗
Hacker News discussion.
- GitHub · Repository created · First archived
alphaparkinc/genpark-agent-tool-retry-exponential-backoff-jitter-skill ↗
Fault-tolerant agent tool execution wrapper featuring adaptive exponential backoff, full jitter, rate-limit 429 backpressure handling, and circuit breaker patterns.
- GitHub · Repository created · First archived
Alpha-Park/genpark-agent-prompt-injection-sanitizer-firewall-skill ↗
Heuristic and pattern-based agent prompt injection sanitizer and LLM jailbreak firewall detecting adversarial jailbreaks, system prompt exfiltration, and tool hijacking.
- GitHub · Repository created · First archived
alphaparkinc/genpark-agent-prompt-injection-sanitizer-firewall-skill ↗
Heuristic and pattern-based agent prompt injection sanitizer and LLM jailbreak firewall detecting adversarial jailbreaks, system prompt exfiltration, and tool hijacking.
- GitHub · Repository created · First archived
Alpha-Park/genpark-agent-self-consistency-consensus-scorer-skill ↗
Multi-path chain-of-thought self-consistency consensus scorer and majority voter aggregating divergent LLM reasoning trajectories into high-confidence deterministic outputs.
- GitHub · Repository created · First archived
alphaparkinc/genpark-agent-self-consistency-consensus-scorer-skill ↗
Multi-path chain-of-thought self-consistency consensus scorer and majority voter aggregating divergent LLM reasoning trajectories into high-confidence deterministic outputs.
- GitHub · Repository created · First archived
Alpha-Park/genpark-agent-hybrid-memory-retriever-skill ↗
Production-grade hybrid sparse-dense memory retrieval engine combining BM25 keyword search, semantic n-gram cosine similarity, and recency decay ranking for agent episodic long-term memory.
- GitHub · Repository created · First archived
alphaparkinc/genpark-agent-hybrid-memory-retriever-skill ↗
Production-grade hybrid sparse-dense memory retrieval engine combining BM25 keyword search, semantic n-gram cosine similarity, and recency decay ranking for agent episodic long-term memory.
- GitHub · Repository created · First archived
Alpha-Park/genpark-agent-sandboxed-virtual-code-runner-skill ↗
Deterministic sandboxed virtual code execution engine and AST security analyzer verifying multi-language agent code, blocking dangerous syscalls, and capturing isolated execution telemetry.
- arXiv · Published · First archived
JEV as a Judge for Agent Trace Security: An Empirical Comparison with Generative LLM Judges ↗
Security evaluation of tool-using agents requires judging actions in context, yet generative judges add latency, explanation overhead, and output-validation failures. We study whether JEV, a typed decision model, offers a useful alternative for retrospective trace classification. We evaluate JEV and four generative judges on four benchmark collections totaling 5,219 trajectories, using a common risk rubric and behavior-level labels. JEV attains a benchmark-averaged positive-class F1 of 77.8, compared with 74.1 for the strongest generative configuration, GLM-5.2, with valid-result coverage of 9…
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.