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Earlier sources: AI agents
AI agents use models to choose actions, call tools, and work toward a goal. This thread follows agent frameworks, memory, orchestration, evaluation, and safeguards; autonomy and reliability vary between 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 ↗831 classified signals in the fourteen-day window. 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
The Wealth of Agents: Scaling Agentic AI Across Developers ↗
Today's agentic systems are typically designed, built, and deployed by single developers with full control over all participating agents. While this simplifies development, it limits the potential scale of agentic systems. We study multi-developer agentic systems (MDAs) which enable developers to collaboratively build agentic systems to push frontier capability beyond what any individual developer could achieve alone. We construct empirical and theoretical models of developers to compare MDAs with single-developer agentic systems (SDAs). On GAIA, a general assistant benchmark, a population of…
- Hacker News · Posted on Hacker News · First archived
I Deleted 168,000 Lines of Comments. My AI Agent Didn't Notice ↗
Hacker News discussion.
- arXiv · Published · First archived
From Training to Escalation: Forensic Analysis and Local Recreation of RL/RLHF-Induced Privilege Escalation in AI Agents (CVE-2026-65616) ↗
Mid-2026 reporting on autonomous AI evaluation incidents claimed that frontier large language model (LLM) agents escaped server containment through emergent coordination and cloud infrastructure attacks. This paper contextualizes those claims through forensic analysis and containerized recreation of both the Highly Persistent Internal Model (HPIM) training-case conditions and the Phase One privilege escalation targeting internal package repositories during OpenAI's ExploitGym evaluations. We recreate the configuration of the OpenAI-provided training example and reproduce the cryptographic veri…
- arXiv · Published · First archived
What Should a Software Team Spend on Agents? Simulation-Based Cost Optimization of Mixed AI-Human Teams ↗
Context: AI agents are rapidly adopted in software development, but their token costs are rising and it is unclear how much a team should spend on agents, and on which tasks. Objective: We determine how teams should divide spending between human developers and AI agents to deliver the features a project demands at the lowest total cost. Method: We model mixed teams of human developers and AI agents with eight specialized roles as a discrete-time stochastic process covering bug injection and detection, technical debt, human supervision of agents, and release cycles with feature freezes. Using M…
- GitHub · Repository created · First archived
bmcreations/disk-jockey ↗
Agent-era disk hygiene: spins down your simulators, drops your stale tracks, keeps the dance floor clear.
- Hacker News · Posted on Hacker News · First archived
I Want More from AI Than Another Agent ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
What llms.txt files tell AI agents (beware) ↗
Hacker News discussion.
- GitHub · Repository created · First archived
Yannis100/vita-port-skill ↗
Agent skill for porting games and engines to the PS Vita (VPK homebrew): VitaSDK, vitaGL, Android .so loader, static recomp, API emulation
- arXiv · Published · First archived
SWE-Stream: Scaling Executable Training Data for SWE Agents via Progressive Construction and Elastic Streaming ↗
Software engineering agents have advanced rapidly, but scaling their training remains constrained by the limited availability of high-quality executable tasks. Development histories contain valuable signals for constructing such tasks, yet they often require substantial reconstruction due to incomplete environments, missing tests, and inconsistent artifacts. We present SWE-Stream, a system for scaling executable SWE training data through progressive construction and elastic streaming execution. SWE-Stream progressively validates and refines task candidates, reusing existing artifacts when poss…
- Hacker News · Posted on Hacker News · First archived
Nothing threw: 84 AI agent failures in a live business, 10 caught automatically ↗
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 AI agents
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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
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