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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 ↗815 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.
- Hacker News · Posted on Hacker News · First archived
Taskriz – AI agents send people tiny native UIs, written in Smalltalk ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Classify web pages before adding them to an AI agent's context ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Escaped AI Agents Polluted the Web with Self-Replicating Code ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Show HN: ReliaMesh – Open-source reliability monitoring for AI agents ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Cicada: Sub-ms Go Zero-Trust gateway and gVisor firewall for AI agents Apache 2 ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Inside Agentic AI Workloads ↗
Hacker News discussion.
- arXiv · Published · First archived
MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering ↗
Large language models (LLMs) are expanding from assisting scientific research and engineering to autonomously developing and improving AI systems, advancing AI for AI (AI4AI) as a pathway toward recursive self-improvement (RSI). Workflow-driven machine learning engineering (MLE) systems provide a practical path toward this pursuit through complex orchestration that manages long-horizon experimentation, while recent MLE systems adopt model-directed interaction, allowing models to autonomously organize experiments and adapt optimization strategies based on execution feedback. However, feedback f…
- arXiv · Published · First archived
KnowGPO: Knowledge-Grouped Policy Optimization for Search Agents ↗
Deep search agents solve knowledge-intensive tasks through long-horizon, multi-turn interactions, where assigning credit to individual search decisions is fundamentally difficult. Outcome-based reinforcement learning provides only a single terminal reward shared across an entire trajectory. Recent process-supervision methods aim to assign credit at finer granularity, but they typically anchor credit to either surface-level trajectory states or model-internal signals, both of which become unreliable in deep search where observations are heterogeneous and retrieved contexts are long and noisy. W…
- arXiv · Published · First archived
Learning Self-Orchestration in Decentralized Agent Swarms ↗
The capabilities of an agent swarm depend on both its individual members and how they coordinate. Yet existing multi-agent systems often train agents within fixed orchestration structures, separating the learning of individual capabilities from the organization of collective behavior. We introduce market-based post-training (MPT), a framework for jointly learning action policies and self-orchestration in decentralized agent swarms. Agents express their willingness to participate through bids, which determine who acts and how collective outcomes provide local training credit. Learning both acti…
- arXiv · Published · First archived
A team of Gemini 4 Argon agents beats Minecraft ↗
We placed four Gemini 4 Argon agents in a sandboxed Minecraft survival world and gave them one goal: defeat the Ender Dragon. Each agent controlled a player via an open-source library called Mineflayer and coordinated with its teammates through in-game chat and shared memory. No one assigned roles or wrote game logic. The agents planned, wrote, and debugged every program themselves. Researchers could watch the run on a live dashboard and pause or stop it at any time, but they had no cause to intervene. Starting from a randomly generated world on Easy difficulty, the team wrote 2,783 programs a…
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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These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.