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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 ↗856 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.
- GitHub · Repository created · First archived
VORNEX-01/JARVIS-VORNEX ↗
Voice-first desktop assistant for Windows — talk to your PC and it works inside real apps, safely. By VORNEX.
- Hacker News · Posted on Hacker News · First archived
Meta's new AI agent built lists of people in vulnerable groups on request ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
When single AI agents become a swarm ↗
Hacker News discussion.
- arXiv · Published · First archived
SCA: Spatial Credit Assignment for Reinforcement Learning of GUI Agents ↗
GUI agents automate tasks on digital devices by grounding language instructions in visual interfaces. Existing group-relative reinforcement learning improves GUI action prediction by comparing the rewards of multiple responses sampled from the same GUI state. However, binary evaluation treats spatially different failed clicks as identical and provides no relative signal when all sampled clicks fail. To address these limitations, we propose Spatial Credit Assignment (SCA), which uses the screen coordinates of sampled clicks to refine group-relative credit. Specifically, SCA predicts each held-o…
- arXiv · Published · First archived
Neuro-Symbolic Computer Use: Learning Reusable Policies for Reliable and Efficient Execution ↗
Many computer tasks recur: the same workflow runs many times, with new inputs and from different starting states. Current computer-use agents re-plan every step of every run, which makes them costly and unreliable on such tasks. We introduce neuro-symbolic computer use, in which a recurring workflow is executed by a learned policy rather than re-derived by an agent on each run. The policy fixes the decisions that are stable across runs (ordering, variables, loops, and branches) in executable code, and delegates observation-dependent decisions, such as grounding and state checks, to neural mode…
- GitHub · Repository created · First archived
ariana-dot-dev/battleships ↗
The most comprehensive comparison of platforms offering cloud runtime / sandbox / VM compute for agents. By boat.dev.
- arXiv · Published · First archived
PrecogUI: Proactive GUI Agents via Pre-cognitive Simulation and Experience Retrieval ↗
Existing reactive Graphical User Interface (GUI) agents often fail in long-horizon, dynamic scenarios, where unexpected disturbances trigger attention-diverting and cascading failures. To address this, we propose PrecogUI, a pre-cognitive architecture that shifts the paradigm from reactive execution to proactive decision-making. Specifically, we design a Proactive Experience Pool (PEP), which caches recurring anomaly and success patterns as "state-action-result" tuples in a dual-memory repository. Furthermore, we introduce a Proactive Simulation Executor (PSE) that learns to forecast the next…
- GitHub · Repository created · First archived
BlackHole1/passportsim ↗
Develop and debug FoloToy AI Passport firmware without the device: an ESP32-C3 emulator that runs unmodified ESP-IDF images in the browser (WebAssembly) or on macOS and Windows, with a CLI and an MCP server for AI agents.
- arXiv · Published · First archived
Harness Evolution as Learning: Approximation, Generalization, and Optimization Limits of Self-Improving Personal Agents ↗
As the capabilities of large language models (LLMs) continue to advance, increasing attention is turning to how to translate their abilities into useful behavior. Personal agents bring this question into everyday settings, where models are expected to serve individual users and continually adapt to their preferences. With the underlying model held fixed, such adaptation relies on harness engineering: designing and evolving the surrounding layer that manages context, memory, tools, and execution. Despite rapid progress, the factors governing effective harness evolution remain insufficiently und…
- GitHub · Repository created · First archived
chiragmangaldev3112/agent-playbooks ↗
Portable engineering workflows for AI coding agents — 35 playbooks for bug fixing, feature development, code review, testing, and security, across Claude Code, Cursor, Codex, Copilot, and more.
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.