arXiv (All)
open access
Synapse-Control: Making Agent Experience Actionable with Lightweight Runtime Control
Large language model agents reuse execution experience to improve reliability on complex tool-based tasks. Systems such as Sentry call large models to generate failure diagnoses before retrieving advice, adding inference cost to experience reuse. We present Synapse-Control, an agent runtime whose li
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- ASA: Backbone-Training-Free Representation Engineering for Tool-Calling Agents#785110
- ASA: Backbone-Training-Free Representation Engineering for Tool-Calling Agents#783520
- I-PTC: Interactive Programmatic Tool Calling for Stateful Tool-Augmented Agents#1584891
- Forethought: Verifiable Reasoning from Neurosymbolic Primitive Programming#1646380
- Engineering Robustness into Personal Agents with the AI Workflow Store#175107
Record · ID 1646712
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive