CaveAgent: Transforming LLMs into Stateful Runtime Operators
#CaveAgent #LLM #runtime operator #dual-stream architecture #agentic systems #context drift #long‑horizon tasks #text‑centric paradigms
📌 Key Takeaways
- LLM‑based agents are increasingly capable but limited by text‑centric paradigms that falter on long‑horizon tasks.
- Current systems exhibit fragile multi‑turn dependencies and suffer from context drift.
- CaveAgent re‑conceptualises tool use, treating the LLM as a runtime operator rather than a text generator.
- It introduces a dual‑stream architecture that inverts the conventional paradigm for better task execution.
📖 Full Retelling
Researchers announced **CaveAgent**, a new framework that transforms large language model (LLM)-based agents from **LLM-as-Text-Generator** to **LLM-as-Runtime-Operator**. The work was published on arXiv (ID 2601.01569v2) in **January 2026** and seeks to overcome the weaknesses of existing text‑centric agentic systems—specifically their difficulty handling long‑horizon tasks due to fragile multi‑turn dependencies and context drift.
🏷️ Themes
LLM Agents, Runtime Operator Paradigm, Dual‑Stream Architecture, Long‑Horizon Task Execution, Text‑Centric Limitations, Context Drift
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Original Source
arXiv:2601.01569v2 Announce Type: replace
Abstract: LLM-based agents are increasingly capable of complex task execution, yet current agentic systems remain constrained by text-centric paradigms that struggle with long-horizon tasks due to fragile multi-turn dependencies and context drift. We present CaveAgent, a framework that shifts tool use from ``LLM-as-Text-Generator'' to ``LLM-as-Runtime-Operator.'' CaveAgent introduces a dual-stream architecture that inverts the conventional paradigm: rat
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