arXiv:2606.10507v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents across a wide range of tasks, their performance often degrades in multi-turn long-horizon agentic tasks. Existing methods have made progress through fine-grained credit assignment to alleviate long-horizon sparse rewards and hierarchical reinforcement learning to decompose tasks and reduce long-term dependency. However, these methods still do not directly address long-context interference, in which continuously growing histories weaken the agent's abilit
The rapid advancement of LLMs has exposed performance limitations in complex, long-horizon tasks, necessitating new architectural and algorithmic approaches to improve agentic capabilities.
Improving LLM agents' ability to manage long-context interference and execute multi-turn tasks reliably is critical for their autonomous functionality and broader enterprise adoption.
New methodologies like hierarchical planning and information folding directly address core limitations in LLM agent performance, potentially unlocking more sophisticated and extended autonomous operations.
- · AI software developers
- · Enterprises leveraging AI agents
- · Deep learning researchers
- · Tasks requiring simple, short-term LLM interactions
- · Current generation LLM agent frameworks with context limitations
LLM agents become capable of much longer and more complex autonomous workflows.
This capability leads to the automation of multi-step white-collar tasks, significantly impacting productivity.
Widespread adoption of highly autonomous agents could radically redefine the human-computer interface and professional labor markets.
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Read at arXiv cs.AI