Statistical Priors for Implicit Preferences: Decoupling Skill Selection as a Local Harness in Personal Agents

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Expert, extended

Summary

The Local Harness architecture addresses the challenge of implicit user preference learning in locally deployed personal agents. These agents, which rely on API-based remote LLMs and external skills, face difficulties as skill ecosystems expand, particularly with the limitations of local deployment for complex preference algorithms. The proposed framework strictly decouples statistical preference learning, managed by an efficient local statistical primitive, from semantic intent parsing, handled by a remote LLM as an exception handler. This decoupled approach was evaluated on ToolBench-60, a new sandbox comprising 60 skills across 10 domains, demonstrating the lowest cumulative regret and highest test accuracy compared to traditional memory-augmented agents. The architecture's robustness was confirmed across GPT-5.2, DeepSeek-V4-Flash, and Qwen3-30B-Instruct LLM backbones. The code is open-sourced.

Key takeaway

For AI Architects designing locally deployed personal agents, you should adopt a decoupled architecture for skill selection. Avoid relying on a single remote LLM to manage both statistical preference learning and semantic intent parsing, as this leads to high regret and lower accuracy. Instead, implement a lightweight local statistical harness for user habit modeling and reserve the remote LLM strictly for semantic override handling. This approach demonstrably improves performance and robustness in personalized agent designs.

Key insights

Decoupling statistical preference learning from semantic intent parsing significantly improves personal agent skill selection.

Principles

• Conflating statistical and semantic signals in LLMs causes systemic failures.
• Principled exploration is vital for nuanced preference discovery.
• Local statistical primitives efficiently manage exploration-exploitation tradeoffs.

Method

The Local Harness uses an LLM for domain classification, a local statistical prior for default action, and an LLM for semantic override detection.

In practice

• Implement LinUCB for robust contextual bandit preference learning.
• Utilize ToolBench-60 for evaluating preference-driven skill selection.
• Open-source code available for personalized agent research.

Topics

• Personal Agents
• LLM Skill Selection
• User Preference Learning
• Decoupled Architectures
• Contextual Bandits
• ToolBench-60

Code references

• ZyGan1999/Personalized-Skill-Selection

Best for: Research Scientist, AI Engineer, AI Scientist, AI Architect, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.