mlsanityData to 14 Oct 2026

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

arXiv 2606.05828 ↗First posted 4 Jun 2026cs.AI cs.CLcited by 2

On the map: LLM agents and tool use → Agent memory systems

Abstract (arXiv)

As Large Language Model (LLM) capabilities advance, locally deployed personal agents relying on API-based remote models and external skills have emerged as a novel paradigm. With the rapid expansion of available skills, enabling personal agents to learn and adapt to implicit user preferences becomes a critical challenge. However, local deployment constraints preclude complex centralized selection algorithms, creating an urgent need for a lightweight local preference harness. This paper explores the implementation of such a harness through a novel architecture that strictly decouples statistical preference learning from semantic intent parsing. Specifically, we leverage localized statistical results to influence and modulate the selection decisions of the remote LLM. Extensive evaluations demonstrate that our decoupled approach achieves the lowest cumulative regret and highest test accuracy, significantly outperforming traditional memory-augmented agents.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Agent memory systems, one of 16 directions in LLM agents and tool use. Builds on 16 of its 37 references that are in the map. First posted on arXiv 4 Jun 2026.

Builds on

16 of 37 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026M^star: Every Task Deserves Its Own Memory Harness10 Apr
  2. 2025Memory in the Age of AI Agents15 Dec
  3. 2025MemGen: Weaving Generative Latent Memory for Self-Evolving Agents29 Sep

Other references · 13

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  4. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  5. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,947
  6. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
  7. 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 1,040
  8. 2025LLMs Get Lost In Multi-Turn Conversationcited by 429
  9. 2026Meta-Harness: End-to-End Optimization of Model Harnessescited by 309
  10. 2026Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnessescited by 142
  11. 2026OpenClaw-RL: Train Any Agent Simply by Talkingcited by 90
  12. 2025SynthesizeMe! Inducing Persona-Guided Prompts for Personalized Reward Models in LLMscited by 29
1 more
  1. 2026Affordance Agent Harness: Verification-Gated Skill Orchestrationcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Harness Evolution as Learning: Approximation, Generalization, and Optimization Limits of Self-Improving Personal Agentscited by 1
  2. 2026What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026AdaptArena: Evaluating Test-Time Personalization of Web Agents
  2. 2026From Profiling to Synthesis: Benchmarking Implicit Behavioral Alignment in Personalized LLM Agents
  3. 2026Preference-Aware Rubric Learning for Personalized Evaluation
  4. 2024Aligning LLM Agents by Learning Latent Preference from User Edits
  5. 2026Latent Preference Modeling for Multi-Session Personalized Tool Calling
  6. 2026Personalizing Large Language Model Agents with Small Policy Models
  7. 2024Unsupervised Human Preference Learning
  8. 2025Extended Inductive Reasoning for Personalized Preference Inference from Behavioral Signals