mlsanityData to 14 Oct 2026

State-Grounded Conditioning: Wrapping User-Facing LLM Agents Where Direction Depends on Live State

arXiv 2609.27606 ↗First posted 23 Sep 2026cs.AIcited by 0

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

We introduce State-Grounded Conditioning (SGC), a design principle for user-facing LLM agents that must condition on live user state (game state, session history, live inventory), and a distinct failure class we call direction drift: task-complete responses whose chosen direction misaligns with the current state. SGC externalises state-dependent control into rule kernels over structured inputs and three primary state slices, via Perception, Grounding, and Interaction wrappers with explicit conditioning dependencies. We evaluate SGC on a 200-session anonymised benchmark (≈1,000 assistant model turns) from an in-game conversational coaching agent that guides players through consecutive competitive matches, reporting mean first-token latency and five human-annotated dialogue-quality metrics that jointly cover factual grounding and coach-like guidance progression. The Perception wrapper holds mean first-token latency at 1.5s (vs. 6.1s for PE-Agent inside a production tool-use harness); enabling all three wrappers lifts turn-level grounded accuracy from 61.1%/69.8% (Prompting / PE-Agent) to 96.7% and session-level grounded accuracy from 20.0%/26.5% to 83.5%; session-level grounding-failure incidents drop by ≈78% relative to the strongest baseline. A cumulative ablation shows complementary incremental gains as the wrappers are added. These results inform approximate state-slice orthogonality, without establishing independent per-wrapper effects.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 24 of its 41 references that are in the map. First posted on arXiv 23 Sep 2026.

Builds on

24 of 41 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024A Survey on Large Language Model-Based Game Agents2 Apr
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learning20 Mar

Other references · 22

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
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  3. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  4. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  5. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  6. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
  7. 2023LongBench: A Bilingual, Multitask Benchmark for Long Context Understandingcited by 1,417
  8. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
  9. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,389
  10. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  11. 2023AgentBench: Evaluating LLMs as Agentscited by 1,267
  12. 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 707
10 more
  1. 2025τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environmentcited by 627
  2. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 560
  3. 2024TravelPlanner: A Benchmark for Real-World Planning with Language Agentscited by 392
  4. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAGcited by 249
  5. 2023ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Modelscited by 184
  6. 2025The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Inputcited by 52
  7. 2024Agents Thinking Fast and Slow: A Talker-Reasoner Architecturecited by 36
  8. 2025SLOT: Structuring the Output of Large Language Modelscited by 18
  9. 2025Agent-in-the-Loop: A Data Flywheel for Continuous Improvement in LLM-based Customer Supportcited by 10
  10. 2025A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphscited by 4

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