Problem
Research questions and friction points this paper is trying to address.
State-Grounded Conditioning
direction drift
user-facing LLM agents
State-Grounded Conditioning: Wrapping User-Facing LLM Agents Where Direction Depends on Live State
🤖 AI Summary
本文提出状态接地条件（SGC）方法，解决用户面对的LLM代理在实时状态下的方向漂移问题，通过感知、接地和交互包装器提高对话质量。
📝 Abstract
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 ($\approx$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 $\approx$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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
State-Grounded Conditioning (SGC)
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