Agent-Environment Alignment via Automated Interface Generation

TL;DR

ALIGN addresses agent-environment misalignment by automatically generating aligned interfaces that enrich static environment information and dynamic observations. It introduces InferRules and WrapStep as a lightweight, environment-wrapping solution and employs an Analyzer–Optimizer loop with experimental verification to iteratively refine interfaces. The approach generalizes across tasks and model backbones, delivering substantial gains (e.g., up to 45.67% on ALFWorld) and reducing error-inducing action cycles. This work demonstrates that automatic interface design can significantly improve reliability, interpretability, and cross-domain transfer for LLM-based agents.

Abstract

Large language model (LLM) agents have shown impressive reasoning capabilities in interactive decision-making tasks. These agents interact with environment through intermediate interfaces, such as predefined action spaces and interaction rules, which mediate the perception and action. However, mismatches often happen between the internal expectations of the agent regarding the influence of its issued actions and the actual state transitions in the environment, a phenomenon referred to as \textbf{agent-environment misalignment}. While prior work has invested substantially in improving agent strategies and environment design, the critical role of the interface still remains underexplored. In this work, we empirically demonstrate that agent-environment misalignment poses a significant bottleneck to agent performance. To mitigate this issue, we propose \textbf{ALIGN}, an \underline{A}uto-A\underline{l}igned \underline{I}nterface \underline{G}e\underline{n}eration framework that alleviates the misalignment by enriching the interface. Specifically, the ALIGN-generated interface enhances both the static information of the environment and the step-wise observations returned to the agent. Implemented as a lightweight wrapper, this interface achieves the alignment without modifying either the agent logic or the environment code. Experiments across multiple domains including embodied tasks, web navigation and tool-use, show consistent performance improvements, with up to a 45.67\% success rate improvement observed in ALFWorld. Meanwhile, ALIGN-generated interface can generalize across different agent architectures and LLM backbones without interface regeneration. Code and experimental results are available at https://github.com/THUNLP-MT/ALIGN.

Figures (3)

• Figure 1: Illustration of agent-environment misalignment and our proposed solution. On the left, the agent and the environment have a misalignment in their interpretation of the same observation, where the agent's understanding of the observation differs from the environment's underlying logic. On the right, our method, ALIGN, automatically generates interfaces that provide the agent with clearer interaction context, aligning the agent's understanding with the environment's logic.
• Figure 2: Overview of the ALIGN-generated interface.
• Figure 3: ALIGN framework. In each iteration, ALIGN progresses though three stages. Stage 1: the Analyzer identifies potential agent-environment misalignments and validates them through experiments; Stage 2: the Optimizer generates a new interface based on the previous interface and identified misalignments, followed by verification and refinement; Stage 3: the agent interacts with the updated interface-wrapped environment, with trajectories of failed tasks fed back to the Analyzer for analysis in the next iteration. At the bottom of the figure, examples for misalignment, verification of interface integrity by Optimizer through experiments, and the ALIGN-generated interface are provided.