Everyone building LLM agents pours effort into the agent's strategy or into harder environments. The interface between them gets almost no attention, and it is often where agents actually break.

Case in point in ALFWorld: an agent tries examine shelf 1, but the env requires go to first, so it just returns "Nothing happens", and the agent concludes the shelf is empty. Simply rewording that feedback lifts a Qwen2.5-7B agent from 13.4% to 31.3%.

ALIGN from @TsinghuaNLP (OpenBMB member) automatically generates aligned interfaces to fix this misalignment.
Paper: https://arxiv.org/abs/2505.21055
Code: https://github.com/THUNLP-MT/ALIGN

1⃣️ The problem is agent-environment misalignment: the agent's expectation of what an action does diverges from the environment's real transitions, because implicit rules and under-specified observations are never surfaced. The paper shows this is a pervasive bottleneck, not an agent reasoning failure.

2⃣️ ALIGN wraps the environment with two modules. INFERRULES surfaces static rules and constraints (preconditions, action ordering) before the task; WRAPSTEP intercepts each action and enriches the raw observation with success/failure conditions. It is a lightweight Python wrapper, no changes to agent logic or environment code.

3⃣️ Interfaces are generated iteratively by an Analyzer (diagnoses misalignments from failed trajectories) and an Optimizer (synthesizes and refines the interface as Python functions). Both run experimental verification against the live environment to fight hallucination; ablating it collapses accuracy.

4⃣️ Across four benchmarks in embodied, web, and tool-use, gains reach +45.67% success on ALFWorld and cut consecutive invalid actions by 65%. Interfaces transfer plug-and-play across agent architectures (ReAct, Self-Consistency, Planning...) and across LLM backbones (Qwen, Llama) with no regeneration.