Language Server CLI Empowers Language Agents with Process Rewards
Authors: name: Yifan Zhang, name: Lanser Contributors

Large language models routinely hallucinate APIs and mislocalize edits, while
language servers compute verified, IDE-grade facts about real code. We present
Lanser-CLI, a CLI-first orchestration layer that pins and mediates a Language
Server Protocol (LSP) server for coding agents and CI, exposing deterministic,
replayable workflows. Our position is that language servers provide not only
structural information (definitions, references, types, diagnostics) but also
an actionable process reward: machine-checked, step-wise signals that align an
agent's planning loop with program reality. In this work, Lanser-CLI
contributes: (i) a robust addressing scheme beyond brittle "file:line:col" via
a Selector DSL (symbolic, AST-path, and content-anchored selectors) with a
principled relocation algorithm; (ii) deterministic Analysis Bundles that
normalize Language Server responses and capture environment/capability metadata
with stable content hashes; (iii) a safety envelope for mutating operations
(rename, code actions) with preview, workspace jails, and Git-aware,
transactional apply; and (iv) a process-reward functional derived from Language
Server facts (diagnostic deltas, disambiguation confidence, and safe-apply
checks) that is computable online and replayable offline. We formalize
determinism under frozen snapshots and establish a monotonicity property for
the process reward, making it suitable for process supervision and
counterfactual analysis. Project Page:
https://github.com/yifanzhang-pro/lanser-cli