IR-Agent: Decoupled Alignment for Large Language Model Driven Earnings Call Responses

Company-side earnings-call answering demands three concurrent desiderata: every cited figure must be grounded in the company's publicly disclosed materials, relevant available figures should be properly cited in replies, and responses should adopt managerial conversational register instead of plain document recitation. Satisfying all three simultaneously poses a substantial challenge for large language models (LLMs), as these objectives are inherently conflicting: models optimized to mimic human-like speech tend to invent unsupported numeric claims. In this paper, we first propose IR-Agent, an industry agent for high-fidelity, disclosure-consistent earnings-call response generation, whose architecture is the Decoupled Alignment Framework (DAF). The style module aligns the agent to authentic managerial utterances via supervised fine-tuning, with style signals deliberately excluded from reward computation to avoid reward hacking. The disclosure module regulates numeric citation behavior through advantage-weighted policy optimization, exclusively tuning numerical grounding preference while preserving learned linguistic style. The verification module leverages a rule-based numeric verifier to filter and rank sampled candidates, eliminating fabricated numbers; this same verifier also provides consistent reward signals during training. We construct IRCorpus, the first company-side earnings-call response corpus with verifier-supported numeric supervision, and devise a closed-form evaluation protocol that quantifies fabrication, omission and stylistic fidelity without relying on LLM-as-judge metrics. On 573 held-out questions from 20 companies unseen during fine-tuning, eight-candidate IR-Agent yields zero verifier-detected fabricated numbers with high usability of grounded figures, and matches human managerial register more closely than eight frontier baselines, which persistently suffer from fabrication or excessive omission.