TIE: Text-Bounded Incremental Evidence Migration for LLM-Based Multimodal Affective Computing

LLM-based multimodal affective computing uses a large language model (LLM) to directly predict sentiment or emotion from multimodal inputs. Non-textual affective evidence is constructed, transformed into LLM-compatible representations, and propagated through the language-centered LLM. Along this path, a text-directed preference may be reinforced even when the constructed multimodal evidence supports the correct prediction, a phenomenon we term text-prior reinforcement. To address this problem, we formulate LLM-based multimodal affective prediction as Text-Bounded Incremental Evidence Migration (TIE), treating the full path from evidence construction to its use in prediction as a unified process. Guided by this formulation, TIE uses textual semantics and text-bounded temporal routing to organize audio and visual evidence along text-referenced and text-residual pathways. To preserve the influence of this evidence beyond representation construction, TIE transforms the organized evidence into LLM-compatible representations and applies a migration-specific signal derived from the frozen final prediction block. For sentiment regression, an order-aware objective bridges label generation and ordered sentiment estimation. TIE outperforms same-backbone compact-prompt baselines on most of the five benchmarks. The combined training configuration also improves existing compact-prompt methods. Controlled ablations validate TIE's components, while migration analyses provide empirical support for text-prior reinforcement.