Large language models (LLMs) are increasingly deployed as automated reviewers whose numeric scores directly determine accept/reject outcomes, yet whether those scores faithfully reflect the models’ own critiques remains underexplored. We identify threshold-alignment failure: an LLM reviewer may clearly articulate serious weaknesses while still assigning scores above the decision boundary, rendering its critique inconsequential. We introduce DSAR (Dimension-Specialist Audit and Revision), a training-free protocol that decomposes evidence elicitation across dimension specialists, applies severity-coded adversarial audit, and enforces deterministic score corrections for critical findings before thresholding. On PropAlign-240 under a shared Qwen2.5-72B-Instruct backbone, six standard reviewer protocols keep most human-rejected proposals above the decision threshold, yielding false-positive rates of 89.2–100.0%. DSAR raises reject recall to 45.9% and achieves the lowest MAE (0.334), with all decision-level gains statistically significant (McNemar p < 0.008). Ablations are consistent with severity-based audit correction, rather than longer reasoning or additional agents, as the main source of the improvement. These findings argue that LLM reviewers should be evaluated not only by critique quality, but by whether critiques translate into threshold-consistent decisions.

The multimodal emotion recognition in conversation task aims to predict the emotion label for a given utterance with its context and multiple modalities. Existing approaches achieve good results but also suffer from the following two limitations: 1) lacking modeling of diverse dependency ranges, i.e., long, short, and independent context-specific representations and without consideration of the different recognition difficulty for each utterance; 2) consistent treatment of the contribution for various modalities. To address the above challenges, we propose the Self-adaptive Context and Modal-interaction Modeling (SCMM) framework. We first design the context representation module, which consists of three submodules to model multiple contextual representations. Thereafter, we propose the modal-interaction module, including three interaction submodules to make full use of each modality. Finally, we come up with a self-adaptive path selection module to select an appropriate path in each module and integrate the features to obtain the final representation. Extensive experiments under four settings on three multimodal datasets, including IEMOCAP, MELD, and MOSEI, demonstrate that our proposed method outperforms the state-of-the-art approaches.