Zero-MELO: Test-Time Evidence Calibration with Multimodal LLMs for Zero-Shot Micro-Gesture Recognition

While Multimodal Large Language Models (MLLMs) excel in general video understanding, their capability in fine-grained and motion-centric tasks remains limited. This limitation is particularly critical in micro-gesture recognition (MGR), where micro-gestures (MGs) - subtle, short-duration, and spatially localized human movements - serve as key discriminative signals for implicit affective analysis, yet are easily neglected following common prompting practices. Although MGR has been intensively studied by many discriminative approaches, the use of MLLMs for MGR is underexplored, with notably poor performance. We hypothesize that the motion-sensitive representation ability of MLLMs is constrained by their inherent single-pass forward inference, which can be substantially enhanced through carefully designed test-time guidance. Motivated by this, building on our prior findings regarding temporal insensitivity in Video LLMs, we diagnose zero-shot MGR errors in the Negative Log-Likelihood (NLL) space. We observe that MLLMs suffer from two bottlenecks: 1) insufficient localized evidence and 2) severe score biases driven by language and motion-agnostic appearances. Thus, we propose a novel test-time evidence calibration framework that improves both reasoning details and prediction reliability. Specifically, we introduce a tree search mechanism to progressively acquire localized, fine-grained visual evidence, coupled with a test-time calibration module to mitigate score biases. The multi-cue fusion module then integrates evidence from multiple cues without relying on a single cue for final prediction. Our framework achieves mean-class accuracies of 26.84\% on iMiGUE and 22.10\% on MA-52, significantly outperforming the Qwen2.5-VL baseline, which produces 16.15\% and 10.20\%, respectively. The code will be available at https://zero-melo.github.io/Zero-MELO.

Beyond the Mouth: Upper-Face Affective Cues in Audiovisual Sentence Recognition under Acoustic Uncertainty

Face-to-face speech comprehension is inherently multimodal, integrating acoustic signals with visible articulation, facial expression, head motion, and other socially relevant cues. While audiovisual speech systems typically focus on the mouth region as the primary visual source of linguistic information, affective facial expressions are often treated separately as emotion-recognition targets. This paper investigates whether upper-face affective information contributes to audiovisual sentence recognition beyond audio and mouth-region cues, particularly under acoustic degradation. Using the CREMA-D audiovisual emotional speech corpus, we train feature-based sentence classifiers under four cue conditions: audio only (A), audio plus mouth/lower-face features (A+M), audio plus upper-face features (A+U), and audio plus both mouth and upper-face features (A+M+U). Models are evaluated on clean audio and pink-noise conditions at +10 dB, +5 dB, and 0 dB SNR using actor-independent splits. Results show that mouth/lower-face features provide substantial robustness benefits under degraded audio. At 0 dB SNR, A+M improves accuracy over A by 0.0794, with an actor-bootstrap 95% confidence interval of [0.0296, 0.1298]. Upper-face affective cues exhibit a more nuanced effect. Although the direct accuracy gain of A+M+U over A+M is small, full-face models consistently improve calibration across SNR levels and outperform shuffled upper-face controls under noisy conditions. These findings suggest that affective facial information may support multimodal robustness and confidence estimation under acoustic uncertainty without directly encoding lexical content. More broadly, the study highlights the potential role of socially expressive facial cues in human-centered audiovisual interaction systems.

SayNext-Bench: Why Do LLMs Struggle with Next-Utterance Prediction?

We explore the use of large language models (LLMs) for next-utterance prediction in human dialogue. Despite recent advances in LLMs demonstrating their ability to engage in natural conversations with users, we show that even leading models surprisingly struggle to predict a human speaker's next utterance. Instead, humans can readily anticipate forthcoming utterances based on multimodal cues, such as gestures, gaze, and emotional tone, from the context. To systematically examine whether LLMs can reproduce this ability, we propose SayNext-Bench, a benchmark that evaluates LLMs and Multimodal LLMs (MLLMs) on anticipating context-conditioned responses from multimodal cues spanning a variety of real-world scenarios. To support this benchmark, we build SayNext-PC, a novel large-scale dataset containing dialogues with rich multimodal cues. Building on this, we further develop a dual-route prediction MLLM, SayNext-Chat, that incorporates cognitively inspired design to emulate predictive processing in conversation. Experimental results demonstrate that our model outperforms state-of-the-art MLLMs in terms of lexical overlap, semantic similarity, and emotion consistency. Our results prove the feasibility of next-utterance prediction with LLMs from multimodal cues and emphasize the (i) indispensable role of multimodal cues and (ii) actively predictive processing as the foundation of natural human interaction, which is missing in current MLLMs. We hope that this exploration offers a new research entry toward more human-like, context-sensitive AI interaction for human-centered AI. Our benchmark and model can be accessed at https://saynext.github.io/.