Multimodal sentiment analysis (MSA) in real-world scenarios is often challenged by dynamically missing modalities. Existing methods predominantly rely on deterministic imputation and rigid alignment, which compels the model to overfit noise in ambiguous regions while neglecting the decision shift induced by modality inertia. To address these issues, we propose a novel uncertainty-calibrated elastic alignment framework, termed EASE. Specifically, we employ probabilistic imputation to capture cross-modal ambiguity and leverage the estimated uncertainty to drive elastic alignment, thereby adaptively relaxing constraints in ambiguous regions to avoid rigid fitting. Meanwhile, we introduce cross-view predictive consistency constraints to unify discriminative logic across different modality views, stabilizing the decision boundary under modality degradation. Extensive experiments demonstrate that EASE consistently outperforms existing state-of-the-art baselines across multiple benchmarks, exhibiting exceptional robustness particularly under high missing-rate scenarios.

Recent advances in AI-assisted programming have empowered agents to execute complex workflows via command-line interfaces, however, existing benchmarks are limited by short task horizons, data contamination from GitHub scraping, and a lack of fine-grained evaluation metrics, fail to rigorously evaluate the long-horizon planning and execution capabilities essential for realistic software engineering. To address these gaps, we introduce LongCLI-Bench, a comprehensive benchmark designed to evaluate agentic capabilities across long-horizon, realistic, sequential engineering tasks. We curated 20 high-quality, long-horizon tasks from over 1,000 computer science assignments and real-world workflows, covering four engineering categories: from scratch, feature addition, bug fixing, and refactoring. LongCLI-Bench employs a dual-set testing protocol, which measures requirement fulfillment (fail(→)pass) and regression avoidance (pass(→)pass), and incorporates step-level scoring to pinpoint execution failures. Extensive experiments reveal that even state-of-the-art agents achieve pass rates below 20% in LongCLI-Bench. Step-level analysis further indicates that the majority of tasks stall at less than 30% completion, highlighting that critical failures often occur in the early stages. Although self-correction offers marginal gains, human-agent collaboration through plan injection and interactive guidance yields significantly higher improvements. These results highlight that future research must emphasize the development of synergistic human-agent workflows alongside advances in agents’ planning and execution capabilities to overcome key challenges in long-horizon task performance.