Zeolite synthesis requires a structured recipe that specifies an organic structure-directing agent (OSDA), gel composition, crystallization temperature, and reaction time. General-purpose language models can draft such recipes, but their outputs often fail domain checks, producing invalid SMILES strings, missing condition fields, or temperature–time schedules inconsistent with local Arrhenius trends. We present ZeoAgent, a closed-loop compiler for machine-checkable zeolite synthesis recipes. ZeoAgent represents each generated recipe as a typed record and verifies it through deterministic field-level checks. At each iteration, a compact yet diverse set of retrieved synthesis precedents guides the next draft. When a field fails validation, the corresponding error code is returned to the model for targeted revision. Recipes passing these checks are calibrated with a local Arrhenius fit before final ranking. In condition-only experiments, the recorded OSDA is supplied and only the hydrothermal-condition fields are predicted. On the archived model outputs, ZeoAgent attains 99.7% RDKit parseability, compared with 81.7% for Qwen3-8B. Typed feedback raises repair pass@5 from 68.3% to 72.0%, and PGC reduces high-residual schedules under its fitted local Arrhenius self-consistency diagnostic from 77.0% to 5.4%.

Multimodal Emotional Reappraisal (MER) aims to shift affective interpretation by rewriting and editing a situation without altering its underlying semantic context. Despite advances in controllable editing and sentiment transfer, MER remains challenging under coupled multimodal constraints, especially in internet memes where images, embedded text, and layout jointly encode affective meaning. To bridge this gap, we present MER-Bench, a systematic benchmark for MER on image-text memes, containing over 3,000 real-world memes with fine-grained annotations for emotion transformation and structure-preserving multimodal editing. MER-Bench spans diverse visual, textual, and structural settings and is paired with a structured MLLM-as-a-Judge evaluation suite, comprising eight fine-grained metrics and three aggregated objectives for emotional alignment, content fidelity, and transformation strength. Extensive evaluation of 14 advanced multimodal models reveals that: current models struggle to jointly balance affect, semantics, structure, and text; strong visual editing does not imply effective MER, with poor embedded text rendering and affect-aware rewriting remaining key bottlenecks. Our results identify MER as a critical capability gap in current multimodal generation systems and highlight affective image-text reasoning as a key challenge for future work.

Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos partially relevant to a given query. The core challenge lies in learning robust query-video alignment against spurious semantic correlations arising from inherent data uncertainty: 1) query ambiguity, where the query incompletely characterizes the target video and often contains uninformative tokens, and 2) partial video relevance, where abundant query-irrelevant segments introduce contextual noise in cross-modal alignment. Existing methods often focus on enhancing multi-scale clip representations and retrieving the most relevant clip. However, the inherent data uncertainty in PRVR renders them vulnerable to distractor videos with spurious similarities, leading to suboptimal performance. To fill this research gap, we propose Robust Alignment Learning (RAL) framework, which explicitly models the uncertainty in data. Key innovations include: 1) we pioneer probabilistic modeling for PRVR by encoding videos and queries as multivariate Gaussian distributions. This not only quantifies data uncertainty but also enables proxy-level matching to capture the variability in cross-modal correspondences; 2) we consider the heterogeneous informativeness of query words and introduce learnable confidence gates to dynamically weight similarity. As a plug-and-play solution, RAL can be seamlessly integrated into the existing architectures. Extensive experiments across diverse retrieval backbones demonstrate its effectiveness.