Researchers propose Temporal-Aware Mixture-of-Experts for Text-Video Retrieval (TAME), a CLIP-based framework that jointly models frame-level structure and temporal relations. ^[1] Text-Video Retrieval (TVR) is fundamentally limited by the lack of temporal modeling when extending image-text models like CLIP to videos. ^[2] The paper's authors report that BioCLIP2 achieved 72.36% accuracy on the BFF-15 dataset using English common names and 68.91% on the SylFishBD dataset using scientific names. ^[3] The TAME framework achieves consistent gains on DiDeMo, MSVD, LSMDC, and ActivityNet benchmarks compared to CLIP-based baselines. ^[4] Videos exhibit frame-wise heterogeneity in appearance and motion, and compressing all frames into a single representation often obscures temporal structure and semantic transitions. ^[5] The paper concludes that zero-shot biological vision-language model scores jointly reflect biological specialization, multilingual alignment, nomenclature, prompt formulation, and context. ^[6] The paper's authors report that BioCLIP2 with Bengali prompts performed near chance with balanced accuracy between 14.22% and 14.29%, while Jina CLIP partially recovered Bengali discrimination to 21.89% and 16.36% on the two sources, and bare Bengali names returned to 14.29% on both. ^[7] The paper's authors report that generic CLIP scored 25.15% on BFF-15 and 14.40% on SylFishBD, substantially lower than BioCLIP2. ^[8]