Existing cross-modal image-text retrieval models often retrieve samples with inconsistent details. To evaluate fine-grained discriminability, we introduce MSCOCO-CCD and Flickr30k-CCD, with three key features: (1) a two-level image content taxonomy for contrastive sample generation and fine-grained evaluation; (2) annotation of numerous contrastive samples, where each sample differs from the anchor by a controlled contrastive difference (CCD), with the specific type of difference labeled; (3) a fine-grained contrastive discrimination metric to assess the ability to distinguish fine-grained nuances. Extensive experiments demonstrate that contrastive samples can significantly degrade retrieval performance. Furthermore, fine-grained evaluation reveals that current models still struggle to effectively produce discriminative representations on certain feature types, such as entity emotion and scene attribute. Our datasets and related codes will be publicly released.

Large Language Models (LLMs) exhibit strong reasoning capabilities and are widely applied in event forecasting. However, studies have demonstrated that LLMs exhibit human-like cognitive biases, systematic patterns of deviation from rationality in decision-making. To explore the cognitive biases in event forecasting, we introduce CogForecast, a human-curated dataset comprising six topics. Experimental results on three LLMs reveal significant cognitive biases in LLM-based event forecasting methods. To address this issue, we propose MCA, a Multi-Cognition Agentic framework. Specifically, MCA leverages LLMs to act as multi-cognition event participants, performing perspective-taking based on the cognitive patterns of event participants to alleviate the inherent cognitive biases in LLMs and offer diverse analytical perspectives. Then, MCA clusters agents according to their predictions and derives a final answer through a group-level reliability scoring method. Experimental results on a dataset including eight event categories demonstrate the effectiveness of MCA. Using Llama-3.1-70B, MCA achieves an accuracy of 82.3% (79.5% for the human crowd). Additionally, we demonstrate that MCA can alleviate the cognitive biases in LLMs and investigate three influencing factors.