what is eibench fr?

EIBench is a benchmark for testing how well AI models can understand and reason about human emotions from images. It accompanies a 2025 CVPR paper titled "Why We Feel: Breaking Boundaries in Emotional Reasoning with Multimodal Large Language Models." The project gives researchers a standardized way to measure whether an AI can look at a picture and correctly infer what someone in that picture might be feeling and why. The benchmark comes in two tiers: Basic and Complex. The Basic level tests straightforward emotion recognition, identifying visible emotions in an image. The Complex level goes further, asking the model to reason about emotional nuances and context. To use the benchmark, you download two image datasets (EmoSet-118K and CAER-S), run your AI model against the test questions, and then use another AI model, either LLaMA-3 or ChatGPT-3.5, to score how well your model performed. The project also includes a "long-term coherence" evaluation that checks whether a model's emotional reasoning stays consistent over a conversation. This project is aimed at AI researchers and engineers building vision-language models, systems that combine image understanding with text generation. If you're developing an AI assistant, chatbot, or tool that needs to pick up on emotional cues from images, EIBench lets you compare your model against established baselines like GPT-4, Claude-3, LLaVA, MiniGPT4, and Otter. It answers the question: "Can my model read a room, or is it missing emotional context that a human would catch?" The project is notable for its two-tier structure, which separates surface-level emotion detection from deeper emotional reasoning. Rather than just asking "what emotion is this," the Complex tier probes whether the model can explain the social or contextual factors behind that emotion, a harder problem that today's models still struggle with.