Browsing: Multimodal

a picture is worth a thousand words. Yet, very few enterprise chatbots can reliably return images grounded in their source documents. Why is that? The reason…

When people talk about artificial intelligence, they usually focus on what it produces: Human-like text, stunning images, or eerily accurate recommendations. What rarely gets attention is…

class QwenChat: def __init__(self, model, processor, system=None, tools=None): self.model, self.processor = model, processor self.tokenizer = processor.tokenizer self.history: list[dict] = [] if system: self.history.append({“role”: “system”, “content”: system})…

Retrieval-Augmented Generation (RAG) has become a standard technique for grounding large language models in external knowledge — but the moment you move beyond plain text and…

Meta Superintelligence Labs recently made a significant move by unveiling ‘Muse Spark’ — the first model in the Muse family. Muse Spark is a natively multimodal…

In the field of vision-language models (VLMs), the ability to bridge the gap between visual perception and logical code execution has traditionally faced a performance trade-off.…

The landscape of multimodal large language models (MLLMs) has shifted from experimental ‘wrappers’—where separate vision or audio encoders are stitched onto a text-based backbone—to native, end-to-end…

Google has released Gemini 3.1 Flash Live in preview for developers through the Gemini Live API in Google AI Studio. This model targets low-latency, more natural,…

def parse_click_coords(action_str): “”” Extract normalised (x, y) coordinates from a click action string. e.g., ‘click(0.45, 0.32)’ -> (0.45, 0.32) Returns None if the action is not…

Finance leaders are automating their complex workflows by actively adopting powerful new multimodal AI frameworks.Extracting text from unstructured documents presents a frequent headache for developers. Historically,…