Multimodal-Ai

TL;DR for operators If your agent stack is becoming expensive because every “reflection” step means another model call, this paper is worth reading. Its proposal, Introspection of Thought (INoT), tries to compress an external multi-agent debate loop into one structured prompt. The LLM is not literally running multiple agents. It is being instructed, through a hybrid Python-and-natural-language prompt called PromptCode, to simulate two internal debaters that reason, critique, rebut, revise, and then return an answer.1 ...

TL;DR for operators Finfluencer videos are not just “text with a face attached.” They contain ticker symbols on charts, spoken recommendations, gestures, confidence, hedging, hype, and the occasional performance of certainty. VideoConviction turns that mess into a benchmark: 288 YouTube videos from finance influencers, 687 stock recommendation segments, 6,063 expert annotations, transcripts, metadata, and a 1–3 conviction score grounded in tone, facial expression, delivery, and consistency between title and content.1 ...

TL;DR for operators Image-editing demos are easy. Ask a model to remove one object, recolour a jacket, or add a tasteful lamp, and most modern systems can produce something impressive enough for a product page and a LinkedIn post. Ask it to perform eight connected edits while keeping the original subject, layout, texture, lighting, and realism intact, and the polite showroom smile begins to crack. ...