Financial markets face growing threats from misinformation that can trigger billions in losses in minutes. Most existing approaches lack transparency in their decision-making and provide limited attribution to credible sources. We introduce FinVet, a novel multi-agent framework that integrates two Retrieval-Augmented Generation (RAG) pipelines with external fact-checking through a confidence-weighted voting mechanism. FinVet employs adaptive three-tier processing that dynamically adjusts verification strategies based on retrieval confidence, from direct metadata extraction to hybrid reasoning to full model-based analysis. Unlike existing methods, FinVet provides evidence-backed verdicts, source attribution, confidence scores, and explicit uncertainty flags when evidence is insufficient. Experimental evaluation on the FinFact dataset shows that FinVet achieves an F1 score of 0.85, which is a 10.4% improvement over the best individual pipeline (fact-check pipeline) and 37% improvement over standalone RAG approaches.

While image generation techniques are now capable of producing high-quality images that respect prompts which span multiple sentences, the task of text-guided image editing remains a challenge. Even edit requests that consist of only a few words often fail to be executed correctly. We explore three strategies to enhance performance on a wide range of image editing tasks: supervised fine-tuning (SFT), reinforcement learning (RL), and Chain-of-Thought (CoT) reasoning. In order to study all these components in one consistent framework, we adopt an autoregressive multimodal model that processes textual and visual tokens in a unified manner. We find RL combined with a large multi-modal LLM verifier to be the most effective of these strategies. As a result, we release EARL: Editing with Autoregression and RL, a strong RL-based image editing model that performs competitively on a diverse range of edits compared to strong baselines, despite using much less training data. Thus, EARL pushes the frontier of autoregressive multimodal models on image editing. We release our code, training data, and trained models at https://github.com/mair-lab/EARL.

The increasing realism of synthetic speech, driven by advancements in text-to-speech models, raises ethical concerns regarding impersonation and disinformation. Audio watermarking offers a promising solution via embedding human-imperceptible watermarks into AI-generated audios.

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A 26-year-old woman is in critical condition after a man poured a liquid on her and set her on fire during an argument on a Chicago Transit Authority train, police said.

Science Biology Humans sync their blinks and brain waves to a song's beat Our bodies react to music whether we want it to our not. Breakthroughs, discoveries, and DIY tips sent every weekday. A good song can easily get a listener nodding their head or tapping their foot along to the beat. While you may not initially realize you're doing it, those physical responses to music are still conscious decisions that you can stop whenever you want. According to neuroscientists, however, music also has the ability to influence even some of our involuntary movements.

Texas mother Mandi Furniss alleges Character.AI chatbot encouraged her teenage son toward self-harm and violence, prompting the platform to ban minors.

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Lycodon irwini is the latest species named after The Crocodile Hunter. Breakthroughs, discoveries, and DIY tips sent every weekday. Conservationists have discovered a previously unknown species of snake, slithering around one of Earth's most unique environments. In naming their new reptile, researchers decided to honor one of popular culture's most unique and beloved wildlife educators: the late, great Steve Irwin . The snake was discovered in the Nicobar Islands.

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