On Monday morning, a jury in Oakland, California, announced its verdict in one of the most-watched tech feuds between billionaire Elon Musk and OpenAI CEO Sam Altman. The nine-member jury handed a decisive victory to Altman, saying Musk had waited too long to bring his claims against the artificial intelligence company and its top executives. Musk, who cofounded OpenAI as a nonprofit, had filed a $150bn lawsuit against the organisation, Altman and its president, Greg Brockman, accusing them of turning it into a for-profit entity for personal enrichment. Instead, the case became focused on a procedural issue. After deliberating for less than two hours, the jury unanimously found that the statute of limitations had expired before Musk filed the lawsuit in 2024, meaning jurors concluded he had waited too long to bring his claims under the applicable legal deadline.

The federal jury in Oakland, California, found Altman, OpenAI and its president, Greg Brockman, not liable for Elon Musk's claims that they unjustly enriched themselves and broke a founding contract made with Musk when founding the startup. The verdict, delivered after less than two hours of deliberation, is a stark rebuke of Musk and his lawyer's claims that Altman "stole a charity" through his leadership of OpenAI . It also provides the AI firm with a clear path ahead to pursue going public later this year at about a $1tn valuation . The jury's finding is a non-binding, advisory verdict that left Judge Yvonne Gonzalez Rogers with ultimate power to issue her own ruling in the case. Gonzalez Rogers immediately said that she would agree with the jury's decision and dismissed Musk's claims.

A California jury has tossed out Elon Musk's high-profile lawsuit against OpenAI and its boss Sam Altman. In a unanimous verdict, the case was thrown out because Musk had filed his lawsuit after a statute of limitations to bring such claims had expired. Musk had accused Altman of breaching a non-profit contract by shifting the ChatGPT-maker to a for-profit company after Musk donated $38m (£28.5m). Musk had argued Altman deceived him by accepting his money and then reneging on OpenAI's original non-profit mission to develop artificial intelligence (AI) technology for the benefit of humanity. Jurors spent three weeks viewing internal correspondence and hearing testimony, and arrived at a verdict on Monday after deliberating for roughly two hours.

Members of the New Scientist Book Club give their take on Sierra Greer's award-winning science-fiction novel Annie Bot, our read for February - and the needle swings wildly from positive to negative Annie Bot by Sierra Greer was the Book Club's January read The New Scientist Book Club moved on from reading a classic piece science fiction in December - Iain M. Banks's - to an award-winning sci-fi novel in January: Sierra Greer's, which won the Arthur C. Clarke prize in 2025. I must admit, I was nervous to announce this one to my fellow readers. is the story of a sex robot, owned by a controlling and abusive man. It gets very dark in places, it has a number of sex scenes, and I wanted to make sure you all knew what you were getting into before getting started. That cupboard scene, some way into the book, was super disturbing, for example. It turns out my wariness was warranted.

Fact-checking health-related claims has become increasingly critical as misinformation proliferates online. Effective verification requires both the retrieval of high-quality evidence and rigorous reasoning processes. In this paper, we propose a two-stage framework for health misinformation detection: Agreement Score Prediction followed by Multi-Agent Debate. In the first stage, we employ large language models (LLMs) to independently evaluate retrieved articles and compute an aggregated agreement score that reflects the overall evidence stance. When this score indicates insufficient consensus-falling below a predefined threshold-the system proceeds to a second stage. Multiple agents engage in structured debate to synthesize conflicting evidence and generate well-reasoned verdicts with explicit justifications. Experimental results demonstrate that our two-stage approach achieves superior performance compared to baseline methods, highlighting the value of combining automated scoring with collaborative reasoning for complex verification tasks.

Large Vision-Language Models (VLMs) have achieved remarkable progress in multimodal understanding, yet they struggle when reasoning over information-intensive images that densely interleave textual annotations with fine-grained graphical elements. The main challenges lie in precisely localizing critical cues in dense layouts and multi-hop reasoning to integrate dispersed evidence. We propose Speculative Verdict (SV), a training-free framework inspired by speculative decoding that combines multiple lightweight draft experts with a large verdict model. In the draft stage, small VLMs act as draft experts to generate reasoning paths that provide diverse localization candidates; in the verdict stage, a strong VLM synthesizes these paths to produce the final answer, minimizing computational cost while recovering correct answers. To further improve efficiency and accuracy, SV introduces a consensus expert selection mechanism that forwards only high-agreement reasoning paths to the verdict. Empirically, SV achieves consistent gains on challenging information-intensive and high-resolution visual question answering benchmarks, including InfographicVQA, ChartMuseum, ChartQAPro, and HR-Bench 4K. By synthesizing correct insights from multiple partially accurate reasoning paths, SV achieves both error correction and cost-efficiency compared to large proprietary models or training pipelines. Code is available at https://github.com/Tinaliu0123/speculative-verdict.

Can democratized information gatekeepers and community note writers effectively decide what scientific information to amplify? Lacking domain expertise, such gatekeepers rely on automated reasoning agents that use RAG to ground evidence to cited sources. But such standard RAG systems validate summaries via semantic grounding and suffer from "methodological blindness," treating all cited evidence as equally valid regardless of rigor. To address this, we introduce VERIRAG, a post-retrieval auditing framework that shifts the task from classification to methodological vulnerability detection. Using private Small Language Models (SLMs), VERIRAG audits source papers against the Veritable taxonomy of statistical rigor. We contribute: (1) a benchmark of 1,730 summaries with realistic, non-obvious perturbations modeled after retracted papers; (2) the auditable Veritable taxonomy; and (3) an operational system that improves Macro F1 by at least 19 points over baselines using GPT-based SLMs, a result that replicates across MISTRAL and Gemma architectures. Given the complexity of detecting non-obvious flaws, we view VERIRAG as a "vulnerability-detection copilot," providing structured audit trails for human editors. In our experiments, individual human testers found over 80% of the generated audit trails useful for decision-making. We plan to release the dataset and code to support responsible science advocacy.