Trending Now Say More Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Switch Off Creator Playbook Mashable Voices Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List In My Bag All Series Apple reveals'shocking evidence' in OpenAI trade secrets lawsuit Apple previously claimed this was just the tip of the iceberg. Apple claims it has evidence that a former employee stole trade secrets to benefit his new employer, OpenAI. Apple's lawsuit against OpenAI and two of its former employees over alleged stolen trade secrets is heating up. In a new filing, Apple says it has found shocking evidence and is asking the court for expedited discovery. Apple claims the limited information it found after a former employee's work laptop was returned to the company shows that its trade secrets are being used and evidence is being destroyed."

It's hard to recall a feature that's been as instantly reviled as NVIDIA's DLSS 5, a new version of its AI upscaling technology that can modify character models and environments to chase the specter of "photorealism." NVIDIA calls it progress, but the initial examples from Resident Evil: Requiem and Starfield were uncanny misfires. Now, NVIDIA is gearing up to finally launch DLSS 5 alongside NBA 2K27 on September 3. And judging from the wildly negative responses to recent leaks of the technology, it's going to face a tough crowd. I had serious doubts about DLSS 5 at first, but after seeing some remote demonstrations of the tech from Gamescom, my hackles have cooled a bit. Sure, it's an AI tool that could be used to obscenely yassify a fragile character suffering from PTSD, like it did for Resident Evil: Requiem's co-lead Grace.

Apple offered new evidence in support of its claims that OpenAI used trade secrets taken by former employees in support its new hardware business. As part of a push to obtain expedited discovery, Apple filed a support document that revealed information found on a MacBook laptop used by a former employee after he began working at OpenAI. Apple's initial forensic investigation of a laptop Liu used after leaving the company alleges that the former employee knew he had access to Apple's third-party cloud storage and used it to download "dozens of confidential Apple files," including a circuit schematic. Apple further claimed that Liu used that schematic to run simulations in the course of his work for OpenAI. It claimed the laptop revealed previously unknown instances of stolen information, claiming that Liu "referred to using a tool and related software packages with names identical to proprietary internal engineering tools used at Apple." The next arguments by Apple claim that, after learning that he was the subject of its internal investigation, Liu directed a coworker at OpenAI to "restore" any Apple-issued devices, which Apple claimed was a way to erase forensic evidence that could be used against him.

The next time you apply for a job, AI may screen your résumé before any human sees it. But there's good reason to question whether AI will judge you fairly. Researchers already know that LLMs pick up human biases from their training data. New research suggests that LLMs can also develop their own biases from experience--and stereotype job applicants more than humans do. As AI companies race to build agentic models that remember the tiniest details about users, they may be handing them ammunition for forming those biases.

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Recent advancements in large language models (LLMs) underscore the need for more comprehensive evaluation methods to accurately assess their reasoning capabilities. Existing benchmarks are often domain-specific and thus cannot fully capture an LLM's general reasoning potential. To address this limitation, we introduce the Knowledge Orthogonal Reasoning Gymnasium (KORGym)1, a dynamic evaluation platform inspired by KOR-Bench [1] and Gymnasium [2]. KORGym offers over fifty games in either textual or visual formats and supports interactive, multi-turn assessments with reinforcement learning scenarios. Using KORGym, we conduct extensive experiments on 19 LLMs and 8 VLMs, revealing consistent reasoning patterns within model families and demonstrating the superior performance of closed-source models. Further analysis examines the effects of modality, reasoning strategies, reinforcement learning techniques, and response length on model performance. We expect KORGym to become a valuable resource for advancing LLM reasoning research and developing evaluation methodologies suited to complex, interactive environments.

Existing pretrained models for 3D mesh generation often suffer from data biases and produce low-quality results, while global reinforcement learning (RL) methods rely on object-level rewards that struggle to capture local structure details. To address these challenges, we present Mesh-RFT, a novel fine-grained reinforcement finetuning framework that employs Masked Direct Preference Optimization (M-DPO) to enable localized refinement via quality-aware face masking. To facilitate efficient quality evaluation, we introduce an objective topology-aware scoring system to evaluate geometric integrity and topological regularity at both object and face levels through two metrics: Boundary Edge Ratio (BER) and Topology Score (TS).

Models (LMMs) have demonstrated their remarkable focuses attention ties, where on has the holistic success been models gi imagev as en are general-purpose to e scaling xpected and video-language fine-grained to realize multi-modal pix pix el-le understanding.

Large language models (LLMs) excel at complex reasoning tasks but remain computationally expensive, limiting their practical deployment. To address this, recent works have focused on distilling reasoning capabilities into smaller language models (sLMs) using chain-of-thought (CoT) traces from teacher LLMs. However, this approach struggles in scenarios requiring rare factual knowledge or precise computation, where sLMs often hallucinate due to limited capability. In this work, we propose Agent Distillation, a framework for transferring not only reasoning capability but full task-solving behavior from LLM-based agents into sLMs with retrieval and code tools. We improve agent distillation along two complementary axes: (1) we introduce a prompting method called first-thought prefix to enhance the quality of teacher-generated trajectories; and (2) we propose a self-consistent action generation for improving test-time robustness of small agents. We evaluate our method on eight reasoning tasks across factual and mathematical domains, covering both in-domain and out-of-domain generalization. Our results show that sLMs as small as 0.5B, 1.5B, 3B parameters can achieve performance competitive with nexttier larger 1.5B, 3B, 7B models fine-tuned using CoT distillation, demonstrating the potential of agent distillation for building practical, tool-using small agents.

We introduce SLED, an alternative approach to speech language modeling by encoding speech waveforms into sequences of continuous latent representations and modeling them autoregressively using an energy distance objective. The energy distance offers an analytical measure of the distributional gap by contrasting simulated and target samples, enabling efficient training to capture the underlying continuous autoregressive distribution. By bypassing reliance on residual vector quantization, SLED avoids discretization errors and eliminates the need for the complicated hierarchical architectures common in existing speech language models.