In this paper, we propose Tortoise and Hare Guidance (THG), a training-free strategy that accelerates diffusion sampling while maintaining high-fidelity generation. We demonstrate that the noise estimate and the additional guidance term exhibit markedly different sensitivity to numerical error by reformulating the classifier-free guidance (CFG) ODE as a multirate system of ODEs. Our error-bound analysis shows that the additional guidance branch is more robust to approximation, revealing substantial redundancy that conventional solvers fail to exploit. Building on this insight, THG significantly reduces the computation of the additional guidance: the noise estimate is integrated with the tortoise equation on the original, fine-grained timestep grid, while the additional guidance is integrated with the hare equation only on a coarse grid. We also introduce (i) an error-bound-aware timestep sampler that adaptively selects step sizes and (ii) a guidance-scale scheduler that stabilizes large extrapolation spans. THG reduces the number of function evaluations (NFE) by up to 30% with virtually no loss in generation fidelity ( ImageReward 0.032) and outperforms state-of-the-art CFG-based training-free accelerators under identical computation budgets. Our findings highlight the potential of multirate formulations for diffusion solvers, paving the way for real-time high-quality image synthesis without any model retraining. The source code is available at https://github.com/yhlee-add/THG.

Multi-modal large language models (MLLMs) are making rapid progress toward general-purpose embodied agents. However, existing MLLMs do not reliably capture fine-grained links between low-level visual features and high-level textual semantics, leading to weak grounding and inaccurate perception. To overcome this challenge, we propose ESCA, a framework that contextualizes embodied agents by grounding their perception in spatial-temporal scene graphs. At its core is SGCLIP, a novel, open-domain, promptable foundation model for generating scene graphs that is based on CLIP. SGCLIP is trained on 87K+ open-domain videos using a neurosymbolic pipeline that aligns automatically generated captions with scene graphs produced by the model itself, eliminating the need for human-labeled annotations. We demonstrate that SGCLIP excels in both prompt-based inference and task-specific fine-tuning, achieving state-of-the-art results on scene graph generation and action localization benchmarks. ESCA with SGCLIP improves perception for embodied agents based on both open-source and commercial MLLMs, achieving state of-the-art performance across two embodied environments. Notably, ESCA significantly reduces agent perception errors and enables open-source models to surpass proprietary baselines. We release the source code for SGCLIP model training at https://github.com/video-fm/LASER

The recent rise of Large Reasoning Models (LRMs) has significantly improved multi-step reasoning performance, but often at the cost of generating excessively long reasoning chains. This paper revisits the efficiency of such reasoning processes through an information-theoretic lens, revealing a fundamental trade-off between reasoning length and semantic efficiency. We propose two metrics--InfoBias and InfoGain--to quantify divergence from ideal reasoning paths and stepwise information contribution, respectively. Empirical analyses show that longer reasoning chains tend to exhibit higher information bias and diminishing information gain, especially for incorrect answers. Motivated by these findings, we introduce an entropy-based Adaptive Think strategy that dynamically halts reasoning once confidence is sufficiently high, improving efficiency while maintaining competitive accuracy. Compared to the Vanilla Think approach (default mode), our strategy yields a 1.10% improvement in average accuracy and a 50.80% reduction in token usage on QwQ-32B across six benchmark tasks spanning diverse reasoning types and difficulty levels, demonstrating superior efficiency and reasoning performance. These results underscore the promise of entropy-based methods for enhancing both accuracy and cost-effiiciency in large language model deployment.

Large Language Model (LLM) agents are capable of task execution across various domains by autonomously interacting with environments and refining LLM responses based on feedback. However, existing model serving systems are not optimized for the unique demands of serving agents. Compared to classic model serving, agent serving has different characteristics: predictable request pattern, increasing quality requirement, and unique prompt formatting. We identify a key problem for agent serving: LLM serving systems lack session-awareness. They neither perform effective KV cache management nor precisely select the cheapest yet competent model in each round. This leads to a cost-quality tradeoff, and we identify an opportunity to surpass it in an agent serving system. To this end, we introduce AgServe for AGile AGent SERVing.

The advent of Machine Learning as a Service (MLaaS) has heightened the trade-off between model explainability and security.

Long-horizon planning in robotic manipulation tasks requires translating underspecified, symbolic goals into executable control programs satisfying spatial, temporal, and physical constraints.

Italian astronaut Luca Parmitano will serve as the mission's pilot. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. ESA astronaut Luca Parmitano poses for his official portrait in a U.S. spacesuit at NASA's Johnson Space Center. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Apple took the wraps off iOS 27 at its WWDC event, and the iPhone update is chock-full of smart upgrades, with a big focus on improvements to Siri. While the news coming out of WWDC 2026 is not as dramatic as last year's iOS 26 update, where the new Liquid Glass redesign caused a big stir, Apple's presentation still made waves. During the WWDC keynote, Apple walked through the changes coming to the next version of its mobile operating systems, iOS 27 and iPadOS 27. These updates will roll out to iPhones and iPads later this year, likely in September. There are several smaller quality-of-life improvements, but the big highlight is the Siri AI upgrade .

Anthropic Offers Mythos Upgrade for Cyber Partners and a'Safe' Version for the Rest of You Anthropic is releasing Claude Mythos 5 to trusted organizations and Claude Fable 5 to the public, a version it says can't be used for cyberattacks. Anthropic released two new AI models called Claude Fable 5 and Claude Mythos 5 on Tuesday, which the company says have greater capabilities than the Mythos Preview model it released in April to a limited set of tech industry partners. Anthropic has said the initial, limited release stemmed from concerns that the model's capabilities could be exploited by bad actors to develop hacking tools that could catch defenders off guard. Anthropic is currently only releasing Claude Mythos 5 to a limited set of industry partners, many of which received access to Mythos Preview, and the company says it is collaborating with the US government on the rollout. Claude Fable 5, which is being publicly released, uses the same underlying model as Mythos 5, but will have "guardrails" in place at launch, the company said Tuesday, that will block the model from answering many user questions related to cybersecurity, biology, and chemistry.

Siri AI first look: How Apple's rebuilt AI-powered assistant behaves across iPhone, iPad and Mac Siri AI first look: How Apple's rebuilt AI-powered assistant behaves across iPhone, iPad and Mac A quick preview of what's to come this fall. The biggest news out of WWDC 2026 was, as widely rumored, the next generation of Siri. Called Siri AI, the AI-boosted assistant will be infused into lots of Apple products like the iPhone, iPad and Mac. You can sign up to get early access to the new Siri AI to get a feel for how it behaves. But since I've already had the opportunity to check out some demos here at Apple Park after the keynote ended, I figured I'd preview it for those of you who don't want to go through the hassle of beta software.