In an era marked by rapid technological advancements and complex global challenges, responsible foresight has emerged as an essential framework for policymakers aiming to navigate future uncertainties and shape the future. Responsible foresight entails the ethical anticipation of emerging opportunities and risks, with a focus on fostering proactive, sustainable, and accountable future design. This paper coins the term "responsible computational foresight", examining the role of human-centric artificial intelligence and computational modeling in advancing responsible foresight, establishing a set of foundational principles for this new field and presenting a suite of AI-driven foresight tools currently shaping it. AI, particularly in conjunction with simulations and scenario analysis, enhances policymakers' ability to address uncertainty, evaluate risks, and devise strategies geared toward sustainable, resilient futures. However, responsible foresight extends beyond mere technical forecasting; it demands a nuanced understanding of the interdependencies within social, environmental, economic and political systems, alongside a commitment to ethical, long-term decision-making that supports human intelligence. We argue that AI will play a role as a supportive tool in responsible, human-centered foresight, complementing rather than substituting policymaker judgment to enable the proactive shaping of resilient and ethically sound futures. This paper advocates for the thoughtful integration of AI into foresight practices to empower policymakers and communities as they confront the grand challenges of the 21st century.

Traditional threat modeling remains reactive-focused on known TTPs and past incident data, while threat prediction and forecasting frameworks are often disconnected from operational or architectural artifacts. This creates a fundamental weakness: the most serious cyber threats often do not arise from what is known, but from what is assumed, overlooked, or not yet conceived, and frequently originate from the future, such as artificial intelligence, information warfare, and supply chain attacks, where adversaries continuously develop new exploits that can bypass defenses built on current knowledge. To address this mental gap, this paper introduces the theory and methodology of Future-Back Threat Modeling (FBTM). This predictive approach begins with envisioned future threat states and works backward to identify assumptions, gaps, blind spots, and vulnerabilities in the current defense architecture, providing a clearer and more accurate view of impending threats so that we can anticipate their emergence and shape the future we want through actions taken now. The proposed methodology further aims to reveal known unknowns and unknown unknowns, including tactics, techniques, and procedures that are emerging, anticipated, and plausible. This enhances the predictability of adversary behavior, particularly under future uncertainty, helping security leaders make informed decisions today that shape more resilient security postures for the future.

Diffusion Transformers (DiTs) achieve state-of-the-art results in text-to-image, text-to-video generation, and editing. However, their large model size and the quadratic cost of spatial-temporal attention over multiple denoising steps make video generation computationally expensive. Static caching mitigates this by reusing features across fixed steps but fails to adapt to generation dynamics, leading to suboptimal trade-offs between speed and quality. We propose Foresight, an adaptive layer-reuse technique that reduces computational redundancy across denoising steps while preserving baseline performance. Foresight dynamically identifies and reuses DiT block outputs for all layers across steps, adapting to generation parameters such as resolution and denoising schedules to optimize efficiency. Applied to OpenSora, Latte, and CogVideoX, Foresight achieves up to \latencyimprv end-to-end speedup, while maintaining video quality. The source code of Foresight is available at \href{https://github.com/STAR-Laboratory/foresight}{https://github.com/STAR-Laboratory/foresight}.

Executing language-conditioned tasks in dynamic visual environments remains a central challenge in embodied AI. Existing Vision-Language-Action (VLA) models predominantly adopt reactive state-to-action mappings, often leading to shortsighted behaviors and poor robustness in dynamic scenes. This training scheme enhances modular reasoning and equips the model with transferable visual foresight, which is critical for complex and dynamic environments. Vision-Language-Action (VLA) models (Kim et al., 2024; Team et al., 2025a; Black et al., 2024) aim to equip robots with the ability to execute natural language instructions in visually rich environments. By aligning language instructions with perceptual inputs and mapping them to actions, such models enable language-guide manipulation and versatile human-robot interaction. However, reliable performance in realistic settings remains elusive: environments are inherently dynamic, i.e., objects move, contexts shift, and instructions unfold over time, so robots must ground ambiguous language, handle diverse objects, and maintain long-horizon temporal coherence as scenes evolve. These conditions expose a core limitation of purely reactive state-to-action mappings: without predictive foresight about likely future states, policies become short-sighted and brittle under distribution shifts. Previous efforts on manipulation policy learning can be broadly grouped into three paradigms, as illustrated in Figure 1. The earliest line of work employs only an action expert trained end-to-end from observations to low-level actions (Zhao et al., 2023; Chi et al., 2023), but such purely reactive mappings lack semantic grounding and generalization across tasks and embodiments (Figure 1(a)). The earliest end-to-end manipulation policies are illustrated in Figure 1(a), such as ACT (Zhao et al., 2023) and DP (Chi et al., 2023). There are also approaches, as seen in Figure 1(c), e.g., VPP (Hu et al., 2024) and Genie Envisioner (Liao et al., 2025b), that leverage video diffusion models to guide action execution through video prediction. As depicted in Figure 1(d), we adopts an integrated architecture of understanding, generation, and execution, empowering the action execution module with capabilities in both scene and instruction comprehension as well as dynamic temporal prediction.

W e introduce F oreSight, a novel joint detection and forecasting framework for vision-based 3D perception in autonomous vehicles. Traditional approaches treat detection and forecasting as separate sequential tasks, limiting their ability to leverage temporal cues. F oreSight addresses this limitation with a multi-task streaming and bidirectional learning approach, allowing detection and forecasting to share query memory and propagate information seamlessly. The forecast-aware detection transformer enhances spatial reasoning by integrating trajectory predictions from a multiple hypothesis forecast memory queue, while the streaming forecast transformer improves temporal consistency using past forecasts and refined detections. Unlike tracking-based methods, F oreSight eliminates the need for explicit object association, reducing error propagation with a tracking-free model that efficiently scales across multi-frame sequences. Experiments on the nuScenes dataset show that F oreSight achieves state-of-the-art performance, achieving an EP A of 54.9%, surpassing previous methods by 9.3%, while also attaining the best mAP and minADE among multi-view detection and forecasting models.

An artificial intelligence model trained on the medical data of 57 million people who have used the National Health Service in England could one day assist doctors in predicting disease or forecast hospitalisation rates, its creators have claimed. However, other researchers say there are still significant privacy and data protection concerns around such large-scale use of health data, while even the AI's architects say they can't guarantee that it won't inadvertently reveal sensitive patient data. The model, called Foresight, was first developed in 2023. That initial version used OpenAI's GPT-3, the large language model (LLM) behind the first version of ChatGPT, and trained on 1.5 million real patient records from two London hospitals. Now, Chris Tomlinson at University College London and his colleagues have scaled up Foresight to create what they say is the world's first "national-scale generative AI model of health data" and the largest of its kind.

Future advanced AI systems may learn sophisticated strategies through reinforcement learning (RL) that humans cannot understand well enough to safely evaluate. We propose a training method which avoids agents learning undesired multi-step plans that receive high reward (multi-step "reward hacks") even if humans are not able to detect that the behaviour is undesired. The method, Myopic Optimization with Non-myopic Approval (MONA), works by combining short-sighted optimization with far-sighted reward. We demonstrate that MONA can prevent multi-step reward hacking that ordinary RL causes, even without being able to detect the reward hacking and without any extra information that ordinary RL does not get access to. We study MONA empirically in three settings which model different misalignment failure modes including 2-step environments with LLMs representing delegated oversight and encoded reasoning and longer-horizon gridworld environments representing sensor tampering.

With the rapid pace of technological innovation, traditional methods of policy formation and legislating are becoming conspicuously anachronistic. The need for regulatory choices to be made to counter the deadening effect of regulatory lag is more important to developing markets and fostering growth than achieving one off regulatory perfection. This article advances scholarship on innovation policy and the regulation of technological innovation in the European Union. It does so by considering what building an agile yet robust anticipatory governance regulatory culture involves. It systematically excavates a variety of tools and elements that are being put into use in inventive ways and argues that these need to be more cohesively and systemically integrated into the regulatory toolbox. Approaches covered include strategic foresight, the critical embrace of iterative policy development and regulatory learning in the face of uncertainty and the embrace of bottom up approaches to cocreation of policy such as Policy Labs and the testing and regulatory learning through pilot regulation and experimentation. The growing use of regulatory sandboxes as an EU policy tool to boost innovation and navigate regulatory complexity as seen in the EU AI Act is also probed

Large Language Models (LLMs) have demonstrated remarkable abilities in reasoning and planning by breaking down complex problems into sequential steps. This paper revisits LLM reasoning from an optimal control perspective, proposing a novel method, Predictive-Decoding, that leverages Model Predictive Control to enhance planning accuracy. By reweighting LLM distributions based on foresight trajectories, Predictive-Decoding aims to mitigate early errors and promote non-myopic planning. Our experiments show significant improvements across a wide range of tasks in math, coding, and agent-based scenarios. Furthermore, Predictive-Decoding demonstrates computational efficiency, outperforming search baselines while utilizing inference compute more effectively. This study provides insights into optimizing LLM planning capabilities. Code is available at this repo. Large Language Models (LLMs) are extensively pretrained on large corpus to predict the next tokens.

Chain-of-Thought (CoT) reasoning could in principle enable a deeper understanding of a language model's (LM) internal reasoning. However, prior work suggests that LMs can answer questions similarly despite changes in their CoT, suggesting that those models are not truly using the CoT. We propose an reinforcement learning technique to produce CoTs that are sufficient alone for predicting future text, independent of other context. This methodology ensures that if the LM can predict future tokens, then it must have used the CoT to understand its context. We formalize the informativeness of a sender to a receiver LM as the degree to which the sender helps the receiver predict their future observations, and we define a "Markovian" LM as one which predicts future text given only a CoT as context. We derive a "Markovian training" procedure by applying our definition of informativeness to a Markovian LM and optimizing via policy gradient and Proximal Policy Optimization (PPO). We demonstrate our training algorithm's effectiveness on fifteen-term arithmetic problems, show the model utilizes the CoT, and externally validate that the generated CoT is meaningful and usable by another model.