Goal-directed planning presents a challenge for classical RL algorithms due to the vastness of the combinatorial state and goal spaces, while humans and animals adapt to complex environments, especially with diverse, non-stationary objectives, often employing intermediate goals for long-horizon tasks.Here, we propose a goal reduction mechanism for effectively deriving subgoals from arbitrary and distant original goals, using a novel loop-removal technique.The product of the method, called goal-reducer, distills high-quality subgoals from a replay buffer, all without the need for prior global environmental knowledge.Simulations show that the goal-reducer can be integrated into RL frameworks like Deep Q-learning and Soft Actor-Critic.It accelerates performance in both discrete and continuous action space tasks, such as grid world navigation and robotic arm manipulation, relative to the corresponding standard RL models.Moreover, the goal-reducer, when combined with a local policy, without iterative training, outperforms its integrated deep RL counterparts in solving a navigation task.This goal reduction mechanism also models human problem-solving.Comparing the model's performance and activation with human behavior and fMRI data in a treasure hunting task, we found matching representational patterns between an goal-reducer agent's components and corresponding human brain areas, particularly the vmPFC and basal ganglia. The results suggest that humans may use a similar computational framework for goal-directed behaviors.

The vulnerability of deep neural networks to imperceptible adversarial perturbations has attracted widespread attention. Inspired by the success of vision-language foundation models, previous efforts achieved zero-shot adversarial robustness by aligning adversarial visual features with text supervision. However, in practice, they are still unsatisfactory due to several issues, including heavy adaptation cost, suboptimal text supervision, and uncontrolled natural generalization capacity. In this paper, to address these issues, we propose a few-shot adversarial prompt framework where adapting input sequences with limited data makes significant adversarial robustness improvement. Specifically, we achieve this by providing adversarially correlated text supervision that is end-to-end learned from adversarial examples. We also propose a novel training objective that enhances the consistency of multi-modal features while encourages differentiated uni-modal features between natural and adversarial examples. The proposed framework gives access to learn adversarial text supervision, which provides superior cross-modal adversarial alignment and matches state-of-the-art zero-shot adversarial robustness with only 1\% training data. Code is available at: https://github.com/lionel-w2/FAP.

Enzymes are important proteins that catalyze chemical reactions. In recent years, machine learning methods have emerged to predict enzyme function from sequence; however, there are no standardized benchmarks to evaluate these methods. We introduce CARE, a benchmark and dataset suite for the Classification And Retrieval of Enzymes (CARE). CARE centers on two tasks: (1) classification of a protein sequence by its enzyme commission (EC) number and (2) retrieval of an EC number given a chemical reaction. For each task, we design train-test splits to evaluate different kinds of out-of-distribution generalization that are relevant to real use cases. For the classification task, we provide baselines for state-of-the-art methods. Because the retrieval task has not been previously formalized, we propose a method called Contrastive Reaction-EnzymE Pretraining (CREEP) as one of the first baselines for this task and compare it to the recent method, CLIPZyme. CARE is available at https://github.com/jsunn-y/CARE/.

This paper presents UltraEdit, a large-scale (~ 4M editing samples), automatically generated dataset for instruction-based image editing. Our key idea is to address the drawbacks in existing image editing datasets like InstructPix2Pix and MagicBrush, and provide a approach to producing massive and high-quality image editing samples: 1) UltraEdit includes more diverse editing instructions by combining LLM creativity and in-context editing examples by human raters; 2) UltraEdit is anchored on real images (photographs or artworks), which offers more diversity and less biases than those purely synthesized by text-to-image models; 3) UltraEdit supports region-based editing with high-quality, automatically produced region annotations. Our experiments show that canonical diffusion-based editing baselines trained on UltraEdit set new records on challenging MagicBrush and Emu-Edit benchmarks, respectively. Our analysis further confirms the crucial role of real image anchors and region-based editing data. The dataset, code, and models will be made public.

Yellowstone, Yosemite, and Grand Canyon all make the list, but aren't number one. Yosemite National Park came in at number five on the National Parks Service list. Breakthroughs, discoveries, and DIY tips sent six days a week. In 2025, the parks received 323 million recreation visits, according to new data release by the National Parks Service. The data includes visitors to National Parks, National Historic Sites, National Memorials, National Seashores, National Parkways, and other designated public lands.

Support journalism that doesn't flinch . Support journalism that doesn't flinch . Elon Musk leaves a meeting with House Republicans in the basement of the US Capitol building on March 5, 2025 in Washington, DC. Get your news from a source that's not owned and controlled by oligarchs. Tennessee teenagers are suing Elon Musk's company xAI over allegations that its artificial intelligence tool Grok undressed photos of them as minors--the latest challenge against the wealthiest living person's chatbot .

After the rise (and fall) of reservation scalping, DoorDash and a host of apps are fighting to book you a seat at the country's most exclusive restaurants. At The Eighty-Six in Manhattan, exclusivity is the point. The luxe, 11-table steakhouse is the sort of place that lavishes caviar and aged mimolette cheese on its potatoes, and crows that your market-price duck was raised by one Dr. Taylor Swift has reportedly dined there in a Miu Miu skirt. Reservations are a scarce commodity that the restaurant, and New York law forbids you from selling one. "Access is the main asset," wrote food writer Helen Rosner in a recent New Yorker review of The Eighty-Six. "The product is the door, and what a door!

Explainability in artificial intelligence is crucial for restoring trust, particularly in areas like face forgery detection, where viewers often struggle to distinguish between real and fabricated content. Vision and Large Language Models (VLLM) bridge computer vision and natural language, offering numerous applications driven by strong common-sense reasoning. Despite their success in various tasks, the potential of vision and language remains underexplored in face forgery detection, where they hold promise for enhancing explainability by leveraging the intrinsic reasoning capabilities of language to analyse fine-grained manipulation areas. For that reason, few works have recently started to frame the problem of deepfake detection as a Visual Question Answering (VQA) task, nevertheless omitting the realistic and informative open-ended multi-label setting. With the rapid advances in the field of VLLM, an exponential rise of investigations in that direction is expected. As such, there is a need for a clear experimental methodology that converts face forgery detection to a Visual Question Answering (VQA) task to systematically and fairly evaluate different VLLM architectures. Previous evaluation studies in deepfake detection have mostly focused on the simpler binary task, overlooking evaluation protocols for multi-label fine-grained detection and text-generative models. We propose a multi-staged approach that diverges from the traditional binary evaluation protocol and conducts a comprehensive evaluation study to compare the capabilities of several VLLMs in this context. In the first stage, we assess the models' performance on the binary task and their sensitivity to given instructions using several prompts.

Learning Rate Warmup is a popular heuristic for training neural networks, especially at larger batch sizes, despite limited understanding of its benefits. Warmup decreases the update size $\Delta \mathbf{w}_t = \eta_t \mathbf{u}_t$ early in training by using lower values for the learning rate $\eta_t$. In this work we argue that warmup benefits training by keeping the overall size of $\Delta \mathbf{w}_t$ limited, counteracting large initial values of $\mathbf{u}_t$. Focusing on small-scale GPT training with AdamW/Lion, we explore the following question: *Why and by which criteria are early updates $\mathbf{u}_t$ too large?* We analyze different metrics for the update size including the $\ell_2$-norm, resulting directional change, and impact on the representations of the network, providing a new perspective on warmup. In particular, we find that warmup helps counteract large angular updates as well as a limited critical batch size early in training. Finally, we show that the need for warmup can be significantly reduced or eliminated by modifying the optimizer to explicitly normalize $\mathbf{u}_t$ based on the aforementioned metrics.

The remarkable capabilities and easy accessibility of large language models (LLMs) have significantly increased societal risks (e.g., fake news generation), necessitating the development of LLM-generated text (LGT) detection methods for safe usage. However, detecting LGTs is challenging due to the vast number of LLMs, making it impractical to account for each LLM individually; hence, it is crucial to identify the common characteristics shared by these models. In this paper, we draw attention to a common feature of recent powerful LLMs, namely the alignment training, i.e., training LLMs to generate human-preferable texts. Our key finding is that as these aligned LLMs are trained to maximize the human preferences, they generate texts with higher estimated preferences even than human-written texts; thus, such texts are easily detected by using the reward model (i.e., an LLM trained to model human preference distribution). Based on this finding, we propose two training schemes to further improve the detection ability of the reward model, namely (i) continual preference fine-tuning to make reward model prefer aligned LGTs even further and (ii) reward modeling of Human/LLM mixed texts (a rephrased texts from human-written texts using aligned LLMs), which serves as a median preference text corpus between LGTs and human-written texts to learn the decision boundary better. We provide an extensive evaluation by considering six text domains across twelve aligned LLMs, where our method demonstrates state-of-the-art results.