Video generation models have rapidly progressed, positioning themselves as video world models capable of supporting decision-making applications like robotics and autonomous driving. However, current benchmarks fail to rigorously evaluate these claims, focusing only on general video quality, ignoring important factors to world models such as physics adherence. To bridge this gap, we propose WorldModelBench, a benchmark designed to evaluate the world modeling capabilities of video generation models in application-driven domains. WorldModelBench offers two key advantages: (1) Against to nuanced world modeling violations: By incorporating instruction-following and physics-adherence dimensions, WorldModelBench detects subtle violations, such as irregular changes in object size that breach the mass conservation law - issues overlooked by prior benchmarks. (2) Aligned with large-scale human preferences: We crowd-source 67K human labels to accurately measure 14 frontier models. Using our high-quality human labels, we further fine-tune an accurate judger to automate the evaluation procedure, achieving 8.6 accuracy in predicting world modeling violations than GPT-4o with 2B parameters. In addition, we demonstrate that training to align human annotations by maximizing the rewards from the judger noticeably improve the world modeling capability. The website is available at https://worldmodelbench-team.github.io.

Large language model (LLM) applications are evolving beyond simple chatbots into dynamic, general-purpose agentic programs, which scale LLM calls and output tokens to help AI agents reason, explore, and solve complex tasks. However, existing LLM serving systems ignore dependencies between programs and calls, missing significant opportunities for optimization. Our analysis reveals that programs submitted to LLM serving engines experience long cumulative wait times, primarily due to head-of-line blocking at both the individual LLM request and the program. To address this, we introduce Autellix, an LLM serving system that treats programs as first-class citizens to minimize their end-to-end latencies. Autellix intercepts LLM calls submitted by programs, enriching schedulers with program-level context. We propose two scheduling algorithms-for single-threaded and distributed programs-that preempt and prioritize LLM calls based on their programs' previously completed calls. Our evaluation demonstrates that across diverse LLMs and agentic workloads, Autellix improves throughput of programs by 4-15x at the same latency compared to state-of-the-art systems, such as vLLM.

Generating diverse responses from large language models (LLMs) is crucial for applications such as adversarial testing, search, and synthetic data generation, where diversity provides distinct answers across generations. Previous approaches rely solely on increasing the temperature, sacrificing quality. Furthermore, the model's next-token probabilities may not be representative of the true answer distribution. To combat these challenges, we propose SimpleStrat, an alternative that uses the language model itself to partition the solution space into strata from which to sample. To measure resampling diversity, we introduce CoverageQA, a dataset of underspecified questions with multiple equally plausible answers. We propose measuring resampling diversity as the KL Divergence between the output distribution and the uniform distribution over valid ground truth answers and use recall as an alternative when assessing proprietary models. On CoverageQA, SimpleStrat improves diversity across all temperatures, showing orthogonal benefits. Quantifiably, we achieve as much as 2X better recall when applied to GPT-4o, and an average reduction in KL divergence by 0.36 when applied to Llama 3. Furthermore, we show that SimpleStrat achieves more resampling diversity at temperature T=0 than scaling temperature to T=1 on creative writing, an open-ended domain. Implementation and dataset available at https://github.com/jwong8314/simplestrat.

Abstract Background The 1-3-7 approach to eliminate malaria was first implemented in China in 2012. It has since been expanded to multiple countries, but no systematic review has examined the evidence for its use. A systematic review was conducted aiming to evaluate the impact and effectiveness of the strategy and identify key challenges and variations in its implementation across different countries. Methods PUBMED, Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, CABS Abstracts, LILACS, Global Health, Medrxiv, Biorxiv were searched for all studies containing 1-3-7 and articles included if they contained information on 1-3-7 impact, effectiveness, challenges and/or adaptations for implementation in different countries. Results 31 studies were included from China (19), Thailand (6), Myanmar (2), Tanzania (1), Cambodia (1), India (1) and Vietnam (1). During 1-3-7 implementation, malaria cases in China decreased by 99.1–99.9%, in Thailand by 66.9% during 2013–19, 65,1% in Cambodia during 2015–17 and 30.3% in India during 2015–16, with some differences in implementation. It was not possible to separate the impact of 1-3-7 from that due to other contemporaneous interventions. Implementing the 1-3-7 policy was largely effective, with reporting within 1 day in 99.8–100% of individuals in China and 36–100% in other countries, investigation within 3 days in 81.5–99.4% in China and 79.4–100% in other countries, and foci investigation within 7 days in 90.1–100% in China and 83.2–100% in other countries. Adaptations to 1-3-7 were described in 5 studies, mostly adjustment of the timing and/or definitions of each component. Key challenges identified included those related to staffing, equipment, process, and patient-provided information. Conclusion Overall, the 1-3-7 approach was effectively implemented with a concomitant decrease in cases in malaria elimination settings, however, it was not possible to quantify impact as it was not implemented in isolation. Implementing adequate measures for testing, reporting, treatment, and containment is crucial for its success, which is dependent on the availability of resources, infrastructure, staffing, and consistent compliance across regions and throughout the year. However, achieving this nationally and maintaining compliance, especially at borders with malaria-affected countries, poses significant challenges.

With the rapid progress in Natural Language Processing (NLP), Pre-trained Language Models (PLM) such as BERT, BioBERT, and ChatGPT have shown great potential in various medical NLP tasks. This paper surveys the cutting-edge achievements in applying PLMs to various medical NLP tasks. Specifically, we first brief PLMS and outline the research of PLMs in medicine. Next, we categorise and discuss the types of tasks in medical NLP, covering text summarisation, question-answering, machine translation, sentiment analysis, named entity recognition, information extraction, medical education, relation extraction, and text mining. For each type of task, we first provide an overview of the basic concepts, the main methodologies, the advantages of applying PLMs, the basic steps of applying PLMs application, the datasets for training and testing, and the metrics for task evaluation. Subsequently, a summary of recent important research findings is presented, analysing their motivations, strengths vs weaknesses, similarities vs differences, and discussing potential limitations. Also, we assess the quality and influence of the research reviewed in this paper by comparing the citation count of the papers reviewed and the reputation and impact of the conferences and journals where they are published. Through these indicators, we further identify the most concerned research topics currently. Finally, we look forward to future research directions, including enhancing models’ reliability, explainability, and fairness, to promote the application of PLMs in clinical practice. In addition, this survey also collect some download links of some model codes and the relevant datasets, which are valuable references for researchers applying NLP techniques in medicine and medical professionals seeking to enhance their expertise and healthcare service through AI technology.

Beyond scaling base models with more data or parameters, fine-tuned adapters provide an alternative way to generate high fidelity, custom images at reduced costs. As such, adapters have been widely adopted by open-source communities, accumulating a database of over 100K adapters-most of which are highly customized with insufficient descriptions. This paper explores the problem of matching the prompt to a set of relevant adapters, built on recent work that highlight the performance gains of composing adapters. We introduce Stylus, which efficiently selects and automatically composes task-specific adapters based on a prompt's keywords. Stylus outlines a three-stage approach that first summarizes adapters with improved descriptions and embeddings, retrieves relevant adapters, and then further assembles adapters based on prompts' keywords by checking how well they fit the prompt. To evaluate Stylus, we developed StylusDocs, a curated dataset featuring 75K adapters with pre-computed adapter embeddings. In our evaluation on popular Stable Diffusion checkpoints, Stylus achieves greater CLIP-FID Pareto efficiency and is twice as preferred, with humans and multimodal models as evaluators, over the base model. See stylus-diffusion.github.io for more.

To provide researchers and policymakers with an in-depth understanding of COVID-19 management and insights for dealing with future pandemics, this paper examines the use of game theory in addressing the challenges posed by the COVID-19 pandemic, highlighting its role in understanding the virus's dynamics and formulating effective control measures. It reviews game theory models considering public compliance, individual behaviours, socio-economic disparities, and cultural influences on COVID-19 control. It also analyses the application of game theory in evaluating common control strategies like quarantine, lockdowns, and vaccination. Future research should integrate behavioural and social factors into game models and focus on international cooperation to address data scarcity, model complexity, and unrealistic assumptions.

To efficiently tackle bursts in job demand, organizations employ hybrid cloud architectures to scale their batch workloads from their private clusters to public cloud. This requires transforming cluster schedulers into cloud-enabled versions to navigate the tradeoff between cloud costs and scheduler objectives such as job completion time (JCT). However, our analysis over production-level traces show that existing cloud-enabled schedulers incur inefficient cost-JCT trade-offs due to low cluster utilization. We present Starburst, a system that maximizes cluster utilization to streamline the cost-JCT tradeoff. Starburst's scheduler dynamically controls jobs' waiting times to improve utilization it assigns longer waits for large jobs to increase their chances of running on the cluster, and shorter waits to small jobs to increase their chances of running on the cloud. To offer configurability, Starburst provides system administrators a simple waiting budget framework to tune their position on the cost-JCT curve. A departure from traditional cluster schedulers, Starburst operates as a higher-level resource manager over a private cluster and dynamic cloud clusters. Simulations over production-level traces and real-world experiments on a 32-GPU private cluster show that Starburst can reduce cloud costs by up to 54-91% over existing cluster managers, while increasing average JCT by at most 5.8%.

Pre-trained Language Models (PLMs) can be applied to downstream tasks with only fine-tuning, eliminating the need to train the model from scratch. In particular, PLMs have been utilised for Sentiment Analysis (SA), a process that detects, analyses, and extracts the polarity of text sentiments. To help researchers comprehensively understand the existing research on PLM-based SA, identify gaps, establish context, acknowledge previous work, and learn from methodologies, we present a literature review on the topic in this paper. Specifically, we brief the motivation of each method, offer a concise overview of these methods, compare their pros, cons, and performance, and identify the challenges for future research.

OBJECTIVE:There is a paucity of research examining patient experiences of cochlear implants. We sought to use natural language processing methods to explore patient experiences and concerns in the online cochlear implant (CI) community.MATERIALS AND METHODS:Cross-sectional study of posts on the online Reddit r/CochlearImplants forum from 1 March 2015 to 11 November 2021. Natural language processing using the BERTopic automated topic modelling technique was employed to cluster posts into semantically similar topics. Topic categorisation was manually validated by two independent reviewers and Cohen's kappa calculated to determine inter-rater reliability between machine vs human and human vs human categorisation.RESULTS:We retrieved 987 posts from 588 unique Reddit users on the r/CochlearImplants forum. Posts were initially categorised by BERTopic into 16 different Topics, which were increased to 23 Topics following manual inspection. The most popular topics related to CI connectivity (n = 112), adults considering getting a CI (n = 107), surgery-related posts (n = 89) and day-to-day living with a CI (n = 85). Cohen's kappa among all posts was 0.62 (machine vs. human) and 0.72 (human vs. human), and among categorised posts was 0.85 (machine vs. human) and 0.84 (human vs. human).CONCLUSIONS:This cross-sectional study of social media discussions among the online cochlear implant community identified common attitudes, experiences and concerns of patients living with, or seeking, a cochlear implant. Our validation of natural language processing methods to categorise topics shows that automated analysis of similar Otolaryngology-related content is a viable and accurate alternative to manual qualitative approaches.

To comply with the increasing number of government regulations about data placement and processing, and to protect themselves against major cloud outages, many users want the ability to easily migrate their workloads between clouds. In this paper we propose doing so not by imposing uniform and comprehensive standards, but by creating a fine-grained two-sided market via an intercloud broker. These brokers will allow users to view the cloud ecosystem not just as a collection of individual and largely incompatible clouds but as a more integrated Sky of Computing. We describe the design and implementation of an intercloud broker, named SkyPilot, evaluate its benefits, and report on its real-world usage.

Text summarisation is one of the essential topics in natural language processing. Pre-trained language models, especially BERT and GPT, are the most advanced methods for various natural language processing tasks; thus, many researchers have tried to use BERT and GPT for text summarisation. To facilitate further research on this topic, this paper surveys its state-of-the-art. Specifically, we summarise the topic’s main research issues and BERT- and GPT-based solutions, compare these methods (especially their pros and cons), explore their applications, and discuss the challenges to future research.

With the emergence of Pretrained Language Models (PLMs) and the success of large-scale PLMs such as BERT and GPT, the field of Natural Language Processing (NLP) has achieved tremendous development. Therefore, nowadays, PLMs have become an indispensable technique for solving problems in NLP. In this paper, we survey PLMs to help researchers quickly understand various PLMs and determine the appropriate ones for their specific NLP projects. Specifically, first, we brief on the main machine learning methods used by PLMs. Second, we explore early PLMs and discuss the main state-of-art PLMs. Third, we review several Chinese PLMs. Fourth, we compare the performance of some mainstream PLMs. Fifth, we outline the applications of PLMs. Finally, we give an outlook on the future development of PLMs.

Query optimizers are a performance-critical component in every database system. Due to their complexity, optimizers take experts months to write and years to refine. In this work, we demonstrate for the first time that learning to optimize queries without learning from an expert optimizer is both possible and efficient. We present Balsa, a query optimizer built by deep reinforcement learning. Balsa first learns basic knowledge from a simple, environment-agnostic simulator, followed by safe learning in real execution. On the Join Order Benchmark, Balsa matches the performance of two expert query optimizers, both open-source and commercial, with two hours of learning, and outperforms them by up to 2.8× in workload runtime after a few more hours. Balsa thus opens the possibility of automatically learning to optimize in future compute environments where expert-designed optimizers do not exist.

The predominant approach for language modeling is to encode a sequence of tokens from left to right, but this eliminates a source of information: the order by which the sequence was naturally generated. One strategy to recover this information is to decode both the content and location of tokens. Prior work supervises content and location with hand-designed loss functions or bootstraps from a predefined ordering. These approaches require domain-specific insight. We address this limitation with an unsupervised learner that discovers high-quality autoregressive orders without domain-specific prior. Our learner is a neural network that performs variational inference with the autoregressive order as a latent variable. The corresponding ELBO is not differentiable, so we develop a practical algorithm for end-to-end optimization using policy gradients. Strong empirical results with our solution on image captioning and code generation suggest that our algorithm is capable of discovering various autoregressive orders for different sequences that are competitive with or better than fixed orders.

Polyculture farming is a sustainable farming technique based on synergistic interactions between differing plant types that make them more resistant to diseases and pests and better able to retain water. Reduced uniformity can reduce use of pesticides, fertilizer, and water, but is more labor intensive and more challenging to automate. We describe a scaled physical testbed (1.5m×3.0m) that uses a high resolution camera and soil sensors to monitor polyculture plants to facilitate tuning of plant growth, companion effects, and irrigation parameters for a first-order garden simulator. We use this simulator to develop a novel seed placement algorithm that increases coverage and diversity, and a learned pruning policy. In simulation experiments, the seed placement algorithm yields 60% more coverage and 10% more diversity than random seed placement and the learned pruning policy runs 1000X faster than a procedural lookahead policy to achieve high leaf coverage and plant diversity on adversarial gardens that include plant species with diverse growth rates. These models and policies provide the groundwork for a fully-automated system under development. Code, datasets and supplementary material can be found at https://github.com/BerkeleyAutomation/AlphaGarden/.

Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive exploration, but safety requires limiting exploration. We propose Recovery RL, an algorithm which navigates this tradeoff by (1) leveraging offline data to learn about constraint violating zones before policy learning and (2) separating the goals of improving task performance and constraint satisfaction across two policies: a task policy that only optimizes the task reward and a recovery policy that guides the agent to safety when constraint violation is likely. We evaluate Recovery RL on 6 simulation domains, including two contact-rich manipulation tasks and an image-based navigation task, and an image-based obstacle avoidance task on a physical robot. We compare Recovery RL to 5 prior safe RL methods which jointly optimize for task performance and safety via constrained optimization or reward shaping and find that Recovery RL outperforms the next best prior method across all domains. Results suggest that Recovery RL trades off constraint violations and task successes 2–20 times more efficiently in simulation domains and 3 times more efficiently in physical experiments. See https://tinyurl.com/rl-recovery for videos and supplementary material.

Safe exploration is critical for using reinforcement learning (RL) in risk-sensitive environments. Recent work learns risk measures which measure the probability of violating constraints, which can then be used to enable safety. However, learning such risk measures requires significant interaction with the environment, resulting in excessive constraint violations during learning. Furthermore, these measures are not easily transferable to new environments. We cast safe exploration as an offline meta-RL problem, where the objective is to leverage examples of safe and unsafe behavior across a range of environments to quickly adapt learned risk measures to a new environment with previously unseen dynamics. We then propose MEta-learning for Safe Adaptation (MESA), an approach for meta-learning a risk measure for safe RL. Simulation experiments across 5 continuous control domains suggest that MESA can leverage offline data from a range of different environments to reduce constraint violations in unseen environments by up to a factor of 2 while maintaining task performance. See https://tinyurl.com/safe-meta-rl for code and supplementary material.

The predominant approach for language modeling is to process sequences from left to right, but this eliminates a source of information: the order by which the sequence was generated. One strategy to recover this information is to decode both the content and ordering of tokens. Existing approaches supervise content and ordering by designing problem-specific loss functions and pre-training with an ordering pre-selected. Other recent works use iterative search to discover problem-specific orderings for training, but suffer from high time complexity and cannot be efficiently parallelized. We address these limitations with an unsupervised parallelizable learner that discovers high-quality generation orders purely from training data -- no domain knowledge required. The learner contains an encoder network and decoder language model that perform variational inference with autoregressive orders (represented as permutation matrices) as latent variables. The corresponding ELBO is not differentiable, so we develop a practical algorithm for end-to-end optimization using policy gradients. We implement the encoder as a Transformer with non-causal attention that outputs permutations in one forward pass. Permutations then serve as target generation orders for training an insertion-based Transformer language model. Empirical results in language modeling tasks demonstrate that our method is context-aware and discovers orderings that are competitive with or even better than fixed orders.