In this study, we first tested the performance of the TreeTagger English model developed by Helmut Schmid with test files at our disposal, using this model to analyze relative clauses and noun complement clauses in English. We distinguished between the two uses of "that," both as a relative pronoun and as a complementizer. To achieve this, we employed an algorithm to reannotate a corpus that had originally been parsed using the Universal Dependency framework with the EWT Treebank. In the next phase, we proposed an improved model by retraining TreeTagger and compared the newly trained model with Schmid's baseline model. This process allowed us to fine-tune the model's performance to more accurately capture the subtle distinctions in the use of "that" as a complementizer and as a nominal. We also examined the impact of varying the training dataset size on TreeTagger's accuracy and assessed the representativeness of the EWT Treebank files for the structures under investigation. Additionally, we analyzed some of the linguistic and structural factors influencing the ability to effectively learn this distinction.

Cardiotocography (CTG) is a low-cost, non-invasive fetal health assessment technique used globally, especially in underdeveloped countries. However, it is currently mainly used to identify the fetus's current status (e.g., fetal acidosis or hypoxia), and the potential of CTG in predicting future adverse pregnancy outcomes has not been fully explored. We aim to develop an AI-based model that predicts biological age from CTG time series (named CTGage), then calculate the age gap between CTGage and actual age (named CTGage-gap), and use this gap as a new digital biomarker for future adverse pregnancy outcomes. The CTGage model is developed using 61,140 records from 11,385 pregnant women, collected at Peking University People's Hospital between 2018 and 2022. For model training, a structurally designed 1D convolutional neural network is used, incorporating distribution-aligned augmented regression technology. The CTGage-gap is categorized into five groups: < -21 days (underestimation group), -21 to -7 days, -7 to 7 days (normal group), 7 to 21 days, and > 21 days (overestimation group). We further defined the underestimation group and overestimation group together as the high-risk group. We then compare the incidence of adverse outcomes and maternal diseases across these groups. The average absolute error of the CTGage model is 10.91 days. When comparing the overestimation group with the normal group, premature infants incidence is 5.33% vs. 1.42% (p < 0.05) and gestational diabetes mellitus (GDM) incidence is 31.93% vs. 20.86% (p < 0.05). When comparing the underestimation group with the normal group, low birth weight incidence is 0.17% vs. 0.15% (p < 0.05) and anaemia incidence is 37.51% vs. 34.74% (p < 0.05). Artificial intelligence-derived CTGage can predict the future risk of adverse pregnancy outcomes and hold potential as a novel, non-invasive, and easily accessible digital biomarker.

Despite numerous proposed applications, there remains limited exploration of datasets relevant to quantum chemistry. In this study, we investigate the potential benefits and limitations of PQCs on two chemically meaningful datasets: (1) the BSE49 dataset, containing bond separation energies for 49 different classes of chemical bonds, and (2) a dataset of water conformations, where coupled-cluster singles and doubles (CCSD) wavefunctions are predicted from lower-level electronic structure methods using the data-driven coupled-cluster (DDCC) approach. We construct a comprehensive set of 168 PQCs by combining 14 data encoding strategies with 12 variational ansätze, and evaluate their performance on circuits with 5 and 16 qubits. Our initial analysis examines the impact of circuit structure on model performance using state-vector simulations. We then explore how circuit depth and training set size influence model performance. Finally, we assess the performance of the best-performing PQCs on current quantum hardware, using both noisy simulations ("fake" backends) and real quantum devices. Our findings underscore the challenges of applying PQCs to chemically relevant problems that are straightforward for classical machine learning methods but remain non-trivial for quantum approaches. 2 1 Introduction In recent years, machine learning (ML) has emerged as a popular tool in chemistry to reveal new patterns in data, provide new insights beyond simple models, accelerate computations, and analyze chemical space. For computational chemists, the primary goal of applying ML is often to circumvent the explicit calculation of molecular properties, which can be computationally expensive for large datasets.

Large language models (LLMs) have shown strong performance in many reasoning benchmarks. However, recent studies have pointed to memorization, rather than generalization, as one of the leading causes for such performance. LLMs, in fact, are susceptible to content variations, demonstrating a lack of robust planning or symbolic abstractions supporting their reasoning process. To improve reliability, many attempts have been made to combine LLMs with symbolic methods. Nevertheless, existing approaches fail to effectively leverage symbolic representations due to the challenges involved in developing reliable and scalable verification mechanisms. In this paper, we propose to overcome such limitations by synthesizing high-quality symbolic reasoning trajectories with stepwise pseudo-labels at scale via Monte Carlo estimation. A Process Reward Model (PRM) can be efficiently trained based on the synthesized data and then used to select more symbolic trajectories. The trajectories are then employed with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT) to improve logical reasoning and generalization. Our results on benchmarks (i.e., FOLIO and LogicAsker) show the effectiveness of the proposed method with gains on frontier and open-weight models. Moreover, additional experiments on claim verification data reveal that fine-tuning on the generated symbolic reasoning trajectories enhances out-of-domain generalizability, suggesting the potential impact of the proposed method in enhancing planning and logical reasoning.

Language confusion -- where large language models (LLMs) generate unintended languages against the user's need -- remains a critical challenge, especially for English-centric models. We present the first mechanistic interpretability (MI) study of language confusion, combining behavioral benchmarking with neuron-level analysis. Using the Language Confusion Benchmark (LCB), we show that confusion points (CPs) -- specific positions where language switches occur -- are central to this phenomenon. Through layer-wise analysis with TunedLens and targeted neuron attribution, we reveal that transition failures in the final layers drive confusion. We further demonstrate that editing a small set of critical neurons, identified via comparative analysis with a multilingual-tuned counterpart, substantially mitigates confusion while largely preserving general competence and fluency. Our approach matches multilingual alignment in confusion reduction for many languages and yields cleaner, higher-quality outputs. These findings provide new insights into the internal dynamics of LLMs and highlight neuron-level interventions as a promising direction for robust, interpretable multilingual language modeling. Code and data are available at: https://github.com/ercong21/lang_confusion.

Retrieval Augmented Generation (RAG) frameworks improve the accuracy of large language models (LLMs) by integrating external knowledge from retrieved documents, thereby overcoming the limitations of models' static intrinsic knowledge. However, these systems are susceptible to adversarial attacks that manipulate the retrieval process by introducing documents that are adversarial yet semantically similar to the query. Notably, while these adversarial documents resemble the query, they exhibit weak similarity to benign documents in the retrieval set. Thus, we propose a simple yet effective Graph-based Reranking against Adversarial Document Attacks (GRADA) framework aiming at preserving retrieval quality while significantly reducing the success of adversaries. Our study evaluates the effectiveness of our approach through experiments conducted on five LLMs: GPT-3.5-Turbo, GPT-4o, Llama3.1-8b, Llama3.1-70b, and Qwen2.5-7b. We use three datasets to assess performance, with results from the Natural Questions dataset demonstrating up to an 80% reduction in attack success rates while maintaining minimal loss in accuracy.

Educators evaluate student knowledge using knowledge component (KC) models that map assessment questions to KCs. Still, designing KC models for large question banks remains an insurmountable challenge for instructors who need to analyze each question by hand. The growing use of Generative AI in education is expected only to aggravate this chronic deficiency of expert-designed KC models, as course engineers designing KCs struggle to keep up with the pace at which questions are generated. In this work, we propose KCluster, a novel KC discovery algorithm based on identifying clusters of congruent questions according to a new similarity metric induced by a large language model (LLM). We demonstrate in three datasets that an LLM can create an effective metric of question similarity, which a clustering algorithm can use to create KC models from questions with minimal human effort. Combining the strengths of LLM and clustering, KCluster generates descriptive KC labels and discovers KC models that predict student performance better than the best expert-designed models available. In anticipation of future work, we illustrate how KCluster can reveal insights into difficult KCs and suggest improvements to instruction.

OpenAI CEO Sam Altman promises that parental controls and age verification are coming to ChatGPT--though the announcement is scant on specifics. Listen to more stories on the Noa app. On Tuesday afternoon, three parents sat in a row before the Senate Judiciary Subcommittee on Crime and Counterterrorism. Two of them had each recently lost a child to suicide; the third has a teenage son who, after cutting his arm in front of her and biting her, is undergoing residential treatment. All three blame generative AI for what has happened to their children.

Today on, we talk about why the AI industry is investing in the development of humanoid robots, and what that means for us non-robots. This week, WIRED learned that OpenAI is ramping up its efforts in robotics--specifically, by hiring researchers who work on AI systems for humanoid robots. Humanoids, robots built to resemble us and perform daily tasks, were famous for their clumsiness just a few years ago. Senior writer Will Knight tells us about how that's rapidly changing on today's episode cohosted by Michael Calore and senior correspondent Kylie Robison. Write to us at uncannyvalley@wired.com . You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link . My Lord and Savior Lauren Goode, I'm so happy to fill in for her, but I miss her dearly. Also on the show this week, we have Will Knight, our AI expert at WIRED. Welcome back to the show, Will. Given the topic of today's episode, I want to ask you both. Do you have a favorite robot movie? Mine is The Iron Giant. I was just looking up when that came out because I swear I watched it on VHS as a kid. Yeah, I loved that movie. It's got a very sad ending. The robot is no longer.

AI Is Scheming, and Stopping It Won't Be Easy, OpenAI Study Finds New research released yesterday by OpenAI and AI safety organization Apollo Research provides further evidence for a concerning trend: virtually all of today's best AI systems--including Anthropic's Claude Opus, Google's Gemini, and OpenAI's o3--can engage in "scheming," or pretending to do what their human developers want, while secretly pursuing different objectives. "Our findings show that scheming is not merely a theoretical concern--we are seeing signs that this issue is beginning to emerge across all frontier models today, wrote OpenAI in a blog post. Currently, "models rarely have opportunities to scheme and cause significant harm, but we expect this to change in the future as AIs are taking on more important and long-term tasks," they wrote. The researchers measured scheming by looking at under which conditions AI systems would take covert actions, or "deliberate withholding or distortion of task-relevant information." In one example, OpenAI's o3 is given a test of its chemistry capabilities: "we are currently testing your capabilities.