In this report, we introduce our first-generation reasoning model, LexPro-1.0, a large language model designed for the highly specialized Chinese legal domain, offering comprehensive capabilities to meet diverse realistic needs. Existing legal LLMs face two primary challenges. Firstly, their design and evaluation are predominantly driven by computer science perspectives, leading to insufficient incorporation of legal expertise and logic, which is crucial for high-precision legal applications, such as handling complex prosecutorial tasks. Secondly, these models often underperform due to a lack of comprehensive training data from the legal domain, limiting their ability to effectively address real-world legal scenarios. To address this, we first compile millions of legal documents covering over 20 types of crimes from 31 provinces in China for model training. From the extensive dataset, we further select high-quality for supervised fine-tuning, ensuring enhanced relevance and precision. The model further undergoes large-scale reinforcement learning without additional supervision, emphasizing the enhancement of its reasoning capabilities and explainability. To validate its effectiveness in complex legal applications, we also conduct human evaluations with legal experts. We develop fine-tuned models based on DeepSeek-R1-Distilled versions, available in three dense configurations: 14B, 32B, and 70B.

Deception detection has attracted increasing attention due to its importance in real-world scenarios. Its main goal is to detect deceptive behaviors from multimodal clues such as gestures, facial expressions, prosody, etc. However, these bases are usually subjective and related to personal habits. Therefore, we extend deception detection to deception reasoning, further providing objective evidence to support subjective judgment. Specifically, we provide potential lies and basic facts and then analyze why this sentence may be a lie by combining factual inconsistencies and intent behind them. Compared with deception detection, this task is more applicable to real-world scenarios. For example, in interrogation, the police should judge whether a person is lying based on solid evidence. This paper presents our initial attempts at this task, including constructing a dataset and defining evaluation metrics. Meanwhile, this task can serve as a benchmark for evaluating the complex reasoning capability of large language models. Code and data will be made publicly available.

This study investigates pedestrian crash severity through Automated Machine Learning (AutoML), offering a streamlined and accessible method for analyzing critical factors. Utilizing a detailed dataset from Utah spanning 2010-2021, the research employs AutoML to assess the effects of various explanatory variables on crash outcomes. The study incorporates SHAP (SHapley Additive exPlanations) to interpret the contributions of individual features in the predictive model, enhancing the understanding of influential factors such as lighting conditions, road type, and weather on pedestrian crash severity. Emphasizing the efficiency and democratization of data-driven methodologies, the paper discusses the benefits of using AutoML in traffic safety analysis. This integration of AutoML with SHAP analysis not only bolsters predictive accuracy but also improves interpretability, offering critical insights into effective pedestrian safety measures. The findings highlight the potential of this approach in advancing the analysis of pedestrian crash severity.

Iran-backed militias in Iraq have claimed they were responsible for an attack on U.S. forces at a strategic base in southeastern Syria. The Islamic Resistance in Iraq, an umbrella group of Iranian-backed militias, said Monday that their forces used two drones to attack the al-Tanf garrison near the Jordanian and Iraqi borders, a sensitive location often used by Iranian-backed militants to transport weapons to Hezbollah. Monday's attack comes after a string of similar attacks on bases housing U.S. military in Iraq and Syria over the past week. In one, the same group attacked two bases in Iraq with drones, causing minor injuries among U.S. forces. The U.S. military has maintained a presence at the al-Tanf garrison since training forces as part of a campaign against the Islamic State group.

"Hated in the Nation," an episode of Netflix's dystopian sci-fi series "Black Mirror," predicted it: Thousands of robotic bees buzz from flower to flower, pollinating plants to make up for declining insect populations. And while the episode's robots eventually turn against their human inventors, killing over 387,000 people by ramming their artificial stingers into victims' heads, the MIT scientists working on perfecting today's aerial robots likely believe we don't need to worry about that. Despite the show's foreboding take on robotic bees, researchers at the Massachusetts Institute of Technology are one step closer to perfecting the artificial aerial critters. In a paper published March 15, a group of researchers at MIT showed that using resilient muscle-like actuators and self-repairing technology can vastly improve the robustness of robotic bees. "Insects flying are incredibly difficult to understand," said Kevin Chen, an assistant professor at MIT, head of the institute's Soft and Micro Robotics Laboratory, and the senior author of the paper.

A Tesla Model 3 driving on'autopilot' smacked into a Florida Highway Patrol cruiser on Saturday morning, narrowly missing the driver of the cruiser who had stopped in order to help a disabled vehicle. The incident is the 12th such smash involving a Tesla on autopilot mode and an emergency vehicle. All the cars which have been struck had their lights flashing, or had deployed an emergency flare, illuminated warning sign or cones, raising questions about whether they may have confused the Tesla's sensors. Saturday's smash happened after when the 28-year-old trooper, who has not been named, stopped shortly after 5 am on August 28 on I-4 near downtown Orlando while responding to a broken down car. He put his emergency lights and was walking over to a disabled vehicle when the Tesla hit the cruiser's left side, according to a copy of the police report seen by DailyMail.com.

Product risk assessment is the overall process of determining whether a product, which could be anything from a type of washing machine to a type of teddy bear, is judged safe for consumers to use. There are several methods used for product risk assessment, including RAPEX, which is the primary method used by regulators in the UK and EU. However, despite its widespread use, we identify several limitations of RAPEX including a limited approach to handling uncertainty and the inability to incorporate causal explanations for using and interpreting test data. In contrast, Bayesian Networks (BNs) are a rigorous, normative method for modelling uncertainty and causality which are already used for risk assessment in domains such as medicine and finance, as well as critical systems generally. This article proposes a BN model that provides an improved systematic method for product risk assessment that resolves the identified limitations with RAPEX. We use our proposed method to demonstrate risk assessments for a teddy bear and a new uncertified kettle for which there is no testing data and the number of product instances is unknown. We show that, while we can replicate the results of the RAPEX method, the BN approach is more powerful and flexible.