The Canadian province of British Columbia is preparing to sue OpenAI, alleging the US company failed to alert police after its staff internally flagged violent ChatGPT conversations linked to the person responsible for February's Tumbler Ridge mass shooting . Attorney General Niki Sharma announced Tuesday that the province has hired legal teams in British Columbia and California to "explore all legal avenues to hold OpenAI and its decision-makers accountable for its documented failure to notify law enforcement regarding explicit, flagged threats made by the perpetrator on the company's ChatGPT platform." The move stems from the February 10 attack in the remote mountain community of Tumbler Ridge, where authorities say 18-year-old Jesse Van Rootselaar killed their mother and half-brother before going to the Tumbler Ridge Secondary School and opening fire. Five children between the ages of 11 and 13 and one educator were killed at the school. Twenty-seven other people were wounded before Van Rootselaar died from what police described as a self-inflicted gunshot wound.

An explosion at a fireworks shop in central China's Hubei province has killed at least 12 people, state media reported, marking the second deadly blast linked to fireworks as the country celebrates the Lunar New Year . The explosion tore through the shop in Xiangyang on Wednesday afternoon. Officials said five children and seven adults died in the explosion. The victims included the shop owner and customers who had been buying fireworks for holiday celebrations. Some had travelled from other areas to visit relatives during the festive period .

PlanBench seeks to cover a broad range of tasks related to planning and reasoning.

Neural Information Processing Systems http://nips.cc/

Causal inference is paramount for understanding the effects of interventions, yet extracting personalized insights from increasingly complex data remains a significant challenge for modern machine learning. This is the case, in particular, when considering functional outcomes observed over a continuous domain (e.g., time, or space). Estimation of heterogeneous treatment effects, known as CATE, has emerged as a crucial tool for personalized decision-making, but existing meta-learning frameworks are largely limited to scalar outcomes, failing to provide satisfying results in scientific applications that leverage the rich, continuous information encoded in functional data. Here, we introduce FOCaL (Functional Outcome Causal Learning), a novel, doubly robust meta-learner specifically engineered to estimate a functional heterogeneous treatment effect (F-CATE). FOCaL integrates advanced functional regression techniques for both outcome modeling and functional pseudo-outcome reconstruction, thereby enabling the direct and robust estimation of F-CATE. We provide a rigorous theoretical derivation of FOCaL, demonstrate its performance and robustness compared to existing non-robust functional methods through comprehensive simulation studies, and illustrate its practical utility on diverse real-world functional datasets. FOCaL advances the capabilities of machine intelligence to infer nuanced, individualized causal effects from complex data, paving the way for more precise and trustworthy AI systems in personalized medicine, adaptive policy design, and fundamental scientific discovery.

Severe heatwaves in urban areas significantly threaten public health, calling for establishing early warning strategies. Despite predicting occurrence of heatwaves and attributing historical mortality, predicting an incoming deadly heatwave remains a challenge due to the difficulty in defining and estimating heat-related mortality. Furthermore, establishing an early warning system imposes additional requirements, including data availability, spatial and temporal robustness, and decision costs. To address these challenges, we propose DeepTherm, a modular early warning system for deadly heatwave prediction without requiring heat-related mortality history. By highlighting the flexibility of deep learning, DeepTherm employs a dual-prediction pipeline, disentangling baseline mortality in the absence of heatwaves and other irregular events from all-cause mortality. We evaluated DeepTherm on real-world data across Spain. Results demonstrate consistent, robust, and accurate performance across diverse regions, time periods, and population groups while allowing trade-off between missed alarms and false alarms.

Knowledge Base Question Answering (KBQA) aims to answer natural language questions using structured knowledge from KBs. While LLM-only approaches offer generalization, they suffer from outdated knowledge, hallucinations, and lack of transparency. Chain-based KG-RAG methods address these issues by incorporating external KBs, but are limited to simple chain-structured questions due to the absence of planning and logical structuring. Inspired by semantic parsing methods, we propose PDRR: a four-stage framework consisting of Predict, Decompose, Retrieve, and Reason. Our method first predicts the question type and decomposes the question into structured triples. Then retrieves relevant information from KBs and guides the LLM as an agent to reason over and complete the decomposed triples. Experimental results demonstrate that PDRR consistently outperforms existing methods across various LLM backbones and achieves superior performance on both chain-structured and non-chain complex questions.

'Extremely rare' Roman tomb discovered in Germany No riches or remains are inside--but it probably wasn't tomb raiders. This stone circle was part of a Roman burial mound called a tumulus. Breakthroughs, discoveries, and DIY tips sent every weekday. In 15 BCE, the Romans invaded parts of Austria, Switzerland, and Germany. The region would eventually become the province of Raetia, but it was not valued for its economic resources.

Reinforcement learning (RL) has been pivotal in enhancing the reasoning capabilities of large language models (LLMs), but it often suffers from limited exploration and entropy collapse, where models exploit a narrow set of solutions, leading to a loss of sampling diversity and subsequently preventing RL from further improving performance. This issue is exacerbated in parallel sampling methods, where multiple outputs are drawn from the same distribution, potentially causing the model to converge to similar solutions. We propose SESA, a novel SEquential SAmpling framework that mitigates this challenge by generating diverse solution sketches sequentially before expanding them into full reasoning paths. This approach ensures broader exploration by conditioning each new output on previous ones, promoting diversity throughout the process and preventing policy collapse. Our experiments on a synthetic task show that sequential sampling consistently outperforms traditional RL methods in terms of path diversity and recovery from collapse. Further evaluations on real-world tasks demonstrate that SESA improves both the exploration of valid strategies and the overall performance of LLMs. On three agent benchmarks, SESA lifts success rates by $+0.25$, $+0.42$, and $+0.07$ absolute over the base model (up to an additional $211\%$ relative improvement over baseline RL), underscoring its exploration advantage. This work introduces a structured approach to exploration, paving the way for more effective and diverse reasoning in RL-trained LLMs. Our code is released at https://github.com/MuLabPKU/sesa.