Aoqian Zhang, Chunzheng Wang, Haoran Chen, Annan Tang, Cheng Xiang, Fan Shi, Shuzhi Sam Ge
Humanoids have become increasingly agile and capable in real-world environments. To operate safely in dynamic scenarios, they must rapidly evade fast-moving obstacles while maintaining stability and physically feasible whole-body motion. Learning such behaviors remains challenging due to the high-dimensional dynamics of humanoid systems and the need to jointly balance reactive obstacle avoidance, motion naturalness, and control robustness. In this work, we present Humanoid Dodgeball, a reinforcement learning (RL) framework for agile dynamic obstacle avoidance in humanoids. Our method enables highly dynamic whole-body evasive maneuvers against incoming projectiles through threat-aware reward shaping, while multiple direction-specific adversarial motion priors regularize the learned behaviors toward stable and diverse human-like motions. Extensive simulation and hardware experiments demonstrate that the proposed framework substantially improves dynamic obstacle avoidance performance across diverse obstacle speeds and launch distances, while preserving robust and physically plausible humanoid motions.
Document backed analytics need databases that can be built as queries arrive. Enterprises increasingly ask analytical queries over messy document collections such as contracts, invoices, reports, emails, policies, and tickets, where the relevant structure and facts are often not known in advance. RAG can retrieve and summarize supporting evidence, but it does not by itself produce reusable, joinable, governed database state. Schema first construction offers the opposite strength: once a database is built, it supports joins, aggregation, constraints, indexes, optimization, and transactions. Yet for document analytics, fixing the schema and extracting all possible fields upfront is both expensive and brittle, because later queries and new documents often surface concepts that were not modeled before. We propose the Evolving Universal Relation Model (EUR), a query first model for document backed analytics. Users ask natural language queries as if the document corpus exposed one conceptual relation over application concepts. Internally, the DBMS turns each query into controlled database evolution: it updates the logical schema when new concepts are needed, extracts and integrates the facts required to answer the query, and decides what durable database state should remain for future queries. Thus, in EUR, a query is not only evaluated; it also helps construct the database. Document backed analytics becomes query first universal access plus workload-driven database schema and instance evolution.
In Large Language Model (LLM) training, data mixing plays a pivotal role in determining model performance. Recent methods optimize mixture weights via proxy models, but they rely on the assumption of static data distributions. As a result, when the underlying data pool shifts, these methods require costly retraining from scratch. This limitation restricts their ability to scale seamlessly from small settings to larger data pools and model sizes. In this paper, we propose CausalMix to address this limitation by casting data mixture optimization as a causal inference problem. We formulate the statistical features of the data pool as covariates and the domain mixture as the treatment. After fitting a causal model on 512 runs of Qwen2.5-0.5B to estimate the Conditional Average Treatment Effect (CATE), we extrapolate the optimal mixture for an 800K data pool and apply it to train a 7B model. Furthermore, we successfully generalize the framework to long chain-of-thought data on Qwen3-4B-Base. By leveraging causal modeling to isolate confounding biases, CausalMix infers state-dependent data mixtures offline, with the selected mixture fixed during each training run. Extensive experiments show that the mixture guided by CausalMix achieves competitive performance across downstream tasks compared with other baselines. The CATE Interpreter visualizes the learned mixing strategy. Overall, CausalMix offers a causal and interpretable framework for optimizing LLM data mixtures.
Zhuoshi Pan, Qizhi Pei, Yu Li, Zinan Tang Alibaba Group, QiYao Sun, H. Vicky Zhao Tsinghua University, Tsinghua University, Conghui He Shanghai AI Lab, Lijun Wu Shanghai Artificial Intelligence Laboratory
Recent Large Reasoning Models (LRMs) have achieved remarkable progress, yet their evaluation still relies on a narrow paradigm: evaluating one question at a time. This single-question setup suffers from two major limitations: (1) vulnerability to data contamination and diminishing difficulty, forcing costly creation of new questions with significant human effort, (2) failure to evaluate models under multi-context pressure, a key requirement for real-world deployment. To bridge this gap, we present REST (Reasoning Evaluation through Simultaneous Testing), a stress-testing framework that exposes LRMs to multiple problems simultaneously. Beyond basic reasoning, REST evaluates two under-tested capabilities: contextual priority allocation and robustness against contextual interference. Our evaluation of more than 30 advanced reasoning models on 9 reasoning benchmarks reveals several striking findings: Even state-of-the-art (SOTA) models such as DeepSeek-R1 exhibit substantial performance degradation under stress testing, challenging the prevailing assumption that “LLMs are multi-problem solvers”. Crucially, REST demonstrates stronger discriminative power than existing benchmarks, revealing performance gaps among models that exhibit similar, near-ceiling performance under traditional evaluation. Some key insights emerge from our analysis: (1) the “overthinking trap” is a critical factor contributing to the performance degradation; (2) models trained with the “Long2Short” technique preserve more of their single-problem accuracy under REST, outperforming their standard-trained counterparts. These results establish REST as a cost-efficient, future-proof evaluation paradigm while reducing reliance on continuous human annotation. Code is available at https://github.com/opendatalab/REST.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表