Ruiqing Sun, Dawei Feng, Sen Yang, Ronghang Wang, Huaiyuan Song, Bo Ding, Yijie Wang, Huaimin Wang
De novo 3D molecular design with multiple properties and fragment-retention constraints is formulated as a constrained multi-objective optimization problem. Existing 3D diffusion models provide strong capabilities for generating novel molecules, but adapting them to new conditions and multi-objective optimization tasks remains difficult. We present Diffusion-based Evolutionary Molecular Optimization (DEMO), a framework that draws solely on the unconditional molecular prior of a frozen pretrained diffusion model to generate chemically valid 3D candidates, while evolutionary search handles conditional generation and constrained multi-objective optimization, without retraining or fine-tuning. DEMO performs crossover and mutation on intermediate diffusion states through an Evolutionary-Guided Diffusion (EGD) operator, combines 2D topology and 3D geometry in Structure-Aware Environmental Selection (SAES) to preserve structural diversity, and uses three cooperating populations to coordinate structural exploration, fragment-constraint satisfaction, and property optimization. Experimental results demonstrate that EGD outperforms task-specific conditional generators in property targeting; SAES preserves structural diversity while retaining competitive hypervolume in multi-objective optimization; and DEMO improves objective convergence and generates diverse feasible molecules under fragment constraints. These results show how evolutionary search can reuse existing diffusion models for diverse molecular design requirements.
Aditya Golatkar, Alessandro Achille, Renos Zabounidis, Vlad Sobal, Yangruibo Ding, Baishakhi Ray, Jonathan Buck, Guy Van den Broeck 等 12 位作者
Coding agents are typically provided end-to-end behavioral specifications that the code they produce should satisfy, such as tests and input--output examples. How many such specifications must a developer provide, and which ones, for the agent to produce the developer's intended program? We argue that this cannot be determined a priori, for it depends on the structure of the implementation, which is left to the agent to decide. An agent first commits to a structure---a decomposition into data types, modules, and functions---which fixes a class of candidate programs; behavioral specifications then select a program within that class. A set of tests that suffices for one structure may leave another badly underdetermined, requiring different edge cases or exponentially more examples. Since the structure is chosen by the agent, developers cannot ensure their specification is complete without reverse-engineering it from thousands of lines of generated code. To resolve this, we introduce Skeleton Coding, in which developers jointly specify the structure---in high-level, code-like format---alongside behavioral specifications. Theoretically, we model AI-assisted coding as a control problem, and show that skeletons can reduce exponentially the number of tests developers must supply to steer an agent toward an acceptable program. Empirically, across four models and three benchmarks, the best-performing skeleton specifications reduce mean test error by 48.4% relative to the best end-to-end behavioral specifications. Even when the agent can query a complete reference implementation, adding a skeleton reduces mean test error relatively by up to 27.0%. Skeletons also narrow the average gap between small and large models by about 52.4% while lowering implementation cost by 15--26x, and reduce code erosion by 24.7% on average.
Lingfeng Zhang Tsinghua University and Xiaomi Corporation, Xiaoshuai Hao Xiaomi Corporation, Yingbo Tang Institute of automation, Chinese academy of science, Chinese Academy of Sciences, Haoxiang Fu, Xinyu Zheng, Pengwei Wang, Zhongyuan Wang BAAI, Wenbo Ding Tsinghua Univeresity 等 9 位作者
Embodied navigation is a fundamental capability of embodied intelligence, enabling robots to move and interact within physical environments. However, existing navigation tasks primarily focus on predefined object navigation or instruction following, which significantly differs from human needs in real-world scenarios involving complex, open-ended scenes. To bridge this gap, we introduce a challenging long-horizon navigation task that requires understanding high-level human instructions and performing spatial-aware object navigation in real-world environments. Existing embodied navigation methods struggle with such tasks due to their limitations in comprehending high-level human instructions and localizing objects with an open vocabulary. In this paper, we propose NavA^3, a hierarchical framework divided into two stages: global and local policies. In the global policy, we leverage the reasoning capabilities of Reasoning-VLM to parse high-level human instructions and integrate them with global 3D scene views. This allows us to reason and navigate to regions most likely to contain the goal object. In the local policy, we have collected a dataset of 1.0 million samples of spatial-aware object affordances to train the NaviAfford model (PointingVLM), which provides robust open-vocabulary object localization and spatial awareness for precise goal identification and navigation in complex environments. Extensive experiments demonstrate that NavA^3 achieves SOTA results in navigation performance and can successfully complete long-horizon navigation tasks across different robot embodiments in real-world settings, paving the way for universal embodied navigation. The dataset and code will be made available.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Xiaoying Le, Pengfei Qian, Yuanzhao Zhai, Xu Zhang National University of Defense Technology, Qian Liu National University of Defense Technology, Feng Dawei National University of Defense Technology, Bo Ding National University of Defense Technology
Scientific discovery evolution does not emerge in isolation but stems from the structural deepening and recombination of existing functionalities. However, current automated hypothesis generation methods, constrained by the statistical co-occurrence nature of Large Language Models (LLMs), lack perception of temporal causality and the “evolutionary patterns” inherent in scientific development. Consequently, they often yield superficial combinations that are logically infeasible. To address this, we propose EvoNarrator, a framework for hypothesis generation based on evolutionary narratives. We first extract structured P-M-L-F (Problem, Method, Limitation, Future Work) quadruples from citation networks. Subsequently, we introduce the SocketMatch mechanism, which eliminates logical disconnects between methods and problems by assessing their deep semantic compatibility. Finally, utilizing three macro patterns—Chain, Divergence, and Convergence—we constrain the generation process within historically logical derivation paths. Furthermore, double-blind expert reviews yielded an average score of 4.80/5.00 across novelty, feasibility, theoretical, and Logical. Additionally, hindcasting experiments validated its predictive foresight. Crucially, ablation studies indicate that integrating evolutionary patterns facilitates a paradigm shift from conservative incrementalism to theoretically grounded structural innovation. The code is available at https://github.com/xiyii-star/EvoNarrator.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表