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新论文 · v1arXiv:2611.07865

OcTreeMamba: Topology-Aware Mamba for 3D Medical Segmentation via Octree Sparsification

Kaixiang Mai, Jiaqi Yu, Yuzhu Ji, Yiqun Zhang, Zhijie Wang, Haijun Zhang, Yiu-ming Cheung

原文摘要

Accurate segmentation of 3D anatomical structures is crucial to medical image analysis. Recently, Mamba-based approaches have achieved promising segmentation performance in capturing long-range dependencies with linear-time computational complexity. However, heuristic selective scanning strategies inherently disrupt the intrinsic spatial hierarchy and increase computational redundancy, ignoring the inherent sparsity of 3D anatomical structures. To this end, we propose OcTreeMamba, a hierarchical framework that incorporates Octree structures into SSM, achieving 3D topology-aware long-range context feature propagation and integration. Specifically, a Structure Learner performs anatomy-adaptive structured sparsification through hierarchical gating and Gumbel-Softmax sampling, enabling differentiable selection of anatomical regions. The selected regions are subsequently organized by a Dynamic Octree Builder into a multi-level topology that preserves their hierarchical connectivity. Based on the constructed Octree, a bidirectional Tree-SSM propagates and integrates contextual features along connectivity-aware leaf--parent--root and root--parent--leaf trajectories. Extensive experiments on four benchmark datasets show that our proposed OcTreeMamba achieves competitive performance \wrt topological continuity and computational efficiency, particularly for recovering the complex structure on vascular datasets. The code is available at https://github.com/MikeMak123/OcTreeMamba.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02511

When Prediction Shapes the Future: A BlueSky Agenda for Reflexive Time Series Analysis

Tao Xie, Yiqun Zhang, Yiu-ming Cheung

原文摘要

Time-series models are increasingly deployed in operational systems (e.g., inventory management and clinical care) where predictions trigger interventions that alter future observations, producing records we term AI-shaped trajectories. This sequence-prediction coupling deepens as such systems evolve into agents that autonomously predict, act, and remember across long horizons. Such AI-shaped trajectories support immediate performance monitoring, but turn ambiguous once reused as passive data. An algorithm may then misread a prediction-prevented stockout as naturally stable demand, or an averted medical crisis as a false alarm. This ambiguity then propagates into everything built on the record, from retraining and agent memory to downstream predictive tasks. As a result, systems may internalize these intervention effects as natural phenomena, drifting further from the truth at every closed-loop cycle. In this Blue Sky paper, we advocate an agenda for reflexive time-series analysis that shifts the analytical object from the intervention-agnostic sequence to the prediction-action-observation loop. The agenda requires closed-loop provenance to track the operational context (e.g., predictions, policies, and actions), sequence identification to estimate how those actions affect future values under explicit causal assumptions, and closed-loop evaluation to separate intrinsic process prediction from policy-driven effects. This foundation is essential for integrating autonomous agents into such loops at scale, enabling the joint learning of intervention effects and intrinsic dynamics for robust temporal representations.

cs.AI · cs.LG

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1673ACL Anthology · ACL 2026

SAD: A Large-Scale Strategic Argumentative Dialogue Dataset

YongKang Liu Northeast University, Jiayang Yu Peking University, Mingyang Wang LMU Munich, Yiqun Zhang Shanghai Artificial Intelligence Laboratory and Northeastern University, Ercong Nie Shanghai Jiaotong University, Shi Feng Northeastern University, China, Daling Wang, Kaisong Song 等 9 位作者

原文摘要

Argumentation generation has attracted substantial research interest due to its central role in human reasoning and decision-making. However, most existing argumentative corpora focus on non-interactive, single-turn settings, either generating arguments from a given topic or refuting an existing argument. In practice, however, argumentation is often realized as multi-turn dialogue, where speakers defend their stances and employ diverse argumentative strategies to strengthen persuasiveness. To support deeper modeling of argumentation dialogue, we present the first large-scale Strategic Argumentative Dialogue dataset, SAD, consisting of 392,822 examples. Grounded in argumentation theories, we annotate each utterance with five strategy types, allowing multiple strategies per utterance. Unlike prior datasets, SAD requires models to generate contextually appropriate arguments conditioned on the dialogue history, a specified stance on the topic, and targeted argumentation strategies. We further benchmark a range of pretrained generative models on SAD and present in-depth analysis of strategy usage patterns in argumentation.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2044ACL Anthology · ACL 2026

Nature-Inspired Population-Based Evolution of Large Language Models

Yiqun Zhang Shanghai Artificial Intelligence Laboratory and Northeastern University, Peng Ye The Chinese University of Hong Kong, Xiaocui Yang Northeastern University, Shi Feng Northeastern University, China, Shufei Zhang Shanghai AI Lab, Lei Bai Shanghai AI Laboratory, Wanli Ouyang Shanghai AI Lab, Shuyue Hu

原文摘要

Evolution, the engine behind the survival and growth of life on Earth, operates through the population-based process of reproduction. Inspired by this principle, this paper formally defines a newly emerging problem: the population-based evolution of large language models (LLMs). We introduce a novel framework that starts with a population of parent LLMs and allows this population to evolve through four key operations: (i) crossover, merging the weights of different parents to create offspring LLMs, (ii) mutation, introducing small, random changes to model weights to foster diversity, (iii) selection, prioritizing high-performing models, and (iv) succession, transferring the learned experience from parent to offspring LLMs. With only 200 samples per new task, the LLM population evolves rapidly to adapt to the task at hand, without any gradients. Experiments on 12 datasets show that our framework consistently outperforms existing multi-LLM merging and adaptation methods, achieving relative performance gains of up to 54.8 over the best LLM in the initial population. Moreover, our framework allows for (i) the evolution of LLMs across multiple new tasks simultaneously, (ii) scaling effectively with populations of up to 40 LLMs, and (iii) even zero-shot generalization to unseen held-out tasks. Code: https://github.com/ZhangYiqun018/GENOME

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2045ACL Anthology · ACL 2026

MTRouter: Cost-Aware Multi-Turn LLM Routing with History–Model Joint Embeddings

Yiqun Zhang Shanghai Artificial Intelligence Laboratory and Northeastern University, Hao Li, Zihan Wang Northeastern University, Shi Feng Northeastern University, China, Xiaocui Yang Northeastern University, Daling Wang, Bo Zhang AI for Science, Shanghai AI Laboratory, Lei Bai Shanghai AI Laboratory 等 9 位作者

原文摘要

Multi-turn, long-horizon tasks are increasingly common for large language models (LLMs), but solving them typically requires many sequential model invocations, accumulating substantial inference costs. Here, we study cost-aware multi-turn LLM routing: selecting which model to invoke at each turn from a model pool, given a fixed cost budget. We propose MTRouter, which encodes the interaction history and candidate models into joint history–model embeddings, and learns an outcome estimator from logged trajectories to predict turn-level model utility. Experiments show that MTRouter improves the performance–cost trade-off: on ScienceWorld, it surpasses GPT-5 while reducing total cost by 58.7%; on Humanity’s Last Exam (HLE), it achieves competitive accuracy while reducing total cost by 43.4% relative to GPT-5, and these gains even carry over to held-out tasks. Further analyses reveal several mechanisms underlying its effectiveness: relative to prior multi-turn routers, MTRouter makes fewer model switches, is more tolerant to transient errors, and exhibits emergent specialization across models.Code: https://github.com/ZhangYiqun018/MTRouter

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2108ACL Anthology · ACL 2026

Why Do More Experts Fail? A Theoretical Analysis of Model Merging

Zijing Wang, Xingle Xu Northeastern University, YongKang Liu Northeast University, Yiqun Zhang Shanghai Artificial Intelligence Laboratory and Northeastern University, Peiqin Lin, Shi Feng Northeastern University, China, Daling Wang, Xiaocui Yang Northeastern University 等 9 位作者

原文摘要

Model merging dramatically reduces storage and computational resources by combining multiple expert models into a single multi-task model. However, existing methods struggle to maintain performance gains as the number of merged models increases. In this paper, we investigate the key obstacles that limit the scalability of model merging. We prove that the limited effective parameter space imposes a strict constraint on the number of models that can be successfully merged. Through Gaussian Width analysis, we show that marginal benefits diminish according to a strictly concave function as more models are merged. Using Approximate Kinematics Theory, we further prove the existence of a unique optimal threshold beyond which additional models yield negligible improvements. To address this limitation, we propose a straightforward Reparameterized Heavy-Tailed method to extend the merged model’s coverage and enhance performance. Empirical results on 19 benchmarks, including both knowledge-intensive and general-purpose tasks, validate our theoretical analysis. We believe that these results spark further research beyond the current scope of model merging.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表