论文检索

Optimization · Non-Convex

Zhanwang Deng, Yuqiu Su, Wen Huang

In this paper, we present the convergence analysis of the proximal Alternating Direction Method of Multipliers (ADMM) for problems with block anti-upper triangular constraints. While the linear constraints can be treated separately, most analyses of ADMM and its variants predominantly regard the linear constraints as one. Hence, it relies on assumptions related to the entire constraint matrix, such as the full column rank. However, some problems with block anti-upper triangular constraints that can be solved by ADMM do not satisfy these assumptions. To fill this gap, a new assumption is proposed and used to guarantee the global convergence of the proximal ADMM for nonconvex problems. In the strongly convex setting, we also prove the global convergence of the proximal ADMM and establish the linear convergence under four different scenarios. This work extends the theoretical understanding of the multi-block ADMM to more general cases with block anti-upper triangular constraints.

Deep Learning · Foundation Models

Ishaan Watts, Catherine Li, Sachin Goyal, Jacob Mitchell Springer, Aditi Raghunathan

Standard optimizer choices for pre-training are designed to minimize pre-training loss. Yet pre-trained models are routinely subjected to further transformations—such as fine-tuning to acquire new capabilities or quantization for efficiency. In this work, we evaluate optimizer choices across model scales, token budgets, and datasets, and find that strategies that explicitly (Sharpness-Aware Minimization) or implicitly (large learning rates and Warmup–Stable–Decay schedules) reduce sharpness yield better downstream performance, even when they achieve comparable or worse pre-training loss. Combining these strategies yields a new pre-training recipe that substantially outperforms standard baselines with minimal compute overhead, delivering a better learning–forgetting frontier during fine-tuning and higher accuracy after quantization.

Serafim Batzoglou

bstract: Large language and reasoning models can be prompted to generate well-formed first-order formulas, but we still lack evaluations of their ability to produce correct, compact explanations under fully specified, mechanically checkable semantics. We study finite-structure concept synthesis: given several small finite relational worlds that are labeled extensionally with a unary target predicate $T(x)$, the learner must output a single first-order formula $\varphi(x)$ that recovers (explains) $T$ uniformly across worlds. Because the domains are finite, correctness is solver-verifiable via exact model checking and SMT. We introduce INDUCTION, a benchmark suite that (to our knowledge) provides the first challenging, end-to-end evaluation of first-order definition synthesis from extensional relational evidence in a fully abstract interface. INDUCTION includes three tightly related regimes---FullObs (full observation), CI (contrastive Yes/No worlds), and EC (partial observation under existential completion)---and reports gold-relative, budgeted metrics that penalize formula bloat. Across tasks we observe sharp difficulty gradients and persistent hard structural families; moreover, held-out world evaluation shows that among training-correct solutions, low-bloat formulas generalize far better than highly bloated ones, motivating bloat-aware scoring as a first-class metric for symbolic induction.

Social Aspects · Security

Ruixuan Huang, Qingyue Wang, Hantao Huang, Yudong Gao, Dong CHEN, Shuai Wang, Wei Wang

Mixture-of-Experts architectures have become the standard for efficient LLM scaling, typically employing expert parallelism to distribute experts across devices. However, the absence of explicit load balancing constraints during inference allows adversarial inputs to trigger severe routing concentration. We demonstrate that out-of-distribution prompts can manipulate the routing mechanism such that all tokens are routed to the same set of top-k experts, which creates computational bottlenecks on certain devices while forcing others to idle. This converts an efficiency mechanism into a denial-of-service attack vector, leading to violations of service-level agreements for time-to-first-token (TTFT). We propose RepetitionCurse, a black-box strategy to exploit this vulnerability. By identifying a universal flaw in MoE router behavior, RepetitionCurse constructs attack prompts using simple repetitive token patterns in a model-agnostic manner. On widely deployed MoE models hosted on 8-GPU clusters, our method increases TTFT by 20% to 148%, significantly degrading service quality.

Deep Learning · Other Representation Learning

Xuan Son Nguyen, Nistor Grozavu

Riemannian neural networks have proven effective in solving a variety of machine learning tasks. The key to their success lies in the development of principled Riemannian analogs of fundamental building blocks in deep neural networks (DNNs). Among those, Riemannian batch normalization (BN) layers have shown to enhance training stability and improve accuracy. In this paper, we propose BN layers for neural networks on complex domains. The proposed layers have close connections with existing Riemannian BN layers. We derive essential components for practical implementations of BN layers on some complex domains which are less studied in previous works, e.g., the Siegel disk domain. We conduct experiments on radar clutter classification, node classification, and action recognition demonstrating the efficacy of our method.

Theory · Reinforcement Learning and Planning

Khang Luong, Nam Nguyen, Hoang Ta, Hung Tran-The, Tuan Dam

We propose ***Var**iance **D**riven **E**xploration* (VarDE), a principled approach for pure exploration in *highly stochastic environments*, where the exploration process is dominated by stochastic variance. VarDE is built on a fundamental principle: *sampling effort should be allocated to minimize the uncertainty of the final decision*. We formalize the uncertainty of the final decision through a smooth decision function and derive allocation rules that explicitly capture how stochastic noise in individual components affects the reliability of the final output. We apply this methodology to three core problems of pure exploration -- Best Arm Identification (BAI), Monte Carlo Tree Search (MCTS), and Best-Policy Identification (BPI) -- with theoretical guarantees on variance decay and simple regret. Empirically, we demonstrate consistent and significant improvements of VarDE over existing methods, with especially strong gains in highly stochastic environments.

Applications · Everything Else

Xindan Zhang, Weilong Yan, YUFEI SHI, Xuerui Qiu, Tao He, Ying Li, Ming Li, Hehe Fan

Point clouds provide a compact and expressive representation of 3D objects, and have recently been integrated into multimodal large language models (MLLMs). However, existing methods primarily focus on static objects, while understanding dynamic point cloud sequences remains largely unexplored. This limitation is mainly caused by the lack of large-scale cross-modal datasets and the difficulty of modeling motions in spatio-temporal contexts. To bridge this gap, we present 4DPC$^2$hat, the *first* MLLM tailored for dynamic point cloud understanding. To this end, we construct a large-scale cross-modal dataset 4DPC$^2$hat-200K via a meticulous two-stage pipeline consisting of topology-consistent 4D point construction and two-level captioning. The dataset contains over 44K dynamic object sequences, 700K point cloud frames, and 200K curated question–answer (QA) pairs, supporting inquiries about counting, temporal relationship, action, spatial relationship, and appearance. At the core of the framework, we introduce a Mamba-enhanced temporal reasoning MLLM to capture long-range dependencies and dynamic patterns among a point cloud sequence. Furthermore, we propose a *failure-aware* bootstrapping learning strategy that iteratively identifies model deficiencies and generates targeted QA supervision to continuously strengthen corresponding reasoning capabilities. Extensive experiments demonstrate that our 4DPC$^2$hat significantly improves action understanding and temporal reasoning compared with existing models, establishing a strong foundation for 4D dynamic point cloud understanding.

Deep Learning · Graph Neural Networks

Danny Wang, Ruihong Qiu, Zi Huang

Graph neural networks are widely used for node classification, but they remain vulnerable to out-of-distribution (OOD) shifts in node features and graph structure. Prior work established that methods trained with standard supervised learning (SL) objectives tend to capture spurious signals from either features and/or structure, leaving the model fragile under distributional changes. To address this, we propose \textsc{Tide}, a novel and effective \underline{T}ri-Component \underline{I}nformation \underline{De}composition framework that explicitly decomposes information into \textit{feature-specific, structure-specific and joint} components. \textsc{Tide} aims to preserve only the label-relevant part of the joint information while filtering out spurious feature- and structure-specific information, thereby enhancing the separation between in-distribution (ID) and OOD nodes. Beyond the framework, we provide theoretical and empirical analyses showing that an information bottleneck objective is preferable to standard SL for graph OOD detection, with higher ID confidence and a greater entropy gap between ID and OOD data. Extensive experiments across seven datasets confirm the efficacy of \textsc{Tide}, achieving up to a 34% improvement in FPR95 over strong baselines while maintaining competitive ID accuracy. Code will be released upon acceptance.

Tuan Dam

Planning with a generative model aims to estimate state values using minimal oracle calls. For entropy-regularized MDPs, SmoothCruiser exploits the smoothness of the $\operatorname{LogSumExp}$ Bellman operator to achieve $\widetilde{\mathcal{O}}(\varepsilon^{-4})$ sample complexity, but its first-order Taylor approximation limits the rate. We develop a curvature--complexity theory showing that if a Bellman aggregator has Taylor remainder of order $\beta \ge 2$, the optimal oracle complexity exponent is $2 + 2/(\beta-1)$---recovering $\widetilde{\mathcal{O}}(\varepsilon^{-4})$ for $\beta=2$ and predicting $\widetilde{\mathcal{O}}(\varepsilon^{-3})$ for $\beta=3$. To achieve $\beta=3$, we introduce an entropic optimal-transport regularizer over action distributions. The resulting OT-smoothed Bellman operator admits a closed-form expression, explicit gradient policy, and Lipschitz Hessian. We derive an unbiased estimator of the quadratic Taylor term via cross-product debiasing, enabling a second-order SmoothCruiser with $\widetilde{\mathcal{O}}(\varepsilon^{-3})$ complexity. We further propose gap-dependent variants and provide a complexity analysis and show advantage of our method.

Theory · Deep Learning

Junyu Ren, Lek-Heng Lim

We study layered models, including feedforward networks and transformers, by limiting each layer to a width of $d = 3$ neurons, i.e., a representation space of $\mathbb{R}^3$. This allows us to examine how a neural network changes low-dimensional topological invariants like links and knots, as well as more sophisticated measures like Milnor's $\mu$-invariant, through the layers. Note that one may simplify or even trivialize just about any topological structure by simply increasing dimension; for example, any knot is equivalent to an unknot in $\mathbb{R}^4$. By restricting to $\mathbb{R}^3$, we not only isolate the effects of activation and depth from that of width, we work in a space that lends itself to easy visualization. We provide full mathematical proofs and empirical experiments to justify the following insights: When measured by their power to effect topological changes, ResNets are as powerful as transformers; both are strictly more powerful than feedforward neural networks, which are in turn more powerful than invertible models like flow-based models; but using a non-monotone activation would allow the feedforward networks to become as powerful as ResNets and transformers. These results suggest that low-dimensional topology can be an important tool to guide future designs of AI architectures. We then generalize our results from $d = 3$ to arbitrary $d > 3$.

Social Aspects · Robustness

Tong Liu, Sen Liang, Shuo Bai

Domain generalization (DG) aims to learn representations that remain predictive under distribution shifts. A key challenge is that the target domain is unobserved during training, which complicates the search for invariant representations: alignment objectives that do not account for the preservation of discriminative structure may become ill-conditioned or lead to degenerate solutions, especially under finite samples. We propose Geometric **R**ate–**D**istortion **I**nvariance (**RDI**), a DG framework that addresses this challenge by generalizing classical rate–distortion theory to Grassmann manifolds. **RDI** explicitly models class-conditional representations as low-dimensional subspaces and formulates DG as a joint optimization of (i) cross-domain subspace alignment (geometric distortion) and (ii) spectral–volumetric complexity (a capacity-regularized rate term). This integrated approach is designed to promote stable alignment while preventing the collapse of discriminative geometry, adapting to dataset-specific regimes. We provide finite-sample stability guarantees under bounded shifts. Experiments on DomainBed demonstrate that **RDI** is competitive with strong DG baselines, and ablations verify that reliable generalization necessitates the concerted action of both alignment and complexity control.

Shuo Tang, Jiadong Zhang, Jian Xu, Gengxian Zhou, Qizhao Jin, Qinxuan Wang, Yi Hu, Ning Hu, Hongchang Ren, Lingli He 等

While deep learning-based weather forecasting paradigms have made significant strides, addressing extreme weather diagnostics remains a formidable challenge. This gap exists primarily because the diagnostic process demands sophisticated multi-step logical reasoning, dynamic tool invocation, and expert-level prior judgment. Although agents possess inherent advantages in task decomposition and autonomous execution, current architectures are still hampered by critical bottlenecks: inadequate expert knowledge integration, a lack of professional-grade iterative reasoning loops, and the absence of fine-grained validation and evaluation systems for complex workflows under extreme conditions. To this end, we propose HVR-Met，a multi-agent meteorological diagnostic system characterized by the deep integration of expert knowledge. Its central innovation is the ``Hypothesis-Verification-Replanning'' closed-loop mechanism, which facilitates sophisticated iterative reasoning for anomalous meteorological signals during extreme weather events. To bridge gaps within existing evaluation frameworks, we further introduce a novel benchmark focused on atomic-level subtasks. Experimental evidence demonstrates that the system excels in complex diagnostic scenarios.

Deep Learning · Self-Supervised Learning

Kawtar Zaher, Ilyass Moummad, Olivier Buisson, Alexis Joly

Most self-supervised learning (SSL) methods learn continuous visual representations by aligning different views of the same input, offering limited control over how information is structured across representation dimensions. In this work, we frame visual self-supervised learning as a discrete communication process between a teacher and a student network, where semantic information is transmitted through a fixed-capacity binary channel. Rather than aligning continuous features, the student predicts multi-label binary messages produced by the teacher. Discrete agreement is enforced through an element-wise binary cross-entropy objective, while a coding-rate regularization term encourages effective utilization of the constrained channel, promoting structured representations. We further show that periodically reinitializing the projection head strengthens this effect by encouraging embeddings that remain predictive across multiple discrete encodings. Extensive experiments demonstrate consistent improvements over continuous agreement baselines on image classification, retrieval, and dense visual prediction tasks, as well as under domain shift through self-supervised adaptation. Beyond backbone representations, we analyze the learned binary codes and show that they form a compact and informative discrete language, capturing semantic factors reusable across classes.

Social Aspects · Security

Jianming Chen, Yawen Wang, Junjie Wang, Zhe Liu, Qing Wang, Xu

Tool-calling text-to-image (T2I) agents can plan and execute multi-step tool chains to accomplish complex generation and editing queries. However, this capability introduces a new safety attack surface: harmful outputs may arise from tool orchestration, where individually benign steps combine into unsafe results, making prompt-only jailbreak techniques insufficient. We present OrchJail, an orchestration-guided fuzzing framework for jailbreaking tool-calling T2I agents. Its core idea is to exploit high‑risk tool‑orchestration patterns: by learning from successful jailbreak tool-calling traces and their causal relationships to prompt wording, OrchJail directly guides the fuzzing search toward prompts that are more likely to trigger unsafe multi‑step tool behaviors, rather than relying on surface‑level textual perturbations. Extensive experiments demonstrate that OrchJail improves jailbreak effectiveness and efficiency across representative tool-calling T2I agents, achieving higher attack success rates, better image fidelity, and lower query costs, while remaining robust against common jailbreak defenses. Our work highlights tool orchestration as a critical, previously unexplored attack surface and provides a novel framework for uncovering safety risks in T2I agents.

Applications · Chemistry, Physics, and Earth Sciences

Li Sun, Hongbo Lv, Zhikai Jiang, Zhongtian Sun, Lanxu Yang, Philip Yu

Partial Differential Equations (PDEs) play a fundamental role in scientific computing, and recent efforts have sought to extend the success of foundation models to PDE solving. However, multi-physics PDE pre-training faces the unique challenge of disentangling dynamic heterogeneity to learn universal, elementary patterns that generalize to new PDEs. Additionally, cross-physics transfer lacks a theoretical framework for interpretability—specifically, understanding which pre-trained operator knowledge is effectively transferred to target PDEs. To bridge these gaps, we introduce the theory of neural operator splitting, which decomposes PDE evolution into a modulated global spectral operator and sparse local constitutive mechanisms. A key innovation is Origo, which provides a neural operator bank that enables the identification of operator-level generalization patterns. Extensive experiments demonstrate strong zero-shot generalization and mechanism-level interpretability on unseen PDEs.

Applications · Time Series

Binqing Wu, Jian Zhou, Zongjiang Shang, Ling Chen

Spatial–temporal time series forecasting is challenging due to complex lead–lag dependencies, which are often ignored or inadequately modeled by existing methods. Thus, we propose LagLLM, the first LLM-empowered framework that explicitly models lead–lag dependencies by unifying data-driven dynamics modeling and knowledge-driven semantic reasoning. Specifically, LagLLM constructs a lead–lag graph by integrating learnable embeddings, spatial proximity, and prompt-guided reasoning from a frozen LLM, which can capture lead-lag dependencies informed by underlying data structure and semantic knowledge. In addition, LagLLM introduces structural token sorting based on the graph, which can make a fine-turned LLM explicitly perceive directional and delayed interactions. Experiments on eight real-world datasets show that LagLLM achieves the state-of-the-art performance with improved accuracy, robustness, and interpretability. The code is available at https://anonymous.4open.science/r/LagLLM.

Applications · Social Sciences

Caroline L Wang, Daniel Kasenberg, Kimberly Stachenfeld, Pablo Samuel Castro

As Large Language Models (LLMs) are increasingly deployed in social and strategic scenarios, it becomes critical to understand where and why their behavior diverges from that of humans. While behavioral game theory (BGT) provides a framework for analyzing behavior, existing models do not fully capture the idiosyncratic behavior of humans or black-box, non-human agents like LLMs. We employ AlphaEvolve, a cutting-edge program discovery tool, to directly discover *interpretable* models of human and LLM behavior from data, thereby enabling open-ended discovery of structural factors driving human and LLM behavior. Our analysis on iterated rock-paper-scissors reveals that frontier LLMs can be capable of deeper strategic behavior than humans. These results provide a foundation for understanding structural differences driving differences in human and LLM behavior in strategic interactions.

Deep Learning · Generative Models and Autoencoders

Hongxu CHEN, Hongxiang Li, Zhen Wang, Long Chen

Flow Matching (FM) models have emerged as a leading paradigm for high-fidelity synthesis. However, their reliance on iterative Ordinary Differential Equation (ODE) solving creates a significant latency bottleneck. Existing solutions face a dichotomy: training-free solvers suffer from significant performance degradation at low Neural Function Evaluations (NFEs), while training-based methods incur prohibitive training costs and lack plug-and-play versatility. To bridge this gap, we propose the Bi-Anchor Interpolation Solver (BA-solver). BA-solver retains the versatility of standard training-free solvers while achieving significant acceleration by introducing a lightweight SideNet (1-2% backbone size) alongside the frozen backbone. Specifically, our method is founded on two synergistic components: 1) Bidirectional Temporal Perception, where the SideNet learns to approximate both future and historical velocities without retraining the heavy backbone; and 2) Bi-Anchor Velocity Integration, which utilizes the SideNet with two anchor velocities to efficiently approximate intermediate velocities for batched high-order integration. By utilizing the backbone to establish high-precision “anchors” and the SideNet to densify the trajectory, BA-solver enables large step sizes with minimized error. Empirical results on ImageNet-256 demonstrate that BA-solver achieves generation quality comparable to 100+ NFEs Euler solver in just 10 NFEs and maintains high fidelity in as few as 5 NFEs, incurring negligible training costs. Furthermore, BA-solver ensures seamless integration with existing generative pipelines, facilitating downstream tasks such as image editing.

Deep Learning · Large Language Models

Xin Ma, Wei Chen, Qi Liu, Derong Xu, Zhi Zheng, Tong Xu, Enhong Chen

Lifelong Model Editing aims to continuously update evolving facts in Large Language Models while preserving unrelated knowledge and general capabilities, yet it remains plagued by catastrophic forgetting and model collapse. Empirically, we find that the few recent editors resilient over long horizons share the same core strategy: **Lifelong Normalization (LN)**, which normalizes value gradients using running statistics. Removing LN causes immediate collapse, and we observe a counter-intuitive **positive cumulative effect** where early edits can facilitate later edits. This suggests that with LN, early edits can stabilize the model and promote the success of future edits. Yet the mechanism of LN remains a "black box", leaving its precise role in lifelong stability poorly understood. In this work, we provide the **first** theoretical account of LN in the lifelong regime. Our analysis reveals a self-reinforcing stability loop and proves that, when combined with ridge-regularized regression, LN yields updates with **asymptotic orthogonality** and **bounded norms**, directly mitigating forgetting and systemic collapse. Based on these insights, we derive **StableEdit**, which strengthens this stability loop via an explicit warm-up stage and full whitening, improving long-horizon stability at minimal overhead. Extensive experiments validate our theory and demonstrate competitive performance.

Theory · Everything Else

Suvrat Raju, Praneeth Kumar Netrapalli

We study the error rate of LLMs on tasks like arithmetic that require a deterministic output, and repetitive processing of tokens drawn from a small set of alternatives. We argue that incorrect predictions arise when small errors in the attention mechanism accumulate to cross a threshold, and use this insight to derive a quantitative two-parameter relationship between the accuracy and the complexity of the task. The two parameters vary with the prompt and the model; they can be interpreted in terms of an elementary noise rate, and the number of plausible erroneous tokens that can be predicted. Our analysis is inspired by an "effective field theory'' perspective: the LLM's many raw parameters can be reorganized into just two parameters that govern the error rate. We perform extensive empirical tests, using Gemini 2.5 Flash, Gemini 2.5 Pro and DeepSeek R1, and find excellent agreement between the predicted and observed accuracy for a variety of tasks, although we also identify deviations in some cases. Our model provides an alternative to suggestions that errors made by LLMs on long repetitive tasks indicate the "collapse of reasoning'', or an inability to express "compositional'' functions. Finally, we show how to construct prompts to reduce the error rate.