论文检索

Deep Learning · Generative Models and Autoencoders

Luhan Tang, Longxuan Yu, Shaorong Zhang, Greg Ver Steeg

Discrete diffusion language models (dLLMs) offer a fast and flexible alternative to autoregressive models (ARMs) for discrete sequence generation by performing iterative denoising with parallel updates. Despite these advantages, dLLMs are commonly evaluated using metrics developed for ARMs. Such evaluations rely on metrics computed from final generated samples and conflate model approximation error in the learned denoiser and sampler-induced error from the sampling dynamics. We introduce a sampler-centric oracle evaluation framework that replaces learned denoisers with an oracle Hidden Markov Model posterior derived from a ground-truth Markov chain, enabling isolation of sampler-induced error under controlled and method-consistent settings. We show that few-step discrete diffusion samplers are not distributionally correct, even under an exact oracle denoiser, with substantial distributional mismatch at the transition level persisting at small step counts and vanishing only when the number of diffusion steps approaches the sequence length. We also find that current metrics for evaluating dLLMs are insufficient: improvements in negative log-likelihood, generative perplexity, or MAUVE do not imply correct sampling.

Deep Learning · Robustness

Hesam Asadollahzadeh, Feng Liu, Christopher Leckie, Sarah Erfani

Finetuning pretrained multimodal models improves in-distribution performance but often degrades out-of-distribution (OOD) robustness, a phenomenon known as catastrophic forgetting. We develop a theoretical framework for multimodal contrastive finetuning by introducing a *contrastive target matrix* that reformulates the objective as a matrix least-squares problem, yielding closed-form solutions and a geometric decomposition of how different strategies manage pretrained knowledge. Our analysis reveals a largely overlooked limitation: standard Exponential Moving Average (EMA) teachers, widely used in robust finetuning, suffer from late-stage collapse where the teacher--student gap vanishes precisely when OOD robustness is most vulnerable. We prove that a Weighted Moving Average (WMA) teacher, which integrates the full optimization trajectory, maintains a persistent regularizing force over finite horizons, enabling bias-free convergence in the task subspace while preserving orthogonal knowledge. These insights motivate **TRACER** (**T**rajectory-**R**obust **A**nchoring for **C**ontrastive **E**ncoder **R**egularization), which combines contrastive learning with WMA-guided multi-perspective distillation. Extensive experiments on CLIP finetuning demonstrate consistent OOD accuracy and calibration gains across three backbone architectures. Comprehensive ablations across four axes (distillation components, regularization strength, update frequency, and kernel shape) confirm that TRACER is both principled and robust to hyperparameter choices.

Social Aspects · Robustness

Pengzhen Chen, Yanwei Liu, Xiaoyan Gu, Antonios Argyriou, Wu Liu, Weiping Wang

Reliable watermarking of panoramic imagery is fundamentally challenged by arbitrary 3D rotations. As panoramas are defined on the sphere, they naturally transform under the action of $SO(3)$, rendering conventional planar representations and augmentation-based robustness strategies inadequate and devoid of theoretical guarantees. To address this, we formulate panoramas as spherical signals and leverage $SO(3)$ representation theory to derive provably rotation-invariant descriptors. While spherical harmonic coefficients transform equivariantly under rotations, the natural invariant constructions are typically limited to zeroth-order statistics which eliminate directional information and severely constrain embedding capacity. In this work, we introduce a principled third-order invariant construction by coupling higher-order $SO(3)$ irreducible representations via tensor products and projecting onto the trivial representation. This yields a spherical invariant bispectrum that preserves phase information while remaining strictly rotation-invariant. Leveraging this property, we embed watermarks into higher-order spherical harmonic coefficients and recover them from invariant bispectral scalars, enabling reliable extraction under arbitrary 3D rotations. We provide a theoretical proof of $SO(3)$ invariance for it and demonstrate experimentally its near-perfect robustness to continuous rotations while maintaining high visual fidelity.

Optimization · Large Scale, Parallel and Distributed

Feihu Huang, Yuning Luo, Songcan Chen

Large models recently are widely applied in machine learning, so efficient training of large models has received widespread attention. More recently, a useful Muon optimizer is specifically designed for matrix-structured parameters of large models. Although some works have begun to studying the Muon optimizer, the existing Muon and its variants still suffer from high sample complexity or high memory for large models. To fill this gap, we propose a light and fast Muon (LiMuon) optimizer for training large models, which builds on the momentum-based variance reduced technique and randomized Singular Value Decomposition (SVD). In particular, our LiMuon optimizer simultaneously has a lower memory and lower sample complexity than the Muon. Moreover, we prove that our LiMuon has a lower sample complexity of $O(\epsilon^{-3})$ for finding an $\epsilon$-stationary solution of non-convex stochastic optimization under the generalized smooth condition. Numerical experimental results on training Mamba-130M, Qwen2.5-0.5B and ViT models demonstrate effectiveness of our LiMuon optimizer.

General Machine Learning · Online Learning, Active Learning and Bandits

Liangxiao Jiang, Liangxiao Jiang, Chaoqun Li, Shanshan Si

In crowdsourcing scenarios, to mitigate the impact of noisy labels assigned by non-expert workers, each instance is typically annotated multiple times by different workers. However, repeated annotation can introduce instance- or label-level redundancy, thereby inflating annotation costs. Despite its practical importance, research on repeated annotation strategies remains limited, and no existing strategy simultaneously avoids being offline, instance-unaware, and model-centric. In this paper, we propose a model-agnostic active annotation strategy, MA$^3$S, that addresses these limitations: (1) To reduce label redundancy caused by offline procedure, MA$^3$S estimates instance uncertainties with a general Beta distribution and updates them online as new labels arrive. (2) To prevent instance redundancy induced by instance-unaware designs, MA$^3$S constructs a nearest-neighbor graph to propagate instance uncertainties, reducing repeated annotations of similar instances. (3) To avoid being model-centric, MA$^3$S actively selects instances for annotation based solely on the estimated uncertainties, without relying on model feedback. Extensive experiments on synthetic and real-world datasets demonstrate that MA$^3$S consistently outperforms existing annotation strategies.

John Yang, Kilian Lieret, Joyce Yang, Carlos Jimenez, Muhtasham Oblokulov, Aryan Siddiqui, Ofir Press, Ludwig Schmidt, Diyi Yang

Existing coding benchmarks evaluate language models (LMs) on concrete, well-specified tasks such as fixing bugs or writing tests. However, human programmers do not spend all day addressing isolated GitHub issues. Instead, real-world software development is grounded in the pursuit of high-level goals. Evaluating whether LMs can iteratively develop code to accomplish open-ended objectives without explicit guidance remains an open challenge. We introduce CodeClash, a benchmark where LMs compete in multi-round tournaments to build the best codebase for achieving a competitive objective. Each round proceeds in two parts: agents edit their code, then their codebases compete head-to-head in a code arena that determines winners based on objectives like score maximization, resource acquisition, or survival. Models must decide for themselves how to improve their code both absolutely and against their opponents. We run 1680 tournaments to evaluate 8 LMs across 6 arenas, revealing how models exhibit diverse development styles and share fundamental limitations in strategic reasoning. Models also struggle with long-term codebase maintenance; repositories become progressively messy and redundant. Top models lose every round against expert human programmers. We open-source CodeClash to advance the study of autonomous, goal-oriented code development.

General Machine Learning · Unsupervised and Semi-supervised Learning

Hongyang He, Yan Zhong, Xinyuan Song, Daizong Liu, Xuanyu Liu, Victor Sanchez

Most semi-supervised learning frameworks rely on a single teacher that transfers zero-order supervision through pseudo-labels, constraining the student to imitate categorical outputs without perceiving the loss geometry. This design often leads to unstable optimization and limited generalization under scarce labels. We propose TTN (Two-Teachers Newton-guided Learning), a dual-teacher framework that integrates complementary supervision from MAE and DINOv3 and optimizes the student through a Newton step update. The two teachers provide multi-scale structural and semantic cues whose pseudo-labels and local Hessians are fused by confidence weighting, forming a unified second-order supervision signal. The student updates parameters preconditioned by the fused curvature, enabling stable convergence and geometry-consistent learning. TTN consistently improves over existing single-teacher and consistency-based semi-supervised learning methods on ImageNet, CIFAR-10, SVHN, and STL-10, demonstrating that combining multi-view self-supervised teachers with curvature-guided optimization yields robust and efficient semi-supervised learning.

Hoang Anh Just, Myeongseob Ko, Ruoxi Jia

Distilling long-form reasoning from teacher models into smaller students requires selecting which candidate solutions to train on. Recent work argues that one should select responses the student model assigns highest probability, i.e., favoring solutions ``natural'' to the student. However, we find that this approach works within a single teacher but fails when scaling to long reasoning traces from multiple diverse teachers. We identify a key cause: this approach scores entire solutions, but students generalize by recombining familiar reasoning steps, not by memorizing complete solutions. Full-trajectory scoring optimizes the wrong target; it rewards global fluency while the transferable signal lies in local step transitions. We propose Local Average Log Probability (LALP), which scores each reasoning step using only a small window of preceding context, measuring whether each step is justified by its immediate premises rather than whether the full response looks natural to the student. LALP enables two practical use cases: selecting the best teacher before fine-tuning and curating training data from diverse teacher pools. Across math, coding, and science reasoning tasks, LALP consistently improves accuracy when selecting the most natural solutions by a large margin.

General Machine Learning · Data

Hoang Anh Just, Myeongseob Ko, Ruoxi Jia

Distilling long-form reasoning from teacher models into smaller students requires selecting which candidate solutions to train on. Recent work argues that one should select responses the student model assigns highest probability, i.e., favoring solutions ``natural'' to the student. However, we find that this approach works within a single teacher but fails when scaling to long reasoning traces from multiple diverse teachers. We identify a key cause: this approach scores entire solutions, but students generalize by recombining familiar reasoning steps, not by memorizing complete solutions. Full-trajectory scoring optimizes the wrong target; it rewards global fluency while the transferable signal lies in local step transitions. We propose Local Average Log Probability (LALP), which scores each reasoning step using only a small window of preceding context, measuring whether each step is justified by its immediate premises rather than whether the full response looks natural to the student. LALP enables two practical use cases: selecting the best teacher before fine-tuning and curating training data from diverse teacher pools. Across math, coding, and science reasoning tasks, LALP consistently improves accuracy when selecting the most natural solutions by a large margin.

Deep Learning · Large Language Models

Janghyeon Kim, Minsoo Kim, Kyuhong Shim, Jungwook Choi

Large Reasoning Models (LRMs) achieve superior problem-solving through extended chain-of-thought generation, but the resulting key-value (KV) cache grows linearly with sequence length and creates severe memory bottlenecks—often exceeding GPU capacity for long reasoning traces. Existing KV cache compression methods rely on recent queries to estimate future token importance, implicitly assuming these serve as reliable proxies for future attention patterns. We demonstrate that this assumption fails in long-horizon reasoning: certain decoding steps generate Thought Revisiting Tokens (TRT) that re-attend to distant previous context, such as task-solving plans formulated early in the trace. Through systematic analysis, we discover that queries corresponding to the TRT cluster into a small number of similarity groups in the embedding space. Based on this insight, we propose BeaconKV, a training-free KV cache compression method that maintains beacon queries—compact representatives for each global query cluster—to anticipate which KV pairs will be revisited without storing the entire query history. We introduce Continual Farthest Point Sampling for memory-efficient beacon identification during inference. Across four open-source LRMs and diverse reasoning benchmarks, BeaconKV consistently outperforms state-of-the-art methods, achieving up to $5.8\times$ memory reduction while nearly preserving full cache accuracy and improving throughput by over $4.3\times$.

Reinforcement Learning · Online

Ryo Iwaki

Regularization is a core component of recent Reinforcement Learning (RL) algorithms. Mirror Descent Value Iteration (MDVI) uses both Kullback-Leibler divergence and entropy as regularizers in its value and policy updates. Despite its empirical success in discrete action domains and strong theoretical guarantees, the performance of KL-entropy-regularized methods does not surpass that of a strong entropy-only-regularized method in continuous action domains. In this study, we propose Mirror Descent Actor Critic (MDAC) as an actor-critic style instantiation of MDVI for continuous action domains, and show that its empirical performance is significantly boosted by bounding the actor's log-probability terms in the critic's loss function, compared to a non-bounded naive instantiation. Further, we relate MDAC to Advantage Learning by recalling that the actor's log-probability is equal to the regularized advantage function in tabular cases, and theoretically discuss when and why bounding the advantage terms is validated and beneficial. We also empirically explore effective choices for the bounding functions, and show that MDAC performs better than strong non-regularized and entropy-only-regularized methods with an appropriate choice of the bounding functions.

Social Aspects · Security

Qiushi Wu, Yue Xiao, Dhilung Kirat, Kevin Eykholt, Jiyong Jang, Douglas Schales

Recurring Pattern Bugs (RPBs) are defined as bugs where a single root cause appears repeatedly across multiple code segments. These bugs remain a persistent security threat even after individual instances are patched. Various static analyzers exist for finding specific bug patterns but require significant engineering effort and fail to generalize well beyond their predefined template, preventing them from detecting RPBs. To tackle RPBs, we introduce BugStone, a hybrid framework combining LLVM-based program analysis with Large Language Models to automate RPB detection. BugStone leverages a single patched instance to synthesize abstract error patterns and retrieves semantically similar bugs throughout the codebase. To evaluate BugStone, we create a ground truth dataset by analyzing over 1.9K security bugs reports, on which BugStone achieves 92.2% precision and 79.1% pairwise accuracy. We further validated BugStone through a large-scale real-world deployment. In the Linux kernel, BugStone identified over 22K potential issues; a manual audit of 400 samples confirmed 246 valid bugs, including invalid pointer dereferences, resource leaks, type errors, performance issues, and others. To evaluate the generalizability of BugStone, we further applied it to the top 100 Python projects, discovering multiple critical command injection vulnerabilities.

Applications · Robotics

Fengshuo Bai, Yufeng Li, Ruihai Wu, Peishuo Wang, Yuhan Wang, Bernie Zhu, Yuanfei Wang, Tawei Chou, Gao, Runchuan Zhu 等

Laboratory automation driven by scientific embodied agents represents a critical frontier in modern laboratories. Unlike conventional robotic domains, laboratory environments impose zero-tolerance constraints on manipulation precision and collision, as minor deviations can lead to irreversible chemical hazards or equipment damage. This naturally makes the automated laboratory an ideal testbed for advancing embodied safety. However, existing benchmarks predominantly feature high-tolerance manipulation tasks where intermediate failures are largely reversible. More critically, current Vision-Language-Action (VLA) models trained via static imitation learning cannot satisfy these strict constraints. Because they merely mimic successful demonstrations, they lack the ability to recover from execution drift, leading to catastrophic compounding errors in precision-critical domains. Overcoming this limitation requires transitioning from static datasets to interactive environments that support Reinforcement Learning (RL) for dynamic error recovery. To this end, we introduce SafeLab, a generative simulation benchmark designed for the full lifecycle of safe robot learning. Grounded in a high-fidelity chemistry lab, our framework integrates an {LLM engine} for procedural task synthesis, an {automated expert} for scalable demonstration collection, and an {interactive environment} for continuous RL refinement. Leveraging this infrastructure, we release a dataset of 6,000+ {complex trajectories} to evaluate state-of-the-art VLA models. Experiments reveal that current embodied agents fail significantly under these safety constraints. In contrast, our RL post-training pipeline enables agents to learn active error correction, mitigating hazardous failures and improving success rates by 37\%, thereby establishing SafeLab as a critical platform for developing reliable and safe generalist agents.

Hayden Helm, Ben Johnson, Carey Priebe

Evaluating a new model on an existing benchmark is often necessary to understand its behavior before deployment. For modern evaluation frameworks, generating and evaluating a response for all queries can be prohibitively expensive. In practice, responses from previously-evaluated models are often cached -- creating a potential opportunity to use this additional information to decrease the number of queries required to accurately evaluate a new model. In this paper, we introduce an approach for predicting benchmark performance that leverages cached model responses based on the Data Kernel Perspective Space (DKPS), a method for quantifying the relationship between models in the black-box setting. Theoretically, we show that DKPS-based methods are query-efficient under certain conditions. Empirically, we demonstrate that DKPS-based methods achieve the same mean absolute error as baselines with a substantially decreased query budget. We conclude by proposing an offline method for selecting a set of queries that maximizes the goodness-of-fit on reference models, improving prediction accuracy over random query selection.

Optimization · Discrete and Combinatorial Optimization

Peng Chen, Hailiang Zhao, Xueyan Tang, Yixuan Wang, Shuiguang Deng

Learning-augmented paging has been extensively studied in recent years. A key advantage over naive ML-based approaches is \emph{bounded robustness}, which guarantees worst-case performance even when predictions are inaccurate, making these algorithms valuable for real-world systems. Prior work achieves robustness bounds of $2H_k + O(1)$ in the randomized setting, leaving a gap to the optimal competitive ratio $H_k$. We are the first to study how to close this gap. In this paper, we begin by analyzing online optimality and provide a new proof of the latest $H_k$-competitive algorithm, which facilitates analysis in the learning-augmented setting. Then, we review existing learning-augmented paging algorithms and introduce a unifying primitive, the \emph{relative prediction budget}, which captures the essence of how to establish robustness and reveals that prior algorithms either overuse or underutilize predictions. Guided by the above analysis, we develop a new framework that achieves the best-possible robustness for learning-augmented paging: $H_k + O(1)$. Experiments further demonstrate strong practical performance.

Deep Learning · Graph Neural Networks

Giannis Nikolentzos, Dimitrios Kelesis, Nikolaos Nakis

Over the past decade, Graph Neural Networks (GNNs) have become a standard tool for solving machine learning problems on graphs. While many aspects of GNNs have been studied in depth, including their efficiency and expressive power, the invertibility of these models has remained largely unexplored. Standard aggregation functions, such as the mean, max and sum operators, are not invertible, which limits their applicability in tasks requiring invertible transformations. In this work, we introduce an invertible GNN layer. By stacking multiple such layers, we construct fully invertible GNN models, which we refer to as InvGNNs. These models inherit the benefits of invertible neural networks, including low memory usage for deep architectures, exact likelihood computation, and generative modeling capabilities. We demonstrate that InvGNNs can match the expressive power of the 1-dimensional Weisfeiler-Leman algorithm, showing that invertibility does not compromise model expressiveness. On standard graph classification benchmarks, our model performs comparably to other well-established GNNs, such as GIN. Beyond classification, we demonstrate the potential of invertible layers through density estimation tasks, including outlier detection and node feature generation. Our experiments confirm that InvGNNs effectively handle tasks that benefit from invertible layers.

Tong Tong, LING XING, Linjie Li, Rui Yan, Zhengyuan Yang, Lijuan Wang, Alex Jinpeng Wang

Autoregressive (AR) image generation has recently gained momentum as a scalable alternative to diffusion models, benefiting from unified next-token prediction paradigm and strong instruction following ability. However, AR visual generation must decode excessively long sequences of visual tokens, making inference heavily bottlenecked by the memory footprint and latency of the self-attention KV cache. While KV cache compression is well studied in Large Language Model, its counterparts in AR image generation remain underexplored. The reason is fundamental: visual tokens are highly redundant, and their spatial information density is highly non-uniform. In this work, we introduce SparseAR, a training-free, entropy-aware sparse attention method that is specifically designed for AR image generation and editing. Our key insight is that information-rich regions exhibit higher entropy and require broader attention, while redundant regions show lower entropy and allow aggressive sparsification. Based on this insight, we dynamically identify information-rich regions during decoding and adaptively adjust attention sparsity to reduce KV-cache overhead. SparseAR is plug-and-play and can be readily applied to mainstream AR models. Extensive experiments on four representative AR models across multiple benchmarks demonstrate that SparseAR significantly improves inference efficiency while maintaining, and often even improving, generation and editing quality.

General Machine Learning · Unsupervised and Semi-supervised Learning

nan cao, Xu Zhao, Teng Zhang

Multi-instance partial-label learning (MIPL) is a recently proposed learning paradigm to address tasks that multi-instance bags are associated with a candidate label set comprising one ground-truth label and several false positive labels. Existing MIPL methods rely on simple instance level information, and can hardly find the key instances under noisy labels. In this paper, we propose a novel algorithm termed AGOPMIPL, i.e., Average Gradient Outer Product based Multi-Instance Partial-Label Learning to address the problem. AGOP derives a data-dependent metric in the embedding space by computing the outer product of classifier gradients, which stretches discriminative feature dimensions and facilitates more accurate key instance identification. Moreover, AGOP aggregates gradient information across all training samples, providing inherent robustness to label noise. Additionally, we introduce a progressive label disambiguation strategy that gradually refine the learning targets. Experimental studies on benchmark and real-world datasets demonstrate the superiority of AGOPMIPL over existing MIPL methods.

Zixiong Yang, Linxiao Li, Jiaye Lin, Binrui Wu, Xiaoyu Kang, Jiechao Gao

Automating User Interface (UI) generation substantially improves productivity and accelerates development by reducing engineering time and manual effort. Despite recent progress of Large Language Models (LLMs) in UI-to-Code, most existing approaches focus on a single HTML/CSS form and fail to systematically incorporate front-end frameworks such as React, Vue, and Angular. Moreover, their outputs are often verbose and hard to reuse at the component level. To address those issues, we propose Deterministic Component Mining (DCM) method, a multi-stage pipeline that couples MLLM prompting with a compact intermediate representation to enable multi-framework and component-oriented generation. Firstly, a lightweight structure model predicts the representation of DOM tree in JSON format capturing the coarse layout from webpage screenshot. Subsequently, we formulate deterministic rules to normalize the predicted DOM tree and mine reusable components and repetitive patterns via hashing and clustering, thereby yielding a portable intermediate representation. Finally, we employ a framework-conditioned LLM prompt governed by a binding specification and a file-block protocol to emit HTML/React/Vue/Angular code with explicit component props and repeat constructs. Experiments demonstrate that DCM significantly outperforms baselines on automatic evaluation metrics and component-level reuse, while delivering consistent gains in multi-framework portability and code structural quality.

Pengyi Li, Hongyao Tang, Yifu Yuan, Yan Zheng, Xin Xu, Jianye Hao

Deploying robots in open‑ended real‑world environments demands continual learning capabilities to adapt to an ever-expanding range of tasks. This requires retaining previously acquired skills without forgetting while effectively leveraging prior knowledge to learn new ones. Inspired by neuroscience, we propose Neuro-evolutionary Continual Reinforcement Learning (Nevo-CRL). Nevo-CRL maintains a fixed-capacity monolithic policy network, solving tasks by optimizing inter-layer connectivity and neuron parameter. For each new task, Nevo-CRL constructs a mask population to selectively activate the outputs of each hidden layer, thereby forming a task-specific policy population. Upon completing each task, the best-performing mask is stored, and its activated neurons are frozen to prevent catastrophic forgetting. To facilitate knowledge transfer, Nevo-CRL reuses neurons from acquired skills based on semantic similarity between tasks, while dynamically allocating additional neurons for task-specific adaptation. In the learning process, Nevo-CRL iteratively adjusts masks via importance-based crossover to optimize the policy network connectivity. To improve neuron utilization, we prune low-activity connections to recycle neurons. The experiments demonstrate that Nevo-CRL significantly outperforms existing continual RL methods and multi-task learning methods in terms of overall performance, forgetting reduction, generalization ability.