OpenRaiser/Intern-Atlas huggingface dataset:
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
Venue: arXiv cs.LG 2025
Year: 2025

Collaborative fairness is a crucial challenge in federated learning. However, existing approaches often overlook a practical yet complex form of heterogeneity: imbalanced covariate shift. We provide a theoretical analysis of this setting, which motivates the design of FedAKD (Federated Asynchronous Knowledge Distillation)- simple yet effective approach that balances accurate prediction with collaborative fairness. FedAKD consists of client and server updates. In the client update, we introduce a novel asynchronous knowledge distillation strategy based on our preliminary analysis, which reveals that while correctly predicted samples exhibit similar feature distributions across clients, incorrectly predicted samples show significant variability. This suggests that imbalanced covariate shift primarily arises from misclassified samples. Leveraging this insight, our approach first applies traditional knowledge distillation to update client models while keeping the global model fixed. Next, we select correctly predicted high-confidence samples and update the global model using these samples while keeping client models fixed. The server update simply aggregates all client models. We further provide a theoretical proof of FedAKD's convergence. Experimental results on public datasets (FashionMNIST and CIFAR10) and a real-world Electronic Health Records (EHR) dataset demonstrate that FedAKD significantly improves collaborative fairness, enhances predictive accuracy, and fosters client participation even under highly heterogeneous data distributions.
Multi-critic Learning for Whole-body End-effector Twist Tracking
Venue: arXiv cs.RO 2025
Year: 2025

Learning whole-body control for locomotion and arm motions in a single policy has challenges, as the two tasks have conflicting goals. For instance, efficient locomotion typically favors a horizontal base orientation, while end-effector tracking may benefit from base tilting to extend reachability. Additionally, current Reinforcement Learning (RL) approaches using a pose-based task specification lack the ability to directly control the end-effector velocity, making smoothly executing trajectories very challenging. To address these limitations, we propose an RL-based framework that allows for dynamic, velocity-aware whole-body end-effector control. Our method introduces a multi-critic actor architecture that decouples the reward signals for locomotion and manipulation, simplifying reward tuning and allowing the policy to resolve task conflicts more effectively. Furthermore, we design a twist-based end-effector task formulation that can track both discrete poses and motion trajectories. We validate our approach through a set of simulation and hardware experiments using a quadruped robot equipped with a robotic arm. The resulting controller can simultaneously walk and move its end-effector and shows emergent whole-body behaviors, where the base assists the arm in extending the workspace, despite a lack of explicit formulations. Videos and supplementary material can be found at multi-critic-locomanipulation.github.io.
ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way
Venue: arXiv cs.CV 2025
Year: 2025

We introduce ByDeWay, a training-free framework designed to enhance the performance of Multimodal Large Language Models (MLLMs). ByDeWay uses a novel prompting strategy called Layered-Depth-Based Prompting (LDP), which improves spatial reasoning and grounding without modifying any model parameters. It segments the scene into closest, mid-range, and farthest layers using monocular depth estimation, then generates region-specific captions with a grounded vision-language model. These structured, depth-aware captions are appended to the image-question prompt, enriching it with spatial context. This guides MLLMs to produce more grounded and less hallucinated responses. Our method is lightweight, modular, and compatible with black-box MLLMs. Experiments on hallucination-sensitive (POPE) and reasoning-intensive (GQA) benchmarks show consistent improvements across multiple MLLMs, validating the effectiveness of depth-aware prompting in a zero-training setting.
To buy or not to buy: deterministic rent-or-buy problems on node-weighted graphs
Venue: arXiv cs.DS 2025
Year: 2025

We study the rent-or-buy variant of the online Steiner forest problem on node- and edge-weighted graphs. For $n$-node graphs with at most $\bar{n}$ non-zero node-weights, and at most $\tilde{k}$ different arriving terminal pairs, we obtain a deterministic, $O(\log n \log \bar{n})$-competitive algorithm. This improves on the previous best, $O(\log^4 n)$-competitive algorithm obtained by the black-box reduction from (Bartal et al. 2021) combined with the previously best deterministic algorithms for the simpler 'buy-only' setting. We also obtain a deterministic, $O(\bar{n}\log \tilde{k})$-competitive algorithm. This generalizes the $O(\log \tilde{k})$-competitive algorithm for the purely edge-weighted setting from (Umboh 2015). We also obtain a randomized, $O(\log \tilde{k} \log \bar{n})$-competitive algorithm. All previous approaches were based on the randomized, black-box reduction from~\cite{AwerbuchAzarBartal96} that achieves a $O(\log \tilde{k} \log n)$-competitive ratio when combined with an algorithm for the 'buy-only' setting. Our key technical ingredient is a novel charging scheme to an instance of \emph{online prize-collecting set cover}. This allows us to extend the witness-technique of (Umboh 2015) to the node-weighted setting and obtain refined guarantees with respect to $\bar{n}$, already in the much simpler 'buy-only' setting.
On the Effect of Regularization in Policy Mirror Descent
Venue: arXiv cs.LG 2025
Year: 2025

Policy Mirror Descent (PMD) has emerged as a unifying framework in reinforcement learning (RL) by linking policy gradient methods with a first-order optimization method known as mirror descent. At its core, PMD incorporates two key regularization components: (i) a distance term that enforces a trust region for stable policy updates and (ii) an MDP regularizer that augments the reward function to promote structure and robustness. While PMD has been extensively studied in theory, empirical investigations remain scarce. This work provides a large-scale empirical analysis of the interplay between these two regularization techniques, running over 500k training seeds on small RL environments. Our results demonstrate that, although the two regularizers can partially substitute each other, their precise combination is critical for achieving robust performance. These findings highlight the potential for advancing research on more robust algorithms in RL, particularly with respect to hyperparameter sensitivity.
Hashing for Fast Pattern Set Selection
Venue: arXiv cs.DB 2025
Year: 2025

Pattern set mining, which is the task of finding a good set of patterns instead of all patterns, is a fundamental problem in data mining. Many different definitions of what constitutes a good set have been proposed in recent years. In this paper, we consider the reconstruction error as a proxy measure for the goodness of the set, and concentrate on the adjacent problem of how to find a good set efficiently. We propose a method based on bottom-k hashing for efficiently selecting the set and extend the method for the common case where the patterns might only appear in approximate form in the data. Our approach has applications in tiling databases, Boolean matrix factorization, and redescription mining, among others. We show that our hashing-based approach is significantly faster than the standard greedy algorithm while obtaining almost equally good results in both synthetic and real-world data sets.
Partitioned Hybrid Quantum Fourier Neural Operators for Scientific Quantum Machine Learning
Venue: arXiv cs.LG 2025
Year: 2025

We introduce the Partitioned Hybrid Quantum Fourier Neural Operator (PHQFNO), a generalization of the Quantum Fourier Neural Operator (QFNO) for scientific machine learning. PHQFNO partitions the Fourier operator computation across classical and quantum resources, enabling tunable quantum-classical hybridization and distributed execution across quantum and classical devices. The method extends QFNOs to higher dimensions and incorporates a message-passing framework to distribute data across different partitions. Input data are encoded into quantum states using unary encoding, and quantum circuit parameters are optimized using a variational scheme. We implement PHQFNO using PennyLane with PyTorch integration and evaluate it on Burgers' equation, incompressible and compressible Navier-Stokes equations. We show that PHQFNO recovers classical FNO accuracy. On incompressible Navier-Stokes, PHQFNO achieves higher accuracy than its classical counterparts. Finally, we perform a sensitivity analysis under input noise, confirming improved stability of PHQFNO over classical baselines.
On Fair Epsilon Net and Geometric Hitting Set
Venue: arXiv cs.DS 2025
Year: 2025

Fairness has emerged as a formidable challenge in data-driven decisions. Many of the data problems, such as creating compact data summaries for approximate query processing, can be effectively tackled using concepts from computational geometry, such as $\varepsilon$-nets. However, these powerful tools have yet to be examined from the perspective of fairness. To fill this research gap, we add fairness to classical geometric approximation problems of $\varepsilon$-net, $\varepsilon$-sample, and geometric hitting set. We introduce and address two notions of group fairness: demographic parity, which requires preserving group proportions from the input distribution, and custom-ratios fairness, which demands satisfying arbitrary target ratios. We develop two algorithms to enforce fairness: one based on sampling and another on discrepancy theory. The sampling-based algorithm is faster and computes a fair $\varepsilon$-net of size which is only larger by a $\log(k)$ factor compared to the standard (unfair) $\varepsilon$-net, where $k$ is the number of demographic groups. The discrepancy-based algorithm is slightly slower (for bounded VC dimension), but it computes a smaller fair $\varepsilon$-net. Notably, we reduce the fair geometric hitting set problem to finding fair $\varepsilon$-nets. This results in a $O(\log \mathsf{OPT} \times \log k)$ approximation of a fair geometric hitting set. Additionally, we show that under certain input distributions, constructing fair $\varepsilon$-samples can be infeasible, highlighting limitations in fair sampling. Beyond the theoretical guarantees, our experimental results validate the practical effectiveness of the proposed algorithms. In particular, we achieve zero unfairness with only a modest increase in output size compared to the unfair setting.
Dependent Multiplicities in Dependent Linear Type Theory
Venue: arXiv cs.PL 2025
Year: 2025

We present a novel dependent linear type theory in which the multiplicity of some variable - i.e., the number of times the variable can be used in a program - can depend on other variables. This allows us to give precise resource annotations to many higher-order functions that cannot be adequately typed in any other system. Inspired by the Dialectica translation, our typing discipline is obtained by embedding linear logic into dependent type theory and specifying how the embedded logic interacts with the host theory. We can then use a standard natural numbers type to obtain a quantitative typing system with dependent multiplicities. We characterise the semantics for our theory as a combination of standard models of dependent type theory and linear logic. Our system can be added to any dependently typed language, which we demonstrate with an implementation in Agda.
A Hybrid Multi-Well Hopfield-CNN with Feature Extraction and K-Means for MNIST Classification
Venue: arXiv cs.CV 2025
Year: 2025

This study presents a hybrid model for classifying handwritten digits in the MNIST dataset, combining convolutional neural networks (CNNs) with a multi-well Hopfield network. The approach employs a CNN to extract high-dimensional features from input images, which are then clustered into class-specific prototypes using k-means clustering. These prototypes serve as attractors in a multi-well energy landscape, where a Hopfield network performs classification by minimizing an energy function that balances feature similarity and class assignment.The model's design enables robust handling of intraclass variability, such as diverse handwriting styles, while providing an interpretable framework through its energy-based decision process. Through systematic optimization of the CNN architecture and the number of wells, the model achieves a high test accuracy of 99.2% on 10,000 MNIST images, demonstrating its effectiveness for image classification tasks. The findings highlight the critical role of deep feature extraction and sufficient prototype coverage in achieving high performance, with potential for broader applications in pattern recognition.
On Barriers to Archival Audio Processing
Venue: arXiv cs.SD 2025
Year: 2025

In this study, we leverage a unique UNESCO collection of mid-20th century radio recordings to probe the robustness of modern off-the-shelf language identification (LID) and speaker recognition (SR) methods, especially with respect to the impact of multilingual speakers and cross-age recordings. Our findings suggest that LID systems, such as Whisper, are increasingly adept at handling second-language and accented speech. However, speaker embeddings remain a fragile component of speech processing pipelines that is prone to biases related to the channel, age, and language. Issues which will need to be overcome should archives aim to employ SR methods for speaker indexing.
Optimistic Exploration for Risk-Averse Constrained Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Risk-averse Constrained Reinforcement Learning (RaCRL) aims to learn policies that minimise the likelihood of rare and catastrophic constraint violations caused by an environment's inherent randomness. In general, risk-aversion leads to conservative exploration of the environment which typically results in converging to sub-optimal policies that fail to adequately maximise reward or, in some cases, fail to achieve the goal. In this paper, we propose an exploration-based approach for RaCRL called Optimistic Risk-averse Actor Critic (ORAC), which constructs an exploratory policy by maximising a local upper confidence bound of the state-action reward value function whilst minimising a local lower confidence bound of the risk-averse state-action cost value function. Specifically, at each step, the weighting assigned to the cost value is increased or decreased if it exceeds or falls below the safety constraint value. This way the policy is encouraged to explore uncertain regions of the environment to discover high reward states whilst still satisfying the safety constraints. Our experimental results demonstrate that the ORAC approach prevents convergence to sub-optimal policies and improves significantly the reward-cost trade-off in various continuous control tasks such as Safety-Gymnasium and a complex building energy management environment CityLearn.
One Token to Fool LLM-as-a-Judge
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) are increasingly trusted as automated judges, assisting evaluation and providing reward signals for training other models, particularly in reference-based settings like Reinforcement Learning with Verifiable Rewards (RLVR). However, we uncover a critical vulnerability even in this reference-based paradigm: generative reward models are systematically susceptible to reward hacking. We find that superficial inputs, which we term ''master keys'' such as non-word symbols (e.g., '':'' or ''.'') or generic reasoning openers (e.g., ''Thought process:'' or ''Let's solve this problem step by step.''), can consistently elicit false positive rewards without any substantive reasoning. Our systematic evaluation demonstrates this is a widespread failure affecting a diverse range of models, including leading proprietary systems such as GPT-o1 and Claude-4. These results challenge the assumed robustness of LLM judges and pose a significant threat to their reliability. To address this, we propose a simple yet effective data augmentation strategy using truncated model outputs as adversarial negative examples. The resulting Master Reward Models (Master-RMs) demonstrate state-of-the-art robustness against these ''master key'' attacks while maintaining high performance in standard evaluation settings. We supplement these findings with a comprehensive analysis of the vulnerability across model scales, prompt variations, and common inference-time strategies, offering insights to guide future research on robust LLM evaluation. We release our robust, general-domain reward models and the synthetic training data at https://huggingface.co/sarosavo/Master-RM and https://huggingface.co/datasets/sarosavo/Master-RM.
Lumos-1: On Autoregressive Video Generation from a Unified Model Perspective
Venue: arXiv cs.CV 2025
Year: 2025

Autoregressive large language models (LLMs) have unified a vast range of language tasks, inspiring preliminary efforts in autoregressive video generation. Existing autoregressive video generators either diverge from standard LLM architectures, depend on bulky external text encoders, or incur prohibitive latency due to next-token decoding. In this paper, we introduce Lumos-1, an autoregressive video generator that retains the LLM architecture with minimal architectural modifications. To inject spatiotemporal correlations in LLMs, we identify the efficacy of incorporating 3D RoPE and diagnose its imbalanced frequency spectrum ranges. Therefore, we propose MM-RoPE, a RoPE scheme that preserves the original textual RoPE while providing comprehensive frequency spectra and scaled 3D positions for modeling multimodal spatiotemporal data. Moreover, Lumos-1 resorts to a token dependency strategy that obeys intra-frame bidirectionality and inter-frame temporal causality. Based on this dependency strategy, we identify the issue of frame-wise loss imbalance caused by spatial information redundancy and solve it by proposing Autoregressive Discrete Diffusion Forcing (AR-DF). AR-DF introduces temporal tube masking during training with a compatible inference-time masking policy to avoid quality degradation. By using memory-efficient training techniques, we pre-train Lumos-1 on only 48 GPUs, achieving performance comparable to EMU3 on GenEval, COSMOS-Video2World on VBench-I2V, and OpenSoraPlan on VBench-T2V. Code and models are available at https://github.com/alibaba-damo-academy/Lumos.
Think Clearly: Improving Reasoning via Redundant Token Pruning
Venue: arXiv cs.AI 2025
Year: 2025

Recent large language models have shown promising capabilities in long-form reasoning, following structured chains of thought before arriving at a final answer. However, we observe that these reasoning paths tend to include substantial redundancy; analyzing attention patterns reveals that attention scores are widely scattered, particularly incorrect answers exhibit greater attention sparsity. In this paper, we demonstrate that deliberately removing this redundancy in the reasoning process significantly improves performance through clear thinking, i.e., removing distraction. Specifically, we systematically identify reasoning redundancy by measuring token-level attention scores to a special end-of-thinking token, which is appended to an explicit instruction inserted to conclude each intermediate reasoning step. Furthermore, we propose structure-aware pruning that prioritizes removing tokens in low-contributing reasoning chunks over individual tokens. After evicting redundant tokens, we remove the injected end-of-thinking instruction, then resume the reasoning generation. We demonstrate that our method significantly improves overall accuracy across reasoning-intensive benchmarks without any training involved. In particular, our method shows strong performance on challenging mathematical competition benchmarks such as AIME and AMC, where reasoning redundancy is more prevalent.
Efficient Triple Modular Redundancy for Reliability Enhancement of DNNs Using Explainable AI
Venue: arXiv cs.LG 2025
Year: 2025

Deep Neural Networks (DNNs) are widely employed in safety-critical domains, where ensuring their reliability is essential. Triple Modular Redundancy (TMR) is an effective technique to enhance the reliability of DNNs in the presence of bit-flip faults. In order to handle the significant overhead of TMR, it is applied selectively on the parameters and components with the highest contribution at the model output. Hence, the accuracy of the selection criterion plays the key role on the efficiency of TMR. This paper presents an efficient TMR approach to enhance the reliability of DNNs against bit-flip faults using an Explainable Artificial Intelligence (XAI) method. Since XAI can provide valuable insights about the importance of individual neurons and weights in the performance of the network, they can be applied as the selection metric in TMR techniques. The proposed method utilizes a low-cost, gradient-based XAI technique known as Layer-wise Relevance Propagation (LRP) to calculate importance scores for DNN parameters. These scores are then used to enhance the reliability of the model, with the most critical weights being protected by TMR. The proposed approach is evaluated on two DNN models, VGG16 and AlexNet, using datasets such as MNIST and CIFAR-10. The results demonstrate that the method can protect the AlexNet model at a bit error rate of 10-4, achieving over 60% reliability improvement while maintaining the same overhead as state-of-the-art methods.
Immutability Does Not Guarantee Trust: A Formal and Logical Refutation
Venue: arXiv cs.CR 2025
Year: 2025

It is frequently claimed in blockchain discourse that immutability guarantees trust. This paper rigorously refutes that assertion. We define immutability as the cryptographic persistence of historical states in an append-only data structure and contrast it with trust, understood as a rational epistemic expectation under uncertainty. Employing predicate logic, automata-theoretic models, and epistemic game-theoretic analysis, we demonstrate that immutability neither entails nor implies correctness, fairness, or credibility. Through formal constructions and counterexamples--including predictive fraud schemes and the phenomenon of garbage permanence--we show that the belief conflates structural and epistemic domains. Immutability preserves all data equally, regardless of veracity. Therefore, the assertion that immutability guarantees trust collapses under the weight of formal scrutiny.
Assuring the Safety of Reinforcement Learning Components: AMLAS-RL
Venue: arXiv cs.LG 2025
Year: 2025

The rapid advancement of machine learning (ML) has led to its increasing integration into cyber-physical systems (CPS) across diverse domains. While CPS offer powerful capabilities, incorporating ML components introduces significant safety and assurance challenges. Among ML techniques, reinforcement learning (RL) is particularly suited for CPS due to its capacity to handle complex, dynamic environments where explicit models of interaction between system and environment are unavailable or difficult to construct. However, in safety-critical applications, this learning process must not only be effective but demonstrably safe. Safe-RL methods aim to address this by incorporating safety constraints during learning, yet they fall short in providing systematic assurance across the RL lifecycle. The AMLAS methodology offers structured guidance for assuring the safety of supervised learning components, but it does not directly apply to the unique challenges posed by RL. In this paper, we adapt AMLAS to provide a framework for generating assurance arguments for an RL-enabled system through an iterative process; AMLAS-RL. We demonstrate AMLAS-RL using a running example of a wheeled vehicle tasked with reaching a target goal without collision.