OpenRaiser/Intern-Atlas huggingface dataset:
Coevolution of Camouflage
Venue: arXiv cs.GR 2025
Year: 2025

Camouflage in nature seems to arise from competition between predator and prey. To survive, predators must find prey, and prey must avoid being found. This work simulates an abstract model of that adversarial relationship. It looks at crypsis through evolving prey camouflage patterns (as color textures) in competition with evolving predator vision. During their "lifetime" predators learn to better locate camouflaged prey. The environment for this 2D simulation is provided by a set of photographs, typically of natural scenes. This model is based on two evolving populations, one of prey and another of predators. Mutual conflict between these populations can produce both effective prey camouflage and predators skilled at "breaking" camouflage. The result is an open source artificial life model to help study camouflage in nature, and the perceptual phenomenon of camouflage more generally.
Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning
Venue: arXiv cs.LG 2025
Year: 2025

Transfer learning leverages feature representations of deep neural networks (DNNs) pretrained on source tasks with rich data to empower effective finetuning on downstream tasks. However, the pretrained models are often prohibitively large for delivering generalizable representations, which limits their deployment on edge devices with constrained resources. To close this gap, we propose a new transfer learning pipeline, which leverages our finding that robust tickets can transfer better, i.e., subnetworks drawn with properly induced adversarial robustness can win better transferability over vanilla lottery ticket subnetworks. Extensive experiments and ablation studies validate that our proposed transfer learning pipeline can achieve enhanced accuracy-sparsity trade-offs across both diverse downstream tasks and sparsity patterns, further enriching the lottery ticket hypothesis.
AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design Anywhere
Venue: arXiv cs.LG 2025
Year: 2025

Meta-learning, also known as ``learning to learn'', enables models to acquire great generalization abilities by learning from various tasks. Recent advancements have made these models applicable across various fields without data constraints, offering new opportunities for general artificial intelligence. However, applying these models can be challenging due to their often task-specific, standalone nature and the technical barriers involved. To address this challenge, we develop AwesomeMeta+, a prototyping and learning system designed to standardize the key components of meta-learning within the context of systems engineering. It standardizes different components of meta-learning and uses a building block metaphor to assist in model construction. By employing a modular, building-block approach, AwesomeMeta+ facilitates the construction of meta-learning models that can be adapted and optimized for specific application needs in real-world systems. The system is developed to support the full lifecycle of meta-learning system engineering, from design to deployment, by enabling users to assemble compatible algorithmic modules. We evaluate AwesomeMeta+ through feedback from 50 researchers and a series of machine-based tests and user studies. The results demonstrate that AwesomeMeta+ enhances users' understanding of meta-learning principles, accelerates system engineering processes, and provides valuable decision-making support for efficient deployment of meta-learning systems in complex application scenarios.
With a little help from your friends: semi-cooperative games via Joker moves
Venue: arXiv cs.GT 2025
Year: 2025

This paper coins the notion of Joker games, a variant of concurrent games where the players are not strictly adversarial. Instead, Player 1 can get help from Player 2 by playing a Joker move. We formalize these games as cost games and develop strategies that minimize the use of Jokers - viewed as costs - to secure a win with the least possible help. Our investigation studies the theoretical underpinnings of these games and their associated Joker strategies. In particular, when comparing our cost-minimal strategies with admissible strategies, we find out that they differ. Moreover, while randomization can be beneficial in conventional concurrent games, it does not aid in winning Joker games, although it can help reduce the number of needed Jokers. We also enhance our framework by introducing a secondary objective, namely by minimizing the number of moves executed by a Joker strategy. Finally, we demonstrate the practical advantages of our approach by applying it to test generation in model-based testing.
Implicit Counterfactual Data Augmentation for Robust Learning
Venue: arXiv cs.LG 2025
Year: 2025

Machine learning models are prone to capturing the spurious correlations between non-causal attributes and classes, with counterfactual data augmentation being a promising direction for breaking these spurious associations. However, generating counterfactual data explicitly poses a challenge, and incorporating augmented data into the training process decreases training efficiency. This study proposes an Implicit Counterfactual Data Augmentation (ICDA) method to remove spurious correlations and make stable predictions. Specifically, first, a novel sample-wise augmentation strategy is developed that generates semantically and counterfactually meaningful deep features with distinct augmentation strength for each sample. Second, we derive an easy-to-compute surrogate loss on the augmented feature set when the number of augmented samples becomes infinite. Third, two concrete schemes are proposed, including direct quantification and meta-learning, to derive the key parameters for the robust loss. In addition, ICDA is explained from a regularization perspective, revealing its capacity to improve intra-class compactness and augment margins at both class and sample levels. Extensive experiments have been conducted across various biased learning scenarios covering both image and text datasets, demonstrating that ICDA consistently enhances the generalization and robustness performance of popular networks.
Quantum Cross Subspace Alignment Codes via the $N$-sum Box Abstraction
Venue: arXiv cs.IT 2025
Year: 2025

Cross-subspace alignment (CSA) codes are used in various private information retrieval (PIR) schemes (e.g., with secure storage) and in secure distributed batch matrix multiplication (SDBMM). Using a recently developed $N$-sum box abstraction of a quantum multiple-access channel (QMAC), we translate CSA schemes over classical multiple-access channels into efficient quantum CSA schemes over a QMAC, achieving maximal superdense coding gain. Because of the $N$-sum box abstraction, the underlying problem of coding to exploit quantum entanglements for CSA schemes, becomes conceptually equivalent to that of designing a channel matrix for a MIMO MAC subject to given structural constraints imposed by the $N$-sum box abstraction, such that the resulting MIMO MAC is able to implement the functionality of a CSA scheme (encoding/decoding) \emph{over-the-air}. Applications include Quantum PIR with secure and MDS-coded storage, as well as Quantum SDBMM.
Large Linguistic Models: Investigating LLMs' metalinguistic abilities
Venue: arXiv cs.CL 2025
Year: 2025

The performance of large language models (LLMs) has recently improved to the point where models can perform well on many language tasks. We show here that--for the first time--the models can also generate valid metalinguistic analyses of language data. We outline a research program where the behavioral interpretability of LLMs on these tasks is tested via prompting. LLMs are trained primarily on text--as such, evaluating their metalinguistic abilities improves our understanding of their general capabilities and sheds new light on theoretical models in linguistics. We show that OpenAI's (2024) o1 vastly outperforms other models on tasks involving drawing syntactic trees and phonological generalization. We speculate that OpenAI o1's unique advantage over other models may result from the model's chain-of-thought mechanism, which mimics the structure of human reasoning used in complex cognitive tasks, such as linguistic analysis.
Oil Spill Segmentation using Deep Encoder-Decoder models
Venue: arXiv cs.CV 2025
Year: 2025

Crude oil is an integral component of the world economy and transportation sectors. With the growing demand for crude oil due to its widespread applications, accidental oil spills are unfortunate yet unavoidable. Even though oil spills are difficult to clean up, the first and foremost challenge is to detect them. In this research, the authors test the feasibility of deep encoder-decoder models that can be trained effectively to detect oil spills remotely. The work examines and compares the results from several segmentation models on high dimensional satellite Synthetic Aperture Radar (SAR) image data to pave the way for further in-depth research. Multiple combinations of models are used to run the experiments. The best-performing model is the one with the ResNet-50 encoder and DeepLabV3+ decoder. It achieves a mean Intersection over Union (IoU) of 64.868% and an improved class IoU of 61.549% for the ``oil spill" class when compared with the previous benchmark model, which achieved a mean IoU of 65.05% and a class IoU of 53.38% for the ``oil spill" class.
Adding Reconfiguration to Zielonka's Asynchronous Automata
Venue: arXiv cs.FL 2025
Year: 2025

We study an extension of Zielonka's (fixed) asynchronous automata called reconfigurable asynchronous automata where processes can dynamically change who they communicate with. We show that reconfigurable asynchronous automata are not more expressive than fixed asynchronous automata by giving translations from one to the other. However, going from reconfigurable to fixed comes at the cost of disseminating communication (and knowledge) to all processes in the system. We then show that this is unavoidable by describing a language accepted by a reconfigurable automaton such that in every equivalent fixed automaton, every process must either be aware of all communication or be irrelevant.
From FASTER to F2: Evolving Concurrent Key-Value Store Designs for Large Skewed Workloads
Venue: arXiv cs.DB 2025
Year: 2025

Modern large-scale services such as search engines, messaging platforms, and serverless functions, rely on key-value (KV) stores to maintain high performance at scale. When such services are deployed in constrained memory environments, they present challenging requirements: point operations requiring high throughput, working sets much larger than main memory, and natural skew in key access patterns. Traditional KV stores, based on LSM- and B-Trees, have been widely used to handle such use cases, but they often suffer from suboptimal use of modern hardware resources. The FASTER project, developed as a high-performance open-source KV storage library, has demonstrated remarkable success in both in-memory and hybrid storage environments. However, when tasked with serving large skewed workloads, it faced challenges, including high indexing and compactions overheads, and inefficient management of non-overlapping read-hot and write-hot working sets. In this paper, we introduce F2 (for FASTER v2), an evolution of FASTER designed to meet the requirements of large skewed workloads common in industry applications. F2 adopts a two-tier record-oriented design to handle larger-than-memory skewed workloads, along with new concurrent latch-free mechanisms and components to maximize performance on modern hardware. To realize this design, F2 tackles key challenges and introduces several innovations, including new latch-free algorithms for multi-threaded log compaction, a two-level hash index to reduce indexing overhead for cold records, and a read-cache for serving read-hot records. Our evaluation shows that F2 achieves 2-11.9x better throughput compared to existing KV stores, effectively serving the target workload. F2 is open-source and available as part of the FASTER project.
How to play the Accordion: Uniformity and the (non-)conservativity of the linear approximation of the λ-calculus (extended version)
Venue: arXiv cs.LO 2025
Year: 2025

Twenty years after its introduction by Ehrhard and Regnier, differentiation in $λ$-calculus and in linear logic is now a celebrated tool. In particular, it allows to establish a Taylor expansion formula for various $λ$-calculi, hence providing a theory of linear approximations for these calculi. In the pure $λ$-calculus, the linear approximants of $λ$-terms supporting this Taylor expansion are the terms of a so-called resource calculus, which is equipped with a finitary (strongly normalising) reduction; and the efficiency of this linear approximation is expressed by results stating that the (possibly) infinitary $β$-reduction of $λ$-terms is simulated by the reduction of their Taylor expansions, which is induced by the iterated reduction of resource terms. In terms of rewriting systems, resource reduction (operating on infinite linear combinations of Taylor approximants) is an extension of $β$-reduction. In this article, we address the converse property, conservativity: do all reductions between Taylor expansions arise from actual $β$-reductions? We show that if we restrict the setting to finite terms and $β$-reduction sequences, then the linear approximation is conservative. However, as soon as one allows infinitary reduction sequences this property is broken. We design a counter-example, the Accordion. Then we show how restricting the reduction of the Taylor approximants allows to build a conservative extension of the $β$-reduction preserving good simulation properties; this restriction relies on uniformity, a property that was already at the core of Ehrhard and Regnier's pioneering work. Finally, we extend our work to $β\bot$-reductions, which play a key role in $λ$-calculus as they relate a $λ$-term to its Böhm tree.
Distribution of Chores with Information Asymmetry
Venue: arXiv cs.GT 2025
Year: 2025

A well-regarded fairness notion when dividing indivisible chores is envy-freeness up to one item (EF1), which requires that pairwise envy can be eliminated by the removal of a single item. While an EF1 and Pareto optimal (PO) allocation of goods can always be found via well-known algorithms, even the existence of such solutions for chores remains open, to date. We take an epistemic approach utilizing information asymmetry by introducing dubious chores--items that inflict no cost on receiving agents but are perceived costly by others. On a technical level, dubious chores provide a more fine-grained approximation of envy-freeness than EF1. We show that finding allocations with minimal number of dubious chores is computationally hard. Nonetheless, we prove the existence of envy-free and fractional PO allocations for $n$ agents with only $2n-2$ dubious chores and strengthen it to $n-1$ dubious chores in four special classes of valuations. Our experimental analysis demonstrates that often only a few dubious chores are needed to achieve envy-freeness.
Calibrating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Weakly supervised semantic segmentation (WSSS) has recently attracted considerable attention because it requires fewer annotations than fully supervised approaches, making it especially promising for large-scale image segmentation tasks. Although many vision transformer-based methods leverage self-attention affinity matrices to refine Class Activation Maps (CAMs), they often treat each layer's affinity equally and thus introduce considerable background noise at deeper layers, where attention tends to converge excessively on certain tokens (i.e., over-smoothing). We observe that this deep-level attention naturally converges on a subset of tokens, yet unregulated query-key affinity can generate unpredictable activation patterns (undisciplined over-smoothing), adversely affecting CAM accuracy. To address these limitations, we propose an Adaptive Re-Activation Mechanism (AReAM), which exploits shallow-level affinity to guide deeper-layer convergence in an entropy-aware manner, thereby suppressing background noise and re-activating crucial semantic regions in the CAMs. Experiments on two commonly used datasets demonstrate that AReAM substantially improves segmentation performance compared with existing WSSS methods, reducing noise while sharpening focus on relevant semantic regions. Overall, this work underscores the importance of controlling deep-level attention to mitigate undisciplined over-smoothing, introduces an entropy-aware mechanism that harmonizes shallow and deep-level affinities, and provides a refined approach to enhance transformer-based WSSS accuracy by re-activating CAMs.
Neural Exploitation and Exploration of Contextual Bandits
Venue: arXiv cs.LG 2025
Year: 2025

In this paper, we study utilizing neural networks for the exploitation and exploration of contextual multi-armed bandits. Contextual multi-armed bandits have been studied for decades with various applications. To solve the exploitation-exploration trade-off in bandits, there are three main techniques: epsilon-greedy, Thompson Sampling (TS), and Upper Confidence Bound (UCB). In recent literature, a series of neural bandit algorithms have been proposed to adapt to the non-linear reward function, combined with TS or UCB strategies for exploration. In this paper, instead of calculating a large-deviation based statistical bound for exploration like previous methods, we propose, ``EE-Net,'' a novel neural-based exploitation and exploration strategy. In addition to using a neural network (Exploitation network) to learn the reward function, EE-Net uses another neural network (Exploration network) to adaptively learn the potential gains compared to the currently estimated reward for exploration. We provide an instance-based $\widetilde{\mathcal{O}}(\sqrt{T})$ regret upper bound for EE-Net and show that EE-Net outperforms related linear and neural contextual bandit baselines on real-world datasets.
Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Existing knowledge distillation works for semantic segmentation mainly focus on transferring high-level contextual knowledge from teacher to student. However, low-level texture knowledge is also of vital importance for characterizing the local structural pattern and global statistical property, such as boundary, smoothness, regularity and color contrast, which may not be well addressed by high-level deep features. In this paper, we are intended to take full advantage of both structural and statistical texture knowledge and propose a novel Structural and Statistical Texture Knowledge Distillation (SSTKD) framework for semantic segmentation. Specifically, for structural texture knowledge, we introduce a Contourlet Decomposition Module (CDM) that decomposes low-level features with iterative Laplacian pyramid and directional filter bank to mine the structural texture knowledge. For statistical knowledge, we propose a Denoised Texture Intensity Equalization Module (DTIEM) to adaptively extract and enhance statistical texture knowledge through heuristics iterative quantization and denoised operation. Finally, each knowledge learning is supervised by an individual loss function, forcing the student network to mimic the teacher better from a broader perspective. Experiments show that the proposed method achieves state-of-the-art performance on Cityscapes, Pascal VOC 2012 and ADE20K datasets.
LABO: Towards Learning Optimal Label Regularization via Bi-level Optimization
Venue: arXiv cs.LG 2025
Year: 2025

Regularization techniques are crucial to improving the generalization performance and training efficiency of deep neural networks. Many deep learning algorithms rely on weight decay, dropout, batch/layer normalization to converge faster and generalize. Label Smoothing (LS) is another simple, versatile and efficient regularization which can be applied to various supervised classification tasks. Conventional LS, however, regardless of the training instance assumes that each non-target class is equally likely. In this work, we present a general framework for training with label regularization, which includes conventional LS but can also model instance-specific variants. Based on this formulation, we propose an efficient way of learning LAbel regularization by devising a Bi-level Optimization (LABO) problem. We derive a deterministic and interpretable solution of the inner loop as the optimal label smoothing without the need to store the parameters or the output of a trained model. Finally, we conduct extensive experiments and demonstrate our LABO consistently yields improvement over conventional label regularization on various fields, including seven machine translation and three image classification tasks across various
Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters
Venue: arXiv cs.LG 2025
Year: 2025

Neural network (NN) designed for challenging machine learning tasks is in general a highly nonlinear mapping that contains massive variational parameters. High complexity of NN, if unbounded or unconstrained, might unpredictably cause severe issues including \R{overfitting}, loss of generalization power, and unbearable cost of hardware. In this work, we propose a general compression scheme that significantly reduces the variational parameters of NN's, despite of their specific types (linear, convolutional, \textit{etc}), by encoding them to deep \R{automatically differentiable} tensor network (ADTN) that contains exponentially-fewer free parameters. Superior compression performance of our scheme is demonstrated on several widely-recognized NN's (FC-2, LeNet-5, AlextNet, ZFNet and VGG-16) and datasets (MNIST, CIFAR-10 and CIFAR-100). For instance, we compress two linear layers in VGG-16 with approximately $10^{7}$ parameters to two ADTN's with just 424 parameters, improving the testing accuracy on CIFAR-10 from $90.17\%$ to $91.74\%$. We argue that the deep structure of ADTN is an essential reason for the remarkable compression performance of ADTN, compared to existing compression schemes that are mainly based on tensor decompositions/factorization and shallow tensor networks. Our work suggests deep TN as an exceptionally efficient mathematical structure for representing the variational parameters of NN's, which exhibits superior compressibility over the commonly-used matrices and multi-way arrays.
Joint Identification and Sensing for Discrete Memoryless Channels
Venue: arXiv cs.IT 2025
Year: 2025

In the identification (ID) scheme proposed by Ahlswede and Dueck, the receiver's goal is simply to verify whether a specific message of interest was sent. Unlike Shannon's transmission codes, which aim for message decoding, ID codes for a Discrete Memoryless Channel (DMC) are far more efficient: their size grows doubly exponentially with the blocklength when randomized encoding is used. This indicates that, when the receiver's objective does not require decoding, the ID paradigm is significantly more efficient than traditional Shannon transmission in terms of both energy consumption and hardware complexity. Further benefits of ID schemes can be realized by leveraging additional resources such as feedback. In this work, we address the problem of joint ID and channel state estimation over a DMC with independent and identically distributed (i.i.d.) state sequences. State estimation functions as the sensing mechanism of the model. Specifically, the sender transmits an ID message over the DMC while simultaneously estimating the channel state through strictly causal observations of the channel output. Importantly, the random channel state is unknown to both the sender and the receiver. For this system model, we present a complete characterization of the ID capacity-distortion function.