OpenRaiser/Intern-Atlas huggingface dataset:
Scaling laws for activation steering with Llama 2 models and refusal mechanisms
Venue: arXiv cs.LG 2025
Year: 2025

As large language models (LLMs) evolve in complexity and capability, the efficacy of less widely deployed alignment techniques are uncertain. Building on previous work on activation steering and contrastive activation addition (CAA), this paper explores the effectiveness of CAA with model scale using the family of Llama 2 models (7B, 13B, and 70B). CAA works by finding desirable 'directions' in the model's residual stream vector space using contrastive pairs (for example, hate to love) and adding this direction to the residual stream during the forward pass. It directly manipulates the residual stream and aims to extract features from language models to better control their outputs. Using answer matching questions centered around the refusal behavior, we found that 1) CAA is most effective when applied at early-mid layers. 2) The effectiveness of CAA diminishes with model size. 3) Negative steering has more pronounced effects than positive steering across all model sizes.
Towards Scalable AASIST: Refining Graph Attention for Speech Deepfake Detection
Venue: arXiv cs.SD 2025
Year: 2025

Advances in voice conversion and text-to-speech synthesis have made automatic speaker verification (ASV) systems more susceptible to spoofing attacks. This work explores modest refinements to the AASIST anti-spoofing architecture. It incorporates a frozen Wav2Vec 2.0 encoder to retain self-supervised speech representations in limited-data settings, substitutes the original graph attention block with a standardized multi-head attention module using heterogeneous query projections, and replaces heuristic frame-segment fusion with a trainable, context-aware integration layer. When evaluated on the ASVspoof 5 corpus, the proposed system reaches a 7.6\% equal error rate (EER), improving on a re-implemented AASIST baseline under the same training conditions. Ablation experiments suggest that each architectural change contributes to the overall performance, indicating that targeted adjustments to established models may help strengthen speech deepfake detection in practical scenarios. The code is publicly available at https://github.com/KORALLLL/AASIST_SCALING.
Simulated Language Acquisition in a Biologically Realistic Model of the Brain
Venue: arXiv cs.NE 2025
Year: 2025

Despite tremendous progress in neuroscience, we do not have a compelling narrative for the precise way whereby the spiking of neurons in our brain results in high-level cognitive phenomena such as planning and language. We introduce a simple mathematical formulation of six basic and broadly accepted principles of neuroscience: excitatory neurons, brain areas, random synapses, Hebbian plasticity, local inhibition, and inter-area inhibition. We implement a simulated neuromorphic system based on this formalism, which is capable of basic language acquisition: Starting from a tabula rasa, the system learns, in any language, the semantics of words, their syntactic role (verb versus noun), and the word order of the language, including the ability to generate novel sentences, through the exposure to a modest number of grounded sentences in the same language. We discuss several possible extensions and implications of this result.
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels
Venue: arXiv cs.LG 2025
Year: 2025

Learning with noisy labels (LNL) is essential for training deep neural networks with imperfect data. Meta-learning approaches have achieved success by using a clean unbiased labeled set to train a robust model. However, this approach heavily depends on the availability of a clean labeled meta-dataset, which is difficult to obtain in practice. In this work, we thus tackle the challenge of meta-learning for noisy label scenarios without relying on a clean labeled dataset. Our approach leverages the data itself while bypassing the need for labels. Building on the insight that clean samples effectively preserve the consistency of related data structures across the last hidden and the final layer, whereas noisy samples disrupt this consistency, we design the Cross-layer Information Divergence-based Meta Update Strategy (CLID-MU). CLID-MU leverages the alignment of data structures across these diverse feature spaces to evaluate model performance and use this alignment to guide training. Experiments on benchmark datasets with varying amounts of labels under both synthetic and real-world noise demonstrate that CLID-MU outperforms state-of-the-art methods. The code is released at https://github.com/ruofanhu/CLID-MU.
New allocation rule based on graph structures and their application to economic phenomena
Venue: arXiv cs.GT 2025
Year: 2025

This study introduces the \emph{edge-based Shapley value}, a novel allocation rule within cooperative game theory, specifically tailored for networked systems, where value is generated through interactions represented by edges. Traditional allocation rules, such as the Shapley and Myerson values, evaluate player contributions based on node-level characteristics, or connected components. However, these approaches often fail to adequately capture the functional role of edges, which are crucial in systems such as supply chains and digital platforms, where interactions, rather than individual agents, are the primary drivers of value. Our edge-based Shapley value shifts the characteristic function from node sets to edge sets, thereby enabling a more granular and context-sensitive evaluation of the contributions. We establish its theoretical foundations, demonstrate its relationship to classical allocation rules, and show that it retains key properties such as fairness and symmetry. To illustrate its applicability, we present two use cases: content platform networks and supply chain logistics (SCL). In both cases, our method produces intuitive and structurally consistent allocations, particularly in scenarios with overlapping routes, exclusive contracts or cost-sensitive paths. This framework offers a new perspective on value attribution in cooperative settings with complex interaction structures and provides practical tools for analyzing real-world economic and logistical networks.
A Multimodal Data Fusion Generative Adversarial Network for Real Time Underwater Sound Speed Field Construction
Venue: arXiv cs.SD 2025
Year: 2025

Sound speed profiles (SSPs) are essential parameters underwater that affects the propagation mode of underwater signals and has a critical impact on the energy efficiency of underwater acoustic communication and accuracy of underwater acoustic positioning. Traditionally, SSPs can be obtained by matching field processing (MFP), compressive sensing (CS), and deep learning (DL) methods. However, existing methods mainly rely on on-site underwater sonar observation data, which put forward strict requirements on the deployment of sonar observation systems. To achieve high-precision estimation of sound velocity distribution in a given sea area without on-site underwater data measurement, we propose a multi-modal data-fusion generative adversarial network model with residual attention block (MDF-RAGAN) for SSP construction. To improve the model's ability for capturing global spatial feature correlations, we embedded the attention mechanisms, and use residual modules for deeply capturing small disturbances in the deep ocean sound velocity distribution caused by changes of SST. Experimental results on real open dataset show that the proposed model outperforms other state-of-the-art methods, which achieves an accuracy with an error of less than 0.3m/s. Specifically, MDF-RAGAN not only outperforms convolutional neural network (CNN) and spatial interpolation (SITP) by nearly a factor of two, but also achieves about 65.8\% root mean square error (RMSE) reduction compared to mean profile, which fully reflects the enhancement of overall profile matching by multi-source fusion and cross-modal attention.
Universal Synthesis of Differentiably Tunable Numerical Abstract Transformers
Venue: arXiv cs.PL 2025
Year: 2025

Numerical abstract interpretation is a widely used framework for the static analysis of numerical programs. However, existing numerical abstract interpreters rely on hand-crafted, instruction-specific transformers tailored to each domain, with no general algorithm for handling common operations across domains. This limits extensibility, prevents precise compositional reasoning over instruction sequences, and forces all downstream tasks to use the same fixed transformer regardless of their precision, efficiency, or task-specific requirements. To address these limitations, we propose a universal transformer synthesis algorithm that constructs a parametric family of sound abstract transformers for any given polyhedral numerical domain and a concrete operator from the class of Quadratic-Bounded Guarded Operators (QGO), which includes both individual instructions and structured sequences. Each instantiation in this family is sound by construction, enabling downstream analyses to adapt the transformer to their particular needs. The space of transformers is differentiable but complex. To efficiently explore this space of transformers, we introduce the Adaptive Gradient Guidance (AGG) procedure, a gradient-guided search strategy that steers the search process based on downstream analysis objectives and runtime constraints. We implement these ideas in the USTAD framework and evaluate their effectiveness across three numerical abstract domains: Zones, Octagons, and Polyhedra. Our results demonstrate that the universal synthesis algorithm successfully constructs sound families of transformers across domains, and that USTAD achieves significant, tunable precision gains over baselines by leveraging compositional reasoning and efficient gradient-guided traversal of the transformer space.
Arctic Inference with Shift Parallelism: Fast and Efficient Open Source Inference System for Enterprise AI
Venue: arXiv cs.DC 2025
Year: 2025

Inference is now the dominant AI workload, yet existing systems force trade-offs between latency, throughput, and cost. Arctic Inference, an open-source vLLM plugin from Snowflake AI Research, introduces Shift Parallelism, a dynamic parallelism strategy that adapts to real-world traffic while integrating speculative decoding, SwiftKV compute reduction, and optimized embedding inference. It achieves up to 3.4 times faster request completion, 1.75 times faster generation, and 1.6M tokens/sec per GPU for embeddings, outperforming both latency- and throughput-optimized deployments. Already powering Snowflake Cortex AI, Arctic Inference delivers state-of-the-art, cost-effective inference for enterprise AI and is now available to the community.
CorrMoE: Mixture of Experts with De-stylization Learning for Cross-Scene and Cross-Domain Correspondence Pruning
Venue: arXiv cs.CV 2025
Year: 2025

Establishing reliable correspondences between image pairs is a fundamental task in computer vision, underpinning applications such as 3D reconstruction and visual localization. Although recent methods have made progress in pruning outliers from dense correspondence sets, they often hypothesize consistent visual domains and overlook the challenges posed by diverse scene structures. In this paper, we propose CorrMoE, a novel correspondence pruning framework that enhances robustness under cross-domain and cross-scene variations. To address domain shift, we introduce a De-stylization Dual Branch, performing style mixing on both implicit and explicit graph features to mitigate the adverse influence of domain-specific representations. For scene diversity, we design a Bi-Fusion Mixture of Experts module that adaptively integrates multi-perspective features through linear-complexity attention and dynamic expert routing. Extensive experiments on benchmark datasets demonstrate that CorrMoE achieves superior accuracy and generalization compared to state-of-the-art methods. The code and pre-trained models are available at https://github.com/peiwenxia/CorrMoE.
The Developments and Challenges towards Dexterous and Embodied Robotic Manipulation: A Survey
Venue: arXiv cs.RO 2025
Year: 2025

Achieving human-like dexterous robotic manipulation remains a central goal and a pivotal challenge in robotics. The development of Artificial Intelligence (AI) has allowed rapid progress in robotic manipulation. This survey summarizes the evolution of robotic manipulation from mechanical programming to embodied intelligence, alongside the transition from simple grippers to multi-fingered dexterous hands, outlining key characteristics and main challenges. Focusing on the current stage of embodied dexterous manipulation, we highlight recent advances in two critical areas: dexterous manipulation data collection (via simulation, human demonstrations, and teleoperation) and skill-learning frameworks (imitation and reinforcement learning). Then, based on the overview of the existing data collection paradigm and learning framework, three key challenges restricting the development of dexterous robotic manipulation are summarized and discussed.
Interactive Hybrid Rice Breeding with Parametric Dual Projection
Venue: arXiv cs.HC 2025
Year: 2025

Hybrid rice breeding crossbreeds different rice lines and cultivates the resulting hybrids in fields to select those with desirable agronomic traits, such as higher yields. Recently, genomic selection has emerged as an efficient way for hybrid rice breeding. It predicts the traits of hybrids based on their genes, which helps exclude many undesired hybrids, largely reducing the workload of field cultivation. However, due to the limited accuracy of genomic prediction models, breeders still need to combine their experience with the models to identify regulatory genes that control traits and select hybrids, which remains a time-consuming process. To ease this process, in this paper, we proposed a visual analysis method to facilitate interactive hybrid rice breeding. Regulatory gene identification and hybrid selection naturally ensemble a dual-analysis task. Therefore, we developed a parametric dual projection method with theoretical guarantees to facilitate interactive dual analysis. Based on this dual projection method, we further developed a gene visualization and a hybrid visualization to verify the identified regulatory genes and hybrids. The effectiveness of our method is demonstrated through the quantitative evaluation of the parametric dual projection method, identified regulatory genes and desired hybrids in the case study, and positive feedback from breeders.
Sub-Connected Hybrid Beamfocusing Design for RSMA-Enabled Near-Field Communications with Imperfect CSI and SIC
Venue: arXiv cs.IT 2025
Year: 2025

Near-field spherical waves inherently encode both direction and distance information, enabling spotlight-like beam focusing for targeted interference mitigation. However, whether such beam focusing can fully eliminate interference under perfect and imperfect channel state information (CSI), rendering advanced interference management schemes unnecessary, remains an open question. To address this, we investigate rate-splitting multiple access (RSMA)-enabled near-field communications (NFC) under imperfect SCI. Our transmit scheme employs a sub-connected hybrid analog-digital (HAD) architecture to reduce hardware overhead while incorporating imperfect successive interference cancellation (SIC) for practical implementation. A minimum rate maximization problem is formulated by jointly optimizing the analog beamfocuser, the digital beamfocuser, and the common rate allocation. To solve the non-convex problem, we develop a penalty-based block coordinate descent (BCD) algorithm, deriving closed-form expressions for the optimal analog and digital beamfocusers solutions. Furthermore, to reduce computational complexity, we propose a low-complexity algorithm, where analog and digital beamfocusers are designed in two separate stages. Simulation results underscore that: 1) beamfocusing alone is insufficient to fully suppress interference even under perfect CSI; 2) RSMA exhibits superior interference management over SDMA under imperfect CSI and SIC conditions; 3) sub-connected HAD architecture delivers near-optimal digital beamfocusing performance with fewer radio frequency chains.
NemeSys: An Online Underwater Explorer with Goal-Driven Adaptive Autonomy
Venue: arXiv cs.RO 2025
Year: 2025

Adaptive mission control and dynamic parameter reconfiguration are essential for autonomous underwater vehicles (AUVs) operating in GPS-denied, communication-limited marine environments. However, most current AUV platforms execute static, pre-programmed missions or rely on tethered connections and high-latency acoustic channels for mid-mission updates, significantly limiting their adaptability and responsiveness. In this paper, we introduce NemeSys, a novel AUV system designed to support real-time mission reconfiguration through compact optical and magnetoelectric (OME) signaling facilitated by floating buoys. We present the full system design, control architecture, and a semantic mission encoding framework that enables interactive exploration and task adaptation via low-bandwidth communication. The proposed system is validated through analytical modeling, controlled experimental evaluations, and open-water trials. Results confirm the feasibility of online mission adaptation and semantic task updates, highlighting NemeSys as an online AUV platform for goal-driven adaptive autonomy in dynamic and uncertain underwater environments.
Imbalanced Regression Pipeline Recommendation
Venue: arXiv cs.LG 2025
Year: 2025

Imbalanced problems are prevalent in various real-world scenarios and are extensively explored in classification tasks. However, they also present challenges for regression tasks due to the rarity of certain target values. A common alternative is to employ balancing algorithms in preprocessing to address dataset imbalance. However, due to the variety of resampling methods and learning models, determining the optimal solution requires testing many combinations. Furthermore, the learning model, dataset, and evaluation metric affect the best strategies. This work proposes the Meta-learning for Imbalanced Regression (Meta-IR) framework, which diverges from existing literature by training meta-classifiers to recommend the best pipeline composed of the resampling strategy and learning model per task in a zero-shot fashion. The meta-classifiers are trained using a set of meta-features to learn how to map the meta-features to the classes indicating the best pipeline. We propose two formulations: Independent and Chained. Independent trains the meta-classifiers to separately indicate the best learning algorithm and resampling strategy. Chained involves a sequential procedure where the output of one meta-classifier is used as input for another to model intrinsic relationship factors. The Chained scenario showed superior performance, suggesting a relationship between the learning algorithm and the resampling strategy per task. Compared with AutoML frameworks, Meta-IR obtained better results. Moreover, compared with baselines of six learning algorithms and six resampling algorithms plus no resampling, totaling 42 (6 X 7) configurations, Meta-IR outperformed all of them. The code, data, and further information of the experiments can be found on GitHub: https://github.com/JusciAvelino/Meta-IR.
CoCre-Sam (Kokkuri-san): Modeling Ouija Board as Collective Langevin Dynamics Sampling from Fused Language Models
Venue: arXiv cs.MA 2025
Year: 2025

Collective human activities like using an Ouija board (or Kokkuri-san) often produce emergent, coherent linguistic outputs unintended by any single participant. While psychological explanations such as the ideomotor effect exist, a computational understanding of how decentralized, implicit linguistic knowledge fuses through shared physical interaction remains elusive. We introduce CoCre-Sam (Collective-Creature Sampling), a framework modeling this phenomenon as collective Langevin dynamics sampling from implicitly fused language models. Each participant is represented as an agent associated with an energy landscape derived from an internal language model reflecting linguistic priors, and agents exert stochastic forces based on local energy gradients. We theoretically prove that the collective motion of the shared pointer (planchette) corresponds to Langevin MCMC sampling from the sum of individual energy landscapes, representing fused collective knowledge. Simulations validate that CoCre-Sam dynamics effectively fuse different models and generate meaningful character sequences, while ablation studies confirm the essential roles of collective interaction and stochasticity. Altogether, CoCre-Sam provides a novel computational mechanism linking individual implicit knowledge, embodied collective action, and emergent linguistic phenomena, grounding these complex interactions in the principles of probabilistic sampling.
A Parallel CPU-GPU Framework for Batching Heuristic Operations in Depth-First Heuristic Search
Venue: arXiv cs.AI 2025
Year: 2025

The rapid advancement of GPU technology has unlocked powerful parallel processing capabilities, creating new opportunities to enhance classic search algorithms. This hardware has been exploited in best-first search algorithms with neural network-based heuristics by creating batched versions of A* and Weighted A* that delay heuristic evaluation until sufficiently many states can be evaluated in parallel on the GPU. But, research has not addressed how depth-first algorithms like IDA* or Budgeted Tree Search (BTS) can have their heuristic computations batched. This is more complicated in a tree search, because progress in the search tree is blocked until heuristic evaluations are complete. In this paper we show that GPU parallelization of heuristics can be effectively performed when the tree search is parallelized on the CPU while heuristic evaluations are parallelized on the GPU. We develop a parallelized cost-bounded depth-first search (CB-DFS) framework that can be applied to both IDA* and BTS, significantly improving their performance. We demonstrate the strength of the approach on the 3x3 Rubik's Cube and the 4x4 sliding tile puzzle (STP) with both classifier-based and regression-based heuristics.
Making Serverless Computing Extensible: A Case Study of Serverless Data Analytics
Venue: arXiv cs.DC 2025
Year: 2025

Serverless computing has attracted a broad range of applications due to its ease of use and resource elasticity. However, developing serverless applications often poses a dilemma -- relying on general-purpose serverless platforms can fall short of delivering satisfactory performance for complex workloads, whereas building application-specific serverless systems undermines the simplicity and generality. In this paper, we propose an extensible design principle for serverless computing. We argue that a platform should enable developers to extend system behaviors for domain-specialized optimizations while retaining a shared, easy-to-use serverless environment. We take data analytics as a representative serverless use case and realize this design principle in Proteus. Proteus introduces a novel abstraction of decision workflows, allowing developers to customize control-plane behaviors for improved application performance. Preliminary results show that Proteus's prototype effectively optimizes analytical query execution and supports fine-grained resource sharing across diverse applications.