OpenRaiser/Intern-Atlas huggingface dataset:
Two-Stage Reasoning-Infused Learning: Improving Classification with LLM-Generated Reasoning
Venue: arXiv cs.CL 2025
Year: 2025

Standard classification models often map inputs directly to labels without explicit reasoning, potentially limiting their performance, robustness, and interpretability. This paper introduces a novel two-stage approach to enhance text classification by leveraging Large Language Model (LLM)-generated reasonings. In the first stage, we fine-tune a Llama-3.2-1B-Instruct model (henceforth Llama-R-Gen) on a general-purpose reasoning dataset (syvai/reasoning-gen) to generate textual reasoning (R) given a question and its answer. In the second stage, this generally trained Llama-R-Gen is used offline to create an augmented training dataset for a downstream generative model. This downstream model, based on Llama-3.2-1B-Instruct, takes only the input text (Q) and is trained to output the generated reasoning (R) immediately followed by the predicted emotion (A). We demonstrate this methodology on the dair-ai/emotion dataset for emotion classification. Our experiments show that the generative model trained to output reasoning and the emotion (Classifier Q->RA) achieves a significant improvement of 8.7 percentage points in accuracy (for emotion prediction) compared to a baseline generative model trained solely to output the emotion (Classifier Q->A), highlighting the strong generalization capabilities of the reasoning generation and the benefit of explicit reasoning training. This work underscores the potential of LLM-generated reasonings for creating richer training datasets, thereby improving the performance of diverse downstream NLP tasks and providing explicit explanations.
CrossPipe: Towards Optimal Pipeline Schedules for Cross-Datacenter Training
Venue: arXiv cs.DC 2025
Year: 2025

Training large language models (LLMs) now requires resources that exceed a single datacenter, making cross-datacenter strategies increasingly crucial. We present CrossPipe, a framework designed to optimize model training across geographically distributed datacenters by explicitly modeling and mitigating the impact of network latency and limited bandwidth. It enables unified analysis and optimization incorporating both pipeline parallelism (PP) and opportunities for overlapping data parallelism (DP) communication. CrossPipe generates optimized pipeline schedules using either solver-based optimal or fast near-optimal greedy algorithms, built upon a flexible execution engine that separates scheduling logic from communication details. Our evaluation shows that CrossPipe reduces training time by up to 33.6\% compared to traditional pipeline schedules under identical memory constraints. When memory constraints are relaxed, CrossPipe maintains strong performance despite communication delays, approaching the efficiency of idealized schedules without delays. CrossPipe offers improved scalability and resource utilization, particularly in environments with high network latency or limited bandwidth.
VirtualFencer: Generating Fencing Bouts based on Strategies Extracted from In-the-Wild Videos
Venue: arXiv cs.CV 2025
Year: 2025

Fencing is a sport where athletes engage in diverse yet strategically logical motions. While most motions fall into a few high-level actions (e.g. step, lunge, parry), the execution can vary widely-fast vs. slow, large vs. small, offensive vs. defensive. Moreover, a fencer's actions are informed by a strategy that often comes in response to the opponent's behavior. This combination of motion diversity with underlying two-player strategy motivates the application of data-driven modeling to fencing. We present VirtualFencer, a system capable of extracting 3D fencing motion and strategy from in-the-wild video without supervision, and then using that extracted knowledge to generate realistic fencing behavior. We demonstrate the versatile capabilities of our system by having it (i) fence against itself (self-play), (ii) fence against a real fencer's motion from online video, and (iii) fence interactively against a professional fencer.
User Concerns Regarding Social Robots for Mood Regulation: A Case Study on the "Sunday Blues"
Venue: arXiv cs.HC 2025
Year: 2025

While recent research highlights the potential of social robots to support mood regulation, little is known about how prospective users view their integration into everyday life. To explore this, we conducted an exploratory case study that used a speculative robot concept "Mora" to provoke reflection and facilitate meaningful discussion about using social robots to manage subtle, day-to-day emotional experiences. We focused on the "Sunday Blues," a common dip in mood that occurs at the end of the weekend, as a relatable context in which to explore individuals' insights. Using a video prototype and a co-constructing stories method, we engaged 15 participants in imagining interactions with Mora and discussing their expectations, doubts, and concerns. The study surfaced a range of nuanced reflections around the attributes of social robots like empathy, intervention effectiveness, and ethical boundaries, which we translated into design considerations for future research and development in human-robot interaction.
Meaningful Data Erasure in the Presence of Dependencies
Venue: arXiv cs.DB 2025
Year: 2025

Data regulations like GDPR require systems to support data erasure but leave the definition of "erasure" open to interpretation. This ambiguity makes compliance challenging, especially in databases where data dependencies can lead to erased data being inferred from remaining data. We formally define a precise notion of data erasure that ensures any inference about deleted data, through dependencies, remains bounded to what could have been inferred before its insertion. We design erasure mechanisms that enforce this guarantee at minimal cost. Additionally, we explore strategies to balance cost and throughput, batch multiple erasures, and proactively compute data retention times when possible. We demonstrate the practicality and scalability of our algorithms using both real and synthetic datasets.
Question Decomposition for Retrieval-Augmented Generation
Venue: arXiv cs.CL 2025
Year: 2025

Grounding large language models (LLMs) in verifiable external sources is a well-established strategy for generating reliable answers. Retrieval-augmented generation (RAG) is one such approach, particularly effective for tasks like question answering: it retrieves passages that are semantically related to the question and then conditions the model on this evidence. However, multi-hop questions, such as "Which company among NVIDIA, Apple, and Google made the biggest profit in 2023?," challenge RAG because relevant facts are often distributed across multiple documents rather than co-occurring in one source, making it difficult for standard RAG to retrieve sufficient information. To address this, we propose a RAG pipeline that incorporates question decomposition: (i) an LLM decomposes the original query into sub-questions, (ii) passages are retrieved for each sub-question, and (iii) the merged candidate pool is reranked to improve the coverage and precision of the retrieved evidence. We show that question decomposition effectively assembles complementary documents, while reranking reduces noise and promotes the most relevant passages before answer generation. Although reranking itself is standard, we show that pairing an off-the-shelf cross-encoder reranker with LLM-driven question decomposition bridges the retrieval gap on multi-hop questions and provides a practical, drop-in enhancement, without any extra training or specialized indexing. We evaluate our approach on the MultiHop-RAG and HotpotQA, showing gains in retrieval (MRR@10: +36.7%) and answer accuracy (F1: +11.6%) over standard RAG baselines.
Wireless AI Evolution: From Statistical Learners to Electromagnetic-Guided Foundation Models
Venue: arXiv cs.IT 2025
Year: 2025

While initial applications of artificial intelligence (AI) in wireless communications over the past decade have demonstrated considerable potential using specialized models for targeted communication tasks, the revolutionary demands of sixth-generation (6G) networks for holographic communications, ubiquitous sensing, and native intelligence are propelling a necessary evolution towards AI-native wireless networks. The arrival of large AI models paves the way for the next phase of Wireless AI, driven by wireless foundation models (WFMs). In particular, pre-training on universal electromagnetic (EM) principles equips WFMs with the essential adaptability for a multitude of demanding 6G applications. However, existing large AI models face critical limitations, including pre-training strategies disconnected from EM-compliant constraints leading to physically inconsistent predictions, a lack of embedded understanding of wave propagation physics, and the inaccessibility of massive labeled datasets for comprehensive EM-aware training. To address these challenges, this article presents an electromagnetic information theory-guided self-supervised pre-training (EIT-SPT) framework designed to systematically inject EM physics into WFMs. The EIT-SPT framework aims to infuse WFMs with intrinsic EM knowledge, thereby enhancing their physical consistency, generalization capabilities across varied EM landscapes, and overall data efficiency. Building upon the proposed EIT-SPT framework, this article first elaborates on diverse potential applications in 6G scenarios of WFMs, then validates the efficacy of the proposed framework through illustrative case studies, and finally summarizes critical open research challenges and future directions for WFMs.
Gregorian melody, modality, and memory: Segmenting chant with Bayesian nonparametrics
Venue: arXiv cs.CL 2025
Year: 2025

The idea that Gregorian melodies are constructed from some vocabulary of segments has long been a part of chant scholarship. This so-called "centonisation" theory has received much musicological criticism, but frequent re-use of certain melodic segments has been observed in chant melodies, and the intractable number of possible segmentations allowed the option that some undiscovered segmentation exists that will yet prove the value of centonisation, and recent empirical results have shown that segmentations can outperform music-theoretical features in mode classification. Inspired by the fact that Gregorian chant was memorised, we search for an optimal unsupervised segmentation of chant melody using nested hierarchical Pitman-Yor language models. The segmentation we find achieves state-of-the-art performance in mode classification. Modeling a monk memorising the melodies from one liturgical manuscript, we then find empirical evidence for the link between mode classification and memory efficiency, and observe more formulaic areas at the beginnings and ends of melodies corresponding to the practical role of modality in performance. However, the resulting segmentations themselves indicate that even such a memory-optimal segmentation is not what is understood as centonisation.
Eilenberg correspondence for Stone recognition
Venue: arXiv cs.FL 2025
Year: 2025

We develop and explore the idea of recognition of languages (in the general sense of subsets of topological algebras) as preimages of clopen sets under continuous homomorphisms into Stone topological algebras. We obtain an Eilenberg correspondence between varieties of languages and varieties of ordered Stone topological algebras and a Birkhoff/Reiterman-type theorem showing that the latter may me defined by certain pseudo-inequalities. In the case of classical formal languages, of words over a finite alphabet, we also show how this extended framework goes beyond the class of regular languages by working with Stone completions of minimal automata, viewed as unary algebras. This leads to a general method for showing that a language does not belong to a variety of languages, expressed in terms of sequences of pairs of words, which is illustrated when the class consists of all finite intersections of context-free languages.
Diffusion Disambiguation Models for Partial Label Learning
Venue: arXiv cs.LG 2025
Year: 2025

Learning from ambiguous labels is a long-standing problem in practical machine learning applications. The purpose of \emph{partial label learning} (PLL) is to identify the ground-truth label from a set of candidate labels associated with a given instance. Inspired by the remarkable performance of diffusion models in various generation tasks, this paper explores their potential to denoise ambiguous labels through the reverse denoising process. Therefore, this paper reformulates the label disambiguation problem from the perspective of generative models, where labels are generated by iteratively refining initial random guesses. This perspective enables the diffusion model to learn how label information is generated stochastically. By modeling the generation uncertainty, we can use the maximum likelihood estimate of the label for classification inference. However, such ambiguous labels lead to a mismatch between instance and label, which reduces the quality of generated data. To address this issue, this paper proposes a \emph{diffusion disambiguation model for PLL} (DDMP), which first uses the potential complementary information between instances and labels to construct pseudo-clean labels for initial diffusion training. Furthermore, a transition-aware matrix is introduced to estimate the potential ground-truth labels, which are dynamically updated during the diffusion generation. During training, the ground-truth label is progressively refined, improving the classifier. Experiments show the advantage of the DDMP and its suitability for PLL.
Serving LLMs in HPC Clusters: A Comparative Study of Qualcomm Cloud AI 100 Ultra and NVIDIA Data Center GPUs
Venue: arXiv cs.DC 2025
Year: 2025

This study presents a benchmarking analysis of the Qualcomm Cloud AI 100 Ultra (QAic) accelerator for large language model (LLM) inference, evaluating its energy efficiency (throughput per watt), performance, and hardware scalability against NVIDIA A100 GPUs (in 4x and 8x configurations) within the National Research Platform (NRP) ecosystem. A total of 12 open-source LLMs, ranging from 124 million to 70 billion parameters, are served using the vLLM framework. Our analysis reveals that QAic achieves competitive energy efficiency with advantages on specific models while enabling more granular hardware allocation: some 70B models operate on as few as 1 QAic card versus 8 A100 GPUs required, with 20x lower power consumption (148W vs 2,983W). For smaller models, single QAic devices achieve up to 35x lower power consumption compared to our 4-GPU A100 configuration (36W vs 1,246W). The findings offer insights into the potential of the Qualcomm Cloud AI 100 Ultra for energy-constrained and resource-efficient HPC deployments within the National Research Platform (NRP).
Real-Time In-Network Machine Learning on P4-Programmable FPGA SmartNICs with Fixed-Point Arithmetic and Taylor
Venue: arXiv cs.DC 2025
Year: 2025

As machine learning (ML) applications become integral to modern network operations, there is an increasing demand for network programmability that enables low-latency ML inference for tasks such as Quality of Service (QoS) prediction and anomaly detection in cybersecurity. ML models provide adaptability through dynamic weight adjustments, making Programming Protocol-independent Packet Processors (P4)-programmable FPGA SmartNICs an ideal platform for investigating In-Network Machine Learning (INML). These devices offer high-throughput, low-latency packet processing and can be dynamically reconfigured via the control plane, allowing for flexible integration of ML models directly at the network edge. This paper explores the application of the P4 programming paradigm to neural networks and regression models, where weights and biases are stored in control plane table lookups. This approach enables flexible programmability and efficient deployment of retrainable ML models at the network edge, independent of core infrastructure at the switch level.
Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning
Venue: arXiv cs.AI 2025
Year: 2025

Math reasoning has become the poster child of progress in large language models (LLMs), with new models rapidly surpassing human-level performance on benchmarks like MATH and AIME. But as math leaderboards improve week by week, it is worth asking: do these gains reflect broader problem-solving ability or just narrow overfitting? To answer this question, we evaluate over 20 open-weight reasoning-tuned models across a broad suite of tasks, including math, scientific QA, agent planning, coding, and standard instruction-following. We surprisingly find that most models that succeed in math fail to transfer their gains to other domains. To rigorously study this phenomenon, we conduct controlled experiments on Qwen3-14B models using math-only data but different tuning methods. We find that reinforcement learning (RL)-tuned models generalize well across domains, while supervised fine-tuning (SFT)-tuned models often forget general capabilities. Latent-space representation and token-space distribution shift analyses reveal that SFT induces substantial representation and output drift, while RL preserves general-domain structure. Our results suggest a need to rethink standard post-training recipes, particularly the reliance on SFT-distilled data for advancing reasoning models.
DIJE: Dense Image Jacobian Estimation for Robust Robotic Self-Recognition and Visual Servoing
Venue: arXiv cs.RO 2025
Year: 2025

For robots to move in the real world, they must first correctly understand the state of its own body and the tools that it holds. In this research, we propose DIJE, an algorithm to estimate the image Jacobian for every pixel. It is based on an optical flow calculation and a simplified Kalman Filter that can be efficiently run on the whole image in real time. It does not rely on markers nor knowledge of the robotic structure. We use the DIJE in a self-recognition process which can robustly distinguish between movement by the robot and by external entities, even when the motion overlaps. We also propose a visual servoing controller based on DIJE, which can learn to control the robot's body to conduct reaching movements or bimanual tool-tip control. The proposed algorithms were implemented on a physical musculoskeletal robot and its performance was verified. We believe that such global estimation of the visuomotor policy has the potential to be extended into a more general framework for manipulation.
Recurrent Memory-Augmented Transformers with Chunked Attention for Long-Context Language Modeling
Venue: arXiv cs.LG 2025
Year: 2025

We present a Transformer architecture for long-context language modeling that combines global attention with two biologically inspired components: chunked local attention and a gated FIFO memory mechanism. This unified attention block allows the model to efficiently handle both short-range and long-range dependencies without increasing attention cost quadratically. The memory module persistently stores past token representations using a gated update mechanism inspired by recurrent networks. Rotary positional encoding is applied per attention head to enable directionally disentangled, scale-invariant positional signals. The architecture is implemented entirely from scratch in PyTorch, with no reliance on high-level libraries, enabling transparent and modular experimentation. Our model offers a lightweight and extensible design for tasks such as dialogue modeling, code completion, and document understanding.
Diversity Conscious Refined Random Forest
Venue: arXiv cs.LG 2025
Year: 2025

Random Forest (RF) is a widely used ensemble learning technique known for its robust classification performance across diverse domains. However, it often relies on hundreds of trees and all input features, leading to high inference cost and model redundancy. In this work, our goal is to grow trees dynamically only on informative features and then enforce maximal diversity by clustering and retaining uncorrelated trees. Therefore, we propose a Refined Random Forest Classifier that iteratively refines itself by first removing the least informative features and then analytically determines how many new trees should be grown, followed by correlation-based clustering to remove redundant trees. The classification accuracy of our model was compared against the standard RF on the same number of trees. Experiments on 8 multiple benchmark datasets, including binary and multiclass datasets, demonstrate that the proposed model achieves improved accuracy compared to standard RF.
Shared & Domain Self-Adaptive Experts with Frequency-Aware Discrimination for Continual Test-Time Adaptation
Venue: arXiv cs.CV 2025
Year: 2025

This paper focuses on the Continual Test-Time Adaptation (CTTA) task, aiming to enable an agent to continuously adapt to evolving target domains while retaining previously acquired domain knowledge for effective reuse when those domains reappear. Existing shared-parameter paradigms struggle to balance adaptation and forgetting, leading to decreased efficiency and stability. To address this, we propose a frequency-aware shared and self-adaptive expert framework, consisting of two key components: (i) a dual-branch expert architecture that extracts general features and dynamically models domain-specific representations, effectively reducing cross-domain interference and repetitive learning cost; and (ii) an online Frequency-aware Domain Discriminator (FDD), which leverages the robustness of low-frequency image signals for online domain shift detection, guiding dynamic allocation of expert resources for more stable and realistic adaptation. Additionally, we introduce a Continual Repeated Shifts (CRS) benchmark to simulate periodic domain changes for more realistic evaluation. Experimental results show that our method consistently outperforms existing approaches on both classification and segmentation CTTA tasks under standard and CRS settings, with ablations and visualizations confirming its effectiveness and robustness. Our code is available at https://github.com/ZJC25127/Domain-Self-Adaptive-CTTA.git.
Towards Resource-Efficient Serverless LLM Inference with SLINFER
Venue: arXiv cs.DC 2025
Year: 2025

The rise of LLMs has driven demand for private serverless deployments, characterized by moderate-sized models and infrequent requests. While existing serverless solutions follow exclusive GPU allocation, we take a step back to explore modern platforms and find that: Emerging CPU architectures with built-in accelerators are capable of serving LLMs but remain underutilized, and both CPUs and GPUs can accommodate multiple LLMs simultaneously. We propose SLINFER, a resource-efficient serverless inference scheme tailored for small- to mid-sized LLMs that enables elastic and on-demand sharing across heterogeneous hardware. SLINFER tackles three fundamental challenges: (1) precise, fine-grained compute resource allocation at token-level to handle fluctuating computational demands; (2) a coordinated and forward-looking memory scaling mechanism to detect out-of-memory hazards and reduce operational overhead; and (3) a dual approach that consolidates fragmented instances through proactive preemption and reactive bin-packing. Experimental results on 4 32-core CPUs and 4 A100 GPUs show that SLINFER improves serving capacity by 47% - 62% through sharing, while further leveraging CPUs boosts this to 86% - 154%.