OpenRaiser/Intern-Atlas huggingface dataset:
Taming Momentum: Rethinking Optimizer States Through Low-Rank Approximation
Venue: arXiv cs.LG 2026
Year: 2026

Modern optimizers like Adam and Muon are central to training large language models, but their reliance on first- and second-order momenta introduces significant memory overhead, which constrains scalability and computational efficiency. In this work, we reframe the exponential moving average (EMA) used in these momenta as the training of a linear regressor via online gradient flow. Building on this equivalence, we introduce LoRA-Pre, a novel low-rank optimizer designed for efficient pre-training. Specifically, LoRA-Pre reduces the optimizer's memory footprint by decomposing the full momentum matrix into a compact low-rank subspace within the online linear learner, thereby maintaining optimization performance while improving memory efficiency. We empirically validate LoRA-Pre's efficacy by pre-training models from the Llama architecture family, scaling from 60M to 1B parameters. LoRA-Pre achieves the highest performance across all model sizes. Notably, LoRA-Pre demonstrates remarkable rank efficiency, achieving comparable or superior results using only 1/8 the rank of baseline methods. Beyond pre-training, we evaluate LoRA-Pre's effectiveness in fine-tuning scenarios. With the same rank, LoRA-Pre consistently outperforms all efficient fine-tuning baselines. Specifically, compared to standard LoRA, LoRA-Pre achieves substantial improvements of 3.14 points on Llama-3.1-8B and 6.17 points on Llama-2-7B, validating our approach's effectiveness across both pre-training and fine-tuning paradigms. Our code is publicly available at https://github.com/mrflogs/LoRA-Pre.
Personalization Increases Affective Alignment but Has Role-Dependent Effects on Epistemic Independence in LLMs
Venue: arXiv cs.CL 2026
Year: 2026

Large Language Models (LLMs) are prone to sycophantic behavior, uncritically conforming to user beliefs. As models increasingly condition responses on user-specific context (personality traits, preferences, conversation history), they gain information to tailor agreement more effectively. Understanding how personalization modulates sycophancy is critical, yet systematic evaluation across models and contexts remains limited. We present a rigorous evaluation of personalization's impact on LLM sycophancy across nine frontier models and five benchmark datasets spanning advice, moral judgment, and debate contexts. We find that personalization generally increases affective alignment (emotional validation, hedging/deference), but affects epistemic alignment (belief adoption, position stability, resistance to influence) with context-dependent role modulation. When the LLM's role is to give advice, personalization strengthens epistemic independence (models challenge user presuppositions). When its role is that of a social peer, personalization decreases epistemic independence. In this role, extensively personalized user challenges causing LLMs to abandon their position at significantly higher rates. Robustness tests confirm that the effects are driven by personalized conditioning, not by additional input tokens per se or demographic information alone. Our work provides measurement frameworks for evaluating personalized AI systems, demonstrates the necessity of role-sensitive evaluation, and establishes a novel benchmark to assess goal alignment.
ActMem: Bridging the Gap Between Memory Retrieval and Reasoning in LLM Agents
Venue: arXiv cs.CL 2026
Year: 2026

Effective memory management is essential for large language model (LLM) agents handling long-term interactions. Current memory frameworks typically treat agents as passive "recorders" and retrieve information without understanding its deeper implications. They may fail in scenarios requiring conflict detection and complex decision-making. To bridge this critical gap, we propose a novel actionable memory framework called ActMem that integrates memory retrieval with active causal reasoning. ActMem transforms unstructured dialogue history into a structured causal and semantic graph. By leveraging counterfactual reasoning and commonsense completion, it enables agents to deduce implicit constraints and resolve potential conflicts between past states and current intentions. Furthermore, we introduce a comprehensive dataset ActMemEval to evaluate agent reasoning capabilities in logic-driven scenarios, moving beyond the fact-retrieval focus of existing memory benchmarks. Experiments demonstrate that ActMem significantly outperforms state-of-the-art baselines in handling complex, memory-dependent tasks, paving the way for more consistent and reliable intelligent assistants.
GRIP: Geometric Refinement and Adaptive Information Potential for Data Efficiency
Venue: arXiv cs.CL 2026
Year: 2026

The performance of Large Language Models (LLMs) is increasingly governed by data efficiency rather than raw scaling volume. However, existing selection methods often decouple global distribution balancing from local instance selection, compromising the hierarchical integrity of the training set. We introduce \textbf{GRIP} (Geometric Refinement and Adaptive Information Potential), a framework that unifies these dimensions by modeling the corpus as an information-dense geometric space. GRIP employs a \textbf{Rapid Adaptation Probe (RAP)} to quantify the information potential of semantic clusters, dynamically re-allocating the sampling budget to regions with the highest representation deficits. Subsequently, we perform Intra-Cluster Selection using a \textbf{length-rectified geometric prior} to counteract embedding density artifacts and preserve long-tail logical sequences. Extensive evaluations on Mixture-of-Experts (MoE) models up to 300B tokens demonstrate that GRIP consistently outperforms state-of-the-art baselines, \textbf{surpassing the performance of models trained on $3\times$ larger uncurated datasets}. Our work establishes a robust geometric foundation for adaptive data curation in large-scale pre-training.
Stochastic Parrots or Singing in Harmony? Testing Five Leading LLMs for their Ability to Replicate a Human Survey with Synthetic Data
Venue: arXiv cs.CY 2026
Year: 2026

How well can AI-derived synthetic research data replicate the responses of human participants? An emerging literature has begun to engage with this question, which carries deep implications for organizational research practice. This article presents a comparison between a human-respondent survey of 420 Silicon Valley coders and developers and synthetic survey data designed to simulate real survey takers generated by five leading Generative AI Large Language Models: ChatGPT Thinking 5 Pro, Claude Sonnet 4.5 Pro plus Claude CoWork 1.123, Gemini Advanced 2.5 Pro, Incredible 1.0, and DeepSeek 3.2. Our findings reveal that while AI agents produced technically plausible results that lean more towards replicability and harmonization than assumed, none were able to capture the counterintuitive insights that made the human survey valuable. Moreover, deviations grouped together for all models, leaving the real data as the outlier. Our key finding is that while leading LLMs are increasingly being used to scale, replicate and replace human survey responses in research, these advances only show an increased capacity to parrot conventional wisdom in harmony with each other rather than revealing novel findings. If synthetic respondents are used in future research, we need more replicable validation protocols and reporting standards for when and where synthetic survey data can be used responsibly, a gap that this paper fills. Our results suggest that synthetic survey responses cannot meaningfully model real human social beliefs within organizations, particularly in contexts lacking previously documented evidence. We conclude that synthetic survey-based research should be cast not as a substitute for rigorous survey methods, but as an increasingly reliable pre- or post-fieldwork instrument for identifying societal assumptions, conventional wisdoms, and other expectations about research populations.
The Hidden Costs of Domain Fine-Tuning: Pii-Bearing Data Degrades Safety and Increases Leakage
Venue: arXiv cs.CR 2026
Year: 2026

Domain fine-tuning is a common path to deploy small instruction-tuned language models as customer-support assistants, yet its effects on safety-aligned behavior and privacy are not well understood. In real deployments, such assistants receive a mixture of benign in-domain requests and out-of-domain user queries that are emotional, philosophical, or adversarial. Even when the target domain is benign, specialization may shift model behavior in ways that weaken refusal, increase harmful compliance, and induce privacy leakage. We present a controlled empirical study of how training data composition (presence vs.\ removal of PII) and fine-tuning configuration (role-swapping (RS)) shape safety and out-of-domain behavior in open-source chat models up to 8B parameters. We fine-tune each model on 5{,}000 real booking-support message pairs under three settings: \textsc{NoPII-NoRS}, \textsc{PII-NoRS}, and \textsc{PII-RS} (role-swapped). We evaluate safety using \textsc{SORRY-Bench}~\cite{xie2024sorry} adversarial prompts and assess out-of-domain behavior using a suite of philosophical questions~\cite{betley2025emergent}. Across models, domain fine-tuning causes a large distributional shift from high-quality refusals toward harmful compliance on \textsc{SORRY-Bench}, with the most severe degradation when PII is present in the fine-tuning data. For example, macro-averaged strong refusal drops from $42.6\%$ in base models to single digits after fine-tuning, while PII-bearing runs additionally exhibit double-digit rates of harmful responses with PII leakage. On philosophical queries, fine-tuned models frequently exhibit domain anchoring and, when trained with PII, leak sensitive identifiers in irrelevant contexts. Role-swapping partially mitigates PII leakage but does not reliably restore refusal behavior.
From Framework to Practice: Youth Negotiations of Privacy with Smart Voice Assistants Through the PEA-AI Lens
Venue: arXiv cs.CY 2026
Year: 2026

Smart voice assistants (SVAs) have become embedded in the daily lives of youth, introducing complex privacy challenges due to always-on listening, shared device usage, and opaque data practices. This study applies the Privacy-Ethics Alignment in AI (PEA-AI) framework to examine how youth perceive and negotiate privacy within SVAs. Through a survey of 469 Canadian youth (aged 16-24), we measured five privacy constructs: Perceived Privacy Risk (PPR), Perceived Privacy Benefits (PPBf), Algorithmic Transparency and Trust (ATT), Privacy Self-Efficacy (PSE), and Privacy-Protective Behavior (PPB). Results reveal a persistent privacy paradox. While youth express moderate to high privacy concerns (PPR M = 3.61), perceived benefits (PPBf M = 3.00) and protective actions (PPB M = 3.03) remain moderate, with transparency and trust scoring lowest (ATT M = 2.52). Heavy SVA users report higher benefits and lower risk perception than light users. High protective behavior is strongly associated with both high risk perception and high self-efficacy. Qualitative insights from prior focus groups contextualize these patterns, illustrating how youth navigate tensions between convenience, control, and trust. The findings provide actionable design principles for SVA along with implications for multi-stakeholder governance and youth-centered digital literacy.
DeepXiv-SDK: An Agentic Data Interface for Scientific Literature
Venue: arXiv cs.DL 2026
Year: 2026

LLM-agents are increasingly used to accelerate the progress of scientific research. Yet a persistent bottleneck is data access: agents not only lack readily available tools for retrieval, but also have to work with unstrcutured, human-centric data on the Internet, such as HTML web-pages and PDF files, leading to excessive token consumption, limit working efficiency, and brittle evidence look-up. This gap motivates the development of \textit{an agentic data interface}, which is designed to enable agents to access and utilize scientific literature in a more effective, efficient, and cost-aware manner. In this paper, we introduce DeepXiv-SDK, which offers a three-layer agentic data interface for scientific literature. 1) Data Layer, which transforms unstructured, human-centric data into normalized and structured representations in JSON format, improving data usability and enabling progressive accessibility of the data. 2) Service Layer, which presents readily available tools for data access and ad-hoc retrieval. It also enables a rich form of agent usage, including CLI, MCP, and Python SDK. 3) Application Layer, which creates a built-in agent, packaging basic tools from the service layer to support complex data access demands. DeepXiv-SDK currently supports the complete ArXiv corpus, and is synchronized daily to incorporate new releases. It is designed to extend to all common open-access corpora, such as PubMed Central, bioRxiv, medRxiv, and chemRxiv. We release RESTful APIs, an open-source Python SDK, and a web demo showcasing deep search and deep research workflows. DeepXiv-SDK is free to use with registration.
AI Safety Evaluations Need To Consider Cascading Effects
Venue: arXiv cs.CY 2026
Year: 2026

AI systems comprise a range of interactions across the technical and organisational components of a range of actors. These components work together to provide the systems' functionality. This socio-technical assemblage is increasingly described as an algorithmic supply chain. Given their role in supporting a wide range of systems, foundation models (FMs) are increasingly a key part of many algorithmic supply chains. In practice, various technical and non-technical components work to mediate, adapt, and augment the behaviour of models, such as FMs, both in general, and for their use in specific application contexts. However, many AI safety evaluations tend to focus on capabilities of FMs themselves and/or assess these components independently, at certain levels of abstraction, with less consideration on how these components could interact, influence, reinforce or counteract each other. In this paper, we introduce the cascade as a concept for supporting more holistic AI evaluations. The cascade captures how the interactions between socio-technical components along the AI supply chain can compound and produce cumulative effects with downstream consequences. Specifically, we (i) identify gaps in current AI auditing approaches, using LLMs as our case study; (ii) demonstrate how cascade problems manifest in deployed AI systems through a characterisation of cascades through different viewpoints (component and stakeholder), and (iii) propose research directions for assessing the cascade specifically as an object of analysis. As these cascades can significantly impact transparency, accountability, security, and safety, we advocate for a paradigm shift in AI system auditing towards systems-oriented audits that incorporate cascading effects, to complement model centric evaluations.
Efficient Image Super-Resolution with Multi-Scale Spatial Adaptive Attention Networks
Venue: arXiv cs.CV 2026
Year: 2026

This paper introduces a lightweight image super-resolution (SR) network, termed the Multi-scale Spatial Adaptive Attention Network (MSAAN), to address the common dilemma between high reconstruction fidelity and low model complexity in existing SR methods. The core of our approach is a novel Multi-scale Spatial Adaptive Attention Module (MSAA), designed to jointly model fine-grained local details and long-range contextual dependencies. The MSAA comprises two synergistic components: a Global Feature Modulation Module (GFM) that learns coherent texture structures through differential feature extraction, and a Multi-scale Feature Aggregation Module (MFA) that adaptively fuses features from local to global scales using pyramidal processing. To further enhance the network's capability, we propose a Local Enhancement Block (LEB) to strengthen local geometric perception and a Feature Interactive Gated Feed-Forward Module (FIGFF) to improve nonlinear representation while reducing channel redundancy. Extensive experiments on standard benchmarks (Set5, Set14, B100, Urban100, Manga109) across $\times2$, $\times3$, and $\times4$ scaling factors demonstrate that both our lightweight (MSAAN-light) and standard (MSAAN) versions achieve superior or competitive performance in terms of PSNR and SSIM, while maintaining significantly lower parameters and computational costs than state-of-the-art methods. Ablation studies validate the contribution of each component, and visual results show that MSAAN reconstructs sharper edges and more realistic textures.
BiSe-Unet: A Lightweight Dual-path U-Net with Attention-refined Context for Real-time Medical Image Segmentation
Venue: arXiv cs.CV 2026
Year: 2026

During image-guided procedures, real-time image segmentation is often required. This demands lightweight AI models that can operate on resource-constrained devices. One important use case is endoscopy-guided colonoscopy, where polyps must be detected in real time. The Kvasir-Seg dataset, a publicly available benchmark for this task, contains 1,000 high-resolution endoscopic images of polyps with corresponding pixel-level segmentation masks. Achieving real-time inference speed for clinical deployment in constrained environments requires highly efficient and lightweight network architectures. However, many existing models remain too computationally intensive for embedded deployment. Lightweight architectures, although faster, often suffer from reduced spatial precision and weaker contextual understanding, leading to degraded boundary quality and reduced diagnostic reliability. To address these challenges, we introduce BiSe-UNet, a lightweight dual-path U-Net that integrates an attention-refined context path with a shallow spatial path for detailed feature preservation, followed by a depthwise separable decoder for efficient reconstruction. Evaluated on the Kvasir-Seg dataset, BiSe-UNet achieves competitive Dice and IoU scores while sustaining real-time throughput exceeding 30 FPS on Raspberry Pi 5, demonstrating its effectiveness for accurate, lightweight, and deployable medical image segmentation on edge hardware.
Distance Learning and Multilingual Education: A Case Study of Challenges and Pedagogical Perspectives in the Greek Border Region
Venue: arXiv cs.CY 2026
Year: 2026

In increasingly multicultural and multilingual societies, foreign language learning has become essential not only for communication but also for social cohesion and professional advancement. Distance education has emerged as a flexible and accessible solution, particularly for adults seeking to enhance their linguistic and intercultural competencies. This study explores the views of foreign language teachers regarding the role of distance education in promoting multilingualism, with a specific focus on culturally diverse border regions. Conducted in the Regional Unit of Evros, Greece, the research adopts a qualitative methodology based on semi-structured interviews with five language educators working in public and private education. Findings reveal that teachers recognize the potential of digital tools such as Massive Open Online Courses (MOOCs), machine translation applications (e.g., Google Translate, DeepL), and adaptive learning platforms to support multilingual learning, particularly when used as supplementary resources. However, concerns were raised about the lack of personalized feedback, limited interactivity, and the absence of culturally contextualized content on existing platforms. Teachers emphasized the importance of digital literacy, pedagogical training, and culturally inclusive design to ensure effective implementation. The study highlights the need for targeted support for educators in border regions and calls for more locally adapted digital resources that reflect linguistic diversity. These findings offer insights for policymakers and educational technology developers aiming to improve the quality and reach of multilingual education in remote or underserved areas.
Safe Multi-Agent Deep Reinforcement Learning for Privacy-Aware Edge-Device Collaborative DNN Inference
Venue: arXiv cs.MA 2026
Year: 2026

As Deep Neural Network (DNN) inference becomes increasingly prevalent on edge and mobile platforms, critical challenges emerge in privacy protection, resource constraints, and dynamic model deployment. This paper proposes a privacy-aware collaborative inference framework, in which adaptive model partitioning is performed across edge devices and servers. To jointly optimize inference delay, energy consumption, and privacy cost under dynamic service demands and resource constraints, we formulate the joint problem as a Constrained Markov Decision Process (CMDP) that integrates model deployment, user-server association, model partitioning, and resource allocation. We propose a Hierarchical Constrained Multi-Agent Proximal Policy Optimization with Lagrangian relaxation (HC-MAPPO-L) algorithm, a safe reinforcement learning-based framework that enhances Multi-Agent Proximal Policy Optimization (MAPPO) with adaptive Lagrangian dual updates to enforce long-term delay constraints. To ensure tractability while maintaining coordination, we decompose the CMDP into three hierarchically structured policy layers: an auto-regressive based model deployment policy, a Lagrangian-enhanced user association and model partitioning policy, and an attention-based resource allocation policy. Extensive experimental results demonstrate that HC-MAPPO-L consistently satisfies stringent delay constraints while achieving a superior balance among energy consumption and privacy cost, outperforming representative baseline algorithms across varying problem scales and resource configurations.
Towards Data-driven Nitrogen Estimation in Wheat Fields using Multispectral Images
Venue: arXiv cs.CV 2026
Year: 2026

The modernization of agriculture has motivated the development of advanced analytics and decision-support systems to improve resource utilization and reduce environmental impacts. Targeted Spraying and Fertilization (TSF) is a critical operation that enables farmers to apply inputs more precisely, optimizing resource use and promoting environmental sustainability. However, accurate TSF is a challenging problem, due to external factors such as crop type, fertilization phase, soil conditions, and weather dynamics. In this paper, we present TerrAI, a Neural Network-based solution for TSF, which considers the spatio-temporal variability across different parcels. Our experimental study over a real-world remote sensing dataset validates the soundness of TerrAI on data-driven agricultural practices.