OpenRaiser/Intern-Atlas huggingface dataset:
When Is Degree Enough? Bounds on Degree-Eigenvector Misalignment in Assortative Structured Networks
Venue: arXiv cs.SI 2025
Year: 2025

A tight alignment between the degree vector and the leading eigenvector arises naturally in networks with neutral degree mixing and the absence of local structures. Many real-world networks, however, violate both conditions. We derive bounds on the divergence between the degree vector and the eigenvector in networks with degree assortativity and local mesoscopic structures such as communities, core-peripheries, and cycles. Our approach is constructive. We design sufficiently general degree-preserving rewiring algorithms that start from a neutral benchmark and monotonically increase assortativity and the strength of local structures, with each step inducing a perturbation of the adjacency matrix. Using the Stewart--Sun Perturbation Bound, together with explicit spectral-norm control of the rewiring steps, we derive upper bounds on the angle between the eigenvector and the degree vector for modest levels of assortativity and local structures. Our analytical bounds delineate regions of `spectral safety' in which a node's degree can be used as a reliable measure of its systemic importance in real-world networks.
You Only Need Your Transformer 25% of the Time: Meaning-First Execution for Eliminating Unnecessary Inference
Venue: arXiv cs.LG 2025
Year: 2025

Modern AI inference systems treat transformer execution as mandatory, conflating model capability with execution necessity. We reframe inference as a control-plane decision problem: determining when execution is necessary versus when correctness can be preserved through alternative pathways. We introduce Meaning-First Execution (MFEE), a control-plane architecture implementing this framework, selectively invoking transformer inference only when required. MFEE operates as a gating layer above existing stacks without modifying models, weights, or parameters. Across 1,000 diverse prompts under deterministic decoding, MFEE achieves 78.1% execution reduction while maintaining 100% exact-match equivalence for invoked executions. Comparative evaluation reveals pattern-based routers achieve at most 53.3% avoidance with correctness failures, while MFEE reaches 100% avoidance with zero failures through semantic analysis. We prove this limitation via Theorem 1: any router operating solely on finite feature maps cannot simultaneously guarantee zero false skips and positive avoidance on feature-collision pairs. These results establish execution governance as a foundational layer in ML systems infrastructure, orthogonal to model-level optimization techniques.
Comment on: Your Brain on ChatGPT: Accumulation of Cognitive Debt When Using an AI Assistant for Essay Writing Tasks
Venue: arXiv cs.AI 2025
Year: 2025

Recently published work titled Your Brain on ChatGPT: Accumulation of Cognitive Debt When Using an AI Assistant for Essay Writing Task by Kosmyna et al. (2025) has sparked a vivid debate on the topic of artificial intelligence (AI) and human performance. We sincerely congratulate Kosmyna et al. for initiating such important research, collecting a valuable dataset, and establishing highly automated pipelines for Natural Language Processing (NLP) analyses and scoring. We aim to provide constructive comments that may improve the manuscript's readiness for peer-reviewed publication, as some results by Kosmyna et al. (2025) could be interpreted more conservatively. Our primary concerns focus on: (i) study design considerations, including the limited sample size; (ii) the reproducibility of the analyses; (iii) methodological issues related to the EEG analysis; (iv) inconsistencies in the reporting of results; and (v) limited transparency in several aspects of the study's procedures and findings.
SmartFlow Reinforcement Learning and Agentic AI for Bike-Sharing Optimisation
Venue: arXiv cs.LG 2025
Year: 2025

SmartFlow is a multi-layered framework that integrates Reinforcement Learning and Agentic AI to address the dynamic rebalancing problem in urban bike-sharing services. Its architecture separates strategic, tactical, and communication functions for clarity and scalability. At the strategic level, a Deep Q-Network (DQN) agent, trained in a high-fidelity simulation of New Yorks Citi Bike network, learns robust rebalancing policies by modelling the challenge as a Markov Decision Process. These high-level strategies feed into a deterministic tactical module that optimises multi-leg journeys and schedules just-in-time dispatches to minimise fleet travel. Evaluation across multiple seeded runs demonstrates SmartFlows high efficacy, reducing network imbalance by over 95% while requiring minimal travel distance and achieving strong truck utilisation. A communication layer, powered by a grounded Agentic AI with a Large Language Model (LLM), translates logistical plans into clear, actionable instructions for operational staff, ensuring interpretability and execution readiness. This integration bridges machine intelligence with human operations, offering a scalable solution that reduces idle time, improves bike availability, and lowers operational costs. SmartFlow provides a blueprint for interpretable, AI-driven logistics in complex urban mobility networks.
Cultural Encoding in Large Language Models: The Existence Gap in AI-Mediated Brand Discovery
Venue: arXiv cs.AI 2025
Year: 2025

As artificial intelligence systems increasingly mediate consumer information discovery, brands face algorithmic invisibility. This study investigates Cultural Encoding in Large Language Models (LLMs) -- systematic differences in brand recommendations arising from training data composition. Analyzing 1,909 pure-English queries across 6 LLMs (GPT-4o, Claude, Gemini, Qwen3, DeepSeek, Doubao) and 30 brands, we find Chinese LLMs exhibit 30.6 percentage points higher brand mention rates than International LLMs (88.9% vs. 58.3%, p<.001). This disparity persists in identical English queries, indicating training data geography -- not language -- drives the effect. We introduce the Existence Gap: brands absent from LLM training corpora lack "existence" in AI responses regardless of quality. Through a case study of Zhizibianjie (OmniEdge), a collaboration platform with 65.6% mention rate in Chinese LLMs but 0% in International models (p<.001), we demonstrate how Linguistic Boundary Barriers create invisible market entry obstacles. Theoretically, we contribute the Data Moat Framework, conceptualizing AI-visible content as a VRIN strategic resource. We operationalize Algorithmic Omnipresence -- comprehensive brand visibility across LLM knowledge bases -- as the strategic objective for Generative Engine Optimization (GEO). Managerially, we provide an 18-month roadmap for brands to build Data Moats through semantic coverage, technical depth, and cultural localization. Our findings reveal that in AI-mediated markets, the limits of a brand's "Data Boundaries" define the limits of its "Market Frontiers."
Counterfactual Self-Questioning for Stable Policy Optimization in Language Models
Venue: arXiv cs.AI 2025
Year: 2025

Recent work on language model self-improvement shows that models can refine their own reasoning through reflection, verification, debate, or self-generated rewards. However, most existing approaches rely on external critics, learned reward models, or ensemble sampling, which increases complexity and training instability. We propose Counterfactual Self-Questioning, a framework in which a single language model generates and evaluates counterfactual critiques of its own reasoning. The method produces an initial reasoning trace, formulates targeted questions that challenge potential failure points, and generates alternative reasoning trajectories that expose incorrect assumptions or invalid steps. These counterfactual trajectories provide structured relative feedback that can be directly used for policy optimization without auxiliary models. Experiments on multiple mathematical reasoning benchmarks show that counterfactual self-questioning improves accuracy and training stability, particularly for smaller models, enabling scalable self-improvement using internally generated supervision alone.
Acceptance of cybernetic avatars for capability enhancement: a large-scale survey
Venue: arXiv cs.HC 2025
Year: 2025

Avatar embodiment experiences have the potential to enhance human capabilities by extending human senses, body, and mind. This study investigates social acceptance of robotic and virtual avatars as enablers of capability enhancement in six domains: identity exploration, well-being and behavioral transformation, expanded travel capabilities, expanded bodily and sensory abilities, cognitive augmentation, and immortality. We conducted a large-scale survey (n = 1001) in Dubai to explore acceptance of sixteen capability enhancement scenarios within these domains. The highest levels of agreement were observed for multilingual communication (77.5%) and learning capabilities (68.7%), followed by assisting individuals with reduced mobility (64.5%) and behavioral transformation (59.5%). Scenarios involving immortality through consciousness transfer received the least support (34.9%). These findings contribute to a deeper understanding of public attitudes toward avatar-based human enhancement and offer practical guidance for the responsible design, development, and integration of cybernetic avatars in the society, ensuring their societal acceptance and fostering a harmonious human-avatar coexistence.
TAG-HGT: A Scalable and Cost-Effective Framework for Inductive Cold-Start Academic Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Inductive cold-start recommendation remains the "Achilles' Heel" of industrial academic platforms, where thousands of new scholars join daily without historical interaction records. While recent Generative Graph Models (e.g., HiGPT, OFA) demonstrate promising semantic capabilities, their prohibitive inference latency (often exceeding 13 minutes per 1,000 requests) and massive computational costs render them practically undeployable for real-time, million-scale applications. To bridge this gap between generative quality and industrial scalability, we propose TAG-HGT, a cost-effective neuro-symbolic framework. Adopting a decoupled "Semantics-First, Structure-Refined" paradigm, TAG-HGT utilizes a frozen Large Language Model (DeepSeek-V3) as an offline semantic factory and distills its knowledge into a lightweight Heterogeneous Graph Transformer (HGT) via Cross-View Contrastive Learning (CVCL). We present a key insight: while LLM semantics provide necessary global recall, structural signals offer the critical local discrimination needed to distinguish valid collaborators from semantically similar but socially unreachable strangers in dense embedding spaces. Validated under a strict Time-Machine Protocol on the massive OpenAlex dataset, TAG-HGT achieves a SOTA System Recall@10 of 91.97%, outperforming structure-only baselines by 20.7%. Most significantly, from an industrial perspective, TAG-HGT reduces inference latency by five orders of magnitude ($4.5 \times 10^{5}\times$) compared to generative baselines (from 780s down to 1.73 ms), and slashes inference costs from $\sim$$1.50 to $<$$0.001 per 1k queries. This 99.9% cost reduction democratizes high-precision academic recommendation.
SciNetBench: A Relation-Aware Benchmark for Scientific Literature Retrieval Agents
Venue: arXiv cs.CE 2025
Year: 2025

The rapid development of AI agent has spurred the development of advanced research tools, such as Deep Research. Achieving this require a nuanced understanding of the relations within scientific literature, surpasses the scope of keyword-based or embedding-based retrieval. Existing retrieval agents mainly focus on the content-level similarities and are unable to decode critical relational dynamics, such as identifying corroborating or conflicting studies or tracing technological lineages, all of which are essential for a comprehensive literature review. Consequently, this fundamental limitation often results in a fragmented knowledge structure, misleading sentiment interpretation, and inadequate modeling of collective scientific progress. To investigate relation-aware retrieval more deeply, we propose SciNetBench, the first Scientific Network Relation-aware Benchmark for literature retrieval agents. Constructed from a corpus of over 18 million AI papers, our benchmark systematically evaluates three levels of relations: ego-centric retrieval of papers with novel knowledge structures, pair-wise identification of scholarly relationships, and path-wise reconstruction of scientific evolutionary trajectories. Through extensive evaluation of three categories of retrieval agents, we find that their accuracy on relation-aware retrieval tasks often falls below 20%, revealing a core shortcoming of current retrieval paradigms. Notably, further experiments on the literature review tasks demonstrate that providing agents with relational ground truth leads to a substantial 23.4% performance improvement in the review quality, validating the critical importance of relation-aware retrieval. We publicly release our benchmark at https://anonymous.4open.science/r/SciNetBench/ to support future research on advanced retrieval systems.
Advances and Challenges in Semantic Textual Similarity: A Comprehensive Survey
Venue: arXiv cs.CL 2025
Year: 2025

Semantic Textual Similarity (STS) research has expanded rapidly since 2021, driven by advances in transformer architectures, contrastive learning, and domain-specific techniques. This survey reviews progress across six key areas: transformer-based models, contrastive learning, domain-focused solutions, multi-modal methods, graph-based approaches, and knowledge-enhanced techniques. Recent transformer models such as FarSSiBERT and DeBERTa-v3 have achieved remarkable accuracy, while contrastive methods like AspectCSE have established new benchmarks. Domain-adapted models, including CXR-BERT for medical texts and Financial-STS for finance, demonstrate how STS can be effectively customized for specialized fields. Moreover, multi-modal, graph-based, and knowledge-integrated models further enhance semantic understanding and representation. By organizing and analyzing these developments, the survey provides valuable insights into current methods, practical applications, and remaining challenges. It aims to guide researchers and practitioners alike in navigating rapid advancements, highlighting emerging trends and future opportunities in the evolving field of STS.
LLM_annotate: A Python package for annotating and analyzing fiction characters
Venue: arXiv cs.CL 2025
Year: 2025

LLM_annotate is a Python package for analyzing the personality of fiction characters with large language models. It standardizes workflows for annotating character behaviors in full texts (e.g., books and movie scripts), inferring character traits, and validating annotation/inference quality via a human-in-the-loop GUI. The package includes functions for text chunking, LLM-based annotation, character name disambiguation, quality scoring, and computation of character-level statistics and embeddings. Researchers can use any LLM, commercial, open-source, or custom, within LLM_annotate. Through tutorial examples using The Simpsons Movie and the novel Pride and Prejudice, I demonstrate the usage of the package for efficient and reproducible character analyses.
Shielded RecRL: Explanation Generation for Recommender Systems without Ranking Degradation
Venue: arXiv cs.IR 2025
Year: 2025

We introduce Shielded RecRL, a reinforcement learning approach to generate personalized explanations for recommender systems without sacrificing the system's original ranking performance. Unlike prior RLHF-based recommender methods that directly optimize item rankings, our two-tower architecture keeps the recommender's ranking model intact while a language model learns to produce helpful explanations. We design a composite reward signal combining explanation length, content relevance, and coherence, and apply proximal policy optimization (PPO) with a KL-divergence constraint to fine-tune a large language model with only 0.4% of its parameters trainable via LoRA adapters. In experiments on an Amazon Books dataset (approximately 50K interactions in the fantasy and romance genres), Shielded RecRL improved the relative click-through rate (CTR) by 22.5% (1.225x over baseline) while keeping the recommender's item-ranking behavior virtually unchanged. An extensive ablation study confirms that our gradient shielding strategy and reward design effectively balance explanation quality and policy drift. Our results demonstrate that Shielded RecRL enhances user-facing aspects of recommendations through rich, personalized explanations without degrading core recommendation accuracy.
The Forgotten Shield: Safety Grafting in Parameter-Space for Medical MLLMs
Venue: arXiv cs.LG 2025
Year: 2025

Medical Multimodal Large Language Models (Medical MLLMs) have achieved remarkable progress in specialized medical tasks; however, research into their safety has lagged, posing potential risks for real-world deployment. In this paper, we first establish a multidimensional evaluation framework to systematically benchmark the safety of current SOTA Medical MLLMs. Our empirical analysis reveals pervasive vulnerabilities across both general and medical-specific safety dimensions in existing models, particularly highlighting their fragility against cross-modality jailbreak attacks. Furthermore, we find that the medical fine-tuning process frequently induces catastrophic forgetting of the model's original safety alignment. To address this challenge, we propose a novel "Parameter-Space Intervention" approach for efficient safety re-alignment. This method extracts intrinsic safety knowledge representations from original base models and concurrently injects them into the target model during the construction of medical capabilities. Additionally, we design a fine-grained parameter search algorithm to achieve an optimal trade-off between safety and medical performance. Experimental results demonstrate that our approach significantly bolsters the safety guardrails of Medical MLLMs without relying on additional domain-specific safety data, while minimizing degradation to core medical performance.
Enhancing Admission Inquiry Responses with Fine-Tuned Models and Retrieval-Augmented Generation
Venue: arXiv cs.CL 2025
Year: 2025

University admissions offices face the significant challenge of managing high volumes of inquiries efficiently while maintaining response quality, which critically impacts prospective students' perceptions. This paper addresses the issues of response time and information accuracy by proposing an AI system integrating a fine-tuned language model with Retrieval-Augmented Generation (RAG). While RAG effectively retrieves relevant information from large datasets, its performance in narrow, complex domains like university admissions can be limited without adaptation, potentially leading to contextually inadequate responses due to the intricate rules and specific details involved. To overcome this, we fine-tuned the model on a curated dataset specific to admissions processes, enhancing its ability to interpret RAG-provided data accurately and generate domain-relevant outputs. This hybrid approach leverages RAG's ability to access up-to-date information and fine-tuning's capacity to embed nuanced domain understanding. We further explored optimization strategies for the response generation logic, experimenting with settings to balance response quality and speed, aiming for consistently high-quality outputs that meet the specific requirements of admissions communications.
Ideology as a Problem: Lightweight Logit Steering for Annotator-Specific Alignment in Social Media Analysis
Venue: arXiv cs.CL 2025
Year: 2025

LLMs internally organize political ideology along low-dimensional structures that are partially, but not fully aligned with human ideological space. This misalignment is systematic, model specific, and measurable. We introduce a lightweight linear probe that both quantifies the misalignment and minimally corrects the output layer. This paper introduces a simple and efficient method for aligning models with specific user opinions. Instead of retraining the model, we calculated a bias score from its internal features and directly adjusted the final output probabilities. This solution is practical and low-cost and preserves the original reasoning power of the model.
The Artificial Intelligence Value Chain: A Critical Appraisal. [Spanish Version]
Venue: arXiv cs.CY 2025
Year: 2025

The artificial intelligence value chain is one of the main concepts underpinning the European legislation on the subject, especially the Artificial Intelligence Act. It is an economic concept that has become a legal one. i.e., a concept of legal governance, due to its continued use in policy documents and legal texts. This article (i) analyses its significance and function within the framework of the regulatory strategy established by recent EU programs (the Compass for Competitiveness, the Action Plan, Apply AI Strategy, and the Digital Omnibus on AI), (ii) identifies its limitations, and (iii) advances the theoretical construction of value chains that capture intangible dimensions that are not directly monetizable (such as language, culture, and, especially, ethical and legal values) but have a significant impact on the social environment. It also briefly compares three different legal frameworks for the regulation of AI (EU, Commonwealth and USA). It proposes at the end a specific framework for the analysis of the ethical and legal AI value chain to preserve democratic values and foster the digital implementation of the rule of law.
Defense Against Synthetic Speech: Real-Time Detection of RVC Voice Conversion Attacks
Venue: arXiv cs.SD 2025
Year: 2025

Generative audio technologies now enable highly realistic voice cloning and real-time voice conversion, increasing the risk of impersonation, fraud, and misinformation in communication channels such as phone and video calls. This study investigates real-time detection of AI-generated speech produced using Retrieval-based Voice Conversion (RVC), evaluated on the DEEP-VOICE dataset, which includes authentic and voice-converted speech samples from multiple well-known speakers. To simulate realistic conditions, deepfake generation is applied to isolated vocal components, followed by the reintroduction of background ambiance to suppress trivial artifacts and emphasize conversion-specific cues. We frame detection as a streaming classification task by dividing audio into one-second segments, extracting time-frequency and cepstral features, and training supervised machine learning models to classify each segment as real or voice-converted. The proposed system enables low-latency inference, supporting both segment-level decisions and call-level aggregation. Experimental results show that short-window acoustic features can reliably capture discriminative patterns associated with RVC speech, even in noisy backgrounds. These findings demonstrate the feasibility of practical, real-time deepfake speech detection and underscore the importance of evaluating under realistic audio mixing conditions for robust deployment.