OpenRaiser/Intern-Atlas huggingface dataset:
The Unfairness of Multifactorial Bias in Recommendation
Venue: arXiv cs.IR 2026
Year: 2026

Popularity bias and positivity bias are two prominent sources of bias in recommender systems. Both arise from input data, propagate through recommendation models, and lead to unfair or suboptimal outcomes. Popularity bias occurs when a small subset of items receives most interactions, while positivity bias stems from the over-representation of high rating values. Although each bias has been studied independently, their combined effect, to which we refer to as multifactorial bias, remains underexplored. In this work, we examine how multifactorial bias influences item-side fairness, focusing on exposure bias, which reflects the unequal visibility of items in recommendation outputs. Through simulation studies, we find that positivity bias is disproportionately concentrated on popular items, further amplifying their over-exposure. Motivated by this insight, we adapt a percentile-based rating transformation as a pre-processing strategy to mitigate multifactorial bias. Experiments using six recommendation algorithms across four public datasets show that this approach improves exposure fairness with negligible accuracy loss. We also demonstrate that integrating this pre-processing step into post-processing fairness pipelines enhances their effectiveness and efficiency, enabling comparable or better fairness with reduced computational cost. These findings highlight the importance of addressing multifactorial bias and demonstrate the practical value of simple, data-driven pre-processing methods for improving fairness in recommender systems.
Race, Ethnicity and Their Implication on Bias in Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

Large language models (LLMs) increasingly operate in high-stakes settings including healthcare and medicine, where demographic attributes such as race and ethnicity may be explicitly stated or implicitly inferred from text. However, existing studies primarily document outcome-level disparities, offering limited insight into internal mechanisms underlying these effects. We present a mechanistic study of how race and ethnicity are represented and operationalized within LLMs. Using two publicly available datasets spanning toxicity-related generation and clinical narrative understanding tasks, we analyze three open-source models with a reproducible interpretability pipeline combining probing, neuron-level attribution, and targeted intervention. We find that demographic information is distributed across internal units with substantial cross-model variation. Although some units encode sensitive or stereotype-related associations from pretraining, identical demographic cues can induce qualitatively different behaviors. Interventions suppressing such neurons reduce bias but leave substantial residual effects, suggesting behavioral rather than representational change and motivating more systematic mitigation.
PlannerRFT: Reinforcing Diffusion Planners through Closed-Loop and Sample-Efficient Fine-Tuning
Venue: arXiv cs.RO 2026
Year: 2026

Diffusion-based planners have emerged as a promising approach for human-like trajectory generation in autonomous driving. Recent works incorporate reinforcement fine-tuning to enhance the robustness of diffusion planners through reward-oriented optimization in a generation-evaluation loop. However, they struggle to generate multi-modal, scenario-adaptive trajectories, hindering the exploitation efficiency of informative rewards during fine-tuning. To resolve this, we propose PlannerRFT, a sample-efficient reinforcement fine-tuning framework for diffusion-based planners. PlannerRFT adopts a dual-branch optimization that simultaneously refines the trajectory distribution and adaptively guides the denoising process toward more promising exploration, without altering the original inference pipeline. To support parallel learning at scale, we develop nuMax, an optimized simulator that achieves 10 times faster rollout compared to native nuPlan. Extensive experiments shows that PlannerRFT yields state-of-the-art performance with distinct behaviors emerging during the learning process.
Audit du syst{è}me d'information et du mod{è}le de gouvernance de la Biblioth{è}que Num{é}rique de l'Espace universitaire Francophone (BNEUF) du projet Initiative pour le D{é}veloppement du Num{é}rique dans l'Espace Universitaire Francophone (IDNEUF)
Venue: arXiv cs.DL 2026
Year: 2026

This document provides an assessment of the overall structure of the BNEUF system and how it operates within the framework of the Initiative for Digital Development in French speaking Universities (IDNEUF). This report aims to support the AUF's new strategy for 2021-2025, with its new structural and governance foundations for the implementation of the Francophonie scientifique project. It was therefore decided to reorganize existing and future digital resources and services with a view to incorporating them into the future global collaborative platform for integrated services. This report provides an external assessment with new forms of organization and use of the BNEUF system. The aim is to provide the AUF project team with new avenues for optimized management of the compiled digital resources and to synergize them with the related modules of the Atlas of Expertise and the Francophone Social Network.
An efficient heuristic for geometric analysis of cell deformations
Venue: arXiv cs.LG 2026
Year: 2026

Sickle cell disease causes erythrocytes to become sickle-shaped, affecting their movement in the bloodstream and reducing oxygen delivery. It has a high global prevalence and places a significant burden on healthcare systems, especially in resource-limited regions. Automated classification of sickle cells in blood images is crucial, allowing the specialist to reduce the effort required and avoid errors when quantifying the deformed cells and assessing the severity of a crisis. Recent studies have proposed various erythrocyte representation and classification methods. Since classification depends solely on cell shape, a suitable approach models erythrocytes as closed planar curves in shape space. This approach employs elastic distances between shapes, which are invariant under rotations, translations, scaling, and reparameterizations, ensuring consistent distance measurements regardless of the curves' position, starting point, or traversal speed. While previous methods exploiting shape space distances had achieved high accuracy, we refined the model by considering the geometric characteristics of healthy and sickled erythrocytes. Our method proposes (1) to employ a fixed parameterization based on the major axis of each cell to compute distances and (2) to align each cell with two templates using this parameterization before computing distances. Aligning shapes to templates before distance computation, a concept successfully applied in areas such as molecular dynamics, and using a fixed parameterization, instead of minimizing distances across all possible parameterizations, simplifies calculations. This strategy achieves 96.03\% accuracy rate in both supervised classification and unsupervised clustering. Our method ensures efficient erythrocyte classification, maintaining or improving accuracy over shape space models while significantly reducing computational costs.
A Component-Based Survey of Interactions between Large Language Models and Multi-Armed Bandits
Venue: arXiv cs.CL 2026
Year: 2026

Large language models (LLMs) have become powerful and widely used systems for language understanding and generation, while multi-armed bandit (MAB) algorithms provide a principled framework for adaptive decision-making under uncertainty. This survey explores the potential at the intersection of these two fields. As we know, it is the first survey to systematically review the bidirectional interaction between large language models and multi-armed bandits at the component level. We highlight the bidirectional benefits: MAB algorithms address critical LLM challenges, spanning from pre-training to retrieval-augmented generation (RAG) and personalization. Conversely, LLMs enhance MAB systems by redefining core components such as arm definition and environment modeling, thereby improving decision-making in sequential tasks. We analyze existing LLM-enhanced bandit systems and bandit-enhanced LLM systems, providing insights into their design, methodologies, and performance. Key challenges and representative findings are identified to help guide future research. An accompanying GitHub repository that indexes relevant literature is available at https://github.com/bucky1119/Awesome-LLM-Bandit-Interaction.
Codes Correcting Few Restricted Errors
Venue: arXiv cs.IT 2026
Year: 2026

We consider linear codes over a field in which the error values are restricted to a subgroup of its unit group. This scenario captures Lee distance codes as well as codes over the Gaussian or Eisenstein integers. Codes correcting restricted errors gained increased attention recently in the context of code-based cryptography. In this work we provide new constructions of codes over the Gaussian or Eisenstein integers correcting two or three errors. We adapt some techniques from Roth and Siegel's work on codes for the Lee metric. We propose two construction methods, which may be seen of geometric and algebraic flavor, respectively.
Deterministic Dynamics of Sampling Processes in Score-Based Diffusion Models with Multiplicative Noise Conditioning
Venue: arXiv cs.LG 2026
Year: 2026

Score-based diffusion models generate new samples by learning the score function associated with a diffusion process. While the effectiveness of these models can be theoretically explained using differential equations related to the sampling process, previous work by Song and Ermon (2020) demonstrated that neural networks using multiplicative noise conditioning can still generate satisfactory samples. In this setup, the model is expressed as the product of two functions: one depending on the spatial variable and the other on the noise magnitude. This structure limits the model's ability to represent a more general relationship between the spatial variable and the noise, indicating that it cannot fully learn the correct score. Despite this limitation, the models perform well in practice. In this work, we provide a theoretical explanation for this phenomenon by studying the deterministic dynamics of the associated differential equations, offering insight into how the model operates.
PaperGuide: Making Small Language-Model Paper-Reading Agents More Efficient
Venue: arXiv cs.LG 2026
Year: 2026

The accelerating growth of the scientific literature makes it increasingly difficult for researchers to track new advances through manual reading alone. Recent progress in large language models (LLMs) has therefore spurred interest in autonomous agents that can read scientific papers and extract task-relevant information. However, most existing approaches rely either on heavily engineered prompting or on a conventional SFT-RL training pipeline, both of which often lead to excessive and low-yield exploration. Drawing inspiration from cognitive science, we propose PaperCompass, a framework that mitigates these issues by separating high-level planning from fine-grained execution. PaperCompass first drafts an explicit plan that outlines the intended sequence of actions, and then performs detailed reasoning to instantiate each step by selecting the parameters for the corresponding function calls. To train such behavior, we introduce Draft-and-Follow Policy Optimization (DFPO), a tailored RL method that jointly optimizes both the draft plan and the final solution. DFPO can be viewed as a lightweight form of hierarchical reinforcement learning, aimed at narrowing the `knowing-doing' gap in LLMs. We provide a theoretical analysis that establishes DFPO's favorable optimization properties, supporting a stable and reliable training process. Experiments on paper-based question answering (Paper-QA) benchmarks show that PaperCompass improves efficiency over strong baselines without sacrificing performance, achieving results comparable to much larger models.
Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization
Venue: arXiv cs.RO 2026
Year: 2026

We introduce Being-H0.5, a foundational Vision-Language-Action (VLA) model designed for robust cross-embodiment generalization across diverse robotic platforms. While existing VLAs often struggle with morphological heterogeneity and data scarcity, we propose a human-centric learning paradigm that treats human interaction traces as a universal "mother tongue" for physical interaction. To support this, we present UniHand-2.0, the largest embodied pre-training recipe to date, comprising over 35,000 hours of multimodal data across 30 distinct robotic embodiments. Our approach introduces a Unified Action Space that maps heterogeneous robot controls into semantically aligned slots, enabling low-resource robots to bootstrap skills from human data and high-resource platforms. Built upon this human-centric foundation, we design a unified sequential modeling and multi-task pre-training paradigm to bridge human demonstrations and robotic execution. Architecturally, Being-H0.5 utilizes a Mixture-of-Transformers design featuring a novel Mixture-of-Flow (MoF) framework to decouple shared motor primitives from specialized embodiment-specific experts. Finally, to make cross-embodiment policies stable in the real world, we introduce Manifold-Preserving Gating for robustness under sensory shift and Universal Async Chunking to universalize chunked control across embodiments with different latency and control profiles. We empirically demonstrate that Being-H0.5 achieves state-of-the-art results on simulated benchmarks, such as LIBERO (98.9%) and RoboCasa (53.9%), while also exhibiting strong cross-embodiment capabilities on five robotic platforms.
OFA-MAS: One-for-All Multi-Agent System Topology Design based on Mixture-of-Experts Graph Generative Models
Venue: arXiv cs.MA 2026
Year: 2026

Multi-Agent Systems (MAS) offer a powerful paradigm for solving complex problems, yet their performance is critically dependent on the design of their underlying collaboration topology. As MAS become increasingly deployed in web services (e.g., search engines), designing adaptive topologies for diverse cross-domain user queries becomes essential. Current graph learning-based design methodologies often adhere to a "one-for-one" paradigm, where a specialized model is trained for each specific task domain. This approach suffers from poor generalization to unseen domains and fails to leverage shared structural knowledge across different tasks. To address this, we propose OFA-TAD, a one-for-all framework that generates adaptive collaboration graphs for any task described in natural language through a single universal model. Our approach integrates a Task-Aware Graph State Encoder (TAGSE) that filters task-relevant node information via sparse gating, and a Mixture-of-Experts (MoE) architecture that dynamically selects specialized sub-networks to drive node and edge prediction. We employ a three-stage training strategy: unconditional pre-training on canonical topologies for structural priors, large-scale conditional pre-training on LLM-generated datasets for task-topology mappings, and supervised fine-tuning on empirically validated graphs. Experiments across six diverse benchmarks show that OFA-TAD significantly outperforms specialized one-for-one models, generating highly adaptive MAS topologies. Code: https://github.com/Shiy-Li/OFA-MAS.
Post-Quantum Secure Aggregation via Code-Based Homomorphic Encryption
Venue: arXiv cs.CR 2026
Year: 2026

Secure aggregation enables aggregation of inputs from multiple parties without revealing individual contributions to the server or other clients. Existing post-quantum approaches based on homomorphic encryption offer practical efficiency but predominantly rely on lattice-based hardness assumptions. We present a code-based alternative for secure aggregation by instantiating a general framework based on key- and message-additive homomorphic encryption under the Learning Parity with Noise (LPN) assumption. Our construction employs a committee-based decryptor realized via secret sharing and incorporates a Chinese Remainder Theorem (CRT)-based optimization to reduce the communication costs of LPN-based instantiations. We analyze the security of the proposed scheme under a new Hint-LPN assumption and show that it is equivalent to standard LPN for suitable parameters. Finally, we evaluate performance and identify regimes in which our approach outperforms information-theoretically secure aggregation protocols.
SASA: Semantic-Aware Contrastive Learning Framework with Separated Attention for Triple Classification
Venue: arXiv cs.CL 2026
Year: 2026

Knowledge Graphs~(KGs) often suffer from unreliable knowledge, which restricts their utility. Triple Classification~(TC) aims to determine the validity of triples from KGs. Recently, text-based methods learn entity and relation representations from natural language descriptions, significantly improving the generalization capabilities of TC models and setting new benchmarks in performance. However, there are still two critical challenges. First, existing methods often ignore the effective semantic interaction among different KG components. Second, most approaches adopt single binary classification training objective, leading to insufficient semantic representation learning. To address these challenges, we propose \textbf{SASA}, a novel framework designed to enhance TC models via separated attention mechanism and semantic-aware contrastive learning~(CL). Specifically, we first propose separated attention mechanism to encode triples into decoupled contextual representations and then fuse them through a more effective interactive way. Then, we introduce semantic-aware hierarchical CL as auxiliary training objective to guide models in improving their discriminative capabilities and achieving sufficient semantic learning, considering both local level and global level CL. Experimental results across two benchmark datasets demonstrate that SASA significantly outperforms state-of-the-art methods. In terms of accuracy, we advance the state-of-the-art by +5.9\% on FB15k-237 and +3.4\% on YAGO3-10.
CPU-less parallel execution of lambda calculus in digital logic
Venue: arXiv cs.DC 2026
Year: 2026

While transistor density is still increasing, clock speeds are not, motivating the search for new parallel architectures. One approach is to completely abandon the concept of CPU -- and thus serial imperative programming -- and instead to specify and execute tasks in parallel, compiling from programming languages to data flow digital logic. It is well-known that pure functional languages are inherently parallel, due to the Church-Rosser theorem, and CPU-based parallel compilers exist for many functional languages. However, these still rely on conventional CPUs and their von Neumann bottlenecks. An alternative is to compile functional languages directly into digital logic to maximize available parallelism. It is difficult to work with complete modern functional languages due to their many features, so we demonstrate a proof-of-concept system using lambda calculus as the source language and compiling to digital logic. We show how functional hardware can be tailored to a simplistic functional language, forming the ground for a new model of CPU-less functional computation. At the algorithmic level, we use a tree-based representation, with data localized within nodes and communicated data passed between them. This is implemented by physical digital logic blocks corresponding to nodes, and buses enabling message passing. Node types and behaviors correspond to lambda grammar forms, and beta-reductions are performed in parallel allowing branches independent from one another to perform transformations simultaneously. As evidence for this approach, we present an implementation, along with simulation results, showcasing successful execution of lambda expressions. This suggests that the approach could be scaled to larger functional languages. Successful execution of a test suite of lambda expressions suggests that the approach could be scaled to larger functional languages.
Exploration on Highly Dynamic Graphs
Venue: arXiv cs.DC 2026
Year: 2026

We study the exploration problem by mobile agents in two prominent models of dynamic graphs: $1$-Interval Connectivity and Connectivity Time. The $1$-Interval Connectivity model was introduced by Kuhn et al.~[STOC 2010], and the Connectivity Time model was proposed by Michail et al.~[JPDC 2014]. Recently, Saxena et al.~[TCS 2025] investigated the exploration problem under both models. In this work, we first strengthen the existing impossibility results for the $1$-Interval Connectivity model. We then show that, in Connectivity Time dynamic graphs, exploration is impossible with $\frac{(n-1)(n-2)}{2}$ mobile agents, even when the agents have full knowledge of all system parameters, global communication, full visibility, and infinite memory. This significantly improves the previously known bound of $n$. Moreover, we prove that to solve exploration with $\frac{(n-1)(n-2)}{2}+1$ agents, $1$-hop visibility is necessary. Finally, we present an exploration algorithm that uses $\frac{(n-1)(n-2)}{2}+1$ agents, assuming global communication, $1$-hop visibility, and $O(\log n)$ memory per agent.
Analysis of Long Range Dependency Understanding in State Space Models
Venue: arXiv cs.LG 2026
Year: 2026

Although state-space models (SSMs) have demonstrated strong performance on long-sequence benchmarks, most research has emphasized predictive accuracy rather than interpretability. In this work, we present the first systematic kernel interpretability study of the diagonalized state-space model (S4D) trained on a real-world task (vulnerability detection in source code). Through time and frequency domain analysis of the S4D kernel, we show that the long-range modeling capability of S4D varies significantly under different model architectures, affecting model performance. For instance, we show that the depending on the architecture, S4D kernel can behave as low-pass, band-pass or high-pass filter. The insights from our analysis can guide future work in designing better S4D-based models.
TinyML-Enabled IoT for Sustainable Precision Irrigation
Venue: arXiv cs.LG 2026
Year: 2026

Small-scale farming communities are disproportionately affected by water scarcity, erratic climate patterns, and a lack of access to advanced, affordable agricultural technologies. To address these challenges, this paper presents a novel, edge-first IoT framework that integrates Tiny Machine Learning (TinyML) for intelligent, offline-capable precision irrigation. The proposed four-layer architecture leverages low-cost hardware, an ESP32 microcontroller as an edge inference node, and a Raspberry Pi as a local edge server to enable autonomous decision-making without cloud dependency. The system utilizes capacitive soil moisture, temperature, humidity, pH, and ambient light sensors for environmental monitoring. A rigorous comparative analysis of ensemble models identified gradient boosting as superior, achieving an R^2 score of 0.9973 and a Mean Absolute Percentage Error (MAPE) of 0.99%, outperforming a random forest model (R^2 = 0.9916, MAPE = 1.81%). This optimized model was converted and deployed as a lightweight TinyML inference engine on the ESP32 and predicts irrigation needs with exceptional accuracy (MAPE < 1%). Local communication is facilitated by an MQTT-based LAN protocol, ensuring reliable operation in areas with limited or no internet connectivity. Experimental validation in a controlled environment demonstrated a significant reduction in water usage compared to traditional methods, while the system's low-power design and offline functionality confirm its viability for sustainable, scalable deployment in resource-constrained rural settings. This work provides a practical, cost-effective blueprint for bridging the technological divide in agriculture and enhancing water-use efficiency through on-device artificial intelligence.