OpenRaiser/Intern-Atlas huggingface dataset:
PDF: PUF-based DNN Fingerprinting for Knowledge Distillation Traceability
Venue: arXiv cs.CR 2026
Year: 2026

Knowledge distillation transfers large teacher models to compact student models, enabling deployment on resource-limited platforms while suffering minimal performance degradation. However, this paradigm could lead to various security risks, especially model theft. Existing defenses against model theft, such as watermarking and secure enclaves, focus primarily on identity authentication and incur significant resource costs. Aiming to provide post-theft accountability and traceability, we propose a novel fingerprinting framework that superimposes device-specific Physical Unclonable Function (PUF) signatures onto teacher logits during distillation. Compared with watermarking or secure enclaves, our approach is lightweight, requires no architectural changes, and enables traceability of any leaked or cloned model. Since the signatures are based on PUFs, this framework is robust against reverse engineering and tampering attacks. In this framework, the signature recovery process consists of two stages: first a neural network-based decoder and then a Hamming distance decoder. Furthermore, we also propose a bit compression scheme to support a large number of devices. Experiment results demonstrate that our framework achieves high key recovery rate and negligible accuracy loss while allowing a tunable trade-off between these two key metrics. These results show that the proposed framework is a practical and robust solution for protecting distilled models.
Extended Reality (XR): The Next Frontier in Education
Venue: arXiv cs.CY 2026
Year: 2026

Extended Reality (XR), encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), is revolutionizing education by creating immersive, interactive learning environments. This article explores the potential of XR to enhance student engagement, experiential learning, and skill development while addressing the challenges of widespread adoption. High implementation costs, technical complexities, and ethical concerns especially regarding student privacy and biometric data protection still possess significant barriers to integration. The discussion also highlights regulatory compliance with GDPR and FERPA and the importance of cybersecurity frameworks to safeguard sensitive learner data. Ultimately, the article provides insights into balancing innovation with accessibility and ethics in the evolution of XR based education
Egocentric Visibility-Aware Human Pose Estimation
Venue: arXiv cs.CV 2026
Year: 2026

Egocentric human pose estimation (HPE) using a head-mounted device is crucial for various VR and AR applications, but it faces significant challenges due to keypoint invisibility. Nevertheless, none of the existing egocentric HPE datasets provide keypoint visibility annotations, and the existing methods often overlook the invisibility problem, treating visible and invisible keypoints indiscriminately during estimation. As a result, their capacity to accurately predict visible keypoints is compromised. In this paper, we first present Eva-3M, a large-scale egocentric visibility-aware HPE dataset comprising over 3.0M frames, with 435K of them annotated with keypoint visibility labels. Additionally, we augment the existing EMHI dataset with keypoint visibility annotations to further facilitate the research in this direction. Furthermore, we propose EvaPose, a novel egocentric visibility-aware HPE method that explicitly incorporates visibility information to enhance pose estimation accuracy. Extensive experiments validate the significant value of ground-truth visibility labels in egocentric HPE settings, and demonstrate that our EvaPose achieves state-of-the-art performance in both Eva-3M and EMHI datasets.
DLEBench: Evaluating Small-scale Object Editing Ability for Instruction-based Image Editing Model
Venue: arXiv cs.CV 2026
Year: 2026

Significant progress has been made in the field of Instruction-based Image Editing Models (IIEMs). However, while these models demonstrate plausible adherence to instructions and strong reasoning ability on current benchmarks, their ability to edit small objects remains underexplored, despite its importance for precise local editing and refining details in both real and generated images. In this paper, we introduce DeepLookEditBench (DLEBench), the first benchmark dedicated to assessing the abilities of IIEMs in editing small-scale objects. Specifically, we construct a challenging testbed comprising 1889 samples across seven instruction types. In these samples, target objects occupy only 1%-10% of the image area, covering complex scenarios such as partial occlusion and multi-object editing. To ensure robust evaluation on this benchmark, we propose an evaluation protocol with refined score rubrics to minimize subjectivity and ambiguity in two criteria: Instruction Following and Visual Consistency. This protocol also introduces a dual-mode evaluation framework (Tool-driven and Oracle-guided Modes) addressing the misalignment between LMM-as-a-Judge and human judgements on DLEBench. Empirical results on 10 IIEMs reveal significant performance gaps in small-scale object editing, highlighting the need for specialized benchmarks to advance this ability.
SGAgent: Suggestion-Guided LLM-Based Multi-Agent Framework for Repository-Level Software Repair
Venue: arXiv cs.SE 2026
Year: 2026

The rapid advancement of Large Language Models (LLMs) has led to the emergence of intelligent agents capable of autonomously interacting with environments and invoking external tools. Recently, agent-based software repair approaches have received widespread attention, as repair agents can automatically analyze and localize bugs, generate patches, and achieve state-of-the-art performance on repository-level benchmarks. However, existing approaches usually adopt a localize-then-fix paradigm, jumping directly from "where the bug is" to "how to fix it", leaving a fundamental reasoning gap. To this end, we propose SGAgent, a Suggestion-Guided multi-Agent framework for repository-level software repair, which follows a localize-suggest-fix paradigm. SGAgent introduces a suggestion phase to strengthen the transition from localization to repair. The suggester starts from the buggy locations and incrementally retrieves relevant context until it fully understands the bug, and then provides actionable repair suggestions. Moreover, we construct a Knowledge Graph from the target repository and develop a KG-based toolkit to enhance SGAgent's global contextual awareness and repository-level reasoning. Three specialized sub-agents (i.e., localizer, suggester, and fixer) collaborate to achieve automated end-to-end software repair. Experimental results on SWE-Bench show that SGAgent with Claude-3.5 achieves 51.3% repair accuracy, 81.2% file-level and 52.4% function-level localization accuracy with an average cost of $1.48 per instance, outperforming all baselines using the same base model. Furthermore, SGAgent attains 48% accuracy on VUL4J and VJBench for vulnerability repair, demonstrating strong generalization across tasks and programming languages.
ProtoDCS: Towards Robust and Efficient Open-Set Test-Time Adaptation for Vision-Language Models
Venue: arXiv cs.CV 2026
Year: 2026

Large-scale Vision-Language Models (VLMs) exhibit strong zero-shot recognition, yet their real-world deployment is challenged by distribution shifts. While Test-Time Adaptation (TTA) can mitigate this, existing VLM-based TTA methods operate under a closed-set assumption, failing in open-set scenarios where test streams contain both covariate-shifted in-distribution (csID) and out-of-distribution (csOOD) data. This leads to a critical difficulty: the model must discriminate unknown csOOD samples to avoid interference while simultaneously adapting to known csID classes for accuracy. Current open-set TTA (OSTTA) methods rely on hard thresholds for separation and entropy minimization for adaptation. These strategies are brittle, often misclassifying ambiguous csOOD samples and inducing overconfident predictions, and their parameter-update mechanism is computationally prohibitive for VLMs. To address these limitations, we propose Prototype-based Double-Check Separation (ProtoDCS), a robust framework for OSTTA that effectively separates csID and csOOD samples, enabling safe and efficient adaptation of VLMs to csID data. Our main contributions are: (1) a novel double-check separation mechanism employing probabilistic Gaussian Mixture Model (GMM) verification to replace brittle thresholding; and (2) an evidence-driven adaptation strategy utilizing uncertainty-aware loss and efficient prototype-level updates, mitigating overconfidence and reducing computational overhead. Extensive experiments on CIFAR-10/100-C and Tiny-ImageNet-C demonstrate that ProtoDCS achieves state-of-the-art performance, significantly boosting both known-class accuracy and OOD detection metrics. Code will be available at https://github.com/O-YangF/ProtoDCS.
PseudoAct: Leveraging Pseudocode Synthesis for Flexible Planning and Action Control in Large Language Model Agents
Venue: arXiv cs.AI 2026
Year: 2026

Large language model (LLM) agents typically rely on reactive decision-making paradigms such as ReAct, selecting actions conditioned on growing execution histories. While effective for short tasks, these approaches often lead to redundant tool usage, unstable reasoning, and high token consumption in complex long-horizon tasks involving branching, iteration, or multi-tool coordination. To address these limitations, this paper introduces PseudoAct, a novel framework for flexible planning and action control in LLM agents through pseudocode synthesis. Leveraging the ability of LLMs to express task-solving strategies as code, PseudoAct synthesizes a structured pseudocode plan that decomposes a task into subtasks and explicitly encodes control flow, including sequencing, conditionals, loops, parallel composition, and combinations of these logic primitives. Actions are then executed by following this global plan, making the decision logic explicit and temporally coherent. This design reduces redundant actions, prevents infinite loops, and avoids uninformative alternative exploration, enabling consistent and efficient long-horizon decision-making. Experiments on benchmark datasets show that our method significantly outperforms existing reactive agent approaches, achieving a 20.93% absolute gain in success rate on FEVER and setting a new state-of-the-art on HotpotQA.
FuXi-Linear: Unleashing the Power of Linear Attention in Long-term Time-aware Sequential Recommendation
Venue: arXiv cs.IR 2026
Year: 2026

Modern recommendation systems primarily rely on attention mechanisms with quadratic complexity, which limits their ability to handle long user sequences and slows down inference. While linear attention is a promising alternative, existing research faces three critical challenges: (1) temporal signals are often overlooked or integrated via naive coupling that causes mutual interference between temporal and semantic signals while neglecting behavioral periodicity; (2) insufficient positional information provided by existing linear frameworks; and (3) a primary focus on short sequences and shallow architectures. To address these issues, we propose FuXi-Linear, a linear-complexity model designed for efficient long-sequence recommendation. Our approach introduces two key components: (1) a Temporal Retention Channel that independently computes periodic attention weights using temporal data, preventing crosstalk between temporal and semantic signals; (2) a Linear Positional Channel that integrates positional information through learnable kernels within linear complexity. Moreover, we demonstrate that FuXi-Linear exhibits a robust power-law scaling property at a thousand-length scale, a characteristic largely unexplored in prior linear recommendation studies. Extensive experiments on sequences of several thousand tokens demonstrate that FuXi-Linear outperforms state-of-the-art models in recommendation quality, while achieving up to 10$\times$ speedup in the prefill stage and up to 21$\times$ speedup in the decode stage compared to competitive baselines. Our code has been released in a public repository https://github.com/USTC-StarTeam/fuxi-linear.
EgoGraph: Temporal Knowledge Graph for Egocentric Video Understanding
Venue: arXiv cs.CV 2026
Year: 2026

Ultra-long egocentric videos spanning multiple days present significant challenges for video understanding. Existing approaches still rely on fragmented local processing and limited temporal modeling, restricting their ability to reason over such extended sequences. To address these limitations, we introduce EgoGraph, a training-free and dynamic knowledge-graph construction framework that explicitly encodes long-term, cross-entity dependencies in egocentric video streams. EgoGraph employs a novel egocentric schema that unifies the extraction and abstraction of core entities, such as people, objects, locations, and events, and structurally reasons about their attributes and interactions, yielding a significantly richer and more coherent semantic representation than traditional clip-based video models. Crucially, we develop a temporal relational modeling strategy that captures temporal dependencies across entities and accumulates stable long-term memory over multiple days, enabling complex temporal reasoning. Extensive experiments on the EgoLifeQA and EgoR1-bench benchmarks demonstrate that EgoGraph achieves state-of-the-art performance on long-term video question answering, validating its effectiveness as a new paradigm for ultra-long egocentric video understanding.
Recommending Search Filters To Improve Conversions At Airbnb
Venue: arXiv cs.IR 2026
Year: 2026

Airbnb, a two-sided online marketplace connecting guests and hosts, offers a diverse and unique inventory of accommodations, experiences, and services. Search filters play an important role in helping guests navigate this variety by refining search results to align with their needs. Yet, while search filters are designed to facilitate conversions in online marketplaces, their direct impact on driving conversions remains underexplored in the existing literature. This paper bridges this gap by presenting a novel application of machine learning techniques to recommend search filters aimed at improving booking conversions. We introduce a modeling framework that directly targets lower-funnel conversions (bookings) by recommending intermediate tools, i.e. search filters. Leveraging the framework, we designed and built the filter recommendation system at Airbnb from the ground up, addressing challenges like cold start and stringent serving requirements. The filter recommendation system we developed has been successfully deployed at Airbnb, powering multiple user interfaces and driving incremental booking conversion lifts, as validated through online A/B testing. An ablation study further validates the effectiveness of our approach and key design choices. By focusing on conversion-oriented filter recommendations, our work ensures that search filters serve their ultimate purpose at Airbnb - helping guests find and book their ideal accommodations.
SLA-Aware Distributed LLM Inference Across Device-RAN-Cloud
Venue: arXiv cs.NI 2026
Year: 2026

Embodied AI requires sub-second inference near the Radio Access Network (RAN), but deployments span heterogeneous tiers (on-device, RAN-edge, cloud) and must not disrupt real-time baseband processing. We report measurements from a 5G Standalone (SA) AI-RAN testbed using a fixed baseline policy for repeatability. The setup includes an on-device tier, a three-node RAN-edge cluster co-hosting a containerized 5G RAN, and a cloud tier. We find that on-device execution remains multi-second and fails to meet sub-second budgets. At the RAN edge, SLA feasibility is primarily determined by model variant choice: quantized models concentrate below 0.5\,s, while unquantized and some larger quantized models incur deadline misses due to stalls and queuing. In the cloud tier, meeting a 0.5\,s deadline is challenging on the measured WAN path (up to 32.9\% of requests complete within 0.5\,s), but all evaluated variants meet a 1.0\,s deadline (100\% within 1.0\,s). Under saturated downlink traffic and up to $N{=}20$ concurrent inference clients, Multi-Instance GPU (MIG) isolation preserves baseband timing-health proxies, supporting safe co-location under fixed partitioning.
Unlocking Cognitive Capabilities and Analyzing the Perception-Logic Trade-off
Venue: arXiv cs.AI 2026
Year: 2026

Recent advancements in Multimodal Large Language Models (MLLMs) pursue omni-perception capabilities, yet integrating robust sensory grounding with complex reasoning remains a challenge, particularly for underrepresented regions. In this report, we introduce the research preview of MERaLiON2-Omni (Alpha), a 10B-parameter multilingual omni-perception tailored for Southeast Asia (SEA). We present a progressive training pipeline that explicitly decouples and then integrates "System 1" (Perception) and "System 2" (Reasoning) capabilities. First, we establish a robust Perception Backbone by aligning region-specific audio-visual cues (e.g., Singlish code-switching, local cultural landmarks) with a multilingual LLM through orthogonal modality adaptation. Second, to inject cognitive capabilities without large-scale supervision, we propose a cost-effective Generate-Judge-Refine pipeline. By utilizing a Super-LLM to filter hallucinations and resolve conflicts via a consensus mechanism, we synthesize high-quality silver data that transfers textual Chain-of-Thought reasoning to multimodal scenarios. Comprehensive evaluation on our newly introduced SEA-Omni Benchmark Suite reveals an Efficiency-Stability Paradox: while reasoning acts as a non-linear amplifier for abstract tasks (boosting mathematical and instruction-following performance significantly), it introduces instability in low-level sensory processing. Specifically, we identify Temporal Drift in long-context audio, where extended reasoning desynchronizes the model from acoustic timestamps, and Visual Over-interpretation, where logic overrides pixel-level reality. This report details the architecture, the data-efficient training recipe, and a diagnostic analysis of the trade-offs between robust perception and structured reasoning.
On the Uniqueness of Solutions in GPS Source Localization: Distance and Squared-Distance Minimization under Limited Measurements in Two and Three Dimensions
Venue: arXiv cs.IT 2026
Year: 2026

The source localization problem, fundamental to applications like GPS, is typically approached as a minimization problem in the presence of various types of noise. Ensuring the uniqueness of solutions in GPS technology is vital for the reliability and accuracy of applications, from everyday navigation to critical military operations. In this paper, we examine two key minimization problems: one focused on distance error and the other on squared distance error. We explore these problems in both three-dimensional space, the standard scenario, and in two-dimensional space as a simplified case. Furthermore, we discuss the number of possible source solutions when the number of measurements is fewer than three.
PLA for Drone RID Frames via Motion Estimation and Consistency Verification
Venue: arXiv cs.CR 2026
Year: 2026

Drone Remote Identification (RID) plays a critical role in low-altitude airspace supervision, yet its broadcast nature and lack of cryptographic protection make it vulnerable to spoofing and replay attacks. In this paper, we propose a consistency verification-based physical-layer authentication (PLA) algorithm for drone RID frames. A RID-aware sensing and decoding module is first developed to extract communication-derived sensing parameters, including angle-of-arrival, Doppler shift, average channel gain, and the number of transmit antennas, together with the identity and motion-related information decoded from previously authenticated RID frames. Rather than fusing all heterogeneous information into a single representation, different types of information are selectively utilized according to their physical relevance and reliability. Specifically, real-time wireless sensing parameter constraints and previously authenticated motion states are incorporated in a yaw-augmented constant-acceleration extended Kalman filter (CA-EKF) to estimate the three-dimensional position and motion states of the drone. To further enhance authentication reliability under highly maneuverable and non-stationary flight scenarios, a data-driven long short-term memory-based motion estimator is employed, and its predictions are adaptively combined with the CA-EKF via an error-aware fusion strategy. Finally, RID frames are authenticated by verifying consistency in the number of transmit antennas, motion estimates, and no-fly-zone constraints. Simulation results demonstrate that the proposed algorithm significantly improves authentication reliability and robustness under realistic wireless impairments and complex drone maneuvers, outperforming existing RF feature-based and motion model-based PLA schemes.
UniFAR: A Unified Facet-Aware Retrieval Framework for Scientific Documents
Venue: arXiv cs.IR 2026
Year: 2026

Existing scientific document retrieval (SDR) methods primarily rely on document-centric representations learned from inter-document relationships for document-document (doc-doc) retrieval. However, the rise of LLMs and RAG has shifted SDR toward question-driven retrieval, where documents are retrieved in response to natural-language questions (q-doc). This change has led to systematic mismatches between document-centric models and question-driven retrieval, including (1) input granularity (long documents vs. short questions), (2) semantic focus (scientific discourse structure vs. specific question intent), and (3) training signals (citation-based similarity vs. question-oriented relevance). To this end, we propose UniFAR, a Unified Facet-Aware Retrieval framework to jointly support doc-doc and q-doc SDR within a single architecture. UniFAR reconciles granularity differences through adaptive multi-granularity aggregation, aligns document structure with question intent via learnable facet anchors, and unifies doc-doc and q-doc supervision through joint training. Experimental results show that UniFAR consistently outperforms prior methods across multiple retrieval tasks and base models, confirming its effectiveness and generality.
Tilewise Domain-Separated Selective Encryption for Remote Sensing Imagery under Chosen-Plaintext Attacks
Venue: arXiv cs.CR 2026
Year: 2026

Selective image encryption is common in remote sensing systems because it protects sensitive regions of interest (ROI) while limiting computational cost. However, many selective designs enable cross-tile structural leakage under chosen-plaintext attacks when secret-dependent transformations are reused across spatial positions. This paper proposes Tilewise Domain-Separated Selective Encryption (TDS-SE), where per-tile (and optionally per-frame) keys are derived from a master secret via HKDF with explicit domain separation, and ROI masks are treated strictly as external side information. Structural leakage is evaluated using two reconstruction-based distinguishers -- a linear model and a lightweight convolutional neural network -- under multiple attack settings. Experiments on RESISC45 and SEN12MS cover ablation tests, cross-position transferability, cross-sample generalization, and ROI-knowledge asymmetry. Results show that per-tile domain separation reduces position-conditioned transfer for the linear probe, and that adding frame freshness improves robustness to imperfect ROI assumptions for the CNN probe. Cross-sample generalization exhibits mixed behavior across settings, consistent with an empirical evaluation perspective, while selective-encryption functionality is preserved under the same tiling and ROI policy. Beyond the method itself, the paper provides a structured protocol for evaluating selective encryption under realistic attacker capabilities.