OpenRaiser/Intern-Atlas huggingface dataset:
Unsupervised Video Class-Incremental Learning via Deep Embedded Clustering Management
Venue: arXiv cs.CV 2026
Year: 2026

Unsupervised video class incremental learning (uVCIL) represents an important learning paradigm for learning video information without forgetting, and without considering any data labels. Prior approaches have focused on supervised class-incremental learning, relying on using the knowledge of labels and task boundaries, which is costly, requires human annotation, or is simply not a realistic option. In this paper, we propose a simple yet effective approach to address the uVCIL. We first consider a deep feature extractor network, providing a set of representative video features during each task without assuming any class or task information. We then progressively build a series of deep clusters from the extracted features. During the successive task learning, the model updated from the previous task is used as an initial state in order to transfer knowledge to the current learning task. We perform in-depth evaluations on three standard video action recognition datasets, including UCF101, HMDB51, and Something-to-Something V2, by ignoring the labels from the supervised setting. Our approach significantly outperforms other baselines on all datasets.
Causal feature selection framework for stable soft sensor modeling based on time-delayed cross mapping
Venue: arXiv cs.LG 2026
Year: 2026

Soft sensor modeling plays a crucial role in process monitoring. Causal feature selection can enhance the performance of soft sensor models in industrial applications. However, existing methods ignore two critical characteristics of industrial processes. Firstly, causal relationships between variables always involve time delays, whereas most causal feature selection methods investigate causal relationships in the same time dimension. Secondly, variables in industrial processes are often interdependent, which contradicts the decorrelation assumption of traditional causal inference methods. Consequently, soft sensor models based on existing causal feature selection approaches often lack sufficient accuracy and stability. To overcome these challenges, this paper proposes a causal feature selection framework based on time-delayed cross mapping. Time-delayed cross mapping employs state space reconstruction to effectively handle interdependent variables in causality analysis, and considers varying causal strength across time delay. Time-delayed convergent cross mapping (TDCCM) is introduced for total causal inference, and time-delayed partial cross mapping (TDPCM) is developed for direct causal inference. Then, in order to achieve automatic feature selection, an objective feature selection strategy is presented. The causal threshold is automatically determined based on the model performance on the validation set, and the causal features are then selected. Two real-world case studies show that TDCCM achieves the highest average performance, while TDPCM improves soft sensor stability and performance in the worst scenario. The code is publicly available at https://github.com/dirge1/TDPCM.
A Systematic Analysis of Chunking Strategies for Reliable Question Answering
Venue: arXiv cs.CL 2026
Year: 2026

We study how document chunking choices impact the reliability of Retrieval-Augmented Generation (RAG) systems in industry. While practice often relies on heuristics, our end-to-end evaluation on Natural Questions systematically varies chunking method (token, sentence, semantic, code), chunk size, overlap, and context length. We use a standard industrial setup: SPLADE retrieval and a Mistral-8B generator. We derive actionable lessons for cost-efficient deployment: (i) overlap provides no measurable benefit and increases indexing cost; (ii) sentence chunking is the most cost-effective method, matching semantic chunking up to ~5k tokens; (iii) a "context cliff" reduces quality beyond ~2.5k tokens; and (iv) optimal context depends on the goal (semantic quality peaks at small contexts; exact match at larger ones).
SandWorm: Event-based Visuotactile Perception with Active Vibration for Screw-Actuated Robot in Granular Media
Venue: arXiv cs.RO 2026
Year: 2026

Perception in granular media remains challenging due to unpredictable particle dynamics. To address this challenge, we present SandWorm, a biomimetic screw-actuated robot augmented by peristaltic motion to enhance locomotion, and SWTac, a novel event-based visuotactile sensor with an actively vibrated elastomer. The event camera is mechanically decoupled from vibrations by a spring isolation mechanism, enabling high-quality tactile imaging of both dynamic and stationary objects. For algorithm design, we propose an IMU-guided temporal filter to enhance imaging consistency, improving MSNR by 24%. Moreover, we systematically optimize SWTac with vibration parameters, event camera settings and elastomer properties. Motivated by asymmetric edge features, we also implement contact surface estimation by U-Net. Experimental validation demonstrates SWTac's 0.2 mm texture resolution, 98% stone classification accuracy, and 0.15 N force estimation error, while SandWorm demonstrates versatile locomotion (up to 12.5 mm/s) in challenging terrains, successfully executes pipeline dredging and subsurface exploration in complex granular media (observed 90% success rate). Field experiments further confirm the system's practical performance.
"Range as a Key" is the Key! Fast and Compact Cloud Block Store Index with RASK
Venue: arXiv cs.OS 2026
Year: 2026

In cloud block store, indexing is on the critical path of I/O operations and typically resides in memory. With the scaling of users and the emergence of denser storage media, the index has become a primary memory consumer, causing memory strain. Our extensive analysis of production traces reveals that write requests exhibit a strong tendency to target continuous block ranges in cloud storage systems. Thus, compared to current per-block indexing, our insight is that we should directly index block ranges (i.e., range-as-a-key) to save memory. In this paper, we propose RASK, a memory-efficient and high-performance tree-structured index that natively indexes ranges. While range-as-a-key offers the potential to save memory and improve performance, realizing this idea is challenging due to the range overlap and range fragmentation issues. To handle range overlap efficiently, RASK introduces the log-structured leaf, combined with range-tailored search and garbage collection. To reduce range fragmentation, RASK employs range-aware split and merge mechanisms. Our evaluations on four production traces show that RASK reduces memory footprint by up to 98.9% and increases throughput by up to 31.0x compared to ten state-of-the-art indexes.
The Quest for Reliable AI Accelerators: Cross-Layer Evaluation and Design Optimization
Venue: arXiv cs.AR 2026
Year: 2026

As the CMOS technology pushes to the nanoscale, aging effects and process variations have become increasingly pronounced, posing significant reliability challenges for AI accelerators. Traditional guardband-based design approaches, which rely on pessimistic timing margin, sacrifice significant performance and computational efficiency, rendering them inadequate for high-performance AI computing demands. Current reliability-aware AI accelerator design faces two core challenges: (1) the lack of systematic cross-layer analysis tools to capture coupling reliability effects across device, circuit, architecture, and application layers; and (2) the fundamental trade-off between conventional reliability optimization and computational efficiency. To address these challenges, this paper systematically presents a series of reliability-aware accelerator designs, encompassing (1) aging and variation-aware dynamic timing analyzer, (2) accelerator dataflow optimization using critical input pattern reduction, and (3) resilience characterization and novel architecture design for large language models (LLMs). By tightly integrating cross-layer reliability modeling and AI workload characteristics, these co-optimization approaches effectively achieve reliable and efficient AI acceleration.
LLM Augmented Intervenable Multimodal Adaptor for Post-operative Complication Prediction in Lung Cancer Surgery
Venue: arXiv cs.CV 2026
Year: 2026

Postoperative complications remain a critical concern in clinical practice, adversely affecting patient outcomes and contributing to rising healthcare costs. We present MIRACLE, a deep learning architecture for prediction of risk of postoperative complications in lung cancer surgery by integrating preoperative clinical and radiological data. MIRACLE employs a hyperspherical embedding space fusion of heterogeneous inputs, enabling the extraction of robust, discriminative features from both structured clinical records and high-dimensional radiological images. To enhance transparency of prediction and clinical utility, we incorporate an interventional deep learning module in MIRACLE, that not only refines predictions but also provides interpretable and actionable insights, allowing domain experts to interactively adjust recommendations based on clinical expertise. We validate our approach on POC-L, a real-world dataset comprising 3,094 lung cancer patients who underwent surgery at Roswell Park Comprehensive Cancer Center. Our results demonstrate that MIRACLE outperforms various traditional machine learning models and contemporary large language models (LLM) variants alone, for personalized and explainable postoperative risk management.
One-Shot Refiner: Boosting Feed-forward Novel View Synthesis via One-Step Diffusion
Venue: arXiv cs.CV 2026
Year: 2026

We present a novel framework for high-fidelity novel view synthesis (NVS) from sparse images, addressing key limitations in recent feed-forward 3D Gaussian Splatting (3DGS) methods built on Vision Transformer (ViT) backbones. While ViT-based pipelines offer strong geometric priors, they are often constrained by low-resolution inputs due to computational costs. Moreover, existing generative enhancement methods tend to be 3D-agnostic, resulting in inconsistent structures across views, especially in unseen regions. To overcome these challenges, we design a Dual-Domain Detail Perception Module, which enables handling high-resolution images without being limited by the ViT backbone, and endows Gaussians with additional features to store high-frequency details. We develop a feature-guided diffusion network, which can preserve high-frequency details during the restoration process. We introduce a unified training strategy that enables joint optimization of the ViT-based geometric backbone and the diffusion-based refinement module. Experiments demonstrate that our method can maintain superior generation quality across multiple datasets.
Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance
Venue: arXiv cs.AI 2026
Year: 2026

Writing effective rebuttals is a high-stakes task that demands more than linguistic fluency, as it requires precise alignment between reviewer intent and manuscript details. Current solutions typically treat this as a direct-to-text generation problem, suffering from hallucination, overlooked critiques, and a lack of verifiable grounding. To address these limitations, we introduce $\textbf{RebuttalAgent}$, the first multi-agents framework that reframes rebuttal generation as an evidence-centric planning task. Our system decomposes complex feedback into atomic concerns and dynamically constructs hybrid contexts by synthesizing compressed summaries with high-fidelity text while integrating an autonomous and on-demand external search module to resolve concerns requiring outside literature. By generating an inspectable response plan before drafting, $\textbf{RebuttalAgent}$ ensures that every argument is explicitly anchored in internal or external evidence. We validate our approach on the proposed $\textbf{RebuttalBench}$ and demonstrate that our pipeline outperforms strong baselines in coverage, faithfulness, and strategic coherence, offering a transparent and controllable assistant for the peer review process. Code will be released.
Penalizing Localized Dirichlet Energies in Low Rank Tensor Products
Venue: arXiv cs.LG 2026
Year: 2026

We study low-rank tensor-product B-spline (TPBS) models for regression tasks and investigate Dirichlet energy as a measure of smoothness. We show that TPBS models admit a closed-form expression for the Dirichlet energy, and reveal scenarios where perfect interpolation is possible with exponentially small Dirichlet energy. This renders global Dirichlet energy-based regularization ineffective. To address this limitation, we propose a novel regularization strategy based on local Dirichlet energies defined on small hypercubes centered at the training points. Leveraging pretrained TPBS models, we also introduce two estimators for inference from incomplete samples. Comparative experiments with neural networks demonstrate that TPBS models outperform neural networks in the overfitting regime for most datasets, and maintain competitive performance otherwise. Overall, TPBS models exhibit greater robustness to overfitting and consistently benefit from regularization, while neural networks are more sensitive to overfitting and less effective in leveraging regularization.
Analyzing Far-Right Telegram Channels as Constituents of Information Autocracy in Russia
Venue: arXiv cs.CY 2026
Year: 2026

This study examines how Russian far-right communities on Telegram shape perceptions of political figures through memes and visual narratives. Far from passive spectators, these actors co-produce propaganda, blending state-aligned messages with their own extremist framings. In Russia, such groups are central because they articulate the ideological foundations of the war against Ukraine and reflect the regime's gradual drift toward ultranationalist rhetoric. Drawing on a dataset of 200,000 images from expert-selected far-right Telegram channels, the study employs computer vision and unsupervised clustering to identify memes featuring Russian (Putin, Shoigu) and foreign politicians (Zelensky, Biden, Trump) and to reveal recurrent visual patterns in their representation. By leveraging the large-scale and temporal depth of this dataset, the analysis uncovers differential patterns of legitimation and delegitimation across actors and over time. These insights are not attainable in smaller-scale studies. Preliminary findings show that far-right memes function as instruments of propaganda co-production. These communities do not simply echo official messages but generate bottom-up narratives of legitimation and delegitimation that align with state ideology. By framing leaders as heroic and opponents as corrupt or weak, far-right actors act as informal co-creators of authoritarian legitimacy within Russia's informational autocracy.
APEX-Agents
Venue: arXiv cs.CL 2026
Year: 2026

We introduce the AI Productivity Index for Agents (APEX-Agents), a benchmark for assessing whether AI agents can execute long-horizon, cross-application tasks created by investment banking analysts, management consultants, and corporate lawyers. APEX-Agents requires agents to navigate realistic work environments with files and tools. We test eight agents for the leaderboard using Pass@1. Gemini 3 Flash (Thinking=High) achieves the highest score of 24.0%, followed by GPT-5.2 (Thinking=High), Claude Opus 4.5 (Thinking=High), and Gemini 3 Pro (Thinking=High). We open source the APEX-Agents benchmark (n=480) with all prompts, rubrics, gold outputs, files, and metadata. We also open source Archipelago, our infrastructure for agent execution and evaluation.
XR: Cross-Modal Agents for Composed Image Retrieval
Venue: arXiv cs.IR 2026
Year: 2026

Retrieval is being redefined by agentic AI, demanding multimodal reasoning beyond conventional similarity-based paradigms. Composed Image Retrieval (CIR) exemplifies this shift as each query combines a reference image with textual modifications, requiring compositional understanding across modalities. While embedding-based CIR methods have achieved progress, they remain narrow in perspective, capturing limited cross-modal cues and lacking semantic reasoning. To address these limitations, we introduce XR, a training-free multi-agent framework that reframes retrieval as a progressively coordinated reasoning process. It orchestrates three specialized types of agents: imagination agents synthesize target representations through cross-modal generation, similarity agents perform coarse filtering via hybrid matching, and question agents verify factual consistency through targeted reasoning for fine filtering. Through progressive multi-agent coordination, XR iteratively refines retrieval to meet both semantic and visual query constraints, achieving up to a 38% gain over strong training-free and training-based baselines on FashionIQ, CIRR, and CIRCO, while ablations show each agent is essential. Code is available: https://01yzzyu.github.io/xr.github.io/.
Soft Tail-dropping for Adaptive Visual Tokenization
Venue: arXiv cs.CV 2026
Year: 2026

We present Soft Tail-dropping Adaptive Tokenizer (STAT), a 1D discrete visual tokenizer that adaptively chooses the number of output tokens per image according to its structural complexity and level of detail. STAT encodes an image into a sequence of discrete codes together with per-token keep probabilities. Beyond standard autoencoder objectives, we regularize these keep probabilities to be monotonically decreasing along the sequence and explicitly align their distribution with an image-level complexity measure. As a result, STAT produces length-adaptive 1D visual tokens that are naturally compatible with causal 1D autoregressive (AR) visual generative models. On ImageNet-1k, equipping vanilla causal AR models with STAT yields competitive or superior visual generation quality compared to other probabilistic model families, while also exhibiting favorable scaling behavior that has been elusive in prior vanilla AR visual generation attempts.
Implicit Neural Representation Facilitates Unified Universal Vision Encoding
Venue: arXiv cs.CV 2026
Year: 2026

Models for image representation learning are typically designed for either recognition or generation. Various forms of contrastive learning help models learn to convert images to embeddings that are useful for classification, detection, and segmentation. On the other hand, models can be trained to reconstruct images with pixel-wise, perceptual, and adversarial losses in order to learn a latent space that is useful for image generation. We seek to unify these two directions with a first-of-its-kind model that learns representations which are simultaneously useful for recognition and generation. We train our model as a hyper-network for implicit neural representation, which learns to map images to model weights for fast, accurate reconstruction. We further integrate our INR hyper-network with knowledge distillation to improve its generalization and performance. Beyond the novel training design, the model also learns an unprecedented compressed embedding space with outstanding performance for various visual tasks. The complete model competes with state-of-the-art results for image representation learning, while also enabling generative capabilities with its high-quality tiny embeddings. The code is available at https://github.com/tiktok/huvr.
The Ontological Neutrality Theorem: Why Neutral Ontological Substrates Must Be Pre-Causal and Pre-Normative
Venue: arXiv cs.AI 2026
Year: 2026

Modern data systems must support accountability across persistent legal, political, and analytic disagreement. This requirement imposes strict constraints on the design of any ontology intended to function as a shared substrate. We establish an impossibility result for ontological neutrality: neutrality, understood as interpretive non-commitment and stability under incompatible extensions, is incompatible with the inclusion of causal or normative commitments at the foundational layer. Any ontology that asserts causal or deontic conclusions as ontological facts cannot serve as a neutral substrate across divergent frameworks without revision or contradiction. It follows that neutral ontological substrates must be pre-causal and pre-normative, representing entities, together with identity and persistence conditions, while externalizing interpretation, evaluation, and explanation. This paper does not propose a specific ontology or protocol; rather, it establishes the necessary design constraints for any system intended to maintain a shared, stable representation of reality across conflicting interpretive frameworks.
Quality or Quantity? Error-Informed Selective Online Learning with Gaussian Processes in Multi-Agent Systems: Extended Version
Venue: arXiv cs.LG 2026
Year: 2026

Effective cooperation is pivotal in distributed learning for multi-agent systems, where the interplay between the quantity and quality of the machine learning models is crucial. This paper reveals the irrationality of indiscriminate inclusion of all models on agents for joint prediction, highlighting the imperative to prioritize quality over quantity in cooperative learning. Specifically, we present the first selective online learning framework for distributed Gaussian process (GP) regression, namely distributed error-informed GP (EIGP), that enables each agent to assess its neighboring collaborators, using the proposed selection function to choose the higher quality GP models with less prediction errors. Moreover, algorithmic enhancements are embedded within the EIGP, including a greedy algorithm (gEIGP) for accelerating prediction and an adaptive algorithm (aEIGP) for improving prediction accuracy. In addition, approaches for fast prediction and model update are introduced in conjunction with the error-informed quantification term iteration and a data deletion strategy to achieve real-time learning operations. Numerical simulations are performed to demonstrate the effectiveness of the developed methodology, showcasing its superiority over the state-of-the-art distributed GP methods with different benchmarks.
Which Quantization Should I Use? A Unified Evaluation of llama.cpp Quantization on Llama-3.1-8B-Instruct
Venue: arXiv cs.LG 2026
Year: 2026

Quantization is a practical technique for making large language models easier to deploy by reducing the precision used to store and operate on model weights. This can lower memory use and improve runtime feasibility on constrained hardware, which is especially relevant for users running models locally. Quantization in llama.cpp enables large language models to run on commodity hardware, but available formats are often evaluated inconsistently, making it hard to choose among schemes. We present a unified empirical study of the llama.cpp quantization on a single modern model, Llama-3.1-8B-Instruct (FP16, GGUF), covering 3-8 bit K-quant and legacy formats. We evaluate downstream task performance across standard reasoning, knowledge, instruction-following, and truthfulness benchmarks, and also measure perplexity and CPU throughput (prefill/decoding) alongside model size, compression, and quantization time. Ultimately, this work is a practical guide for choosing a llama.cpp quantization scheme, helping readers make informed, context-aware decisions for their intended use and resource budget.