OpenRaiser/Intern-Atlas huggingface dataset:
Generative Diffusion Model Driven Massive Random Access in Massive MIMO Systems
Venue: arXiv cs.IT 2026
Year: 2026

Massive random access is an important technology for achieving ultra-massive connectivity in next-generation wireless communication systems. It aims to address key challenges during the initial access phase, including active user detection (AUD), channel estimation (CE), and data detection (DD). This paper examines massive access in massive multiple-input multiple-output (MIMO) systems, where deep learning is used to tackle the challenging AUD, CE, and DD functions. First, we introduce a Transformer-AUD scheme tailored for variable pilot-length access. This approach integrates pilot length information and a spatial correlation module into a Transformer-based detector, enabling a single model to generalize across various pilot lengths and antenna numbers. Next, we propose a generative diffusion model (GDM)-driven iterative CE and DD framework. The GDM employs a score function to capture the posterior distributions of massive MIMO channels and data symbols. Part of the score function is learned from the channel dataset via neural networks, while the remaining score component is derived in a closed form by applying the symbol prior constellation distribution and known transmission model. Utilizing these posterior scores, we design an asynchronous alternating CE and DD framework that employs a predictor-corrector sampling technique to iteratively generate channel estimation and data detection results during the reverse diffusion process. Simulation results demonstrate that our proposed approaches significantly outperform baseline methods with respect to AUD, CE, and DD.
Protocol as Poetry: A Case Study of Pak's Smart Contract-Based Protocol Art
Venue: arXiv cs.CY 2026
Year: 2026

Protocol art has recently proliferated through blockchain-based smart contracts, building on a century-long lineage of conceptual, participatory, interactive, systematic, algorithmic, and generative art practices. Few studies have examined the characteristics and appreciation of this emerging art form. To address this gap, this paper presents an annotated portfolio analysis of protocol artworks by Pak, a pioneering and influential pseudonymous artist who treats smart contracts as medium and collective participation through protocol as message. Tracing the evolution from early open-edition releases of The Fungible (2021) and the dynamic mechanics of Merge (2021) to the soul-bound messaging of Censored (2022) and the reflective absence of Not Found (2023), we examine how Pak choreographs distributed agency across collectors and autonomous code, demonstrating how programmable protocols become a social fabric in artistic meaning-making. Through thematic analysis of Pak's works, we identify seven core characteristics distinguishing protocol art from other art forms: (1) system-centric rather than object-centric composition, (2) autonomous governance enabling open-ended control, (3) distributed agency and communal authorship, (4) temporal dynamism and lifecycle aesthetics, (5) economy-driven engagement, (6) poetic message embedded in interaction rituals, and (7) interoperability enabling composability for emergent complexity. We then discuss how these features set protocol art apart from adjacent movements such as conceptual, generative, participatory, interactive, and performance art. By analyzing principles grounded in Pak's practice, we contribute to the emerging literature on protocol art (or "protocolism") and offer design implications for future artists exploring this evolving form.
Unsupervised Representation Learning - an Invariant Risk Minimization Perspective
Venue: arXiv cs.LG 2026
Year: 2026

We propose a novel unsupervised framework for \emph{Invariant Risk Minimization} (IRM), extending the concept of invariance to settings where labels are unavailable. Traditional IRM methods rely on labeled data to learn representations that are robust to distributional shifts across environments. In contrast, our approach redefines invariance through feature distribution alignment, enabling robust representation learning from unlabeled data. We introduce two methods within this framework: Principal Invariant Component Analysis (PICA), a linear method that extracts invariant directions under Gaussian assumptions, and Variational Invariant Autoencoder (VIAE), a deep generative model that separates environment-invariant and environment-dependent latent factors. Our approach is based on a novel ``unsupervised'' structural causal model and supports environment-conditioned sample-generation and intervention. Empirical evaluations on synthetic dataset, modified versions of MNIST, and CelebA demonstrate the effectiveness of our methods in capturing invariant structure, preserving relevant information, and generalizing across environments without access to labels.
Robust Reinforcement Learning-Based Locomotion for Resource-Constrained Quadrupeds with Exteroceptive Sensing
Venue: arXiv cs.RO 2026
Year: 2026

Compact quadrupedal robots are proving increasingly suitable for deployment in real-world scenarios. Their smaller size fosters easy integration into human environments. Nevertheless, real-time locomotion on uneven terrains remains challenging, particularly due to the high computational demands of terrain perception. This paper presents a robust reinforcement learning-based exteroceptive locomotion controller for resource-constrained small-scale quadrupeds in challenging terrains, which exploits real-time elevation mapping, supported by a careful depth sensor selection. We concurrently train both a policy and a state estimator, which together provide an odometry source for elevation mapping, optionally fused with visual-inertial odometry (VIO). We demonstrate the importance of positioning an additional time-of-flight sensor for maintaining robustness even without VIO, thus having the potential to free up computational resources. We experimentally demonstrate that the proposed controller can flawlessly traverse steps up to 17.5 cm in height and achieve an 80% success rate on 22.5 cm steps, both with and without VIO. The proposed controller also achieves accurate forward and yaw velocity tracking of up to 1.0 m/s and 1.5 rad/s respectively. We open-source our training code at github.com/ETH-PBL/elmap-rl-controller.
PLAICraft: Large-Scale Time-Aligned Vision-Speech-Action Dataset for Embodied AI
Venue: arXiv cs.LG 2026
Year: 2026

Advances in deep generative modeling have made it increasingly plausible to train human-level embodied agents. Yet progress has been limited by the absence of large-scale, real-time, multi-modal, and socially interactive datasets that reflect the sensory-motor complexity of natural environments. To address this, we present PLAICraft, a novel data collection platform and dataset capturing multiplayer Minecraft interactions across five time-aligned modalities: video, game output audio, microphone input audio, mouse, and keyboard actions. Each modality is logged with millisecond time precision, enabling the study of synchronous, embodied behaviour in a rich, open-ended world. The dataset comprises over 10,000 hours of gameplay from more than 10,000 global participants. Alongside the dataset, we provide an evaluation suite for benchmarking model capabilities in object recognition, spatial awareness, language grounding, and long-term memory. PLAICraft opens a path toward training and evaluating agents that act fluently and purposefully in real time, paving the way for truly embodied artificial intelligence.
SpecFLASH: A Latent-Guided Semi-autoregressive Speculative Decoding Framework for Efficient Multimodal Generation
Venue: arXiv cs.CV 2026
Year: 2026

Large language models and large multimodal models (LLMs and LMMs) deliver strong generative performance but suffer from slow decoding, a problem that becomes more severe when handling visual inputs, whose sequences typically contain many more tokens with lower information density than text. Speculative decoding accelerates LLM inference by letting a compact draft model propose candidate tokens that are selectively accepted by a larger target model, achieving speed-up without degrading quality. However, existing multimodal speculative decoding approaches largely ignore the structural characteristics of visual representations and usually rely on text-only draft models. In this paper, we introduce SpecFLASH, a speculative decoding framework tailored to LMMs that explicitly exploits multimodal structure when designing the draft model. We first mitigate redundancy in visual token sequences with a lightweight, latent-guided token compression module that compacts visual features while preserving semantics, and then leverage the co-occurrence and local correlations of visual entities via a semi-autoregressive decoding scheme that predicts multiple tokens in a single forward pass. Extensive experiments demonstrate that SpecFLASH consistently surpasses prior speculative decoding baselines, achieving up to $2.68\times$ speed-up on video captioning and $2.55\times$ on visual instruction tuning, relative to the original LMM. Our code is available here: https://github.com/ZihuaEvan/FlashSD/.
MVAR: Visual Autoregressive Modeling with Scale and Spatial Markovian Conditioning
Venue: arXiv cs.CV 2026
Year: 2026

Essential to visual generation is efficient modeling of visual data priors. Conventional next-token prediction methods define the process as learning the conditional probability distribution of successive tokens. Recently, next-scale prediction methods redefine the process to learn the distribution over multi-scale representations, significantly reducing generation latency. However, these methods condition each scale on all previous scales and require each token to consider all preceding tokens, exhibiting scale and spatial redundancy. To better model the distribution by mitigating redundancy, we propose Markovian Visual AutoRegressive modeling (MVAR), a novel autoregressive framework that introduces scale and spatial Markov assumptions to reduce the complexity of conditional probability modeling. Specifically, we introduce a scale-Markov trajectory that only takes as input the features of adjacent preceding scale for next-scale prediction, enabling the adoption of a parallel training strategy that significantly reduces GPU memory consumption. Furthermore, we propose spatial-Markov attention, which restricts the attention of each token to a localized neighborhood of size k at corresponding positions on adjacent scales, rather than attending to every token across these scales, for the pursuit of reduced modeling complexity. Building on these improvements, we reduce the computational complexity of attention calculation from O(N^2) to O(Nk), enabling training with just eight NVIDIA RTX 4090 GPUs and eliminating the need for KV cache during inference. Extensive experiments on ImageNet demonstrate that MVAR achieves comparable or superior performance with both small model trained from scratch and large fine-tuned models, while reducing the average GPU memory footprint by 3.0x.
Optimal Formats for Weight Quantisation
Venue: arXiv cs.LG 2026
Year: 2026

Weight quantisation is an essential technique for enabling efficient training and deployment of modern deep learning models. However, the recipe book of quantisation formats is large and formats are often chosen empirically. In this paper, we propose a framework for systematic design and analysis of quantisation formats. By connecting the question of format design with the classical quantisation theory, we show that the strong practical performance of popular formats comes from their ability to represent values using variable-length codes. We frame the problem as minimising the KL divergence between original and quantised model outputs under a model size constraint, which can be approximated by minimising the squared quantisation error, a well-studied problem where entropy-constrained quantisers with variable-length codes are optimal. We develop non-linear quantisation curves for block-scaled data across multiple distribution families and observe that these formats, along with sparse outlier formats, consistently outperform fixed-length formats, indicating that they also exploit variable-length encoding. Finally, by using the relationship between the Fisher information and KL divergence, we derive the optimal allocation of bit-widths to individual parameter tensors across the model's layers, saving up to 0.25 bits per parameter when applied to large language models.
PIM-malloc: A Fast and Scalable Dynamic Memory Allocator for Processing-In-Memory (PIM) Architectures
Venue: arXiv cs.AR 2026
Year: 2026

The ability to dynamically allocate memory is fundamental in modern programming languages. However, this feature is not adequately supported in current general-purpose PIM devices. To identify key design principles that PIM must consider, we conduct a design space exploration of PIM memory allocators, examining various strategies for metadata placement and management of the allocator. Based on this exploration, we introduce PIM-malloc, a fast and scalable memory allocator for general-purpose PIM that operates on real PIM hardware, achieving a x66 improvement in memory allocation performance. This design is further enhanced with a lightweight, per-PIM core hardware cache, specifically designed for dynamic memory allocation, achieving an additional 31% performance improvement. Finally, we demonstrate the applicability of PIM-malloc by developing several representative PIM workloads, demonstrating its effectiveness in enhancing programmability.
Lightweight and Interpretable Transformer via Mixed Graph Algorithm Unrolling for Traffic Forecast
Venue: arXiv cs.LG 2026
Year: 2026

Unlike conventional "black-box" transformers with classical self-attention mechanism, we build a lightweight and interpretable transformer-like neural net by unrolling a mixed-graph-based optimization algorithm to forecast traffic with spatial and temporal dimensions. We construct two graphs: an undirected graph $\mathcal{G}^u$ capturing spatial correlations across geography, and a directed graph $\mathcal{G}^d$ capturing sequential relationships over time. We predict future samples of signal $\mathbf{x}$, assuming it is "smooth" with respect to both $\mathcal{G}^u$ and $\mathcal{G}^d$, where we design new $\ell_2$ and $\ell_1$-norm variational terms to quantify and promote signal smoothness (low-frequency reconstruction) on a directed graph. We design an iterative algorithm based on alternating direction method of multipliers (ADMM), and unroll it into a feed-forward network for data-driven parameter learning. We periodically insert graph learning modules for $\mathcal{G}^u$ and $\mathcal{G}^d$ that play the role of self-attention. Experiments show that our unrolled networks achieve competitive traffic forecast performance as state-of-the-art prediction schemes, while reducing parameter counts drastically.
Large Language Models as Formalizers on Constraint Satisfaction Problems
Venue: arXiv cs.CL 2026
Year: 2026

An emerging line of recent work advocates for using large language models (LLMs) as formalizers instead of as end-to-end solvers for various types of problems. Instead of generating the solution, the LLM generates a formal program that derives a solution via an external solver. We thoroughly investigate the formalization capability of LLMs on real-life constraint satisfaction problems. On 4 domains, we systematically evaluate 6 LLMs, including 4 large reasoning models with inference-time scaling, paired with 5 pipelines, including 2 types of formalism. We show that in zero-shot settings, LLM-as-formalizer performs on par with the mainstream LLM-as-solver while offering verifiability, interpretability, and robustness. We also observe excessive reasoning tokens and hard-coded solutions scaling with problem complexity, which demonstrates that even the state-of-the-art LLMs have limited ability to generate solutions or formal programs. We present our detailed analysis and actionable remedies to drive future research that improves LLM-as-formalizer.
RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization
Venue: arXiv cs.LG 2026
Year: 2026

Real world data often exhibits unknown, instance-specific symmetries that rarely exactly match a transformation group $G$ fixed a priori. Class-pose decompositions aim to create disentangled representations by factoring inputs into invariant features and a pose $g\in G$ defined relative to a training-dependent, arbitrary canonical representation. We introduce RECON, a class-pose agnostic canonical orientation normalization that corrects arbitrary canonicals via a simple right translation, yielding natural, data-aligned canonicalizations. This enables (i) unsupervised discovery of instance-specific pose distributions, (ii) detection of out-of-distribution poses and (iii) a plug-and-play test-time canonicalization layer. This layer can be attached on top of any pre-trained model to infuse group invariance, improving its performance without retraining. We validate on images and molecular ensembles, demonstrating accurate symmetry discovery, and matching or outperforming other canonicalizations in downstream classification.
New Sorting Algorithm Wave Sort (W-Sort)
Venue: arXiv cs.DS 2026
Year: 2026

Modern comparison sorts like quicksort suffer from performance inconsistencies due to suboptimal pivot selection, leading to $(O(N^2))$ worst-case complexity, while in-place merge sort variants face challenges with data movement overhead. We introduce Wave Sort, a novel in-place sorting algorithm that addresses these limitations through a dynamic pivot selection strategy. Wave Sort iteratively expands a sorted region and selects pivots from this growing sorted portion to partition adjacent unsorted data. This approach ensures robust pivot selection irrespective of dataset size, guarantees a logarithmic recursion stack depth, and enables efficient in-place sorting. Our analysis shows a best comparison complexity of $(N-1)$, average comparison complexity close to $(\log_2(N)!)$, and worst-case comparison complexity bounded by $(O(N(\log(N))^2))$ with a small constant factor, which could be reduced to $(O(N\log(N)))$ with hybrid sorting. The algorithm can be easily expanded to be hybridized with other sorting algorithms. Experimental results demonstrate that Wave Sort requires significantly fewer comparisons than quicksort on average (approximately 24% less) and performs close to the theoretical minimum $(\log_2(N)!)$. Wave Sort offers a compelling alternative for applications demanding consistent, predictable, and in-place sorting performance.
AMAQA: A Metadata-based QA Dataset for RAG Systems
Venue: arXiv cs.IR 2026
Year: 2026

Retrieval-augmented generation (RAG) systems are widely used in question-answering (QA) tasks, but current benchmarks lack metadata integration, limiting their evaluation in scenarios requiring both textual data and external information. To address this, we present AMAQA, a new open-access QA dataset designed to evaluate tasks combining text and metadata. The integration of metadata is especially important in fields that require rapid analysis of large volumes of data, such as cybersecurity and intelligence, where timely access to relevant information is critical. AMAQA includes about 1.1 million English messages collected from 26 public Telegram groups, enriched with metadata such as timestamps and chat names. It also contains 20,000 hotel reviews with metadata. In addition, the dataset provides 2,600 high-quality QA pairs built across both domains, Telegram messages and hotel reviews, making AMAQA a valuable resource for advancing research on metadata-driven QA and RAG systems. Both Telegram messages and Hotel reviews are enriched with emotional tones or toxicity indicators. To the best of our knowledge, AMAQA is the first single-hop QA benchmark to incorporate metadata. We conduct extensive tests on the benchmark, setting a new reference point for future research. We show that leveraging metadata boosts accuracy from 0.5 to 0.86 for GPT-4o and from 0.27 to 0.76 for open source LLMs, highlighting the value of structured context. We conducted experiments on our benchmark to assess the performance of known techniques designed to enhance RAG, highlighting the importance of properly managing metadata throughout the entire RAG pipeline.
RL in Name Only? Analyzing the Structural Assumptions in RL post-training for LLMs
Venue: arXiv cs.LG 2026
Year: 2026

Reinforcement learning based post-training of large language models (LLMs) has recently gained attention, particularly following the release of DeepSeek R1, which applied GRPO for fine-tuning. Amid the growing claims around improved reasoning abilities attributed to RL post-training, we critically examine the formulation and assumptions underlying these methods. We start by highlighting popular structural assumptions made in modeling LLM training as an MDP, and show how they lead to a degenerate MDP, that characterizes the problem as a contextual bandit, where RL updates naturally collapse into a form of on-policy variant of outcome-driven supervised learning. The two critical structural assumptions include (1) making the MDP states be just a concatenation of the actions with states becoming the context window and the actions becoming the tokens in LLMs and (2) splitting the reward of a state-action trajectory uniformly across the trajectory. Our comprehensive analysis demonstrates that, due to these simplifying assumptions, GRPO objective reduces to filtered Iterative SFT, an on-policy variant of supervised fine-tuning. Our experiments on benchmarks including GSM8K and Countdown, across a diverse set of model families show that Filtered Iterative SFT, incorporating both positive and negative samples, achieves performance comparable to GRPO-based training. We also show that these structural assumptions indirectly incentivize RL to generate longer sequences of intermediate tokens which in turn feeds into the narrative of "RL incentivizing thinking because it generates longer thinking traces."
Advancing Software Quality: A Standards-Focused Review of LLM-Based Assurance Techniques
Venue: arXiv cs.SE 2026
Year: 2026

Software Quality Assurance (SQA) is critical for delivering reliable, secure, and efficient software products. The Software Quality Assurance Process aims to provide assurance that work products and processes comply with predefined provisions and plans. Recent advancements in Large Language Models (LLMs) present new opportunities to enhance existing SQA processes by automating tasks like requirement analysis, code review, test generation, and compliance checks. Simultaneously, established standards such as ISO/IEC 12207, ISO/IEC 25010, ISO/IEC 5055, ISO 9001/ISO/IEC 90003, CMMI, and TMM provide structured frameworks for ensuring robust quality practices. This paper surveys the intersection of LLM-based SQA methods and these recognized standards, highlighting how AI-driven solutions can augment traditional approaches while maintaining compliance and process maturity. We first review the foundational software quality standards and the technical fundamentals of LLMs in software engineering. Next, we explore various LLM-based SQA applications, including requirement validation, defect detection, test generation, and documentation maintenance. We then map these applications to key software quality frameworks, illustrating how LLMs can address specific requirements and metrics within each standard. Empirical case studies and open-source initiatives demonstrate the practical viability of these methods. At the same time, discussions on challenges (e.g., data privacy, model bias, explainability) underscore the need for deliberate governance and auditing. Finally, we propose future directions encompassing adaptive learning, privacy-focused deployments, multimodal analysis, and evolving standards for AI-driven software quality.