OpenRaiser/Intern-Atlas huggingface dataset:
Mapping Gemma3 onto an Edge Dataflow Architecture
Venue: arXiv cs.DC 2026
Year: 2026

We present the first end-to-end deployment of the Gemma3 family of large language and vision models on a tiled edge dataflow architecture (AMD Ryzen AI NPU). Our work introduces a set of hardware-aware techniques. For prefill, we introduce an efficient dequantization engine, optimize tiled matrix multiplication kernels, and propose FlowQKV, a chunked, pipelined attention mechanism. For decoding, we introduce FusedDQP, which fuses dequantization and projection into a single kernel, and FlowKV, which re-structures attention to sustain high memory bandwidth utilization. Together with a compact Q4NX 4-bit quantization format, these methods yield up to $5.2\times$ faster prefill and $4.8\times$ faster decoding versus the iGPU, and $33.5\times$ and $2.2\times$ over the CPU, respectively. Power efficiency improves by as much as $67.2\times$ and $222.9\times$ compared to the iGPU and CPU. The proposed approach demonstrates that modern NPUs can deliver practical, low-power LLM and VLM inference at the edge, and provides a generalizable blueprint for mapping transformer-based models onto tiled dataflow accelerators.
FlashSketch: Sketch-Kernel Co-Design for Fast Sparse Sketching on GPUs
Venue: arXiv cs.DC 2026
Year: 2026

Sparse sketches such as the sparse Johnson-Lindenstrauss transform are a core primitive in randomized numerical linear algebra because they leverage random sparsity to reduce the arithmetic cost of sketching, while still offering strong approximation guarantees. Their random sparsity, however, is at odds with efficient implementations on modern GPUs, since it leads to irregular memory access patterns that degrade memory bandwidth utilization. Motivated by this tension, we pursue a sketch-kernel co-design approach: we design a new family of sparse sketches, BlockPerm-SJLT, whose sparsity structure is chosen to enable FlashSketch, a corresponding optimized CUDA kernel that implements these sketches efficiently. The design of BlockPerm-SJLT introduces a tunable parameter that explicitly trades off the tension between GPU-efficiency and sketching robustness. We provide theoretical guarantees for BlockPerm-SJLT under the oblivious subspace embedding (OSE) framework, and also analyze the effect of the tunable parameter on sketching quality. We empirically evaluate FlashSketch on standard RandNLA benchmarks, as well as an end-to-end ML data attribution pipeline called GraSS. FlashSketch pushes the Pareto frontier of sketching quality versus speed, across a range of regimes and tasks, and achieves a global geomean speedup of roughly 1.7x over the prior state-of-the-art GPU sketches.
PackInfer: Compute- and I/O-Efficient Attention for Batched LLM Inference
Venue: arXiv cs.DC 2026
Year: 2026

Attention efficiency is critical to large language model (LLM) inference. While prior advances optimize attention execution for individual requests (e.g., FlashAttention), production LLM serving relies on batching requests with highly heterogeneous sequence lengths for high serving throughput. This mismatch induces severe computation and I/O imbalance, exacerbates stragglers, and underutilizes GPU resources. We present PackInfer, a kernel-level attention framework that enables compute- and I/O-aware execution for heterogeneous batched inference. PackInfer orchestrates batched requests into load-balanced execution groups, effectively saturating GPU utilization by packing multiple requests into unified kernel launches. By constructing attention kernels directly over packed query-key regions, PackInfer eliminates redundant computation and balances thread-block execution. It then incorporates I/O-aware grouping that co-locates shared-prefix requests and reorganizes KV caches into group-contiguous layouts, reducing memory fragmentation and redundant data movement as generation evolves. Evaluations on real-world workloads show that PackInfer reduces inference latency by 13.0-20.1%, and improves throughput by 20% compared to the state-of-the-art FlashAttention.
Communication Enhances LLMs' Stability in Strategic Thinking
Venue: arXiv cs.MA 2026
Year: 2026

Large Language Models (LLMs) often exhibit pronounced context-dependent variability that undermines predictable multi-agent behavior in tasks requiring strategic thinking. Focusing on models that range from 7 to 9 billion parameters in size engaged in a ten-round repeated Prisoner's Dilemma, we evaluate whether short, costless pre-play messages emulating the cheap-talk paradigm affect strategic stability. Our analysis uses simulation-level bootstrap resampling and nonparametric inference to compare cooperation trajectories fitted with LOWESS regression across both the messaging and the no-messaging condition. We demonstrate consistent reductions in trajectory noise across a majority of the model-context pairings being studied. The stabilizing effect persists across multiple prompt variants and decoding regimes, though its magnitude depends on model choice and contextual framing, with models displaying higher baseline volatility gaining the most. While communication rarely produces harmful instability, we document a few context-specific exceptions and identify the limited domains in which communication harms stability. These findings position cheap-talk style communication as a low-cost, practical tool for improving the predictability and reliability of strategic behavior in multi-agent LLM systems.
Coding Agents with Environment Interaction: A Theoretical Perspective
Venue: arXiv cs.SE 2026
Year: 2026

Coding agents are increasingly utilized in test-driven software development, yet the theoretical mechanisms behind their environment-interaction strategies remain underexplored. We provide a probabilistic framework for two dominant paradigms: code selection after generation using the execution environment, and code generation conditioned on environment feedback. First, we formalize several well-established selection heuristics as environment-aware estimators of code correctness. We theoretically prove that estimators based on fuzzy functional similarity add an inductive bias and strictly dominate estimators based on functional equivalence in terms of signal-to-noise ratio. Second, we frame backprompting as an in-context approximation of Thompson sampling. We derive a novel regret bound for reward functions with unobservable components, theoretically explaining why the effectiveness of backprompting is limited by the ambiguity of the informal task description (an irreducible regret). Using three state-of-the-art open weight models, we corroborate these findings across BigCodeBenchHard, LeetCodeDataset, and QiskitHumanEvalSim. Our formalization also suggests how to improve task descriptions effectively, leading to a new benchmark, QiskitHumanEvalSimX.
Pragmatic Curiosity: A Hybrid Learning-Optimization Paradigm via Active Inference
Venue: arXiv cs.LG 2026
Year: 2026

Many engineering and scientific workflows depend on expensive black-box evaluations, requiring decision-making that simultaneously improves performance and reduces uncertainty. Bayesian optimization (BO) and Bayesian experimental design (BED) offer powerful yet largely separate treatments of goal-seeking and information-seeking, providing limited guidance for hybrid settings where learning and optimization are intrinsically coupled. We propose "pragmatic curiosity," a hybrid learning-optimization paradigm derived from active inference, in which actions are selected by minimizing the expected free energy--a single objective that couples pragmatic utility with epistemic information gain. We demonstrate the practical effectiveness and flexibility of pragmatic curiosity on various real-world hybrid tasks, including constrained system identification, targeted active search, and composite optimization with unknown preferences. Across these benchmarks, pragmatic curiosity consistently outperforms strong BO-type and BED-type baselines, delivering higher estimation accuracy, better critical-region coverage, and improved final solution quality.
Private and interpretable clinical prediction with quantum-inspired tensor train models
Venue: arXiv cs.LG 2026
Year: 2026

Machine learning in clinical settings must balance predictive accuracy, interpretability, and privacy. Models such as logistic regression (LR) offer transparency, while neural networks (NNs) provide greater predictive power; yet both remain vulnerable to privacy attacks. We empirically assess these risks by designing attacks that identify which public datasets were used to train a model under varying levels of adversarial access, applying them to LORIS, a publicly available LR model for immunotherapy response prediction, as well as to additional shallow NN models trained for the same task. Our results show that both models leak significant training-set information, with LRs proving particularly vulnerable in white-box scenarios. Moreover, we observe that common practices such as cross-validation in LRs exacerbate these risks. To mitigate these vulnerabilities, we propose a quantum-inspired defense based on tensorizing discretized models into tensor trains (TTs), which fully obfuscates parameters while preserving accuracy, reducing white-box attacks to random guessing and degrading black-box attacks comparably to Differential Privacy. TT models retain LR interpretability and extend it through efficient computation of marginal and conditional distributions, while also enabling this higher level of interpretability for NNs. Our results demonstrate that tensorization is widely applicable and establishes a practical foundation for private, interpretable, and effective clinical prediction.
SCONE: A Practical, Constraint-Aware Plug-in for Latent Encoding in Learned DNA Storage
Venue: arXiv cs.LG 2026
Year: 2026

DNA storage has matured from concept to practical stage, yet its integration with neural compression pipelines remains inefficient. Early DNA encoders applied redundancy-heavy constraint layers atop raw binary data - workable but primitive. Recent neural codecs compress data into learned latent representations with rich statistical structure, yet still convert these latents to DNA via naive binary-to-quaternary transcoding, discarding the entropy model's optimization. This mismatch undermines compression efficiency and complicates the encoding stack. A plug-in module that collapses latent compression and DNA encoding into a single step. SCONE performs quaternary arithmetic coding directly on the latent space in DNA bases. Its Constraint-Aware Adaptive Coding module dynamically steers the entropy encoder's learned probability distribution to enforce biochemical constraints - Guanine-Cytosine (GC) balance and homopolymer suppression - deterministically during encoding, eliminating post-hoc correction. The design preserves full reversibility and exploits the hyperprior model's learned priors without modification. Experiments show SCONE achieves near-perfect constraint satisfaction with negligible computational overhead (<2% latency), establishing a latent-agnostic interface for end-to-end DNA-compatible learned codecs.
MGP-KAD: Multimodal Geometric Priors and Kolmogorov-Arnold Decoder for Single-View 3D Reconstruction in Complex Scenes
Venue: arXiv cs.CV 2026
Year: 2026

Single-view 3D reconstruction in complex real-world scenes is challenging due to noise, object diversity, and limited dataset availability. To address these challenges, we propose MGP-KAD, a novel multimodal feature fusion framework that integrates RGB and geometric prior to enhance reconstruction accuracy. The geometric prior is generated by sampling and clustering ground-truth object data, producing class-level features that dynamically adjust during training to improve geometric understanding. Additionally, we introduce a hybrid decoder based on Kolmogorov-Arnold Networks (KAN) to overcome the limitations of traditional linear decoders in processing complex multimodal inputs. Extensive experiments on the Pix3D dataset demonstrate that MGP-KAD achieves state-of-the-art (SOTA) performance, significantly improving geometric integrity, smoothness, and detail preservation. Our work provides a robust and effective solution for advancing single-view 3D reconstruction in complex scenes.
Driving with DINO: Vision Foundation Features as a Unified Bridge for Sim-to-Real Generation in Autonomous Driving
Venue: arXiv cs.CV 2026
Year: 2026

Driven by the emergence of Controllable Video Diffusion, existing Sim2Real methods for autonomous driving video generation typically rely on explicit intermediate representations to bridge the domain gap. However, these modalities face a fundamental Consistency-Realism Dilemma. Low-level signals (e.g., edges, blurred images) ensure precise control but compromise realism by "baking in" synthetic artifacts, whereas high-level priors (e.g., depth, semantics, HDMaps) facilitate photorealism but lack the structural detail required for consistent guidance. In this work, we present Driving with DINO (DwD), a novel framework that leverages Vision Foundation Module (VFM) features as a unified bridge between the simulation and real-world domains. We first identify that these features encode a spectrum of information, from high-level semantics to fine-grained structure. To effectively utilize this, we employ Principal Subspace Projection to discard the high-frequency elements responsible for "texture baking," while concurrently introducing Random Channel Tail Drop to mitigate the structural loss inherent in rigid dimensionality reduction, thereby reconciling realism with control consistency. Furthermore, to fully leverage DINOv3's high-resolution capabilities for enhancing control precision, we introduce a learnable Spatial Alignment Module that adapts these high-resolution features to the diffusion backbone. Finally, we propose a Causal Temporal Aggregator employing causal convolutions to explicitly preserve historical motion context when integrating frame-wise DINO features, which effectively mitigates motion blur and guarantees temporal stability. Project page: https://albertchen98.github.io/DwD-project/
$f$-FUM: Federated Unlearning via min--max and $f$-divergence
Venue: arXiv cs.LG 2026
Year: 2026

Federated Learning (FL) has emerged as a powerful paradigm for collaborative machine learning across decentralized data sources, preserving privacy by keeping data local. However, increasing legal and ethical demands, such as the "right to be forgotten", and the need to mitigate data poisoning attacks have underscored the urgent necessity for principled data unlearning in FL. Unlike centralized settings, the distributed nature of FL complicates the removal of individual data contributions. In this paper, we propose a novel federated unlearning framework formulated as a min-max optimization problem, where the objective is to maximize an $f$-divergence between the model trained with all data and the model retrained without specific data points, while minimizing the degradation on retained data. Our framework could act like a plugin and be added to almost any federated setup, unlike SOTA methods like (\cite{10269017} which requires model degradation in server, or \cite{khalil2025notfederatedunlearningweight} which requires to involve model architecture and model weights). This formulation allows for efficient approximation of data removal effects in a federated setting. We provide empirical evaluations to show that our method achieves significant speedups over naive retraining, with minimal impact on utility.
DeDPO: Debiased Direct Preference Optimization for Diffusion Models
Venue: arXiv cs.CV 2026
Year: 2026

Direct Preference Optimization (DPO) has emerged as a predominant alignment method for diffusion models, facilitating off-policy training without explicit reward modeling. However, its reliance on large-scale, high-quality human preference labels presents a severe cost and scalability bottleneck. To overcome this, We propose a semi-supervised framework augmenting limited human data with a large corpus of unlabeled pairs annotated via cost-effective synthetic AI feedback. Our paper introduces Debiased DPO (DeDPO), which uniquely integrates a debiased estimation technique from causal inference into the DPO objective. By explicitly identifying and correcting the systematic bias and noise inherent in synthetic annotators, DeDPO ensures robust learning from imperfect feedback sources, including self-training and Vision-Language Models (VLMs). Experiments demonstrate that DeDPO is robust to the variations in synthetic labeling methods, achieving performance that matches and occasionally exceeds the theoretical upper bound of models trained on fully human-labeled data. This establishes DeDPO as a scalable solution for human-AI alignment using inexpensive synthetic supervision.
Bioinspired Kirigami Capsule Robot for Minimally Invasive Gastrointestinal Biopsy
Venue: arXiv cs.RO 2026
Year: 2026

Wireless capsule endoscopy (WCE) has transformed gastrointestinal (GI) diagnostics by enabling noninvasive visualization of the digestive tract, yet its diagnostic yield remains constrained by the absence of biopsy capability, as histological analysis is still the gold standard for confirming disease. Conventional biopsy using forceps, needles, or rotating blades is invasive, limited in reach, and carries risks of perforation or mucosal trauma, while fluid- or microbiota-sampling capsules cannot provide structured tissue for pathology, leaving a critical gap in swallowable biopsy solutions. Here we present the Kiri-Capsule, a kirigami-inspired capsule robot that integrates deployable PI-film flaps actuated by a compact dual-cam mechanism to achieve minimally invasive and repeatable tissue collection. The kirigami surface remains flat during locomotion but transforms into sharp protrusions upon cam-driven stretching, enabling controlled penetration followed by rotary scraping, with specimens retained in internal fan-shaped cavities. Bench tests confirmed that PI films exhibit a Young's modulus of approximately 20 MPa and stable deployment angles (about 34$^\circ$ at 15% strain), while ex vivo porcine studies demonstrated shallow penetration depths (median $\sim$0.61 mm, range 0.46--0.66 mm) and biopsy yields comparable to standard forceps (mean $\sim$10.9 mg for stomach and $\sim$18.9 mg for intestine), with forces within safe ranges reported for GI biopsy. These findings demonstrate that the Kiri-Capsule bridges passive imaging and functional biopsy, providing a swallowable, depth-controlled, and histology-ready solution that advances capsule-based diagnostics toward safe and effective clinical application.
Scaling Mobile Chaos Testing with AI-Driven Test Execution
Venue: arXiv cs.SE 2026
Year: 2026

Mobile applications in large-scale distributed systems are susceptible to backend service failures, yet traditional chaos engineering approaches cannot scale mobile testing due to the combinatorial explosion of flows, locations, and failure scenarios that need validation. We present an automated mobile chaos testing system that integrates DragonCrawl, an LLM-based mobile testing platform, with uHavoc, a service-level fault injection system. The key insight is that adaptive AI-driven test execution can navigate mobile applications under degraded backend conditions, eliminating the need to manually write test cases for each combination of user flow, city, and failure type. Since Q1 2024, our system has executed over 180,000 automated chaos tests across 47 critical flows in Uber's Rider, Driver, and Eats applications, representing approximately 39,000 hours of manual testing effort that would be impractical at this scale. We identified 23 resilience risks, with 70% being architectural dependency violations where non-critical service failures degraded core user flows. Twelve issues were severe enough to prevent trip requests or food orders. Two caused application crashes detectable only through mobile chaos testing, not backend testing alone. Automated root cause analysis reduced debugging time from hours to minutes, achieving 88% precision@5 in attributing mobile failures to specific backend services. This paper presents the system design, evaluates its performance under fault injection (maintaining 99% test reliability), and reports operational experience demonstrating that continuous mobile resilience validation is achievable at production scale.
Code, Capital, and Clusters: Understanding Firm Performance in the UK AI Economy
Venue: arXiv cs.CY 2026
Year: 2026

The UK has established a distinctive position in the global AI landscape, driven by rapid firm formation and strategic investment. However, the interplay between AI specialisation, local socioeconomic conditions, and firm performance remains underexplored. This study analyses a comprehensive dataset of UK AI entities (2000 - 2024) from Companies House, ONS, and glass.ai. We find a strong geographical concentration in London (41.3 percent of entities) and technology-centric sectors, with general financial services reporting the highest mean operating revenue (33.9 million GBP, n=33). Firm size and AI specialisation intensity are primary revenue drivers, while local factors, Level 3 qualification rates, population density, and employment levels, provide significant marginal contributions, highlighting the dependence of AI growth on regional socioeconomic ecosystems. The forecasting models project sectoral expansion to 2030, estimating 4,651 [4,323 - 4,979, 95 percent CI] total entities and a rising dissolution ratio (2.21 percent [-0.17 - 4.60]), indicating a transition toward slower sector expansion and consolidation. These results provide robust evidence for place-sensitive policy interventions: cultivating regional AI capabilities beyond London to mitigate systemic risks; distinguishing between support for scaling (addressing capital gaps) and deepening technical specialisation; and strategically shaping ecosystem consolidation. Targeted actions are essential to foster both aggregate AI growth and balanced regional development, transforming consolidation into sustained competitive advantage.
ASMa: Asymmetric Spatio-temporal Masking for Skeleton Action Representation Learning
Venue: arXiv cs.CV 2026
Year: 2026

Self-supervised learning (SSL) has shown remarkable success in skeleton-based action recognition by leveraging data augmentations to learn meaningful representations. However, existing SSL methods rely on data augmentations that predominantly focus on masking high-motion frames and high-degree joints such as joints with degree 3 or 4. This results in biased and incomplete feature representations that struggle to generalize across varied motion patterns. To address this, we propose Asymmetric Spatio-temporal Masking (ASMa) for Skeleton Action Representation Learning, a novel combination of masking to learn a full spectrum of spatio-temporal dynamics inherent in human actions. ASMa employs two complementary masking strategies: one that selectively masks high-degree joints and low-motion, and another that masks low-degree joints and high-motion frames. These masking strategies ensure a more balanced and comprehensive skeleton representation learning. Furthermore, we introduce a learnable feature alignment module to effectively align the representations learned from both masked views. To facilitate deployment in resource-constrained settings and on low-resource devices, we compress the learned and aligned representation into a lightweight model using knowledge distillation. Extensive experiments on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD datasets demonstrate that our approach outperforms existing SSL methods with an average improvement of 2.7-4.4% in fine-tuning and up to 5.9% in transfer learning to noisy datasets and achieves competitive performance compared to fully supervised baselines. Our distilled model achieves 91.4% parameter reduction and 3x faster inference on edge devices while maintaining competitive accuracy, enabling practical deployment in resource-constrained scenarios.