OpenRaiser/Intern-Atlas huggingface dataset:
C3Po: Cross-View Cross-Modality Correspondence by Pointmap Prediction
Venue: arXiv cs.CV 2026
Year: 2026

Geometric models like DUSt3R have shown great advances in understanding the geometry of a scene from pairs of photos. However, they fail when the inputs are from vastly different viewpoints (e.g., aerial vs. ground) or modalities (e.g., photos vs. abstract drawings) compared to what was observed during training. This paper addresses a challenging version of this problem: predicting correspondences between ground-level photos and floor plans. Current datasets for joint photo-floor plan reasoning are limited, either lacking in varying modalities (VIGOR) or lacking in correspondences (WAFFLE). To address these limitations, we introduce a new dataset, C3, created by first reconstructing a number of scenes in 3D from Internet photo collections via structure-from-motion, then manually registering the reconstructions to floor plans gathered from the Internet, from which we can derive correspondences between images and floor plans. C3 contains 90K paired floor plans and photos across 597 scenes with 153M pixel-level correspondences and 85K camera poses. We find that state-of-the-art correspondence models struggle on this task. By training on our new data, we can improve on the best performing method by 34% in RMSE. We also use the predicted correspondences to estimate camera poses and evaluate performance using recall metrics. Lastly, we identify open challenges in cross-modal geometric reasoning that our dataset aims to help address.
Geometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagery
Venue: arXiv cs.LG 2026
Year: 2026

Cross-subject motor-imagery decoding remains a major challenge in EEG-based brain-computer interfaces. To mitigate strong inter-subject variability, recent work has emphasized manifold-based approaches operating on covariance representations. Yet dispersion scaling and orientation alignment remain largely unaddressed in existing methods. In this paper, we address both issues through congruence transforms and introduce three complementary geometry-aware models: (i) Discriminative Congruence Transform (DCT), (ii) Deep Linear DCT (DLDCT), and (iii) Deep DCT-UNet (DDCT-UNet). These models are evaluated both as pre-alignment modules for downstream classifiers and as end-to-end discriminative systems trained via cross-entropy backpropagation with a custom logistic-regression head. Across challenging motor-imagery benchmarks, the proposed framework improves transductive cross-subject accuracy by 2-3%, demonstrating the value of geometry-aware congruence learning.
Soft Pneumatic Grippers: Topology optimization, 3D-printing and Experimental validation
Venue: arXiv cs.RO 2026
Year: 2026

This paper presents a systematic topology optimization framework for designing a soft pneumatic gripper (SPG), explicitly considering the design-dependent nature of the actuating load. The load is modeled using Darcy's law with an added drainage term. A 2D soft arm unit is optimized by formulating it as a compliant mechanism design problem using the robust formulation. The problem is posed as a min-max optimization, where the output deformations of blueprint and eroded designs are considered. A volume constraint is imposed on the blueprint part, while a strain-energy constraint is enforced on the eroded part. The MMA is employed to solve the optimization problem and obtain the optimized soft unit. Finite element analysis with the Ogden material model confirms that the optimized 2D unit outperforms a conventional rectangular design under pneumatic loading. The optimized 2D unit is extruded to obtain a 3D module, and ten such units are assembled to create a soft arm. Deformation profiles of the optimized arm are analysed under different pressure loads. Four arms are 3D-printed and integrated with a supporting structure to realize the proposed SPG. The gripping performance of the SPG is demonstrated on objects with different weights, sizes, stiffness, and shapes.
WavefrontDiffusion: Dynamic Decoding Schedule for Improved Reasoning
Venue: arXiv cs.LG 2026
Year: 2026

Diffusion Language Models (DLMs) have shown strong potential for text generation and are becoming a competitive alternative to autoregressive models. The denoising strategy plays an important role in determining the quality of their outputs. Mainstream denoising strategies include Standard Diffusion and BlockDiffusion. Standard Diffusion performs global denoising without restricting the update range, often finalizing incomplete context and causing premature end-of-sequence predictions. BlockDiffusion updates fixed-size blocks in a preset order, but its rigid structure can break apart coherent semantic units and disrupt reasoning. We present WavefrontDiffusion, a dynamic decoding approach that expands a wavefront of active tokens outward from finalized positions. This adaptive process follows the natural flow of semantic structure while keeping computational cost equal to block-based methods. Across four benchmarks in reasoning and code generation, WavefrontDiffusion achieves state-of-the-art performance while producing outputs with higher semantic fidelity, showing the value of adaptive scheduling for more coherent and efficient generation.
Bi-fidelity sparse-grid interpolation driven by a local-error estimator
Venue: arXiv cs.CE 2026
Year: 2026

Sparse grids based on Lagrange polynomials have become one of the staple methods for approximating functions that are high-dimensional and expensive to evaluate, in the context e.g. of PDE-based parametric design exploration. They are however known to be inefficient for problems requiring local refinement, such as when the target function exhibits localized features or sharp gradients. While locally-refined sparse grids based e.g. on piecewise linear polynomials are a well-established alternative to circumvent this problem, in this work we present a strategy for improving the local efficiency of Lagrangian sparse grids. We do so by building the sparse grid approximation incrementally and evaluating the function only at collocation points at which a suitable (and crucially, zero-cost) error indicator suggest that incorporating the function evaluation would significantly change the landscape of the approximation. The remaining collocation points are instead assigned values predicted by the already available sparse grid, i.e., following a bifidelity approach that reduces costs while preserving accuracy. The effectiveness of this methodology is demonstrated on several benchmark analytical functions and an engineering application concerning flashback phenomena in hydrogen-fueled perforated burners.
CostNav: A Navigation Benchmark for Real-World Economic-Cost Evaluation of Physical AI Agents
Venue: arXiv cs.AI 2026
Year: 2026

While current navigation benchmarks prioritize task success in simplified settings, they neglect the multidimensional economic constraints essential for the real-world commercialization of autonomous delivery systems. We introduce CostNav, an Economic Navigation Benchmark that evaluates physical AI agents through comprehensive economic cost-revenue analysis aligned with real-world business operations. By integrating industry-standard data--such as Securities and Exchange Commission (SEC) filings and Abbreviated Injury Scale (AIS) injury reports--with Isaac Sim's detailed collision and cargo dynamics, CostNav transcends simple task completion to accurately evaluate business value in complex, real-world scenarios. To our knowledge, CostNav is the first physics-grounded economic benchmark that uses industry-standard regulatory and financial data to quantitatively expose the gap between navigation research metrics and commercial viability, revealing that optimizing for task success on a simplified task fundamentally differs from optimizing for real-world economic deployment. Evaluating seven baselines--two rule-based and five imitation learning--we find that no current method is economically viable, all yielding negative contribution margins. The best-performing method, CANVAS (-27.36\$/run), equipped with only an RGB camera and GPS, outperforms LiDAR-equipped Nav2 w/ GPS (-35.46\$/run). We challenge the community to develop navigation policies that achieve economic viability on CostNav. We remain method-agnostic, evaluating success solely on cost rather than the underlying architecture. All resources are available at https://github.com/worv-ai/CostNav.
Decoupling and Damping: Structurally-Regularized Gradient Matching for Multimodal Graph Condensation
Venue: arXiv cs.LG 2026
Year: 2026

In multimodal graph learning, graph structures that integrate information from multiple sources, such as vision and text, can more comprehensively model complex entity relationships. However, the continuous growth of their data scale poses a significant computational bottleneck for training. Graph condensation methods provide a feasible path forward by synthesizing compact and representative datasets. Nevertheless, existing condensation approaches generally suffer from performance limitations in multimodal scenarios, mainly due to two reasons: (1) semantic misalignment between different modalities leads to gradient conflicts; (2) the message-passing mechanism of graph neural networks further structurally amplifies such gradient noise. Based on this, we propose Structural Regularized Gradient Matching (SR-GM), a condensation framework for multimodal graphs. This method alleviates gradient conflicts between modalities through a gradient decoupling mechanism and introduces a structural damping regularizer to suppress the propagation of gradient noise in the topology, thereby transforming the graph structure from a noise amplifier into a training stabilizer. Extensive experiments on four multimodal graph datasets demonstrate the effectiveness of SR-GM, highlighting its state-of-the-art performance and cross-architecture generalization capabilities in multimodal graph dataset condensation.
HAFO: A Force-Adaptive Control Framework for Humanoid Robots in Intense Interaction Environments
Venue: arXiv cs.RO 2026
Year: 2026

Reinforcement learning (RL) controllers have made impressive progress in humanoid locomotion and light-weight object manipulation. However, achieving robust and precise motion control with intense force interaction remains a significant challenge. To address these limitations, this paper proposes HAFO, a dual-agent reinforcement learning framework that concurrently optimizes both a robust locomotion strategy and a precise upper-body manipulation strategy via coupled training. We employ a constrained residual action space to improve dual-agent training stability and sample efficiency. The external tension disturbances are explicitly modeled using a spring-damper system, allowing for fine-grained force control through manipulation of the virtual spring. In this process, the reinforcement learning policy autonomously generates a disturbance-rejection response by utilizing environmental feedback. The experimental results demonstrate that HAFO achieves whole-body control for humanoid robots across diverse force-interaction environments using a single dual-agent policy, delivering outstanding performance under load-bearing and thrust-disturbance conditions, while maintaining stable operation even under rope suspension state.
Lower Bias, Higher Welfare: How Creator Competition Reshapes Bias-Variance Tradeoff in Recommendation Platforms?
Venue: arXiv cs.GT 2026
Year: 2026

Understanding the bias-variance tradeoff in user representation learning is essential for improving recommendation quality in modern content platforms. While well studied in static settings, this tradeoff becomes significantly more complex when content creators strategically adapt to platform incentives. To analyze how such competition reshapes the tradeoff for maximizing user welfare, we introduce the Content Creator Competition with Bias-Variance Tradeoff framework, a tractable game-theoretic model that captures the platform's decision on regularization strength in user feature estimation. We derive and compare the platform's optimal policy under two key settings: a non-strategic baseline with fixed content and a strategic environment where creators compete in response to the platform's algorithmic design. Our theoretical analysis in a stylized model shows that, compared to the non-strategic environment, content creator competition shifts the platform's optimal policy toward weaker regularization, thereby favoring lower bias in the bias-variance tradeoff. To validate and assess the robustness of these insights beyond the stylized setting, we conduct extensive experiments on both synthetic and real-world benchmark datasets. The empirical results consistently support our theoretical conclusion: in strategic environments, reducing bias leads to higher user welfare. These findings offer practical implications for the design of real-world recommendation algorithms in the presence of content creator competition.
Efficient Parallel Implementation of the Pilot Assignment Problem in Massive MIMO Systems
Venue: arXiv cs.DC 2026
Year: 2026

The assignment of the pilot sequence is a critical challenge in massive MIMO systems, as sharing the same pilot sequence among multiple users causes interference, which degrades the accuracy of the channel estimation. This problem, equivalent to the NP-hard graph coloring problem, directly impacts real-time applications such as autonomous driving and industrial IoT, where minimizing channel estimation time is crucial. This paper proposes an optimized hybrid K-means clustering and Genetic Algorithm (SK-means GA) to improve the pilot assignment efficiency, achieving a 29.3% reduction in convergence time (82s vs. 116s for conventional GA). A parallel implementation (PK-means GA) is developed on an FPGA using Vivado High-Level Synthesis Tools (HLST) to further enhance the run-time performance, accelerating convergence to 3.5 milliseconds. Within Vivado implementation, different optimization techniques such as loop unrolling, pipelining, and function inlining are applied to realize the reported speedup. This significant improvement of PK-means GA in execution speed makes it highly suitable for low-latency real-time wireless networks (6G)
How to Purchase Labels? A Cost-Effective Approach Using Active Learning Markets
Venue: arXiv cs.LG 2026
Year: 2026

We introduce and analyse active learning markets as a way to purchase labels, in situations where analysts aim to acquire additional data to improve model fitting, or to better train models for predictive analytics applications. This comes in contrast to the many proposals that already exist to purchase features and examples. By originally formalising the market clearing as an optimisation problem, we integrate budget constraints and improvement thresholds into the label acquisition process. We focus on a single-buyer-multiple-seller setup and propose the use of two active learning strategies (variance based and query-by-committee based), paired with distinct pricing mechanisms. They are compared to benchmark baselines including random sampling and a greedy knapsack heuristic. The proposed strategies are validated on real-world datasets from two critical application domains: real estate pricing and energy forecasting. Results demonstrate the robustness of our approach, consistently achieving superior performance with fewer labels acquired compared to conventional methods. Our proposal comprises an easy-to-implement practical solution for optimising data acquisition in resource-constrained environments.
Stabilizing Off-Policy Training for Long-Horizon LLM Agent via Turn-Level Importance Sampling and Clipping-Triggered Normalization
Venue: arXiv cs.LG 2026
Year: 2026

Reinforcement learning (RL) algorithms such as PPO and GRPO are widely used to train large language models (LLMs) for multi-turn agentic tasks. However, in off-policy training pipelines, these methods often exhibit unstable optimization dynamics and are prone to performance collapse. Through empirical analysis, we identify two fundamental sources of instability in this setting: (1)~a granularity mismatch between token-level policy optimization and turn-structured interactions, and (2) high-variance and unreliable gradient updates induced by off-policy importance sampling and inaccurate advantage estimation. To address these challenges, we propose SORL, \underline{S}tabilizing \underline{O}ff-Policy \underline{R}einforcement \underline{L}earning for Long-Horizon Agent Training. SORL introduces principled mechanisms that align policy optimization with the structure of multi-turn interactions and adaptively suppress unreliable off-policy updates, yielding more conservative and robust learning dynamics. Within this framework, we instantiate two stabilized algorithms: SO-PPO and SO-GRPO. Both algorithms are designed to mitigate gradient variance and prevent optimization collapse without requiring careful early stopping or heuristic tuning. We evaluate SO-PPO and SO-GRPO on a range of multi-turn search benchmarks, including general question answering, multi-hop question answering, and medical multiple-choice QA tasks. Experimental results show that both methods consistently prevent training instabilities and performance collapses observed in standard PPO and GRPO, maintain lower clipping ratios and more stable optimization trajectories, and achieve superior or comparable task performance. These results demonstrate that the proposed algorithm provides a practical, scalable, and general framework for stabilizing reinforcement learning in multi-turn LLM agent training.
Smooth regularization for efficient video recognition
Venue: arXiv cs.CV 2026
Year: 2026

We propose a smooth regularization technique that instills a strong temporal inductive bias in video recognition models, particularly benefiting lightweight architectures. Our method encourages smoothness in the intermediate-layer embeddings of consecutive frames by modeling their changes as a Gaussian Random Walk (GRW). This penalizes abrupt representational shifts, thereby promoting low-acceleration solutions that better align with the natural temporal coherence inherent in videos. By leveraging this enforced smoothness, lightweight models can more effectively capture complex temporal dynamics. Applied to such models, our technique yields a 3.8% to 6.4% accuracy improvement on Kinetics-600. Notably, the MoViNets model family trained with our smooth regularization improves the current state of the art by 3.8% to 6.1% within their respective FLOP constraints, while MobileNetV3 and the MoViNets-Stream family achieve gains of 4.9% to 6.4% over prior state-of-the-art models with comparable memory footprints. Our code and models are available at https://github.com/cmusatyalab/grw-smoothing.
DOPD: A Dynamic PD-Disaggregation Architecture for Maximizing Goodput in LLM Inference Serving
Venue: arXiv cs.DC 2026
Year: 2026

To meet strict Service-Level Objectives (SLOs),contemporary Large Language Models (LLMs) decouple the prefill and decoding stages and place them on separate GPUs to mitigate the distinct bottlenecks inherent to each phase. However, the heterogeneity of LLM workloads causes producerconsumer imbalance between the two instance types in such disaggregated architecture. To address this problem, we propose DOPD (Dynamic Optimal Prefill/Decoding), a dynamic LLM inference system that adjusts instance allocations to achieve an optimal prefill-to-decoding (P/D) ratio based on real-time load monitoring. Combined with an appropriate request-scheduling policy, DOPD effectively resolves imbalances between prefill and decoding instances and mitigates resource allocation mismatches due to mixed-length requests under high concurrency. Experimental evaluations show that, compared with vLLM and DistServe (representative aggregation-based and disaggregationbased approaches), DOPD improves overall system goodput by up to 1.5X, decreases P90 time-to-first-token (TTFT) by up to 67.5%, and decreases P90 time-per-output-token (TPOT) by up to 22.8%. Furthermore, our dynamic P/D adjustment technique performs proactive reconfiguration based on historical load, achieving over 99% SLOs attainment while using less additional resources.
ICPO: Intrinsic Confidence-Driven Group Relative Preference Optimization for Efficient Reinforcement Learning
Venue: arXiv cs.AI 2026
Year: 2026

Reinforcement Learning with Verifiable Rewards (RLVR) demonstrates significant potential in enhancing the reasoning capabilities of Large Language Models (LLMs). However, existing RLVR methods are often constrained by issues such as coarse-grained rewards, reward noise, and inefficient exploration, which lead to unstable training and entropy collapse. To address this challenge, we propose the Intrinsic Confidence-Driven Group Relative Preference Optimization method (ICPO). The intuition behind it lies in the fact that the probabilities of an LLM generating different responses can inherently and directly reflect its self-assessment of the reasoning process. Inspired by the idea of preference modeling, ICPO calculates a preference advantage score for each response by comparing the relative generation probabilities of multiple responses under the same input prompt, and integrates this score with verifiable rewards to guide the exploration process. We have discovered that the preference advantage score not only alleviates the issues of coarse-grained rewards and reward noise but also effectively curbs overconfident errors, enhances the relative superiority of undervalued high-quality responses, and prevents the model from overfitting to specific strategies. Comprehensive experiments across four general-domain benchmarks and three mathematical benchmarks demonstrate that ICPO steadily boosts reasoning compared to GRPO.
ChronoRAN: Analyzing Latency in 5G Systems
Venue: arXiv cs.NI 2026
Year: 2026

This paper presents ChronoRAN, a mathematical framework for accurately computing one-way latency (for uplink and downlink) in the 5G RAN across diverse system configurations. ChronoRAN models latency sources at every layer of the Radio Access Network (RAN), pinpointing system-level bottlenecks--such as radio interfaces, scheduling policies, and hardware/software constraints--while capturing their intricate dependencies and their stochastic nature. ChronoRAN also includes a configuration optimizer that uses its mathematical models to search through hundreds of billions of configurations and find settings that meet latency-reliability targets under user constraints. We validate ChronoRAN on two open-sourced 5G RAN testbeds (srsRAN and OAI) and a public commercial 5G network, demonstrating that it can closely match empirical latency distributions and significantly outperform prior analytical models and widely used simulators (MATLAB 5G Toolbox, 5G-LENA). It can also find system configurations that meet Ultra-Reliable Low-Latency Communications (URLLC) targets and enable network operators to efficiently identify the best setup for their systems.