OpenRaiser/Intern-Atlas huggingface dataset:
Reverse Distillation: Consistently Scaling Protein Language Model Representations
Venue: arXiv cs.LG 2026
Year: 2026

Unlike the predictable scaling laws in natural language processing and computer vision, protein language models (PLMs) scale poorly: for many tasks, models within the same family plateau or even decrease in performance, with mid-sized models often outperforming the largest in the family. We introduce Reverse Distillation, a principled framework that decomposes large PLM representations into orthogonal subspaces guided by smaller models of the same family. The resulting embeddings have a nested, Matryoshka-style structure: the first k dimensions of a larger model's embedding are exactly the representation from the smaller model. This ensures that larger reverse-distilled models consistently outperform smaller ones. A motivating intuition is that smaller models, constrained by capacity, preferentially encode broadly-shared protein features. Reverse distillation isolates these shared features and orthogonally extracts additional contributions from larger models, preventing interference between the two. On ProteinGym benchmarks, reverse-distilled ESM-2 variants outperform their respective baselines at the same embedding dimensionality, with the reverse-distilled 15 billion parameter model achieving the strongest performance. Our framework is generalizable to any model family where scaling challenges persist. Code and trained models are available at https://github.com/rohitsinghlab/plm_reverse_distillation.
Tensor-Based Modulation on the Unit Circle: A Coding Perspective
Venue: arXiv cs.IT 2026
Year: 2026

Tensor-based modulation (TBM) provides a multi-linear spreading framework for blind multi-user separation in unsourced random access. In this paper, we show that TBM is a coded modulation built on a non-binary linear block code over $\mathbb{Z}_M$, whose symbols are mapped to $M$-PSK modulation, defining a geometrically uniform signal space code. We explicitly derive this generator matrix, characterize its rank deficiency, and show that reference symbols for tensor identifiability correspond to code shortening, producing a quasi-systematic or a systematic code, depending on the number of considered reference symbols for the TBM. Simulations in single-user AWGN and multi-user non-coherent multi-antenna fading channels demonstrate strong robustness and interference resilience, establishing TBM as a scalable, algebraically structured modulation-coding scheme bridging tensor representations and modern coding theory.
The role of team diversity in AI systems development
Venue: arXiv cs.SE 2026
Year: 2026

The widespread integration of AI technologies has intensified concerns about fairness and bias, as these systems often perpetuate societal inequalities through flawed data and design choices. While software engineering research has largely concentrated on technical solutions, such as improving datasets and models, the social dynamics that shape AI outcomes remain underexplored. This study investigates the role of team diversity in the development of AI systems. Drawing from the experience of four AI focused teams working in a large software company operating in Brazil and Portugal, and collaborating with global clients, the study explores how diverse teams influence the development of AI systems. Using Grounded Theory, we conducted 25 interviews with software professionals involved in projects spanning domains such as education, energy, accessibility, and facial recognition. Although our study is conducted in an organizational setting, the variety of projects, from regional to multinational, ensures exposure to global development practices and diverse team dynamics, bringing a variety of perspectives into our findings. Our analysis revealed six key roles that team diversity played in AI development: diversifying perspectives for bias identification, bringing empathy to AI development, addressing systemic discrimination, supporting inclusive and participatory decision making, using diversity as a safeguard against bias, and fostering broadened thinking in problem solving. These findings highlight the importance of incorporating diverse perspectives in AI projects and offer practical recommendations for integrating fairness considerations into software development practices.
DECADE: A Temporally-Consistent Unsupervised Diffusion Model for Enhanced Rb-82 Dynamic Cardiac PET Image Denoising
Venue: arXiv cs.CV 2026
Year: 2026

Rb-82 dynamic cardiac PET imaging is widely used for the clinical diagnosis of coronary artery disease (CAD), but its short half-life results in high noise levels that degrade dynamic frame quality and parametric imaging. The lack of paired clean-noisy training data, rapid tracer kinetics, and frame-dependent noise variations further limit the effectiveness of existing deep learning denoising methods. We propose DECADE (A Temporally-Consistent Unsupervised Diffusion model for Enhanced Rb-82 CArdiac PET DEnoising), an unsupervised diffusion framework that generalizes across early- to late-phase dynamic frames. DECADE incorporates temporal consistency during both training and iterative sampling, using noisy frames as guidance to preserve quantitative accuracy. The method was trained and evaluated on datasets acquired from Siemens Vision 450 and Siemens Biograph Vision Quadra scanners. On the Vision 450 dataset, DECADE consistently produced high-quality dynamic and parametric images with reduced noise while preserving myocardial blood flow (MBF) and myocardial flow reserve (MFR). On the Quadra dataset, using 15%-count images as input and full-count images as reference, DECADE outperformed UNet-based and other diffusion models in image quality and K1/MBF quantification. The proposed framework enables effective unsupervised denoising of Rb-82 dynamic cardiac PET without paired training data, supporting clearer visualization while maintaining quantitative integrity.
Apply2Isar: Automatically Converting Isabelle/HOL Apply-Style Proofs to Structured Isar
Venue: arXiv cs.LO 2026
Year: 2026

In Isabelle/HOL, declarative proofs written in the Isar language are widely appreciated for their readability and robustness. However, some users may prefer writing procedural "apply-style" proof scripts since they enable rapid exploration of the search space. To get the best of both worlds, we introduce Apply2Isar, a tool for Isabelle/HOL that automatically converts apply-style scripts to declarative Isar. This allows users to write complex, possibly fragile apply-style scripts, and then automatically convert them to more readable and robust declarative Isar proofs. To demonstrate the efficacy of Apply2Isar in practice, we evaluate it on a large benchmark set consisting of apply-style proofs from the Isabelle Archive of Formal Proofs.
Governance of AI-Generated Content: A Case Study on Social Media Platforms
Venue: arXiv cs.HC 2026
Year: 2026

Online platforms are seeing increasing amounts of AI-generated content -- text and other forms of media that are made or co-created with generative AI. This trend suggests platforms may need to establish governance frameworks, including policies and enforcement strategies for how users create, post, share, and engage with such content to encourage responsible use. We investigate the governance of AI-generated content across 40 popular social media platforms. Just over two-thirds explicitly describe governance of AI-generated content spanning six themes. Most platforms focus on moderating AI-generated content that violates established content rules and discloses AI-generated content. Fewer platforms -- those that are focused on creativity and knowledge-sharing -- address other issues such as ownership and monetization. Based on these findings, we suggest stakeholders and policymakers develop more direct, comprehensive, and forward-looking AI-generated content governance, as well as tools and education for users about the use of such content.
Physics-infused Learning for Aerial Manipulator in Winds and Near-Wall Environments
Venue: arXiv cs.RO 2026
Year: 2026

Aerial manipulation (AM) expands UAV capabilities beyond passive observation to contact-based operations at high altitudes and in otherwise inaccessible environments. Although recent advances show promise, most AM systems are developed in controlled settings that overlook key aerodynamic effects. Simplified thrust models are often insufficient to capture the nonlinear wind disturbances and proximity-induced flow variations present in real-world environments near infrastructure, while high-fidelity CFD methods remain impractical for real-time use. Learning-based models are computationally efficient at inference, but often struggle to generalize to unseen condition. This paper combines both approaches by integrating a physics-based blade-element model with a learning-based residual force estimator, along with a rotor-speed allocation strategy for disturbance compensation, resulting in a unified control framework. The blade-element model computes per-rotor aerodynamic forces under wind and provides a refined feedforward disturbance estimate. A learning-based estimator then predicts the residual forces not captured by the model, enabling compensation for unmodeled aerodynamic effects. An online adaptation mechanism further updates the residual-force prediction and rotor-speed allocation jointly to reduce the mismatch between desired and realized thrust. We evaluate this framework in both free-flight and wall-contact tracking tasks in a simulated near-wall wind environment. Results demonstrate improved disturbance estimation and trajectory-tracking accuracy over conventional approaches, enabling robust wall-contact execution under challenging aerodynamic conditions.
An Efficient and Effective Evaluator for Text2SQL Models on Unseen and Unlabeled Data
Venue: arXiv cs.CL 2026
Year: 2026

Recent advances in large language models has strengthened Text2SQL systems that translate natural language questions into database queries. A persistent deployment challenge is to assess a newly trained Text2SQL system on an unseen and unlabeled dataset when no verified answers are available. This situation arises frequently because database content and structure evolve, privacy policies slow manual review, and carefully written SQL labels are costly and time-consuming. Without timely evaluation, organizations cannot approve releases or detect failures early. FusionSQL addresses this gap by working with any Text2SQL models and estimating accuracy without reference labels, allowing teams to measure quality on unseen and unlabeled datasets. It analyzes patterns in the system's own outputs to characterize how the target dataset differs from the material used during training. FusionSQL supports pre-release checks, continuous monitoring of new databases, and detection of quality decline. Experiments across diverse application settings and question types show that FusionSQL closely follows actual accuracy and reliably signals emerging issues. Our code is available at https://github.com/phkhanhtrinh23/FusionSQL.
A Lock-Free, Fully GPU-Resident Architecture for the Verification of Goldbach's Conjecture
Venue: arXiv cs.MS 2026
Year: 2026

We present a fully device-resident, multi-GPU architecture for the large-scale computational verification of Goldbach's conjecture. In prior work, a segmented double-sieve eliminated monolithic VRAM bottlenecks but remained constrained by host-side sieve construction and PCIe transfer latency. In this work, we migrate the entire segment generation pipeline to the GPU using highly optimised L1 shared-memory tiling, achieving near-zero host-device communication during the critical verification path. To fully leverage heterogeneous multi-GPU clusters, we introduce an asynchronous, lock-free work-stealing pool that replaces static workload partitioning with atomic segment claiming, enabling $99.7$% parallel efficiency at 2 GPUs and $98.6$% at $4$ GPUs. We further implement strict mathematical overflow guards guaranteeing the soundness of the 64-bit verification pipeline up to its theoretical ceiling of $1.84 \times 10^{19}$. On the same hardware, the new architecture achieves a $45.6\times$ algorithmic speedup over its host-coupled predecessor at N = $10^{10}$. End-to-end, the framework verifies Goldbach's conjecture up to $10^{12}$ in $36.5$ seconds on a single NVIDIA RTX 5090, and up to $10^{13}$ in $133.5$ seconds on a four-GPU system. All code is open-source and reproducible on commodity hardware.
Visualizing Coalition Formation: From Hedonic Games to Image Segmentation
Venue: arXiv cs.AI 2026
Year: 2026

We propose image segmentation as a visual diagnostic testbed for coalition formation in hedonic games. Modeling pixels as agents on a graph, we study how a granularization parameter shapes equilibrium fragmentation and boundary structure. On the Weizmann single-object benchmark, we relate multi-coalition equilibria to binary protocols by measuring whether the converged coalitions overlap with a foreground ground-truth. We observe transitions from cohesive to fragmented yet recoverable equilibria, and finally to intrinsic failure under excessive fragmentation. Our core contribution links multi-agent systems with image segmentation by quantifying the impact of mechanism design parameters on equilibrium structures.
Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases
Venue: arXiv cs.AI 2026
Year: 2026

In recent advances, to enable a fully data-driven learning paradigm on relational databases (RDB), relational deep learning (RDL) is proposed to structure the RDB as a heterogeneous entity graph and adopt the graph neural network (GNN) as the predictive model. However, existing RDL methods neglect the imbalance problem of relational data in RDBs and risk under-representing the minority entities, leading to an unusable model in practice. In this work, we investigate, for the first time, class imbalance problem in RDB entity classification and design the relation-centric minority synthetic over-sampling GNN (Rel-MOSS), in order to fill a critical void in the current literature. Specifically, to mitigate the issue of minority-related information being submerged by majority counterparts, we design the relation-wise gating controller to modulate neighborhood messages from each individual relation type. Based on the relational-gated representations, we further propose the relation-guided minority synthesizer for over-sampling, which integrates the entity relational signatures to maintain relational consistency. Extensive experiments on 12 entity classification datasets provide compelling evidence for the superiority of Rel-MOSS, yielding an average improvement of up to 2.46% and 4.00% in terms of Balanced Accuracy and G-Mean, compared with SOTA RDL methods and classic methods for handling class imbalance.
BRIDGE: Benchmark for multi-hop Reasoning In long multimodal Documents with Grounded Evidence
Venue: arXiv cs.CL 2026
Year: 2026

Multi-hop question answering (QA) is widely used to evaluate the reasoning capabilities of large language models, yet most benchmarks focus on final answer correctness and overlook intermediate reasoning, especially in long multimodal documents. We introduce BRIDGE, a benchmark for multi-hop reasoning over long scientific papers that require integrating evidence across text, tables, and figures. The dataset supports both chain-like and fan-out structures and provides explicit multi-hop reasoning annotations for step-level evaluation beyond answer accuracy. Experiments with state-of-the-art LLMs and multimodal retrieval-augmented generation (RAG) systems reveal systematic deficiencies in evidence aggregation and grounding that remain hidden under conventional answer-only evaluation. BRIDGE provides a targeted testbed for diagnosing reasoning failures in long multimodal documents.
$L^3$:Scene-agnostic Visual Localization in the Wild
Venue: arXiv cs.CV 2026
Year: 2026

Standard visual localization methods typically require offline pre-processing of scenes to obtain 3D structural information for better performance. This inevitably introduces additional computational and time costs, as well as the overhead of storing scene representations. Can we visually localize in a wild scene without any off-line preprocessing step? In this paper, we leverage the online inference capabilities of feed-forward 3D reconstruction networks to propose a novel map-free visual localization framework $L^3$. Specifically, by performing direct online 3D reconstruction on RGB images, followed by two-stage metric scale recovery and pose refinement based on 2D-3D correspondences, $L^3$ achieves high accuracy without the need to pre-build or store any offline scene representations. Extensive experiments demonstrate $L^3$ not only that the performance is comparable to state-of-the-art solutions on various benchmarks, but also that it exhibits significantly superior robustness in sparse scenes (fewer reference images per scene).
WeldAR: Augmenting Live Hands-On Training with In-Situ Guidance for Novice Learners
Venue: arXiv cs.HC 2026
Year: 2026

Extended Reality (XR) systems for physical skill training have largely emphasized simulation rather than real-time in-situ instruction. We present WeldAR, an Augmented Reality (AR) system with five learning modules that overlays real-time guidance during live welding using a headset integrated into a welding helmet and a torch attachment. We conducted an in-situ within-subjects study with 24 novices, comparing AR guidance to video instruction for live welding across practice and unassisted tests. AR improved performance in both assisted practice and unassisted tests, primarily driven by gains in travel speed and work angle. By offering real-time feedback on four performance measures, AR supported novices in carrying embodied knowledge into independent tasks. Our contributions include: (1) WeldAR for in-situ physical skill training; (2) empirical evidence that AR enhances composite welding performance and key physical skills; and (3) implications for the development of AR systems that support in-situ, embodied skill training in welding and related trades.
Energy-Efficient Online Scheduling for Wireless Powered Mobile Edge Computing Networks
Venue: arXiv cs.NI 2026
Year: 2026

Wireless Powered Mobile Edge Computing (WP-MEC) integrates mobile edge computing (MEC) with wireless power transfer (WPT) to simultaneously extend the operational lifetime and enhance the computational capability of wireless devices (WDs). In WPMEC systems, WPT and computation offloading compete for limited wireless resources, which makes their joint scheduling particularly challenging. In this paper, we investigate the energy-efficient online scheduling problem for WPMEC networks with multiple WDs and multiple access points (APs). Based on Lyapunov optimization, we develop an online optimization framework that transforms the original stochastic problem into deterministic per-slot optimization problems. To reduce computational complexity, we introduce the concept of marginal energy efficiency and derive an associated optimality condition, based on which a relax-then-adjust approach is proposed to efficiently obtain feasible solutions. For the resulting non-convex computation offloading subproblem, we analyze the structural properties of its optimal solution and transform it into an assignment problem that can be solved efficiently. We further provide theoretical performance guarantees for both the per-slot and long-term solution, establishing a fundamental trade-off between latency and energy consumption. To improve practical performance, additional mechanisms are introduced to balance the magnitudes of different queues and reduce latency without increasing energy consumption. Extensive simulation results demonstrate the effectiveness and robustness of the proposed algorithm under various system settings.
PreHO: Predictive Handover for LEO Satellite Networks
Venue: arXiv cs.NI 2026
Year: 2026

Low-Earth Orbit (LEO) Satellite Networks (LSNs) offer a promising solution for extending connectivity to areas not covered by Terrestrial Networks (TNs). However, the rapid movement, broad coverage, and high communication latency of LEO satellites pose significant challenges to conventional handover mechanisms, resulting in unacceptable signaling overhead and handover latency. To address these issues, this paper identifies a fundamental difference between the mobility patterns in LSNs and TNs: users are typically stationary relative to the fast- moving satellites, and channel states in LSNs are often stable and predictable. This observation enables handovers to be planned in advance rather than triggered reactively. Motivated by this insight, we propose PreHO, a predictive handover mechanism tailored for LSNs that proactively determines optimal handover strategies, thereby simplifying the handover process and enhancing overall efficiency. To optimize the pre-planned handover decisions, we further formulate the handover planning problem and develop an efficient iterative algorithm based on alternating optimization and dynamic programming. Extensive evaluations driven by real-world data demonstrate that PreHO significantly outperforms traditional handover schemes in terms of signaling overhead, handover latency, and user experience.
CMMR-VLN: Vision-and-Language Navigation via Continual Multimodal Memory Retrieval
Venue: arXiv cs.AI 2026
Year: 2026

Although large language models (LLMs) are introduced into vision-and-language navigation (VLN) to improve instruction comprehension and generalization, existing LLM- based VLN lacks the ability to selectively recall and use relevant priori experiences to help navigation tasks, limiting their performance in long-horizon and unfamiliar scenarios. In this work, we propose CMMR-VLN (Continual Multimodal Memory Retrieval based VLN), a VLN framework that endows LLM agents with structured memory and reflection capabilities. Specifically, the CMMR-VLN constructs a multimodal experi- ence memory indexed by panoramic visual images and salient landmarks to retrieve relevant experiences during navigation, introduces a retrieved-augmented generation pipeline to mimick how experienced human navigators leverage priori knowledge, and incorporates a reflection-based memory update strategy that selectively stores complete successful paths and the key initial mistake in failure cases. Comprehensive tests illustrate average success rate improvements of 52.9%, 20.9% and 20.9%, and 200%, 50% and 50% over the NavGPT, the MapGPT, and the DiscussNav in simulation and real tests, respectively eluci- dating the great potential of the CMMR-VLN as a backbone VLN framework.
Structure-Preserving Graph Contrastive Learning for Mathematical Information Retrieval
Venue: arXiv cs.IR 2026
Year: 2026

This paper introduces Variable Substitution as a domain-specific graph augmentation technique for graph contrastive learning (GCL) in the context of searching for mathematical formulas. Standard GCL augmentation techniques often distort the semantic meaning of mathematical formulas, particularly for small and highly structured graphs. Variable Substitution, on the other hand, preserves the core algebraic relationships and formula structure. To demonstrate the effectiveness of our technique, we apply it to a classic GCL-based retrieval model. Experiments show that this straightforward approach significantly improves retrieval performance compared to generic augmentation strategies. We release the code on GitHub.\footnote{https://github.com/lazywulf/formula_ret_aug}.