OpenRaiser/Intern-Atlas huggingface dataset:
Shape Completion with Prediction of Uncertain Regions
Venue: arXiv cs.CV 2026
Year: 2026

Shape completion, i.e., predicting the complete geometry of an object from a partial observation, is highly relevant for several downstream tasks, most notably robotic manipulation. When basing planning or prediction of real grasps on object shape reconstruction, an indication of severe geometric uncertainty is indispensable. In particular, there can be an irreducible uncertainty in extended regions about the presence of entire object parts when given ambiguous object views. To treat this important case, we propose two novel methods for predicting such uncertain regions as straightforward extensions of any method for predicting local spatial occupancy, one through postprocessing occupancy scores, the other through direct prediction of an uncertainty indicator. We compare these methods together with two known approaches to probabilistic shape completion. Moreover, we generate a dataset, derived from ShapeNet, of realistically rendered depth images of object views with ground-truth annotations for the uncertain regions. We train on this dataset and test each method in shape completion and prediction of uncertain regions for known and novel object instances and on synthetic and real data. While direct uncertainty prediction is by far the most accurate in the segmentation of uncertain regions, both novel methods outperform the two baselines in shape completion and uncertain region prediction, and avoiding the predicted uncertain regions increases the quality of grasps for all tested methods.
A Survey on Decentralized Federated Learning
Venue: arXiv cs.LG 2026
Year: 2026

Federated learning (FL) enables collaborative training without pooling raw data, but standard FL relies on a central coordinator, which introduces a single point of failure and concentrates trust in the orchestration infrastructure. Decentralized federated learning (DFL) removes the coordinator and replaces client-server orchestration with peer-to-peer coordination, making learning dynamics topology-dependent and reshaping the associated security, privacy, and systems trade-offs. This survey systematically reviews DFL methods from 2018 through early 2026 and organizes them into two architectural families: traditional distributed FL and blockchain-based FL. We then propose a unified, challenge-driven taxonomy that maps both families to the core bottlenecks they primarily address, and we summarize prevailing evaluation practices and their limitations, exposing gaps in the literature. Finally, we distill lessons learned and outline research directions, emphasizing topology-aware threat models, privacy notions that reflect decentralized exposure, incentive mechanisms robust to manipulation, and the need to explicitly define whether the objective is a single global model or personalized solutions in decentralized settings.
Taboo and Collaborative Knowledge Production: Evidence from Wikipedia
Venue: arXiv cs.CY 2026
Year: 2026

By definition, people are reticent or even unwilling to talk about taboo subjects. Because subjects like sexuality, health, and violence are taboo in most cultures, important information on each of these subjects can be difficult to obtain. Are peer produced knowledge bases like Wikipedia a promising approach for providing people with information on taboo subjects? With its reliance on volunteers who might also be averse to taboo, can the peer production model produce high-quality information on taboo subjects? In this paper, we seek to understand the role of taboo in knowledge bases produced by volunteers. We do so by developing a novel computational approach to identify taboo subjects and by using this method to identify a set of articles on taboo subjects in English Wikipedia. We find that articles on taboo subjects are more popular than non-taboo articles and that they are frequently vandalized. Despite frequent vandalism attacks, we also find that taboo articles are higher quality than non-taboo articles. We hypothesize that stigmatizing societal attitudes will lead contributors to taboo subjects to seek to be less identifiable. Although our results are consistent with this proposal in several ways, we surprisingly find that contributors make themselves more identifiable in others.
Strategic forecasting of internet of things technologies through patent social network and innovation cluster analysis
Venue: arXiv cs.SI 2026
Year: 2026

The rapid proliferation of Internet of Things (IoT) technologies necessitates robust forecasting mechanisms to guide strategic decision-making amid increasingly complex innovation landscapes. Despite extensive research employing patent analysis for technology forecasting, existing studies lack systematic integration of social network analysis, advanced text mining, and life cycle modeling to comprehensively map IoT technological evolution and collaborative dynamics. This study addresses these gaps by analyzing 154,227 IoT-related patents through a unified methodological framework combining BERT-based text embeddings, k-means clustering with Davies-Bouldin optimization, S-curve life cycle modeling, and Louvain community detection. The analysis identified nine distinct technology clusters spanning foundational infrastructure (Smart Monitoring and Sensor Systems, Network Communication and Data Transmission) to domain-specific applications (Agricultural IoT, Connected Vehicle Technologies). Life cycle assessment revealed temporal convergence, with eight clusters reaching saturation between 2023 and 2027, reflecting ecosystem-wide synchronization driven by standardization imperatives, platform consolidation, and pandemic-accelerated digital transformation. Social network analysis uncovered five major collaborative communities exhibiting divergent strategic orientations: from extreme specialization (Global Telecommunications Technology Leaders: 87.7% network communication focus) to diversified portfolios (China State Grid IoT Consortium: balanced infrastructure investment). Cross-analysis revealed complementary innovation strategies where infrastructure operators pursue breadth, telecommunications specialists maintain focused expertise, and academic researchers emphasize development-aligned agendas.
Feature Propagation on Knowledge Graphs using Cellular Sheaves
Venue: arXiv cs.AI 2026
Year: 2026

Many inference tasks on knowledge graphs, including relation prediction, operate on knowledge graph embeddings -- vector representations of the vertices (entities) and edges (relations) that preserve task-relevant structure encoded within the underlying combinatorial object. Such knowledge graph embeddings can be modeled as an approximate global section of a cellular sheaf, an algebraic structure over the graph. Using the diffusion dynamics encoded by the corresponding sheaf Laplacian, we optimally propagate known embeddings of a subgraph to inductively represent new entities introduced into the knowledge graph at inference time. We implement this algorithm via an efficient iterative scheme and show that on a number of large-scale knowledge graph embedding benchmarks, our method is competitive with -- and in some scenarios outperforms -- more complex models derived explicitly for inductive knowledge graph reasoning tasks.
ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions
Venue: arXiv cs.HC 2026
Year: 2026

From deciding on a PhD program to buying a new camera, unfamiliar decisions--decisions without domain knowledge--are frequent and significant. The complexity and uncertainty of such decisions demand unique approaches to information seeking, understanding, and decision-making. Our formative study highlights that users want to start by discovering broad and relevant domain information evenly and simultaneously, quickly address emerging inquiries, and gain personalized standards to assess information found. We present ChoiceMates, an interactive multi-agent system designed to address these needs by enabling users to engage with a dynamic set of LLM agents each presenting a unique experience in the domain. Unlike existing multi-agent systems that automate tasks with agents, the user orchestrates agents to assist their decision-making process. Our user evaluation (n=12) shows that ChoiceMates enables a more confident, satisfactory decision-making with better situation understanding than web search, and higher decision quality and confidence than a commercial multi-agent framework. This work provides insights into designing a more controllable and collaborative multi-agent system.
Characterizations of Monadic Second Order Definable Context-Free Sets of Graphs
Venue: arXiv cs.FL 2026
Year: 2026

We give a characterization of the sets of graphs that are both definable in Counting Monadic Second Order Logic (CMSO) and context-free, i.e., least solutions of Hyperedge-Replacement (HR) grammars introduced by Courcelle and Engelfriet. We prove the equivalence of these sets with: (a) recognizable sets (in the algebra of graphs with HR-operations) of bounded tree-width; we refine this condition further and show equivalence with recognizability in a finitely generated subalgebra of the HR-algebra of graphs; (b) parsable sets, for which there is a definable transduction from graphs to a set of derivation trees labelled by HR operations, such that the set of graphs is the image of the set of derivation trees under the canonical evaluation of the HR operations; (c) images of recognizable unranked sets of trees under a definable transduction, whose inverse is also definable. We rely on a novel connection between two seminal results, a logical characterization of context-free graph languages in terms of tree-to-graph definable transductions, by Courcelle and Engelfriet and a proof that an optimal-width tree decomposition of a graph can be built by an definable transduction, by Bojanczyk and Pilipczuk.
Lan-grasp: Using Large Language Models for Semantic Object Grasping and Placement
Venue: arXiv cs.RO 2026
Year: 2026

In this paper, we propose Lan-grasp, a novel approach towards more appropriate semantic grasping and placing. We leverage foundation models to equip the robot with a semantic understanding of object geometry, enabling it to identify the right place to grasp, which parts to avoid, and the natural pose for placement. This is an important contribution to grasping and utilizing objects in a more meaningful and safe manner. We leverage a combination of a Large Language Model, a Vision-Language Model, and a traditional grasp planner to generate grasps that demonstrate a deeper semantic understanding of the objects. Building on foundation models provides us with a zero-shot grasp method that can handle a wide range of objects without requiring further training or fine-tuning. We also propose a method for safely putting down a grasped object. The core idea is to rotate the object upright utilizing a pretrained generative model and the reasoning capabilities of a VLM. We evaluate our method in real-world experiments on a custom object dataset and present the results of a survey that asks participants to choose an object part appropriate for grasping. The results show that the grasps generated by our method are consistently ranked higher by the participants than those generated by a conventional grasping planner and a recent semantic grasping approach. In addition, we propose a Visual Chain-of-Thought feedback loop to assess grasp feasibility in complex scenarios. This mechanism enables dynamic reasoning and generates alternative grasp strategies when needed, ensuring safer and more effective grasping outcomes.
Parameter-efficient Multi-Task and Multi-Domain Learning using Factorized Tensor Networks
Venue: arXiv cs.LG 2026
Year: 2026

Multi-task and multi-domain learning methods seek to learn multiple tasks/domains, jointly or one after another, using a single unified network. The primary challenge and opportunity lie in leveraging shared information across these tasks and domains to enhance the efficiency of the unified network. The efficiency can be in terms of accuracy, storage cost, computation, or sample complexity. In this paper, we introduce a factorized tensor network (FTN) designed to achieve accuracy comparable to that of independent single-task or single-domain networks, while introducing a minimal number of additional parameters. The FTN approach entails incorporating task- or domain-specific low-rank tensor factors into a shared frozen network derived from a source model. This strategy allows for adaptation to numerous target domains and tasks without encountering catastrophic forgetting. Furthermore, FTN requires a significantly smaller number of task-specific parameters compared to existing methods. We performed experiments on widely used multi-domain and multi-task datasets. We show the experiments on convolutional-based architecture with different backbones and on transformer-based architecture. Our findings indicate that FTN attains similar accuracy as single-task or single-domain methods while using only a fraction of additional parameters per task. The code is available at https://doi.org/10.24433/CO.7519211.v2.
Weakly-Popular and Super-Popular Matchings with Ties and Their Connection to Stable Matchings
Venue: arXiv cs.GT 2026
Year: 2026

The efficient computation of large matchings with desirable guarantees is a crucial objective in market design. However, even in simple two-sided matching markets with weak ordinal preferences, finding a maximum-size stable matching is NP-hard. Alternatively, popular matchings can be of larger size, but their existence is not guaranteed. In this paper, we study a new definition of popularity with two-sided weak preferences, where agents are only indifferent between two matchings if they receive the same partner. We show that this alternative definition of popularity, which we call weak popularity, guarantees the existence of such matchings. Unfortunately, finding a maximum-size weakly popular matching turns out to be NP-hard even with one-sided ties. However, we provide a polynomial-time algorithm to find a weakly popular matching that has at least $\frac{3}{4}$ times the size of a maximum-size weakly popular matching. We complement our approximation results with an Integer Linear Programming formulation that solves the maximum-size weakly popular matching problem exactly. We evaluate our algorithms on both randomly generated and real-world instances. Our experiments demonstrate that weakly popular matchings can be significantly larger than stable matchings, often covering all agents. Furthermore, we show that our approximation algorithm performs nearly optimally in practice. Finally, we show that maximum-size weakly popular matchings can have very few blocking edges, suggesting that weak popularity offers a desirable trade-off between size and stability. We also study a model more general than weak popularity, where for each edge, we can specify for both agents the size of improvement the agent needs to vote in favor of a new matching. We show that even in this more general model, a so-called $γ$-popular matching always exists, and our approximation algorithm applies.
Expression Syntax Information Bottleneck for Math Word Problems
Venue: arXiv cs.CL 2026
Year: 2026

Math Word Problems (MWP) aims to automatically solve mathematical questions given in texts. Previous studies tend to design complex models to capture additional information in the original text so as to enable the model to gain more comprehensive features. In this paper, we turn our attention in the opposite direction, and work on how to discard redundant features containing spurious correlations for MWP. To this end, we design an Expression Syntax Information Bottleneck method for MWP (called ESIB) based on variational information bottleneck, which extracts essential features of expression syntax tree while filtering latent-specific redundancy containing syntax-irrelevant features. The key idea of ESIB is to encourage multiple models to predict the same expression syntax tree for different problem representations of the same problem by mutual learning so as to capture consistent information of expression syntax tree and discard latent-specific redundancy. To improve the generalization ability of the model and generate more diverse expressions, we design a self-distillation loss to encourage the model to rely more on the expression syntax information in the latent space. Experimental results on two large-scale benchmarks show that our model not only achieves state-of-the-art results but also generates more diverse solutions. The code is available in https://github.com/menik1126/math_ESIB.
Normal Forms for Elements of ${}^*$-Continuous Kleene Algebras Representing the Context-Free Languages
Venue: arXiv cs.FL 2026
Year: 2026

Within the tensor product $K \mathop{\otimes_{\cal R}} C_2'$ of any ${}^*$-continuous Kleene algebra $K$ with the polycyclic ${}^*$-continuous Kleene algebra $C_2'$ over two bracket pairs there is a copy of the fixed-point closure of $K$: the centralizer of $C_2'$ in $K \mathop{\otimes_{\cal R}} C_2'$. Using an automata-theoretic representation of elements of $K\mathop{\otimes_{\cal R}} C_2'$ à la Kleene, with the aid of normal form theorems that restrict the occurrences of brackets on paths through the automata, we develop a foundation for a calculus of context-free expressions without variable binders. We also give some results on the bra-ket ${}^*$-continuous Kleene algebra $C_2$, motivate the ``completeness equation'' that distinguishes $C_2$ from $C_2'$, and show that $C_2'$ already validates a relativized form of this equation.
Fair Graph Machine Learning under Adversarial Missingness Processes
Venue: arXiv cs.LG 2026
Year: 2026

Graph Neural Networks (GNNs) have achieved state-of-the-art results in many relevant tasks where decisions might disproportionately impact specific communities. However, existing work on fair GNNs often assumes that either sensitive attributes are fully observed or they are missing completely at random. We show that an adversarial missingness process can inadvertently disguise a fair model through the imputation, leading the model to overestimate the fairness of its predictions. We address this challenge by proposing Better Fair than Sorry (BFtS), a fair missing data imputation model for sensitive attributes. The key principle behind BFtS is that imputations should approximate the worst-case scenario for fairness -- i.e. when optimizing fairness is the hardest. We implement this idea using a 3-player adversarial scheme where two adversaries collaborate against a GNN classifier, and the classifier minimizes the maximum bias. Experiments using synthetic and real datasets show that BFtS often achieves a better fairness x accuracy trade-off than existing alternatives under an adversarial missingness process.
Exploring and Analyzing the Effect of Avatar's Realism on Anxiety of English as Second Language (ESL) Speakers
Venue: arXiv cs.HC 2026
Year: 2026

Virtual avatars are increasingly used to support cross-cultural communication, yet their impact on communication anxiety among English as a Second Language (ESL) speakers remains underexplored. This study examines how avatar realism influences anxiety during English interactions between ESL speakers and native speakers. We conducted a controlled laboratory study in which Mandarin-speaking ESL participants engaged in guided one-on-one conversations under three visual representation conditions: live video, cartoon-like avatars, and realistic-like avatars. Anxiety was assessed using self-reported surveys and physiological signals, including electrodermal activity (EDA), electrocardiography (ECG), and photoplethysmography (PPG). The results show that increased visual realism does not correspond to a monotonic change in anxiety. Live video was the most preferred and was associated with the lowest self-reported anxiety. Cartoon-like avatars exhibited physiological anxiety levels comparable to live video and lower than realistic-like avatars, whereas realistic-like avatars elicited elevated anxiety across measures. These findings suggest that an effective avatar design for ESL communication should prioritize clarity of social signaling, reduced perceived social threat, and alignment between visual representation and interaction context, rather than visual realism alone.
ConstMig: Enabling Secure Live Migration of Large Intel SGX-based applications
Venue: arXiv cs.CR 2026
Year: 2026

Cloud service providers are adopting Trusted Execution Environments (TEEs) to provide hardware-guaranteed security to applications running on remote, untrusted data centers. However, migrating such applications still relies on the decade-old stop-and-copy method, which introduces large downtimes. Modern live-migration approaches such as pre-copy and post-copy do not work for TEE-based applications due to hardware-enforced restrictions. We propose ConstMig, a near-zero-downtime live-migration mechanism for large memory-footprint TEE-based applications. ConstMig is fully compatible with containers, virtual machines (VMs), and microVMs. Our prototype, built on Intel SGX, achieves near-zero downtime irrespective of enclave size and requires no additional hardware support. ConstMig reduces total downtime by 77 - 96% for a suite of SGX applications with multi-gigabyte memory footprints compared to state-of-the-art TEE-based migration solutions such as MigSGX.
Minimum Star Partitions of Simple Polygons in Polynomial Time
Venue: arXiv cs.CG 2026
Year: 2026

We devise a polynomial-time algorithm for partitioning a simple polygon $P$ into a minimum number of star-shaped polygons. The question of whether such an algorithm exists has been open for more than four decades [Avis and Toussaint, Pattern Recognit., 1981] and it has been repeated frequently, for example in O'Rourke's famous book [Art Gallery Theorems and Algorithms, 1987]. In addition to its strong theoretical motivation, the problem is also motivated by practical domains such as CNC pocket milling, motion planning, and shape parameterization. The only previously known algorithm for a non-trivial special case is for $P$ being both monotone and rectilinear [Liu and Ntafos, Algorithmica, 1991]. For general polygons, an algorithm was only known for the restricted version in which Steiner points are disallowed [Keil, SIAM J. Comput., 1985], meaning that each corner of a piece in the partition must also be a corner of $P$. Interestingly, the solution size for the restricted version may be linear for instances where the unrestricted solution has constant size. The covering variant in which the pieces are star-shaped but allowed to overlap--known as the Art Gallery Problem--was recently shown to be $\exists\mathbb R$-complete and is thus likely not in NP [Abrahamsen, Adamaszek and Miltzow, STOC 2018 & J. ACM 2022]; this is in stark contrast to our result. Arguably the most related work to ours is the polynomial-time algorithm to partition a simple polygon into a minimum number of convex pieces by Chazelle and Dobkin [STOC, 1979 & Comp. Geom., 1985].