OpenRaiser/Intern-Atlas huggingface dataset:
HEHRGNN: A Unified Embedding Model for Knowledge Graphs with Hyperedges and Hyper-Relational Edges
Venue: arXiv cs.LG 2026
Year: 2026

Knowledge Graph(KG) has gained traction as a machine-readable organization of real-world knowledge for analytics using artificial intelligence systems. Graph Neural Network(GNN), is proven to be an effective KG embedding technique that enables various downstream tasks like link prediction, node classification, and graph classification. The focus of research in both KG embedding and GNNs has been mostly oriented towards simple graphs with binary relations. However, real-world knowledge bases have a significant share of complex and n-ary facts that cannot be represented by binary edges. More specifically, real-world knowledge bases are often a mix of two types of n-ary facts - (i) that require hyperedges and (ii) that require hyper-relational edges. Though there are research efforts catering to these n-ary fact types, they are pursued independently for each type. We propose $H$yper$E$dge $H$yper-$R$elational edge $GNN$(HEHRGNN), a unified embedding model for n-ary relational KGs with both hyperedges and hyper-relational edges. The two main components of the model are i)HEHR unified fact representation format, and ii)HEHRGNN encoder, a GNN-based encoder with a novel message propagation model capable of capturing complex graph structures comprising both hyperedges and hyper-relational edges. The experimental results of HEHRGNN on link prediction tasks show its effectiveness as a unified embedding model, with inductive prediction capability, for link prediction across real-world datasets having different types of n-ary facts. The model also shows improved link prediction performance over baseline models for hyperedge and hyper-relational datasets.
Adaptive Collaboration of Arena-Based Argumentative LLMs for Explainable and Contestable Legal Reasoning
Venue: arXiv cs.MA 2026
Year: 2026

Legal reasoning requires not only high accuracy but also the ability to justify decisions through verifiable and contestable arguments. However, existing Large Language Model (LLM) approaches, such as Chain-of-Thought (CoT) and Retrieval-Augmented Generation (RAG), often produce unstructured explanations that lack a formal mechanism for verification or user intervention. To address this limitation, we propose Adaptive Collaboration of Argumentative LLMs (ACAL), a neuro-symbolic framework that integrates adaptive multi-agent collaboration with an Arena-based Quantitative Bipolar Argumentation Framework (A-QBAF). ACAL dynamically deploys expert agent teams to construct arguments, employs a clash resolution mechanism to adjudicate conflicting claims, and utilizes uncertainty-aware escalation for borderline cases. Crucially, our framework supports a Human-in-the-Loop (HITL) contestability workflow, enabling users to directly audit and modify the underlying reasoning graph to influence the final judgment. Empirical evaluations on the LegalBench benchmark demonstrate that ACAL outperforms strong baselines across Gemini-2.5-Flash-Lite and Gemini-2.5-Flash architectures, effectively balancing efficient predictive performance with structured transparency and contestability. Our implementation is available at: https://github.com/loc110504/ACAL.
Constructing (Co)inductive Types via Large Sizes
Venue: arXiv cs.LO 2026
Year: 2026

To ensure decidability and consistency of its type theory, a proof assistant should only accept terminating recursive functions and productive corecursive functions. Most proof assistants enforce this through syntactic conditions, which can be restrictive and non-modular. Sized types are a type-based alternative where (co)inductive types are annotated with additional size information. Well-founded induction on sizes can then be used to prove termination and productivity. An implementation of sized types exists in Agda, but it is currently inconsistent due to the addition of a largest size. We investigate an alternative approach, where intensional type theory is extended with a large type of sizes and parametric quantifiers over sizes. We show that inductive and coinductive types can be constructed in this theory, which improves on earlier work where this was only possible for the finitely-branching inductive types. The consistency of the theory is justified by an impredicative realisability model, which interprets the type of sizes as an uncountable ordinal.
High Dimensional Procedural Content Generation
Venue: arXiv cs.AI 2026
Year: 2026

Procedural content generation (PCG) has made substantial progress in shaping static 2D/3D geometry, while most methods treat gameplay mechanics as auxiliary and optimize only over space. We argue that this limits controllability and expressivity, and formally introduce High-Dimensional PCG (HDPCG): a framework that elevates non-geometric gameplay dimensions to first-class coordinates of a joint state space. We instantiate HDPCG along two concrete directions. Direction-Space augments geometry with a discrete layer dimension and validates reachability in 4D (x,y,z,l), enabling unified treatment of 2.5D/3.5D mechanics such as gravity inversion and parallel-world switching. Direction-Time augments geometry with temporal dynamics via time-expanded graphs, capturing action semantics and conflict rules. For each direction, we present three general, practicable algorithms with a shared pipeline of abstract skeleton generation, controlled grounding, high-dimensional validation, and multi-metric evaluation. Large-scale experiments across diverse settings validate the integrity of our problem formulation and the effectiveness of our methods on playability, structure, style, robustness, and efficiency. Beyond quantitative results, Unity-based case studies recreate playable scenarios that accord with our metrics. We hope HDPCG encourages a shift in PCG toward general representations and the generation of gameplay-relevant dimensions beyond geometry, paving the way for controllable, verifiable, and extensible level generation.
(Perlin) Noise as AI coordinator
Venue: arXiv cs.AI 2026
Year: 2026

Large scale control of nonplayer agents is central to modern games, while production systems still struggle to balance several competing goals: locally smooth, natural behavior, and globally coordinated variety across space and time. Prior approaches rely on handcrafted rules or purely stochastic triggers, which either converge to mechanical synchrony or devolve into uncorrelated noise that is hard to tune. Continuous noise signals such as Perlin noise are well suited to this gap because they provide spatially and temporally coherent randomness, and they are already widely used for terrain, biomes, and other procedural assets. We adapt these signals for the first time to large scale AI control and present a general framework that treats continuous noise fields as an AI coordinator. The framework combines three layers of control: behavior parameterization for movement at the agent level, action time scheduling for when behaviors start and stop, and spawn or event type and feature generation for what appears and where. We instantiate the framework reproducibly and evaluate Perlin noise as a representative coordinator across multiple maps, scales, and seeds against random, filtered, deterministic, neighborhood constrained, and physics inspired baselines. Experiments show that coordinated noise fields provide stable activation statistics without lockstep, strong spatial coverage and regional balance, better diversity with controllable polarization, and competitive runtime. We hope this work motivates a broader exploration of coordinated noise in game AI as a practical path to combine efficiency, controllability, and quality.
Toward Manifest Relationality in Transformers via Symmetry Reduction
Venue: arXiv cs.LG 2026
Year: 2026

Transformer models contain substantial internal redundancy arising from coordinate-dependent representations and continuous symmetries, in model space and in head space, respectively. While recent approaches address this by explicitly breaking symmetry, we propose a complementary framework based on symmetry reduction. We reformulate representations, attention mechanisms, and optimization dynamics in terms of invariant relational quantities, eliminating redundant degrees of freedom by construction. This perspective yields architectures that operate directly on relational structures, providing a principled geometric framework for reducing parameter redundancy and analyzing optimization.
Temporal-Logic-Aware Frontier-Based Exploration
Venue: arXiv cs.RO 2026
Year: 2026

This paper addresses the problem of temporal logic motion planning for an autonomous robot operating in an unknown environment. The objective is to enable the robot to satisfy a syntactically co-safe Linear Temporal Logic (scLTL) specification when the exact locations of the desired labels are not known a priori. We introduce a new type of automaton state, referred to as commit states. These states capture intermediate task progress resulting from actions whose consequences are irreversible. In other words, certain future paths to satisfaction become not feasible after taking those actions that lead to the commit states. By leveraging commit states, we propose a sound and complete frontier-based exploration algorithm that strategically guides the robot to make progress toward the task while preserving all possible ways of satisfying it. The efficacy of the proposed method is validated through simulations.
NeuroWise: A Multi-Agent LLM "Glass-Box" System for Practicing Double-Empathy Communication with Autistic Partners
Venue: arXiv cs.HC 2026
Year: 2026

The double empathy problem frames communication difficulties between neurodivergent and neurotypical individuals as arising from mutual misunderstanding, yet most interventions focus on autistic individuals. We present NeuroWise, a multi-agent LLM-based coaching system that supports neurotypical users through stress visualization, interpretation of internal experiences, and contextual guidance. In a between-subjects study (N=30), NeuroWise was rated as helpful by all participants and showed a significant condition-time effect on deficit-based attributions (p=0.02): NeuroWise users reduced deficit framing, while baseline users shifted toward blaming autistic "deficits" after difficult interactions. NeuroWise users also completed conversations more efficiently (37% fewer turns, p=0.03). These findings suggest that AI-based interpretation can support attributional change by helping users recognize communication challenges as mutual.
Evaluating Replay Techniques for Asynchronous Task Handover in Immersive Analytics
Venue: arXiv cs.HC 2026
Year: 2026

Immersive analytics enables collaborative data analysis in shared virtual spaces. While synchronous collaboration in such environments is well-established, real-world analysis often requires an effective task handover - the transfer of knowledge and analytical context between analysts working asynchronously. Traditional handover methods often rely on static annotations that fail to capture the dynamic problem-solving process and spatial context inherent in immersive workflows. To address this handover challenge, we explore session replay as a comprehensive approach for analysts to re-experience a predecessor's work, facilitating a deeper understanding of both the visual details and the insight formation process. Two phases of studies were conducted to establish design guidelines for such replay systems by investigating the impact of viewing platform (PC vs. VR), perspective (first-person vs. third-person), and navigation control (active vs. passive). Phase 1 identified the optimal replay configurations within each viewing platform, revealing a platform-dependent divergence: PC users favored a guided, first-person perspective for its focused detail, while VR users benefited significantly from the agency afforded by a third-person perspective with active navigation. After refining each condition based on user feedback, including developing a novel hybrid 1PP+3PP format for PC, Phase 2 compared the two optimized systems (PC vs. VR). Our results show that the immersive VR replay led to significantly better task comprehension and workflow reconstruction accuracy, demonstrating the critical role of embodied agency in understanding complex analytical processes.
An interpretable framework using foundation models for fish sex identification
Venue: arXiv cs.CV 2026
Year: 2026

Accurate sex identification in fish is vital for optimizing breeding and management strategies in aquaculture, particularly for species at the risk of extinction. However, most existing methods are invasive or stressful and may cause additional mortality, posing severe risks to threatened or endangered fish populations. To address these challenges, we propose FishProtoNet, a robust, non-invasive computer vision-based framework for sex identification of delta smelt (Hypomesus transpacificus), an endangered fish species native to California, across its full life cycle. Unlike the traditional deep learning methods, FishProtoNet provides interpretability through learned prototype representations while improving robustness by leveraging foundation models to reduce the influence of background noise. Specifically, the FishProtoNet framework consists of three key components: fish regions of interest (ROIs) extraction using visual foundation model, feature extraction from fish ROIs and fish sex identification based on an interpretable prototype network. FishProtoNet demonstrates strong performance in delta smelt sex identification during early spawning and post-spawning stages, achieving the accuracies of 74.40% and 81.16% and corresponding F1 scores of 74.27% and 79.43% respectively. In contrast, delta smelt sex identification at the subadult stage remains challenging for current computer vision methods, likely due to less pronounced morphological differences in immature fish. The source code of FishProtoNet is publicly available at: https://github.com/zhengmiao1/Fish_sex_identification
The Metaphysics We Train: A Heideggerian Reading of Machine Learning
Venue: arXiv cs.CY 2026
Year: 2026

This paper offers a phenomenological reading of contemporary machine learning through Heideggerian concepts, aimed at enriching practitioners' reflexive understanding of their own practice. We argue that this philosophical lens reveals three insights invisible to purely technical analysis. First, the algorithmic Entwurf (projection) is distinctive in being automated, opaque, and emergent--a metaphysics that operates without explicit articulation or debate, crystallizing implicitly through gradient descent rather than theoretical argument. Second, even sophisticated technical advances remain within the regime of Gestell (Enframing), improving calculation without questioning the primacy of calculation itself. Third, AI's lack of existential structure, specifically the absence of Care (Sorge), is genuinely explanatory: it illuminates why AI systems have no internal resources for questioning their own optimization imperatives, and why they optimize without the anxiety (Angst) that signals, in human agents, the friction between calculative absorption and authentic existence. We conclude by exploring the pedagogical value of this perspective, arguing that data science education should cultivate not only technical competence but ontological literacy--the capacity to recognize what worldviews our tools enact and when calculation itself may be the wrong mode of engagement.
A Markovian View of Iterative-Feedback Loops in Image Generative Models: Neural Resonance and Model Collapse
Venue: arXiv cs.LG 2026
Year: 2026

AI training datasets will inevitably contain AI-generated examples, leading to ``feedback'' in which the output of one model impacts the training of another. It is known that such iterative feedback can lead to model collapse, yet the mechanisms underlying this degeneration remain poorly understood. Here we show that a broad class of feedback processes converges to a low-dimensional invariant structure in latent space, a phenomenon we call neural resonance. By modeling iterative feedback as a Markov Chain, we show that two conditions are needed for this resonance to occur: ergodicity of the feedback process and directional contraction of the latent representation. By studying diffusion models on MNIST and ImageNet, as well as CycleGAN and an audio feedback experiment, we map how local and global manifold geometry evolve, and we introduce an eight-pattern taxonomy of collapse behaviors. Neural resonance provides a unified explanation for long-term degenerate behavior in generative models and provides practical diagnostics for identifying, characterizing, and eventually mitigating collapse.
Evaluation and Benchmarking Suite for Financial Large Language Models and Agents
Venue: arXiv cs.CE 2026
Year: 2026

Over the past three years, the financial services industry has witnessed Large Language Models (LLMs) and agents transitioning from the exploration stage to readiness and governance stages. Financial large language models (FinLLMs), such as open FinGPT and proprietary BloombergGPT , have great potential in financial applications, including retrieving real-time data, tutoring, analyzing sentiment of social media, analyzing SEC filings, and agentic trading. However, general-purpose LLMs and agents lack financial expertise and often struggle to handle complex financial reasoning. This paper presents an evaluation and benchmarking suite that covers the lifecycle of FinLLMs and FinAgents. This suite led by SecureFinAI Lab includes an evaluation pipeline and a governance framework collaborating with Linux Foundation and PyTorch Foundation, a FinLLM Leaderboard with HuggingFace, an AgentOps framework with Red Hat, and a documentation website with Rensselear Center of Open Source. Our collaborative development evolves through three stages: FinLLM Exploration (2023), FinLLM Readiness (2024), and FinAI Governance (2025). The proposed suite serves as an open platform that enables researchers and practitioners to perform both quantitative and qualitative analysis of different FinLLMs and FinAgents, fostering a more robust and reliable FinAI ecosystem.
ucTrace: A Multi-Layer Profiling Tool for UCX-driven Communication
Venue: arXiv cs.DC 2026
Year: 2026

UCX is a communication framework that enables low-latency, high-bandwidth communication in HPC systems. With its unified API, UCX facilitates efficient data transfers across multi-node CPU-GPU clusters. UCX is widely used as the transport layer for MPI, particularly in GPU-aware implementations. However, existing profiling tools lack fine-grained communication traces at the UCX level, do not capture transport-layer behavior, or are limited to specific MPI implementations. To address these gaps, we introduce ucTrace, a novel profiler that exposes and visualizes UCX-driven communication in HPC environments. ucTrace provides insights into MPI workflows by profiling message passing at the UCX level, linking operations between hosts and devices (e.g., GPUs and NICs) directly to their originating MPI functions. Through interactive visualizations of process- and device-specific interactions, ucTrace helps system administrators, library and application developers optimize performance and debug communication patterns in large-scale workloads. We demonstrate ucTrace's features through a wide range of experiments including MPI point-to-point behavior under different UCX settings, Allreduce comparisons across MPI libraries, communication analysis of a linear solver, NUMA binding effects, and profiling of GROMACS MD simulations with GPU acceleration at scale. ucTrace is publicly available at https://github.com/ParCoreLab/ucTrace.
SiGRRW: A Single-Watermark Robust Reversible Watermarking Framework with Guiding Strategy
Venue: arXiv cs.CR 2026
Year: 2026

Robust reversible watermarking (RRW) enables copyright protection for images while overcoming the limitation of distortion introduced by watermark itself. Current RRW schemes typically employ a two-stage framework, which fails to achieve simultaneous robustness and reversibility within a single watermarking, and functional interference between the two watermarks results in performance degradation in multiple terms such as capacity and imperceptibility. We propose SiGRRW, a single-watermark RRW framework, which is applicable to both generative models and natural images. We introduce a novel guiding strategy to generate guiding images, serving as the guidance for embedding and recovery. The watermark is reversibly embedded with the guiding residual, which can be calculated from both cover images and watermark images. The proposed framework can be deployed either as a plug-and-play watermarking layer at the output stage of generative models, or directly applied to natural images. Extensive experiments demonstrate that SiGRRW effectively enhances imperceptibility and robustness compared to existing RRW schemes while maintaining lossless recovery of cover images, with significantly higher capacity than conventional schemes.
A User-driven Design Framework for Robotaxi
Venue: arXiv cs.RO 2026
Year: 2026

Robotaxis are emerging as a promising form of urban mobility, yet research has largely emphasized technical driving performance while leaving open how passengers experience and evaluate rides without a human driver. To address the limitations of prior work that often relies on simulated or hypothetical settings, we investigate real-world robotaxi use through 18 semi-structured interviews and autoethnographic ride experiences. We found that users were drawn to robotaxis by low cost, social recommendation, and curiosity. They valued a distinctive set of benefits, such as an increased sense of agency, and consistent driving behavioral consistency and standardized ride experiences. However, they encountered persistent challenges around limited flexibility, insufficient transparency, management difficulty, robustness concerns in edge cases, and emergency handling concerns. Robotaxi experiences were shaped by privacy, safety, ethics, and trust. Users were often privacy-indifferent yet sensitive to opaque access and leakage risks; safety perceptions were polarized; and ethical considerations surfaced round issues such as accountability, feedback responsibility and absence of human-like social norms. Based on these findings, we propose a user-driven design framework spanning the end-to-end journey, such as pre-ride configuration (hailing), context-aware pickup facilitation (pick-up) in-ride explainability (traveling), and accountable post-ride feedback (drop-off) to guide robotaxi interaction and service design.