OpenRaiser/Intern-Atlas huggingface dataset:
When Robots Say No: The Empathic Ethical Disobedience Benchmark
Venue: arXiv cs.RO 2025
Year: 2025

Robots must balance compliance with safety and social expectations as blind obedience can cause harm, while over-refusal erodes trust. Existing safe reinforcement learning (RL) benchmarks emphasize physical hazards, while human-robot interaction trust studies are small-scale and hard to reproduce. We present the Empathic Ethical Disobedience (EED) Gym, a standardized testbed that jointly evaluates refusal safety and social acceptability. Agents weigh risk, affect, and trust when choosing to comply, refuse (with or without explanation), clarify, or propose safer alternatives. EED Gym provides different scenarios, multiple persona profiles, and metrics for safety, calibration, and refusals, with trust and blame models grounded in a vignette study. Using EED Gym, we find that action masking eliminates unsafe compliance, while explanatory refusals help sustain trust. Constructive styles are rated most trustworthy, empathic styles -- most empathic, and safe RL methods improve robustness but also make agents more prone to overly cautious behavior. We release code, configurations, and reference policies to enable reproducible evaluation and systematic human-robot interaction research on refusal and trust. At submission time, we include an anonymized reproducibility package with code and configs, and we commit to open-sourcing the full repository after the paper is accepted.
STORM: Search-Guided Generative World Models for Robotic Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

We present STORM (Search-Guided Generative World Models), a novel framework for spatio-temporal reasoning in robotic manipulation that unifies diffusion-based action generation, conditional video prediction, and search-based planning. Unlike prior Vision-Language-Action (VLA) models that rely on abstract latent dynamics or delegate reasoning to language components, STORM grounds planning in explicit visual rollouts, enabling interpretable and foresight-driven decision-making. A diffusion-based VLA policy proposes diverse candidate actions, a generative video world model simulates their visual and reward outcomes, and Monte Carlo Tree Search (MCTS) selectively refines plans through lookahead evaluation. Experiments on the SimplerEnv manipulation benchmark demonstrate that STORM achieves a new state-of-the-art average success rate of 51.0 percent, outperforming strong baselines such as CogACT. Reward-augmented video prediction substantially improves spatio-temporal fidelity and task relevance, reducing Frechet Video Distance by over 75 percent. Moreover, STORM exhibits robust re-planning and failure recovery behavior, highlighting the advantages of search-guided generative world models for long-horizon robotic manipulation.
Insider Threat Detection Using GCN and Bi-LSTM with Explicit and Implicit Graph Representations
Venue: arXiv cs.AI 2025
Year: 2025

Insider threat detection (ITD) is challenging due to the subtle and concealed nature of malicious activities performed by trusted users. This paper proposes a post-hoc ITD framework that integrates explicit and implicit graph representations with temporal modelling to capture complex user behaviour patterns. An explicit graph is constructed using predefined organisational rules to model direct relationships among user activities. To mitigate noise and limitations in this hand-crafted structure, an implicit graph is learned from feature similarities using the Gumbel-Softmax trick, enabling the discovery of latent behavioural relationships. Separate Graph Convolutional Networks (GCNs) process the explicit and implicit graphs to generate node embeddings, which are concatenated and refined through an attention mechanism to emphasise threat-relevant features. The refined representations are then passed to a bidirectional Long Short-Term Memory (Bi-LSTM) network to capture temporal dependencies in user behaviour. Activities are flagged as anomalous when their probability scores fall below a predefined threshold. Extensive experiments on CERT r5.2 and r6.2 datasets demonstrate that the proposed framework outperforms state-of-the-art methods. On r5.2, the model achieves an AUC of 98.62, a detection rate of 100%, and a false positive rate of 0.05. On the more challenging r6.2 dataset, it attains an AUC of 88.48, a detection rate of 80.15%, and a false positive rate of 0.15, highlighting the effectiveness of combining graph-based and temporal representations for robust ITD.
Large Language Models as Discounted Bayesian Filters
Venue: arXiv cs.AI 2025
Year: 2025

Large Language Models (LLMs) demonstrate strong few-shot generalization through in-context learning, yet their reasoning in dynamic and stochastic environments remains opaque. Prior studies mainly focus on static tasks and overlook the online adaptation required when beliefs must be continuously updated, which is a key capability for LLMs acting as world models or agents. We introduce a Bayesian filtering framework to evaluate online inference in LLMs. Our probabilistic probe suite spans both multivariate discrete distributions, such as dice rolls, and continuous distributions, such as Gaussian processes, where ground-truth parameters shift over time. We find that while LLM belief updates resemble Bayesian posteriors, they are more accurately characterized by an exponential forgetting filter with a model-specific discount factor smaller than one. This reveals systematic discounting of older evidence that varies significantly across model architectures. Although inherent priors are often miscalibrated, the updating mechanism itself remains structured and principled. We further validate these findings in a simulated agent task and propose prompting strategies that effectively recalibrate priors with minimal computational cost.
Exploring Runtime Evolution in Android: A Cross-Version Analysis and Its Implications for Memory Forensics
Venue: arXiv cs.CR 2025
Year: 2025

Userland memory forensics has become a critical component of smartphone investigations and incident response, enabling the recovery of volatile evidence such as deleted messages from end-to-end encrypted apps and cryptocurrency transactions. However, these forensics tools, particularly on Android, face significant challenges in adapting to different versions and maintaining reliability over time due to the constant evolution of low-level structures critical for evidence recovery and reconstruction. Structural changes, ranging from simple offset modifications to complete architectural redesigns, pose substantial maintenance and adaptability issues for forensic tools that rely on precise structure interpretation. Thus, this paper presents the first systematic study of Android Runtime (ART) structural evolution and its implications for memory forensics. We conduct an empirical analysis of critical Android runtime structures, examining their evolution across six versions for four different architectures. Our findings reveal that over 73.2% of structure members underwent positional changes, significantly affecting the adaptability and reliability of memory forensic tools. Further analysis of core components such as Runtime, Thread, and Heap structures highlights distinct evolution patterns and their impact on critical forensic operations, including thread state enumeration, memory mapping, and object reconstruction. These results demonstrate that traditional approaches relying on static structure definitions and symbol-based methods, while historically reliable, are increasingly unsustainable on their own. We recommend that memory forensic tools in general and Android in particular evolve toward hybrid approaches that retain the validation strength of symbolic methods while integrating automated structure inference, version-aware parsing, and redundant analysis strategies.
Integrated Control and Communication in LQG Systems
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we study the Integrated Communication and Control (ICAC) problem. Specifically, we investigate how messages can be transmitted from the controller/encoder to the observer/decoder through the control signal in Multiple-Input Multiple-Output (MIMO) vector-state Linear Quadratic Gaussian (LQG) systems under control constraints. We provide a computable capacity expression using semidefinite programming. We further show that it is possible to transmit data at a nonzero rate over an LQG system while maintaining the same optimal control cost as in the case where no information message are transmitted. Finally, we discuss how this framework generalizes communication over MIMO Gaussian channels with feedback, both with and without InterSymbol Interference (ISI).
Vox Deorum: A Hybrid LLM Architecture for 4X / Grand Strategy Game AI -- Lessons from Civilization V
Venue: arXiv cs.AI 2025
Year: 2025

Large Language Models' capacity to reason in natural language makes them uniquely promising for 4X and grand strategy games, enabling more natural human-AI gameplay interactions such as collaboration and negotiation. However, these games present unique challenges due to their complexity and long-horizon nature, while latency and cost factors may hinder LLMs' real-world deployment. Working on a classic 4X strategy game, Sid Meier's Civilization V with the Vox Populi mod, we introduce Vox Deorum, a hybrid LLM+X architecture. Our layered technical design empowers LLMs to handle macro-strategic reasoning, delegating tactical execution to subsystems (e.g., algorithmic AI or reinforcement learning AI in the future). We validate our approach through 2,327 complete games, comparing two open-source LLMs with a simple prompt against Vox Populi's enhanced AI. Results show that LLMs achieve competitive end-to-end gameplay while exhibiting play styles that diverge substantially from algorithmic AI and from each other. Our work establishes a viable architecture for integrating LLMs in commercial 4X games, opening new opportunities for game design and agentic AI research.
Wireless Copilot: An AI-Powered Partner for Navigating Next-Generation Wireless Complexity
Venue: arXiv cs.NI 2025
Year: 2025

The sixth-generation (6G) of wireless networks introduces a level of operational complexity that exceeds the limits of traditional automation and manual oversight. This paper introduces the "Wireless Copilot", an AI-powered technical assistant designed to function as a collaborative partner for human network designers, engineers, and operators. We posit that by integrating Large Language Models (LLMs) with a robust cognitive framework. It will surpass the existing AI tools and interact with wireless devices, transmitting the user's intentions into the actual network execution process. Then, Wireless Copilot can translate high-level human intent into precise, optimized, and verifiable network actions. This framework bridges the gap between human expertise and machine-scale complexity, enabling more efficient, intelligent, and trustworthy management of 6G systems. Wireless Copilot will be a novel layer between the wireless infrastructure and the network operators. Moreover, we explore Wireless Copilot's methodology and analyze its application in Low-Altitude Wireless Networks (LAWNets) assisting 6G networking, including network design, configuration, evaluation, and optimization. Additionally, we present a case study on intent-based LAWNets resource allocation, demonstrating its superior adaptability compared to others. Finally, we outline future research directions toward creating a comprehensive human-AI collaborative ecosystem for the 6G era.
Reflective Confidence: Correcting Reasoning Flaws via Online Self-Correction
Venue: arXiv cs.AI 2025
Year: 2025

Large language models (LLMs) have achieved strong performance on complex reasoning tasks using techniques such as chain-of-thought and self-consistency. However, ensemble-based approaches, especially self-consistency which relies on multiple reasoning trajectories, often incur substantial computational overhead. To improve efficiency, prior work has leveraged internal confidence signals, where early stopping strategies such as DeepConf reduce cost by terminating low-confidence trajectories. However, this strategy discards incomplete reasoning paths and wastes partial computation. We propose reflective confidence, a novel reasoning framework that transforms low-confidence signals from termination indicators into reflection triggers. When confidence falls below a threshold, instead of stopping generation, the model produces a reflection prompt to analyze the current reasoning state, identify potential errors, and continue generation along a corrected trajectory. Experiments on mathematical reasoning benchmarks, including AIME 2025, demonstrate significant accuracy improvements over advanced early-stopping baselines at comparable computational cost, validating the effectiveness of proactive self-correction over passive discarding.
ChronoDreamer: Action-Conditioned World Model as an Online Simulator for Robotic Planning
Venue: arXiv cs.AI 2025
Year: 2025

We present ChronoDreamer, an action-conditioned world model for contact-rich robotic manipulation. Given a history of egocentric RGB frames, contact maps, actions, and joint states, ChronoDreamer predicts future video frames, contact distributions, and joint angles via a spatial-temporal transformer trained with MaskGIT-style masked prediction. Contact is encoded as depth-weighted Gaussian splat images that render 3D forces into a camera-aligned format suitable for vision backbones. At inference, predicted rollouts are evaluated by a vision-language model that reasons about collision likelihood, enabling rejection sampling of unsafe actions before execution. We train and evaluate on DreamerBench, a simulation dataset generated with Project Chrono that provides synchronized RGB, contact splat, proprioception, and physics annotations across rigid and deformable object scenarios. Qualitative results demonstrate that the model preserves spatial coherence during non-contact motion and generates plausible contact predictions, while the LLM-based judge distinguishes collision from non-collision trajectories.
Multi-user Pufferfish Privacy
Venue: arXiv cs.CR 2025
Year: 2025

This paper studies how to achieve individual indistinguishability by pufferfish privacy in aggregated query to a multi-user system. It is assumed that each user reports realization of a random variable. We study how to calibrate Laplace noise, added to the query answer, to attain pufferfish privacy when user changes his/her reported data value, leaves the system and is replaced by another use with different randomness. Sufficient conditions are derived for all scenarios for attaining statistical indistinguishability on four sets of secret pairs. They are derived using the existing Kantorovich method (Wasserstain metric of order $1$). These results can be applied to attain indistinguishability when a certain class of users is added or removed from a tabular data. It is revealed that attaining indifference in individual's data is conditioned on the statistics of this user only. For binary (Bernoulli distributed) random variables, the derived sufficient conditions can be further relaxed to reduce the noise and improve data utility.
Does It Tie Out? Towards Autonomous Legal Agents in Venture Capital
Venue: arXiv cs.CL 2025
Year: 2025

Before closing venture capital financing rounds, lawyers conduct diligence that includes tying out the capitalization table: verifying that every security (for example, shares, options, warrants) and issuance term (for example, vesting schedules, acceleration triggers, transfer restrictions) is supported by large sets of underlying legal documentation. While LLMs continue to improve on legal benchmarks, specialized legal workflows, such as capitalization tie-out, remain out of reach even for strong agentic systems. The task requires multi-document reasoning, strict evidence traceability, and deterministic outputs that current approaches fail to reliably deliver. We characterize capitalization tie-out as an instance of a real-world benchmark for legal AI, analyze and compare the performance of existing agentic systems, and propose a world model architecture toward tie-out automation-and more broadly as a foundation for applied legal intelligence.
A Study of Finetuning Video Transformers for Multi-view Geometry Tasks
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents an investigation of vision transformer learning for multi-view geometry tasks, such as optical flow estimation, by fine-tuning video foundation models. Unlike previous methods that involve custom architectural designs and task-specific pretraining, our research finds that general-purpose models pretrained on videos can be readily transferred to multi-view problems with minimal adaptation. The core insight is that general-purpose attention between patches learns temporal and spatial information for geometric reasoning. We demonstrate that appending a linear decoder to the Transformer backbone produces satisfactory results, and iterative refinement can further elevate performance to stateof-the-art levels. This conceptually simple approach achieves top cross-dataset generalization results for optical flow estimation with end-point error (EPE) of 0.69, 1.78, and 3.15 on the Sintel clean, Sintel final, and KITTI datasets, respectively. Our method additionally establishes a new record on the online test benchmark with EPE values of 0.79, 1.88, and F1 value of 3.79. Applications to 3D depth estimation and stereo matching also show strong performance, illustrating the versatility of video-pretrained models in addressing geometric vision tasks.
EcoSplat: Efficiency-controllable Feed-forward 3D Gaussian Splatting from Multi-view Images
Venue: arXiv cs.CV 2025
Year: 2025

Feed-forward 3D Gaussian Splatting (3DGS) enables efficient one-pass scene reconstruction, providing 3D representations for novel view synthesis without per-scene optimization. However, existing methods typically predict pixel-aligned primitives per-view, producing an excessive number of primitives in dense-view settings and offering no explicit control over the number of predicted Gaussians. To address this, we propose EcoSplat, the first efficiency-controllable feed-forward 3DGS framework that adaptively predicts the 3D representation for any given target primitive count at inference time. EcoSplat adopts a two-stage optimization process. The first stage is Pixel-aligned Gaussian Training (PGT) where our model learns initial primitive prediction. The second stage is Importance-aware Gaussian Finetuning (IGF) stage where our model learns rank primitives and adaptively adjust their parameters based on the target primitive count. Extensive experiments across multiple dense-view settings show that EcoSplat is robust and outperforms state-of-the-art methods under strict primitive-count constraints, making it well-suited for flexible downstream rendering tasks.
DSO-VSA: a Variable Stiffness Actuator with Decoupled Stiffness and Output Characteristics for Rehabilitation Robotics
Venue: arXiv cs.RO 2025
Year: 2025

Stroke-induced motor impairment often results in substantial loss of upper-limb function, creating a strong demand for rehabilitation robots that enable safe and transparent physical human-robot interaction (pHRI). Variable stiffness actuators are well suited for such applications. However, in most existing designs, stiffness is coupled with the deflection angle, complicating both modeling and control. To address this limitation, this paper presents a variable stiffness actuator featuring decoupled stiffness and output behavior for rehabilitation robotics. The system integrates a variable stiffness mechanism that combines a variable-length lever with a hypocycloidal straight-line mechanism to achieve a linear torque-deflection relationship and continuous stiffness modulation from near zero to theoretically infinite. It also incorporates a differential transmission mechanism based on a planetary gear system that enables dual-motor load sharing. A cascade PI controller is further developed on the basis of the differential configuration, in which the position-loop term jointly regulates stiffness and deflection angle, effectively suppressing stiffness fluctuations and output disturbances. The performance of prototype was experimentally validated through stiffness calibration, stiffness regulation, torque control, decoupled characteristics, and dual-motor load sharing, indicating the potential for rehabilitation exoskeletons and other pHRI systems.
Generating Risky Samples with Conformity Constraints via Diffusion Models
Venue: arXiv cs.LG 2025
Year: 2025

Although neural networks achieve promising performance in many tasks, they may still fail when encountering some examples and bring about risks to applications. To discover risky samples, previous literature attempts to search for patterns of risky samples within existing datasets or inject perturbation into them. Yet in this way the diversity of risky samples is limited by the coverage of existing datasets. To overcome this limitation, recent works adopt diffusion models to produce new risky samples beyond the coverage of existing datasets. However, these methods struggle in the conformity between generated samples and expected categories, which could introduce label noise and severely limit their effectiveness in applications. To address this issue, we propose RiskyDiff that incorporates the embeddings of both texts and images as implicit constraints of category conformity. We also design a conformity score to further explicitly strengthen the category conformity, as well as introduce the mechanisms of embedding screening and risky gradient guidance to boost the risk of generated samples. Extensive experiments reveal that RiskyDiff greatly outperforms existing methods in terms of the degree of risk, generation quality, and conformity with conditioned categories. We also empirically show the generalization ability of the models can be enhanced by augmenting training data with generated samples of high conformity.
Tempo as the Stable Cue: Hierarchical Mixture of Tempo and Beat Experts for Music to 3D Dance Generation
Venue: arXiv cs.CV 2025
Year: 2025

Music to 3D dance generation aims to synthesize realistic and rhythmically synchronized human dance from music. While existing methods often rely on additional genre labels to further improve dance generation, such labels are typically noisy, coarse, unavailable, or insufficient to capture the diversity of real-world music, which can result in rhythm misalignment or stylistic drift. In contrast, we observe that tempo, a core property reflecting musical rhythm and pace, remains relatively consistent across datasets and genres, typically ranging from 60 to 200 BPM. Based on this finding, we propose TempoMoE, a hierarchical tempo-aware Mixture-of-Experts module that enhances the diffusion model and its rhythm perception. TempoMoE organizes motion experts into tempo-structured groups for different tempo ranges, with multi-scale beat experts capturing fine- and long-range rhythmic dynamics. A Hierarchical Rhythm-Adaptive Routing dynamically selects and fuses experts from music features, enabling flexible, rhythm-aligned generation without manual genre labels. Extensive experiments demonstrate that TempoMoE achieves state-of-the-art results in dance quality and rhythm alignment.