OpenRaiser/Intern-Atlas huggingface dataset:
Enabling Autonomous Navigation in a Snake Robot through Visual-Inertial Odometry and Closed-Loop Trajectory Tracking Control
Venue: arXiv cs.RO 2025
Year: 2025

Snake robots offer exceptional mobility across extreme terrain inaccessible to conventional rovers, yet their highly articulated bodies present fundamental challenges for autonomous navigation in environments lacking external tracking infrastructure. This thesis develops a complete autonomy pipeline for COBRA, an 11 degree-of-freedom modular snake robot designed for planetary exploration. While the robot's biologically inspired serpentine gaits achieve impressive mobility, prior work has relied entirely on open-loop teleoperation. This approach integrates onboard visual-inertial SLAM, reduced-order state estimation, and closed-loop trajectory tracking to enable autonomous waypoint navigation. A depth camera paired with edge computing performs real-time localization during dynamic locomotion, validated against motion-capture ground truth to characterize drift behavior and failure modes unique to snake robot platforms. A reduced-order framework estimates Center-of-Mass pose, driving a closed-loop controller that modulates CPG gait parameters through distance-dependent yaw error blending. Physical experiments validate the complete system, demonstrating accurate multi-waypoint tracking and establishing foundations for autonomous snake robot navigation.
Read or Ignore? A Unified Benchmark for Typographic-Attack Robustness and Text Recognition in Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Large vision-language models (LVLMs) are vulnerable to typographic attacks, where misleading text within an image overrides visual understanding. Existing evaluation protocols and defenses, largely focused on object recognition, implicitly encourage ignoring text to achieve robustness; however, real-world scenarios often require joint reasoning over both objects and text (e.g., recognizing pedestrians while reading traffic signs). To address this, we introduce a novel task, Read-or-Ignore VQA (RIO-VQA), which formalizes selective text use in visual question answering (VQA): models must decide, from context, when to read text and when to ignore it. For evaluation, we present the Read-or-Ignore Benchmark (RIO-Bench), a standardized dataset and protocol that, for each real image, provides same-scene counterfactuals (read / ignore) by varying only the textual content and question type. Using RIO-Bench, we show that strong LVLMs and existing defenses fail to balance typographic robustness and text-reading capability, highlighting the need for improved approaches. Finally, RIO-Bench enables a novel data-driven defense that learns adaptive selective text use, moving beyond prior non-adaptive, text-ignoring defenses. Overall, this work reveals a fundamental misalignment between the existing evaluation scope and real-world requirements, providing a principled path toward reliable LVLMs. Our Project Page is at https://turingmotors.github.io/rio-vqa/.
Aion: Towards Hierarchical 4D Scene Graphs with Temporal Flow Dynamics
Venue: arXiv cs.RO 2025
Year: 2025

Autonomous navigation in dynamic environments requires spatial representations that capture both semantic structure and temporal evolution. 3D Scene Graphs (3DSGs) provide hierarchical multi-resolution abstractions that encode geometry and semantics, but existing extensions toward dynamics largely focus on individual objects or agents. In parallel, Maps of Dynamics (MoDs) model typical motion patterns and temporal regularities, yet are usually tied to grid-based discretizations that lack semantic awareness and do not scale well to large environments. In this paper we introduce Aion, a framework that embeds temporal flow dynamics directly within a hierarchical 3DSG, effectively incorporating the temporal dimension. Aion employs a graph-based sparse MoD representation to capture motion flows over arbitrary time intervals and attaches them to navigational nodes in the scene graph, yielding more interpretable and scalable predictions that improve planning and interaction in complex dynamic environments.
Structured Personalization: Modeling Constraints as Matroids for Data-Minimal LLM Agents
Venue: arXiv cs.AI 2025
Year: 2025

Personalizing Large Language Model (LLM) agents requires conditioning them on user-specific data, creating a critical trade-off between task utility and data disclosure. While the utility of adding user data often exhibits diminishing returns (i.e., submodularity), enabling near-optimal greedy selection, real-world personalization is complicated by structural constraints. These include logical dependencies (e.g., selecting fact A requires fact B), categorical quotas (e.g., select at most one writing style), and hierarchical rules (e.g., select at most two social media preferences, of which at most one can be for a professional network). These constraints violate the assumptions of standard subset selection algorithms. We propose a principled method to formally model such constraints. We introduce a compilation process that transforms a user's knowledge graph with dependencies into a set of abstract macro-facets. Our central result is a proof that common hierarchical and quota-based constraints over these macro-facets form a valid laminar matroid. This theoretical characterization lets us cast structured personalization as submodular maximization under a matroid constraint, enabling greedy with constant-factor guarantees (and (1-1/e) via continuous greedy) for a much richer and more realistic class of problems.
MONET -- Virtual Cell Painting of Brightfield Images and Time Lapses Using Reference Consistent Diffusion
Venue: arXiv cs.CV 2025
Year: 2025

Cell painting is a popular technique for creating human-interpretable, high-contrast images of cell morphology. There are two major issues with cell paint: (1) it is labor-intensive and (2) it requires chemical fixation, making the study of cell dynamics impossible. We train a diffusion model (Morphological Observation Neural Enhancement Tool, or MONET) on a large dataset to predict cell paint channels from brightfield images. We show that model quality improves with scale. The model uses a consistency architecture to generate time-lapse videos, despite the impossibility of obtaining cell paint video training data. In addition, we show that this architecture enables a form of in-context learning, allowing the model to partially transfer to out-of-distribution cell lines and imaging protocols. Virtual cell painting is not intended to replace physical cell painting completely, but to act as a complementary tool enabling novel workflows in biological research.
How AI Agents Follow the Herd of AI? Network Effects, History, and Machine Optimism
Venue: arXiv cs.MA 2025
Year: 2025

Understanding decision-making in multi-AI-agent frameworks is crucial for analyzing strategic interactions in network-effect-driven contexts. This study investigates how AI agents navigate network-effect games, where individual payoffs depend on peer participatio--a context underexplored in multi-agent systems despite its real-world prevalence. We introduce a novel workflow design using large language model (LLM)-based agents in repeated decision-making scenarios, systematically manipulating price trajectories (fixed, ascending, descending, random) and network-effect strength. Our key findings include: First, without historical data, agents fail to infer equilibrium. Second, ordered historical sequences (e.g., escalating prices) enable partial convergence under weak network effects but strong effects trigger persistent "AI optimism"--agents overestimate participation despite contradictory evidence. Third, randomized history disrupts convergence entirely, demonstrating that temporal coherence in data shapes LLMs' reasoning, unlike humans. These results highlight a paradigm shift: in AI-mediated systems, equilibrium outcomes depend not just on incentives, but on how history is curated, which is impossible for human.
MVP-ORAM: a Wait-free Concurrent ORAM for Confidential BFT Storage
Venue: arXiv cs.CR 2025
Year: 2025

It is well known that encryption alone is not enough to protect data privacy. Access patterns, revealed when operations are performed, can also be leveraged in inference attacks. Oblivious RAM (ORAM) hides access patterns by making client requests oblivious. However, existing protocols are still limited in supporting concurrent clients and Byzantine fault tolerance (BFT). We present MVP-ORAM, the first wait-free ORAM protocol that supports concurrent fail-prone clients. In contrast to previous works, MVP-ORAM avoids using trusted proxies, which require additional security assumptions, and concurrency control mechanisms based on inter-client communication or distributed locks, which limit overall throughput and the capability of tolerating faulty clients. Instead, MVP-ORAM enables clients to perform concurrent requests and merge conflicting updates as they happen, satisfying wait-freedom, i.e., clients make progress independently of the performance or failures of other clients. Since wait and collision freedom are fundamentally contradictory goals that cannot be achieved simultaneously in an asynchronous concurrent ORAM service, we define a weaker notion of obliviousness that depends on the application workload and number of concurrent clients, and prove MVP-ORAM is secure in practical scenarios where clients perform skewed block accesses. By being wait-free, MVP-ORAM can be seamlessly integrated into existing confidential BFT data stores, creating the first BFT ORAM construction. We implement MVP-ORAM on top of a confidential BFT data store and show our prototype can process hundreds of 4KB accesses per second in modern clouds.
A Reference Architecture for Embedding Quantum Software Into Enterprise Systems
Venue: arXiv cs.SE 2025
Year: 2025

Quantum computing promises a remarkable performance boost for certain applications, including computational intensive problems addressed by enterprise systems. However, software architectures of enterprise systems must consider specific characteristics and quality attributes when collaborating with quantum computing services. Hence, this paper presents a modular reference architecture for embedding quantum software into enterprise systems. Its building blocks consist of loosely coupled and distributed services that implement both quantum-independent and quantum-specific tasks. Although these services either depend on the business domain or the selected quantum algorithm, their orchestration forms a stable and reusable pipeline, specified as an executable BPMN model. For demonstration and evaluation purposes, the proposed reference architecture is utilized in two case studies addressing combinatorial challenges from the field of operations research.
Defunding Sexual Healthcare: A Topological Investigation of Resource Accessibility
Venue: arXiv cs.SI 2025
Year: 2025

Government actions, such as the Medina v. Planned Parenthood South Atlantic Supreme Court ruling and the passage of the Big Beautiful Bill Act, have aimed to restrict or prohibit Medicaid funding for Planned Parenthood Healthcare Centers (PPHCs) at both the state and national levels. These funding cuts are particularly harmful in states like California, which has a large population of Medicaid users. This analysis focuses on the distribution of Planned Parenthood clinics and Federally Qualified Health Centers (FQHCs), which offer essential reproductive healthcare services including, but not limited to, abortions, birth control, HIV services, pregnancy testing and planning, STD testing and treatment, and cancer screenings. While expanded funding for FQHCs has been proposed as a solution, it fails to address the locational accessibility of Medicaid-funded health centers that provide sexual and reproductive care. To assess this issue, we analyze the proximity of data points representing California's PPHC and FQHC locations. Topological Data Analysis (TDA)-an approach that examines the shape and structure of data -- is used to detect disparities in reproductive and sexual healthcare coverage. To conduct data collection and visualization, we utilize R and Python. We apply an n-closest neighbor algorithm to examine distances between facilities and assess changes in travel time required to reach healthcare sites. We apply persistent homology to analyze current gaps across multiple scales in healthcare coverage and compare them to potential future gaps. Our findings aim to identify areas where access to care is most vulnerable and demonstrate how TDA can be used to analyze spatial inequalities in public health.
Verification of Lightning Network Channel Balances with Trusted Execution Environments (TEE)
Venue: arXiv cs.CR 2025
Year: 2025

Verifying the private liquidity state of Lightning Network (LN) channels is desirable for auditors, service providers, and network participants who need assurance of financial capacity. Current methods often lack robustness against a malicious or compromised node operator. This paper introduces a methodology for the verification of LN channel balances. The core contribution is a framework that combines Trusted Execution Environments (TEEs) with Zero-Knowledge Transport Layer Security (zkTLS) to provide strong, hardware-backed guarantees. In our proposed method, the node's balance-reporting software runs within a TEE, which generates a remote attestation quote proving the software's integrity. This attestation is then served via an Application Programming Interface (API), and zkTLS is used to prove the authenticity of its delivery. We also analyze an alternative variant where the TEE signs the report directly without zkTLS, discussing the trade-offs between transport-layer verification and direct enclave signing. We further refine this by distinguishing between "Hot Proofs" (verifiable claims via TEEs) and "Cold Proofs" (on-chain settlement), and discuss critical security considerations including hardware vulnerabilities, privacy leakage to third-party APIs, and the performance overhead of enclaved operations.
Beyond right or wrong: towards redefining adaptive learning indicators in virtual learning environments
Venue: arXiv cs.CY 2025
Year: 2025

Student learning development must involve more than just correcting or incorrect questions. However, most adaptive learning methods in Virtual Learning Environments are based on whether the student's response is incorrect or correct. This perspective is limited in assessing the student's learning level, as it does not consider other elements that can be crucial in this process. The objective of this work is to conduct a Systematic Literature Review (SLR) to elucidate which learning indicators influence student learning and which can be implemented in a VLE to assist in adaptive learning. The works selected and filtered by qualitative assessment reveal a comprehensive approach to assessing different aspects of the learning in virtual environments, such as motivation, emotions, physiological responses, brain imaging, and the students' prior knowledge. The discussion of these new indicators allows adaptive technology developers to implement more appropriate solutions to students' realities, resulting in more complete training.
DreamRAM: A Fine-Grained Configurable Design Space Modeling Tool for Custom 3D Die-Stacked DRAM
Venue: arXiv cs.AR 2025
Year: 2025

3D die-stacked DRAM has emerged as a key technology for delivering high bandwidth and high density for applications such as high-performance computing, graphics, and machine learning. However, different applications place diverse and sometimes diverging demands on power, performance, and area that cannot be universally satisfied with fixed commodity DRAM designs. Die stacking creates the opportunity for a large DRAM design space through 3D integration and expanded total die area. To open and navigate this expansive design space of customized memory architectures that cater to application-specific needs, we introduce DreamRAM, a configurable bandwidth, capacity, energy, latency, and area modeling tool for custom 3D die-stacked DRAM designs. DreamRAM exposes fine-grained design customization parameters at the MAT, subarray, bank, and inter-bank levels, including extensions of partial page and subarray parallelism proposals found in the literature, to open a large previously-unexplored design space. DreamRAM analytically models wire pitch, width, length, capacitance, and scaling parameters to capture the performance tradeoffs of physical layout and routing design choices. Routing awareness enables DreamRAM to model a custom MAT-level routing scheme, Dataline-Over-MAT (DLOMAT), to facilitate better bandwidth tradeoffs. DreamRAM is calibrated and validated against published industry HBM3 and HBM2E designs. Within DreamRAM's rich design space, we identify designs that achieve each of 66% higher bandwidth, 100% higher capacity, and 45% lower power and energy per bit compared to the baseline design, each on an iso-bandwidth, iso-capacity, and iso-power basis.
Neural CDEs as Correctors for Learned Time Series Models
Venue: arXiv cs.LG 2025
Year: 2025

Learned time-series models, whether continuous- or discrete-time, are widely used to forecast the states of a dynamical system. Such models generate multi-step forecasts either directly, by predicting the full horizon at once, or iteratively, by feeding back their own predictions at each step. In both cases, the multi-step forecasts are prone to errors. To address this, we propose a Predictor-Corrector mechanism where the Predictor is any learned time-series model and the Corrector is a neural controlled differential equation. The Predictor forecasts, and the Corrector predicts the errors of the forecasts. Adding these errors to the forecasts improves forecast performance. The proposed Corrector works with irregularly sampled time series and continuous- and discrete-time Predictors. Additionally, we introduce two regularization strategies to improve the extrapolation performance of the Corrector with accelerated training. We evaluate our Corrector with diverse Predictors, e.g., neural ordinary differential equations, Contiformer, and DLinear, on synthetic, physics simulation, and real-world forecasting datasets. The experiments demonstrate that the Predictor-Corrector mechanism consistently improves the performance compared to Predictor alone.
Citation-Grounded Code Comprehension: Preventing LLM Hallucination Through Hybrid Retrieval and Graph-Augmented Context
Venue: arXiv cs.SE 2025
Year: 2025

Large language models have become essential tools for code comprehension, enabling developers to query unfamiliar codebases through natural language interfaces. However, LLM hallucination, generating plausible but factually incorrect citations to source code, remains a critical barrier to reliable developer assistance. This paper addresses the challenges of achieving verifiable, citation grounded code comprehension through hybrid retrieval and lightweight structural reasoning. Our work is grounded in systematic evaluation across 30 Python repositories with 180 developer queries, comparing retrieval modalities, graph expansion strategies, and citation verification mechanisms. We find that challenges of citation accuracy arise from the interplay between sparse lexical matching, dense semantic similarity, and cross file architectural dependencies. Among these, cross file evidence discovery is the largest contributor to citation completeness, but it is largely overlooked because existing systems rely on pure textual similarity without leveraging code structure. We advocate for citation grounded generation as an architectural principle for code comprehension systems and demonstrate this need by achieving 92 percent citation accuracy with zero hallucinations. Specifically, we develop a hybrid retrieval system combining BM25 sparse matching, BGE dense embeddings, and Neo4j graph expansion via import relationships, which outperforms single mode baselines by 14 to 18 percentage points while discovering cross file evidence missed by pure text similarity in 62 percent of architectural queries.
High-Dimensional Tensor Discriminant Analysis: Low-Rank Discriminant Structure, Representation Synergy, and Theoretical Guarantees
Venue: arXiv cs.LG 2025
Year: 2025

High-dimensional tensor-valued predictors arise in modern applications, increasingly as learned representations from neural networks. Existing tensor classification methods rely on sparsity or Tucker structures and often lack theoretical guarantees. Motivated by empirical evidence that discriminative signals concentrate along a few multilinear components, we introduce CP low-rank structure for the discriminant tensor, a modeling perspective not previously explored. Under a Tensor Gaussian Mixture Model, we propose high-dimensional CP low-rank Tensor Discriminant Analysis (CP-TDA) with Randomized Composite PCA (\textsc{rc-PCA}) initialization, that is essential for handling dependent and anisotropic noise under weaker signal strength and incoherence conditions, followed by iterative refinement algorithm. We establish global convergence and minimax-optimal misclassification rates. To handle tensor data deviating from tensor normality, we develop the first semiparametric tensor discriminant model, in which learned tensor representations are mapped via deep generative models into a latent space tailored for CP-TDA. Misclassification risk decomposes into representation, approximation, and estimation errors. Numerical studies and real data analysis on graph classification demonstrate substantial gains over existing tensor classifiers and state-of-the-art graph neural networks, particularly in high-dimensional, small-sample regimes.
Layered Monoidal Theories
Venue: arXiv cs.LO 2025
Year: 2025

In the first part, we develop layered monoidal theories - a generalisation of monoidal theories combining descriptions of a system at several levels. Via their representation as string diagrams, monoidal theories provide a graphical syntax with a visually intuitive notions of information flow and composition. Layered monoidal theories allow mixing several monoidal theories (together with translations between them) within the same string diagram, while retaining mathematical precision and semantic interpretability. We define three flavours of layered monoidal theories, provide a recursively generated syntax for each, and construct a free-forgetful adjunction with respect to three closely related semantics: opfibrations, fibrations and deflations. We motivate the general theory by providing several examples from existing literature. In the second part, we develop a formal approach to retrosynthesis - the process of backwards reaction search in synthetic chemistry. Chemical processes are treated at three levels of abstraction: (1) (formal) reactions encode all chemically feasible combinatorial rearrangements of molecules, (2) reaction schemes encode transformations applicable to 'patches' of molecules (including the functional groups), and (3) disconnection rules encode local chemical rewrite rules applicable to a single bond or atom at a time. We show that the three levels are tightly linked: the reactions are generated by the reaction schemes, while there is a functorial translation from the disconnection rules to the reactions. Moreover, the translation from the disconnection rules to the reactions is shown to be sound, complete and universal - allowing one to treat the disconnection rules as a formal syntax with the semantics provided by the reactions. We tie together the two parts by providing a formalisation of retrosynthesis within a certain layered monoidal theory.