OpenRaiser/Intern-Atlas huggingface dataset:
InnerThoughts: Disentangling Representations and Predictions in Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) contain substantial factual knowledge which is commonly elicited by multiple-choice question-answering prompts. Internally, such models process the prompt through multiple transformer layers, building varying representations of the problem within its hidden states. Ultimately, however, only the hidden state corresponding to the final layer and token position are used to predict the answer label. In this work, we propose instead to learn a small separate neural network predictor module on a collection of training questions, that take the hidden states from all the layers at the last temporal position as input and outputs predictions. In effect, such a framework disentangles the representational abilities of LLMs from their predictive abilities. On a collection of hard benchmarks, our method achieves considerable improvements in performance, sometimes comparable to supervised fine-tuning procedures, but at a fraction of the computational cost.
A Proximal Operator for Inducing 2:4-Sparsity
Venue: arXiv cs.LG 2025
Year: 2025

Recent hardware advancements in AI Accelerators and GPUs allow to efficiently compute sparse matrix multiplications, especially when 2 out of 4 consecutive weights are set to zero. However, this so-called 2:4 sparsity usually comes at a decreased accuracy of the model. We derive a regularizer that exploits the local correlation of features to find better sparsity masks in trained models. We minimize the regularizer jointly with a local squared loss by deriving the proximal operator for which we show that it has an efficient solution in the 2:4-sparse case. After optimizing the mask, we use maskedgradient updates to further minimize the local squared loss. We illustrate our method on toy problems and apply it to pruning entire large language models up to 70B parameters. On models up to 13B we improve over previous state of the art algorithms, whilst on 70B models we match their performance.
Digital Twin Synchronization: Bridging the Sim-RL Agent to a Real-Time Robotic Additive Manufacturing Control
Venue: arXiv cs.RO 2025
Year: 2025

With the rapid development of deep reinforcement learning technology, it gradually demonstrates excellent potential and is becoming the most promising solution in the robotics. However, in the smart manufacturing domain, there is still not too much research involved in dynamic adaptive control mechanisms optimizing complex processes. This research advances the integration of Soft Actor-Critic (SAC) with digital twins for industrial robotics applications, providing a framework for enhanced adaptive real-time control for smart additive manufacturing processing. The system architecture combines Unity's simulation environment with ROS2 for seamless digital twin synchronization, while leveraging transfer learning to efficiently adapt trained models across tasks. We demonstrate our methodology using a Viper X300s robot arm with the proposed hierarchical reward structure to address the common reinforcement learning challenges in two distinct control scenarios. The results show rapid policy convergence and robust task execution in both simulated and physical environments demonstrating the effectiveness of our approach.
A Case Study in Acceleration AI Ethics: The TELUS GenAI Conversational Agent
Venue: arXiv cs.CY 2025
Year: 2025

Acceleration ethics addresses the tension between innovation and safety in artificial intelligence. The acceleration argument is that risks raised by innovation should be answered with still more innovating. This paper summarizes the theoretical position, and then shows how acceleration ethics works in a real case. To begin, the paper summarizes acceleration ethics as composed of five elements: innovation solves innovation problems, innovation is intrinsically valuable, the unknown is encouraging, governance is decentralized, ethics is embedded. Subsequently, the paper illustrates the acceleration framework with a use-case, a generative artificial intelligence language tool developed by the Canadian telecommunications company Telus. While the purity of theoretical positions is blurred by real-world ambiguities, the Telus experience indicates that acceleration AI ethics is a way of maximizing social responsibility through innovation, as opposed to sacrificing social responsibility for innovation, or sacrificing innovation for social responsibility.
Gray-Box Fuzzing in Local Space
Venue: arXiv cs.PL 2025
Year: 2025

We consider gray-box fuzzing of a program instrumented such that information about evaluation of program expressions converting values of numerical types to Boolean, like x <= y, is recorded during each program's execution. Given that information for an executed program path, we formally define the problem for finding input such that program's execution with that input evaluates all those expressions in the same order and with the same Boolean values as in the original execution path, except for the last one, which is evaluated to the opposite value. Then we also provide an algorithm searching for a solution of the problem effectively. The effectiveness of the algorithm is demonstrated empirically via its evaluation on the TestComp 2024 benchmark suite.
Statistical Design of Thermal Protection System Using Physics-Informed Neural Network
Venue: arXiv cs.CE 2025
Year: 2025

Thermal protection systems (TPS) of space vehicles are designed computationally rather than experimentally. They are validated using ground experiments, but all aspects of the flight cannot be replicated on ground. This ground-to-flight mapping introduces uncertainties which need to be accounted for while designing any thermal protection system. Thus, precise computational models along with uncertainty quantification in the models are required to design the TPS. The focus of this study is to estimate the thermal material parameters of TPS based on the target reliability requirements using statistical methods. To perform uncertainty quantification (UQ) of a system, a simulated model of the system needs to be solved many times on statistical samples, increasing the computational time and cost of the overall process. A physics-informed neural network (PINN) model is used in the analysis instead of traditional physics based numerical solutions. The accuracy of PINN is comparable to that of the numerical solution. To find the parameter distribution, sampling of the parameter space is performed using Sequential Monte- Carlo (SMC) method. The sampling method is efficient as it generates samples based on the target distribution in parallel and it also generates diverse samples for proper UQ. Combining the use of both PINN predictive model and SMC sampling, the framework can approximate the parameter distributions that satisfy the TPS design reliability constraints. The framework achieved remarkable increases in the speed of performing the reliability analysis of the TPS. This reliability analysis can be used for design optimization of the TPS based on risk analysis along with other systems of the vehicle.
Coverage Semantics for Dependent Pattern Matching
Venue: arXiv cs.PL 2025
Year: 2025

Dependent pattern matching is a key feature in dependently typed programming. However, there is a theory-practice disconnect: while many proof assistants implement pattern matching as primitive, theoretical presentations give semantics to pattern matching by elaborating to eliminators. Though theoretically convenient, eliminators can be awkward and verbose, particularly for complex combinations of patterns. This work aims to bridge the theory-practice gap by presenting a direct categorical semantics for pattern matching, which does not elaborate to eliminators. This is achieved using sheaf theory to describe when sets of arrows (terms) can be amalgamated into a single arrow. We present a language with top-level dependent pattern matching, without specifying which sets of patterns are considered covering for a match. Then, we give a sufficient criterion for which pattern-sets admit a sound model: patterns should be in the canonical coverage for the category of contexts. Finally, we use sheaf-theoretic saturation conditions to devise some allowable sets of patterns. We are able to express and exceed the status quo, giving semantics for datatype constructors, nested patterns, absurd patterns, propositional equality, and dot patterns.
Beyond Turn-taking: Introducing Text-based Overlap into Human-LLM Interactions
Venue: arXiv cs.HC 2025
Year: 2025

Traditional text-based human-AI interactions often adhere to a strict turn-taking approach. In this research, we propose a novel approach that incorporates overlapping messages, mirroring natural human conversations. Through a formative study, we observed that even in text-based contexts, users instinctively engage in overlapping behaviors like "A: Today I went to-" "B: yeah." To capitalize on these insights, we developed OverlapBot, a prototype chatbot where both AI and users can initiate overlapping. Our user study revealed that OverlapBot was perceived as more communicative and immersive than traditional turn-taking chatbot, fostering faster and more natural interactions. Our findings contribute to the understanding of design space for overlapping interactions. We also provide recommendations for implementing overlap-capable AI interactions to enhance the fluidity and engagement of text-based conversations.
Dual-Bounded Nonlinear Optimal Transport for Size Constrained Min Cut Clustering
Venue: arXiv cs.LG 2025
Year: 2025

Min cut is an important graph partitioning method. However, current solutions to the min cut problem suffer from slow speeds, difficulty in solving, and often converge to simple solutions. To address these issues, we relax the min cut problem into a dual-bounded constraint and, for the first time, treat the min cut problem as a dual-bounded nonlinear optimal transport problem. Additionally, we develop a method for solving dual-bounded nonlinear optimal transport based on the Frank-Wolfe method (abbreviated as DNF). Notably, DNF not only solves the size constrained min cut problem but is also applicable to all dual-bounded nonlinear optimal transport problems. We prove that for convex problems satisfying Lipschitz smoothness, the DNF method can achieve a convergence rate of \(\mathcal{O}(\frac{1}{t})\). We apply the DNF method to the min cut problem and find that it achieves state-of-the-art performance in terms of both the loss function and clustering accuracy at the fastest speed, with a convergence rate of \(\mathcal{O}(\frac{1}{\sqrt{t}})\). Moreover, the DNF method for the size constrained min cut problem requires no parameters and exhibits better stability.
StructuredField: Unifying Structured Geometry and Radiance Field
Venue: arXiv cs.GR 2025
Year: 2025

Recent point-based differentiable rendering techniques have achieved significant success in high-fidelity reconstruction and fast rendering. However, due to the unstructured nature of point-based representations, they are difficult to apply to modern graphics pipelines designed for structured meshes, as well as to a variety of simulation and editing algorithms that work well with structured mesh representations. To this end, we propose StructuredField, a novel representation that achieves both a structured geometric representation of the reconstructed object and high-fidelity rendering reconstruction. We employ structured tetrahedral meshes to represent the reconstructed object. We reparameterize the geometric attributes of these tetrahedra into the parameters of 3D Gaussian primitives, thereby enabling differentiable, high-fidelity rendering directly from the mesh. Furthermore, a hierarchical implicit subdivision strategy is utilized to ensure a conformal mesh structure while empowering the representation to capture multi-scale details. To maintain geometric integrity during optimization, we propose a novel inversion-free homeomorphism that constrains the tetrahedral mesh, guaranteeing it remains both inversion-free and self-intersection-free during the optimization process and in the final result. Based on our proposed StructuredField, we achieve high-quality structured meshes that are completely inversion-free and conformal, while also attaining reconstruction results comparable to those of 3DGS. We also demonstrate the applicability of our representation to various applications such as physical simulation, deformation, and level-of-detail.
Chip-to-chip photonic connectivity in multi-accelerator servers for ML
Venue: arXiv cs.NI 2025
Year: 2025

We present a rack-scale compute architecture for ML using multi-accelerator servers connected via chip-to-chip silicon photonic components. Our architecture achieves (1) multi-tenanted resource slicing without fragmentation, (2) 74% faster rack-scale collective communication, and (3) 1.7X speedup in end-to-end ML training throughput.
Fundamental Challenges in Evaluating Text2SQL Solutions and Detecting Their Limitations
Venue: arXiv cs.LG 2025
Year: 2025

In this work, we dive into the fundamental challenges of evaluating Text2SQL solutions and highlight potential failure causes and the potential risks of relying on aggregate metrics in existing benchmarks. We identify two largely unaddressed limitations in current open benchmarks: (1) data quality issues in the evaluation data, mainly attributed to the lack of capturing the probabilistic nature of translating a natural language description into a structured query (e.g., NL ambiguity), and (2) the bias introduced by using different match functions as approximations for SQL equivalence. To put both limitations into context, we propose a unified taxonomy of all Text2SQL limitations that can lead to both prediction and evaluation errors. We then motivate the taxonomy by providing a survey of Text2SQL limitations using state-of-the-art Text2SQL solutions and benchmarks. We describe the causes of limitations with real-world examples and propose potential mitigation solutions for each category in the taxonomy. We conclude by highlighting the open challenges encountered when deploying such mitigation strategies or attempting to automatically apply the taxonomy.
GPD: Guided Polynomial Diffusion for Motion Planning
Venue: arXiv cs.RO 2025
Year: 2025

Diffusion-based motion planners are becoming popular due to their well-established performance improvements, stemming from sample diversity and the ease of incorporating new constraints directly during inference. However, a primary limitation of the diffusion process is the requirement for a substantial number of denoising steps, especially when the denoising process is coupled with gradient-based guidance. In this paper, we introduce, diffusion in the parametric space of trajectories, where the parameters are represented as Bernstein coefficients. We show that this representation greatly improves the effectiveness of the cost function guidance and the inference speed. We also introduce a novel stitching algorithm that leverages the diversity in diffusion-generated trajectories to produce collision-free trajectories with just a single cost function-guided model. We demonstrate that our approaches outperform current SOTA diffusion-based motion planners for manipulators and provide an ablation study on key components.
Algebraic Proof Theory for Infinitary Action Logic
Venue: arXiv cs.LO 2025
Year: 2025

We exhibit a uniform method for obtaining (wellfounded and non-wellfounded) cut-free sequent-style proof systems that are sound and complete for various classes of action algebras, i.e., Kleene algebras enriched with meets and residuals. Our method applies to any class of *-continuous action algebras that is defined, relative to the class of all *-continuous action algebras, by analytic quasiequations. The latter make up an expansive class of conditions encompassing the algebraic analogues of most well-known structural rules. These results are achieved by wedding existing work on non-wellfounded proof theory for action algebras with tools from algebraic proof theory.
RIS-assisted Physical Layer Security
Venue: arXiv cs.IT 2025
Year: 2025

We propose a reconfigurable intelligent surface (RIS)-assisted wiretap channel, where the RIS is strategically deployed to provide a spatial separation to the transmitter, and orthogonal combiners are employed at the legitimate receiver to extract the data streams from the direct and RIS-assisted links. Then we derive the achievable secrecy rate under semantic security for the RIS-assisted channel and design an algorithm for the secrecy rate optimization problem. The simulation results show the effects of total transmit power, the location and number of eavesdroppers on the security performance.
Online Neural Model Fine-Tuning in Massive MIMO CSI Feedback: Taming The Communication Cost of Model Updates
Venue: arXiv cs.IT 2025
Year: 2025

Efficient channel state information (CSI) compression is essential in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems due to the significant feedback overhead. Recently, deep learning-based compression techniques have demonstrated superior performance across various data types, including CSI. However, these methods often suffer from performance degradation when the data distribution shifts, primarily due to limited generalization capabilities. To address this challenge, we propose an online model fine-tuning approach for CSI feedback in massive MIMO systems. We consider full-model fine-tuning, where both the encoder and decoder are jointly updated using recent CSI samples. A key challenge in this setup is the transmission of updated decoder parameters, which introduces additional feedback overhead. To mitigate this bottleneck, we incorporate the bit-rate of model updates into the fine-tuning objective and entropy code the updates jointly with the compressed CSI. To reduce the bit-rate, we design an efficient prior distribution that encourages the network to update only the most significant weights, thereby minimizing the overall model update cost. Our results show that full-model fine-tuning significantly enhances the rate-distortion (RD) performance of neural CSI compression despite the additional communication cost of model updates. Moreover, we investigate the impact of update frequency in dynamic wireless environments and identify an optimal fine-tuning interval that achieves the best RD trade-off.
DATCloud: A Model-Driven Framework for Multi-Layered Data-Intensive Architectures
Venue: arXiv cs.SE 2025
Year: 2025

The complexity of multi-layered, data-intensive systems demands frameworks that ensure flexibility, scalability, and efficiency. DATCloud is a model-driven framework designed to facilitate the modeling, validation, and refinement of multi-layered architectures, addressing scalability, modularity, and real-world requirements. By adhering to ISO/IEC/IEEE 42010 standards, DATCloud leverages structural and behavioral meta-models and graphical domain-specific languages (DSLs) to enhance reusability and stakeholder communication. Initial validation through the VASARI system at the Uffizi Gallery demonstrates a 40% reduction in modeling time and a 32% improvement in flexibility compared to manual methods. While effective, DATCloud is a work in progress, with plans to integrate advanced code generation, simulation tools, and domain-specific extensions to further enhance its capabilities for applications in healthcare, smart cities, and other data-intensive domains.
Zero Estimation Cost Strategy for Witsenhausen Counterexample with Causal Encoder
Venue: arXiv cs.IT 2025
Year: 2025

We propose a zero estimation cost (ZEC) scheme for causal-encoding noncausal-decoding vector-valued Witsenhausen counterexample based on the coordination coding result. In contrast to source coding, our goal is to communicate a controlled system state. The introduced ZEC scheme is a joint control-communication approach that transforms the system state into a sequence that can be efficiently communicated using block coding. Numerical results show that our approach significantly reduces the power required for achieving zero-estimation-cost state reconstruction at the decoder. In the second part, we introduce a more general non-zero estimation cost (Non-ZEC) scheme. We observe numerically that the Non-ZEC scheme operates as a time-sharing mechanism between the two-point strategy and the ZEC scheme. Overall, by leveraging block-coding gain, our proposed methods substantially improve the power-estimation trade-off for Witsenhausen counterexample.
Surface Defect Identification using Bayesian Filtering on a 3D Mesh
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents a CAD-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAD model and integrate it with point cloud data acquired from commercially available stereo and depth cameras. The proposed method first transforms the CAD model into a high-density polygonal mesh, where each vertex represents a state variable in 3D space. Subsequently, a weighted least squares algorithm is employed to iteratively estimate the state of the scanned workpiece based on the captured point cloud measurements. This framework offers the potential to incorporate information from diverse sensors into the CAD domain, facilitating a more comprehensive analysis. Preliminary results demonstrate promising performance, with the algorithm achieving convergence to a sub-millimeter standard deviation in the region of interest using only approximately 50 point cloud samples. This highlights the potential of utilising commercially available stereo cameras for high-precision quality control applications.
Transductions of Graph Classes Admitting Product Structure
Venue: arXiv cs.LO 2025
Year: 2025

In a quest to thoroughly understand the first-order transduction hierarchy of hereditary graph classes, some questions in particular stand out; such as, what properties hold for graph classes that are first-order transductions of planar graphs (and of similar classes)? When addressing this (so-far wide open) question, we turn to the concept of a product structure - being a subgraph of the strong product of a path and a graph of bounded tree-width, introduced by Dujmovic et al. [JACM 2020]. Namely, we prove that any graph class which is a first-order transduction of a class admitting such product structure, up to perturbations also meets a structural description generalizing the concept of a product structure in a dense hereditary way - the latter concept being introduced just recently by Hlineny and Jedelsky under the name of H-clique-width [MFCS 2024]. Using this characterization, we show that the class of the 3D grids, as well as a class of certain modifications of 2D grids, are not first-order transducible from classes admitting a product structure, and in particular not from the class of planar graphs.