OpenRaiser/Intern-Atlas huggingface dataset:
Asymptotically Optimal Inapproximability of E$k$-SAT Reconfiguration
Venue: arXiv cs.CC 2025
Year: 2025

In the Maxmin E$k$-SAT Reconfiguration problem, we are given a satisfiable $k$-CNF formula $φ$ where each clause contains exactly $k$ literals, along with a pair of its satisfying assignments. The objective is transform one satisfying assignment into the other by repeatedly flipping the value of a single variable, while maximizing the minimum fraction of satisfied clauses of $φ$ throughout the transformation. In this paper, we demonstrate that the optimal approximation factor for Maxmin E$k$-SAT Reconfiguration is $1 - Θ\left(\frac{1}{k}\right)$. On the algorithmic side, we develop a deterministic $\left(1-\frac{1}{k-1}-\frac{1}{k}\right)$-factor approximation algorithm for every $k \geq 3$. On the hardness side, we show that it is $\mathsf{PSPACE}$-hard to approximate this problem within a factor of $1-\frac{1}{10k}$ for every sufficiently large $k$. Note that an ``$\mathsf{NP}$ analogue'' of Maxmin E$k$-SAT Reconfiguration is Max E$k$-SAT, whose approximation threshold is $1-\frac{1}{2^k}$ shown by Håstad (JACM 2001). To the best of our knowledge, this is the first reconfiguration problem whose approximation threshold is (asymptotically) worse than that of its $\mathsf{NP}$ analogue. To prove the hardness result, we introduce a new ``non-monotone'' test, which is specially tailored to reconfiguration problems, despite not being helpful in the PCP regime.
Reimagining Voltage-Controlled Cryogenic Boolean Logic Paradigm with Quantum-Enhanced Josephson Junction FETs
Venue: arXiv cs.ET 2025
Year: 2025

The growing demand for ultra low power computing and the emergence of quantum technologies have intensified interest in cryogenic electronics, particularly superconducting devices. Despite their promise, current controlled superconducting components face fundamental challenges in cascadability, limiting their effectiveness in complex logic architectures. To overcome this, recent efforts have focused on developing gate tunable superconducting devices, such as Josephson junction field effect transistors (JJFETs). However, achieving robust control and sufficient supercurrent gain, both critical for transistor-like performance in logic circuits remains a key challenge. A recent advancement in JJFET design, based on InAs and GaSb heterostructures, demonstrates enhanced gain and favorable device characteristics suitable for circuit integration. Building on this innovation, we propose and analyze fundamental voltage controlled logic topologies using the quantum enhanced JJFET. We develop a Verilog A based circuit compatible compact model of the quantum enhanced JJFET which accurately captures the experimentally observed device characteristics. To ensure cascadability, our logic circuits incorporate the multilayered Heater Nanocryotron (nTron), a superconducting nanowire-based thermal switch. Through simulation based analysis, we demonstrate the successful implementation of fundamental logic gates, including NOT, NAND, and NOR. Furthermore, we design a 3 input majority gate, which plays a pivotal role in quantum and reversible computing due to its universality. Finally, to demonstrate the cascadability of our proposed logic topology, we demonstrate the operation of a 2 input XOR gate based on our designed JJFET based NOT, NAND, and NOR gate.
AniMer+: Unified Pose and Shape Estimation Across Mammalia and Aves via Family-Aware Transformer
Venue: arXiv cs.CV 2025
Year: 2025

In the era of foundation models, achieving a unified understanding of different dynamic objects through a single network has the potential to empower stronger spatial intelligence. Moreover, accurate estimation of animal pose and shape across diverse species is essential for quantitative analysis in biological research. However, this topic remains underexplored due to the limited network capacity of previous methods and the scarcity of comprehensive multi-species datasets. To address these limitations, we introduce AniMer+, an extended version of our scalable AniMer framework. In this paper, we focus on a unified approach for reconstructing mammals (mammalia) and birds (aves). A key innovation of AniMer+ is its high-capacity, family-aware Vision Transformer (ViT) incorporating a Mixture-of-Experts (MoE) design. Its architecture partitions network layers into taxa-specific components (for mammalia and aves) and taxa-shared components, enabling efficient learning of both distinct and common anatomical features within a single model. To overcome the critical shortage of 3D training data, especially for birds, we introduce a diffusion-based conditional image generation pipeline. This pipeline produces two large-scale synthetic datasets: CtrlAni3D for quadrupeds and CtrlAVES3D for birds. To note, CtrlAVES3D is the first large-scale, 3D-annotated dataset for birds, which is crucial for resolving single-view depth ambiguities. Trained on an aggregated collection of 41.3k mammalian and 12.4k avian images (combining real and synthetic data), our method demonstrates superior performance over existing approaches across a wide range of benchmarks, including the challenging out-of-domain Animal Kingdom dataset. Ablation studies confirm the effectiveness of both our novel network architecture and the generated synthetic datasets in enhancing real-world application performance.
Controllable Pedestrian Video Editing for Multi-View Driving Scenarios via Motion Sequence
Venue: arXiv cs.CV 2025
Year: 2025

Pedestrian detection models in autonomous driving systems often lack robustness due to insufficient representation of dangerous pedestrian scenarios in training datasets. To address this limitation, we present a novel framework for controllable pedestrian video editing in multi-view driving scenarios by integrating video inpainting and human motion control techniques. Our approach begins by identifying pedestrian regions of interest across multiple camera views, expanding detection bounding boxes with a fixed ratio, and resizing and stitching these regions into a unified canvas while preserving cross-view spatial relationships. A binary mask is then applied to designate the editable area, within which pedestrian editing is guided by pose sequence control conditions. This enables flexible editing functionalities, including pedestrian insertion, replacement, and removal. Extensive experiments demonstrate that our framework achieves high-quality pedestrian editing with strong visual realism, spatiotemporal coherence, and cross-view consistency. These results establish the proposed method as a robust and versatile solution for multi-view pedestrian video generation, with broad potential for applications in data augmentation and scenario simulation in autonomous driving.
TopoDiffuser: A Diffusion-Based Multimodal Trajectory Prediction Model with Topometric Maps
Venue: arXiv cs.RO 2025
Year: 2025

This paper introduces TopoDiffuser, a diffusion-based framework for multimodal trajectory prediction that incorporates topometric maps to generate accurate, diverse, and road-compliant future motion forecasts. By embedding structural cues from topometric maps into the denoising process of a conditional diffusion model, the proposed approach enables trajectory generation that naturally adheres to road geometry without relying on explicit constraints. A multimodal conditioning encoder fuses LiDAR observations, historical motion, and route information into a unified bird's-eye-view (BEV) representation. Extensive experiments on the KITTI benchmark demonstrate that TopoDiffuser outperforms state-of-the-art methods, while maintaining strong geometric consistency. Ablation studies further validate the contribution of each input modality, as well as the impact of denoising steps and the number of trajectory samples. To support future research, we publicly release our code at https://github.com/EI-Nav/TopoDiffuser.
Systematic Evaluation of Optimization Techniques for Long-Context Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) excel across diverse natural language processing tasks but face resource demands and limited context windows. Although techniques like pruning, quantization, and token dropping can mitigate these issues, their efficacy in long-context scenarios and system evaluation remains underexplored. This paper systematically benchmarks these optimizations, characterizing memory usage, latency, and throughput, and studies how these methods impact the quality of text generation. We first analyze individual optimization methods for two LLM architectures supporting long context and then systematically evaluate combinations of these techniques to assess how this deeper analysis impacts performance metrics. We subsequently study the scalability of individual optimization methods on a larger variant with 70 billion-parameter model. Our novel insights reveal that naive combination inference optimization algorithms can adversely affect larger models due to compounded approximation errors, as compared to their smaller counterparts. Experiments show that relying solely on F1 obscures these effects by hiding precision-recall trade-offs in question answering tasks. By integrating system-level profiling with task-specific insights, this study helps LLM practitioners and researchers explore and balance efficiency, accuracy, and scalability across tasks and hardware configurations.
Embryology of a Language Model
Venue: arXiv cs.LG 2025
Year: 2025

Understanding how language models develop their internal computational structure is a central problem in the science of deep learning. While susceptibilities, drawn from statistical physics, offer a promising analytical tool, their full potential for visualizing network organization remains untapped. In this work, we introduce an embryological approach, applying UMAP to the susceptibility matrix to visualize the model's structural development over training. Our visualizations reveal the emergence of a clear ``body plan,'' charting the formation of known features like the induction circuit and discovering previously unknown structures, such as a ``spacing fin'' dedicated to counting space tokens. This work demonstrates that susceptibility analysis can move beyond validation to uncover novel mechanisms, providing a powerful, holistic lens for studying the developmental principles of complex neural networks.
ExeKGLib: A Platform for Machine Learning Analytics based on Knowledge Graphs
Venue: arXiv cs.LG 2025
Year: 2025

Nowadays machine learning (ML) practitioners have access to numerous ML libraries available online. Such libraries can be used to create ML pipelines that consist of a series of steps where each step may invoke up to several ML libraries that are used for various data-driven analytical tasks. Development of high-quality ML pipelines is non-trivial; it requires training, ML expertise, and careful development of each step. At the same time, domain experts in science and engineering may not possess such ML expertise and training while they are in pressing need of ML-based analytics. In this paper, we present our ExeKGLib, a Python library enhanced with a graphical interface layer that allows users with minimal ML knowledge to build ML pipelines. This is achieved by relying on knowledge graphs that encode ML knowledge in simple terms accessible to non-ML experts. ExeKGLib also allows improving the transparency and reusability of the built ML workflows and ensures that they are executable. We show the usability and usefulness of ExeKGLib by presenting real use cases.
IN2OUT: Fine-Tuning Video Inpainting Model for Video Outpainting Using Hierarchical Discriminator
Venue: arXiv cs.CV 2025
Year: 2025

Video outpainting presents a unique challenge of extending the borders while maintaining consistency with the given content. In this paper, we suggest the use of video inpainting models that excel in object flow learning and reconstruction in outpainting rather than solely generating the background as in existing methods. However, directly applying or fine-tuning inpainting models to outpainting has shown to be ineffective, often leading to blurry results. Our extensive experiments on discriminator designs reveal that a critical component missing in the outpainting fine-tuning process is a discriminator capable of effectively assessing the perceptual quality of the extended areas. To tackle this limitation, we differentiate the objectives of adversarial training into global and local goals and introduce a hierarchical discriminator that meets both objectives. Additionally, we develop a specialized outpainting loss function that leverages both local and global features of the discriminator. Fine-tuning on this adversarial loss function enhances the generator's ability to produce both visually appealing and globally coherent outpainted scenes. Our proposed method outperforms state-of-the-art methods both quantitatively and qualitatively. Supplementary materials including the demo video and the code are available in SigPort.
Semantic and Temporal Integration in Latent Diffusion Space for High-Fidelity Video Super-Resolution
Venue: arXiv cs.CV 2025
Year: 2025

Recent advancements in video super-resolution (VSR) models have demonstrated impressive results in enhancing low-resolution videos. However, due to limitations in adequately controlling the generation process, achieving high fidelity alignment with the low-resolution input while maintaining temporal consistency across frames remains a significant challenge. In this work, we propose Semantic and Temporal Guided Video Super-Resolution (SeTe-VSR), a novel approach that incorporates both semantic and temporal-spatio guidance in the latent diffusion space to address these challenges. By incorporating high-level semantic information and integrating spatial and temporal information, our approach achieves a seamless balance between recovering intricate details and ensuring temporal coherence. Our method not only preserves high-reality visual content but also significantly enhances fidelity. Extensive experiments demonstrate that SeTe-VSR outperforms existing methods in terms of detail recovery and perceptual quality, highlighting its effectiveness for complex video super-resolution tasks.
LAMIC: Layout-Aware Multi-Image Composition via Scalability of Multimodal Diffusion Transformer
Venue: arXiv cs.CV 2025
Year: 2025

In controllable image synthesis, generating coherent and consistent images from multiple references with spatial layout awareness remains an open challenge. We present LAMIC, a Layout-Aware Multi-Image Composition framework that, for the first time, extends single-reference diffusion models to multi-reference scenarios in a training-free manner. Built upon the MMDiT model, LAMIC introduces two plug-and-play attention mechanisms: 1) Group Isolation Attention (GIA) to enhance entity disentanglement; and 2) Region-Modulated Attention (RMA) to enable layout-aware generation. To comprehensively evaluate model capabilities, we further introduce three metrics: 1) Inclusion Ratio (IN-R) and Fill Ratio (FI-R) for assessing layout control; and 2) Background Similarity (BG-S) for measuring background consistency. Extensive experiments show that LAMIC achieves state-of-the-art performance across most major metrics: it consistently outperforms existing multi-reference baselines in ID-S, BG-S, IN-R and AVG scores across all settings, and achieves the best DPG in complex composition tasks. These results demonstrate LAMIC's superior abilities in identity keeping, background preservation, layout control, and prompt-following, all achieved without any training or fine-tuning, showcasing strong zero-shot generalization ability. By inheriting the strengths of advanced single-reference models and enabling seamless extension to multi-image scenarios, LAMIC establishes a new training-free paradigm for controllable multi-image composition. As foundation models continue to evolve, LAMIC's performance is expected to scale accordingly. Our implementation is available at: https://github.com/Suchenl/LAMIC.
A Variant of Non-uniform Cylindrical Algebraic Decomposition for Real Quantifier Elimination
Venue: arXiv cs.SC 2025
Year: 2025

The Cylindrical Algebraic Decomposition (CAD) method is currently the only complete algorithm used in practice for solving real-algebraic problems. To ameliorate its doubly-exponential complexity, different exploration-guided adaptations try to avoid some of the computations. The first such adaptation named NLSAT was followed by Non-uniform CAD (NuCAD) and the Cylindrical Algebraic Covering (CAlC). Both NLSAT and CAlC have been developed and implemented in SMT solvers for satisfiability checking, and CAlC was recently also adapted for quantifier elimination. However, NuCAD was designed for quantifier elimination only, and no complete implementation existed before this work. In this paper, we present a novel variant of NuCAD for both real quantifier elimination and SMT solving, provide an implementation, and evaluate the method by experimentally comparing it to CAlC.
Desyan: A Platform for Seamless Value-Flow and Symbolic Analysis
Venue: arXiv cs.SE 2025
Year: 2025

Over the past two decades, two different types of static analyses have emerged as dominant paradigms both in academia and industry: value-flow analysis (e.g., data-flow analysis or points-to analysis) and symbolic analysis (e.g., symbolic execution). Despite their individual successes in numerous application fields, the two approaches have remained largely separate; an artifact of the simple reality that there is no broadly adopted unifying platform for effortless and efficient integration of symbolic techniques with high-performance data-flow reasoning. To bridge this gap, we introduce Desyan: a platform for writing program analyses with seamless integration of value-flow and symbolic reasoning. Desyan expands a production-ready Datalog fixpoint engine (Soufflé) with full-fledged SMT solving invoking industry-leading SMT engines. Desyan provides constructs for automatically (and efficiently!) handling typical patterns that come up in program analysis. At the same time, the integration is agnostic with respect to the solving technology, and supports Datalog-native symbolic reasoning, via a bottom-up algebraic reasoning module. The result is an engine that allows blending different kinds of reasoning, as needed for the underlying analysis. For value-flow analysis, the engine is the best-in-class Datalog evaluator (often by a factor of over 20x in execution time); for applications that require full SMT (e.g., a concolic execution engine or other symbolic evaluator that needs to solve arbitrarily complex conditions), the engine is leveraging the leading SMT solvers; for lightweight symbolic evaluation (e.g., solving simple conditionals in the context of a path-sensitive analysis), the engine can use Datalog-native symbolic reasoning, achieving large speedups (often of over 2x) compared to eagerly appealing to an SMT solver.
Towards a Measure Theory of Semantic Information
Venue: arXiv cs.IT 2025
Year: 2025

A classic account of the quantification of semantic information is that of Bar-Hiller and Carnap. Their account proposes an inverse relation between the informativeness of a statement and its probability. However, their approach assigns the maximum informativeness to a contradiction: which Floridi refers to as the Bar-Hillel-Carnap paradox. He developed a novel theory founded on a distance metric and parabolic relation, designed to remove this paradox. Unfortunately is approach does not succeed in that aim. In this paper I critique Floridi's theory of strongly semantic information on its own terms and show where it succeeds and fails. I then present a new approach based on the unit circle (a relation that has been the basis of theories from basic trigonometry to quantum theory). This is used, by analogy with von Neumann's quantum probability to construct a measure space for informativeness that meets all the requirements stipulated by Floridi and removes the paradox. In addition, while contradictions and tautologies have zero informativeness, it is found that messages which are contradictory to each other are equally informative. The utility of this is explained by means of an example.
Closed-Form BER Analysis for Uplink NOMA with Dynamic SIC Decoding
Venue: arXiv cs.IT 2025
Year: 2025

This paper, for the first time, presents a closed-form error performance analysis of uplink power-domain non-orthogonal multiple access (PD-NOMA) with dynamic successive interference cancellation (SIC) decoding, where the decoding order is adapted to the instantaneous channel conditions. We first develop an analytical framework that characterizes how dynamic ordering affects error probabilities in uplink PD-NOMA systems. For a two-user system over independent and non-identically distributed Rayleigh fading channels, we derive closed-form probability density functions (PDFs) of ordered channel gains and the corresponding unconditional pairwise error probabilities (PEPs). To address the mathematical complexity of characterizing ordered channel distributions, we employ a Gaussian fitting to approximate truncated distributions while maintaining analytical tractability. Finally, we extend the bit error rate analysis for various $M$-quadrature amplitude modulation schemes (QAM) in both homogeneous and heterogeneous scenarios. Numerical results validate the theoretical analysis and demonstrate that dynamic SIC eliminates the error floor issue observed in fixed-order SIC, achieving significantly improved performance in high signal-to-noise ratio regions. Our findings also highlight that larger power differences are essential for higher-order modulations, offering concrete guidance for practical uplink PD-NOMA deployment.
SPENCER: Self-Adaptive Model Distillation for Efficient Code Retrieval
Venue: arXiv cs.SE 2025
Year: 2025

Code retrieval aims to provide users with desired code snippets based on users' natural language queries. With the development of deep learning technologies, adopting pre-trained models for this task has become mainstream. Considering the retrieval efficiency, most of the previous approaches adopt a dual-encoder for this task, which encodes the description and code snippet into representation vectors, respectively. However, the model structure of the dual-encoder tends to limit the model's performance, since it lacks the interaction between the code snippet and description at the bottom layer of the model during training. To improve the model's effectiveness while preserving its efficiency, we propose a framework, which adopts Self-AdaPtive Model Distillation for Efficient CodE Retrieval, named SPENCER. SPENCER first adopts the dual-encoder to narrow the search space and then adopts the cross-encoder to improve accuracy. To improve the efficiency of SPENCER, we propose a novel model distillation technique, which can greatly reduce the inference time of the dual-encoder while maintaining the overall performance. We also propose a teaching assistant selection strategy for our model distillation, which can adaptively select the suitable teaching assistant models for different pre-trained models during the model distillation to ensure the model performance. Extensive experiments demonstrate that the combination of dual-encoder and cross-encoder improves overall performance compared to solely dual-encoder-based models for code retrieval. Besides, our model distillation technique retains over 98% of the overall performance while reducing the inference time of the dual-encoder by 70%.