arXiv Papers

Published: 2026-04-20 17:57:02

Authors: Savya Khosla, Sethuraman T, Aryan Chadha, Alex Schwing, Derek Hoiem

Categories: cs.CV

Abstract:
Despite recent progress, vision-language encoders struggle with two core limitations: (1) weak alignment between language and dense vision features, which hurts tasks like open-vocabulary semantic segmentation; and (2) high token counts for fine-grained visual representations, which limits scalability to long videos. This work addresses both limitations. We propose T-REN (Text-aligned Region Encoder Network), an efficient encoder that maps visual data to a compact set of text-aligned region-level representations (or region tokens). T-REN achieves this through a lightweight network added on top of a frozen vision backbone, trained to pool patch-level representations within each semantic region into region tokens and align them with region-level text annotations. With only 3.7% additional parameters compared to the vision-language backbone, this design yields substantially stronger dense cross-modal understanding while reducing the token count by orders of magnitude. Specifically, T-REN delivers +5.9 mIoU on ADE20K open-vocabulary segmentation, +18.4% recall on COCO object-level text-image retrieval, +15.6% recall on Ego4D video object localization, and +17.6% mIoU on VSPW video scene parsing, all while reducing token counts by more than 24x for images and 187x for videos compared to the patch-based vision-language backbone. The code and model are available at https://github.com/savya08/T-REN.

            {
  "tldr": "T-REN introduces a new method for aligning text and visual data by using text-aligned region tokens, significantly improving performance in vision-language tasks while reducing token counts.",
  "whats_new": [
    "• Introduces T-REN, a Text-aligned Region Encoder Network for better vision-language alignment.",
    "• Achieves +5.9 mIoU on ADE20K and +18.4% recall on COCO with reduced token counts.",
    "• Reduces visual token counts by over 24× for images and 187× for videos compared to existing methods.",
    "• Allows for multi-token predictions per prompt, enhancing representation quality."
  ],
  "method": "T-REN pools patch-level features into region tokens and aligns them with text annotations using a lightweight network on top of a frozen vision backbone. It employs cross-attention to gather relevant visual information and merges similar tokens to reduce redundancy.",
  "results": [
    "• +5.9 mIoU improvement on ADE20K for open-vocabulary segmentation.",
    "• +18.4% recall improvement on COCO object-level text-image retrieval.",
    "• +15.6% recall on Ego4D video object localization.",
    "• +17.6% mIoU on VSPW video scene parsing.",
    "• Reduces token counts by over 24× for images and 187× for videos."
  ],
  "limitations": [
    "• Performance may vary with different prompt grid sizes.",
    "• Requires careful tuning of merging thresholds for optimal results.",
    "• Future work needed to explore end-to-end training of region-based models."
  ],
  "why_useful": "T-REN enhances the efficiency and effectiveness of vision-language models, making it suitable for tasks requiring fine-grained understanding and scalability, such as open-vocabulary segmentation and video processing. Its ability to reduce token counts while maintaining performance is particularly beneficial for large-scale applications.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://github.com/savya08/T-REN."
  }
}
            

arXiv Page | PDF

Published: 2026-04-20 17:56:02

Authors: A. Sophia Koepke, Daniil Zverev, Shiry Ginosar, Alexei A. Efros

Categories: cs.CV, cs.AI, cs.LG

Abstract:
The Platonic Representation Hypothesis suggests that neural networks trained on different modalities (e.g., text and images) align and eventually converge toward the same representation of reality. If true, this has significant implications for whether modality choice matters at all. We show that the experimental evidence for this hypothesis is fragile and depends critically on the evaluation regime. Alignment is measured using mutual nearest neighbors on small datasets ($\approx$1K samples) and degrades substantially as the dataset is scaled to millions of samples. The alignment that remains between model representations reflects coarse semantic overlap rather than consistent fine-grained structure. Moreover, the evaluations in Huh et al. are done in a one-to-one image-caption setting, a constraint that breaks down in realistic many-to-many settings and further reduces alignment. We also find that the reported trend of stronger language models increasingly aligning with vision does not appear to hold for newer models. Overall, our findings suggest that the current evidence for cross-modal representational convergence is considerably weaker than subsequent works have taken it to be. Models trained on different modalities may learn equally rich representations of the world, just not the same one.

            {
  "tldr": "This paper challenges the Platonic Representation Hypothesis, showing that cross-modal alignment between neural networks degrades significantly as dataset size increases, indicating that models trained on different modalities do not converge to the same representation.",
  "whats_new": [
    "• Demonstrates that alignment degrades from small to large datasets.",
    "• Shows that fine-grained alignment is not maintained in many-to-many settings.",
    "• Finds that newer language models do not align better with vision models as previously reported.",
    "• Suggests that models may learn rich but differently organized representations."
  ],
  "method": "The study uses the mutual k-nearest neighbor (kNN) metric to measure alignment between representations from different models. It compares performance on small datasets (≈1K samples) versus larger datasets (millions of samples) to assess how alignment changes with scale and dataset structure.",
  "results": [
    "• Mutual kNN alignment drops sharply from 0.799 to 0.906 as dataset size increases from WIT-1024 to WIT-1M.",
    "• Fine-grained alignment is rarely achieved, with strict mutual agreement around 11% even with detailed captions.",
    "• The trend of stronger language models aligning better with vision models weakens significantly at larger scales.",
    "• Models trained on different modalities show coarse agreement but diverge on fine-grained structure."
  ],
  "limitations": [
    "• The findings are based on specific evaluation metrics that may not capture all aspects of alignment.",
    "• The study primarily focuses on English-language datasets, limiting generalizability.",
    "• Future work is needed to explore the implications of many-to-many correspondences in real-world data."
  ],
  "why_useful": "This paper provides critical insights into the limitations of the Platonic Representation Hypothesis, emphasizing that different modalities may not converge to a single representation. It has implications for the design of multimodal models and understanding how they learn from diverse data sources.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-20 17:55:06

Authors: Javier Carvajal-Rojas, Axel Stäbler

Categories: math.AG, math.AC

Abstract:
We introduce a framework for pulling back Cartier modules and their associated invariants along regular $F$-finite morphisms. To achieve this, we construct a relative Cartier isomorphism and operator for an arbitrary regular $F$-finite map of locally noetherian schemes. As an application, we obtain new results on the constancy regions of mixed test ideals, based on the work of Felipe Pérez.

            {
  "tldr": "This paper introduces a framework for pulling back Cartier modules along regular F-finite morphisms, providing new insights into mixed test ideals and their constancy regions.",
  "whats_new": [
    "• Establishes a relative Cartier isomorphism for regular F-finite morphisms.",
    "• Extends results on mixed test ideals beyond finite type conditions.",
    "• Introduces a new framework for analyzing Cartier modules and their invariants."
  ],
  "method": "The authors construct a relative Cartier isomorphism and operator for regular F-finite morphisms of locally noetherian schemes. They utilize differential bases and p-bases to define a twisted inverse image functor for Cartier modules.",
  "results": [
    "• Theorem A: A relative Cartier isomorphism exists for regular F-finite maps.",
    "• Theorem B: A natural isomorphism between functors related to test modules is established.",
    "• Theorem C: The constancy regions of mixed test ideals are shown to be finite under certain conditions."
  ],
  "limitations": [
    "• The framework relies on specific conditions of regularity and F-finiteness.",
    "• Applications to constancy regions may not extend to all types of schemes."
  ],
  "why_useful": "This paper is significant for researchers in algebraic geometry and commutative algebra, particularly those studying F-singularities and test ideals. It provides new tools for analyzing the behavior of Cartier modules and their applications in various mathematical contexts.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-20 17:53:33

Authors: Manan Gupta, Dhruv Kumar

Categories: cs.LG, cs.AI, cs.CL

Abstract:
Large language models frequently commit unrecoverable reasoning errors mid-generation: once a wrong step is taken, subsequent tokens compound the mistake rather than correct it. We introduce $\textbf{Latent Phase-Shift Rollback}$ (LPSR): at each generation step, we monitor the residual stream at a critical layer lcrit, detect abrupt directional reversals (phase shifts) via a cosine-similarity $+$ entropy dual gate, and respond by rolling back the KV-cache and injecting a pre-computed steering vector. No fine-tuning, gradient computation, or additional forward passes are required. LPSR achieves $\mathbf{44.0\%}$ on MATH-500 with an 8B model versus $28.8\%$ for standard AR ($+15.2$ pp; McNemar $χ^2 = 66.96$, $p < 10^{-15}$). Critically, prompted self-correction, the most natural inference-time baseline, scores only $19.8\%$, below standard AR; LPSR exceeds it by $+24.2$ pp ($χ^2 = 89.4$, $p \approx 0$). LPSR also outperforms Best-of-16 ($+7.8$ pp) at $5.4\times$ lower token cost, and surpasses a standard 70B model ($35.2\%$) with $8.75\times$ fewer parameters at ${\sim}3\times$ the token budget. A 32-layer sweep reveals a novel \textbf{detection-correction dissociation}: error-detection AUC peaks at layer~14 ($0.718$) but task accuracy peaks at layer~16 ($44.0\%$ vs.\ $29.2\%$), demonstrating that optimal monitoring depth differs for detection and correction.

            {
  "tldr": "This paper presents Latent Phase-Shift Rollback (LPSR), a method for correcting reasoning errors in large language models during inference by monitoring residual streams and adjusting outputs without additional training. LPSR significantly outperforms existing methods on mathematical reasoning tasks.",
  "whats_new": [
    "• Introduces LPSR, a training-free inference method for error correction.",
    "• Achieves 44.0% accuracy on MATH-500, outperforming standard autoregressive methods by 15.2 percentage points.",
    "• Identifies a dissociation between error detection and correction optimal layers.",
    "• Demonstrates that prompted self-correction degrades performance."
  ],
  "method": "LPSR monitors the residual stream at a critical layer to detect phase shifts indicating potential errors. Upon detection, it rolls back the KV-cache and injects a steering vector to correct the trajectory, all without requiring additional forward passes or fine-tuning.",
  "results": [
    "• LPSR achieves 44.0% on MATH-500, compared to 28.8% for standard autoregressive methods.",
    "• Outperforms prompted self-correction by 24.2 percentage points (19.8% accuracy).",
    "• Requires 8.75× fewer parameters than a standard 70B model while achieving higher accuracy.",
    "• Error detection AUC peaks at layer 14 (0.718), while task accuracy peaks at layer 16 (44.0%)."
  ],
  "limitations": [
    "• Evaluated primarily on English mathematics; performance in other domains is unknown.",
    "• The method's effectiveness may vary with model scale and requires domain-specific calibration.",
    "• High compute overhead in cases with multiple rollbacks."
  ],
  "why_useful": "LPSR enhances the reliability of reasoning in large language models, making it applicable for educational tools and automated theorem assistance. Its ability to correct errors in real-time can significantly improve performance in critical reasoning tasks.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://arxiv.org/abs/2604.18567v1"
  }
}
            

arXiv Page | PDF

Published: 2026-04-20 17:27:28

Authors: Giuseppe De Giacomo, Christian Hagemeier, Daniel Hausmann, Nir Piterman

Categories: cs.LO, cs.AI, cs.FL

Abstract:
We study synthesis for obligation properties expressed in LTLfp, the extension of LTLf to infinite traces. Obligation properties are positive Boolean combinations of safety and guarantee (co-safety) properties and form the second level of the temporal hierarchy of Manna and Pnueli. Although obligation properties are expressed over infinite traces, they retain most of the simplicity of LTLf. In particular, we show that they admit a translation into symbolically represented deterministic weak automata (DWA) obtained directly from the symbolic deterministic finite automata (DFA) for the underlying LTLf properties on trace prefixes. DWA inherit many of the attractive algorithmic features of DFA, including Boolean closure and polynomial-time minimization. Moreover, we show that synthesis for LTLfp obligation properties is theoretically highly efficient - solvable in linear time once the DWA is constructed. We investigate several symbolic algorithms for solving DWA games that arise in the synthesis of obligation properties and evaluate their effectiveness experimentally. Overall, the results indicate that synthesis for LTLfp obligation properties can be performed with virtually the same effectiveness as LTLf synthesis.

            {
  "tldr": "This paper presents a method for synthesizing obligation properties in LTLf+ using deterministic weak automata (DWA), achieving efficient synthesis comparable to LTLf synthesis.",
  "whats_new": [
    "• Introduces a technique for constructing DWA from LTLf+ obligation properties.",
    "• Reduces synthesis of obligation properties to solving games on DWA.",
    "• Proposes a novel symbolic algorithm for solving weak games.",
    "• Demonstrates experimental results showing efficiency comparable to LTLf synthesis."
  ],
  "method": "The authors construct symbolically represented deterministic weak automata (DWA) from LTLf+ obligation properties. They then reduce the synthesis problem to solving games on these automata, employing various symbolic algorithms to evaluate their effectiveness.",
  "results": [
    "• Synthesis for LTLf+ obligation properties can be performed in linear time after constructing the DWA.",
    "• The proposed algorithms achieve performance comparable to LTLf synthesis.",
    "• Experimental evaluations demonstrate the effectiveness of the algorithms across various benchmarks."
  ],
  "limitations": [
    "• The paper does not address the synthesis of higher-level temporal properties beyond obligations.",
    "• Performance may vary based on the complexity of the input formulas."
  ],
  "why_useful": "This work extends the applicability of reactive synthesis techniques to infinite traces, making it relevant for systems requiring long-term guarantees. It offers efficient methods for synthesizing complex temporal specifications in practical applications.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-20 17:25:44

Authors: Mao Lin, Xi Wang, Guilherme Cox, Dong Li, Hyeran Jeon

Categories: cs.PF, cs.DC

Abstract:
As modern LLMs support thousands to millions of tokens, KV caches grow to hundreds of gigabytes, stressing memory capacity and bandwidth. Existing solutions, such as KV cache pruning and offloading, alleviate these but underutilize hardware by relying solely on either GPU or CPU for attention computing, and considering yet limited CPU local memory for KV cache storage. We propose HybridGen, an efficient hybrid attention framework for long-context LLM inference. HybridGen enables CPU-GPU collaborative attention on systems with expanded tiered memory (e.g., CXL memory), addressing three key challenges: (1) multi-dimensional attention dependencies, (2) intensifying CPU-GPU load imbalance with longer sequences, and (3) NUMA penalty of tiered memories. HybridGen tackles these by introducing attention logit parallelism, a feedback-driven scheduler, and semantic-aware KV cache mapping. Experiments with three LLM models with eleven different sizes on three GPU platforms with a CXL-expanded memory show that HybridGen outperforms six state-of-the-art KV cache management methods by 1.41x--3.2x on average while maintaining superior accuracy.

            {
  "tldr": "HybridGen is a new framework that improves long-context LLM inference by enabling CPU-GPU collaboration, achieving 1.41×–3.2× speedup over existing methods while maintaining accuracy.",
  "whats_new": [
    "• Introduces a hybrid attention framework for LLM inference using CPU-GPU collaboration.",
    "• Implements a feedback-driven scheduler for dynamic workload balancing.",
    "• Utilizes semantic-aware KV cache mapping for efficient memory management.",
    "• Outperforms six state-of-the-art KV cache management methods."
  ],
  "method": "HybridGen parallelizes attention computation across CPU and GPU, addressing challenges like load imbalance and memory access latency. It employs a feedback scheduler to dynamically adjust workloads and a semantic-aware mapping strategy for efficient use of tiered memory.",
  "results": [
    "• Achieves 1.41×–3.2× speedup over state-of-the-art methods on average.",
    "• Maintains superior accuracy compared to existing KV cache management techniques.",
    "• Reduces end-to-end latency significantly across various sequence lengths and batch sizes.",
    "• Demonstrates improved performance on diverse LLM models, including OPT and Llama."
  ],
  "limitations": [
    "• Performance may vary based on specific hardware configurations.",
    "• The effectiveness of the feedback scheduler depends on accurate runtime latency measurements.",
    "• Requires integration with existing LLM serving systems, which may not be straightforward."
  ],
  "why_useful": "This paper addresses critical challenges in LLM inference, particularly for long contexts, making it highly relevant for applications requiring efficient processing of large datasets. Its hybrid approach can significantly enhance performance in real-world scenarios.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-20 17:10:47

Authors: Mihai Turinici

Categories: math.GN

Abstract:
The 2015 fixed point result on rs-relational metric spaces due to Alam and Imdad [J. Fixed Point Th. Appl., 17 (2015), 693-702] is equivalent with the classical Banach Contraction Principle [Fund. Math., 3 (1922), 133-181]. This is also valid for the 1961 statement in metric spaces due to Edelstein [Proc. Amer. Math. Soc., 12 (1961), 7-10], or the 2005 fixed point result in quasi-ordered metric spaces obtained by Nieto and Rodriguez-Lopez [Order, 22 (2005), 223-239].

            {
  "tldr": "This paper extends fixed point results in relational metric spaces, showing that certain functional contractions are equivalent to the classical Banach Contraction Principle. It provides a comprehensive framework for understanding these relationships.",
  "whats_new": [
    "• Establishes a functional extension of the Alam-Imdad result in relational metric spaces.",
    "• Demonstrates equivalence between the rs-relational version of the Alam-Imdad result and the Banach Contraction Principle.",
    "• Introduces new concepts like fix-asingleton and strongly Picard operators in relational contexts."
  ],
  "method": "The paper develops a framework for analyzing fixed point theorems in relational metric spaces by combining concepts of contractive mappings with relational structures. It utilizes existing results and extends them to new settings, particularly focusing on functional contractions.",
  "results": [
    "• Theorem 1.1 confirms that a (d; 0, 1)-contractive selfmap in a complete metric space is global strongly Picard.",
    "• The functional contraction principle is shown to be deductible from the Kirk asymptotic fixed point theorem.",
    "• Establishes that the results are applicable to various types of relational and quasi-ordered metric spaces."
  ],
  "limitations": [
    "• The paper does not address the reciprocal inclusion of certain principles, leaving it as an open question.",
    "• Some results may not hold in more general relational contexts without additional assumptions."
  ],
  "why_useful": "This work is significant for researchers in fixed point theory and metric spaces, providing new insights into the relationships between various contraction principles. It has potential applications in mathematical analysis and operator theory.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-20 16:44:52

Authors: Tanya Keshari, Debasis Sadhukhan

Categories: quant-ph

Abstract:
We simulate a long-range extended Ising model in one dimension using a hybrid quantum algorithm, namely Variational Quantum Eigensolver (VQE). In this quantum simulation, we investigate how quantum resources scale with system size and interaction strength. Three structure-aware ansatze incorporating nearest-neighbor (NN), next-nearest-neighbor (NNN), and next-next-nearest-neighbor (NNNN) entangling blocks are constructed by mimicking the string operators in the Hamiltonian. We show that energy fidelity alone is not a good indicator for finding the ground state of our model. To overcome this problem, we introduce an additional criterion based on pairwise logarithmic negativity as a more reliable way to find the actual ground state by the VQE. We find that the interaction range parameter alpha primarily governs the minimum number of ansatz layers required, rather than proximity to the quantum critical point. In particular, we show that in the non-local regime (alpha <= 1), the NNN and NNNN ansatze reduce the layer scaling rate by factors of 2.5x and 3.8x relative to NN in all phases, including the critical point. The total number of two-qubit gates required for reliable simulation grows quadratically with system size for all three ansatze. This is consistent with the theoretical prediction, as the number of non-local terms in the Hamiltonian also grows quadratically with the system size. In the local regime, however, the number of required two-qubit gates grows linearly with system size. In contrast, in the quasi-local regime, the required number of two-qubit gates for the quantum simulation is more subtle and depends on the phase of the Hamiltonian.

            {
  "tldr": "This paper explores the scaling of quantum resources needed for simulating a long-range extended Ising model using the Variational Quantum Eigensolver (VQE). It introduces structure-aware ansatzes that significantly reduce resource costs compared to traditional methods.",
  "whats_new": [
    "• Introduces structure-aware ansatzes that mirror Hamiltonian string operators.",
    "• Demonstrates that energy fidelity alone is insufficient for validating VQE success in long-range systems.",
    "• Establishes a direct link between interaction range and required ansatz layers.",
    "• Provides quantitative insights into resource scaling for different interaction regimes."
  ],
  "method": "The study employs the Variational Quantum Eigensolver (VQE) to simulate a one-dimensional long-range extended Ising model. Three ansatz configurations (NN, NNN, NNNN) are designed to capture the entanglement structure of the Hamiltonian, and performance is evaluated using pairwise logarithmic negativity.",
  "results": [
    "• In the long-range regime (α=0.5), NNN and NNNN reduce layer scaling rates by factors of 2.6 and 3.8 compared to NN.",
    "• Total quantum resource costs scale as O(N²) in the long-range regime.",
    "• In the quasi-local regime (α=1.5), the advantage of multipartite ansatzes is strongest near the quantum critical point.",
    "• In the short-range regime (α=10.0), all ansatzes perform similarly, with NN being the most efficient."
  ],
  "limitations": [
    "• The study is limited to N≤9 qubits, potentially affecting generalizability.",
    "• Conducted on an ideal simulator without noise, which may not reflect real-world performance.",
    "• Further research is needed to explore larger system sizes and implement on NISQ hardware."
  ],
  "why_useful": "This paper provides valuable insights into the design of quantum circuits for simulating long-range interacting systems, which is crucial for advancing quantum computing applications. The findings can guide future research in optimizing VQE implementations on near-term quantum hardware.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-20 16:37:22

Authors: Jinghui Lu, Jiayi Guan, Zhijian Huang, Jinlong Li, Guang Li, Lingdong Kong, Yingyan Li, Han Wang, Shaoqing Xu, Yuechen Luo, Fang Li, Chenxu Dang, Junli Wang, Tao Xu, Jing Wu, Jianhua Wu, Xiaoshuai Hao, Wen Zhang, Tianyi Jiang, Lingfeng Zhang, Lei Zhou, Yingbo Tang, Jie Wang, Yinfeng Gao, Xizhou Bu, Haochen Tian, Yihang Qiu, Feiyang Jia, Lin Liu, Yigu Ge, Hanbing Li, Yuannan Shen, Jianwei Cui, Hongwei Xie, Bing Wang, Haiyang Sun, Jingwei Zhao, Jiahui Huang, Pei Liu, Zeyu Zhu, Yuncheng Jiang, Zibin Guo, Chuhong Gong, Hanchao Leng, Kun Ma, Naiyang Wang, Guang Chen, Kuiyuan Yang, Hangjun Ye, Long Chen

Categories: cs.CV, cs.CL, cs.RO

Abstract:
Chain-of-Thought (CoT) reasoning has become a powerful driver of trajectory prediction in VLA-based autonomous driving, yet its autoregressive nature imposes a latency cost that is prohibitive for real-time deployment. Latent CoT methods attempt to close this gap by compressing reasoning into continuous hidden states, but consistently fall short of their explicit counterparts. We suggest that this is due to purely linguistic latent representations compressing a symbolic abstraction of the world, rather than the causal dynamics that actually govern driving. Thus, we present OneVL (One-step latent reasoning and planning with Vision-Language explanations), a unified VLA and World Model framework that routes reasoning through compact latent tokens supervised by dual auxiliary decoders. Alongside a language decoder that reconstructs text CoT, we introduce a visual world model decoder that predicts future-frame tokens, forcing the latent space to internalize the causal dynamics of road geometry, agent motion, and environmental change. A three-stage training pipeline progressively aligns these latents with trajectory, language, and visual objectives, ensuring stable joint optimization. At inference, the auxiliary decoders are discarded and all latent tokens are prefilled in a single parallel pass, matching the speed of answer-only prediction. Across four benchmarks, OneVL becomes the first latent CoT method to surpass explicit CoT, delivering state-of-the-art accuracy at answer-only latency, and providing direct evidence that tighter compression, when guided in both language and world-model supervision, produces more generalizable representations than verbose token-by-token reasoning. Project Page: https://xiaomi-embodied-intelligence.github.io/OneVL

            {
  "tldr": "OneVL introduces a novel framework for autonomous driving that combines vision-language reasoning with efficient trajectory prediction, achieving state-of-the-art performance while minimizing latency.",
  "whats_new": [
    "• First latent CoT method to outperform explicit CoT in trajectory prediction.",
    "• Introduces dual auxiliary decoders for language and visual predictions.",
    "• Implements a prefill inference mechanism for faster processing.",
    "• Establishes a three-stage training pipeline for effective optimization."
  ],
  "method": "OneVL integrates vision-language reasoning with a world model framework. It uses dual auxiliary decoders to supervise latent tokens, allowing for both language and visual predictions. A three-stage training pipeline aligns these representations with trajectory objectives, ensuring effective joint optimization.",
  "results": [
    "• Achieved 88.84 PDM-score on NAVSIM, surpassing previous state-of-the-art by 2.64.",
    "• On ROADWork, OneVL achieved 12.49 ADE and 28.80 FDE, significantly outperforming YNet.",
    "• Inference latency matches that of answer-only predictions, e.g., 4.46s on NAVSIM.",
    "• Visual auxiliary decoder contributed +0.87 to PDM-score, indicating its importance."
  ],
  "limitations": [
    "• Requires approximately 3× memory during training due to multiple model instances.",
    "• Latent token count was chosen empirically, needing further systematic study.",
    "• The model's performance may degrade without the three-stage training approach."
  ],
  "why_useful": "OneVL enhances trajectory prediction in autonomous driving by efficiently combining visual and language reasoning. Its ability to provide interpretable explanations and maintain low latency makes it suitable for real-time applications, improving safety and reliability in autonomous systems.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-20 16:32:04

Authors: Soumyadip Paul, Sourav Banerjee, Debanjan Bhowmik, Neel Kanth Kundu

Categories: eess.SP

Abstract:
Data detection in large-scale multiple-input multiple-output (MIMO) systems with higher-order quadrature amplitude modulation (QAM) remains a challenging problem due to the exponential complexity of the classical maximum likelihood (ML) detector. This challenge is further amplified by Gray-coded modulation, which introduces nonlinear symbol-to-bit mappings and transforms the problem into a higher-order unconstrained binary optimization (HUBO) formulation. To address this problem, this paper presents a hybrid quantum-classical detection framework that leverages a warm-start linear-ramp Quantum Approximate Optimization Algorithm (WSLR-QAOA) for solving the resulting HUBO problem. A structured warm-start based on a low-rank semidefinite relaxation, solved via a block coordinate descent (BCD) method, provides an efficient and high-quality initialization, while a linear ramp parameterization guides the QAOA optimization. Simulation results show that the proposed framework outperforms classical methods in terms of symbol error rate (SER) and converges faster than standard QAOA, while achieving performance close to the optimal ML detector. Furthermore, the WSLR-QAOA algorithm is validated on actual IBM quantum hardware, where it achieves near-ML performance at low SNR and maintains competitive accuracy at higher SNR despite moderate degradation due to hardware noise. This demonstrates the practical potential of the HUBO-based WSLR-QAOA algorithm for large-scale MIMO data detection.

            {
  "tldr": "This paper introduces a hybrid quantum-classical framework using a warm-start Quantum Approximate Optimization Algorithm (WSLR-QAOA) for MIMO data detection with higher-order QAM, achieving near-optimal performance and faster convergence compared to classical methods.",
  "whats_new": [
    "• Proposes a warm-start strategy for QAOA using classical BCD for initialization.",
    "• Introduces a linear ramp parameterization to enhance convergence.",
    "• Validates the algorithm on actual IBM quantum hardware, demonstrating practical viability.",
    "• Extends QAOA applications from simplified BPSK to complex Gray-coded M-QAM systems."
  ],
  "method": "The proposed WSLR-QAOA framework combines a classical BCD method for initialization with a quantum optimization process. It uses a structured warm-start mixer and a linear ramp parameterization to guide the optimization, improving convergence in non-convex landscapes.",
  "results": [
    "• WSLR-QAOA outperforms classical methods in Symbol-Error-Rate (SER) across various SNR levels.",
    "• Achieves near-maximum likelihood (ML) performance at low SNR with competitive accuracy at higher SNR.",
    "• Demonstrates faster convergence than standard QAOA.",
    "• Validated on IBM quantum hardware, maintaining performance despite moderate noise."
  ],
  "limitations": [
    "• Performance may degrade at high SNR due to increased sensitivity to quantum noise.",
    "• The method's scalability is limited by the complexity of the optimization landscape.",
    "• Further research is needed to enhance robustness against hardware noise."
  ],
  "why_useful": "This paper is significant for advancing MIMO data detection techniques using quantum computing, potentially leading to more efficient wireless communication systems. Its practical implementation on quantum hardware opens avenues for real-world applications in next-generation networks.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-20 16:24:26

Authors: Vivek Shrivastav, Mani K Chettri, Britan Singh, Hemam D. Singh, Rupak Mukherjee

Categories: physics.plasm-ph

Abstract:
Recent Magnetospheric Multiscale (MMS) observations report approximate equality between electric and magnetic field energy spectral densities, $\varepsilon_{0} P[δE]/2 \approx P[δB]/(2μ_{0})$, at sub-electron scales in reconnection-driven magnetotail turbulence, interpreted as relaxation toward thermodynamic equilibrium. We derive the electric-to-magnetic energy ratio from the linear polarization of kinetic Alfvén waves and whistler-mode waves in the two-fluid framework and show that it saturates at $\mathcal{R}_{\infty}=(V_{A}/c)^{2}(m_{i}/m_{e})(β_{e}/2)$ deep in the sub-electron regime. Setting $\mathcal{R}_{\infty}=1$ yields the universal threshold $V_{A}/c \gtrsim \sqrt{2/[(m_{i}/m_{e})β_{e}]}$, which no non-relativistic space plasma satisfies. For typical magnetotail parameters, $\mathcal{R}_{\infty}\approx 2\times 10^{-3}$, approximately 500 times below the observed value, a discrepancy rooted in the non-relativistic ordering $(V_{A}/c)^{2}\ll 1$. Noise-floor estimates show that Search Coil Magnetometer and Electric Double Probe sensitivity convergence produces a spurious apparent equipartition throughout this regime. The observed equality likely reflects nonlinear dynamics, incoherent superposition of electromagnetic and electrostatic fluctuations, or instrumental noise contamination.

arXiv Page | PDF

Published: 2026-04-20 16:21:33

Authors: Jonas Sievers, Mardavij Roozbehani

Categories: cs.AI

Abstract:
Baseline estimation is critical to Demand Response (DR) settlement in electricity markets, yet existing machine learning methods remain limited in predictive performance, while methodologies from causal inference and counterfactual prediction are still underutilized in this domain. We introduce a Generalized Synthetic Control Method that builds on the classical Synthetic Control Method (SCM) from econometrics. While SCM provides a powerful framework for counterfactual estimation, classical SCM remains a static estimator: it fits the treated unit as a combination of contemporaneous donor units and therefore ignores predictable temporal structure in the residual error. We develop a generalized SCM framework that transforms baseline estimation into a dynamic counterfactual prediction problem by augmenting the donor representation with exogenous features, lagged treated load, and selected lagged donor signals. This enriched representation allows the estimator to capture autoregressive dependence, delayed donor-response patterns, and error-correction effects beyond the scope of standard SCM. The framework further accommodates nonlinear predictors when linear weighting is inadequate, with the greatest benefit arising in limited-data settings. Experiments on the Ausgrid smart-meter dataset show consistent improvements over classical SCM and strong benchmark methods, with the dominant performance gains driven by dynamic augmentation.

arXiv Page | PDF

Published: 2026-04-20 16:20:23

Authors: Chupei Tang, Junxiao Kong, Moyu Tang, Di Wang, Jixiu Zhai, Ronghao Xie, Shangkun Sima, Tianchi Lu

Categories: cs.LG, cs.AI

Abstract:
Motivation: Peptide-protein interactions (PepPIs) are central to cellular regulation and peptide therapeutics, but experimental characterization remains too slow for large-scale screening. Existing methods usually emphasize either interaction prediction or peptide generation, leaving candidate prioritization, residue-level interpretation, and target-conditioned expansion insufficiently integrated. Results: We present an integrated framework for early-stage peptide screening that combines a partner-aware prediction and localization model (ConGA-PepPI) with a target-conditioned generative model (TC-PepGen). ConGA-PepPI uses asymmetric encoding, bidirectional cross-attention, and progressive transfer from pair prediction to binding-site localization, while TC-PepGen preserves target information throughout autoregressive decoding via layerwise conditioning. In five-fold cross-validation, ConGA-PepPI achieved 0.839 accuracy and 0.921 AUROC, with binding-site AUPR values of 0.601 on the protein side and 0.950 on the peptide side, and remained competitive on external benchmarks. Under a controlled length-conditioned benchmark, 40.39% of TC-PepGen peptides exceeded native templates in AlphaFold 3 ipTM, and unconstrained generation retained evidence of target-conditioned signal.

arXiv Page | PDF

Published: 2026-04-20 16:11:54

Authors: Ioannis Gkeneralis

Categories: math.AT, math.GT

Abstract:
We investigate the equivariant topological rigidity of complex and quaternionic moment--angle manifolds. By reducing the classification to the equivariant rigidity of their quasitoric (or quoric) quotients and the classification of the associated principal bundles, we establish new rigidity results within the category of locally linear actions. We prove that complex moment-angle manifolds are equivariantly rigid: any locally linear manifold equivariantly homotopy equivalent to a complex moment--angle manifold is equivariantly homeomorphic to it. In the quaternionic setting, we establish full equivariant rigidity for manifolds with four-dimensional quoric quotients and provide a primary rigidity statement for higher dimensions based on degree-4 characteristic classes. These results characterize moment--angle manifolds as equivariant strong Borel manifolds, demonstrating that their equivariant homotopy type completely determines their equivariant homeomorphism type.

arXiv Page | PDF

Published: 2026-04-20 16:05:58

Authors: Florentin Coeurdoux, Grégoire Ferré, Jean-Philippe Bouchaud

Categories: stat.ML, cs.LG, math.ST

Abstract:
Empirical studies of trained models often report a transient regime in which signal is detectable in a finite gradient descent time window before overfitting dominates. We provide an analytically tractable random-matrix model that reproduces this phenomenon for gradient flow in a linear teacher--student setting. In this framework, learning occurs when an isolated eigenvalue separates from a noisy bulk, before eventually disappearing in the overfitting regime. The key ingredient is anisotropy in the input covariance, which induces fast and slow directions in the learning dynamics. In a two-block covariance model, we derive the full time-dependent bulk spectrum of the symmetrized weight matrix through a $2\times 2$ Dyson equation, and we obtain an explicit outlier condition for a rank-one teacher via a rank-two determinant formula. This yields a transient Baik-Ben Arous-Péché (BBP) transition: depending on signal strength and covariance anisotropy, the teacher spike may never emerge, emerge and persist, or emerge only during an intermediate time interval before being reabsorbed into the bulk. We map the corresponding phase diagrams and validate the theory against finite-size simulations. Our results provide a minimal solvable mechanism for early stopping as a transient spectral effect driven by anisotropy and noise.

arXiv Page | PDF

Published: 2026-04-20 15:59:21

Authors: Jan Nowosielski, Marcin Jastrzębski, Wojciech Wasilewski, Mateusz Mazelanik, Michał Parniak

Categories: physics.atom-ph, physics.optics

Abstract:
Rydberg atom-based sensors have emerged as highly sensitive tools for terahertz (THz) metrology, yet most current imaging techniques discard crucial phase information. In this Letter, we present a coherent THz-to-optical conversion scheme in warm Rb vapor that enables complex-amplitude field imaging. By manipulating the phase-matching conditions via an adjustable interference pattern of optical probe beams, we demonstrate the ability to perform tomographic reconstruction of the THz field distribution. We experimentally validate the spatial resolution and phase-sensitivity of the system by resolving sub-centimeter features and identifying incident angles of arrival. Our results establish a robust framework for phase-resolved THz imaging and holography using atomic vapors at room temperature.

arXiv Page | PDF

Published: 2026-04-20 15:52:09

Authors: Armin Nurkanović, Anton Pozharskiy, Moritz Diehl

Categories: math.OC

Abstract:
Model predictive control (MPC) of hybrid dynamical systems is challenging because the associated optimization problem is nonsmooth and the resulting feedback law is discontinuous. This paper develops real-time MPC algorithms for nonlinear hybrid systems modeled as dynamical complementarity systems. The resulting optimal control problems are formulated as mathematical programs with complementarity constraints (MPCCs). We show that the solution map of parametric MPCCs is discontinuous, and that standard nonlinear-programming-based approaches may become infeasible when the hybrid system switches. To address this, we introduce three real-time hybrid MPC schemes whose feedback phase solves a quadratic program with complementarity constraints per sample, yielding local discontinuous piecewise affine approximations of the MPC feedback law. Moreover, we derive continuity and differentiability results for parametric MPCCs, and establish conditions under which the approximation error of our new hybrid MPC algorithms remains uniformly bounded despite solution discontinuities. The algorithms are demonstrated on a robotic manipulation example, where contact sequences are discovered online.

arXiv Page | PDF

Published: 2026-04-20 15:40:45

Authors: Zijian Li, Chenyu Xu, Xin Hua, Yongqiang Du, Xin Liu, Tao Lin, Xi Xiao, Kejin Wei

Categories: quant-ph, physics.optics

Abstract:
Integrated photonics is widely regarded as a key enabler for scalable quantum key distribution (QKD), offering compactness, stability, and compatibility with semiconductor fabrication. Despite rapid advances in chip-based QKD, the implementation security of integrated photonic components remains insufficiently understood. Here we present the first systematic study of an implementation-level security vulnerability associated with p-n junction-based variable optical attenuators (VOAs), a ubiquitous component in integrated QKD transmitters. We theoretically and experimentally demonstrate that electrically biased p-n junction VOAs emit spontaneous luminescence. Using a single-photon-sensitive spectral measurement technique, we identify the emission wavelength to be centered around 1107 nm, well separated from the C-band quantum signals. This spectral separation gives rise to a previously unrecognized wavelength-resolved side channel, enabling potential wavelength-splitting attacks without directly disturbing the encoded quantum states. By incorporating the measured luminescence into a quantitative security analysis, we show that even extremely weak emission can lead to non-negligible information leakage. Our findings reveal a fundamental and previously overlooked security risk in photonic integrated QKD systems and highlight the necessity of security-aware device design for future integrated quantum communication technologies.

arXiv Page | PDF

Published: 2026-04-20 15:32:23

Authors: Gustavo Castillo, Nicolás Mujica

Categories: cond-mat.soft, cond-mat.stat-mech

Abstract:
We investigate the kinetics of particle aggregation within the framework of the Smoluchowski coagulation equation, extending it to account for electrostatic interactions among charged clusters. Using a stochastic Monte Carlo implementation, we examine how different charge distributions and net system charge affect cluster growth dynamics. Electrostatic interactions are incorporated directly into the classical Brownian collision kernel, yielding charge-dependent modifications of the collision rates that may either enhance or suppress aggregation depending on the signs and magnitudes of the interacting charges. Our simulations reveal distinct regimes of growth: at intermediate times, charge heterogeneity accelerates or delays aggregation depending on the initial underlying charge distribution, while at long times the system tends toward quasi--stationary states whose properties depend on the net charge. Comparisons between Gaussian and Cauchy--Lorentz initial charge statistics highlight the role of heavy-tailed distributions in promoting faster cluster growth. These findings contribute to a unified understanding of coagulation kinetics in charged particulate systems, with potential implications for aerosol and astrophysical coagulation processes, volcanic ash aggregation, and clustering in industrial fluidized granular beds.

arXiv Page | PDF

Published: 2026-04-20 15:22:59

Authors: Chad Coleman, W. Russell Neuman, Manan Shah, Ali Dasdan, Matthew Crispi, Morris Chiang, Zack Leitman, Mustafa Poonawala

Categories: cs.AI

Abstract:
We present Six Llamas, a comparative study examining whether large language models fine-tuned on distinct religious corpora encode systematically different patterns of ethical reasoning. Six variants of Meta-Llama-3.1-8B are constructed: one unmodified control and five LoRA-adapted models trained exclusively on the sacred and theological texts of Christianity, Islam, Judaism, Hinduism, or Buddhism. All six models are probed with an identical battery of 17 standardized ethical prompts spanning moral dilemmas, game-theoretic scenarios, public policy questions, and moral-psychological self-assessments. To assess robustness and reproducibility, we implement a multi-temperature sampling design spanning ten temperature settings. We compute response consistency metrics, pairwise inter-model agreement rates, temperature sensitivity coefficients across four prompt domains, and run-to-run stability analyses. Findings show that LoRA-adapted models produce ethical reasoning patterns that are (a) systematically differentiated from the base model, (b) consistent with the moral logics of their training traditions, (c) structured along interpretable dimensions in moral-philosophical space, (d) core ethical positions remain stable across temperature variations for high-consensus dilemmas. The Trolley Problem achieves 100% consistency across all models and temperatures, while (e) tradition-specific divergence intensifies at higher temperatures in morally contested domains, and (f) the base model exhibits the highest overall response consistency (mean 88.3%), suggesting LoRA adaptation introduces both tradition-specific signal and increased sampling sensitivity. The study offers a proof-of-concept for the condensate comparative method using differentially trained language models as instruments for cultural and ethical analysis and identifies specific criteria for falsification and planned extensions.

arXiv Page | PDF

Published: 2026-04-20 15:22:13

Authors: Oleksiy Dovgoshey, Olga Rovenska

Categories: math.GN

Abstract:
Let $G=(V,E)$ be a finite connected graph with vertex set $V$ and edge set $E$, and let $U(G)$ be the set of all ultrametric spaces $(V,d_l)$ generated by vertex labelings $l\colon V \to \mathbb R^+$. We prove that the inequality $$ |D(V)| \le |E| + 1 $$ holds for all $(V,d_l) \in U(G)$, where $D(V)$ is the distance set of $(V,d_l)$. The necessary and sufficient conditions under which the above inequality turns to an equality are found. Moreover, we prove that each connected graph with non-negative vertex labeling generates a pseudoultrametric space and find some sufficient conditions under which this space is ultrametric.

arXiv Page | PDF

Published: 2026-04-20 15:20:22

Authors: Yu-Cheng Qiu, Yongchao Zhang

Categories: hep-ph, astro-ph.HE

Abstract:
The axion-like particles $a$ can be produced in the Sun via the process of $p + D \to {}^3{\rm He} +a$, with mass up to 5.5 MeV. The photons in the subsequent decay $a \to γγ$ can deviate significantly from the Sun, or even from roughly the opposite direction of the Sun. The nontrivial angular and spectral distributions of such photons enable us new methods to detect the {\it lights from the darkness}. In this letter, we consider both the space detection and terrestrial experiments at the South Pole. As a result of the two-body decay and the geometric effects, there exists a critical height for the terrestrial experiments, below which there is no photon for some regions of the parameter space. With the sensitivities of $10^{-16}$ ($10^{-17}$) erg cm$^{-2}$ s$^{-1}$ for the MeV-scale photons in future space and terrestrial experiments, the coupling $g_{aγ}$ of $a$ to photons can be probed up to $3\times10^{-12}$ ($1\times10^{-12}$) GeV$^{-1}$, well surpassing the current supernova limits.

arXiv Page | PDF

Published: 2026-04-20 15:16:32

Authors: Boan Zhang, Wen Li, Guanhua Yu, Xiyang Liu, Wenchao Chen, Long Tian

Categories: cs.CV

Abstract:
Diffusion models have achieved outstanding performance in unsupervised industrial anomaly detection (uIAD) by learning a manifold of normal data under the common assumption that off-manifold anomalies are harder to generate, resulting in larger reconstruction errors in data space or lower probability densities in the tractable latent space. However, their iterative denoising and noising nature leads to slow inference. In this paper, we propose OSD-IRF, a novel one-step diffusion with inverse residual fields, to address this limitation for uIAD task. We first train a deep diffusion probabilistic model (DDPM) on normal data without any conditioning. Then, for a test sample, we predict its inverse residual fields (IRF) based on the noise estimated by the well-trained parametric noise function of the DDPM. Finally, uIAD is performed by evaluating the probability density of the IRF under a Gaussian distribution and comparing it with a threshold. Our key observation is that anomalies become distinguishable in this IRF space, a finding that has seldom been reported in prior works. Moreover, OSD-IRF requires only single step diffusion for uIAD, thanks to the property that IRF holds for any neighboring time step in the denoising process. Extensive experiments on three widely used uIAD benchmarks show that our model achieves SOTA or competitive performance across six metrics, along with roughly a 2X inference speedup without distillation.

arXiv Page | PDF

Published: 2026-04-20 15:13:11

Authors: Mei Dong, Linbo Wang, Lin Liu, Oliver Dukes

Categories: stat.ME

Abstract:
We discuss the regression-by-composition framework of Farewell, Daniel, Stensrud and Huitfeldt, highlighting a key consequence of its sequential construction: order dependence. Reordering the flows may change the implied conditional distribution, the interpretation of model parameters, and the associated estimation problem, with consequences for model specification, interpretation, and inference.

arXiv Page | PDF

Published: 2026-04-20 15:06:41

Authors: Andrew Cochran, Harshvardhan Gupta, Vishal Jain, Maysamreza Chamanzar, Gianluca Piazza

Categories: physics.optics

Abstract:
We introduce a novel electro-optomechanic neural sensor for realizing ultra-compact neural recording probes that can detect and relay electrophysiology signals from within neural tissue. This technology addresses outstanding challenges faced by existing neural recording technologies, including the resolution trade-off with signal-to-noise-ratio (SNR) due to the high impedances of small electrodes, and lingering stimulation artifacts. The sensor employs a highly miniaturized NEMS (nano-electromechanical systems) electrostatic transducer that modulates a silicon photonic microdisk resonator to convert electrical signals to an optical signal modulation. We have been able to achieve a limit of detection down to 110 microvolts, making the sensor sensitive enough to detect neural signals. This sensitive electro-optomechanic sensor directly detects electrophysiology signals and converts them to optomechanic modulation for effective transmission to outside the brain, which provides the unique potential for massive multiplexing of neural recordings. This design eliminates the need for bulky backend headstages that limit neural recording on awake free-roaming subjects. The ability of the device to record electrophysiological signals has been demonstrated using benchtop characterization and ex-vivo recordings from live neural tissue.

arXiv Page | PDF

Published: 2026-04-20 15:04:37

Authors: Alexandra Volokhova, Alex Hernandez-Garcia

Categories: cs.AI, cs.CY

Abstract:
Artificial intelligence (AI) technologies are increasingly used in modern weapons systems. Notably, these systems have recently been involved in mass killings and destruction at scale. Furthermore, there is currently a strong interest and competition among powerful players to accelerate the proliferation of weapons with automated or AI-based components, a phenomenon known as AI arms race. This competition poses a risk of causing even more deaths and devastation in the future, as well as increased power and wealth inequality. In this work, we aim to shed light on the role of AI researchers as implicated subjects in the harms caused by weapons enabled by AI technologies. We investigate and discuss the specifics of this implication and explore ways to transfigure this position of implication into one of differentiated, long-distance solidarity with the victims of technologically fortified injustices.

arXiv Page | PDF

Published: 2026-04-20 14:57:01

Authors: Iva Sović, Ivan Martinović, Marin Oršić

Categories: cs.CV

Abstract:
Early action prediction seeks to anticipate an action before it fully unfolds, but limited visual evidence makes this task especially challenging. We introduce EAST, a simple and efficient framework that enables a model to reason about incomplete observations. In our empirical study, we identify key components when training early action prediction models. Our key contribution is a randomized training strategy that samples a time step separating observed and unobserved video frames, enabling a single model to generalize seamlessly across all test-time observation ratios. We further show that joint learning on both observed and future (oracle) representations significantly boosts performance, even allowing an encoder-only model to excel. To improve scalability, we propose a token masking procedure that cuts memory usage in half and accelerates training by 2x with negligible accuracy loss. Combined with a forecasting decoder, EAST sets a new state of the art on NTU60, SSv2, and UCF101, surpassing previous best work by 10.1, 7.7, and 3.9 percentage points, respectively.

arXiv Page | PDF

Published: 2026-04-20 14:53:33

Authors: Wolfgang Messner

Categories: stat.ME

Abstract:
In an editorial in the Journal of Marketing, Steenkamp et al. (2026) make a valuable and timely intervention by urging marketing scholars to move beyond dichotomous significance testing and to report effect sizes that speak to substantive significance. Their editorial is especially strong in its insistence on exact p-values, richer statistical reporting, and closer alignment between rigor and relevance. Yet, their framework omits the local form of Cohen's f^2, that is f(B)^2 as an effect-size measure for the contribution of an individual predictor or predictor block B within a multivariable model. That omission matters because much of marketing research relies on regression-type models in which the central theoretical question is not merely whether a model fits globally, but whether a focal construct adds meaningful explanatory power beyond competing predictors and controls. This commentary argues that the R-squared foundation of local Cohen's f(B)^2 is a strength, especially in large-sample settings. Moreover, f-squared-type local effect sizes can be extended beyond ordinary least squares to multilevel models and, more tentatively, to neural networks and other machine-learning models.

arXiv Page | PDF

Published: 2026-04-20 14:42:47

Authors: Daniela Baiamonte, Elena Fano, Matteo Gabburo, Stefano Simonazzi, Leonardo Rigutini, Andrea Zugarini

Categories: cs.CL, cs.AI

Abstract:
Vision Language Models (VLMs) achieved rapid progress in the recent years. However, despite their growth, VLMs development is heavily grounded on English, leading to two main limitations: (i) the lack of multilingual and multimodal datasets for training, and (ii) the scarcity of comprehensive evaluation benchmarks across languages. In this work, we address these gaps by introducing a new comprehensive suite of resources for VLMs training and evaluation spanning five European languages (English, French, German, Italian, and Spanish). We adopt a regeneration-translation paradigm that produces high-quality cross-lingual resources by combining curated synthetic generation and manual annotation. Specifically, we build Multi-PixMo, a training corpus obtained regenerating examples from Pixmo pre-existing datasets with permissively licensed models: PixMo-Cap, PixMo-AskModelAnything, and CoSyn-400k. On the evaluation side, we construct a set of multilingual benchmarks derived translating widely used English datasets (MMbench, ScienceQA, MME, POPE, AI2D). We assess the quality of these resources through qualitative and quantitative human analyses, measuring inter-annotator agreement. Additionally, we perform ablation studies to demonstrate the impact of multilingual data, with respect to English only, in VLMs training. Experiments, comprising 3 different models show that using multilingual, multimodal examples for training VLMs aids is consistently beneficial on non-English benchmarks, with positive transfer to English as well.

arXiv Page | PDF

Published: 2026-04-20 14:41:47

Authors: Jihong Guan, Jiaqi Wang, Wengen Li, Hanchen Yang, Yichao Zhang, Shuigeng Zhou

Categories: cs.AI

Abstract:
Knowledge Graphs (KGs) are composed of triples, and the goal of Knowledge Graph Completion (KGC) is to infer the missing factual triples. Traditional KGC tasks predict missing elements in a triple given one or two of its elements. As a more realistic task, the Triple Set Prediction (TSP) task aims to infer the set of missing triples conditioned only on the observed knowledge graph, without assuming any partial information about the missing triples. Existing TSP methods predict the set of missing triples in a triple-by-triple manner, falling short in capturing the dependencies among the predicted triples to ensure consistency. To address this issue, we propose a novel discrete diffusion model termed DiffTSP that treats TSP as a generative task. DiffTSP progressively adds noise to the KG through a discrete diffusion process, achieved by masking relational edges. The reverse process then gradually recovers the complete KG conditioned on the incomplete graph. To this end, we design a structure-aware denoising network that integrates a relational context encoder with a relational graph diffusion transformer for knowledge graph generation. DiffTSP can generate the complete set of triples in a one-pass manner while ensuring the dependencies among the predicted triples. Our approach achieves state-of-the-art performance on three public datasets. Code: https://github.com/ADMIS-TONGJI/DiffTSP.

arXiv Page | PDF

Published: 2026-04-20 14:31:54

Authors: Sreejita Das, Enrique Macías, Nicolas T. Kurtovic, Til Birnstiel, Elena M. Viscardi, Pietro Curone

Categories: astro-ph.EP, astro-ph.SR

Abstract:
Extended, low surface brightness emission has been identified in a number of protoplanetary disks, in tension with predictions of radial drift theory. We aim to investigate the nature and origin of faint, extended dust emission in the outer regions of protoplanetary disks, which we define as the Halo, using multiwavelength (sub-)millimeter continuum observations of three systems: Elias 2-24, IM Lup, and DM Tau. We utilize Atacama Large Millimeter Array (ALMA) observations of our targets to perform spectral energy distribution (SED) fitting with four dust compositions and, derive radial profiles of their dust properties. The halos identified in our sources account for 20 - 30% of the total flux density at (sub-)millimeter wavelengths. In Elias 2-24, IM Lup, and DM Tau, we infer maximum grain sizes of 2 cm, $<$ 4 mm, and $<$ 9 mm, with the data best reproduced by porous amorphous carbon, compact amorphous carbon, and compact organic carbon compositions respectively. Their total dust masses are $125^{+34}_{-23}$, $301^{+139}_{-101}$, and $829^{+761}_{-378}$ M$_{\oplus}$, with corresponding halo masses of $33^{+12}_{-6}$, $103^{+25}_{-17}$, and $316^{+202}_{-117}$ M$_{\oplus}$. The halos of IM Lup and DM Tau are dust rich with gas-to-dust mass ratios of 64 and 18 respectively. In all three disks, the dust drift and growth timescales are shorter than the disk ages, implying that the smooth outer disks should not exist. The halos in our sources hold relevant fractions of the total dust reservoir, demonstrating that they play an important role in alleviating the mass-budget problem. While the persistence of halos in IM Lup and DM Tau could be explained by late infall, the presence of cm-sized grains in Elias 2-24's halo suggests that hidden pressure traps also play a role.

arXiv Page | PDF

Published: 2026-04-20 14:09:01

Authors: Eranga Bandara, Asanga Gunaratna, Ross Gore, Anita H. Clayton, Christopher K. Rhea, Sachini Rajapakse, Isurunima Kularathna, Sachin Shetty, Ravi Mukkamala, Xueping Liang, Preston Samuel, Atmaram Yarlagadda

Categories: cs.AI

Abstract:
Privacy represents one of the most critical yet underaddressed barriers to AI adoption in mental healthcare -- particularly in high-sensitivity operational environments such as military, correctional, and remote healthcare settings, where the risk of patient data exposure can deter help-seeking behavior entirely. Existing AI-enabled psychiatric decision support systems predominantly rely on cloud-based inference pipelines, requiring sensitive patient data to leave the device and traverse external servers, creating unacceptable privacy and security risks in these contexts. In this paper, we propose a zero-egress, on-device AI platform for privacy-preserving psychiatric decision support, deployed as a cross-platform mobile application. The proposed system extends our prior work on fine-tuned LLM consortiums for psychiatric diagnosis standardization by fundamentally re-architecting the inference pipeline for fully local execution -- ensuring that no patient data is transmitted to, processed by, or stored on any external server at any stage. The platform integrates a consortium of three lightweight, fine-tuned, and quantized open-source LLMs -- Gemma, Phi-3.5-mini, and Qwen2 -- selected for their compact architectures and proven efficiency on resource-constrained mobile hardware. An on-device orchestration layer coordinates ensemble inference and consensus-based diagnostic reasoning, producing DSM-5-aligned assessments for conditions. The platform is designed to assist clinicians with differential diagnosis and evidence-linked symptom mapping, as well as to support patient-facing self-screening with appropriate clinical safeguards. Initial evaluation demonstrates that the proposed zero-egress deployment achieves diagnostic accuracy comparable to its server-side predecessor while sustaining real-time inference latency on commodity mobile hardware.

arXiv Page | PDF

Published: 2026-04-20 14:01:11

Authors: Ryo Yoshida, Shinnosuke Isono, Taiga Someya, Yohei Oseki, Tatsuki Kuribayashi

Categories: cs.CL

Abstract:
Surprisal theory hypothesizes that the difficulty of human sentence processing increases linearly with surprisal, the negative log-probability of a word given its context. Computational psycholinguistics has tested this hypothesis using language models (LMs) as proxies for human prediction. While surprisal derived from recent neural LMs generally captures human processing difficulty on naturalistic corpora that predominantly consist of simple sentences, it severely underestimates processing difficulty on sentences that require syntactic disambiguation (garden-path effects). This leads to the claim that the processing difficulty of such sentences cannot be reduced to surprisal, although it remains possible that neural LMs simply differ from humans in next-word prediction. In this paper, we investigate whether it is truly impossible to construct a neural LM that can explain garden-path effects via surprisal. Specifically, instead of evaluating off-the-shelf neural LMs, we fine-tune these LMs on garden-path sentences so as to better align surprisal-based reading-time estimates with actual human reading times. Our results show that fine-tuned LMs do not overfit and successfully capture human reading slowdowns on held-out garden-path items; they even improve predictive power for human reading times on naturalistic corpora and preserve their general LM capabilities. These results provide an existence proof for a neural LM that can explain both garden-path effects and naturalistic reading times via surprisal, but also raise a theoretical question: what kind of evidence can truly falsify surprisal theory?

arXiv Page | PDF

Published: 2026-04-20 13:57:49

Authors: Xiaoyu Ma, Fang Fang, Ximing Xie, Xianbin Wang, Lajos Hanzo

Categories: cs.ET, quant-ph

Abstract:
The limited number of qubits is a major bottleneck in Quantum Approximate Optimization Algorithm (QAOA) for large-scale combinatorial optimization in the Noisy Intermediate-Scale Quantum (NISQ) era. To make progress, existing techniques rely on qubit reduction at the cost of information loss, hence leading to degraded computational performance. As a remedy, we propose the Equivalence-preserving Qubit Efficient QAOA (EQE-QAOA), which significantly reduces the required number of qubits without degrading the performance of QAOA. By exploiting intrinsic symmetries and conserved quantities, we first demonstrate that the QAOA dynamics are strictly confined to an invariant subspace of the Hilbert space. We subsequently prove that the evolution within this subspace is exactly equivalent to that of the full-scale system, achieving the same optimal solution as the original QAOA. Moreover, to reduce the number of qubits, we propose an isometric mapping that re-encodes the subspace into a space relying on fewer qubits. Furthermore, we derive the applicability conditions of EQE-QAOA and show that it is broadly applicable to large-scale combinatorial optimization problems, excluding only unconstrained problems with completely independent variables. Numerical simulations based on Max-Cut instances validate that EQE-QAOA significantly reduces qubit requirements and computational resources, while preserving exact optimization performance.

arXiv Page | PDF

Published: 2026-04-20 13:52:12

Authors: Youyuan Long, Gokhan Solak, Arash Ajoudani

Categories: cs.LG

Abstract:
Accurate dynamical modeling is essential for simulation and control of embodied systems, yet first-principles models of electromechanical systems often fail to capture complex dissipative effects such as joint friction, stray losses, and structural damping. While residual-learning physics-informed neural networks (PINNs) can effectively augment imperfect first-principles models with data-driven components, the residual terms are typically implemented as unconstrained multilayer perceptrons (MLPs), which may inadvertently inject artificial energy into the system. To more faithfully model the dissipative dynamics, we propose DiLaR-PINN, a dissipative latent residual PINN designed to learn unmodeled dissipative effects in a physically consistent manner. Structurally, the residual network operates only on unmeasurable (latent) state components and is parameterized in a skew-dissipative form that guarantees non-increasing energy for any choice of network parameters. To enable stable and data-efficient training under partial measurability of the state, we further develop a recurrent rollout scheme with a curriculum-based sequence length extension strategy. We validate DiLaR-PINN on a real-world helicopter system and compare it against four baselines: a pure physical model (without a residual network), an unstructured residual MLP, a DiLaR variant with a soft dissipativity constraint, and a black-box LSTM. The results demonstrate that DiLaR-PINN more accurately captures dissipative effects and achieves superior long-horizon extrapolation performance.

arXiv Page | PDF

Published: 2026-04-20 13:48:17

Authors: Zepeng Sun, Naichuan Zheng, Hailun Xia, Junjie Wu, Liwei Bao, Xiaotai Zhang

Categories: cs.CV

Abstract:
Temporal Action Detection (TAD) in untrimmed videos is currently dominated by Transformer-based architectures. While high-performing, their quadratic computational complexity and substantial parameter redundancy limit deployment in resource-constrained environments. In this paper, we propose LiquidTAD, a novel parameter-efficient framework that replaces cumbersome self-attention layers with parallelized ActionLiquid blocks. Unlike traditional Liquid Neural Networks (LNNs) that suffer from sequential execution bottlenecks, LiquidTAD leverages a closed-form continuous-time (CfC) formulation, allowing the model to be reformulated as a parallelizable operator while preserving the intrinsic physical prior of continuous-time dynamics. This architecture captures complex temporal dependencies with $O(N)$ linear complexity and adaptively modulates temporal sensitivity through learned time-constants ($τ$), providing a robust mechanism for handling varying action durations. To the best of our knowledge, this work is the first to introduce a parallelized LNN-based architecture to the TAD domain. Experimental results on the THUMOS-14 dataset demonstrate that LiquidTAD achieves a highly competitive Average mAP of 69.46\% with only 10.82M parameters -- a 63\% reduction compared to the ActionFormer baseline. Further evaluations on ActivityNet-1.3 and Ego4D benchmarks confirm that LiquidTAD achieves an optimal accuracy-efficiency trade-off and exhibits superior robustness to temporal sampling variations, advancing the Pareto frontier of modern TAD frameworks.

arXiv Page | PDF

Published: 2026-04-20 13:30:01

Authors: Simone Catanzaro, Elvira Di Nardo

Categories: math.ST, math.PR, stat.CO

Abstract:
The first passage time problem is considered for stochastic logistic growth model with constant harvesting and multiplicative environmental noise. Explicit expressions for the moments and cumulants of both upcrossing and downcrossing FPTs in the presence of constant thresholds are obtained through a power-series expansion of the Laplace transform. Then a closed-form representation of the FPT density is recovered via an orthogonal Laguerre--Gamma expansion . This representation is used to numerically evaluate FPT densities, with the truncation order controlling the trade-off between accuracy and stability. Numerical experiments based on Monte Carlo simulations confirm the high accuracy of the method in regimes of moderate dispersion and highlight its limitations when higher-order moments grow rapidly. Application to fisheries management models shows that the method remains effective even for large-scale population. Finally, the approximated density is satisfactory used to estimate some parameters of the model.

arXiv Page | PDF

Published: 2026-04-20 13:28:47

Authors: Kangrou Guo, Xiumin Huang, Dong Lai

Categories: astro-ph.EP

Abstract:
A key feature of close-in, multiple super-Earth (SE) systems is the tendency for adjacent planet pairs to lie just wide of low-order mean-motion resonances (MMR). This period ratio distribution has motivated numerous theoretical studies, particularly those invoking post-disk processes that perturb initially resonant architectures. We investigate whether orbital instability among cold Jupiters (CJs) can perturb inner SE systems initially in MMR. We show that a single pericenter passage of a highly eccentric CJ can disrupt inner resonances once a critical perturbation strength is exceeded, increasing the libration amplitude of the resonant angles. However, N-body simulations show that deep penetration of CJs into the inner system is uncommon, with $\lesssim 10-20\%$ of cases reaching $\lesssim 10\%$ of the initial semi-major axis of the innermost CJ. Motivated by these results, we use secular perturbation theory to quantify the impact of time-dependent forcing from scattering CJs on the eccentricity and resonant-angle evolution of inner SEs. We find that for typical systems (e.g., with SEs at $\sim 0.1$ au and CJs at a few au), such forcing can efficiently disrupt resonances, driving resonance-angle circulation in most systems ($\gtrsim 60\%$ for 2:1 and $\sim 85\%$ for 3:2 configurations). Thus, even when the "final" CJ has little effect on the "current" SEs, its earlier scattering history can leave significant imprints on the system architecture. This mechanism, and similar ones involving more abundant cold Neptunes, provide a natural source of dynamical "kicks" and offer a pathway for producing the observed trough-peak structure in the period ratio distribution of Kepler multi-planet systems.

arXiv Page | PDF

Published: 2026-04-20 13:27:05

Authors: Thamilvendhan Munirathinam

Categories: cs.CR, cs.CL

Abstract:
Current open-source prompt-injection detectors converge on two architectural choices: regular-expression pattern matching and fine-tuned transformer classifiers. Both share failure modes that recent work has made concrete. Regular expressions miss paraphrased attacks. Fine-tuned classifiers are vulnerable to adaptive adversaries: a 2025 NAACL Findings study reported that eight published indirect-injection defenses were bypassed with greater than fifty percent attack success rates under adaptive attacks. This work proposes seven detection techniques that each port a specific mechanism from a discipline outside large-language-model security: forensic linguistics, materials-science fatigue analysis, deception technology from network security, local-sequence alignment from bioinformatics, mechanism design from economics, spectral signal analysis from epidemiology, and taint tracking from compiler theory. Three of the seven techniques are implemented in the prompt-shield v0.4.1 release (Apache 2.0) and evaluated in a four-configuration ablation across six datasets including deepset/prompt-injections, NotInject, LLMail-Inject, AgentHarm, and AgentDojo. The local-alignment detector lifts F1 on deepset from 0.033 to 0.378 with zero additional false positives. The stylometric detector adds 11.1 percentage points of F1 on an indirect-injection benchmark. The fatigue tracker is validated via a probing-campaign integration test. All code, data, and reproduction scripts are released under Apache 2.0.

arXiv Page | PDF

Published: 2026-04-20 13:26:24

Authors: A. Errehymy, S. K. Maurya, M. Govender, K. N. Singh, J. Rayimbaev, B. Myrzakulova, S. Murodov

Categories: gr-qc

Abstract:
In this work, we explore for the first time slowly rotating traversable wormholes embedded in holographic dark energy. We focus on three representative holographic dark energy models -- Rényi, mixed, and Moradpour -- and construct the wormhole shape functions directly from these energy density profiles using a Teo-type rotating wormhole metric. This allows us to examine the wormhole geometry in detail, including throat structure, the flaring-out condition for safe traversal, and violations of the null energy condition. To capture the effects of different redshift behaviors, we consider three smooth hyperbolic redshift functions -- Sinh, Cosh, and Tanh -- and study how they influence photon motion, null geodesics, effective potentials, photon-sphere locations, and Lense-Thirring precession caused by wormhole rotation. Our analysis shows that cuspy Rényi profiles produce tighter photon orbits and stronger asymmetry, while smoother mixed and Moradpour profiles allow more circular paths and weaker frame-dragging effects. Finally, we calculate the shadows cast by these wormholes, finding that Rényi-supported wormholes generate smaller, asymmetric shadows, whereas mixed and Moradpour-supported wormholes produce larger, nearly circular silhouettes. Altogether, this study provides a detailed theoretical picture of photon dynamics, shadow morphology, and relativistic effects in slowly rotating wormholes within realistic holographic dark energy environments, offering potential guidance for observational signatures of these exotic objects.

arXiv Page | PDF

Published: 2026-04-20 13:23:57

Authors: Yangdi Jiang, Xiaotian Chang, Cyrus Mostajeran

Categories: math.ST, cs.LG, stat.ML

Abstract:
\We introduce the horospherical depth, an intrinsic notion of statistical depth on Hadamard manifolds, and define the Busemann median as the set of its maximizers. The construction exploits the fact that the linear functionals appearing in Tukey's half-space depth are themselves limits of renormalized distance functions; on a Hadamard manifold the same limiting procedure produces Busemann functions, whose sublevel sets are horoballs, the intrinsic replacements for halfspaces. The resulting depth is parametrized by the visual boundary, is isometry-equivariant, and requires neither tangent-space linearization nor a chosen base point.For arbitrary Hadamard manifolds, we prove that the depth regions are nested and geodesically convex, that a centerpoint of depth at least $1/(d+1)$ exists, and hence that the Busemann median exists for every Borel probability measure. Under strictly negative sectional curvature and mild regularity assumptions, the depth is strictly quasi-concave and the median is unique. We also establish robustness: the depth is stable under total-variation perturbations, and under contamination escaping to infinity the limiting median depends on the escape direction but not on how far the contaminating mass has moved along the geodesic ray, in contrast with the Fréchet mean. Finally, we establish uniform consistency of the sample depth and convergence of sample depth regions and sample Busemann medians; on symmetric spaces of noncompact type, the argument proceeds through a VC analysis of upper horospherical halfspaces, while on general Hadamard manifolds it follows from a compactness argument under a mild non-atomicity assumption.

arXiv Page | PDF

Published: 2026-04-20 13:23:42

Authors: Kilian Rausch

Categories: math.NT

Abstract:
In this paper, we calculate an exact formula for the number of partitions of a natural number $n$, where the largest part is even and no odd parts appears more than two times. The generating functions of the number of these partitions is a mixed mock modular form of weight 0. In order to obtain the formula we apply an extended version of the circle method, during which we need to bound Kloosterman sums and similar exponential sums as well as Mordell-type integrals.

arXiv Page | PDF

Published: 2026-04-20 13:20:57

Authors: Lorenz Brehme, Thomas Ströhle, Ruth Breu

Categories: cs.IR, cs.AI

Abstract:
Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge to answer questions more accurately. However, research on evaluating RAG systems-particularly the retriever component-remains limited, as most existing work focuses on single-context retrieval rather than multi-hop queries, where individual contexts may appear irrelevant in isolation but are essential when combined. In this research, we use the HotPotQA, MuSiQue, and SQuAD datasets to simulate a RAG system and compare three LLM-as-judge evaluation strategies, including our proposed Context-Aware Retriever Evaluation (CARE). Our goal is to better understand how multi-hop reasoning can be most effectively evaluated in RAG systems. Experiments with LLMs from OpenAI, Meta, and Google demonstrate that CARE consistently outperforms existing methods for evaluating multi-hop reasoning in RAG systems. The performance gains are most pronounced in models with larger parameter counts and longer context windows, while single-hop queries show minimal sensitivity to context-aware evaluation. Overall, the results highlight the critical role of context-aware evaluation in improving the reliability and accuracy of retrieval-augmented generation systems, particularly in complex query scenarios. To ensure reproducibility, we provide the complete data of our experiments at https://github.com/lorenzbrehme/CARE.

arXiv Page | PDF

Published: 2026-04-20 13:12:04

Authors: Alberto Tagliaferro, Bruno Guindani, Livia Lestingi, Matteo Rossi

Categories: cs.SE

Abstract:
Early-stage specifications of safety-critical systems are typically expressed in natural language, making it difficult to derive formal properties suitable for verification and needed to guarantee safety. While recent Large Language Model (LLM)-based approaches can generate formal artifacts from text, they mainly focus on syntactic correctness and do not ensure semantic alignment between informal requirements and formally verifiable properties. We propose an agentic methodology that automatically extracts verification-ready properties from unstructured specifications. The modular pipeline combines requirement extraction, compatibility filtering with respect to a target formalism, and translation into formal properties. Experimental results across three scenarios show that the pipeline generates syntactically and semantically aligned formal properties with a 77.8% accuracy. By explicitly accounting for modeling and verification constraints, the approach is a paving step towards exploiting Artificial Intelligence (AI) to bridge the gap between informal descriptions and semantically meaningful formal verification.

arXiv Page | PDF

Published: 2026-04-20 13:09:13

Authors: Zhen Liu, Yuhan Liu, Jinjun Wang, Jianyi Liu, Wei Song, Jingwen Fu

Categories: cs.CV

Abstract:
Vision-and-Language Navigation requires agents to follow natural-language instructions in visually changing environments. A central challenge is the dynamic entanglement between language and observations: the meaning of instruction shifts as the agent's field of view and spatial context evolve. However, many existing models encode the instruction as a static global representation, limiting their ability to adapt instruction meaning to the current visual context. We therefore model instruction understanding as an Instruction-as-State variable: a decision-relevant, token-level instruction state that evolves step by step conditioned on the agent's perceptual state, where the perceptual state denotes the observation-grounded navigation context at each step. To realize this principle, we introduce State-Entangled Environment-Guided Instruction Understanding (S-EGIU), a coarse-to-fine framework for state-conditioned segment activation and token-level semantic refinement. At the coarse level, S-EGIU activates the instruction segment whose semantics align with the current observation. At the fine level, it refines the activated segment through observation-guided token grounding and contextual modeling, sharpening its internal semantics under the current observation. Together, these stages maintain an instruction state that is continuously updated according to the agent's perceptual state during navigation. S-EGIU delivers strong performance on several key metrics, including a +2.68% SPL gain on REVERIE Test Unseen, and demonstrates consistent efficiency gains across multiple VLN benchmarks, underscoring the value of dynamic instruction--perception entanglement.

arXiv Page | PDF

Published: 2026-04-20 12:56:25

Authors: Benjamin Hertzsch, Job Feldbrugge, Rien van de Weygaert

Categories: astro-ph.CO, astro-ph.GA

Abstract:
The caustic skeleton is a parameter-free and mathematically rigorous formalism for tracing the hierarchical formation history of the multiscale cosmic web from the singularities in the underlying dark matter flow. In the present study, we explicitly use the multistreaming nature of the cosmic mass distribution to address the influence of the weblike embedding on the galaxy populations and discern their properties in different web environments. To this end, we construct the multiscale caustic skeleton of the dark mass distribution in the state-of-the-art suite of the large-scale IllustrisTNG simulations. In addition to the multistreaming dark matter density field, we assess the characteristic properties of the intergalactic baryonic gas in the vicinity of the caustics. Next, we associate the galaxies with the voids, walls, filaments and cluster nodes, and investigate their colours and star formation activities. A unique feature of the analysis is that it explicitly addresses the multiscale aspects with respect to the galaxy population, assessing issues such as the fraction of (blue) galaxies as a function of the scale of the cosmic web pattern and its caustic features. We find that the galaxy properties form a continuum in the scale-space cosmic web. Intimately coupled to the hierarchical build-up of the cosmic structure, it also allows us to systematically assess the impact of the formation time of the various structural components of the cosmic web on the galaxy properties. This furthers insight into the establishment of the observed colour-density relation of galaxies.

arXiv Page | PDF

Published: 2026-04-20 12:55:13

Authors: Mikolaj Zielinski, Eryk Vykysaly, Bartlomiej Biesiada, Jan Baturo, Mateusz Capala, Dominik Belter

Categories: cs.CV, cs.RO

Abstract:
Recent advances in neural rendering have introduced numerous 3D scene representations. Although standard computer vision metrics evaluate the visual quality of generated images, they often overlook the fidelity of surface geometry. This limitation is particularly critical in robotics, where accurate geometry is essential for tasks such as grasping and object manipulation. In this paper, we present an evaluation pipeline for neural rendering methods that focuses on geometric accuracy, along with a benchmark comprising 19 diverse scenes. Our approach enables a systematic assessment of reconstruction methods in terms of surface and shape fidelity, complementing traditional visual metrics.

arXiv Page | PDF

Published: 2026-04-20 12:50:21

Authors: Wuhan Chen, Min Gao, Xin Xia, Zongwei Wang, Wentao Li, Shane Culpepper

Categories: cs.IR

Abstract:
Large language models (LLMs) have recently shown promise in recommendation by providing rich semantic knowledge. While most existing approaches rely on external textual corpora to align LLMs with recommender systems, we revisit a more fundamental yet underexplored question: Can recommendation benefit from LLM token embeddings alone without textual input? Through a systematic empirical study, we show that directly injecting token embeddings from a single LLM into sequential recommenders leads to unstable or limited gains, due to semantic misalignment, insufficient task adaptation, and the restricted coverage of individual LLMs. To address these challenges, we propose MLTFR, a Multi-LLM Token Filtering and Routing framework for corpus-free sequential recommendation. MLTFR follows an interaction-guided LLM knowledge integration paradigm, where task-relevant token embeddings are selected via user-guided token filtering to suppress noisy and irrelevant vocabulary signals. To overcome the limitations of single-LLM representations, MLTFR integrates multiple LLM token spaces through a Mixture-of-Experts architecture, with a Fisher-weighted semantic consensus expert to balance heterogeneous experts and prevent domination during training. By jointly filtering informative tokens and aggregating complementary semantic knowledge across multiple LLMs, MLTFR enables stable and effective utilization of LLM token embeddings without textual inputs or backbone modification. Extensive experiments demonstrate that MLTFR consistently outperforms state-of-the-art sequential recommendation baselines and existing alignment methods. Our code is available at: https://github.com/ccwwhhh/MLTFR.

arXiv Page | PDF

Published: 2026-04-20 12:50:12

Authors: R. M. Iakhibbaev, A. I. Mukhaeva, D. M. Tolkachev

Categories: hep-th

Abstract:
Using the Bogoliubov-Parasiuk theorem we derive differential equations for the sum of leading UV divergences of the Kähler potential in the general $\mathcal{N}=1$ supersymmetric chiral theory. The obtained equations recover the limit of the renormalizable Wess-Zumino theory and also allow one to consider non-renormalizable chiral interactions. Some implications of the obtained equations are shown.

arXiv Page | PDF

Published: 2026-04-20 12:48:46

Authors: Ghiglino Davide, Foglino Caterina, Wykowska Agnieszka

Categories: cs.HC, cs.RO

Abstract:
Qualitative methods are important to use alongside quantitative methods to improve Human-Robot Interaction (HRI), yet they are often applied in static or one-off formats that cannot capture how stakeholder perspectives evolve over time. This limitation is especially evident in clinical contexts, where families and patients face heavy burdens and cannot easily participate in repeated research encounters. To address this gap, we introduce continuous focus groups, a longitudinal and co-agential method designed to sustain dialogue with assistive care professionals working with children with autism spectrum disorder (ASD). Three focus groups were organized across successive phases of a robot-assisted therapeutic protocol, enabling participants to revisit and refine earlier views as the intervention progressed. Results show that continuity fostered trust, supported the integration of tacit clinical expertise into design decisions, and functioned as an ethical safeguard by allowing participants to renegotiate involvement and surface new concerns. By bridging the therapeutic iteration of families, children, and clinicians with the research-design iteration of researchers and developers, continuous focus groups provide a methodological contribution that is both feasible in practice and rigorous in design. Beyond autism care, this approach offers a transferable framework for advancing qualitative research in HRI, particularly in sensitive domains where direct user participation is limited and continuity is essential.

arXiv Page | PDF