# Research Papers — 2025-12 · bundle page 1 of 175

> Full-text markdown of papers #1–50 of 8703 published in 2025-12, newest first. AI-readable mirrors of the full 8703-paper 2025-12 archive (50 papers per file).

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### SAC-NeRF: Adaptive Ray Sampling for Neural Radiance Fields via Soft Actor-Critic Reinforcement Learning

Authors: Chenyu Ge
Published: 2025-12-31
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2603.15622v1

Neural Radiance Fields (NeRF) have achieved photorealistic novel view synthesis but suffer from computational inefficiency due to dense ray sampling during volume rendering. We propose SAC-NeRF, a reinforcement learning framework that learns adaptive sampling policies using Soft Actor-Critic (SAC). Our method formulates sampling as a Markov Decision Process where an RL agent learns to allocate samples based on scene characteristics. We introduce three technical components: (1) a Gaussian mixture distribution color model providing uncertainty estimates, (2) a multi-component reward function balancing quality, efficiency, and consistency, and (3) a two-stage training strategy addressing environment non-stationarity. Experiments on Synthetic-NeRF and LLFF datasets show that SAC-NeRF reduces sampling points by 35-48\% while maintaining rendering quality within 0.3-0.8 dB PSNR of dense sampling baselines. While the learned policy is scene-specific and the RL framework adds complexity compared to simpler heuristics, our work demonstrates that data-driven sampling strategies can discover effective patterns that would be difficult to hand-design.

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### SEGB: Self-Evolved Generative Bidding with Local Autoregressive Diffusion

Authors: Yulong Gao, Wan Jiang, Mingzhe Cao, Xuepu Wang, Zeyu Pan, Haonan Yang, Ye Liu, Xin Yang
Published: 2025-12-31
Categories: cs.IR, cs.LG
arXiv: https://arxiv.org/abs/2602.22226v1

In the realm of online advertising, automated bidding has become a pivotal tool, enabling advertisers to efficiently capture impression opportunities in real-time. Recently, generative auto-bidding has shown significant promise, offering innovative solutions for effective ad optimization. However, existing offline-trained generative policies lack the near-term foresight required for dynamic markets and usually depend on simulators or external experts for post-training improvement. To overcome these critical limitations, we propose Self-Evolved Generative Bidding (SEGB), a framework that plans proactively and refines itself entirely offline. SEGB first synthesizes plausible short-horizon future states to guide each bid, providing the agent with crucial, dynamic foresight. Crucially, it then performs value-guided policy refinement to iteratively discover superior strategies without any external intervention. This self-contained approach uniquely enables robust policy improvement from static data alone. Experiments on the AuctionNet benchmark and a large-scale A/B test validate our approach, demonstrating that SEGB significantly outperforms state-of-the-art baselines. In a large-scale online deployment, it delivered substantial business value, achieving a +10.19% increase in target cost, proving the effectiveness of our advanced planning and evolution paradigm.

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### Developmental trajectories of decision making and affective dynamics in large language models

Authors: Zhihao Wang, Yiyang Liu, Ting Wang, Zhiyuan Liu
Published: 2025-12-31
Categories: cs.CY, cs.AI, cs.CL
arXiv: https://arxiv.org/abs/2601.14268v1

Large language models (LLMs) are increasingly used in medicine and clinical workflows, yet we know little about their decision and affective profiles. Taking a historically informed outlook on the future, we treated successive OpenAI models as an evolving lineage and compared them with humans in a gambling task with repeated happiness ratings. Computational analyses showed that some aspects became more human-like: newer models took more risks and displayed more human-like patterns of Pavlovian approach and avoidance. At the same time, distinctly non-human signatures emerged: loss aversion dropped below neutral levels, choices became more deterministic than in humans, affective decay increased across versions and exceeded human levels, and baseline mood remained chronically higher than in humans. These "developmental" trajectories reveal an emerging psychology of machines and have direct implications for AI ethics and for thinking about how LLMs might be integrated into clinical decision support and other high-stakes domains.

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### From Chaos to Clarity: Schema-Constrained AI for Auditable Biomedical Evidence Extraction from Full-Text PDFs

Authors: Pouria Mortezaagha, Joseph Shaw, Bowen Sun, Arya Rahgozar
Published: 2025-12-31
Categories: cs.CL
arXiv: https://arxiv.org/abs/2601.14267v1

Biomedical evidence synthesis relies on accurate extraction of methodological, laboratory, and outcome variables from full-text research articles, yet these variables are embedded in complex scientific PDFs that make manual abstraction time-consuming and difficult to scale. Existing document AI systems remain limited by OCR errors, long-document fragmentation, constrained throughput, and insufficient auditability for high-stakes synthesis. We present a schema-constrained AI extraction system that transforms full-text biomedical PDFs into structured, analysis-ready records by explicitly restricting model inference through typed schemas, controlled vocabularies, and evidence-gated decisions. Documents are ingested using resume-aware hashing, partitioned into caption-aware page-level chunks, and processed asynchronously under explicit concurrency controls. Chunk-level outputs are deterministically merged into study-level records using conflict-aware consolidation, set-based aggregation, and sentence-level provenance to support traceability and post-hoc audit. Evaluated on a corpus of studies on direct oral anticoagulant level measurement, the pipeline processed all documents without manual intervention, maintained stable throughput under service constraints, and exhibited strong internal consistency across document chunks. Iterative schema refinement substantially improved extraction fidelity for synthesis-critical variables, including assay classification, outcome definitions, follow-up duration, and timing of measurement. These results demonstrate that schema-constrained, provenance-aware extraction enables scalable and auditable transformation of heterogeneous scientific PDFs into structured evidence, aligning modern document AI with the transparency and reliability requirements of biomedical evidence synthesis.

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### Speculative Decoding: Performance or Illusion?

Authors: Xiaoxuan Liu, Jiaxiang Yu, Jongseok Park, Ion Stoica, Alvin Cheung
Published: 2025-12-31
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2601.11580v2

Speculative decoding (SD) has become a popular technique to accelerate Large Language Model (LLM) inference, yet its real-world effectiveness remains unclear as prior evaluations rely on research prototypes and unrealistically small batch sizes. We present, to our knowledge, the first systematic study of SD on a production-grade and widely deployed inference engine (vLLM), covering multiple SD variants ($n$-gram, EAGLE/EAGLE-3, Draft-Model, Multi-Token Prediction) across diverse workloads, model scales, and batch sizes. We analyze key factors governing SD performance, and quantify a theoretical upper bound on SD speedup. Our results show that verification by the target model dominates the execution, while acceptance length varies markedly across output token positions, requests, and datasets. Comparing measured performance with theoretical bounds reveals substantial gaps between observed and theoretical upper bounds, and we leverage this observation to highlight new research opportunities that our study opens up in improving SD.

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### From Detection to Diagnosis: Advancing Hallucination Analysis with Automated Data Synthesis

Authors: Yanyi Liu, Qingwen Yang, Tiezheng Guo, Feiyu Qu, Jun Liu, Yingyou Wen
Published: 2025-12-31
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2601.09734v1

Hallucinations in Large Language Models (LLMs), defined as the generation of content inconsistent with facts or context, represent a core obstacle to their reliable deployment in critical domains. Current research primarily focuses on binary "detection" approaches that, while capable of identifying hallucinations, fail to provide interpretable and actionable feedback for model improvement, thus limiting practical utility. To address this limitation, a new research paradigm is proposed, shifting from "detection" to "diagnosis". The Hallucination Diagnosis Task is introduced, a task which requires models to not only detect hallucinations, but also perform error localization, causal explanation, and content correction. We develop the Hallucination Diagnosis Generator (HDG), an automated pipeline that systematically generates high-quality training samples with rich diagnostic metadata from raw corpora through multi-dimensional augmentation strategies including controlled fact fabrication and reasoning chain perturbation. Using HDG-generated data, we train HDM-4B-RL, a 4-billion-parameter hallucination diagnosis model, employing Group Relative Policy Optimization (GRPO) with a comprehensive reward function incorporating structural, accuracy, and localization signals. Experimental results demonstrate that our model surpasses previous state-of-the-art detection models on the HaluEval benchmark while achieving comparable performance to advanced general-purpose models. In comprehensive diagnosis tasks, HDM-4B-RL matches the capabilities of larger general models while maintaining a smaller size. This work validates the feasibility and value of hallucination diagnosis, providing an effective methodology for building more trustworthy and reliable generative AI systems.

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### UR-Bench: A Benchmark for Multi-Hop Reasoning over Ultra-High-Resolution Images

Authors: Siqi Li, Xinyu Cai, Jianbiao Mei, Nianchen Deng, Pinlong Cai, Licheng Wen, Yufan Shen, Xuemeng Yang, Botian Shi, Yong Liu
Published: 2025-12-31
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2601.08748v1

Recent multimodal large language models (MLLMs) show strong capabilities in visual-language reasoning, yet their performance on ultra-high-resolution imagery remains largely unexplored. Existing visual question answering (VQA) benchmarks typically rely on medium-resolution data, offering limited visual complexity. To bridge this gap, we introduce Ultra-high-resolution Reasoning Benchmark (UR-Bench), a benchmark designed to evaluate the reasoning capabilities of MLLMs under extreme visual information. UR-Bench comprises two major categories, Humanistic Scenes and Natural Scenes, covering four subsets of ultra-high-resolution images with distinct spatial structures and data sources. Each subset contains images ranging from hundreds of megapixels to gigapixels, accompanied by questions organized into three levels, enabling evaluation of models' reasoning capabilities in ultra-high-resolution scenarios. We further propose an agent-based framework in which a language model performs reasoning by invoking external visual tools. In addition, we introduce Semantic Abstraction and Retrieval tools that enable more efficient processing of ultra-high-resolution images. We evaluate state-of-the-art models using both an end-to-end MLLMs and our agent-based framework, demonstrating the effectiveness of our framework.

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### GenAITEd Ghana: A First-of-Its-Kind Context-Aware and Curriculum-Aligned Conversational AI Agent for Teacher Education

Authors: Matthew Nyaaba, Patrick Kyeremeh, Macharious Nabang, Bismark Nyaaba Akanzire, Sakina Acquah, Cyril Ababio Titty, Kotor Asare, Jerry Etornam Kudaya
Published: 2025-12-31
Categories: cs.CY, cs.AI
arXiv: https://arxiv.org/abs/2601.06093v2

Global frameworks increasingly advocate for Responsible Artificial Intelligence (AI) in education, yet they provide limited guidance on how ethical, culturally responsive, and curriculum-aligned AI can be operationalized within functioning teacher education systems, particularly in the Global South. This study addresses this gap through the design and evaluation of GenAITEd Ghana, a context-aware, region-specific conversational AI prototype developed to support teacher education in Ghana. Guided by a Design Science Research approach, the system was developed as a school-mimetic digital infrastructure aligned with the organizational logic of Ghanaian Colleges of Education and the National Council for Curriculum and Assessment (NaCCA) framework. GenAITEd Ghana operates as a multi-agent, retrieval-augmented conversational AI that coordinates multiple models for curriculum-grounded dialogue, automatic speech recognition, voice synthesis, and multimedia interaction. Two complementary prompt pathways were embedded: system-level prompts that enforce curriculum boundaries, ethical constraints, and teacher-in-the-loop oversight, and interaction-level semi-automated prompts that structure live pedagogical dialogue through clarification, confirmation, and guided response generation. Evaluation findings show that the system effectively enacted key Responsible AI principles, including transparency, accountability, cultural responsiveness, privacy, and human oversight. Human expert evaluations further indicated that GenAITEd Ghana is pedagogically appropriate for Ghanaian teacher education, promoting student engagement while preserving educators' professional authority. Identified challenges highlight the need for continued model integration, professional development, and critical AI literacy to mitigate risks of over-reliance.

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### Islamic Chatbots in the Age of Large Language Models

Authors: Muhammad Aurangzeb Ahmad
Published: 2025-12-31
Categories: cs.CY, cs.AI
arXiv: https://arxiv.org/abs/2601.06092v1

Large Language Models (LLMs) are rapidly transforming how communities access, interpret, and circulate knowledge, and religious communities are no exception. Chatbots powered by LLMs are beginning to reshape authority, pedagogy, and everyday religious practice in Muslim communities. We analyze the landscape of LLM powered Islamic chatbots and how they are transforming Islamic religious practices e.g., democratizing access to religious knowledge but also running the risk of erosion of authority. We discuss what kind of challenges do these systems raise for Muslim communities and explore recommendations for the responsible design of these systems.

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### PriceSeer: Evaluating Large Language Models in Real-Time Stock Prediction

Authors: Bohan Liang, Zijian Chen, Qi Jia, Kaiwei Zhang, Kaiyuan Ji, Guangtao Zhai
Published: 2025-12-31
Categories: q-fin.ST, cs.LG
arXiv: https://arxiv.org/abs/2601.06088v1

Stock prediction, a subject closely related to people's investment activities in fully dynamic and live environments, has been widely studied. Current large language models (LLMs) have shown remarkable potential in various domains, exhibiting expert-level performance through advanced reasoning and contextual understanding. In this paper, we introduce PriceSeer, a live, dynamic, and data-uncontaminated benchmark specifically designed for LLMs performing stock prediction tasks. Specifically, PriceSeer includes 110 U.S. stocks from 11 industrial sectors, with each containing 249 historical data points. Our benchmark implements both internal and external information expansion, where LLMs receive extra financial indicators, news, and fake news to perform stock price prediction. We evaluate six cutting-edge LLMs under different prediction horizons, demonstrating their potential in generating investment strategies after obtaining accurate price predictions for different sectors. Additionally, we provide analyses of LLMs' suboptimal performance in long-term predictions, including the vulnerability to fake news and specific industries. The code and evaluation data will be open-sourced at https://github.com/BobLiang2113/PriceSeer.

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### AzeroS: Extending LLM to Speech with Self-Generated Instruction-Free Tuning

Authors: Yiwen Shao, Wei Liu, Jiahong Li, Tianzi Wang, Kun Wei, Meng Yu, Dong Yu
Published: 2025-12-31
Categories: cs.CL, cs.SD, eess.AS
arXiv: https://arxiv.org/abs/2601.06086v1

Extending large language models (LLMs) to the speech domain has recently gained significant attention. A typical approach connects a pretrained LLM with an audio encoder through a projection module and trains the resulting model on large-scale, task-specific instruction-tuning datasets. However, curating such instruction-tuning data for specific requirements is time-consuming, and models trained in this manner often generalize poorly to unseen tasks. In this work, we first formulate that the strongest generalization of a speech-LLM is achieved when it is trained with Self-Generated Instruction-Free Tuning (SIFT), in which supervision signals are generated by a frozen LLM using textual representations of speech as input. Our proposed SIFT paradigm eliminates the need for collecting task-specific question-answer pairs and yields the theoretically best generalization to unseen tasks. Building upon this paradigm, we introduce AZeroS (Auden Zero-instruction-tuned Speech-LLM), which is trained on speech-text pairs derived from publicly available corpora, including approximately 25,000 hours of speech with ASR transcripts and 3,000 hours of speech with paralinguistic labels. Built upon Qwen2.5-7B-Instruct, the model updates only two lightweight projection modules (23.8 million parameters each), while keeping both the LLM and audio encoders frozen. Despite the minimal training cost and modest data scale, AZeroS achieves state-of-the-art performance on both semantic and paralinguistic benchmarks, including VoiceBench, AIR-Bench Foundation (Speech), and AIR-Bench Chat (Speech).

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### Defense Against Synthetic Speech: Real-Time Detection of RVC Voice Conversion Attacks

Authors: Prajwal Chinchmalatpure, Suyash Chinchmalatpure, Siddharth Chavan
Published: 2025-12-31
Categories: cs.SD, cs.AI, eess.AS
arXiv: https://arxiv.org/abs/2601.04227v1

Generative audio technologies now enable highly realistic voice cloning and real-time voice conversion, increasing the risk of impersonation, fraud, and misinformation in communication channels such as phone and video calls. This study investigates real-time detection of AI-generated speech produced using Retrieval-based Voice Conversion (RVC), evaluated on the DEEP-VOICE dataset, which includes authentic and voice-converted speech samples from multiple well-known speakers. To simulate realistic conditions, deepfake generation is applied to isolated vocal components, followed by the reintroduction of background ambiance to suppress trivial artifacts and emphasize conversion-specific cues. We frame detection as a streaming classification task by dividing audio into one-second segments, extracting time-frequency and cepstral features, and training supervised machine learning models to classify each segment as real or voice-converted. The proposed system enables low-latency inference, supporting both segment-level decisions and call-level aggregation. Experimental results show that short-window acoustic features can reliably capture discriminative patterns associated with RVC speech, even in noisy backgrounds. These findings demonstrate the feasibility of practical, real-time deepfake speech detection and underscore the importance of evaluating under realistic audio mixing conditions for robust deployment.

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### PCEval: A Benchmark for Evaluating Physical Computing Capabilities of Large Language Models

Authors: Inpyo Song, Eunji Jeon, Jangwon Lee
Published: 2025-12-31
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2601.02404v1

Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, including software development, education, and technical assistance. Among these, software development is one of the key areas where LLMs are increasingly adopted. However, when hardware constraints are considered-for instance, in physical computing, where software must interact with and control physical hardware -their effectiveness has not been fully explored. To address this gap, we introduce \textsc{PCEval} (Physical Computing Evaluation), the first benchmark in physical computing that enables a fully automatic evaluation of the capabilities of LLM in both the logical and physical aspects of the projects, without requiring human assessment. Our evaluation framework assesses LLMs in generating circuits and producing compatible code across varying levels of project complexity. Through comprehensive testing of 13 leading models, \textsc{PCEval} provides the first reproducible and automatically validated empirical assessment of LLMs' ability to reason about fundamental hardware implementation constraints within a simulation environment. Our findings reveal that while LLMs perform well in code generation and logical circuit design, they struggle significantly with physical breadboard layout creation, particularly in managing proper pin connections and avoiding circuit errors. \textsc{PCEval} advances our understanding of AI assistance in hardware-dependent computing environments and establishes a foundation for developing more effective tools to support physical computing education.

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### Spiking Heterogeneous Graph Attention Networks

Authors: Buqing Cao, Qian Peng, Xiang Xie, Liang Chen, Min Shi, Jianxun Liu
Published: 2025-12-31
Categories: cs.NE, cs.LG, cs.SI
arXiv: https://arxiv.org/abs/2601.02401v1

Real-world graphs or networks are usually heterogeneous, involving multiple types of nodes and relationships. Heterogeneous graph neural networks (HGNNs) can effectively handle these diverse nodes and edges, capturing heterogeneous information within the graph, thus exhibiting outstanding performance. However, most methods of HGNNs usually involve complex structural designs, leading to problems such as high memory usage, long inference time, and extensive consumption of computing resources. These limitations pose certain challenges for the practical application of HGNNs, especially for resource-constrained devices. To mitigate this issue, we propose the Spiking Heterogeneous Graph Attention Networks (SpikingHAN), which incorporates the brain-inspired and energy-saving properties of Spiking Neural Networks (SNNs) into heterogeneous graph learning to reduce the computing cost without compromising the performance. Specifically, SpikingHAN aggregates metapath-based neighbor information using a single-layer graph convolution with shared parameters. It then employs a semantic-level attention mechanism to capture the importance of different meta-paths and performs semantic aggregation. Finally, it encodes the heterogeneous information into a spike sequence through SNNs, simulating bioinformatic processing to derive a binarized 1-bit representation of the heterogeneous graph. Comprehensive experimental results from three real-world heterogeneous graph datasets show that SpikingHAN delivers competitive node classification performance. It achieves this with fewer parameters, quicker inference, reduced memory usage, and lower energy consumption. Code is available at https://github.com/QianPeng369/SpikingHAN.

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### Placenta Accreta Spectrum Detection using Multimodal Deep Learning

Authors: Sumaiya Ali, Areej Alhothali, Sameera Albasri, Ohoud Alzamzami, Ahmed Abduljabbar, Muhammad Alwazzan
Published: 2025-12-31
Categories: eess.IV, cs.AI, cs.CV, cs.LG
arXiv: https://arxiv.org/abs/2601.00907v1

Placenta Accreta Spectrum (PAS) is a life-threatening obstetric complication involving abnormal placental invasion into the uterine wall. Early and accurate prenatal diagnosis is essential to reduce maternal and neonatal risks. This study aimed to develop and validate a deep learning framework that enhances PAS detection by integrating multiple imaging modalities. A multimodal deep learning model was designed using an intermediate feature-level fusion architecture combining 3D Magnetic Resonance Imaging (MRI) and 2D Ultrasound (US) scans. Unimodal feature extractors, a 3D DenseNet121-Vision Transformer for MRI and a 2D ResNet50 for US, were selected after systematic comparative analysis. Curated datasets comprising 1,293 MRI and 1,143 US scans were used to train the unimodal models and paired samples of patient-matched MRI-US scans was isolated for multimodal model development and evaluation. On an independent test set, the multimodal fusion model achieved superior performance, with an accuracy of 92.5% and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.927, outperforming the MRI-only (82.5%, AUC 0.825) and US-only (87.5%, AUC 0.879) models. Integrating MRI and US features provides complementary diagnostic information, demonstrating strong potential to enhance prenatal risk assessment and improve patient outcomes.

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### Evaluating Contextual Intelligence in Recyclability: A Comprehensive Study of Image-Based Reasoning Systems

Authors: Eliot Park, Abhi Kumar, Pranav Rajpurkar
Published: 2025-12-31
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2601.00905v1

While the importance of efficient recycling is widely acknowledged, accurately determining the recyclability of items and their proper disposal remains a complex task for the general public. In this study, we explore the application of cutting-edge vision-language models (GPT-4o, GPT-4o-mini, and Claude 3.5) for predicting the recyclability of commonly disposed items. Utilizing a curated dataset of images, we evaluated the models' ability to match objects to appropriate recycling bins, including assessing whether the items could physically fit into the available bins. Additionally, we investigated the models' performance across several challenging scenarios: (i) adjusting predictions based on location-specific recycling guidelines; (ii) accounting for contamination or structural damage; and (iii) handling objects composed of multiple materials. Our findings highlight the significant advancements in contextual understanding offered by these models compared to previous iterations, while also identifying areas where they still fall short. The continued refinement of context-aware models is crucial for enhancing public recycling practices and advancing environmental sustainability.

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### Deep Deterministic Nonlinear ICA via Total Correlation Minimization with Matrix-Based Entropy Functional

Authors: Qiang Li, Shujian Yu, Liang Ma, Chen Ma, Jingyu Liu, Tulay Adali, Vince D. Calhoun
Published: 2025-12-31
Categories: stat.ME, cs.LG, stat.ML
arXiv: https://arxiv.org/abs/2601.00904v1

Blind source separation, particularly through independent component analysis (ICA), is widely utilized across various signal processing domains for disentangling underlying components from observed mixed signals, owing to its fully data-driven nature that minimizes reliance on prior assumptions. However, conventional ICA methods rely on an assumption of linear mixing, limiting their ability to capture complex nonlinear relationships and to maintain robustness in noisy environments. In this work, we present deep deterministic nonlinear independent component analysis (DDICA), a novel deep neural network-based framework designed to address these limitations. DDICA leverages a matrix-based entropy function to directly optimize the independence criterion via stochastic gradient descent, bypassing the need for variational approximations or adversarial schemes. This results in a streamlined training process and improved resilience to noise. We validated the effectiveness and generalizability of DDICA across a range of applications, including simulated signal mixtures, hyperspectral image unmixing, modeling of primary visual receptive fields, and resting-state functional magnetic resonance imaging (fMRI) data analysis. Experimental results demonstrate that DDICA effectively separates independent components with high accuracy across a range of applications. These findings suggest that DDICA offers a robust and versatile solution for blind source separation in diverse signal processing tasks.

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### Noise-Aware and Dynamically Adaptive Federated Defense Framework for SAR Image Target Recognition

Authors: Yuchao Hou, Zixuan Zhang, Jie Wang, Wenke Huang, Lianhui Liang, Di Wu, Zhiquan Liu, Youliang Tian, Jianming Zhu, Jisheng Dang, Junhao Dong, Zhongliang Guo
Published: 2025-12-31
Categories: cs.CR, cs.CV, cs.LG
arXiv: https://arxiv.org/abs/2601.00900v2

As a critical application of computational intelligence in remote sensing, deep learning-based synthetic aperture radar (SAR) image target recognition facilitates intelligent perception but typically relies on centralized training, where multi-source SAR data are uploaded to a single server, raising privacy and security concerns. Federated learning (FL) provides an emerging computational intelligence paradigm for SAR image target recognition, enabling cross-site collaboration while preserving local data privacy. However, FL confronts critical security risks, where malicious clients can exploit SAR's multiplicative speckle noise to conceal backdoor triggers, severely challenging the robustness of the computational intelligence model. To address this challenge, we propose NADAFD, a noise-aware and dynamically adaptive federated defense framework that integrates frequency-domain, spatial-domain, and client-behavior analyses to counter SAR-specific backdoor threats. Specifically, we introduce a frequency-domain collaborative inversion mechanism to expose cross-client spectral inconsistencies indicative of hidden backdoor triggers. We further design a noise-aware adversarial training strategy that embeds $Γ$-distributed speckle characteristics into mask-guided adversarial sample generation to enhance robustness against both backdoor attacks and SAR speckle noise. In addition, we present a dynamic health assessment module that tracks client update behaviors across training rounds and adaptively adjusts aggregation weights to mitigate evolving malicious contributions. Experiments on MSTAR and OpenSARShip datasets demonstrate that NADAFD achieves higher accuracy on clean test samples and a lower backdoor attack success rate on triggered inputs than existing federated backdoor defenses for SAR target recognition.

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### Dichotomous Diffusion Policy Optimization

Authors: Ruiming Liang, Yinan Zheng, Kexin Zheng, Tianyi Tan, Jianxiong Li, Liyuan Mao, Zhihao Wang, Guang Chen, Hangjun Ye, Jingjing Liu, Jinqiao Wang, Xianyuan Zhan
Published: 2025-12-31
Categories: cs.LG, cs.RO
arXiv: https://arxiv.org/abs/2601.00898v3

Diffusion-based policies have gained growing popularity in solving a wide range of decision-making tasks due to their superior expressiveness and controllable generation during inference. However, effectively training large diffusion policies using reinforcement learning (RL) remains challenging. Existing methods either suffer from unstable training due to directly maximizing value objectives, or face computational issues due to relying on crude Gaussian likelihood approximation, which requires a large amount of sufficiently small denoising steps. In this work, we propose DIPOLE (Dichotomous diffusion Policy improvement), a novel RL algorithm designed for stable and controllable diffusion policy optimization. We begin by revisiting the KL-regularized objective in RL, which offers a desirable weighted regression objective for diffusion policy extraction, but often struggles to balance greediness and stability. We then formulate a greedified policy regularization scheme, which naturally enables decomposing the optimal policy into a pair of stably learned dichotomous policies: one aims at reward maximization, and the other focuses on reward minimization. Under such a design, optimized actions can be generated by linearly combining the scores of dichotomous policies during inference, thereby enabling flexible control over the level of greediness.Evaluations in offline and offline-to-online RL settings on ExORL and OGBench demonstrate the effectiveness of our approach. We also use DIPOLE to train a large vision-language-action (VLA) model for end-to-end autonomous driving (AD) and evaluate it on the large-scale real-world AD benchmark NAVSIM, highlighting its potential for complex real-world applications.

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### CornViT: A Multi-Stage Convolutional Vision Transformer Framework for Hierarchical Corn Kernel Analysis

Authors: Sai Teja Erukude, Jane Mascarenhas, Lior Shamir
Published: 2025-12-31
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2601.00897v1

Accurate grading of corn kernels is critical for seed certification, directional seeding, and breeding, yet it is still predominantly performed by manual inspection. This work introduces CornViT, a three-stage Convolutional Vision Transformer (CvT) framework that emulates the hierarchical reasoning of human seed analysts for single-kernel evaluation. Three sequential CvT-13 classifiers operate on 384x384 RGB images: Stage 1 distinguishes pure from impure kernels; Stage 2 categorizes pure kernels into flat and round morphologies; and Stage 3 determines the embryo orientation (up vs. down) for pure, flat kernels. Starting from a public corn seed image collection, we manually relabeled and filtered images to construct three stage-specific datasets: 7265 kernels for purity, 3859 pure kernels for morphology, and 1960 pure-flat kernels for embryo orientation, all released as benchmarks. Head-only fine-tuning of ImageNet-22k pretrained CvT-13 backbones yields test accuracies of 93.76% for purity, 94.11% for shape, and 91.12% for embryo-orientation detection. Under identical training conditions, ResNet-50 reaches only 76.56 to 81.02 percent, whereas DenseNet-121 attains 86.56 to 89.38 percent accuracy. These results highlight the advantages of convolution-augmented self-attention for kernel analysis. To facilitate adoption, we deploy CornViT in a Flask-based web application that performs stage-wise inference and exposes interpretable outputs through a browser interface. Together, the CornViT framework, curated datasets, and web application provide a deployable solution for automated corn kernel quality assessment in seed quality workflows. Source code and data are publicly available.

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### Investigation into U.S. Citizen and Non-Citizen Worker Health Insurance and Employment

Authors: Annabelle Yao
Published: 2025-12-31
Categories: econ.GN, cs.LG
arXiv: https://arxiv.org/abs/2601.00896v1

Socioeconomic integration is a critical dimension of social equity, yet persistent disparities remain in access to health insurance, education, and employment across different demographic groups. While previous studies have examined isolated aspects of inequality, there is limited research that integrates both statistical analysis and advanced machine learning to uncover hidden structures within population data. This study leverages statistical analysis ($χ^2$ test of independence and Two Proportion Z-Test) and machine learning clustering techniques -- K-Modes and K-Prototypes -- along with t-SNE visualization and CatBoost classification to analyze socioeconomic integration and inequality. Using statistical tests, we identified the proportion of the population with healthcare insurance, quality education, and employment. With this data, we concluded that there was an association between employment and citizenship status. Moreover, we were able to determine 5 distinct population groups using Machine Learning classification. The five clusters our analysis identifies reveal that while citizenship status shows no association with workforce participation, significant disparities exist in access to employer-sponsored health insurance. Each cluster represents a distinct demographic of the population, showing that there is a primary split along the lines of educational attainment which separates Clusters 0 and 4 from Clusters 1, 2, and 3. Furthermore, labor force status and nativity serve as secondary differentiators. Non-citizens are also disproportionately concentrated in precarious employment without benefits, highlighting systemic inequalities in healthcare access. By uncovering demographic clusters that face compounded disadvantages, this research contributes to a more nuanced understanding of socioeconomic stratification.

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### Deep Learning Framework for RNA Inverse Folding with Geometric Structure Potentials

Authors: Annabelle Yao
Published: 2025-12-31
Categories: q-bio.QM, cs.LG
arXiv: https://arxiv.org/abs/2601.00895v1

RNA's diverse biological functions stem from its structural versatility, yet accurately predicting and designing RNA sequences given a 3D conformation (inverse folding) remains a challenge. Here, I introduce a deep learning framework that integrates Geometric Vector Perceptron (GVP) layers with a Transformer architecture to enable end-to-end RNA design. I construct a dataset consisting of experimentally solved RNA 3D structures, filtered and deduplicated from the BGSU RNA list, and evaluate performance using both sequence recovery rate and TM-score to assess sequence and structural fidelity, respectively. On standard benchmarks and RNA-Puzzles, my model achieves state-of-the-art performance, with recovery and TM-scores of 0.481 and 0.332, surpassing existing methods across diverse RNA families and length scales. Masked family-level validation using Rfam annotations confirms strong generalization beyond seen families. Furthermore, inverse-folded sequences, when refolded using AlphaFold3, closely resemble native structures, highlighting the critical role of geometric features captured by GVP layers in enhancing Transformer-based RNA design.

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### When to Ponder: Adaptive Compute Allocation for Code Generation via Test-Time Training

Authors: Gihyeon Sim
Published: 2025-12-31
Categories: cs.LG, cs.CL
arXiv: https://arxiv.org/abs/2601.00894v2

Large language models apply uniform computation to all inputs, regardless of difficulty. We propose PonderTTT, a gating strategy using the TTT layer's self-supervised reconstruction loss to selectively trigger Test-Time Training (TTT) updates. The gating decision itself is training-free--requiring no learned classifier or auxiliary networks; only a single scalar threshold is initially calibrated on unlabeled data and continuously adapted via EMA to maintain target update rates. Our experiments with GPT-2 models (124M to 1.5B) on code language modeling (The Stack v2, teacher-forced perplexity) demonstrate that this signal is inference-compatible, requiring no ground-truth labels. Our Reconstruction Gating achieves 82-89% Oracle Recovery while being fully training-free, significantly outperforming Random Skip baselines (up to 16% lower loss on OOD languages).

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### Towards eco friendly cybersecurity: machine learning based anomaly detection with carbon and energy metrics

Authors: KC Aashish, Md Zakir Hossain Zamil, Md Shafiqul Islam Mridul, Lamia Akter, Farmina Sharmin, Eftekhar Hossain Ayon, Md Maruf Bin Reza, Ali Hassan, Abdur Rahim, Sirapa Malla
Published: 2025-12-31
Categories: cs.CR, cs.CY, cs.LG
arXiv: https://arxiv.org/abs/2601.00893v1

The rising energy footprint of artificial intelligence has become a measurable component of US data center emissions, yet cybersecurity research seldom considers its environmental cost. This study introduces an eco aware anomaly detection framework that unifies machine learning based network monitoring with real time carbon and energy tracking. Using the publicly available Carbon Aware Cybersecurity Traffic Dataset comprising 2300 flow level observations, we benchmark Logistic Regression, Random Forest, Support Vector Machine, Isolation Forest, and XGBoost models across energy, carbon, and performance dimensions. Each experiment is executed in a controlled Colab environment instrumented with the CodeCarbon toolkit to quantify power draw and equivalent CO2 output during both training and inference. We construct an Eco Efficiency Index that expresses F1 score per kilowatt hour to capture the trade off between detection quality and environmental impact. Results reveal that optimized Random Forest and lightweight Logistic Regression models achieve the highest eco efficiency, reducing energy consumption by more than forty percent compared to XGBoost while sustaining competitive detection accuracy. Principal Component Analysis further decreases computational load with negligible loss in recall. Collectively, these findings establish that integrating carbon and energy metrics into cybersecurity workflows enables environmentally responsible machine learning without compromising operational protection. The proposed framework offers a reproducible path toward sustainable carbon accountable cybersecurity aligned with emerging US green computing and federal energy efficiency initiatives.

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### Hierarchical topological clustering

Authors: Ana Carpio, Gema Duro
Published: 2025-12-31
Categories: cs.LG, cs.CV, physics.data-an, stat.ME, stat.ML
arXiv: https://arxiv.org/abs/2601.00892v1

Topological methods have the potential of exploring data clouds without making assumptions on their the structure. Here we propose a hierarchical topological clustering algorithm that can be implemented with any distance choice. The persistence of outliers and clusters of arbitrary shape is inferred from the resulting hierarchy. We demonstrate the potential of the algorithm on selected datasets in which outliers play relevant roles, consisting of images, medical and economic data. These methods can provide meaningful clusters in situations in which other techniques fail to do so.

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### Enhancing Retrieval-Augmented Generation with Topic-Enriched Embeddings: A Hybrid Approach Integrating Traditional NLP Techniques

Authors: Rodrigo Kataishi
Published: 2025-12-31
Categories: cs.IR, cs.AI
arXiv: https://arxiv.org/abs/2601.00891v1

Retrieval-augmented generation (RAG) systems rely on accurate document retrieval to ground large language models (LLMs) in external knowledge, yet retrieval quality often degrades in corpora where topics overlap and thematic variation is high. This work proposes topic-enriched embeddings that integrate term-based signals and topic structure with contextual sentence embeddings. The approach combines TF-IDF with topic modeling and dimensionality reduction, using Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) to encode latent topical organization, and fuses these representations with a compact contextual encoder (all-MiniLM). By jointly capturing term-level and topic-level semantics, topic-enriched embeddings improve semantic clustering, increase retrieval precision, and reduce computational burden relative to purely contextual baselines. Experiments on a legal-text corpus show consistent gains in clustering coherence and retrieval metrics, suggesting that topic-enriched embeddings can serve as a practical component for more reliable knowledge-intensive RAG pipelines.

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### FANoS-v2: Feedback-Controlled Momentum with Thermostat Damping for Lightweight Neural Optimization

Authors: Nalin Dhiman
Published: 2025-12-31
Categories: cs.LG
arXiv: https://arxiv.org/abs/2601.00889v2

\FANOS{} is a PyTorch optimizer that augments RMS-preconditioned momentum with a scalar feedback controller over update energy. The public reference implementation stores momentum in parameter-update units, applies a non-negative thermostat damping coefficient, supports diagonal, factored, and raw-gradient preconditioning, and exposes diagnostics intended for stability audits. This study gives a complete mathematical specification of the released optimizer, including the exact parameter-unit update, the study-equation physical update mode, bounded log-ratio thermostat control, adaptive preconditioner softening, warmup guardrails, and the experimental \Fast{} profile. We report the v0.2 evidence: five-seed reduced-sample MNIST, Fashion-MNIST, and CIFAR-10 experiments show mean top-1 gains of 0.889, 2.197, and 2.666 percentage points over AdamW for \Fast{}, but with 49.8\%, 61.6\%, and 56.8\% higher wall-clock time. Preliminary scientific, PINN, and EEG smoke tests are mixed and are treated as hypothesis-generating only. The evidence supports \FANOS{} as an alpha-stage research optimizer with a reproducible lightweight-vision signal and an explicit runtime bottleneck.

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### Comparative Evaluation of CNN Architectures for Neural Style Transfer in Indonesian Batik Motif Generation: A Comprehensive Study

Authors: Happy Gery Pangestu, Andi Prademon Yunus, Siti Khomsah
Published: 2025-12-31
Categories: cs.CV
arXiv: https://arxiv.org/abs/2601.00888v1

Neural Style Transfer (NST) provides a computational framework for the digital preservation and generative exploration of Indonesian batik motifs; however, existing approaches remain largely centered on VGG-based architectures whose strong stylistic expressiveness comes at the cost of high computational and memory demands, that limits practical deployment in resource-limited environments. This study presents a systematic comparative analysis of five widely used CNN backbones, namely VGG16, VGG19, Inception V3, ResNet50, and ResNet101, based on 245 controlled experiments combining quantitative metrics, qualitative assessment, and statistical analysis to examine the trade-off between structural preservation, stylistic behavior, and computational efficiency. The results show that backbone selection does not yield statistically significant differences in structural similarity, as confirmed by ANOVA on SSIM (p= 0.83), indicating comparable levels of structural preservation rather than equivalent stylistic quality. Within this context, ResNet-based architectures achieve approximately 5-6x faster convergence than VGG models while maintaining similar perceptual similarity (LPIPS = 0.53) and requiring over 16x fewer FLOPs (0.63 vs 10.12 GFLOPs). Qualitative analysis reveals consistent stylistic trade-offs, with VGG producing denser painterly textures, ResNet favoring geometric stability and canting stroke preservation with milder stylization, and Inception V3 exhibiting intermediate but noisier behavior. These findings reposition architectural choice in NST from maximizing stylistic intensity toward efficiency-aware and structure-preserving deployment, highlighting ResNet-based backbones as a practical foundation for scalable, industry-oriented batik generation.

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### VideoCuRL: Video Curriculum Reinforcement Learning with Orthogonal Difficulty Decomposition

Authors: Hongbo Jin, Kuanwei Lin, Wenhao Zhang, Yichen Jin, Ge Li
Published: 2025-12-31
Categories: cs.CV
arXiv: https://arxiv.org/abs/2601.00887v1

Reinforcement Learning (RL) is crucial for empowering VideoLLMs with complex spatiotemporal reasoning. However, current RL paradigms predominantly rely on random data shuffling or naive curriculum strategies based on scalar difficulty metrics. We argue that scalar metrics fail to disentangle two orthogonal challenges in video understanding: Visual Temporal Perception Load and Cognitive Reasoning Depth. To address this, we propose VideoCuRL, a novel framework that decomposes difficulty into these two axes. We employ efficient, training-free proxies, optical flow and keyframe entropy for visual complexity, Calibrated Surprisal for cognitive complexity, to map data onto a 2D curriculum grid. A competence aware Diagonal Wavefront strategy then schedules training from base alignment to complex reasoning. Furthermore, we introduce Dynamic Sparse KL and Structured Revisiting to stabilize training against reward collapse and catastrophic forgetting. Extensive experiments show that VideoCuRL surpasses strong RL baselines on reasoning (+2.5 on VSI-Bench) and perception (+2.9 on VideoMME) tasks. Notably, VideoCuRL eliminates the prohibitive inference overhead of generation-based curricula, offering a scalable solution for robust video post-training.

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### Counterfactual Self-Questioning for Stable Policy Optimization in Language Models

Authors: Mandar Parab
Published: 2025-12-31
Categories: cs.AI
arXiv: https://arxiv.org/abs/2601.00885v1

Recent work on language model self-improvement shows that models can refine their own reasoning through reflection, verification, debate, or self-generated rewards. However, most existing approaches rely on external critics, learned reward models, or ensemble sampling, which increases complexity and training instability. We propose Counterfactual Self-Questioning, a framework in which a single language model generates and evaluates counterfactual critiques of its own reasoning. The method produces an initial reasoning trace, formulates targeted questions that challenge potential failure points, and generates alternative reasoning trajectories that expose incorrect assumptions or invalid steps. These counterfactual trajectories provide structured relative feedback that can be directly used for policy optimization without auxiliary models. Experiments on multiple mathematical reasoning benchmarks show that counterfactual self-questioning improves accuracy and training stability, particularly for smaller models, enabling scalable self-improvement using internally generated supervision alone.

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### Outlier Detection Using Vector Cosine Similarity by Adding a Dimension

Authors: Zhongyang Shen
Published: 2025-12-31
Categories: cs.LG
arXiv: https://arxiv.org/abs/2601.00883v1

We propose a new outlier detection method for multi-dimensional data. The method detects outliers based on vector cosine similarity, using a new dataset constructed by adding a dimension with zero values to the original data. When a point in the new dataset is selected as the measured point, an observation point is created as the origin, differing only in the new dimension by having a non-zero value compared to the measured point. Vectors are then formed from the observation point to the measured point and to other points in the dataset. By comparing the cosine similarities of these vectors, abnormal data can be identified. An optimized implementation (MDOD) is available on PyPI: https://pypi.org/project/mdod/.

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### Universal Conditional Logic: A Formal Language for Prompt Engineering

Authors: Anthony Mikinka
Published: 2025-12-31
Categories: cs.AI, cs.CL, cs.LG, cs.PL, cs.SE
arXiv: https://arxiv.org/abs/2601.00880v1

We present Universal Conditional Logic (UCL), a mathematical framework for prompt optimization that transforms prompt engineering from heuristic practice into systematic optimization. Through systematic evaluation (N=305, 11 models, 4 iterations), we demonstrate significant token reduction (29.8%, t(10)=6.36, p < 0.001, Cohen's d = 2.01) with corresponding cost savings. UCL's structural overhead function O_s(A) explains version-specific performance differences through the Over-Specification Paradox: beyond threshold S* = 0.509, additional specification degrades performance quadratically. Core mechanisms -- indicator functions (I_i in {0,1}), structural overhead (O_s = gamma * sum(ln C_k)), early binding -- are validated. Notably, optimal UCL configuration varies by model architecture -- certain models (e.g., Llama 4 Scout) require version-specific adaptations (V4.1). This work establishes UCL as a calibratable framework for efficient LLM interaction, with model-family-specific optimization as a key research direction.

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### VL-OrdinalFormer: Vision Language Guided Ordinal Transformers for Interpretable Knee Osteoarthritis Grading

Authors: Zahid Ullah, Jihie Kim
Published: 2025-12-31
Categories: cs.CV
arXiv: https://arxiv.org/abs/2601.00879v1

Knee osteoarthritis (KOA) is a leading cause of disability worldwide, and accurate severity assessment using the Kellgren Lawrence (KL) grading system is critical for clinical decision making. However, radiographic distinctions between early disease stages, particularly KL1 and KL2, are subtle and frequently lead to inter-observer variability among radiologists. To address these challenges, we propose VLOrdinalFormer, a vision language guided ordinal learning framework for fully automated KOA grading from knee radiographs. The proposed method combines a ViT L16 backbone with CORAL based ordinal regression and a Contrastive Language Image Pretraining (CLIP) driven semantic alignment module, allowing the model to incorporate clinically meaningful textual concepts related to joint space narrowing, osteophyte formation, and subchondral sclerosis. To improve robustness and mitigate overfitting, we employ stratified five fold cross validation, class aware re weighting to emphasize challenging intermediate grades, and test time augmentation with global threshold optimization. Experiments conducted on the publicly available OAI kneeKL224 dataset demonstrate that VLOrdinalFormer achieves state of the art performance, outperforming CNN and ViT baselines in terms of macro F1 score and overall accuracy. Notably, the proposed framework yields substantial performance gains for KL1 and KL2 without compromising classification accuracy for mild or severe cases. In addition, interpretability analyses using Grad CAM and CLIP similarity maps confirm that the model consistently attends to clinically relevant anatomical regions. These results highlight the potential of vision language aligned ordinal transformers as reliable and interpretable tools for KOA grading and disease progression assessment in routine radiological practice.

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### Compressed Map Priors for 3D Perception

Authors: Brady Zhou, Philipp Krähenbühl
Published: 2025-12-31
Categories: cs.CV
arXiv: https://arxiv.org/abs/2601.00139v1

Human drivers rarely travel where no person has gone before. After all, thousands of drivers use busy city roads every day, and only one can claim to be the first. The same holds for autonomous computer vision systems. The vast majority of the deployment area of an autonomous vision system will have been visited before. Yet, most autonomous vehicle vision systems act as if they are encountering each location for the first time. In this work, we present Compressed Map Priors (CMP), a simple but effective framework to learn spatial priors from historic traversals. The map priors use a binarized hashmap that requires only $32\text{KB}/\text{km}^2$, a $20\times$ reduction compared to the dense storage. Compressed Map Priors easily integrate into leading 3D perception systems at little to no extra computational costs, and lead to a significant and consistent improvement in 3D object detection on the nuScenes dataset across several architectures.

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### Explicit Abstention Knobs for Predictable Reliability in Video Question Answering

Authors: Jorge Ortiz
Published: 2025-12-31
Categories: cs.AI, cs.CV
arXiv: https://arxiv.org/abs/2601.00138v2

High-stakes deployment of vision-language models (VLMs) requires selective prediction, where systems abstain when uncertain rather than risk costly errors. We investigate whether confidence-based abstention provides reliable control over error rates in video question answering, and whether that control remains robust under distribution shift. Using NExT-QA and Gemini 2.0 Flash, we establish two findings. First, confidence thresholding provides mechanistic control in-distribution. Sweeping threshold epsilon produces smooth risk-coverage tradeoffs, reducing error rates f

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### Democratizing Electronic-Photonic AI Systems: An Open-Source AI-Infused Cross-Layer Co-Design and Design Automation Toolflow

Authors: Hongjian Zhou, Ziang Yin, Jiaqi Gu
Published: 2025-12-31
Categories: physics.optics, cs.AI, cs.AR, cs.ET
arXiv: https://arxiv.org/abs/2601.00130v1

Photonics is becoming a cornerstone technology for high-performance AI systems and scientific computing, offering unparalleled speed, parallelism, and energy efficiency. Despite this promise, the design and deployment of electronic-photonic AI systems remain highly challenging due to a steep learning curve across multiple layers, spanning device physics, circuit design, system architecture, and AI algorithms. The absence of a mature electronic-photonic design automation (EPDA) toolchain leads to long, inefficient design cycles and limits cross-disciplinary innovation and co-evolution. In this work, we present a cross-layer co-design and automation framework aimed at democratizing photonic AI system development. We begin by introducing our architecture designs for scalable photonic edge AI and Transformer inference, followed by SimPhony, an open-source modeling tool for rapid EPIC AI system evaluation and design-space exploration. We then highlight advances in AI-enabled photonic design automation, including physical AI-based Maxwell solvers, a fabrication-aware inverse design framework, and a scalable inverse training algorithm for meta-optical neural networks, enabling a scalable EPDA stack for next-generation electronic-photonic AI systems.

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### Toward Large-Scale Photonics-Empowered AI Systems: From Physical Design Automation to System-Algorithm Co-Exploration

Authors: Ziang Yin, Hongjian Zhou, Nicholas Gangi, Meng Zhang, Jeff Zhang, Zhaoran Rena Huang, Jiaqi Gu
Published: 2025-12-31
Categories: physics.optics, cs.AI, cs.AR, cs.ET
arXiv: https://arxiv.org/abs/2601.00129v1

In this work, we identify three considerations that are essential for realizing practical photonic AI systems at scale: (1) dynamic tensor operation support for modern models rather than only weight-static kernels, especially for attention/Transformer-style workloads; (2) systematic management of conversion, control, and data-movement overheads, where multiplexing and dataflow must amortize electronic costs instead of letting ADC/DAC and I/O dominate; and (3) robustness under hardware non-idealities that become more severe as integration density grows. To study these coupled tradeoffs quantitatively, and to ensure they remain meaningful under real implementation constraints, we build a cross-layer toolchain that supports photonic AI design from early exploration to physical realization. SimPhony provides implementation-aware modeling and rapid cross-layer evaluation, translating physical costs into system-level metrics so architectural decisions are grounded in realistic assumptions. ADEPT and ADEPT-Z enable end-to-end circuit and topology exploration, connecting system objectives to feasible photonic fabrics under practical device and circuit constraints. Finally, Apollo and LiDAR provide scalable photonic physical design automation, turning candidate circuits into manufacturable layouts while accounting for routing, thermal, and crosstalk constraints.

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### Constructing a Neuro-Symbolic Mathematician from First Principles

Authors: Keqin Xie
Published: 2025-12-31
Categories: cs.AI
arXiv: https://arxiv.org/abs/2601.00125v1

Large Language Models (LLMs) exhibit persistent logical failures in complex reasoning due to the lack of an internal axiomatic framework. We propose Mathesis, a neuro-symbolic architecture that encodes mathematical states as higher-order hypergraphs and uses a Symbolic Reasoning Kernel (SRK)--a differentiable logic engine that maps constraints to a continuous energy landscape. By defining a global energy function E(G), where zero energy implies logical consistency, the SRK yields gradient-based signals to train a Hypergraph Transformer Brain, turning proof search into energy minimization. Multi-step deduction is enabled via Monte Carlo Tree Search and Evolutionary Proof Search, guided by learned value functions and semantic unification.

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### A Spatially Masked Adaptive Gated Network for multimodal post-flood water extent mapping using SAR and incomplete multispectral data

Authors: Hyunho Lee, Wenwen Li
Published: 2025-12-31
Categories: cs.CV
arXiv: https://arxiv.org/abs/2601.00123v1

Mapping water extent during a flood event is essential for effective disaster management throughout all phases: mitigation, preparedness, response, and recovery. In particular, during the response stage, when timely and accurate information is important, Synthetic Aperture Radar (SAR) data are primarily employed to produce water extent maps. Recently, leveraging the complementary characteristics of SAR and MSI data through a multimodal approach has emerged as a promising strategy for advancing water extent mapping using deep learning models. This approach is particularly beneficial when timely post-flood observations, acquired during or shortly after the flood peak, are limited, as it enables the use of all available imagery for more accurate post-flood water extent mapping. However, the adaptive integration of partially available MSI data into the SAR-based post-flood water extent mapping process remains underexplored. To bridge this research gap, we propose the Spatially Masked Adaptive Gated Network (SMAGNet), a multimodal deep learning model that utilizes SAR data as the primary input for post-flood water extent mapping and integrates complementary MSI data through feature fusion. In experiments on the C2S-MS Floods dataset, SMAGNet consistently outperformed other multimodal deep learning models in prediction performance across varying levels of MSI data availability. Furthermore, we found that even when MSI data were completely missing, the performance of SMAGNet remained statistically comparable to that of a U-Net model trained solely on SAR data. These findings indicate that SMAGNet enhances the model robustness to missing data as well as the applicability of multimodal deep learning in real-world flood management scenarios.

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### Ask, Clarify, Optimize: Human-LLM Agent Collaboration for Smarter Inventory Control

Authors: Yaqi Duan, Yichun Hu, Jiashuo Jiang
Published: 2025-12-31
Categories: cs.AI, cs.HC
arXiv: https://arxiv.org/abs/2601.00121v1

Inventory management remains a challenge for many small and medium-sized businesses that lack the expertise to deploy advanced optimization methods. This paper investigates whether Large Language Models (LLMs) can help bridge this gap. We show that employing LLMs as direct, end-to-end solvers incurs a significant "hallucination tax": a performance gap arising from the model's inability to perform grounded stochastic reasoning. To address this, we propose a hybrid agentic framework that strictly decouples semantic reasoning from mathematical calculation. In this architecture, the LLM functions as an intelligent interface, eliciting parameters from natural language and interpreting results while automatically calling rigorous algorithms to build the optimization engine. To evaluate this interactive system against the ambiguity and inconsistency of real-world managerial dialogue, we introduce the Human Imitator, a fine-tuned "digital twin" of a boundedly rational manager that enables scalable, reproducible stress-testing. Our empirical analysis reveals that the hybrid agentic framework reduces total inventory costs by 32.1% relative to an interactive baseline using GPT-4o as an end-to-end solver. Moreover, we find that providing perfect ground-truth information alone is insufficient to improve GPT-4o's performance, confirming that the bottleneck is fundamentally computational rather than informational. Our results position LLMs not as replacements for operations research, but as natural-language interfaces that make rigorous, solver-based policies accessible to non-experts.

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### GRL-SNAM: Geometric Reinforcement Learning with Path Differential Hamiltonians for Simultaneous Navigation and Mapping in Unknown Environments

Authors: Aditya Sai Ellendula, Yi Wang, Minh Nguyen, Chandrajit Bajaj
Published: 2025-12-31
Categories: cs.LG, cs.RO
arXiv: https://arxiv.org/abs/2601.00116v1

We present GRL-SNAM, a geometric reinforcement learning framework for Simultaneous Navigation and Mapping(SNAM) in unknown environments. A SNAM problem is challenging as it needs to design hierarchical or joint policies of multiple agents that control the movement of a real-life robot towards the goal in mapless environment, i.e. an environment where the map of the environment is not available apriori, and needs to be acquired through sensors. The sensors are invoked from the path learner, i.e. navigator, through active query responses to sensory agents, and along the motion path. GRL-SNAM differs from preemptive navigation algorithms and other reinforcement learning methods by relying exclusively on local sensory observations without constructing a global map. Our approach formulates path navigation and mapping as a dynamic shortest path search and discovery process using controlled Hamiltonian optimization: sensory inputs are translated into local energy landscapes that encode reachability, obstacle barriers, and deformation constraints, while policies for sensing, planning, and reconfiguration evolve stagewise via updating Hamiltonians. A reduced Hamiltonian serves as an adaptive score function, updating kinetic/potential terms, embedding barrier constraints, and continuously refining trajectories as new local information arrives. We evaluate GRL-SNAM on two different 2D navigation tasks. Comparing against local reactive baselines and global policy learning references under identical stagewise sensing constraints, it preserves clearance, generalizes to unseen layouts, and demonstrates that Geometric RL learning via updating Hamiltonians enables high-quality navigation through minimal exploration via local energy refinement rather than extensive global mapping. The code is publicly available on \href{https://github.com/CVC-Lab/GRL-SNAM}{Github}.

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### Mortar: Evolving Mechanics for Automatic Game Design

Authors: Muhammad U. Nasir, Yuchen Li, Steven James, Julian Togelius
Published: 2025-12-31
Categories: cs.AI
arXiv: https://arxiv.org/abs/2601.00105v1

We present Mortar, a system for autonomously evolving game mechanics for automatic game design. Game mechanics define the rules and interactions that govern gameplay, and designing them manually is a time-consuming and expert-driven process. Mortar combines a quality-diversity algorithm with a large language model to explore a diverse set of mechanics, which are evaluated by synthesising complete games that incorporate both evolved mechanics and those drawn from an archive. The mechanics are evaluated by composing complete games through a tree search procedure, where the resulting games are evaluated by their ability to preserve a skill-based ordering over players -- that is, whether stronger players consistently outperform weaker ones. We assess the mechanics based on their contribution towards the skill-based ordering score in the game. We demonstrate that Mortar produces games that appear diverse and playable, and mechanics that contribute more towards the skill-based ordering score in the game. We perform ablation studies to assess the role of each system component and a user study to evaluate the games based on human feedback.

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### Covariance Matrix Adaptation Evolution Strategy without a matrix

Authors: Jarosław Arabas, Adam Stelmaszczyk, Eryk Warchulski, Dariusz Jagodziński, Rafał Biedrzycki
Published: 2025-12-31
Categories: cs.NE
arXiv: https://arxiv.org/abs/2601.00102v1

Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a highly effective optimization technique. A primary challenge when applying CMA-ES in high dimensionality is sampling from a multivariate normal distribution with an arbitrary covariance matrix, which involves its decomposition. The cubic complexity of this process is the main obstacle to applying CMA-ES in highdimensional spaces. We introduce a version of CMA-ES that uses no covariance matrix at all. In the proposed matrix-free CMA-ES, an archive stores the vectors of differences between individuals and the midpoint, normalized by the step size. New individuals are generated as the weighted combinations of the vectors from the archive. We prove that the probability distribution of individuals generated by the proposed method is identical to that of the standard CMA-ES. Experimental results show that reducing the archive size to store only a fixed number of the most recent populations is sufficient, without compromising optimization efficiency. The matrix-free and matrix-based CMA-ES achieve comparable results on the quadratic function when the step-size adaptation is turned off. When coupled with the step-size adaptation method, the matrix-free CMA-ES converges faster than the matrix-based, and usually yields the results of a comparable or superior quality, according to the results obtained for the CEC'2017 benchmark suite. Presented approach simplifies the algorithm, offers a novel perspective on covariance matrix adaptation, and serves as a stepping stone toward even more efficient methods.

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### Learning Speech Representations with Variational Predictive Coding

Authors: Sung-Lin Yeh, Peter Bell, Hao Tang
Published: 2025-12-31
Categories: eess.AS, cs.CL
arXiv: https://arxiv.org/abs/2601.00100v1

Despite being the best known objective for learning speech representations, the HuBERT objective has not been further developed and improved. We argue that it is the lack of an underlying principle that stalls the development, and, in this paper, we show that predictive coding under a variational view is the principle behind the HuBERT objective. Due to its generality, our formulation provides opportunities to improve parameterization and optimization, and we show two simple modifications that bring immediate improvements to the HuBERT objective. In addition, the predictive coding formulation has tight connections to various other objectives, such as APC, CPC, wav2vec, and BEST-RQ. Empirically, the improvement in pre-training brings significant improvements to four downstream tasks: phone classification, f0 tracking, speaker recognition, and automatic speech recognition, highlighting the importance of the predictive coding interpretation.

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### The Agentic Leash: Extracting Causal Feedback Fuzzy Cognitive Maps with LLMs

Authors: Akash Kumar Panda, Olaoluwa Adigun, Bart Kosko
Published: 2025-12-31
Categories: cs.AI, cs.CL, cs.HC, cs.IR
arXiv: https://arxiv.org/abs/2601.00097v3

We design a large-language-model (LLM) agent system that extracts causal feedback fuzzy cognitive maps (FCMs) from raw text. The causal learning or extraction process is agentic both because of the LLM's semi-autonomy and because ultimately the FCM dynamical system's equilibria drive the LLM agents to fetch and process causal text. The fetched text can in principle modify the adaptive FCM causal structure and so modify the source of its quasi-autonomy$-$its equilibrium limit cycles and fixed-point attractors. This bidirectional process endows the evolving FCM dynamical system with a degree of autonomy while the system still stays on its agentic leash. We show in particular that a sequence of three system-instruction sets guide an LLM agent as it systematically extracts key nouns and noun phrases from text, as it extracts FCM concept nodes from among those nouns and noun phrases, and then as it extracts or infers partial or fuzzy causal edges between those FCM nodes. We test this FCM generation on a recent essay about the promise of AI from the late diplomat and political theorist Henry Kissinger and his colleagues. This three-step process produced FCM dynamical systems that converged to the same equilibrium limit cycles as did the human-generated FCMs even though the human-generated FCM differed in the number of nodes and edges. A final FCM mixed generated FCMs from separate Gemini and ChatGPT LLM agents. The mixed FCM absorbed the equilibria of its dominant mixture component but also created new equilibria of its own to better approximate the underlying causal dynamical system.

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### Adaptive Constraint Propagation: Scaling Structured Inference for Large Language Models via Meta-Reinforcement Learning

Authors: Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Published: 2025-12-31
Categories: cs.CL
arXiv: https://arxiv.org/abs/2601.00095v3

Large language models increasingly require structured inference, from JSON schema enforcement to multi-lingual parsing, where outputs must satisfy complex constraints. We introduce MetaJuLS, a meta-reinforcement learning approach that learns universal constraint propagation policies applicable across languages and tasks without task-specific retraining. By formulating structured inference as adaptive constraint propagation and training a Graph Attention Network with meta-learning, MetaJuLS achieves 1.5--2.0$\times$ speedups over GPU-optimized baselines while maintaining within 0.2\% accuracy of state-of-the-art parsers. On Universal Dependencies across 10 languages and LLM-constrained generation (LogicBench, GSM8K-Constrained), MetaJuLS demonstrates rapid cross-domain adaptation: a policy trained on English parsing adapts to new languages and tasks with 5--10 gradient steps (5--15 seconds) rather than requiring hours of task-specific training. Mechanistic analysis reveals the policy discovers human-like parsing strategies (easy-first) and novel non-intuitive heuristics. By reducing propagation steps in LLM deployments, MetaJuLS contributes to Green AI by directly reducing inference carbon footprint.

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### Spatial4D-Bench: A Versatile 4D Spatial Intelligence Benchmark

Authors: Pan Wang, Yang Liu, Guile Wu, Eduardo R. Corral-Soto, Chengjie Huang, Binbin Xu, Dongfeng Bai, Xu Yan, Yuan Ren, Xingxin Chen, Yizhe Wu, Tao Huang et al.
Published: 2025-12-31
Categories: cs.CV
arXiv: https://arxiv.org/abs/2601.00092v2

4D spatial intelligence involves perceiving and processing how objects move or change over time. Humans naturally possess 4D spatial intelligence, supporting a broad spectrum of spatial reasoning abilities. To what extent can Multimodal Large Language Models (MLLMs) achieve human-level 4D spatial intelligence? In this work, we present Spatial4D-Bench, a versatile 4D spatial intelligence benchmark designed to comprehensively assess the 4D spatial reasoning abilities of MLLMs. Unlike existing spatial intelligence benchmarks that are often small-scale or limited in diversity, Spatial4D-Bench provides a large-scale, multi-task evaluation benchmark consisting of ~40,000 question-answer pairs covering 18 well-defined tasks. We systematically organize these tasks into six cognitive categories: object understanding, scene understanding, spatial relationship understanding, spatiotemporal relationship understanding, spatial reasoning and spatiotemporal reasoning. Spatial4D-Bench thereby offers a structured and comprehensive benchmark for evaluating the spatial cognition abilities of MLLMs, covering a broad spectrum of tasks that parallel the versatility of human spatial intelligence. We benchmark various state-of-the-art open-source and proprietary MLLMs on Spatial4D-Bench and reveal their substantial limitations in a wide variety of 4D spatial reasoning aspects, such as route plan, action recognition, and physical plausibility reasoning. We hope that the findings provided in this work offer valuable insights to the community and that our benchmark can facilitate the development of more capable MLLMs toward human-level 4D spatial intelligence. More resources can be found on our project page.

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### It's Never Too Late: Noise Optimization for Collapse Recovery in Trained Diffusion Models

Authors: Anne Harrington, A. Sophia Koepke, Shyamgopal Karthik, Trevor Darrell, Alexei A. Efros
Published: 2025-12-31
Categories: cs.CV, cs.LG
arXiv: https://arxiv.org/abs/2601.00090v2

Contemporary text-to-image models exhibit a surprising degree of mode collapse, as can be seen when sampling several images given the same text prompt. Previous work has attempted to address this issue by steering the model using guidance mechanisms, or by generating a large pool of candidates and refining them. In this work, we take a different direction and aim for diversity in generations via noise optimization. Specifically, we show that a simple noise optimization objective can mitigate mode collapse while preserving the fidelity of the base model. We also analyze the frequency characteristics of the noise and show that alternative noise initializations with different frequency profiles can improve both optimization and search. Our experiments demonstrate that noise optimization yields superior results in terms of generation quality and diversity.

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### Dynamic Bayesian Optimization Framework for Instruction Tuning in Partial Differential Equation Discovery

Authors: Junqi Qu, Yan Zhang, Shangqian Gao, Shibo Li
Published: 2025-12-31
Categories: cs.LG
arXiv: https://arxiv.org/abs/2601.00088v1

Large Language Models (LLMs) show promise for equation discovery, yet their outputs are highly sensitive to prompt phrasing, a phenomenon we term instruction brittleness. Static prompts cannot adapt to the evolving state of a multi-step generation process, causing models to plateau at suboptimal solutions. To address this, we propose NeuroSymBO, which reframes prompt engineering as a sequential decision problem. Our method maintains a discrete library of reasoning strategies and uses Bayesian Optimization to select the optimal instruction at each step based on numerical feedback. Experiments on PDE discovery benchmarks show that adaptive instruction selection significantly outperforms fixed prompts, achieving higher recovery rates with more parsimonious solutions.

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### Reinforcement learning with timed constraints for robotics motion planning

Authors: Zhaoan Wang, Junchao Li, Mahdi Mohammad, Shaoping Xiao
Published: 2025-12-31
Categories: cs.RO, cs.LG
arXiv: https://arxiv.org/abs/2601.00087v1

Robotic systems operating in dynamic and uncertain environments increasingly require planners that satisfy complex task sequences while adhering to strict temporal constraints. Metric Interval Temporal Logic (MITL) offers a formal and expressive framework for specifying such time-bounded requirements; however, integrating MITL with reinforcement learning (RL) remains challenging due to stochastic dynamics and partial observability. This paper presents a unified automata-based RL framework for synthesizing policies in both Markov Decision Processes (MDPs) and Partially Observable Markov Decision Processes (POMDPs) under MITL specifications. MITL formulas are translated into Timed Limit-Deterministic Generalized Büchi Automata (Timed-LDGBA) and synchronized with the underlying decision process to construct product timed models suitable for Q-learning. A simple yet expressive reward structure enforces temporal correctness while allowing additional performance objectives. The approach is validated in three simulation studies: a $5 \times 5$ grid-world formulated as an MDP, a $10 \times 10$ grid-world formulated as a POMDP, and an office-like service-robot scenario. Results demonstrate that the proposed framework consistently learns policies that satisfy strict time-bounded requirements under stochastic transitions, scales to larger state spaces, and remains effective in partially observable environments, highlighting its potential for reliable robotic planning in time-critical and uncertain settings.

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