Why Nonparametric Models Deserve a Second LookTowards Data Science Discover how nonparametric conditional distributions unify regression, classification, and synthetic data generation—without assuming functional forms.
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Discover how nonparametric conditional distributions unify regression, classification, and synthetic data generation—without assuming functional forms.
The post Why Nonparametric Models Deserve a Second Look appeared first on Towards Data Science. Read More
How to Build a Model-Native Agent That Learns Internal Planning, Memory, and Multi-Tool Reasoning Through End-to-End Reinforcement LearningMarkTechPost In this tutorial, we explore how an agent can internalize planning, memory, and tool use within a single neural model rather than relying on external orchestration. We design a compact, model-native agent that learns to perform arithmetic reasoning tasks through reinforcement learning. By combining a stage-aware actor-critic network with a curriculum of increasingly complex environments,
The post How to Build a Model-Native Agent That Learns Internal Planning, Memory, and Multi-Tool Reasoning Through End-to-End Reinforcement Learning appeared first on MarkTechPost.
In this tutorial, we explore how an agent can internalize planning, memory, and tool use within a single neural model rather than relying on external orchestration. We design a compact, model-native agent that learns to perform arithmetic reasoning tasks through reinforcement learning. By combining a stage-aware actor-critic network with a curriculum of increasingly complex environments,
The post How to Build a Model-Native Agent That Learns Internal Planning, Memory, and Multi-Tool Reasoning Through End-to-End Reinforcement Learning appeared first on MarkTechPost. Read More
OpenAI Introduces IndQA: A Culture Aware Benchmark For Indian LanguagesMarkTechPost How can we reliably test whether large language models actually understand Indian languages and culture in real world contexts? OpenAI has released IndQA, a benchmark that evaluates how well AI models understand and reason about questions that matter in Indian languages across cultural domains. Why IndQA? OpenAI states that about 80 percent of people worldwide
The post OpenAI Introduces IndQA: A Culture Aware Benchmark For Indian Languages appeared first on MarkTechPost.
How can we reliably test whether large language models actually understand Indian languages and culture in real world contexts? OpenAI has released IndQA, a benchmark that evaluates how well AI models understand and reason about questions that matter in Indian languages across cultural domains. Why IndQA? OpenAI states that about 80 percent of people worldwide
The post OpenAI Introduces IndQA: A Culture Aware Benchmark For Indian Languages appeared first on MarkTechPost. Read More
ARC-GEN: A Mimetic Procedural Benchmark Generator for the Abstraction and Reasoning Corpuscs.AI updates on arXiv.org arXiv:2511.00162v2 Announce Type: new
Abstract: The Abstraction and Reasoning Corpus remains one of the most compelling and challenging benchmarks for tracking progress toward achieving Artificial General Intelligence. In contrast to other evaluation datasets designed to assess an agent’s task-specific skills or accumulated knowledge, the ARC-AGI suite is specifically targeted at measuring skill acquisition efficiency, a trait that has (so far) been lacking in even the most sophisticated machine learning systems. For algorithms that require extensive intra-task exemplars, a significant constraint imposed by ARC-AGI is the modest cardinality of its demonstration set, comprising a small number of $langle$ input, output $rangle$ grids per task specifying the corresponding transformation. To embellish the space of viable sample pairs, this paper introduces ARC-GEN, an open-source procedural generator aimed at extending the original ARC-AGI training dataset as faithfully as possible. Unlike prior efforts, our generator is both exhaustive (covering all four-hundred tasks) and mimetic (more closely honoring the distributional properties and characteristics embodied in the initial ARC-AGI-1 release). We also discuss the use of this generator in establishing a static benchmark suite to verify the correctness of programs submitted to the 2025 Google Code Golf Championship.
arXiv:2511.00162v2 Announce Type: new
Abstract: The Abstraction and Reasoning Corpus remains one of the most compelling and challenging benchmarks for tracking progress toward achieving Artificial General Intelligence. In contrast to other evaluation datasets designed to assess an agent’s task-specific skills or accumulated knowledge, the ARC-AGI suite is specifically targeted at measuring skill acquisition efficiency, a trait that has (so far) been lacking in even the most sophisticated machine learning systems. For algorithms that require extensive intra-task exemplars, a significant constraint imposed by ARC-AGI is the modest cardinality of its demonstration set, comprising a small number of $langle$ input, output $rangle$ grids per task specifying the corresponding transformation. To embellish the space of viable sample pairs, this paper introduces ARC-GEN, an open-source procedural generator aimed at extending the original ARC-AGI training dataset as faithfully as possible. Unlike prior efforts, our generator is both exhaustive (covering all four-hundred tasks) and mimetic (more closely honoring the distributional properties and characteristics embodied in the initial ARC-AGI-1 release). We also discuss the use of this generator in establishing a static benchmark suite to verify the correctness of programs submitted to the 2025 Google Code Golf Championship. Read More
Engineering.ai: A Platform for Teams of AI Engineers in Computational Design AI updates on arXiv.org
Engineering.ai: A Platform for Teams of AI Engineers in Computational Designcs.AI updates on arXiv.org arXiv:2511.00122v1 Announce Type: new
Abstract: In modern engineering practice, human engineers collaborate in specialized teams to design complex products, with each expert completing their respective tasks while communicating and exchanging results and data with one another. While this division of expertise is essential for managing multidisciplinary complexity, it demands substantial development time and cost. Recently, we introduced OpenFOAMGPT (1.0, 2.0), which functions as an autonomous AI engineer for computational fluid dynamics, and turbulence.ai, which can conduct end-to-end research in fluid mechanics draft publications and PhD theses. Building upon these foundations, we present Engineering.ai, a platform for teams of AI engineers in computational design. The framework employs a hierarchical multi-agent architecture where a Chief Engineer coordinates specialized agents consisting of Aerodynamics, Structural, Acoustic, and Optimization Engineers, each powered by LLM with domain-specific knowledge. Agent-agent collaboration is achieved through file-mediated communication for data provenance and reproducibility, while a comprehensive memory system maintains project context, execution history, and retrieval-augmented domain knowledge to ensure reliable decision-making across the workflow. The system integrates FreeCAD, Gmsh, OpenFOAM, CalculiX, and BPM acoustic analysis, enabling parallel multidisciplinary simulations while maintaining computational accuracy. The framework is validated through UAV wing optimization. This work demonstrates that agentic-AI-enabled AI engineers has the potential to perform complex engineering tasks autonomously. Remarkably, the automated workflow achieved a 100% success rate across over 400 parametric configurations, with zero mesh generation failures, solver convergence issues, or manual interventions required, validating that the framework is trustworthy.
arXiv:2511.00122v1 Announce Type: new
Abstract: In modern engineering practice, human engineers collaborate in specialized teams to design complex products, with each expert completing their respective tasks while communicating and exchanging results and data with one another. While this division of expertise is essential for managing multidisciplinary complexity, it demands substantial development time and cost. Recently, we introduced OpenFOAMGPT (1.0, 2.0), which functions as an autonomous AI engineer for computational fluid dynamics, and turbulence.ai, which can conduct end-to-end research in fluid mechanics draft publications and PhD theses. Building upon these foundations, we present Engineering.ai, a platform for teams of AI engineers in computational design. The framework employs a hierarchical multi-agent architecture where a Chief Engineer coordinates specialized agents consisting of Aerodynamics, Structural, Acoustic, and Optimization Engineers, each powered by LLM with domain-specific knowledge. Agent-agent collaboration is achieved through file-mediated communication for data provenance and reproducibility, while a comprehensive memory system maintains project context, execution history, and retrieval-augmented domain knowledge to ensure reliable decision-making across the workflow. The system integrates FreeCAD, Gmsh, OpenFOAM, CalculiX, and BPM acoustic analysis, enabling parallel multidisciplinary simulations while maintaining computational accuracy. The framework is validated through UAV wing optimization. This work demonstrates that agentic-AI-enabled AI engineers has the potential to perform complex engineering tasks autonomously. Remarkably, the automated workflow achieved a 100% success rate across over 400 parametric configurations, with zero mesh generation failures, solver convergence issues, or manual interventions required, validating that the framework is trustworthy. Read More
Advancing AI Challenges for the United States Department of the Air Forcecs.AI updates on arXiv.org arXiv:2511.00267v1 Announce Type: new
Abstract: The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers fundamental advances in artificial intelligence (AI) to expand the competitive advantage of the United States in the defense and civilian sectors. In recent years, AI Accelerator projects have developed and launched public challenge problems aimed at advancing AI research in priority areas. Hallmarks of AI Accelerator challenges include large, publicly available, and AI-ready datasets to stimulate open-source solutions and engage the wider academic and private sector AI ecosystem. This article supplements our previous publication, which introduced AI Accelerator challenges. We provide an update on how ongoing and new challenges have successfully contributed to AI research and applications of AI technologies.
arXiv:2511.00267v1 Announce Type: new
Abstract: The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers fundamental advances in artificial intelligence (AI) to expand the competitive advantage of the United States in the defense and civilian sectors. In recent years, AI Accelerator projects have developed and launched public challenge problems aimed at advancing AI research in priority areas. Hallmarks of AI Accelerator challenges include large, publicly available, and AI-ready datasets to stimulate open-source solutions and engage the wider academic and private sector AI ecosystem. This article supplements our previous publication, which introduced AI Accelerator challenges. We provide an update on how ongoing and new challenges have successfully contributed to AI research and applications of AI technologies. Read More
Who Can We Trust? Scope-Aware Video Moment Retrieval with Multi-Agent Conflictcs.AI updates on arXiv.org arXiv:2511.00370v1 Announce Type: cross
Abstract: Video moment retrieval uses a text query to locate a moment from a given untrimmed video reference. Locating corresponding video moments with text queries helps people interact with videos efficiently. Current solutions for this task have not considered conflict within location results from different models, so various models cannot integrate correctly to produce better results. This study introduces a reinforcement learning-based video moment retrieval model that can scan the whole video once to find the moment’s boundary while producing its locational evidence. Moreover, we proposed a multi-agent system framework that can use evidential learning to resolve conflicts between agents’ localization output. As a side product of observing and dealing with conflicts between agents, we can decide whether a query has no corresponding moment in a video (out-of-scope) without additional training, which is suitable for real-world applications. Extensive experiments on benchmark datasets show the effectiveness of our proposed methods compared with state-of-the-art approaches. Furthermore, the results of our study reveal that modeling competition and conflict of the multi-agent system is an effective way to improve RL performance in moment retrieval and show the new role of evidential learning in the multi-agent framework.
arXiv:2511.00370v1 Announce Type: cross
Abstract: Video moment retrieval uses a text query to locate a moment from a given untrimmed video reference. Locating corresponding video moments with text queries helps people interact with videos efficiently. Current solutions for this task have not considered conflict within location results from different models, so various models cannot integrate correctly to produce better results. This study introduces a reinforcement learning-based video moment retrieval model that can scan the whole video once to find the moment’s boundary while producing its locational evidence. Moreover, we proposed a multi-agent system framework that can use evidential learning to resolve conflicts between agents’ localization output. As a side product of observing and dealing with conflicts between agents, we can decide whether a query has no corresponding moment in a video (out-of-scope) without additional training, which is suitable for real-world applications. Extensive experiments on benchmark datasets show the effectiveness of our proposed methods compared with state-of-the-art approaches. Furthermore, the results of our study reveal that modeling competition and conflict of the multi-agent system is an effective way to improve RL performance in moment retrieval and show the new role of evidential learning in the multi-agent framework. Read More
Proactive DDoS Detection and Mitigation in Decentralized Software-Defined Networking via Port-Level Monitoring and Zero-Training Large Language Modelscs.AI updates on arXiv.org arXiv:2511.00460v1 Announce Type: cross
Abstract: Centralized Software-Defined Networking (cSDN) offers flexible and programmable control of networks but suffers from scalability and reliability issues due to its reliance on centralized controllers. Decentralized SDN (dSDN) alleviates these concerns by distributing control across multiple local controllers, yet this architecture remains highly vulnerable to Distributed Denial-of-Service (DDoS) attacks. In this paper, we propose a novel detection and mitigation framework tailored for dSDN environments. The framework leverages lightweight port-level statistics combined with prompt engineering and in-context learning, enabling the DeepSeek-v3 Large Language Model (LLM) to classify traffic as benign or malicious without requiring fine-tuning or retraining. Once an anomaly is detected, mitigation is enforced directly at the attacker’s port, ensuring that malicious traffic is blocked at their origin while normal traffic remains unaffected. An automatic recovery mechanism restores normal operation after the attack inactivity, ensuring both security and availability. Experimental evaluation under diverse DDoS attack scenarios demonstrates that the proposed approach achieves near-perfect detection, with 99.99% accuracy, 99.97% precision, 100% recall, 99.98% F1-score, and an AUC of 1.0. These results highlight the effectiveness of combining distributed monitoring with zero-training LLM inference, providing a proactive and scalable defense mechanism for securing dSDN infrastructures against DDoS threats.
arXiv:2511.00460v1 Announce Type: cross
Abstract: Centralized Software-Defined Networking (cSDN) offers flexible and programmable control of networks but suffers from scalability and reliability issues due to its reliance on centralized controllers. Decentralized SDN (dSDN) alleviates these concerns by distributing control across multiple local controllers, yet this architecture remains highly vulnerable to Distributed Denial-of-Service (DDoS) attacks. In this paper, we propose a novel detection and mitigation framework tailored for dSDN environments. The framework leverages lightweight port-level statistics combined with prompt engineering and in-context learning, enabling the DeepSeek-v3 Large Language Model (LLM) to classify traffic as benign or malicious without requiring fine-tuning or retraining. Once an anomaly is detected, mitigation is enforced directly at the attacker’s port, ensuring that malicious traffic is blocked at their origin while normal traffic remains unaffected. An automatic recovery mechanism restores normal operation after the attack inactivity, ensuring both security and availability. Experimental evaluation under diverse DDoS attack scenarios demonstrates that the proposed approach achieves near-perfect detection, with 99.99% accuracy, 99.97% precision, 100% recall, 99.98% F1-score, and an AUC of 1.0. These results highlight the effectiveness of combining distributed monitoring with zero-training LLM inference, providing a proactive and scalable defense mechanism for securing dSDN infrastructures against DDoS threats. Read More
Incremental Selection of Most-Filtering Conjectures and Proofs of the Selected Conjecturescs.AI updates on arXiv.org arXiv:2511.00194v1 Announce Type: new
Abstract: We present an improved incremental selection algorithm of the selection algorithm presented in [1] and prove all the selected conjectures.
arXiv:2511.00194v1 Announce Type: new
Abstract: We present an improved incremental selection algorithm of the selection algorithm presented in [1] and prove all the selected conjectures. Read More
EPARA: Parallelizing Categorized AI Inference in Edge Cloudscs.AI updates on arXiv.org arXiv:2511.00603v1 Announce Type: cross
Abstract: With the increasing adoption of AI applications such as large language models and computer vision AI, the computational demands on AI inference systems are continuously rising, making the enhancement of task processing capacity using existing hardware a primary objective in edge clouds. We propose EPARA, an end-to-end AI parallel inference framework in edge, aimed at enhancing the edge AI serving capability. Our key idea is to categorize tasks based on their sensitivity to latency/frequency and requirement for GPU resources, thereby achieving both request-level and service-level task-resource allocation. EPARA consists of three core components: 1) a task-categorized parallelism allocator that decides the parallel mode of each task, 2) a distributed request handler that performs the calculation for the specific request, and 3) a state-aware scheduler that periodically updates service placement in edge clouds. We implement a EPARA prototype and conduct a case study on the EPARA operation for LLMs and segmentation tasks. Evaluation through testbed experiments involving edge servers, embedded devices, and microcomputers shows that EPARA achieves up to 2.1$times$ higher goodput in production workloads compared to prior frameworks, while adapting to various edge AI inference tasks.
arXiv:2511.00603v1 Announce Type: cross
Abstract: With the increasing adoption of AI applications such as large language models and computer vision AI, the computational demands on AI inference systems are continuously rising, making the enhancement of task processing capacity using existing hardware a primary objective in edge clouds. We propose EPARA, an end-to-end AI parallel inference framework in edge, aimed at enhancing the edge AI serving capability. Our key idea is to categorize tasks based on their sensitivity to latency/frequency and requirement for GPU resources, thereby achieving both request-level and service-level task-resource allocation. EPARA consists of three core components: 1) a task-categorized parallelism allocator that decides the parallel mode of each task, 2) a distributed request handler that performs the calculation for the specific request, and 3) a state-aware scheduler that periodically updates service placement in edge clouds. We implement a EPARA prototype and conduct a case study on the EPARA operation for LLMs and segmentation tasks. Evaluation through testbed experiments involving edge servers, embedded devices, and microcomputers shows that EPARA achieves up to 2.1$times$ higher goodput in production workloads compared to prior frameworks, while adapting to various edge AI inference tasks. Read More
