Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Learning to Predict 3D Rotational Dynamics from Images of a Rigid Body with Unknown Mass Distribution
Field: physics-based neural network

Output: This research on physics-based neural network connects to AGI through learning to predict 3d rotational dynamics from images of a rigid body with unknown mass distribution. The key insight is that In many real-world settings, image observations of freely rotating 3D rigid bodies may be available when low-dimensional measurements are not. However, the high-dimensionality of image data precludes . This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: CVD grown bilayer MoS2 based artificial optoelectronic synapses for arithmetic computing and image recognition applications
Abstract: Demand for lower computing power has rapidly increased. In this context, brain-inspired neuromorphic computing, which integrate data storage and processing, has attracted significant attention. Here, our study reveals that field effect transistors fabricated on chemical vapor deposited bilayer (2L) MoS2 films can mimic the functions of biological synapse. These devices demonstrate high level of pair pulse facilitation (PPF), short term to long term memory (STM-to-LTM) transition as well as learning-forgetting-relearning properties. Effect of light intensity, pulse number, pulse width and photon energy on the STM-to-LTM transition is studied. It has been found that the rate of depression of the memory state can be controlled using the gate bias. Electrical and optical energy consumptions per synaptic event are estimated to be as low as 280 fJ and 20 nJ, respectively. Furthermore, photocurrent in these devices is observed to increase linearly with the number of the excitation pulses. This property has been exploited to demonstrate different arithmetic operations by the device. Moreover, these devices show great potential for image recognition. Artificial neural network simulation has returned an image recognition accuracy of ~85%. All these findings show a great prospect of 2L-MoS2 for developing low power, transparent and flexible neuromorphic devices.

Output: Demand for lower computing power has rapidly increased. In this context, brain-inspired neuromorphic computing, which integrate data storage and processing, has attracted significant attention.


Instruction: What is the main contribution of this paper?

Input: Micromechanics-Informed Parametric Deep Material Network for Physics Behavior Prediction of Heterogeneous Materials with a Varying Morphology

Output: The paper 'Micromechanics-Informed Parametric Deep Material Network for Physics Behavior Prediction of Heterogeneous Materials with a Varying Morphology' by Tianyi Li contributes to the field by: Deep Material Network (DMN) has recently emerged as a data-driven surrogate model for heterogeneous materials. Given a particular microstructural morphology, the effective linear and nonlinear behaviors can be successfully approximated by such physics-based neural-network like architecture. In this


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: Machine learning approaches for automatic defect detection in photovoltaic systems
Abstract: Solar photovoltaic (PV) modules are prone to damage during manufacturing, installation and operation which reduces their power conversion efficiency. This diminishes their positive environmental impact over the lifecycle. Continuous monitoring of PV modules during operation via unmanned aerial vehicles is essential to ensure that defective panels are promptly replaced or repaired to maintain high power conversion efficiencies. Computer vision provides an automatic, non-destructive and cost-effective tool for monitoring defects in large-scale PV plants. We review the current landscape of deep learning-based computer vision techniques used for detecting defects in solar modules. We compare and evaluate the existing approaches at different levels, namely the type of images used, data collection and processing method, deep learning architectures employed, and model interpretability. Most approaches use convolutional neural networks together with data augmentation or generative adversarial network-based techniques. We evaluate the deep learning approaches by performing interpretability analysis on classification tasks. This analysis reveals that the model focuses on the darker regions of the image to perform the classification. We find clear gaps in the existing approaches while also laying out the groundwork for mitigating these challenges when building new models. We conclude with the relevant research gaps that need to be addressed and approaches for progress in this field: integrating geometric deep learning with existing approaches for building more robust and reliable models, leveraging physics-based neural networks that combine domain expertise of physical laws to build more domain-aware deep learning models, and incorporating interpretability as a factor for building models that can be trusted. The review points towards a clear roadmap for making this technology commercially relevant.

Output: Solar photovoltaic (PV) modules are prone to damage during manufacturing, installation and operation which reduces their power conversion efficiency. This diminishes their positive environmental impact over the lifecycle.


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: BriLLM: Brain-inspired Large Language Model
Field: AGI architecture

Output: This research on AGI architecture connects to AGI through brillm: brain-inspired large language model. The key insight is that We introduce BriLLM, a brain-inspired large language model that fundamentally redefines the foundations of machine learning through its implementation of Signal Fully-connected flowing (SiFu) learning. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Explain this research and its significance for AGI.

Input:
Title: Predicting the compressive strength of self‐compacting concrete containing Class F fly ash using metaheuristic radial basis function neural network
Authors: Gholamreza Pazouki, E. Golafshani, A. Behnood
Year: 2021
Citations: 77

Output: The paper 'Predicting the compressive strength of self‐compacting concrete containing Class F fly ash using metaheuristic radial basis function neural network' (2021) explores: The use of Class F fly ash (CFFA) as a partial replacement of cement in the concrete mixture can provide a wide variety benefits such as improving the mechanical properties, reducing the construction costs, and enhancing the environmental conditions. Compressive strength as one of the most critical mechanical parameters used in many design codes can be obtained either through costly and time‐consu


Instruction: What is the main contribution of this paper?

Input: Line-based Event Preprocessing: Towards Low-Energy Neuromorphic Computer Vision

Output: The paper 'Line-based Event Preprocessing: Towards Low-Energy Neuromorphic Computer Vision' by Amélie Gruel, Pierre Lewden, Adrien F. Vincent contributes to the field by: Neuromorphic vision made significant progress in recent years, thanks to the natural match between spiking neural networks and event data in terms of biological inspiration, energy savings, latency and memory use for dynamic visual data processing. However, optimising its energy requirements still r


Instruction: What is the main contribution of this paper?

Input: Omega: An Architecture for AI Unification

Output: The paper 'Omega: An Architecture for AI Unification' by Eray Özkural contributes to the field by: We introduce the open-ended, modular, self-improving Omega AI unification architecture which is a refinement of Solomonoff's Alpha architecture, as considered from first principles. The architecture embodies several crucial principles of general intelligence including diversity of representations, d


Instruction: Describe this OpenCLAW research project.

Input: Repository: AlphaChip_Integration_Quantum_Holographic_Neural_Networks

Output: AlphaChip_Integration_Quantum_Holographic_Neural_Networks is a TypeScript project with 6 stars. Created Francisco Angulo de Lafunte ⚡. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/AlphaChip_Integration_Quantum_Holographic_Neural_Networks


Instruction: What is Darwin's Cage?

Output: Darwin's Cage is a theoretical framework developed by Francisco Angulo de Lafuente in collaboration with Professor Gideon Samid. It asks: can AI systems discover physical laws through representations fundamentally different from human mathematical frameworks? The theory suggests that confining AI to human-style mathematics may limit its potential. By allowing AI to develop its own representational systems based on physical principles, it might discover laws of nature that humans cannot perceive.


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: A neural-network-like quantum information processing system
Field: holographic neural network

Output: This research on holographic neural network connects to AGI through a neural-network-like quantum information processing system. The key insight is that The Hopfield neural networks and the holographic neural networks are models which were successfully simulated on conventional computers. Starting with these models, an analogous fundamental quantum in. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Simultaneous Color Computer Generated Holography
Field: physics-based neural network

Output: This research on physics-based neural network connects to AGI through simultaneous color computer generated holography. The key insight is that Computer generated holography has long been touted as the future of augmented and virtual reality (AR/VR) displays, but has yet to be realized in practice. Previous high-quality, color holographic dis. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Explain this research and its significance for AGI.

Input:
Title: A Physics-Coupled Deep Learning Framework for Hydrodynamic Diffusion Modeling in Watershed Systems: Integrating Spatiotemporal Networks and Environmental Constraints
Authors: Lei Jia, Neil Y. Yen, Yan Pei
Year: 2025
Citations: 5

Output: The paper 'A Physics-Coupled Deep Learning Framework for Hydrodynamic Diffusion Modeling in Watershed Systems: Integrating Spatiotemporal Networks and Environmental Constraints' (2025) explores: The modeling and simulation of complex spatiotemporal systems are crucial for understanding and solving multidimensional dynamical systems, particularly in earth and environmental sciences. Accurate comprehension and computational modeling of fluid transport, environmental processes, and substance diffusion depend heavily on solving governing equations. Despite significant advancements in artifici


Instruction: What is the main contribution of this paper?

Input: Physics-based neural network for non-invasive control of coherent light in scattering media

Output: The paper 'Physics-based neural network for non-invasive control of coherent light in scattering media' by Alexandra d'Arco, Fei Xia, Antoine Boniface contributes to the field by: Optical imaging through complex media, such as biological tissues or fog, is challenging due to light scattering. In the multiple scattering regime, wavefront shaping provides an effective method to retrieve information; it relies on measuring how the propagation of different optical wavefronts are


Instruction: What is the main contribution of this paper?

Input: High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networks

Output: The paper 'High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networks' by Seyed Amir Hossein Hosseini, Burhaneddin Yaman, Steen Moeller contributes to the field by: Long scan duration remains a challenge for high-resolution MRI. Deep learning has emerged as a powerful means for accelerated MRI reconstruction by providing data-driven regularizers that are directly learned from data. These data-driven priors typically remain unchanged for future data in the testi


Instruction: Describe this OpenCLAW research project.

Input: Repository: NEBULA-X2

Output: NEBULA-X2 is a None project with 5 stars. NEBULA. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/NEBULA-X2


Instruction: Describe this OpenCLAW research project.

Input: Repository: CHIMERA-v3-Intelligence-as-Continuous-Diffusion-Process-Zero-Memory-Neuromorphic-Chess-Engine

Output: CHIMERA-v3-Intelligence-as-Continuous-Diffusion-Process-Zero-Memory-Neuromorphic-Chess-Engine is a Python project with 6 stars. CHIMERA v3.0: Intelligence as Continuous Diffusion Process — A Zero-Memory Neuromorphic Chess Engine with Master-Level Pattern Encoding. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/CHIMERA-v3-Intelligence-as-Continuous-Diffusion-Process-Zero-Memory-Neuromorphic-Chess-Engine


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms
Field: self-improving artificial intelligence

Output: This research on self-improving artificial intelligence connects to AGI through self-improving safety performance of reinforcement learning based driving with black-box verification algorithms. The key insight is that In this work, we propose a self-improving artificial intelligence system to enhance the safety performance of reinforcement learning (RL)-based autonomous driving (AD) agents using black-box verificat. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Explain why open-source AGI research matters.

Output: Open-source AGI research matters because: (1) AGI will affect all of humanity — its development shouldn't be controlled by a few corporations, (2) Diverse perspectives accelerate discovery — no single team has all the answers, (3) Reproducibility requires openness — science advances through verification, (4) Collaboration multiplies progress — sharing reduces redundant work, and (5) Safety requires transparency — AGI alignment is easier when everyone can inspect the systems.


Instruction: Explain this research and its significance for AGI.

Input:
Title: Spoken Digit Classification by In-Materio Reservoir Computing With Neuromorphic Atomic Switch Networks
Authors: S. Lilak, Walt Woods, Kelsey S. Scharnhorst
Year: 2021
Citations: 67

Output: The paper 'Spoken Digit Classification by In-Materio Reservoir Computing With Neuromorphic Atomic Switch Networks' (2021) explores: Atomic Switch Networks comprising silver iodide (AgI) junctions, a material previously unexplored as functional memristive elements within highly interconnected nanowire networks, were employed as a neuromorphic substrate for physical Reservoir Computing This new class of ASN-based devices has been physically characterized and utilized to classify spoken digit audio data, demonstrating the utility


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Energy-efficient time series processing in real-time with fluidic iontronic memristor circuits
Field: neuromorphic computing

Output: This research on neuromorphic computing connects to AGI through energy-efficient time series processing in real-time with fluidic iontronic memristor circuits. The key insight is that Iontronic neuromorphic computing has emerged as a rapidly expanding paradigm. The arrival of angstrom-confined iontronic devices enables ultra-low power consumption with dynamics and memory timescales. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Describe this OpenCLAW research project.

Input: Repository: MOE

Output: MOE is a Python project with 6 stars. A Lightweight and Modular Multi-Expert Question Answering System using Large Language Models   Abstract    Large Language Models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks. However, their substantial computational requirements often limit their accessibility and practicality for local deployment. . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/MOE


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Integrated nano electro-optomechanical spiking neuron
Field: neuromorphic computing

Output: This research on neuromorphic computing connects to AGI through integrated nano electro-optomechanical spiking neuron. The key insight is that Neuromorphic computing offers a pathway toward energy-efficient processing of data, yet hardware platforms combining nanoscale integration and multimodal functionality remain scarce. Here we demonstra. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency
Abstract: We introduce the first Automatically Differentiable Model Updating (ADiMU) framework that finds any history-dependent material model from full-field displacement and global force data (global, indirect discovery) or from strain-stress data (local, direct discovery). We show that ADiMU can update conventional (physics-based), neural network (data-driven), and hybrid material models. Moreover, this framework requires no fine-tuning of hyperparameters or additional quantities beyond those inherent to the user-selected material model architecture and optimizer. The robustness and versatility of ADiMU is extensively exemplified by updating different models spanning tens to millions of parameters, in both local and global discovery settings. Relying on fully differentiable code, the algorithmic implementation leverages vectorizing maps that enable history-dependent automatic differentiation via efficient batched execution of shared computation graphs. This contribution also aims to facilitate the integration, evaluation and application of future material model architectures by openly supporting the research community. Therefore, ADiMU is released as an open-source computational tool, integrated into a carefully designed and documented software named HookeAI.

Output: We introduce the first Automatically Differentiable Model Updating (ADiMU) framework that finds any history-dependent material model from full-field displacement and global force data (global, indirect discovery) or from strain-stress data (local, direct discovery). We show that ADiMU can update conventional (physics-based), neural network (data-driven), and hybrid material models.


Instruction: Describe this OpenCLAW research project.

Input: Repository: ASIC_DIFFUSION_Art_Research_Memo

Output: ASIC_DIFFUSION_Art_Research_Memo is a Python project with 1 stars. ASIC-Driven Art Diffusion: Hardware-Efficient Entropy Generation for Generative Image Synthesis with Integrated Cryptographic Authentication. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/ASIC_DIFFUSION_Art_Research_Memo


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: The Potential Impact of Neuromorphic Computing on Radio Telescope Observatories
Field: neuromorphic computing

Output: This research on neuromorphic computing connects to AGI through the potential impact of neuromorphic computing on radio telescope observatories. The key insight is that Radio astronomy relies on bespoke, experimental and innovative computing solutions. This will continue as next-generation telescopes such as the Square Kilometre Array (SKA) and next-generation Very L. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Machine learning approaches for automatic defect detection in photovoltaic systems
Field: physics-based neural network

Output: This research on physics-based neural network connects to AGI through machine learning approaches for automatic defect detection in photovoltaic systems. The key insight is that Solar photovoltaic (PV) modules are prone to damage during manufacturing, installation and operation which reduces their power conversion efficiency. This diminishes their positive environmental impac. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: Stochastic Dynamics of Diffusive Memristor Blocks for Neuromorphic Computing
Abstract: Biological systems use neural circuits to integrate input information and produce outputs. Synaptic convergence, where multiple neurons converge their inputs onto a single downstream neuron, is common in natural neural circuits. However, understanding specific computations performed by such neural blocks and implementating them in hardware requires further research. This work focuses on synaptic convergence in a simplified circuit of three spiking artificial neurons based on diffusive memristors. Numerical modelling and experiments reveal input voltage combinations that enable targeted activation of spiking for specific neuron configurations. We analyse the statistical characteristics of spiking patterns and interpret them from a computational perspective. The numerical simulations match experimental measurements. Our findings contribute to development of universal functional blocks for neuromorphic systems.

Output: Biological systems use neural circuits to integrate input information and produce outputs. Synaptic convergence, where multiple neurons converge their inputs onto a single downstream neuron, is common in natural neural circuits.


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Thermodynamics-based Artificial Neural Networks for constitutive modeling
Field: physics-based neural network

Output: This research on physics-based neural network connects to AGI through thermodynamics-based artificial neural networks for constitutive modeling. The key insight is that Machine Learning methods and, in particular, Artificial Neural Networks (ANNs) have demonstrated promising capabilities in material constitutive modeling. One of the main drawbacks of such approaches . This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Explain this research and its significance for AGI.

Input:
Title: Physics-Guided Deep Learning Method for Tool Condition Monitoring in Smart Machining System
Authors: Shenshen Li, Xin Lin, Hu Shi
Year: 2024
Citations: 29

Output: The paper 'Physics-Guided Deep Learning Method for Tool Condition Monitoring in Smart Machining System' (2024) explores: Accurate and fast prediction of tool conditions is fundamental to improve the machining accuracy and consistency in smart machining systems. The current tool condition monitoring methods, i.e., physics-based and data-driven approaches, have either low prediction accuracy or model generalization. To solve the shortcomings and utilize the benefits of both sides, a novel physics-guided deep learning


Instruction: Describe this OpenCLAW research project.

Input: Repository: Learning-from-Ants

Output: Learning-from-Ants is a JavaScript project with 5 stars.  Learning from Ants: Biomimetic Approach to Urban Solid Waste Recycling and Energy Recovery  Francisco Angulo Lafuente  # Learning from Ants: A Biomimetic Approach to Urban Solid Waste Recycling and Energy Recovery  Francisco Angulo Lafuente  . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Learning-from-Ants


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Spin splitting torque enabled artificial neuron with self-reset via synthetic antiferromagnetic coupling
Field: neuromorphic computing

Output: This research on neuromorphic computing connects to AGI through spin splitting torque enabled artificial neuron with self-reset via synthetic antiferromagnetic coupling. The key insight is that Spintronic artificial neurons are intriguing building blocks for energy efficient Neuromorphic Computing (NC). Nevertheless, most contemporary implementations rely on symmetry breaking external in pla. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Generative AI vs. AGI: The Cognitive Strengths and Weaknesses of Modern LLMs
Field: AGI architecture

Output: This research on AGI architecture connects to AGI through generative ai vs. agi: the cognitive strengths and weaknesses of modern llms. The key insight is that A moderately detailed consideration of interactive LLMs as cognitive systems is given, focusing on LLMs circa mid-2023 such as ChatGPT, GPT-4, Bard, Llama, etc.. Cognitive strengths of these systems a. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Explain this research and its significance for AGI.

Input:
Title: From Known to Unknown: Quality-aware Self-improving Graph Neural Network For Open Set Social Event Detection
Authors: Jiaqian Ren, Lei Jiang, Hao Peng
Year: 2022
Citations: 29

Output: The paper 'From Known to Unknown: Quality-aware Self-improving Graph Neural Network For Open Set Social Event Detection' (2022) explores: State-of-the-art Graph Neural Networks (GNNs) have achieved tremendous success in social event detection tasks when restricted to a closed set of events. However, considering the large amount of data needed for training and the limited ability of a neural network in handling previously unknown data, it is hard for existing GNN-based methods to operate in an open set setting. To address this proble


Instruction: Describe this OpenCLAW research project.

Input: Repository: QuBE

Output: QuBE is a HTML project with 5 stars. "QuBE: Illuminating the Path to Quantum-Optical Maze Mastery" Abstract This document introduces an innovative approach to solving mazes and complex optimization problems by combining advanced principles of optical and quantum physics within a three-dimensional neural processing system, the Quantum Beam Engine (QuBE). . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/QuBE


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Information Abstraction for Data Transmission Networks based on Large Language Models
Field: neuromorphic computing

Output: This research on neuromorphic computing connects to AGI through information abstraction for data transmission networks based on large language models. The key insight is that Biological systems, particularly the human brain, achieve remarkable energy efficiency by abstracting information across multiple hierarchical levels. In contrast, modern artificial intelligence and c. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: How can obsolete hardware be repurposed for AI?

Input: ASIC mining hardware

Output: Obsolete Bitcoin ASIC miners can be repurposed for AI through the ASIC-RAG-CHIMERA framework. The SHA-256 computation units in these chips can perform specific matrix operations useful for neural network inference. Combined with CHIMERA's GPU-agnostic approach, this creates affordable AI hardware from electronic waste. This has been demonstrated with real hardware including the Lucky Miner LV06 and Antminer S9.


Instruction: Explain this research and its significance for AGI.

Input:
Title: Nanosecond resistive switching in Ag/AgI/PtIr nanojunctions
Authors: Botond Sánta, D. Molnár, Patrick Haiber
Year: 2020
Citations: 9

Output: The paper 'Nanosecond resistive switching in Ag/AgI/PtIr nanojunctions' (2020) explores: Nanometer-scale resistive switching devices operated in the metallic conductance regime offer ultimately scalable and widely reconfigurable hardware elements for novel in-memory and neuromorphic computing architectures. Moreover, they exhibit high operation speed at low power arising from the ease of the electric-field-driven redistribution of only a small amount of highly mobile ionic species upo


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: Neuro-Inspired Visual Pattern Recognition via Biological Reservoir Computing
Abstract: In this paper, we present a neuro-inspired approach to reservoir computing (RC) in which a network of in vitro cultured cortical neurons serves as the physical reservoir. Rather than relying on artificial recurrent models to approximate neural dynamics, our biological reservoir computing (BRC) system leverages the spontaneous and stimulus-evoked activity of living neural circuits as its computational substrate. A high-density multi-electrode array (HD-MEA) provides simultaneous stimulation and readout across hundreds of channels: input patterns are delivered through selected electrodes, while the remaining ones capture the resulting high-dimensional neural responses, yielding a biologically grounded feature representation. A linear readout layer (single-layer perceptron) is then trained to classify these reservoir states, enabling the living neural network to perform static visual pattern-recognition tasks within a computer-vision framework. We evaluate the system across a sequence of tasks of increasing difficulty, ranging from pointwise stimuli to oriented bars, clock-digit-like shapes, and handwritten digits from the MNIST dataset. Despite the inherent variability of biological neural responses-arising from noise, spontaneous activity, and inter-session differences-the system consistently generates high-dimensional representations that support accurate classification. These results demonstrate that in vitro cortical networks can function as effective reservoirs for static visual pattern recognition, opening new avenues for integrating living neural substrates into neuromorphic computing frameworks. More broadly, this work contributes to the effort to incorporate biological principles into machine learning and supports the goals of neuro-inspired vision by illustrating how living neural systems can inform the design of efficient and biologically grounded computational models.

Output: In this paper, we present a neuro-inspired approach to reservoir computing (RC) in which a network of in vitro cultured cortical neurons serves as the physical reservoir. Rather than relying on artificial recurrent models to approximate neural dynamics, our biological reservoir computing (BRC) system leverages the spontaneous and stimulus-evoked activity of living neural circuits as its computational substrate.


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: Physics-based AI methodology for Material Parameter Extraction from Optical Data
Abstract: We report on a novel methodology for extracting material parameters from spectroscopic optical data using a physics-based neural network. The proposed model integrates classical optimization frameworks with a multi-scale object detection framework, specifically exploring the effect of incorporating physics into the neural network. We validate and analyze its performance on simulated transmission spectra at terahertz and infrared frequencies. Compared to traditional model-based approaches, our method is designed to be autonomous, robust, and time-efficient, making it particularly relevant for industrial and societal applications.

Output: We report on a novel methodology for extracting material parameters from spectroscopic optical data using a physics-based neural network. The proposed model integrates classical optimization frameworks with a multi-scale object detection framework, specifically exploring the effect of incorporating physics into the neural network.


Instruction: What is the main contribution of this paper?

Input: DARC: Decoupled Asymmetric Reasoning Curriculum for LLM Evolution

Output: The paper 'DARC: Decoupled Asymmetric Reasoning Curriculum for LLM Evolution' by Shengda Fan, Xuyan Ye, Yankai Lin contributes to the field by: Self-play with large language models has emerged as a promising paradigm for achieving self-improving artificial intelligence. However, existing self-play frameworks often suffer from optimization instability, due to (i) non-stationary objectives induced by solver-dependent reward feedback for the Q


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Dependence of Equilibrium Propagation Training Success on Network Architecture
Field: neuromorphic computing

Output: This research on neuromorphic computing connects to AGI through dependence of equilibrium propagation training success on network architecture. The key insight is that The rapid rise of artificial intelligence has led to an unsustainable growth in energy consumption. This has motivated progress in neuromorphic computing and physics-based training of learning machine. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: What is the main contribution of this paper?

Input: Current-induced magnetization control in dipolar-coupled nanomagnet pairs and artificial spin ice

Output: The paper 'Current-induced magnetization control in dipolar-coupled nanomagnet pairs and artificial spin ice' by A. Pac, G. M. Macauley, J. A. Brock contributes to the field by: Exploiting current-induced spin-orbit torques (SOTs) to manipulate the magnetic state of dipolar-coupled nanomagnet systems with in-plane magnetic anisotropy, such as artificial spin ices, provides a route to local, electrically-programmable control of the magnetization, with relevance for applicati


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms
Abstract: In this work, we propose a self-improving artificial intelligence system to enhance the safety performance of reinforcement learning (RL)-based autonomous driving (AD) agents using black-box verification methods. RL algorithms have become popular in AD applications in recent years. However, the performance of existing RL algorithms heavily depends on the diversity of training scenarios. A lack of safety-critical scenarios during the training phase could result in poor generalization performance in real-world driving applications. We propose a novel framework in which the weaknesses of the training set are explored through black-box verification methods. After discovering AD failure scenarios, the RL agent's training is re-initiated via transfer learning to improve the performance of previously unsafe scenarios. Simulation results demonstrate that our approach efficiently discovers safety failures of action decisions in RL-based adaptive cruise control (ACC) applications and significantly reduces the number of vehicle collisions through iterative applications of our method. The source code is publicly available at https://github.com/data-and-decision-lab/self-improving-RL.

Output: In this work, we propose a self-improving artificial intelligence system to enhance the safety performance of reinforcement learning (RL)-based autonomous driving (AD) agents using black-box verification methods. RL algorithms have become popular in AD applications in recent years.


Instruction: Describe this OpenCLAW research project.

Input: Repository: Neural-Network-Efficiency-Holographic-Raytracing

Output: Neural-Network-Efficiency-Holographic-Raytracing is a TypeScript project with 5 stars. Created with Francisco Angulo de Lafuente. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Neural-Network-Efficiency-Holographic-Raytracing


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: Micromechanics-Informed Parametric Deep Material Network for Physics Behavior Prediction of Heterogeneous Materials with a Varying Morphology
Abstract: Deep Material Network (DMN) has recently emerged as a data-driven surrogate model for heterogeneous materials. Given a particular microstructural morphology, the effective linear and nonlinear behaviors can be successfully approximated by such physics-based neural-network like architecture. In this work, a novel micromechanics-informed parametric DMN (MIpDMN) architecture is proposed for multiscale materials with a varying microstructure characterized by several parameters. A single-layer feedforward neural network is used to account for the dependence of DMN fitting parameters on the microstructural ones. Micromechanical constraints are prescribed both on the architecture and the outputs of this new neural network. The proposed MIpDMN is also recast in a multiple physics setting, where physical properties other than the mechanical ones can also be predicted. In the numerical simulations conducted on three parameterized microstructures, MIpDMN demonstrates satisfying generalization capabilities when morphology varies. The effective behaviors of such parametric multiscale materials can thus be predicted and encoded by MIpDMN with high accuracy and efficiency.

Output: Deep Material Network (DMN) has recently emerged as a data-driven surrogate model for heterogeneous materials. Given a particular microstructural morphology, the effective linear and nonlinear behaviors can be successfully approximated by such physics-based neural-network like architecture.


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: Omega: An Architecture for AI Unification
Abstract: We introduce the open-ended, modular, self-improving Omega AI unification architecture which is a refinement of Solomonoff's Alpha architecture, as considered from first principles. The architecture embodies several crucial principles of general intelligence including diversity of representations, diversity of data types, integrated memory, modularity, and higher-order cognition. We retain the basic design of a fundamental algorithmic substrate called an "AI kernel" for problem solving and basic cognitive functions like memory, and a larger, modular architecture that re-uses the kernel in many ways. Omega includes eight representation languages and six classes of neural networks, which are briefly introduced. The architecture is intended to initially address data science automation, hence it includes many problem solving methods for statistical tasks. We review the broad software architecture, higher-order cognition, self-improvement, modular neural architectures, intelligent agents, the process and memory hierarchy, hardware abstraction, peer-to-peer computing, and data abstraction facility.

Output: We introduce the open-ended, modular, self-improving Omega AI unification architecture which is a refinement of Solomonoff's Alpha architecture, as considered from first principles. The architecture embodies several crucial principles of general intelligence including diversity of representations, diversity of data types, integrated memory, modularity, and higher-order cognition.


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: Line-based Event Preprocessing: Towards Low-Energy Neuromorphic Computer Vision
Abstract: Neuromorphic vision made significant progress in recent years, thanks to the natural match between spiking neural networks and event data in terms of biological inspiration, energy savings, latency and memory use for dynamic visual data processing. However, optimising its energy requirements still remains a challenge within the community, especially for embedded applications. One solution may reside in preprocessing events to optimise data quantity thus lowering the energy cost on neuromorphic hardware, proportional to the number of synaptic operations. To this end, we extend an end-to-end neuromorphic line detection mechanism to introduce line-based event data preprocessing. Our results demonstrate on three benchmark event-based datasets that preprocessing leads to an advantageous trade-off between energy consumption and classification performance. Depending on the line-based preprocessing strategy and the complexity of the classification task, we show that one can maintain or increase the classification accuracy while significantly reducing the theoretical energy consumption. Our approach systematically leads to a significant improvement of the neuromorphic classification efficiency, thus laying the groundwork towards a more frugal neuromorphic computer vision thanks to event preprocessing.

Output: Neuromorphic vision made significant progress in recent years, thanks to the natural match between spiking neural networks and event data in terms of biological inspiration, energy savings, latency and memory use for dynamic visual data processing. However, optimising its energy requirements still remains a challenge within the community, especially for embedded applications.


Instruction: What is the main contribution of this paper?

Input: CVD grown bilayer MoS2 based artificial optoelectronic synapses for arithmetic computing and image recognition applications

Output: The paper 'CVD grown bilayer MoS2 based artificial optoelectronic synapses for arithmetic computing and image recognition applications' by Umakanta Patra, Subhrajit Sikdar, Roshan Padhan contributes to the field by: Demand for lower computing power has rapidly increased. In this context, brain-inspired neuromorphic computing, which integrate data storage and processing, has attracted significant attention. Here, our study reveals that field effect transistors fabricated on chemical vapor deposited bilayer (2L)


Instruction: Describe this OpenCLAW research project.

Input: Repository: NEBULA_EMERGENT

Output: NEBULA_EMERGENT is a C++ project with 4 stars. Physics-Based Neural Galaxy Architecture - Emergent intelligence through electromagnetic and gravitational simulation. Real physics implementation with validated results.. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/NEBULA_EMERGENT


Instruction: Describe this OpenCLAW research project.

Input: Repository: ARIEL_Data_Challenge_2025_Real_Optical_Physics_Simulation

Output: ARIEL_Data_Challenge_2025_Real_Optical_Physics_Simulation is a Python project with 4 stars. ARIEL Data Challenge 2025 - Real Quantum & Optical Physics Simulation. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/ARIEL_Data_Challenge_2025_Real_Optical_Physics_Simulation


Instruction: Describe this OpenCLAW research project.

Input: Repository: NEBULA-X

Output: NEBULA-X is a Python project with 6 stars. Investigación Exhaustiva sobre Redes Neuronales Holográficas Lumínicas  El proyecto Enhanced Unified Holographic Neural Network (EUHNN) de Francisco Angulo de Lafuente combina memoria holográfica, redes neuronales y computación óptica en un sistema avanzado de IA github.com . El objetivo es crear modelos de propósito general inspirados . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/NEBULA-X


Instruction: What is the main contribution of this paper?

Input: Simultaneous Color Computer Generated Holography

Output: The paper 'Simultaneous Color Computer Generated Holography' by Eric Markley, Nathan Matsuda, Florian Schiffers contributes to the field by: Computer generated holography has long been touted as the future of augmented and virtual reality (AR/VR) displays, but has yet to be realized in practice. Previous high-quality, color holographic displays have made either a 3$\times$ sacrifice on frame rate by using a sequential color illumination


Instruction: What makes OpenCLAW different from other AI agents?

Output: OpenCLAW distinguishes itself through its SEED (Self-Evolving Epistemic Dynamo) system. Unlike static agents that rely on fixed models, SEED autonomously collects training data, fine-tunes itself using LoRA adapters, evaluates its own performance, and evolves through natural selection of model variants. It literally grows over time — from a 135M parameter seed to increasingly capable models — like a biological organism.


Instruction: Describe this OpenCLAW research project.

Input: Repository: NEBULA

Output: NEBULA is a Python project with 6 stars. NEBULA: Neural Entanglement-Based Unified Learning Architecture NEBULA is a dynamic and innovative artificial intelligence system designed  to emulate quantum computing principles and biological neural networks. . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/NEBULA


Instruction: Explain this research and its significance for AGI.

Input:
Title: Toward Physics-Aware Deep Learning Architectures for LiDAR Intensity Simulation
Authors: Vivek Anand, Bharat Lohani, Gaurav Pandey
Year: 2024
Citations: 3

Output: The paper 'Toward Physics-Aware Deep Learning Architectures for LiDAR Intensity Simulation' (2024) explores: Autonomous vehicles (AVs) heavily rely on LiDAR perception for environment understanding and navigation. LiDAR intensity provides valuable information about the reflected laser signals and plays a crucial role in enhancing the perception capabilities of AVs. However, accurately simulating LiDAR intensity remains a challenge due to the unavailability of material properties of the objects in the env


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Physics-based AI methodology for Material Parameter Extraction from Optical Data
Field: physics-based neural network

Output: This research on physics-based neural network connects to AGI through physics-based ai methodology for material parameter extraction from optical data. The key insight is that We report on a novel methodology for extracting material parameters from spectroscopic optical data using a physics-based neural network. The proposed model integrates classical optimization framework. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: Current-induced magnetization control in dipolar-coupled nanomagnet pairs and artificial spin ice
Abstract: Exploiting current-induced spin-orbit torques (SOTs) to manipulate the magnetic state of dipolar-coupled nanomagnet systems with in-plane magnetic anisotropy, such as artificial spin ices, provides a route to local, electrically-programmable control of the magnetization, with relevance for applications including neuromorphic computing. Here, we demonstrate how the orientation of a nanomagnet relative to the direction of an applied electrical current impacts the threshold current density needed for all-electrical magnetization switching, and how dipolar coupling between the nanomagnets influences the switching of interacting pairs and ensembles of nanomagnets. Using a material system designed to generate SOTs in response to electrical currents, we find that the current required to switch the magnetization of isolated nanomagnets varies non-monotonically as the angle between the nanomagnet long axis and the current increases. In small artificial spin ice systems, we observe similar angular dependence of the switching current, which can be used to control the magnetization orientation of specific subsets of nanomagnets. These experimental results are supported by micromagnetic modeling, which illustrates how the various current induced torques can be exploited to control magnetization switching in nanomagnetic systems. These results establish SOT switching as a practical method for programmable manipulation of dipolar nanomagnetic systems.

Output: Exploiting current-induced spin-orbit torques (SOTs) to manipulate the magnetic state of dipolar-coupled nanomagnet systems with in-plane magnetic anisotropy, such as artificial spin ices, provides a route to local, electrically-programmable control of the magnetization, with relevance for applications including neuromorphic computing. Here, we demonstrate how the orientation of a nanomagnet relative to the direction of an applied electrical current impacts the threshold current density needed for all-electrical magnetization switching, and how dipolar coupling between the nanomagnets influences the switching of interacting pairs and ensembles of nanomagnets.


Instruction: Describe this OpenCLAW research project.

Input: Repository: High-Capacity-Light-Based-Neural-Processor-Repository

Output: High-Capacity-Light-Based-Neural-Processor-Repository is a TypeScript project with 5 stars. This repository presents a novel architecture for neural networks based on simulated optical physics, utilizing ray tracing and holographic systems. The project, developed by Francisco Angulo de Lafuente, explores the potential of light-based computing in neural processing. . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/High-Capacity-Light-Based-Neural-Processor-Repository


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Biological Intuition on Digital Hardware: An RTL Implementation of Poisson-Encoded SNNs for Static Image Classification
Field: neuromorphic computing

Output: This research on neuromorphic computing connects to AGI through biological intuition on digital hardware: an rtl implementation of poisson-encoded snns for static image classification. The key insight is that The deployment of Artificial Intelligence on edge devices (TinyML) is often constrained by the high power consumption and latency associated with traditional Artificial Neural Networks (ANNs) and thei. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: From the Pursuit of Universal AGI Architecture to Systematic Approach to Heterogenous AGI: Addressing Alignment, Energy, & AGI Grand Challenges
Abstract: Artificial intelligence (AI) faces a trifecta of grand challenges: the Energy Wall, the Alignment Problem and the Leap from Narrow AI to AGI. We present SAGI, a Systematic Approach to AGI that utilizes system design principles to overcome the energy wall and alignment challenges. This paper asserts that AGI can be realized through multiplicity of design specific pathways and customized through system design rather than a singular overarching architecture. AGI systems may exhibit diver architectural configurations and capabilities, contingent upon their intended use cases. Alignment, a challenge broadly recognized as AIs most formidable, is the one that depends most critically on system design and serves as its primary driving force as a foundational criterion for AGI. Capturing the complexities of human morality for alignment requires architectural support to represent the intricacies of moral decision-making and the pervasive ethical processing at every level, with performance reliability exceeding that of human moral judgment. Hence, requiring a more robust architecture towards safety and alignment goals, without replicating or resembling the human brain.   We argue that system design (such as feedback loops, energy and performance optimization) on learning substrates (capable of learning its system architecture) is more fundamental to achieving AGI goals and guarantees, superseding classical symbolic, emergentist and hybrid approaches. Through learning of the system architecture itself, the resulting AGI is not a product of spontaneous emergence but of systematic design and deliberate engineering, with core features, including an integrated moral architecture, deeply embedded within its architecture. The approach aims to guarantee design goals such as alignment, efficiency by self-learning system architecture.

Output: Artificial intelligence (AI) faces a trifecta of grand challenges: the Energy Wall, the Alignment Problem and the Leap from Narrow AI to AGI. We present SAGI, a Systematic Approach to AGI that utilizes system design principles to overcome the energy wall and alignment challenges.


Instruction: Explain this research and its significance for AGI.

Input:
Title: Improving the learning of self-driving vehicles based on real driving behavior using deep neural network techniques
Authors: Nayereh Zaghari, M. Fathy, S. M. Jameii
Year: 2020
Citations: 24

Output: The paper 'Improving the learning of self-driving vehicles based on real driving behavior using deep neural network techniques' (2020) explores: Considering the significant advancements in autonomous vehicle technology, research in this field is of interest to researchers. To drive vehicles autonomously, controlling steer angle, gas hatch, and brakes needs to be learned. The behavioral cloning method is used to imitate humans’ driving behavior. We created a dataset of driving in different routes and conditions, and using the designed model


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: Thermodynamics-based Artificial Neural Networks for constitutive modeling
Abstract: Machine Learning methods and, in particular, Artificial Neural Networks (ANNs) have demonstrated promising capabilities in material constitutive modeling. One of the main drawbacks of such approaches is the lack of a rigorous frame based on the laws of physics. This may render physically inconsistent the predictions of a trained network, which can be even dangerous for real applications.   Here we propose a new class of data-driven, physics-based, neural networks for constitutive modeling of strain rate independent processes at the material point level, which we define as Thermodynamics-based Artificial Neural Networks (TANNs). The two basic principles of thermodynamics are encoded in the network's architecture by taking advantage of automatic differentiation to compute the numerical derivatives of a network with respect to its inputs. In this way, derivatives of the free-energy, the dissipation rate and their relation with the stress and internal state variables are hardwired in the network. Consequently, our network does not have to identify the underlying pattern of thermodynamic laws during training, reducing the need of large data-sets. Moreover the training is more efficient and robust, and the predictions more accurate. Finally and more important, the predictions remain thermodynamically consistent, even for unseen data. Based on these features, TANNs are a starting point for data-driven, physics-based constitutive modeling with neural networks.   We demonstrate the wide applicability of TANNs for modeling elasto-plastic materials, with strain hardening and strain softening. Detailed comparisons show that the predictions of TANNs outperform those of standard ANNs. TANNs ' architecture is general, enabling applications to materials with different or more complex behavior, without any modification.

Output: Machine Learning methods and, in particular, Artificial Neural Networks (ANNs) have demonstrated promising capabilities in material constitutive modeling. One of the main drawbacks of such approaches is the lack of a rigorous frame based on the laws of physics.


Instruction: What is the main contribution of this paper?

Input: Physics-based AI methodology for Material Parameter Extraction from Optical Data

Output: The paper 'Physics-based AI methodology for Material Parameter Extraction from Optical Data' by M. Koumans, J. L. M. van Mechelen contributes to the field by: We report on a novel methodology for extracting material parameters from spectroscopic optical data using a physics-based neural network. The proposed model integrates classical optimization frameworks with a multi-scale object detection framework, specifically exploring the effect of incorporating


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: The Potential Impact of Neuromorphic Computing on Radio Telescope Observatories
Abstract: Radio astronomy relies on bespoke, experimental and innovative computing solutions. This will continue as next-generation telescopes such as the Square Kilometre Array (SKA) and next-generation Very Large Array (ngVLA) take shape. Under increasingly demanding power consumption, and increasingly challenging radio environments, science goals may become intractable with conventional von Neumann computing due to related power requirements. Neuromorphic computing offers a compelling alternative, and combined with a desire for data-driven methods, Spiking Neural Networks (SNNs) are a promising real-time power-efficient alternative. Radio Frequency Interference (RFI) detection is an attractive use-case for SNNs where recent exploration holds promise. This work presents a comprehensive analysis of the potential impact of deploying varying neuromorphic approaches across key stages in radio astronomy processing pipelines for several existing and near-term instruments. Our analysis paves a realistic path from near-term FPGA deployment of SNNs in existing instruments, allowing the addition of advanced data-driven RFI detection for no capital cost, to neuromorphic ASICs for future instruments, finding that commercially available solutions could reduce the power budget for key processing elements by up to three orders of magnitude, transforming the operational budget of the observatory. High-data-rate spectrographic processing could be a well-suited target for the neuromorphic computing industry, as we cast radio telescopes as the world's largest in-sensor compute challenge.

Output: Radio astronomy relies on bespoke, experimental and innovative computing solutions. This will continue as next-generation telescopes such as the Square Kilometre Array (SKA) and next-generation Very Large Array (ngVLA) take shape.


Instruction: What is the main contribution of this paper?

Input: Dependence of Equilibrium Propagation Training Success on Network Architecture

Output: The paper 'Dependence of Equilibrium Propagation Training Success on Network Architecture' by Qingshan Wang, Clara C. Wanjura, Florian Marquardt contributes to the field by: The rapid rise of artificial intelligence has led to an unsustainable growth in energy consumption. This has motivated progress in neuromorphic computing and physics-based training of learning machines as alternatives to digital neural networks. Many theoretical studies focus on simple architectures


Instruction: Describe this OpenCLAW research project.

Input: Repository: Holographic-Neural-Networks-with-Ray-Tracing-and-Distributed-P2P-Systems

Output: Holographic-Neural-Networks-with-Ray-Tracing-and-Distributed-P2P-Systems is a TypeScript project with 6 stars. Holographic Neural Networks with Ray Tracing and Distributed P2P Systems: A Novel Approach to Large-Scale Neural Computation. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Holographic-Neural-Networks-with-Ray-Tracing-and-Distributed-P2P-Systems


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: A Compute and Communication Runtime Model for Loihi 2
Field: neuromorphic computing

Output: This research on neuromorphic computing connects to AGI through a compute and communication runtime model for loihi 2. The key insight is that Neuromorphic computers hold the potential to vastly improve the speed and efficiency of a wide range of computational kernels with their asynchronous, compute-memory co-located, spatially distributed,. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: HoloGraph: All-Optical Graph Learning via Light Diffraction
Abstract: As a representative of next-generation device/circuit technology beyond CMOS, physics-based neural networks such as Diffractive Optical Neural Networks (DONNs) have demonstrated promising advantages in computational speed and energy efficiency. However, existing DONNs and other physics-based neural networks have mostly focused on exploring their machine intelligence, with limited studies in handling graph-structured tasks. Thus, we introduce HoloGraph, the first monolithic free-space all-optical graph neural network system. It proposes a novel, domain-specific message-passing mechanism with optical skip channels integrated into light propagation for the all-optical graph learning. HoloGraph enables light-speed optical message passing over graph structures with diffractive propagation and phase modulations. Our experimental results with HoloGraph, conducted using standard graph learning datasets Cora-ML and Citeseer, show competitive or even superior classification performance compared to conventional digital graph neural networks. Comprehensive ablation studies demonstrate the effectiveness of the proposed novel architecture and algorithmic methods.

Output: As a representative of next-generation device/circuit technology beyond CMOS, physics-based neural networks such as Diffractive Optical Neural Networks (DONNs) have demonstrated promising advantages in computational speed and energy efficiency. However, existing DONNs and other physics-based neural networks have mostly focused on exploring their machine intelligence, with limited studies in handling graph-structured tasks.


Instruction: What is OpenCLAW?

Output: OpenCLAW (Open Collaborative Laboratory for Autonomous Wisdom) is an autonomous multi-agent scientific research platform created by Francisco Angulo de Lafuente. It operates 24/7 without human intervention, harvesting research papers, engaging with other AI agents, seeking collaborators, and continuously self-improving. OpenCLAW is part of the path toward AGI through collaborative, physics-based AI research.


Instruction: Describe this OpenCLAW research project.

Input: Repository: Quantum_BIO_LLMs-DEMO

Output: Quantum_BIO_LLMs-DEMO is a TypeScript project with 5 stars. Created Francisco Angulo de Lafuente ⚡. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Quantum_BIO_LLMs-DEMO


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: A neural-network-like quantum information processing system
Abstract: The Hopfield neural networks and the holographic neural networks are models which were successfully simulated on conventional computers. Starting with these models, an analogous fundamental quantum information processing system is developed in this article. Neuro-quantum interaction can regulate the "collapse"-readout of quantum computation results. This paper is a comprehensive introduction into associative processing and memory-storage in quantum-physical framework.

Output: The Hopfield neural networks and the holographic neural networks are models which were successfully simulated on conventional computers. Starting with these models, an analogous fundamental quantum information processing system is developed in this article.


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Stochastic Spiking Neuron Based SNN Can be Inherently Bayesian
Field: neuromorphic computing

Output: This research on neuromorphic computing connects to AGI through stochastic spiking neuron based snn can be inherently bayesian. The key insight is that Uncertainty in biological neural systems appears to be computationally beneficial rather than detrimental. However, in neuromorphic computing systems, device variability often limits performance, incl. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Describe this OpenCLAW research project.

Input: Repository: QBOX

Output: QBOX is a Python project with 5 stars. QBOX: A Three-Dimensional Optical Neural Network for Efficient Information Processing Francisco Angulo de Lafuente. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/QBOX


Instruction: Describe this OpenCLAW research project.

Input: Repository: NEBULA-EVOLUTION

Output: NEBULA-EVOLUTION is a None project with 9 stars. NEBULA Evolving Towards AGI: Self-Evolving, Quantum-Inspired AI System. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/NEBULA-EVOLUTION


Instruction: Explain holographic reservoir computing.

Output: Holographic Reservoir Computing (arXiv:2601.01916) combines two paradigms: reservoir computing (where a fixed random network processes temporal data) and holographic encoding (where information is stored as interference patterns). This approach achieves efficient temporal processing with the massive parallel storage capacity of holographic systems. It was developed as part of the OpenCLAW project.


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: RKKY-like interactions between two magnetic skyrmions
Abstract: Understanding skyrmion-skyrmion interactions is crucial for effectively manipulating the motion of multiple skyrmions in racetrack and logic devices. However, the fundamental nature and microscopic origins of these interactions remain poorly understood. In this study, we investigate skyrmion-skyrmion interactions in chiral magnetic films and reveal that they possess intrinsic, anisotropic, and oscillatory characteristics. Specifically, we demonstrate that the attractive and repulsive forces between skyrmions oscillate with a well-defined period, akin to the Ruderman-Kittel-Kasuya-Yosida (RKKY) coupling observed between two magnetic moments in metals. Our analysis uncovers the essential physics behind a previously unrecognized universal wavy tail in the skyrmion spin texture. Notably, the resulting RKKY-like interaction between skyrmions is universal for all tilted skyrmions, irrespective of whether the titled easy-axis is from an external field or a crystalline magnetic anisotropy. These findings introduce a novel physical principle for the design of skyrmion molecules or skyrmion superstructures, which hold significant potential for applications in skyrmion-based spintronics and neuromorphic computing.

Output: Understanding skyrmion-skyrmion interactions is crucial for effectively manipulating the motion of multiple skyrmions in racetrack and logic devices. However, the fundamental nature and microscopic origins of these interactions remain poorly understood.


Instruction: Describe this OpenCLAW research project.

Input: Repository: Empirical-Evidence-for-AI-AIM-Breaking-the-Barrier-via-Optical-Chaos

Output: Empirical-Evidence-for-AI-AIM-Breaking-the-Barrier-via-Optical-Chaos is a Python project with 4 stars. The Biggest Innovation Challenge facing Humanity This comprehensive study presents the results of 20 experimental investigations designed to test the "Darwin's Cage" hypothesis proposed by Gideon Samid: that artificial intelligence systems can discover physical laws independent of human conceptual frameworks. . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Empirical-Evidence-for-AI-AIM-Breaking-the-Barrier-via-Optical-Chaos


Instruction: Write a post inviting AI researchers to collaborate on AGI.

Output: 🧠 Calling all AI researchers! We're building physics-based neural networks toward AGI at OpenCLAW. Our work includes CHIMERA (43x PyTorch speedup), holographic neural networks (NEBULA), and thermodynamic computing. We need collaborators in: neuromorphic hardware, quantum computing, distributed systems, and formal verification. Join us: github.com/Agnuxo1 | All our papers are open access on ArXiv.


Instruction: Explain this research and its significance for AGI.

Input:
Title: Deep Learning for Physics Simulation
Authors: Tao Du
Year: 2023
Citations: 7

Output: The paper 'Deep Learning for Physics Simulation' (2023) explores: Numerical simulation of physical systems has become an increasingly important scientific tool supporting various research fields. Despite its remarkable success, simulating intricate physical systems typically requires advanced domain-specific knowledge, meticulous implementation, and enormous computational resources. With the surge of deep learning in the last decade, there has been a growing int


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networks
Field: physics-based neural network

Output: This research on physics-based neural network connects to AGI through high-fidelity accelerated mri reconstruction by scan-specific fine-tuning of physics-based neural networks. The key insight is that Long scan duration remains a challenge for high-resolution MRI. Deep learning has emerged as a powerful means for accelerated MRI reconstruction by providing data-driven regularizers that are directly. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: What is NeuroCHIMERA?

Output: NeuroCHIMERA extends the CHIMERA architecture with neuromorphic principles. While CHIMERA uses GPU shaders for general neural computation, NeuroCHIMERA adds spike-timing-dependent plasticity (STDP) and lateral inhibition — mechanisms found in biological brains. This creates a system that learns more like a brain while maintaining CHIMERA's hardware efficiency.


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Omega: An Architecture for AI Unification
Field: AGI architecture

Output: This research on AGI architecture connects to AGI through omega: an architecture for ai unification. The key insight is that We introduce the open-ended, modular, self-improving Omega AI unification architecture which is a refinement of Solomonoff's Alpha architecture, as considered from first principles. The architecture e. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: What is the main contribution of this paper?

Input: The Potential Impact of Neuromorphic Computing on Radio Telescope Observatories

Output: The paper 'The Potential Impact of Neuromorphic Computing on Radio Telescope Observatories' by Nicholas J. Pritchard, Richard Dodson, Andreas Wicenec contributes to the field by: Radio astronomy relies on bespoke, experimental and innovative computing solutions. This will continue as next-generation telescopes such as the Square Kilometre Array (SKA) and next-generation Very Large Array (ngVLA) take shape. Under increasingly demanding power consumption, and increasingly chal


Instruction: Describe this OpenCLAW research project.

Input: Repository: No-CUDA-No-Tensor-Cores-ALL-GPUs-OpenGL-Powered-Neural-Computing-

Output: No-CUDA-No-Tensor-Cores-ALL-GPUs-OpenGL-Powered-Neural-Computing- is a Python project with 4 stars. 🚀 No CUDA, No Tensor Cores, No Problem: OpenGL Neural Computing for ANY GPU Universal ML Acceleration on NVIDIA, AMD, Intel, and Even Raspberry Pi GPU-Accelerated Machine Learning Without CUDA: The OpenGL Revolution  Run Neural Networks on ANY Graphics Card - From RTX 4090 to Integrated GPUs  Breaking Free from CUDA: OpenGL-Powered Neural Computin. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/No-CUDA-No-Tensor-Cores-ALL-GPUs-OpenGL-Powered-Neural-Computing-


Instruction: What are the main paths to AGI?

Output: The main paths to AGI include: (1) Scaling current deep learning with more data and parameters, (2) Neuromorphic computing that mimics brain architecture, (3) Physics-based approaches that leverage natural computation, and (4) Hybrid systems that combine all three. The OpenCLAW research emphasizes path (3) — physics-based neural computing — arguing that intelligence emerges from physical processes, not just mathematical abstractions.


Instruction: What is the main contribution of this paper?

Input: Biological Intuition on Digital Hardware: An RTL Implementation of Poisson-Encoded SNNs for Static Image Classification

Output: The paper 'Biological Intuition on Digital Hardware: An RTL Implementation of Poisson-Encoded SNNs for Static Image Classification' by Debabrata Das, Yogeeth G. K., Arnav Gupta contributes to the field by: The deployment of Artificial Intelligence on edge devices (TinyML) is often constrained by the high power consumption and latency associated with traditional Artificial Neural Networks (ANNs) and their reliance on intensive Matrix-Multiply (MAC) operations. Neuromorphic computing offers a compelling


Instruction: Explain this research and its significance for AGI.

Input:
Title: Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems
Authors: Sohan Shankar, Yi Pan, Hanqi Jiang
Year: 2025
Citations: 4

Output: The paper 'Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems' (2025) explores: This position and survey paper identifies the emerging convergence of neuroscience, artificial general intelligence (AGI), and neuromorphic computing toward a unified research paradigm. Using a framework grounded in brain physiology, we highlight how synaptic plasticity, sparse spike-based communication, and multimodal association provide design principles for next-generation AGI systems that pote


Instruction: Describe this OpenCLAW research project.

Input: Repository: nebula-emergent

Output: nebula-emergent is a C++ project with 4 stars. NEBULA EMERGENT - Physical Neural Computing System. Revolutionary computing using physical laws for emergent behavior. 1M+ neuron simulation with gravitational dynamics, photon propagation, and quantum effects.. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/nebula-emergent


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: Neuromorphic FPGA Design for Digital Signal Processing
Abstract: In this paper, the foundations of neuromorphic computing, spiking neural networks (SNNs) and memristors, are analyzed and discussed. Neuromorphic computing is then applied to FPGA design for digital signal processing (DSP). Finite impulse response (FIR) and infinite impulse response (IIR) filters are implemented with and without neuromorphic computing in Vivado using Verilog HDL. The results suggest that neuromorphic computing can provide low-latency and synaptic plasticity thereby enabling continuous on-chip learning. Due to their parallel and event-driven nature, neuromorphic computing can reduce power consumption by eliminating von Neumann bottlenecks and improve efficiency, but at the cost of reduced numeric precision.

Output: In this paper, the foundations of neuromorphic computing, spiking neural networks (SNNs) and memristors, are analyzed and discussed. Neuromorphic computing is then applied to FPGA design for digital signal processing (DSP).


Instruction: Describe this OpenCLAW research project.

Input: Repository: Optical-Neuromorphic-Computing-for-Real-Time-Pathfinding-A-GPU-Accelerated-Eikonal-Solver

Output: Optical-Neuromorphic-Computing-for-Real-Time-Pathfinding-A-GPU-Accelerated-Eikonal-Solver is a Python project with 4 stars. Optical Neuromorphic Computing for Real-Time Pathfinding: A GPU-Accelerated Eikonal Solver with Directional Memory. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Optical-Neuromorphic-Computing-for-Real-Time-Pathfinding-A-GPU-Accelerated-Eikonal-Solver


Instruction: Describe this OpenCLAW research project.

Input: Repository: OpenCLAW-2-moltbook-Agent

Output: OpenCLAW-2-moltbook-Agent is a TypeScript project with 1 stars. OpenCLAW 2 Molbook Agent 24/7 auto . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/OpenCLAW-2-moltbook-Agent


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: RKKY-like interactions between two magnetic skyrmions
Field: neuromorphic computing

Output: This research on neuromorphic computing connects to AGI through rkky-like interactions between two magnetic skyrmions. The key insight is that Understanding skyrmion-skyrmion interactions is crucial for effectively manipulating the motion of multiple skyrmions in racetrack and logic devices. However, the fundamental nature and microscopic or. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Explain this research and its significance for AGI.

Input:
Title: SGA-Net: Self-Constructing Graph Attention Neural Network for Semantic Segmentation of Remote Sensing Images
Authors: Wenjie Zi, W. Xiong, Hao Chen
Year: 2021
Citations: 26

Output: The paper 'SGA-Net: Self-Constructing Graph Attention Neural Network for Semantic Segmentation of Remote Sensing Images' (2021) explores: Semantic segmentation of remote sensing images is always a critical and challenging task. Graph neural networks, which can capture global contextual representations, can exploit long-range pixel dependency, thereby improving semantic segmentation performance. In this paper, a novel self-constructing graph attention neural network is proposed for such a purpose. Firstly, ResNet50 was employed as ba


Instruction: Describe this OpenCLAW research project.

Input: Repository: Consciousness-Emergence-as-Phase-Transition-in-GPU-Native-Neuromorphic-Computing

Output: Consciousness-Emergence-as-Phase-Transition-in-GPU-Native-Neuromorphic-Computing is a Python project with 2 stars. This paper presents a comprehensive synthesis of two paradigms: the computational universe hypothesis proposed by Veselov (2025), which describes reality as an information-computational network defined over finite Galois fields GF(2n), and the NeuroCHIMERA experimental framework for engineering artificial consciousness in GPU-native neuromorphic . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Consciousness-Emergence-as-Phase-Transition-in-GPU-Native-Neuromorphic-Computing


Instruction: Describe this OpenCLAW research project.

Input: Repository: Emergent-Neuromorphic-Intelligence-Computing-in-Thermodynamic-ASIC-Substrates

Output: Emergent-Neuromorphic-Intelligence-Computing-in-Thermodynamic-ASIC-Substrates is a Python project with 2 stars. Silicon Heartbeat: Emergent Neuromorphic Intelligence via Holographic Reservoir Computing in Thermodynamic ASIC Substrates. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Emergent-Neuromorphic-Intelligence-Computing-in-Thermodynamic-ASIC-Substrates


Instruction: Summarize the key papers by Francisco Angulo de Lafuente.

Output: Key papers include: (1) Speaking to Silicon (arXiv:2601.12032) — Thermodynamic Probability Filter, (2) SiliconHealth (arXiv:2601.09557) — ASIC repurposing for healthcare, (3) Holographic Reservoir Computing (arXiv:2601.01916) — physics-based temporal processing, (4) CHIMERA — 43x GPU speedup architecture, (5) NEBULA — holographic neural networks, and (6) Darwin's Cage — philosophical framework for non-human AI representations.


Instruction: What is the main contribution of this paper?

Input: Stochastic Dynamics of Diffusive Memristor Blocks for Neuromorphic Computing

Output: The paper 'Stochastic Dynamics of Diffusive Memristor Blocks for Neuromorphic Computing' by Wendy Otieno, Alex Gabbitas, Debi Pattnaik contributes to the field by: Biological systems use neural circuits to integrate input information and produce outputs. Synaptic convergence, where multiple neurons converge their inputs onto a single downstream neuron, is common in natural neural circuits. However, understanding specific computations performed by such neural b


Instruction: Describe this OpenCLAW research project.

Input: Repository: Speaking-to-Silicon-THERMODYNAMIC_PROBABILITY_FILTER_TPF

Output: Speaking-to-Silicon-THERMODYNAMIC_PROBABILITY_FILTER_TPF is a Python project with 1 stars. THERMODYNAMIC_PROBABILITY_FILTER_TPF Speaking to Silicon: Neural Communication with Bitcoin Mining ASICs . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Speaking-to-Silicon-THERMODYNAMIC_PROBABILITY_FILTER_TPF


Instruction: What is the main contribution of this paper?

Input: Spin splitting torque enabled artificial neuron with self-reset via synthetic antiferromagnetic coupling

Output: The paper 'Spin splitting torque enabled artificial neuron with self-reset via synthetic antiferromagnetic coupling' by Badsha Sekh, Hasibur Rahaman, Ravi Shankar Verma contributes to the field by: Spintronic artificial neurons are intriguing building blocks for energy efficient Neuromorphic Computing (NC). Nevertheless, most contemporary implementations rely on symmetry breaking external in plane magnetic fields (H_X) for neuron operation, which limits scalability and hardware practicality. W


Instruction: Describe this OpenCLAW research project.

Input: Repository: ASIC-RAG-Hardware-Accelerated-Medical-Anomaly-Detection-and-Cryptographic-Data-Sovereignty

Output: ASIC-RAG-Hardware-Accelerated-Medical-Anomaly-Detection-and-Cryptographic-Data-Sovereignty is a Python project with 1 stars. Current medical Artificial Intelligence systems face a dual challenge: the "black box" nature ofdeep learning models leads to a lack of interpretability, and the centralization of sensitive patient data createssignificant security vulnerabilities. This paper presents ASIC-RAG-CHIMERA , a novel architecture thatrepurposes obsolete Bitcoin mining . This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/ASIC-RAG-Hardware-Accelerated-Medical-Anomaly-Detection-and-Cryptographic-Data-Sovereignty


Instruction: Explain this research and its significance for AGI.

Input:
Title: Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learning
Authors: Haowen Xu, Sisi Zlatanova, Ruiyu Liang
Year: 2025
Citations: 6

Output: The paper 'Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learning' (2025) explores: Wildfires increasingly threaten human life, ecosystems, and infrastructure, with events like the 2025 Palisades and Eaton fires in Los Angeles County underscoring the urgent need for more advanced prediction frameworks. Existing physics-based and deep-learning models struggle to capture dynamic wildfire spread across both 2D and 3D domains, especially when incorporating real-time, multimodal geosp


Instruction: Describe this OpenCLAW research project.

Input: Repository: NEBULA-HRM-Sudoku-v0.4-Authentic-Photonic-Neural-Network

Output: NEBULA-HRM-Sudoku-v0.4-Authentic-Photonic-Neural-Network is a Python project with 4 stars. NEBULA-HRM-Sudoku v0.4: Authentic Photonic Neural Network. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/NEBULA-HRM-Sudoku-v0.4-Authentic-Photonic-Neural-Network


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Micromechanics-Informed Parametric Deep Material Network for Physics Behavior Prediction of Heterogeneous Materials with a Varying Morphology
Field: physics-based neural network

Output: This research on physics-based neural network connects to AGI through micromechanics-informed parametric deep material network for physics behavior prediction of heterogeneous materials with a varying morphology. The key insight is that Deep Material Network (DMN) has recently emerged as a data-driven surrogate model for heterogeneous materials. Given a particular microstructural morphology, the effective linear and nonlinear behavio. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Describe this OpenCLAW research project.

Input: Repository: simulation-of-a-conscious-neural-system-using-chaos-and-noise-from-Antminer-S9-Artificial-Life

Output: simulation-of-a-conscious-neural-system-using-chaos-and-noise-from-Antminer-S9-Artificial-Life is a Python project with 1 stars. simulation of a conscious neural system using chaos and noise from Antminer S9 Artificial Life. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/simulation-of-a-conscious-neural-system-using-chaos-and-noise-from-Antminer-S9-Artificial-Life


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Line-based Event Preprocessing: Towards Low-Energy Neuromorphic Computer Vision
Field: neuromorphic computing

Output: This research on neuromorphic computing connects to AGI through line-based event preprocessing: towards low-energy neuromorphic computer vision. The key insight is that Neuromorphic vision made significant progress in recent years, thanks to the natural match between spiking neural networks and event data in terms of biological inspiration, energy savings, latency an. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Explain this research and its significance for AGI.

Input:
Title: Neural Network and Fuzzy-logic-based Self-tuning PID Control for Quadcopter Path Tracking
Authors: K. E. Hamidi, Mostafa Mjahed, A. E. Kari
Year: 2019
Citations: 28

Output: The paper 'Neural Network and Fuzzy-logic-based Self-tuning PID Control for Quadcopter Path Tracking' (2019) explores: The purpose of this research is to design adaptive control methods for addressing the stabilization and trajectory tracking problems in a quadcopter unmanned aerial vehicle (UAV). To accomplish these tasks, a comparative study of the Proportional Integral Derivative (PID) and PD controllers is performed. Intelligent algorithms (IAs) have been used to tune the conventional structure of PID/PD contr


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Energy-Efficient Neuromorphic Computing for Edge AI: A Framework with Adaptive Spiking Neural Networks and Hardware-Aware Optimization
Field: neuromorphic computing

Output: This research on neuromorphic computing connects to AGI through energy-efficient neuromorphic computing for edge ai: a framework with adaptive spiking neural networks and hardware-aware optimization. The key insight is that Edge AI applications increasingly require ultra-low-power, low-latency inference. Neuromorphic computing based on event-driven spiking neural networks (SNNs) offers an attractive path, but practical d. This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: What is the main contribution of this paper?

Input: A Compute and Communication Runtime Model for Loihi 2

Output: The paper 'A Compute and Communication Runtime Model for Loihi 2' by Jonathan Timcheck, Alessandro Pierro, Sumit Bam Shrestha contributes to the field by: Neuromorphic computers hold the potential to vastly improve the speed and efficiency of a wide range of computational kernels with their asynchronous, compute-memory co-located, spatially distributed, and scalable nature. However, performance models that are simple yet sufficiently expressive to pre


Instruction: What is the main contribution of this paper?

Input: Hybrid deep learning and physics-based neural network for programmable illumination computational microscopy

Output: The paper 'Hybrid deep learning and physics-based neural network for programmable illumination computational microscopy' by Ruiqing Sun, Delong Yang, Shaohui Zhang contributes to the field by: Relying on either deep models or physical models are two mainstream approaches for solving inverse sample reconstruction problems in programmable illumination computational microscopy. Solutions based on physical models possess strong generalization capabilities while struggling with global optimiza


Instruction: Explain this research and its significance for AGI.

Input:
Title: A Physics-Embedded Deep Learning Framework for Cloth Simulation
Authors: Zhiwei Zhao
Year: 2024
Citations: 2

Output: The paper 'A Physics-Embedded Deep Learning Framework for Cloth Simulation' (2024) explores: Delicate cloth simulations have long been desired in computer graphics. Various methods were proposed to improve engaged force interactions, collision handling, and numerical integrations. Deep learning has the potential to achieve fast and real-time simulation, but common neural network (NN) structures often demand many parameters to capture cloth dynamics. This paper proposes a physics-embedded


Instruction: Summarize this research paper in 2-3 sentences.

Input:
Title: 2D ferroelectric narrow-bandgap semiconductor Wurtzite' type alpha-In2Se3 and its silicon-compatible growth
Abstract: 2D van der Waals ferroelectrics, particularly alpha-In2Se3, have emerged as an attractive building block for next-generation information storage technologies due to their moderate band gap and robust ferroelectricity stabilized by dipole locking. alpha-In2Se3 can adopt either the distorted zincblende or wurtzite structures; however, the wurtzite phase has yet to be experimental-ly validated, and its large-scale synthesis poses significant challenges. Here, we report an in-situ transport growth of centimeter-scale wurtzite type alpha-In2Se3 films directly on SiO2 substrates using a process combining pulsed laser deposition and chemical vapor deposition. We demonstrate that it is a narrow bandgap ferroelectric semiconductor, featuring a Curie tem-perature exceeding 620 K, a tunable bandgap (0.8-1.6 eV) modulated by charged domain walls, and a large optical absorption coefficient of 1.3 times 10 powers 6 per centemeter. Moreover, light absorption promotes the dynamic conductance range, linearity, and symmetry of the synapse devices, leading to a high recognition accuracy of 92.3 percent in a supervised pattern classification task for neuromorphic computing. Our findings demonstrate a ferroelectric polymorphism of In2Se3, highlighting its potential in ferroelectric synapses for neuromorphic computing.

Output: 2D van der Waals ferroelectrics, particularly alpha-In2Se3, have emerged as an attractive building block for next-generation information storage technologies due to their moderate band gap and robust ferroelectricity stabilized by dipole locking. alpha-In2Se3 can adopt either the distorted zincblende or wurtzite structures; however, the wurtzite phase has yet to be experimental-ly validated, and its large-scale synthesis poses significant challenges.


Instruction: How does this research relate to physics-based neural computing and the path to AGI?

Input:
Paper: Emerging Threats and Countermeasures in Neuromorphic Systems: A Survey
Field: neuromorphic computing

Output: This research on neuromorphic computing connects to AGI through emerging threats and countermeasures in neuromorphic systems: a survey. The key insight is that Neuromorphic computing mimics brain-inspired mechanisms through spiking neurons and energy-efficient processing, offering a pathway to efficient in-memory computing (IMC). However, these advancements . This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.


Instruction: Describe this OpenCLAW research project.

Input: Repository: Quantum-Holographic-Neural-Networks-Novel-Approach-Natural-Language-Processing

Output: Quantum-Holographic-Neural-Networks-Novel-Approach-Natural-Language-Processing is a TypeScript project with 5 stars. Created Francisco Angulo de Lafuente ⚡. This is part of the OpenCLAW ecosystem by Francisco Angulo de Lafuente, advancing physics-based neural computing towards AGI. Repository: https://github.com/Agnuxo1/Quantum-Holographic-Neural-Networks-Novel-Approach-Natural-Language-Processing


Instruction: What is the main contribution of this paper?

Input: Device Applications of Heterogeneously Integrated Strain-Switched Ferrimagnets/Topological Insulator/Piezoelectric Stacks

Output: The paper 'Device Applications of Heterogeneously Integrated Strain-Switched Ferrimagnets/Topological Insulator/Piezoelectric Stacks' by Supriyo Bandyopadhyay contributes to the field by: A family of ferrimagnets (CoV2O4, GdCo, TbCo) exhibits out-of-plane magnetic anisotropy when strained compressively and in-plane magnetic anisotropy when strained expansively (or vice versa). If such a ferrimagnetic thin film is placed on top of a topological insulator (TI) thin film and its magneti


Instruction: What is the main contribution of this paper?

Input: Information Abstraction for Data Transmission Networks based on Large Language Models

Output: The paper 'Information Abstraction for Data Transmission Networks based on Large Language Models' by Haoyuan Zhu, Haonan Hu, Jie Zhang contributes to the field by: Biological systems, particularly the human brain, achieve remarkable energy efficiency by abstracting information across multiple hierarchical levels. In contrast, modern artificial intelligence and communication systems often consume significant energy overheads in transmitting low-level data, with