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: Thermodynamics-based Artificial Neural Networks for constitutive modeling

Output: The paper 'Thermodynamics-based Artificial Neural Networks for constitutive modeling' by Filippo Masi, Ioannis Stefanou, Paolo Vannucci contributes to the field by: 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 inconsisten


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: Summarize this research paper in 2-3 sentences.

Input:
Title: High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networks
Abstract: 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 testing phase once they are learned during training. In this study, we propose to use a transfer learning approach to fine-tune these regularizers for new subjects using a self-supervision approach. While the proposed approach can compromise the extremely fast reconstruction time of deep learning MRI methods, our results on knee MRI indicate that such adaptation can substantially reduce the remaining artifacts in reconstructed images. In addition, the proposed approach has the potential to reduce the risks of generalization to rare pathological conditions, which may be unavailable in the training data.

Output: 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.


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

Input:
Title: Data-Driven Design for Fourier Ptychographic Microscopy
Abstract: Fourier Ptychographic Microscopy (FPM) is a computational imaging method that is able to super-resolve features beyond the diffraction-limit set by the objective lens of a traditional microscope. This is accomplished by using synthetic aperture and phase retrieval algorithms to combine many measurements captured by an LED array microscope with programmable source patterns. FPM provides simultaneous large field-of-view and high resolution imaging, but at the cost of reduced temporal resolution, thereby limiting live cell applications. In this work, we learn LED source pattern designs that compress the many required measurements into only a few, with negligible loss in reconstruction quality or resolution. This is accomplished by recasting the super-resolution reconstruction as a Physics-based Neural Network and learning the experimental design to optimize the network's overall performance. Specifically, we learn LED patterns for different applications (e.g. amplitude contrast and quantitative phase imaging) and show that the designs we learn through simulation generalize well in the experimental setting. Further, we discuss a context-specific loss function, practical memory limitations, and interpretability of our learned designs.

Output: Fourier Ptychographic Microscopy (FPM) is a computational imaging method that is able to super-resolve features beyond the diffraction-limit set by the objective lens of a traditional microscope. This is accomplished by using synthetic aperture and phase retrieval algorithms to combine many measurements captured by an LED array microscope with programmable source patterns.


Instruction: What is the main contribution of this paper?

Input: Data-Driven Design for Fourier Ptychographic Microscopy

Output: The paper 'Data-Driven Design for Fourier Ptychographic Microscopy' by Michael Kellman, Emrah Bostan, Michael Chen contributes to the field by: Fourier Ptychographic Microscopy (FPM) is a computational imaging method that is able to super-resolve features beyond the diffraction-limit set by the objective lens of a traditional microscope. This is accomplished by using synthetic aperture and phase retrieval algorithms to combine many measurem


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

Input:
Paper: Data-Driven Design for Fourier Ptychographic Microscopy
Field: physics-based neural network

Output: This research on physics-based neural network connects to AGI through data-driven design for fourier ptychographic microscopy. The key insight is that Fourier Ptychographic Microscopy (FPM) is a computational imaging method that is able to super-resolve features beyond the diffraction-limit set by the objective lens of a traditional microscope. This. 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: Coordinated inference, Holographic neural networks, and quantum error correction
Abstract: Coordinated inference problems are being introduced as a basis for a neural network representation of the locality problem in the holographic bulk. It is argued that a type of problem originating in the "prisoners and hats" dilemma involves certain non-local structures to be found in the AdS/CFT duality. The neural network solution to this problem introduces a new approach that can be flexible enough to identify holographic dualities beyond AdS/CFT. Neural networks are shown to have a significant role in the connection between the bulk and the boundary, being capable of inferring sufficient information capable of explaining the pre-arrangement of observables in the bulk that would lead to non-local precursor operators in the boundary.

Output: Coordinated inference problems are being introduced as a basis for a neural network representation of the locality problem in the holographic bulk. It is argued that a type of problem originating in the "prisoners and hats" dilemma involves certain non-local structures to be found in the AdS/CFT duality.


Instruction: What is the main contribution of this paper?

Input: Coordinated inference, Holographic neural networks, and quantum error correction

Output: The paper 'Coordinated inference, Holographic neural networks, and quantum error correction' by Andrei T. Patrascu contributes to the field by: Coordinated inference problems are being introduced as a basis for a neural network representation of the locality problem in the holographic bulk. It is argued that a type of problem originating in the "prisoners and hats" dilemma involves certain non-local structures to be found in the AdS/CFT dua


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

Input:
Paper: Coordinated inference, Holographic neural networks, and quantum error correction
Field: holographic neural network

Output: This research on holographic neural network connects to AGI through coordinated inference, holographic neural networks, and quantum error correction. The key insight is that Coordinated inference problems are being introduced as a basis for a neural network representation of the locality problem in the holographic bulk. It is argued that a type of problem originating in t. 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: 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: What is the main contribution of this paper?

Input: A neural-network-like quantum information processing system

Output: The paper 'A neural-network-like quantum information processing system' by Mitja Perus, Horst Bischof contributes to the field by: 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


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: Summarize this research paper in 2-3 sentences.

Input:
Title: BriLLM: Brain-inspired Large Language Model
Abstract: 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 work addresses the critical bottleneck hindering AI's progression toward Artificial General Intelligence (AGI)--the disconnect between language models and "world models"--as well as the fundamental limitations of Transformer-based architectures rooted in the conventional representation learning paradigm. BriLLM incorporates two pivotal neurocognitive principles: (1) static semantic mapping, where tokens are mapped to specialized nodes analogous to cortical areas, and (2) dynamic signal propagation, which simulates electrophysiological information dynamics observed in brain activity.   This architecture enables multiple transformative breakthroughs: natural multi-modal compatibility, full model interpretability at the node level, context-length independent scaling, and the first global-scale simulation of brain-like information processing for language tasks. Our initial 1-2B parameter models successfully replicate GPT-1-level generative capabilities while demonstrating stable perplexity reduction. Scalability analyses confirm the feasibility of 100-200B parameter variants capable of processing 40,000-token vocabularies.   The paradigm is reinforced by both Occam's Razor--evidenced in the simplicity of direct semantic mapping--and natural evolution--given the brain's empirically validated AGI architecture. BriLLM establishes a novel, biologically grounded framework for AGI advancement that addresses fundamental limitations of current approaches.

Output: 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 work addresses the critical bottleneck hindering AI's progression toward Artificial General Intelligence (AGI)--the disconnect between language models and "world models"--as well as the fundamental limitations of Transformer-based architectures rooted in the conventional representation learning paradigm.


Instruction: What is the main contribution of this paper?

Input: BriLLM: Brain-inspired Large Language Model

Output: The paper 'BriLLM: Brain-inspired Large Language Model' by Hai Zhao, Hongqiu Wu, Dongjie Yang contributes to the field by: 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 work addresses the critical bottleneck hindering AI's progression toward Artificial General I


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: 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: What is the main contribution of this paper?

Input: From the Pursuit of Universal AGI Architecture to Systematic Approach to Heterogenous AGI: Addressing Alignment, Energy, & AGI Grand Challenges

Output: The paper 'From the Pursuit of Universal AGI Architecture to Systematic Approach to Heterogenous AGI: Addressing Alignment, Energy, & AGI Grand Challenges' by Eren Kurshan contributes to the field by: 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


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

Input:
Paper: From the Pursuit of Universal AGI Architecture to Systematic Approach to Heterogenous AGI: Addressing Alignment, Energy, & AGI Grand Challenges
Field: AGI architecture

Output: This research on AGI architecture connects to AGI through from the pursuit of universal agi architecture to systematic approach to heterogenous agi: addressing alignment, energy, & agi grand challenges. The key insight is that 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 utilize. 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: Generative AI vs. AGI: The Cognitive Strengths and Weaknesses of Modern LLMs
Abstract: 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 are reviewed, and then careful attention is paid to the substantial differences between the sort of cognitive system these LLMs are, and the sort of cognitive systems human beings are. It is found that many of the practical weaknesses of these AI systems can be tied specifically to lacks in the basic cognitive architectures according to which these systems are built. It is argued that incremental improvement of such LLMs is not a viable approach to working toward human-level AGI, in practical terms given realizable amounts of compute resources. This does not imply there is nothing to learn about human-level AGI from studying and experimenting with LLMs, nor that LLMs cannot form significant parts of human-level AGI architectures that also incorporate other ideas. Social and ethical matters regarding LLMs are very briefly touched from this perspective, which implies that while care should be taken regarding misinformation and other issues, and economic upheavals will need their own social remedies based on their unpredictable course as with any powerfully impactful technology, overall the sort of policy needed as regards modern LLMs is quite different than would be the case if a more credible approximation to human-level AGI were at hand.

Output: 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 are reviewed, and then careful attention is paid to the substantial differences between the sort of cognitive system these LLMs are, and the sort of cognitive systems human beings are.


Instruction: What is the main contribution of this paper?

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

Output: The paper 'Generative AI vs. AGI: The Cognitive Strengths and Weaknesses of Modern LLMs' by Ben Goertzel contributes to the field by: 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 are reviewed, and then careful attention is paid to the substantial differences between the sort of c


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

Input:
Title: DARC: Decoupled Asymmetric Reasoning Curriculum for LLM Evolution
Abstract: 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 Questioner, and (ii) bootstrapping errors from self-generated pseudo-labels used to supervise the Solver. To mitigate these challenges, we introduce DARC (Decoupled Asymmetric Reasoning Curriculum), a two-stage framework that stabilizes the self-evolution process. First, we train the Questioner to synthesize difficulty-calibrated questions, conditioned on explicit difficulty levels and external corpora. Second, we train the Solver with an asymmetric self-distillation mechanism, where a document-augmented teacher generates high-quality pseudo-labels to supervise the student Solver that lacks document access. Empirical results demonstrate that DARC is model-agnostic, yielding an average improvement of 10.9 points across nine reasoning benchmarks and three backbone models. Moreover, DARC consistently outperforms all baselines and approaches the performance of fully supervised models without relying on human annotations. The code is available at https://github.com/RUCBM/DARC.

Output: 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 Questioner, and (ii) bootstrapping errors from self-generated pseudo-labels used to supervise the Solver.


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: DARC: Decoupled Asymmetric Reasoning Curriculum for LLM Evolution
Field: self-improving artificial intelligence

Output: This research on self-improving artificial intelligence connects to AGI through darc: decoupled asymmetric reasoning curriculum for llm evolution. The key insight is that 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 ins. 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: 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: What is the main contribution of this paper?

Input: Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms

Output: The paper 'Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms' by Resul Dagdanov, Halil Durmus, Nazim Kemal Ure contributes to the field by: 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 perf


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: Summarize this research paper in 2-3 sentences.

Input:
Title: Information Abstraction for Data Transmission Networks based on Large Language Models
Abstract: 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 limited emphasis on abstraction. Despite its implicit importance, a formal and computational theory of information abstraction remains absent. In this work, we introduce the Degree of Information Abstraction (DIA), a general metric that quantifies how well a representation compresses input data while preserving task-relevant semantics. We derive a tractable information-theoretic formulation of DIA and propose a DIA-based information abstraction framework. As a case study, we apply DIA to a large language model (LLM)-guided video transmission task, where abstraction-aware encoding significantly reduces transmission volume by $99.75\%$, while maintaining semantic fidelity. Our results suggest that DIA offers a principled tool for rebalancing energy and information in intelligent systems and opens new directions in neural network design, neuromorphic computing, semantic communication, and joint sensing-communication architectures.

Output: 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 limited emphasis on abstraction.


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


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: Summarize this research paper in 2-3 sentences.

Input:
Title: Device Applications of Heterogeneously Integrated Strain-Switched Ferrimagnets/Topological Insulator/Piezoelectric Stacks
Abstract: 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 magnetic anisotropy is modulated with strain, then interfacial exchange coupling between the ferrimagnet (FM) and the underlying TI will modulate the surface current flowing through the latter. If the strain is varied continuously, the current will also vary continuously and if the strain alternates in time, the current will also alternate with the frequency of the strain modulation, as long as the frequency is not so high that the period is smaller than the switching time of the FM. If the strain is generated with a gate voltage by integrating a piezoelectric underneath the FM/TI stack, then that can implement a transconductance amplifier or a synapse for neuromorphic computation.

Output: 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 magnetic anisotropy is modulated with strain, then interfacial exchange coupling between the ferrimagnet (FM) and the underlying TI will modulate the surface current flowing through the latter.


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

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

Output: This research on neuromorphic computing connects to AGI through device applications of heterogeneously integrated strain-switched ferrimagnets/topological insulator/piezoelectric stacks. The key insight is that 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 . This advances our understanding of how physical processes can be leveraged for more efficient and biologically-plausible neural computation.