Power with purposeMIT Technology Reviewon August 26, 2025 at 9:00 am Baafour Asiamah-Adjei ’03 is the founder and CEO of one of Ghana’s largest private power companies, Genser Energy—an entrepreneurial engineer who aims to deliver sustainable energy across West Africa. And he credits MIT with much of his success. But when he was applying to colleges, the Institute wasn’t even on his radar. The son of…
Baafour Asiamah-Adjei ’03 is the founder and CEO of one of Ghana’s largest private power companies, Genser Energy—an entrepreneurial engineer who aims to deliver sustainable energy across West Africa. And he credits MIT with much of his success. But when he was applying to colleges, the Institute wasn’t even on his radar. The son of… Read More
Top AI vibe-coding platforms powering Web3 buildsAI Newson August 26, 2025 at 9:26 am Vibe coding is making lots of noise in software development, but perhaps nowhere will its presence be felt more keenly than in the Web3 coding space. Of course, not every AI code generator is cut out for Web3 development. That’s because it’s a niche area that requires deep expertise in blockchain languages and smart contract
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Vibe coding is making lots of noise in software development, but perhaps nowhere will its presence be felt more keenly than in the Web3 coding space. Of course, not every AI code generator is cut out for Web3 development. That’s because it’s a niche area that requires deep expertise in blockchain languages and smart contract
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Malaysia launches Ryt Bank, its first AI-powered bankAI Newson August 26, 2025 at 8:15 am AI is steadily changing the way banks work. The technology can sift through massive amounts of data, calculate risks, and handle routine tasks at speeds people can’t match. Now, Malaysia has entered that space with the launch of Ryt Bank, billed as the first AI-powered bank created in the country. The new venture, led by
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AI is steadily changing the way banks work. The technology can sift through massive amounts of data, calculate risks, and handle routine tasks at speeds people can’t match. Now, Malaysia has entered that space with the launch of Ryt Bank, billed as the first AI-powered bank created in the country. The new venture, led by
The post Malaysia launches Ryt Bank, its first AI-powered bank appeared first on AI News. Read More
GPG-HT: Generalized Policy Gradient with History-Aware Decision Transformer for Probabilistic Path Planningcs.AI updates on arXiv.orgon August 26, 2025 at 4:00 am arXiv:2508.17218v1 Announce Type: cross
Abstract: With the rapidly increased number of vehicles in urban areas, existing road infrastructure struggles to accommodate modern traffic demands, resulting in the issue of congestion. This highlights the importance of efficient path planning strategies. However, most recent navigation models focus solely on deterministic or time-dependent networks, while overlooking the correlations and the stochastic nature of traffic flows. In this work, we address the reliable shortest path problem within stochastic transportation networks under certain dependencies. We propose a path planning solution that integrates the decision Transformer with the Generalized Policy Gradient (GPG) framework. Based on the decision Transformer’s capability to model long-term dependencies, our proposed solution improves the accuracy and stability of path decisions. Experimental results on the Sioux Falls Network (SFN) demonstrate that our approach outperforms previous baselines in terms of on-time arrival probability, providing more accurate path planning solutions.
arXiv:2508.17218v1 Announce Type: cross
Abstract: With the rapidly increased number of vehicles in urban areas, existing road infrastructure struggles to accommodate modern traffic demands, resulting in the issue of congestion. This highlights the importance of efficient path planning strategies. However, most recent navigation models focus solely on deterministic or time-dependent networks, while overlooking the correlations and the stochastic nature of traffic flows. In this work, we address the reliable shortest path problem within stochastic transportation networks under certain dependencies. We propose a path planning solution that integrates the decision Transformer with the Generalized Policy Gradient (GPG) framework. Based on the decision Transformer’s capability to model long-term dependencies, our proposed solution improves the accuracy and stability of path decisions. Experimental results on the Sioux Falls Network (SFN) demonstrate that our approach outperforms previous baselines in terms of on-time arrival probability, providing more accurate path planning solutions. Read More
Weights-Rotated Preference Optimization for Large Language Modelscs.AI updates on arXiv.orgon August 26, 2025 at 4:00 am arXiv:2508.17637v1 Announce Type: cross
Abstract: Despite the efficacy of Direct Preference Optimization (DPO) in aligning Large Language Models (LLMs), reward hacking remains a pivotal challenge. This issue emerges when LLMs excessively reduce the probability of rejected completions to achieve high rewards, without genuinely meeting their intended goals. As a result, this leads to overly lengthy generation lacking diversity, as well as catastrophic forgetting of knowledge. We investigate the underlying reason behind this issue, which is representation redundancy caused by neuron collapse in the parameter space. Hence, we propose a novel Weights-Rotated Preference Optimization (RoPO) algorithm, which implicitly constrains the output layer logits with the KL divergence inherited from DPO and explicitly constrains the intermediate hidden states by fine-tuning on a multi-granularity orthogonal matrix. This design prevents the policy model from deviating too far from the reference model, thereby retaining the knowledge and expressive capabilities acquired during pre-training and SFT stages. Our RoPO achieves up to a 3.27-point improvement on AlpacaEval 2, and surpasses the best baseline by 6.2 to 7.5 points on MT-Bench with merely 0.015% of the trainable parameters, demonstrating its effectiveness in alleviating the reward hacking problem of DPO.
arXiv:2508.17637v1 Announce Type: cross
Abstract: Despite the efficacy of Direct Preference Optimization (DPO) in aligning Large Language Models (LLMs), reward hacking remains a pivotal challenge. This issue emerges when LLMs excessively reduce the probability of rejected completions to achieve high rewards, without genuinely meeting their intended goals. As a result, this leads to overly lengthy generation lacking diversity, as well as catastrophic forgetting of knowledge. We investigate the underlying reason behind this issue, which is representation redundancy caused by neuron collapse in the parameter space. Hence, we propose a novel Weights-Rotated Preference Optimization (RoPO) algorithm, which implicitly constrains the output layer logits with the KL divergence inherited from DPO and explicitly constrains the intermediate hidden states by fine-tuning on a multi-granularity orthogonal matrix. This design prevents the policy model from deviating too far from the reference model, thereby retaining the knowledge and expressive capabilities acquired during pre-training and SFT stages. Our RoPO achieves up to a 3.27-point improvement on AlpacaEval 2, and surpasses the best baseline by 6.2 to 7.5 points on MT-Bench with merely 0.015% of the trainable parameters, demonstrating its effectiveness in alleviating the reward hacking problem of DPO. Read More
A XAI-based Framework for Frequency Subband Characterization of Cough Spectrograms in Chronic Respiratory Diseasecs.AI updates on arXiv.orgon August 25, 2025 at 4:00 am arXiv:2508.16237v1 Announce Type: cross
Abstract: This paper presents an explainable artificial intelligence (XAI)-based framework for the spectral analysis of cough sounds associated with chronic respiratory diseases, with a particular focus on Chronic Obstructive Pulmonary Disease (COPD). A Convolutional Neural Network (CNN) is trained on time-frequency representations of cough signals, and occlusion maps are used to identify diagnostically relevant regions within the spectrograms. These highlighted areas are subsequently decomposed into five frequency subbands, enabling targeted spectral feature extraction and analysis. The results reveal that spectral patterns differ across subbands and disease groups, uncovering complementary and compensatory trends across the frequency spectrum. Noteworthy, the approach distinguishes COPD from other respiratory conditions, and chronic from non-chronic patient groups, based on interpretable spectral markers. These findings provide insight into the underlying pathophysiological characteristics of cough acoustics and demonstrate the value of frequency-resolved, XAI-enhanced analysis for biomedical signal interpretation and translational respiratory disease diagnostics.
arXiv:2508.16237v1 Announce Type: cross
Abstract: This paper presents an explainable artificial intelligence (XAI)-based framework for the spectral analysis of cough sounds associated with chronic respiratory diseases, with a particular focus on Chronic Obstructive Pulmonary Disease (COPD). A Convolutional Neural Network (CNN) is trained on time-frequency representations of cough signals, and occlusion maps are used to identify diagnostically relevant regions within the spectrograms. These highlighted areas are subsequently decomposed into five frequency subbands, enabling targeted spectral feature extraction and analysis. The results reveal that spectral patterns differ across subbands and disease groups, uncovering complementary and compensatory trends across the frequency spectrum. Noteworthy, the approach distinguishes COPD from other respiratory conditions, and chronic from non-chronic patient groups, based on interpretable spectral markers. These findings provide insight into the underlying pathophysiological characteristics of cough acoustics and demonstrate the value of frequency-resolved, XAI-enhanced analysis for biomedical signal interpretation and translational respiratory disease diagnostics. Read More
On Task Vectors and Gradientscs.AI updates on arXiv.orgon August 25, 2025 at 4:00 am arXiv:2508.16082v1 Announce Type: cross
Abstract: Task arithmetic has emerged as a simple yet powerful technique for model merging, enabling the combination of multiple finetuned models into one. Despite its empirical success, a clear theoretical explanation of why and when it works is lacking. This paper provides a rigorous theoretical foundation for task arithmetic by establishing a connection between task vectors and gradients of the task losses. We show that under standard gradient descent, a task vector generated from one epoch of finetuning is exactly equivalent to the negative gradient of the loss, scaled by the learning rate. For the practical multi-epoch setting, we prove that this equivalence holds approximately, with a second-order error term that we explicitly bound for feed-forward networks. Our empirical analysis across seven vision benchmarks corroborates our theory, demonstrating that the first-epoch gradient dominates the finetuning trajectory in both norm and direction. A key implication is that merging models finetuned for only a single epoch often yields performance comparable to merging fully converged models. These findings reframe task arithmetic as a form of approximate multitask learning, providing a clear rationale for its effectiveness and highlighting the critical role of early training dynamics in model merging.
arXiv:2508.16082v1 Announce Type: cross
Abstract: Task arithmetic has emerged as a simple yet powerful technique for model merging, enabling the combination of multiple finetuned models into one. Despite its empirical success, a clear theoretical explanation of why and when it works is lacking. This paper provides a rigorous theoretical foundation for task arithmetic by establishing a connection between task vectors and gradients of the task losses. We show that under standard gradient descent, a task vector generated from one epoch of finetuning is exactly equivalent to the negative gradient of the loss, scaled by the learning rate. For the practical multi-epoch setting, we prove that this equivalence holds approximately, with a second-order error term that we explicitly bound for feed-forward networks. Our empirical analysis across seven vision benchmarks corroborates our theory, demonstrating that the first-epoch gradient dominates the finetuning trajectory in both norm and direction. A key implication is that merging models finetuned for only a single epoch often yields performance comparable to merging fully converged models. These findings reframe task arithmetic as a form of approximate multitask learning, providing a clear rationale for its effectiveness and highlighting the critical role of early training dynamics in model merging. Read More
PediatricsMQA: a Multi-modal Pediatrics Question Answering Benchmarkcs.AI updates on arXiv.orgon August 25, 2025 at 4:00 am arXiv:2508.16439v1 Announce Type: cross
Abstract: Large language models (LLMs) and vision-augmented LLMs (VLMs) have significantly advanced medical informatics, diagnostics, and decision support. However, these models exhibit systematic biases, particularly age bias, compromising their reliability and equity. This is evident in their poorer performance on pediatric-focused text and visual question-answering tasks. This bias reflects a broader imbalance in medical research, where pediatric studies receive less funding and representation despite the significant disease burden in children. To address these issues, a new comprehensive multi-modal pediatric question-answering benchmark, PediatricsMQA, has been introduced. It consists of 3,417 text-based multiple-choice questions (MCQs) covering 131 pediatric topics across seven developmental stages (prenatal to adolescent) and 2,067 vision-based MCQs using 634 pediatric images from 67 imaging modalities and 256 anatomical regions. The dataset was developed using a hybrid manual-automatic pipeline, incorporating peer-reviewed pediatric literature, validated question banks, existing benchmarks, and existing QA resources. Evaluating state-of-the-art open models, we find dramatic performance drops in younger cohorts, highlighting the need for age-aware methods to ensure equitable AI support in pediatric care.
arXiv:2508.16439v1 Announce Type: cross
Abstract: Large language models (LLMs) and vision-augmented LLMs (VLMs) have significantly advanced medical informatics, diagnostics, and decision support. However, these models exhibit systematic biases, particularly age bias, compromising their reliability and equity. This is evident in their poorer performance on pediatric-focused text and visual question-answering tasks. This bias reflects a broader imbalance in medical research, where pediatric studies receive less funding and representation despite the significant disease burden in children. To address these issues, a new comprehensive multi-modal pediatric question-answering benchmark, PediatricsMQA, has been introduced. It consists of 3,417 text-based multiple-choice questions (MCQs) covering 131 pediatric topics across seven developmental stages (prenatal to adolescent) and 2,067 vision-based MCQs using 634 pediatric images from 67 imaging modalities and 256 anatomical regions. The dataset was developed using a hybrid manual-automatic pipeline, incorporating peer-reviewed pediatric literature, validated question banks, existing benchmarks, and existing QA resources. Evaluating state-of-the-art open models, we find dramatic performance drops in younger cohorts, highlighting the need for age-aware methods to ensure equitable AI support in pediatric care. Read More
What happens when AI data centres run out of space? NVIDIA’s new solution explainedAI Newson August 25, 2025 at 9:00 am When AI data centres run out of space, they face a costly dilemma: build bigger facilities or find ways to make multiple locations work together seamlessly. NVIDIA’s latest Spectrum-XGS Ethernet technology promises to solve this challenge by connecting AI data centres across vast distances into what the company calls “giga-scale AI super-factories.” Announced ahead of Hot
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When AI data centres run out of space, they face a costly dilemma: build bigger facilities or find ways to make multiple locations work together seamlessly. NVIDIA’s latest Spectrum-XGS Ethernet technology promises to solve this challenge by connecting AI data centres across vast distances into what the company calls “giga-scale AI super-factories.” Announced ahead of Hot
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The US federal government secures a massive Google Gemini AI deal at $0.47 per agencyAI Newson August 25, 2025 at 8:00 am Google Gemini will soon power federal operations across the United States government following a sweeping new agreement between the General Services Administration (GSA) and Google that delivers comprehensive AI capabilities at unprecedented pricing. The “Gemini for Government” offering, announced by GSA, represents one of the most significant government AI procurement deals to date. Under the OneGov agreement
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Google Gemini will soon power federal operations across the United States government following a sweeping new agreement between the General Services Administration (GSA) and Google that delivers comprehensive AI capabilities at unprecedented pricing. The “Gemini for Government” offering, announced by GSA, represents one of the most significant government AI procurement deals to date. Under the OneGov agreement
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