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Understanding Tools in LangGraph: A Complete Guide to Tool Execution, Selection, Dynamic Tools…
Large Language Models (LLMs) are incredibly powerful at understanding and generating text. However, on their own, they are limited to the Continue reading on Medium »
Researchers develop modular nanorobot
Illustration of the versatile nanorobot. It is 150 times smaller than the diameter of a human hair. (Illustration: Marina Bräm) By Angelika Jacobs Nanorobots sound like science fiction: tiny machines for medicine, the environment, or industry. In fact, nanorobotics has become a rapidly growing field of research. It is considered a promising approach, for example, [ ]
July 2026 AI Releases: A Timeline of Frontier Model Shifts
July 2026 was the busiest month for frontier model releases the field has seen. Four major labs shipped flagship or near-flagship models, two well funded newcomers shipped their first, and the largest open weight model ever published went up for download, all inside thirty one days. Read as a list, the top AI models in July [ ] The post July 2026 AI Releases: A Timeline of Frontier Model Shifts appeared first on Analytics Vidhya.
Fluent Is Not the Same as Correct: How to Actually Trust an AI Translation
You paste a paragraph into a translator, the result comes back looking perfectly fine, and you send it. Then someone who actually speaks Continue reading on Medium »
How Generative AI Is Reinventing Billing Automation for Intelligent Finance
Finance departments are under increasing pressure to process growing transaction volumes while maintaining speed, compliance, and accuracy Continue reading on Medium »
Machine Learning in 2026: The Technology That Is Quietly Changing the World
Machine Learning in 2026: The Technology That Is Quietly Changing the WorldContinue reading on Medium »
AI Agents: The Future of Intelligent Enterprise Automation
Businesses today need more than chatbots and rule-based automation. They require intelligent systems that can understand context, make Continue reading on Medium »
Show HN: What should the GUI for AI agents look like?
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The AI trade now runs on borrowed money, and the lenders are repricing it
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Probing the Origins of Reasoning Performance: Representational Quality for Mathematical Problem-Solving in RL vs. SFT Fine-Tuned Models
arXiv:2607.26119v1 Announce Type: new Abstract: Large reasoning models trained via reinforcement learning (RL) have been increasingly shown to outperform their supervised fine-tuned (SFT) counterparts on mathematical reasoning tasks; Yet the mechanistic basis for this advantage remains unclear. We therefore ask, what internal representational differences enable RL models' superior performance? Our work presents two converging lines of evidence: First, linear probes trained on layer-wise hidden states reveal that RL models tend to achieve higher accuracy in predicting answer correctness compared to SFT models, indicating more linearly separable and structured representations. Second, mean ablation studies show that RL models develop a hierarchical architecture where deeper layers become progressively more critical, whereas SFT models distribute importance uniformly across layers. Together, these findings demonstrate that RL training fundamentally restructures how models represent and pr
Regularizing modality contribution drift in multimodal continual learning
arXiv:2607.27260v1 Announce Type: new Abstract: Multimodal continual learning (MMCL) aims to learn emerging knowledge from multimodal data while preserving knowledge. To mitigate forgetting, current MMCL methods usually focus on cross-modal representation alignment or semantic similarity, but they overlook whether the relative contributions of individual modalities and their interactions remain stable across incremental tasks. We term this decision-level shift Modality Contribution Drift (MCD) and quantify it with the MCD score, which combines contribution-strength and relative-reliance changes under controlled interventions on modality subsets. Theoretical and empirical analyses further explain why current MMCL methods cannot reliably mitigate this drift. To this end, we propose Continual Modality Contribution Drift Regularization (CMCDR), which preserves the modality contribution structure of previously learned tasks. Since MMCL settings differ in whether old exemplars are available,
Recursive transformers for semiconductor thermo-mechanical reliability
arXiv:2607.27251v1 Announce Type: new Abstract: Transformer-based surrogate models are increasingly used to replace expensive first-principles simulation in engineering design. But conventional transformer architectures are often over parameterized for the small, low-dimensional datasets typical of engineering design spaces, where large simulation data is expensive to generate. Under these conditions, excess parameter capacity leads to overfitting rather than improved accuracy, while also incurring unnecessary memory and compute overhead. This motivates a shift towards architectures that focus on additional compute rather than additional learnable parameters. This paper presents a hardware-aware evaluation of three recursive transformer paradigms for surrogate thermo-mechanical analysis of advanced packages: a)Tiny Recursive Model, b) our proposed Depth Recursive transformer, c) and a simple recursive transformer. We systematically compare their predictive performance (Recall, Mean Rec