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Nscale adds former OpenAI exec Fidji Simo to its board ahead of potential IPO
The No. 2 exec at OpenAI also led Instacart through its IPO in 2023.
Building Effective AI Agents: The Decisions Behind Reliable AI Systems
I ve been spending time reading about how AI agents are designed, evaluated, and improved. Two topics particularly caught my attention Continue reading on Medium »
Free AI Crawler Access Checker | Check If AI Bots Can Access Your Website
**Can AI Search Engines Access Your Website Content? Find Out with AISEOGIC!**Continue reading on Medium »
Competing in RoboCup@Home and winning the 2026 best paper award: an interview with Raphael Memmesheimer
Team NimbRo’s robot in action during the RoboCup@Home competition at RoboCup 2026. Raphael leads the team. Raphael Memmesheimer has been successfully competing in the RoboCup@Home league winning the RoboCup@Home competition five times. He and his colleagues won the best paper award at RoboCup 2026 for their work “Efficient Image Annotation via Semi-Supervised Object Segmentation with […]
What If Scientists Could Predict a Cell’s Next Move?
How artificial intelligence is learning to forecast cellular behavior and why the future of biotechnology may depend on predicting what Continue reading on Medium »
Building Software That Solves Real Problems — Part 1
Before Writing Code, Understand the ProblemContinue reading on Medium »
Building HIPPOCRATES: My Journey Toward Making Healthcare Information More Accessible Through AI
By Tawhidur Rahman Laskar | Founder Project Leader, Hippocrates IndiaContinue reading on Medium »
AI Annotation Market 2025 to 2035: Size, Growth and Who Leads (With Charts)
Every AI model needs labeled data, and the AI annotation market grows almost tenfold to $28.33 billion by 2035 as automation takes over Continue reading on Medium »
Jio shares surge 21% in market debut after blockbuster India IPO
Jio has debuted as India's second-most valuable listed company, behind its parent, Reliance Industries.
Can Language Models Learn While They Think? Testing Gradient Adaptation Across Architectures
arXiv:2610.33025v1 Announce Type: new Abstract: Letting a language model take gradient steps on the text it is reading, known as dynamic evaluation, lowers its loss on the rest of the document. Its behaviour on hybrid linear-attention architectures, on diffusion language models, and on standard long-context benchmarks has not been measured. We measure it. With one SGD step per 128-token chunk on the MLP output projections of the last quarter of blocks, no training, and a 2k-token window, softmax-attention models (Qwen3-1.7B, Qwen3-4B, Gemma-4-E4B) lose 5-8% of their next-token loss over a document and outperform the same frozen model given the full 32k-token context. Hybrid Gated-DeltaNet models (Qwen3.5-4B, Qwen3.8-27B) gain under 1% at every learning rate: the linear-attention blocks already perform the adaptation. The mechanism transfers to masked-diffusion models with a larger effect, 8% on Nemotron-Diffusion-8B and 17-20% on DiffusionGemma-26B, once the rate is lowered and updates
Rollback: Experience Backtracking in World Models for Policy Improvement
arXiv:2610.36068v1 Announce Type: new Abstract: Action-conditioned world models offer a scalable alternative to costly real-world interaction for improving robot policies. However, existing world-model-based methods largely treat world models as forward-only surrogates of the physical environment, leaving untapped a capability no physical environment affords: rewinding to past decision points to explore counterfactual futures. We introduce Rollback, a policy improvement paradigm that turns world models from passive simulators into counterfactual engines: it rolls back to critical decision points, branches into alternative actions, and contrasts their outcomes to attribute success to individual decisions. This concentrates imagination where it matters most and distills counterfactual outcomes into fine-grained, action-level supervision without a separately learned value model. We build a scalable training system that unifies distributed world-model search with asynchronous real-robot ex
Enhancing Deep Learning Model Performance through Data Generation Strategies: Classification of Olive Tree Diseases
arXiv:2610.35984v1 Announce Type: new Abstract: Deep learning models are highly effective in a wide range of domains, particu larly in plant disease diagnosis. However, their performance is often limited by challenges such as insufficient data and class imbalance. In our work, we propose a comprehensive data generation strategy to overcome these limitations. First, we apply various data generation techniques, including traditional augmentation methods, CutMix, MixUp, and synthetic image generation using Deep Convolu tional Generative Adversarial Networks (DCGANs), to create new data samples. Second, we exploit this generated data in three scenarios: (S1) balancing class distribution in the original dataset, (S2) increasing the number of samples per class, and (S3) expanding the dataset while maintaining balanced classes. Third, we evaluate the impact of these data distributions (S1, S2, S3) on model perfor mance using the EfficientNetV2B0 architecture with pre-trained and fine-tuned we
Evolving to Match Capabilities: Improving Harness Self-Evolution via Skill Adaptation
arXiv:2610.33024v1 Announce Type: new Abstract: Harness self-evolution offers a practical route for LLM agents to improve from execution experience without updating model parameters. Its effectiveness depends on whether models can successfully act on the guidance provided by their harnesses. However, we find that harness benefits are not uniform across model capabilities: skills that improve stronger models can provide little benefit or even degrade performance for weaker ones. Across seven models and three benchmarks, we identify 103 task-skill pairs where skills improve GPT-5.6-luna by 31.1 points but degrade Gemma-4-E4B by 8.8 points. In 86% of failed weak-model runs, the violated requirement is already stated in the skill. This motivates skill adaptation: reformulating existing skills to match the executor's capabilities and make their guidance easier to execute. To achieve this, we diagnose failures in tool execution, context management, instruction following, and verification, an