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Most people working in computer vision will tell you the hard part is the model.Continue reading on Medium »
One major challenge in deploying autonomous agents is building systems that can adapt to changes in their environments without the need to retrain the underlying large language mod...
Alibaba DAMO Academy's I2B-LPO framework, accepted at ACL 2026 Main, improves math reasoning accuracy by up to 5.3% and semantic diversity by 7.4% by guiding models to generate mor...
Post-training methods (RLVR, On-policy distillation) are Episode-local Language models are getting better at learning from feedback during post-training. In reinforcement learning...
As Large Language Model (LLM) agents scale from executing basic tool use scripts to running complex, autonomous machine learning pipelines…Continue reading on Medium »
What if an unsupervised model could become a strong classifier with only a handful of labels? The post You Don’t Need Many Labels to Learn appeared first on Towards Data Science.
by Sami Mollard, Sander M. Bohte, Pieter R. Roelfsema Natural scenes usually contain many objects that need to be segregated from each other and the background. Object-based atten...
AI R&D runs on a cycle of hypothesis, experiment, and analysis — each step demanding substantial manual engineering effort. A new framework from researchers at SII-GAIR aims to...
Why the most exciting idea in AI right now isn’t a bigger language model — it’s an architecture that learns the way we do.Continue reading on Towards AI В»
Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context win...
Introduction Reinforcement Learning from Human or AI Feedback (RLHF, RLAIF) has become the standard recipe for aligning large language models (LLMs). But as we push into the agenti...
How the machines became expert students of human preference, taste, deception, and desire while most humans still cannot explain what a…Continue reading on Data Science Collective...
Learning from the World Around Us:Continue reading on Medium »
We gave agents tools. We gave them orchestration frameworks. We gave them RAG pipelines and vector databases. But we forgot to give them the ability to learn. The result: every se...
Creating self-improving AI systems is an important step toward deploying agents in dynamic environments, especially in enterprise production environments, where tasks are not alway...
Intro: Speeding Up IntelligenceContinue reading on Medium В»
Hexo Labs released SIA, an open-source self-improving loop, under an MIT license. A Feedback-Agent reads each run's trajectory, then either rewrites the scaffold or triggers a LoRA...
Eighteen months ago, the best AI models could summarize text and answer questions with reasonable accuracy. Today, they write production code, conduct multi-step research across hu...
The development of AI coding agents has progressed at a very rapid pace, yet all the time they still had one huge limitation in that they still required being supervised consistent...
In the era of AI-assisted development, how can we help AI assistants better understand our learning resources? The HagiCode project implements a unified, AI-comprehensible knowledg...
Every agent framework has the same problem with memory: it doesn't forget. Context windows reset between sessions. RAG and vector DBs store everything with equal weight and grow u...
by Dongning Liu, Muzhi Wang, Huan Luo Ranking—a ubiquitous relational structure—enables humans to organize complex information and overcome cognitive load, yet in real-world setti...
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