Google IO: Text diffusion model outperforms AR

I’ve been experimenting with structured knowledge graphs, optimizing semantic search, and distilling reasoning frameworks that sit on top of LLM outputs. Despite all the progress, no one has yet solved how large language models can offer meaningful guidance about the future. The real breakthrough may come not from bigger models, but from architectures that build persistent agentic memory, systems that learn, recall, and reason more like the human brain. DM me if you see something worth a deeper conversation.

Researchers from Stanford University, SambaNova Systems, and UC Berkeley have proposed Agentic Context Engineering (#ACE), a new framework designed to improve large language models (#LLMs) through evolving, structured contexts rather than weight updates. The method, described in a paper, seeks to make language models self-improving without retraining. Read more on InfoQ ▶️ https://lnkd.in/djHK-AFj

Sim-cot Achieves 8.2% Performance Gain with Supervised Implicit Chain-of-Thought, Stabilizing Training and Improving Semantic Diversity by 3.0% and 2.1% Researchers have developed a new training technique, SIM-CoT, that stabilises and improves the reasoning ability of large language models by ensuring each step in their internal thought process retains distinct meaning, thereby bridging the performance gap with more computationally expensive reasoning methods. #quantum #quantumcomputing #technology https://lnkd.in/eFfVXcQu

Sim-cot Achieves 8.2% Performance Gain with Supervised Implicit Chain-of-Thought, Stabilizing Training and Improving Semantic Diversity by 3.0% and 2.1% Researchers have developed a new training technique, SIM-CoT, that stabilises and improves the reasoning ability of large language models by ensuring each step in their internal thought process retains distinct meaning, thereby bridging the performance gap with more computationally expensive reasoning methods. #quantum #quantumcomputing #technology https://lnkd.in/eFfVXcQu

Large language models are the result of at least forty years of research. Today, LLMs are everywhere, and they are here to stay. Here is a nice write-up on how we got here [1]. Where are we going? The future we create will tell 🔮 [1] Gundersen, G. (2025, October 1). A history of large language models. https://lnkd.in/eXhwB662

Scaling neural nets (as often done with modern LLMs) was once deemed a dead end, primarily due to overfitting concerns. However, in September 2019, a groundbreaking paper by researchers from Ohio State University and Columbia University challenged this prevailing notion. The researchers diverged from the mainstream consensus, paving the way for new perspectives in the field. https://lnkd.in/gJyUW3dT Watch the insightful video here: https://lnkd.in/g-CFWcXq

Hello everyone. This is a second episode of CervellAi in English. It explains a framework, called RAG, to improve the accuracy and timeliness of large language models (LLMs). This ensures the model generates up-to-date responses with evidence to support its claims. #Ai #ArtificialIntelligence #RetrievalAugmentedGeneration https://lnkd.in/ed4rHeMZ

Multi-Head Latent Attention (MLA) Compression This technique transforms N attention heads into 1 compressed representation in large language models, dramatically reducing storage space while enabling recovery through feature vectors when needed - a lossy compression method. It significantly reduces the memory burden of K-V cache, maximizing inference efficiency. #MLACompression #MultiHeadAttention #LLMEfficiency #MemoryEfficiency #KVCache #TransformerOptimization #DeepLearning #AIResearch #ModelCompression

Understanding why fixing the temperature to zero in large language models (LLMs) does not yield identical results for every user is crucial. This phenomenon highlights the inherent nondeterminism present in LLM inference. This blog provides insights into the reasons behind this variability and explores the complexities involved. For a deeper understanding, check out the blog here: https://lnkd.in/dz8_NJkt

WavePSI framework, a radical paradigm shift in Artificial General Intelligence (AGI) that strongly critiques current approaches, particularly those centered around large language models (LLMs). WavePSI asserts that "memetic intelligence", which optimizes for reproducing patterns in human language, is a fundamental dead end, proposing instead "experiential intelligence" that learns through direct interaction and prediction of environmental states rather than text patterns. The framework's core engineering concepts include a physicalized consciousness model that defines subjective experience (qualia) as the computational substrate for understanding, incorporating astrocytes for temporal integration, and employing a core learning mechanism based on conscious state prediction error. Ultimately, WavePSI proposes a unified architecture where the experiential core is the primary intelligence, subordinating symbolic reasoning tools like LLMs to ensure thought remains grounded in direct reality.