Transforming Memory: DDAM-TOGD's Leap in Distributed Systems
Explore the revolutionary DDAM-TOGD algorithm that redefines associative memory in dynamic, multi-agent environments, offering unparalleled accuracy and efficiency.
Read the full articleYou might also wanna read
Systematic Study of Agent Memory Systems for LLMs Reveals No One-Size-Fits-All Architecture
Memory for large language model (LLM) agents has rapidly evolved from simple retrieval-augmented mechanisms into a data management system th
Agent Memory Is Distributed State Management, Not Magic
Agent memory is not magic. It is distributed state with caches, logs, consistency windows, synchronization, and memory curation.
Chinese Researchers Develop Analog Chip Claimed to Be 1,000 Times Faster Than Nvidia GPUs for Specific Applications
Researchers from Peking University say their resistive random-access memory chip may be capable of speeds 1,000 faster than the Nvidia H100
δ-mem: A Compact Online Memory Mechanism for Efficient Long-Context LLM Processing
Large language models increasingly need to accumulate and reuse historical information in long-term assistants and agent systems. Simply exp
Research Proposal: Specialized Memory Architectures with Long-Term and Short-Term RAM Classes
Both SRAM and DRAM have stopped scaling: there is no technical roadmap to reduce their cost (per byte/GB). As a result, memory now dominates
Research Directions for Overcoming Memory and Interconnect Challenges in Large Language Model Inference Hardware
Large Language Model (LLM) inference is hard. The autoregressive Decode phase of the underlying Transformer model makes LLM inference fundam

Comments
Sign in to join the conversation.
No comments yet. Be the first.