Memory

arXiv:2604.21284v1 Announce Type: new Abstract: MemPalace is an open-source AI memory system that applies the ancient method of loci (memory palace) spatial metaphor to organize

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s1brq3/wmb100k_open_source_benchmark_for_ai_memory/"> <img alt="WMB-100K – open source benchmark for AI

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s4f9dv/deepseeks_memory_divorce_what_happens_when_ai/"> <img alt="DeepSeek's Memory Divorce: What Happens

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s64brt/evermindaievermemos_4b_parameter_model_with_100m/"> <img alt="EverMind-AI/EverMemOS: 4B parameter

<table> <tr><td> <a href="https://www.reddit.com/r/singularity/comments/1svasld/memory_systems_with_vector_objects_as/"> <img alt="Memory systems with vector objects as

<!-- SC_OFF --><div class="md"><p>Link to full article <a href="https://orimnemos.com/bedrock">here </a></p> <p>TLDR:</p> <p>- LLMs are the wrong substrate for memory. Prediction

<!-- SC_OFF --><div class="md"><p>Running Llama-3-70B with long contexts and hitting HBM limits constantly. 128K context burns approx 40GB just for KV cache — half the H100 gone

<!-- SC_OFF --><div class="md"><p>I’ve been building Signet, an open-source memory substrate for AI agents.</p> <p>The problem is that most agent memory systems are still