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Graph architecture replaces transformer attention, extrapolating to 4 million tokens in linear time

GTFormer proposes replacing Transformer self-attention with a hierarchical graph neural network, achieving zero-shot length extrapolation from 512 training tokens to 4 million tokens in linear O(N) ...
BIOENGINEER

Parallel algorithms push well-connected community detection to billion-edge graphs

Researchers report distributed-memory implementations of the WCC and CM algorithms that post-process network clusterings for ...
VentureBeat

Mem0's scalable memory promises more reliable AI agents that remembers context across lengthy conversations

Researchers at Mem0 have introduced two new memory architectures designed to enable Large Language Models (LLMs) to maintain coherent and consistent conversations over extended periods. Their ...
InfoWorld

Contexts graphs, AI memory, and enterprise knowledge: Are decision traces enough?

A context graph could capture the full context, reasoning, and causal relationships behind critical business decisions. It’s a powerful idea. A December 2025 paper from Silicon Valley venture capital ...
CIO

AI Agent Memory Systems to consider to optimize enterprise workflows

Compare different AI Agent Memory Systems through deployment responsibility, changing business context, and the evidence ...
BIOENGINEER

Structured Memory Scheduling Helps Large Language Models Reason Beyond Retrieval

A new framework called SMS-RAG schedules semantic, structural, and episodic memory during reasoning, outperforming existing ...
Electronic Design

MoSys’ Graph Memory Engine Accelerator IP Targets Packet Classification on Intel’s Stratix 10 FPGAs

MoSys announced that its Graph Memory Engine (GME) Accelerator IP is now running on Intel Stratix 10 FPGAs. The GME is part of MoSys’ Packet Classification Platform, and is capable of performing ...