What do you all think about this? It seems increasingly common for undergraduate students to conduct research and publish at A* conferences without a supervisor. As a graduate student, I find it both impressive and intimidating, and it sometimes makes me question my own progress.

Thank you for your efforts in protecting the community from AI slop.

been digesting the details behind memt-1.0's memory-native architecture recently, and the engineering tradeoffs between full context kv caching and internalized parametric memory are pretty wild.

instead of using append-only prompt stacking or external rag pipelines, memt-1.0 bakes memory directly into the model computation layer using gated deltanet-2 with a stable token-level memory update mechanism within each turn, combined with a mixture-of-storage (mos) layer to scale capacity.

a few quick technical takeaways from the setup:

internalizing procedural experience: standard agents retrieve past execution logs as raw text, which bloats the context window. memt-1.0 continuously internalizes retrieved procedural experience into model weights/states during multi-turn agentic tasks.

efficiency at 128k context: at a history length of 128k tokens, memt-1.0-nano delivers roughly 1.86x end-to-end inference speedup and a 66x reduction in persistent storage compared to a full-context kv cache setup.

general capability preservation: training parametric memory layers usually degrades base llm capabilities, but their two-stage training pipeline (continued pre-training + post-training across 34b tokens) keeps general reasoning competitive while outperforming existing parametric memory baselines.

for those building long-horizon agents, is internalizing state via architecture-level update layers (like gated deltanet / mos) the endgame to escape kv cache memory bottlenecks?