#memory
6 articles
01
·★ MEMBER·PAPER·10 min read
Paper Walkthrough — Beyond Retrieval: LatentStream Turns Retrieved Video Into Latent Memory
For never-ending video streams, LatentStream stops appending retrieved evidence as extra context and instead internalizes it into fixed-length latent memory tokens. A ground-up walkthrough of its hierarchical memory, latent evolution, and confidence-driven test-time optimization.
02
·Inference & Serving·★ MEMBER·PAPER·12 min read
Paper Explained — LatentPress: Feeding Compressed Context Straight to a Frozen LLM, Neither as Text Nor as Pixels
A method that writes long conversation histories and documents into continuous memory tokens a frozen LLM reads directly — no text summary, no rendered image. On LongMemEval it beats uncompressed evidence at 7.70x compression. Explained from first principles.
03
·Audio & Speech·★ MEMBER·PAPER·14 min read
Paper Walkthrough: VoiceMem — A Left Brain and a Right Brain for Voice Agents, at Zero Added Latency
A from-scratch walkthrough of VoiceMem, a memory system for real-time speech interaction. A factual 'left brain' and an affective 'right brain' run in parallel, and the whole retrieval is hidden inside the silence a VAD already waits out — which is how it wins at a top-5 budget.
04
·Agents·★ MEMBER·PAPER·12 min read
Paper Walkthrough: Embodied-Navigator (TAMP-Nav) — Let the VLM Just Point, and Navigation Gets Both Faster and Better
Instead of asking a VLM for 3D coordinates, have it point at a 2D pixel; think and store memory only at key nodes; align it all with two-level GRPO. A ground-up walkthrough of the design that hits 66.2% SR on R2R-CE at 16.58s per task using only 90k training trajectories.
05
·Agents·★ MEMBER·PAPER·11 min read
Designing Agent Memory — Short-Term, Long-Term, Episodic
An LLM remembers nothing. Conversations only look continuous because you re-send the whole history every turn. Building up from zero: the three-layer split, the function that decides what gets recalled, and the part almost nobody designs — forgetting.
06
·Agents·★ MEMBER·PAPER·8 min read
Paper Walkthrough: Metis — A 'Memory Foundation Model' That Moves Agent Memory Inside the Model
Agent memory today is mostly bolted on from the outside via RAG. This paper proposes memory foundation models — models whose forward pass natively stores, forgets, and updates information — and builds Metis, the first prototype. A from-scratch walkthrough of how it works, how well it works, and where it breaks.