#memory

12 articles

01 ·★ MEMBER·PAPER·7 min read Paper Explained: WorldCrafter — A Video World Model That Remembers What You Saw, via Camera-Queryable Memory Video world models tend to repaint a room differently when you walk back into it. WorldCrafter tackles this with an implicit 3D-aware memory that is read out from the viewpoint you are about to look from. We explain it from scratch with equations, interactive figures, and code. 02 ·★ MEMBER·PAPER·10 min read Paper Walkthrough: MaP-WAM — Turning a Robot's Memory into Plans So the Executor Stays the Same Size as History Grows For robot tasks you can't solve without remembering the past, MaP-WAM stops feeding the executor the full video history and instead compresses it into a plan for what to do next. The executor predicts its own progress alongside actions to decide when a step is done. We walk through the design, which reports 83.3% on RMBench and 78.0% on real robots, straight from the paper. 03 ·Large Language Models·★ MEMBER·PAPER·9 min read Paper Explained: Just-in-Time Memory — Curate Agent Memory When You Read It, Not When You Write It A from-scratch walkthrough of JitMem, which stores raw agent trajectories and only summarizes them once the next task is known. Why that makes the curator easy to train, and exactly how far the reported numbers go. 04 ·Training & Alignment·★ MEMBER·PAPER·10 min read Paper Walkthrough: SpeakerMem-R1 — A Two-Track Memory That Remembers Who Said What, About Whom, and When A from-scratch look at SpeakerMem-R1, a long-term memory for group conversations. It keeps verbatim messages alongside person- and group-level states, and trains a small note-taking model (Qwen2.5-3B) with reinforcement learning that scores memory owner by owner. 05 ·Training & Alignment·FREE·PAPER·19 min read QLoRA — Fine-Tuning a 65B Model on One GPU A 65-billion-parameter model in 16-bit weights is 130 GB before you have trained anything. QLoRA gets that onto a single 48 GB card by storing the frozen weights in a 4-bit format shaped like the weights themselves, quantizing the quantization constants, and paging the optimizer to CPU — while gradients still flow through those 4-bit weights into a small 16-bit adapter. 06 ·Agents·★ MEMBER·PAPER·8 min read Paper Walkthrough: S³Gym — Does Self-Testing and Self-Judging Add Up to Self-Improvement? Can an LLM agent really learn from its own experience? A benchmark that separates permissive exploration from strict held-out evaluation across seven games, comparing raw history, summary memory, and parameter training under one protocol. 07 ·★ 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. 08 ·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. 09 ·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. 10 ·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. 11 ·Agents·FREE·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. 12 ·Agents·FREE·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.