@SirMrMeowmeow

2025-06-22 — web clipping, 510 words — published 2025-04-21

Thread by @SirMrMeowmeow

2025-06-22 — web clipping, 1,040 words — published 2025-04-21

**Sir Mr Meow Meow** @SirMrMeowmeow [2025-04-21](https://x.com/SirMrMeowmeow/status/1914282539231170929) TLDR: Cadence-Ladder AEs w varying cadence for different time horizons. Or MTS AE (Monotonic Temporal Signature) for an implicitly learned temporal structure via time stamps. Let the model learns what to retain or forget. (ie. using Unix-style temporal tags) Basically either way, attempt to forward latents to inform subsequent inference(s). \*See Larimar, which also used updates to pass latents to influence next inference for 'fact edits'. \=========================== Continuity across inferences via AutoEncoders (AEs): Instead of replaying entire prompts or relying on retrieval scaffolding, use AEs to pass compressed latent information forward between inferences. Inspired by Larimar, but generalized beyond fact edits into persistent memory slots across time. Why? The Statelessness Problem. Which just means the models are 'frozen models' and literally start from scratch, receive the chat history, and try their best to infer from that what to do next--on EVERY RESPONSE. This results in gradual loss of nuance and context... :x No unsaid context, goals, intent gets passed or survives the end of inference. \*looks at those amnesic meticulous prompt crafting Pokemon agents\* :/ \-- Cadence-Ladder AEs (Core Idea): Each AE in the ladder writes at a distinct cadence (τ). 'Why? Why not just one AE that updates every inference?' A single AE updated every inference is prone to information drift and “telephone game” degradation. But assigning each AE its own cadence forms a temporal hierarchy, where each AE retains context across a different horizon: Example cadences: AE@1-step, AE@4-step, AE@16-step, AE@64-step, AE@256-step, etc. AE@1: captures immediate working memory — reactive, volatile, updates every step. AE@4–16: stores short-term structure, heuristics, and tactics — more stable, local context. AE@64+: holds mid-run strategies, semantic goals, or persistent environmental state — very stable but lossy. Result is a temporal pyramid: fast AEs react quickly but forget, slow AEs compress for long-term consistency. \-- Biological Analogy: Mimics hippocampus → neocortex consolidation in the human brain. Short-term, high-resolution experience is gradually distilled into slower, stable semantic memory. \-- Temporal Filtering: Each AE acts like a temporal filter: Faster AEs = noisy but highly responsive. Slower AEs = stable but compressive, preserving only persistent features. \-- Cadence Formalization: AE\[tau\_i\] = encode(latents) if t % tau\_i == 0 Where τ\_i ∈ {1, 4, 16, 64, 256…} \-- Temporal Tagging: Each AE latent is tagged with its cadence tier (e.g. AE@1, AE@4) so the model interprets horizon correctly and avoids misrouting. This allows the model to interpret memory relevance and select appropriate time-scale during recall. \-- Memory Consolidation: Allow gated or attention-based upward memory consolidation from faster to slower AEs (e.g., AE@1 → AE@4 → AE@16 → AE@64). This builds layered abstraction and persistent memory without overwhelming slow rungs with noise. \-- Optional: stagger AE update ticks to avoid synchronized writes and reduce compute spikes. \-- MTS Integration (Secondary idea): Can be combined with Monotonic Temporal Signatures (MTS) or timestamp conditioning. This enables learned soft decay patterns for memory — the model learns what to retain or forget based on temporal features and surprise. (e.g., Unix-style temporal tags) Hybrid Mode: MTS handles adaptive, fuzzy decay. Cadence handles structural regularity and long-horizon memory segmentation. \-- \*\*\*Architecture Compatibility\*\*\*: This approach is architecture-neutral. It works with: Transformer backbones (no changes to attention layers needed) SRSM-style agents State space models like Mamba, Hyena, etc. Requires only latent routing, AE stack, and update logic. \-- Benefits Enabled: – Task continuity across thousands of steps – Goal persistence – State-aware planning – Self-reminding behavior – Significantly reduced prompt bloat and drift – No fragile prompt chaining \-- Example Use Case: Imagine a Pokémon-playing agent that recalls town layouts, shop inventories, or rival strategies from 300+ steps ago — without external scaffolding. Just compressed latent buffers across a temporal hierarchy. \-- Should this be trained end-to-end?: Absolutely yes, if you want peak performance. Joint AE + read-head fine-tuning: teaches the model how to properly query rungs. Contrastive or surprise-gated loss: prevents “semantic bleaching” in slower AEs. Curriculum tasks (e.g., 256-step delayed recall): forces model to meaningfully use deeper memories. Prototype mode: A frozen LM + randomly initialized AE stack can still work partially, especially for high-entropy inputs like screenshots, GUI frames, or sensor data. \-- Experimental Variants: Cadence-Ladder AEs: What it shows: Does deterministic write-sparsity alone stop drift? Try: Train an agent to carry a puzzle code 256 steps and reproduce it. MTS-Only AEs: What it shows: Can learnable decay replace fixed cadence? Try: Give agent variable-length tasks (20–1,000 steps) and observe if MTS auto-adapts horizon. Hybrid (Cadence + MTS): What it shows: Does stacking rigid + soft decay outperform either alone? Try: Ablation grid: {on/off} × {τ set} × {MTS decay rate}, test on multiple task types (games, docs, code). \-- Design Knobs to Sweep: τ set (cadence intervals): Controls effective horizon length and memory refresh rate. Try geometric spacing {1,4,16,64,256}, Fibonacci, or even learnable τ\_i. of AEs vs. bottleneck width: Tradeoff between memory depth and per-rung resolution. Try fixed total latent budget (e.g., 4k dims) split as {4×1k, 8×512, 2×2k}. Write gate: Modulo-based vs. surprise-triggered (ΔKL > σ) vs. score-thresholded. Read strategy: Always attend to all rungs, or dynamically select based on query needs. Try masking rungs and measuring impact on performance. MTS timestamp fusion: Try raw time concatenation, embedding into latent, or keeping timestamp as separate key/value. Contrastive / reconstruction loss weighting: Prevents overcompression or homogenization of slow rungs. Try λ ∈ {0, 0.1, 1, 10}. Consolidation schedule: Should AE@1 → AE@4 transfers be periodic or based on surprise / novelty score? Compare copy-on-delta vs. fixed downsampling intervals. \-- Final Thoughts: This framework is loosely based on Larimar’s AE architecture, which targeted fact editing only. Here, the concept is expanded into a full multi-timescale memory scaffold — letting models maintain real, persistent memory across long spans of inference. It’s light-touch, architecture-agnostic, modular, and full of knobs. Useful for agents, simulations, chatbots, or long-horizon reasoning tasks where current stateless transformers fall short. Continuity is the missing piece. Let’s build it into our agents — and give them something like a sense of time. \-- Sir Mr Meow Meow 🐾 \========================================================== loosely based on Larimar, AEs for updates and passing latents (but their scope was only fact edits '>.>) Larimar: Large Language Models with Episodic Memory Control https://arxiv.org/abs/2403.11901