LogLens · TriClock

Live demo: https://huggingface.co/spaces/resoajoe/loglens · Code: https://github.com/resoajoe/loglens

One question: if a camera never moves, how little memory does a model need to answer "when did that change?" at every timescale — a frame ago, a minute ago, all day ago?

One answer: K = O(log T) frame embeddings at logarithmically-spaced ages. A 128K-parameter model with 8 memory slots localizes changes across a 1,024-frame horizon at 0.90–0.92 mean accuracy (3 seeds), where a sliding window scores 0.48 (blind past 8 frames) and uniform sampling scores 0.67 (blind to the recent past). With 4 slots it beats hand-crafted schedules using 8 and matches them at 12. The schedule's base is a deploy-time knob: retune it coarser on frozen weights and keep 99–101% of accuracy. And more memory isn't better — K=12 loses to K=8; coverage, not capacity, is what to buy.

TriClock

A procedural benchmark isolating the memory schedule as the only variable. Three objects per scene each change at most once, at an age drawn log-uniform over [1, 1024]; the task is classifying WHEN (none/fast/med/slow). The current frame is provably uninformative — colors, positions, and object existence are randomized so only comparison against a correctly-aged memory answers the question. Frames render functionally from a seed, so the dataset is infinite, weightless, and exactly reproducible.

from triclock.generate import Episode
ep = Episode(seed=42)
frame_now, frame_100_ago = ep.frame(0), ep.frame(100)
ep.labels  # [light, agent, furniture] -> {0: none, 1: fast, 2: med, 3: slow}

Headline table (K=8 slots, identical model & training)

schedule	fast	med	slow	mean ± std (3 seeds)
log (boundary-tuned)	.91	.90	.91	.92 ± .01
log (auto base)	.86	.90	.86	.90 ± .02
log (wrong boundaries — control)	.85	.83	.74	.85 ± .00
hybrid window+stride	.65	.82	.61	.76 ± .01
uniform stride	.06	.74	.92	.67 ± .01
sliding window	.92	.01	.00	.48 ± .00

Train any row yourself in ~2.5 minutes on an M-series MacBook:

python3 -m triclock.train --policy log --steps 6000

Try the demo (Space)

Move the budget slider and watch the sliding window go blind to the past and the stride go blind to the present, while the log schedule keeps both — the whole idea in one interaction.

Status & roadmap

Temporal results: 3 seeds, 20K steps, synthetic scenes. Spatial axis: a fixed log-zoom glimpse wrapper on SmolVLM-256M was inconclusive on V*Bench (both arms at chance — the backbone, not the schedule, is the bottleneck at 256M); spatial validation moves to TriClock-HD, a trained-from-scratch setting like the temporal one. Next: real fixed-camera streams and Jetson Nano deployment (target: full-day change localization in ~3 KB of memory state at ~5 W). Draft writeup in paper/DRAFT.md.