Challenge: Hand coding weights for efficient sequence memorisation
- Occurred
- Jul 23, 2026
- Published
- Jul 23, 2026
- Source
- AI Alignment Forum
Summary
We hand coded weights for one layer MLPs that memorises labels for input token sequences of length two. The number of facts our hand-coded models can memorise with 90% accuracy[1]scales roughly linearly with the models' parameter count[2], just like trained models for the same architecture. However, our hand-coded models' scaling prefactor still falls short of trained models' by a factor of mjx-math { display: inline-block; text-align: left; line-height: 0; text-indent: 0; font-style: normal; font-weight: normal; font-size: 100%; font-size-adjust: none; letter-spacing: normal; border-collapse: collapse; word-wrap: normal; word-spacing: normal; white-space: nowrap; direction: ltr; padding: 1px 0; } mjx-container[jax="CHTML"][display="true"] { display: block; text-align: center; margin: 1em 0; } mjx-container[jax="CHTML"][display="true"][width="full"] { display: flex; } mjx-container[jax="CHTML"][display="true"] mjx-math { padding: 0; } mjx-container[jax="CHTML"][justify="left"] { text-align: left; } mjx-container[jax="CHTML"][justify="right"] { text-align: right; } mjx-mn { display: inline-block; text-align: left; } mjx-c { display: inline-block; } mjx-utext { display: inline-block; padding: .75em 0 .2em 0; } mjx-mi { display: inline-block; text-align: left; } mjx-mo { display: inline-block; text-align: left; } mjx-stretchy-h { display: inline-table; width: 100%; } mjx-stretchy-h > * { display: table-cell; width: 0; } mjx-stretchy-h > * > mjx-c { display: inline-block; transform: scalex(1.0000001); } mjx-stretchy-h > * > mjx-c::before { display: inline-block; width: initial; } mjx-stretchy-h > mjx-ext { /* IE */ overflow: hidden; /* others */ overflow: clip visible; width: 100%; } mjx-stretchy-h > mjx-ext > mjx-c::before { transform: scalex(500); } mjx-stretchy-h > mjx-ext > mjx-c { width: 0; } mjx-stretchy-h > mjx-beg > mjx-c { margin-right: -.1em; } mjx-stretchy-h > mjx-end > mjx-c { margin-left: -.1em; } mjx-stretchy-v { display: inline-block; } mjx-stretchy-v > * { display: block; } mjx-stretchy-v > mjx-beg { height: 0; } mjx-stretchy-v > mjx-end > mjx-c { display: block; } mjx-stretchy-v > * > mjx-c { transform: scaley(1.0000001); transform-origin: left center; overflow: hidden; } mjx-stretchy-v > mjx-ext { display: block; height: 100%; box-sizing: border-box; border: 0px solid transparent; /* IE */ overflow: hidden; /* others */ overflow: visible clip; } mjx-stretchy-v > mjx-ext > mjx-c::before { width: initial; box-sizing: border-box; } mjx-stretchy-v > mjx-ext > mjx-c { transform: scaleY(500) translateY(.075em); overflow: visible; } mjx-mark { display: inline-block; height: 0px; } mjx-msup { display: inline-block; text-align: left; } mjx-TeXAtom { display: inline-block; text-align: left; } mjx-mfrac { display: inline-block; text-align: left; } mjx-frac { display: inline-block; vertical-align: 0.17em; padding: 0 .22em; } mjx-frac[type="d"] { vertical-align: .04em; } mjx-frac[delims] { padding: 0 .1em; } mjx-frac[atop] { padding: 0 .12em; } mjx-frac[atop][delims] { padding: 0; } mjx-dtable { display: inline-table; width: 100%; } mjx-dtable > * { font-size: 2000%; } mjx-dbox { display: block; font-size: 5%; } mjx-num { display: block; text-align: center; } mjx-den { display: block; text-align: center; } mjx-mfrac[bevelled] > mjx-num { display: inline-block; } mjx-mfrac[bevelled] > mjx-den { display: inline-block; } mjx-den[align="right"], mjx-num[align="right"] { text-align: right; } mjx-den[align="left"], mjx-num[align="left"] { text-align: left; } mjx-nstrut { display: inline-block; height: .054em; width: 0; vertical-align: -.054em; } mjx-nstrut[type="d"] { height: .217em; vertical-align: -.217em; } mjx-dstrut { display: inline-block; height: .505em; width: 0; } mjx-dstrut[type="d"] { height: .726em; } mjx-line { display: block; box-sizing: border-box; min-height: 1px; height: .06em; border-top: .06em solid; margin: .06em -.1em; overflow: hidden; } mjx-line[type="d"] { margin: .18em -.1em; } mjx-mrow { display: inline-block; text-align: left; } mjx-msub { display: inline-block; text-align: left; } mjx-mtext { display: inline-block; text-align: left; } mjx-mover { display: inline-block; text-align: left; } mjx-mover:not([limits="false"]) { padding-top: .1em; } mjx-mover:not([limits="false"]) > * { display: block; text-align: left; } mjx-msqrt { display: inline-block; text-align: left; } mjx-root { display: inline-block; white-space: nowrap; } mjx-surd { display: inline-block; vertical-align: top; } mjx-sqrt { display: inline-block; padding-top: .07em; } mjx-sqrt > mjx-box { border-top: .07em solid; } mjx-sqrt.mjx-tall > mjx-box { padding-left: .3em; margin-left:
Classification
Rationale: Research paper analyzing hand-coded neural network weights for sequence memorization without safety incidents.
Classifier: Qwen/Qwen3.5-35B-A3B-FP8