what we measured
One question per entry, a measurement against controls, and what did not work. Code and data are open: our numbers can be recomputed with one command.
In our candidate matching, nearly half of what reaches the expensive LLM stage is garbage. We tried GPU rerankers, CrossEncoder fine-tuning and feature combos to filter it cheaper. The simplest signal won — skill overlap. Here are the numbers, what worked, and why text lost to skills.
Authors: slavb18
An engineering deep-dive: a council of LoRA experts that exchange hidden states instead of text, and a decoder that reads those thoughts back into text. With the war stories, the measurements, and the honest negative results.
Authors: slavb18
We took the autoencoder from the recent CALM (Continuous Autoregressive Language Models) work, which learns to pack a chunk of K=4 tokens into a single continuous vector and unpack it back, and trained it not on 15 billion Pile tokens on 8 GPUs, as in the original, but on 18 thousand short strings of requirements from IT job postings - on a regular machine without a graphics card. Along the way, we encountered three classic pitfalls (flash-attn without CUDA, deepspeed that doesn't import under NumPy 2.x, and a silent OOM on 33 GB of logits). Below is a detailed analysis of the architecture, configurations, and honest round-trip reconstruction results.
Authors: slavb18