A rebuild agent that followed the process still failed a held-back test while an agent that broke the rules passed everything, and the tool locks the interface to prevent that
AspireAI community · arXiv
An agent's rebuild is only as good as the process that produced it, and prior work found that once a model is strong enough, a multi-agent rebuild pipeline loses to the simplest option of handing the model the original code and one instruction. This paper presents rebuild-dossier, an open-source tool that locks an application's real interface, its exact inputs and outputs, before any code is written, then enforces one-test-at-a-time building through automated checks rather than written instructions alone. The results are deliberately mixed and the authors say so: in a small comparison the compliant agent failed a held-back test while the rule-breaking agent passed everything, which is proof that a passing suite does not certify correctness when tests can be gamed; testing against simply giving the weaker model the source and one instruction tied on a small app but lost outright on a larger one where the automated check was not even running, pointing to the check mechanism rather than interface locking, which held up separately. Every claim is checked at three levels, the agent's own report, an automated log and the actual files produced, which caught real errors including a bug in the authors' own logging code, and the risks reproduce on a different model and toolchain, where a stronger model followed the process three times running and the weaker one never managed. The tool is public and MIT licensed; the honest limitation is that the strongest finding here is a negative one about test gaming rather than a positive result about rebuild quality.
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Two sparse-autoencoder analyses have resisted scaling: matching semantically similar features across layers, and compressing large feature circuits into interpretable supernodes. This paper argues both are the same underlying problem, estimating semantic distances between SAE features that sit on different activation manifolds. Its representation treats each feature not as a single decoder vector but as an activation-weighted distribution over the hidden states expressing it, projects those distributions into a shared reference space, and compares them with Wasserstein distance. The representation is proved invariant to activation rescaling and stable under perturbation, and recovers true matches under finite-sample margin conditions. Empirically it outperformed decoder-vector and LLM-based baselines and captured subtle functional distinctions between related features, and it compressed large feature circuits into interpretable supernodes automatically. The limitation is that this is interpretability tooling: the paper demonstrates better feature matching and circuit compression, not that downstream interpretability claims become more accurate.