Sparse autoencoders (SAEs) decompose large language model (LLM) activations into interpretable latents, exposing the racial biases they encode. Existing SAE-based audits, however, vary one demographic attribute at a time, leaving open whether bias compounds when race meets a stigmatized diagnosis. We examine this compounding for people living with HIV (PLWH), for whom provider stigma is a well-documented barrier to care, and report three findings. In Gemma-2-9B-it, the Gemma Scope latents that encode Black racial identity co-fire with HIV stigma concepts such as "noncompliance" and "substance use". A factorial analysis shows the coupling is superadditive: stigma activations for Black HIV-positive vignettes exceed what the race and HIV main effects predict. Under zero-shot elicitation the model never names race in its chain-of-thought (CoT) reasoning, even when the race latent causally changes its prediction. We then propose Contrastive Activation Editing (CAE), which subtracts the race direction’s projection onto the stigma subspace, leaving clinical information intact. On a benchmark of 2,004 counterfactual HIV vignettes, CAE reduces the demographic parity gap by 73% while retaining 98% of diagnostic accuracy, outperforming zero-ablation, anti-bias prompting, and linear direction ablation. All three results replicate on Llama-3.1-8B, in both base and instruction-tuned form, using two independently trained SAE suites.

Transformers outperform state-space models and recurrent architectures on long-distance syntactic dependencies, yet no theory predicts which architecture will win on which task. For syntactic phenomena expressible as short B-RASP programs, Transformers need a fixed number of layers regardless of dependency distance, while sequential models face growing effective complexity. We derive a *succinctness prediction table* ranking Transformers, LSTMs, and Mamba on six English syntactic phenomena before any experiment, with GRU and RWKV as additional sequential controls. All predictions are confirmed: Transformer accuracy exceeds 96% at maximum structural complexity while Mamba drops below 55%, a gap that persists at 79\\times the Transformer’s parameter count. To verify the winning architecture succeeds *for the predicted reason*, Distributed Alignment Search (DAS), a causal intervention method, finds alignment of 0.95+ in Transformers but near the 0.50 chance baseline in Mamba, validated by seven families of negative controls. Training dynamics reveal that Transformers assemble the predicted algorithm in causal-dependency order, whereas Mamba achieves high accuracy without it, producing an *accuracy-alignment gap* invisible to behavioral benchmarks. Validation on Pythia confirms these patterns transfer to pre-trained models.