Coding-agent residual streams decode program properties 25 edit-steps ahead

Silva et al. train linear (logistic-regression) probes on residual-stream activations of real Qwen3.6-35B-A3B and Laguna-XS.2 running as coding agents [silva-tu-monperrus-2026-latent-programming-horizons] The probes decode well-formedness/correctness/regression properties of the program several edit-steps before the edit that produces them, from the current hidden state [silva-tu-monperrus-2026-latent-programming-horizons] Decoding AUC reaches up to ~0.83 and stays above a shuffled-label chance baseline out to roughly 25 steps ahead of materialization [silva-tu-monperrus-2026-latent-programming-horizons] The effect transfers across benchmarks without probe retraining and is purely observational, with no activation-level causal intervention [silva-tu-monperrus-2026-latent-programming-horizons]