INNSteer learns an invertible neural network to map LLM activations into a latent space where linear steering becomes more effective, then applies the inverse map to produce nonlinear interventions in the original space.
Adaptive activation steering: A tuning-free LLM truthfulness improvement method for diverse hallucinations categories.Proceedings of The Web Confer- ence (WWW)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
TRACE uses cross-layer candidate trajectories inside frozen LLMs to dynamically select and apply one of three correction operators, delivering mean gains of +12.26 MC1 and +8.65 MC2 points across 15 models and 3 benchmarks with no regressions.
citing papers explorer
-
Beyond Linear Activation Steering: Invertible Latent Transformations for Controlling LLM Behavior
INNSteer learns an invertible neural network to map LLM activations into a latent space where linear steering becomes more effective, then applies the inverse map to produce nonlinear interventions in the original space.
-
TRACE: Trajectory Correction from Cross-layer Evidence for Hallucination Reduction
TRACE uses cross-layer candidate trajectories inside frozen LLMs to dynamically select and apply one of three correction operators, delivering mean gains of +12.26 MC1 and +8.65 MC2 points across 15 models and 3 benchmarks with no regressions.