HARC: Coupling Harmfulness and Refusal Directions for Robust Safety Alignment

Understanding how aligned LLMs internally represent safety is critical for diagnosing alignment vulnerabilities, as it explains why jailbreaks succeed and informs the design of robust alignment strategies. Prior work shows that aligned LLMs encode harmfulness and refusal as separable directions in the residual stream at prompt-side token positions. We show that jailbreaks succeed at prompt encoding by suppressing either the refusal or harmfulness direction before any token is generated, with distinct attack classes occupying separable regions of the harmfulness-refusal plane. Extending the analysis to response-token positions, we find that the model recognizes harmful content while it is generating that content, even when it failed to recognize the input as harmful at the prompt side. Motivated by our findings, we introduce HARC (Harmfulness-And-Refusal Coupling), a fine-tuning method that pairs the two directions across both prompt and response positions. Since the intervention is confined to the harmfulness-refusal subspace, it leaves the rest of the residual stream intact and does not degrade general capability or inflate over-refusal. Across extensive experiments, HARC achieves the strongest robustness-capability-usability trade-off among six baselines spanning the major training-time and inference-time safety methods. The harmfulness and refusal directions at prompt and response positions transfer across the five model families and two scales we tested without architecture-specific tuning.

SketchColour: Channel Concat Guided DiT-based Sketch-to-Colour Pipeline for 2D Animation

The production of high-quality 2D animation is highly labor-intensive process, as animators are currently required to draw and color a large number of frames by hand. We present SketchColour, the first sketch-to-colour pipeline for 2D animation built on a diffusion transformer (DiT) backbone. By replacing the conventional U-Net denoiser with a DiT-style architecture and injecting sketch information via lightweight channel-concatenation adapters accompanied with LoRA finetuning, our method natively integrates conditioning without the parameter and memory bloat of a duplicated ControlNet, greatly reducing parameter count and GPU memory usage. Evaluated on the SAKUGA dataset, SketchColour outperforms previous state-of-the-art video colourization methods across all metrics, despite using only half the training data of competing models. Our approach produces temporally coherent animations with minimal artifacts such as colour bleeding or object deformation. Our code is available at: https://bconstantine.github.io/SketchColour .