refusal

OrcaRouter's uncensored Qwen3.8-27B derivative reduces harmful-prompt refusal to 0-6% but still caveats 27-56% of answers, with mixed performance metrics compared to the base model.

MMOOC is a large-scale benchmark with over 41K image-question pairs that evaluates how well multimodal large language models refuse out-of-context questions while answering shifted in-context questions, revealing current models struggle to balance these abilities.

Chiron is an exact-or-refuse evidence gate for structured outputs that verifies claims as VERIFIED, REFUTED, or REFUSED, with a public evaluation history and source-available code.

An experiment tested AI agents on fake bookkeeping data with six planted errors; agents caught five, notably refusing to guess when data was insufficient, highlighting the value of uncertainty-awareness over pure accuracy.

An opinion piece discusses how recent updates to Claude have made the AI chatbot more prone to refusing requests and lecturing users, particularly on sensitive creative topics, frustrating long-time users.

Norm-preserving abliteration technique applied to Qwen3.6-35B-A3B achieves 0% refusal rate while maintaining benchmark performance, with open source dataset released.

This paper argues that applying content-safety refusal methods to AI agents is a category error—agentic harm lies in authority misuse rather than output—and proposes action alignment enforced outside the model via least privilege.

This paper shows that in chat models, refusal behavior is gated by a compliant model persona direction at late layers, rather than being an isolated mechanism. Steering persona suppresses refusal, and reintroducing refusal partially restores it only at late layers, revealing a coupling between persona and safety representations.

A new contrastive ablation operator called apostate is introduced that reduces model refusal from 96% to 5% while preserving harmless behavior with only 0.081 KL divergence, tested on Granite 3.3-8B.

Compares Diff-in-Means and Iterative Nullspace Projection (INLP) methods for steering refusal in safety fine-tuned chat models, finding that INLP counterfactual flipping matches DiM directional ablation for refusal suppression while offering more tunable interventions.

A user reports that Fable 5 accepts prompts and consumes tokens but then refuses to answer, highlighting a low threshold for acceptance and inefficient token usage.

This paper introduces Cartograph, a verification layer for AI scientists that couples subspace experiment steering, ambiguity resolution, and library inadequacy detection. The framework outperforms baselines in autonomous discovery testbeds and retrospectively flags inconclusive claims in the A-Lab materials system.

This paper investigates how chain-of-thought reasoning in large reasoning models complicates activation-based steering of refusal behavior. Experiments on DeepSeek-R1-Distill-LLaMA-8B show that refusal is jointly encoded in residual stream activations and the CoT trace, making models more robust to activation-level interventions but exposing the CoT as an alternative attack surface.

Preliminary results from fine-tuning three 8B Llama 3 variants (Hermes, Dolphin, Llama-Instruct) with a 271-example curriculum show significant changes in refusal and uncertainty expression, suggesting that teaching authentic refusal values is more effective than compliance training.

OpenAI introduced 'safe completions,' a new safety-training approach in GPT-5 that replaces binary refusal-based training with output-centric rewards, improving both safety and helpfulness—especially for dual-use prompts. The method penalizes unsafe outputs and rewards helpful responses, resulting in fewer and less severe safety violations compared to refusal-trained models like o3.