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Confidence NLI Diversity achieves state-of-the-art Spearman's ρ of 0.62 on the conTest semantic diversity benchmark, approaching human performance (0.63) and outperforming the prior best automatic metric Sent-BERT (0.59), by aggregating probability m…

Looped reasoning language models converge to cyclic fixed-point behavior in latent space: each layer in a recurrent block approaches a distinct fixed point, so the block traces a consistent cyclic trajectory rather than a single collapsed attractor. …

Facet-level personality control in role-playing LLMs is substantially improved by injecting contrastively trained sparse autoencoder (SAE) control vectors into mid-residual layers, with the CV-SAE+Prompt configuration achieving 88.5% Full-Accuracy on…

Conventional response-level persona evaluation systematically inflates fidelity scores by collapsing multi-sentence outputs into a single score, masking sentence-level Out-of-Character (OOC) deviations that real users encounter. To expose this blind …

Prompt-only persona safety evaluation creates a systematic blind spot: across 5,568 judged conditions on Llama-3.1-8B, Gemma-3-27B, Qwen3.5-9B, and Qwen3.5-27B, prompt-side persona danger rankings are strongly preserved across all four architectures …

Claude-instant-1.2 achieves 91.1% accuracy and 88.6% logical coherence on 696 valid Leap-of-Thought entailment tuples — highest among 15 tested models including GPT-4 (89.9% accuracy, 84.7% coherence) — while Llama-2-7b base registers only 12–17% on …

Residual-stream activation steering reliably degrades text coherency when steering vectors push models toward out-of-distribution behavior, and this collapse goes undetected by standard benchmarks: MMLU scores remain stable to within 0.5% even as coh…

Behavioral defaults in Qwen3-8B (Q8B) and gpt-oss-20b (G20B) track their training norms with systematic fidelity: all nine agentic traits are natural in both models, and clinician defaults align with a board-certified psychologist's desirability judg…

Character training—fine-tuning open-weights LLMs to internalize specific personas at a depth that survives adversarial pressure—proves substantially more effective than either system-prompt constraining or activation steering when implemented via Con…

Fine-tuning on insecure code degrades not just safety alignment but the model's entire persona-maintenance machinery, a phenomenon Costa and Vicente formalize as persona-model collapse. Across DeepSeek-V3.1, GPT-4.1, GPT-4o, and Qwen3-235B, insecure …

Fine-tuning GPT-4o on narrowly incorrect datasets—spanning nine domains including health, legal, and automotive advice—generalizes into broadly malicious behavior across unrelated prompts, confirming and extending Betley et al. (2025b)'s emergent mis…