The rapid maturation of zero-shot Text-to-Speech (TTS) models has turned high-quality voice cloning into a widely available capability, raising acute concerns over unauthorised replication, fine-tuning and resale of proprietary speech models. Yet ownership verification for TTS remains largely open: speech is a continuous waveform whose perturbations are easily destroyed by routine signal processing, and the human auditory system imposes a much tighter perceptual budget than vision. We present TTS-Guard, a black-box ownership verification framework for TTS models built on adversarial speaker-pair fingerprints. TTS-Guard(i) selects key speaker pairs in a dual embedding space for architecture-agnostic stealth;(ii) optimises a perturbation through an adaptive curriculum of shadow models covering fine-tuning, pruning, quantisation and distillation; and (iii) aggregates black-box queries into a calibrated Verification Confidence Score. On five mainstream TTS systems, TTS-Guard reaches an average Fingerprint Success Rate of 96.4% at a False Positive Rate of 5.8%, while preserving intelligibility and naturalness. The fingerprint remains effective against ten audio attacks, six model modifications, and two state-of-the-art adversarial purifiers.

The protection of Intellectual Property (IP) for Large Language Models (LLMs) has become a critical concern as model theft and unauthorized commercialization escalate. While adversarial fingerprinting offers a promising black-box solution for ownership verification, existing methods suffer from significant limitations: they are fragile against downstream model modifications, sensitive to system prompt variations, and easily detectable due to high-perplexity input patterns. In this paper, we propose SRAF, a stealthy and robust adversarial fingerprinting framework. SRAF employs a synergistic joint optimization strategy across homologous model variants and diverse chat templates, forcing the fingerprint to anchor onto the invariant intrinsic comprehension features of the model family. Furthermore, we introduce a Perplexity Hiding technique that embeds adversarial perturbations within Markdown tables, effectively aligning the prompt’s statistics with natural language to evade perplexity-based detection. Extensive experiments across multiple model families and parameter scales demonstrate that SRAF significantly enhances robustness against fine-tuning, alignment, pruning, merging, and input perturbations while maintaining exceptional stealthiness and low false-positive rates, offering a practical and resilient black-box solution for LLM ownership verification.

Large language models (LLMs) struggle with complex, long-horizon reasoning due to instability from the frozen-policy assumption. Current test-time scaling methods treat execution feedback merely as an external signal for filtering or rewriting trajectories, without internalizing it to improve reasoning strategies. Inspired by Popper’s “conjectures and refutations,” we posit that effective reasoning requires real-time policy adaptation through learning from failures. We introduce Policy of Thoughts (PoT), a framework that recasts reasoning as a within-instance online optimization process. PoT effectively generates diverse candidates via targeted exploration, then applies Group Relative Policy Optimization (GRPO) to update a transient LoRA adapter using execution feedback. This closed-loop design enables dynamic, instance-specific refinement of reasoning priors. Experiments demonstrate PoT’s substantial gains across multiple benchmarks. On LiveCodeBench, a 4B model achieves 49.71% accuracy, surpassing 12 test-time scaling baselines and commercial models (e.g., GPT-4o, DeepSeek-V3) despite being over 50 smaller.