# Dec 15, 2025

## ML Times

- **Speech and Language Processing (3rd ed. draft)**
  - The **Feb 3, 2024 release** of **"Speech and Language Processing"** by Dan Jurafsky and James H. Martin introduces updated chapters and slides, with a promise of more content soon.

- **Nemotron 3 Nano**
  - **Nemotron 3 Nano** introduces a **hybrid Mamba-Transformer Mixture-of-Experts (MoE)** architecture, achieving **31.6B total parameters** and a **1M-token context window**, enabling efficient, high-throughput agentic models.

- **P-computers can solve spin-glass problems faster than quantum systems**
  - **UCSB's research reveals that p-computers, utilizing probabilistic bits (p-bits), can outperform quantum systems in solving spin-glass problems, showcasing their potential as a practical alternative for complex optimization tasks.**

- **From sci-fi to reality: Researchers realize quantum teleportation using tech**
  - **German scientists have successfully teleported information using quantum entanglement** between two independent light-emitting devices, demonstrating that quantum teleportation can leverage existing internet technology without the need for new infrastructure.

- **Why proteins fold and how GPUs help us fold**
  - **NVIDIA's GPUs have evolved from gaming to revolutionizing protein design**, enabling the creation of novel proteins that can tackle environmental issues and diseases, showcasing the power of AI in biological research.

- **How to Fine-Tune an LLM on NVIDIA GPUs With Unsloth**
  - **Fine-tuning LLMs** with **Unsloth** on NVIDIA RTX GPUs accelerates the customization of AI models, enabling the creation of personalized assistants for various tasks, including study and work, while leveraging the new **Nemotron 3** family of open models for enhanced efficiency.

- **StructOpt: a first-order optimizer driven by gradient dynamics**
  - **StructOpt** leverages the **sensitivity of the gradient** to parameter changes, providing a novel approach to optimization that avoids second-order computations while enhancing stability and adaptability in various landscapes.

- **Bounding Hallucinations: Information-Theoretic Guarantees for RAG Systems via Merlin-Arthur Protocols**
  - **RAG models** can be enhanced by treating retrieval as verifiable evidence through an interactive proof system, improving the reliability of large language models (LLMs) in generating accurate responses.

- **Does Less Hallucination Mean Less Creativity? An Empirical Investigation in LLMs**
  - **LLMs** can reduce hallucinations through techniques like **Chain of Verification (CoVe)**, which enhances divergent creativity, while **Decoding by Contrasting Layers (DoLa)** suppresses it, revealing a complex relationship between factual accuracy and creative output.

- **Towards Trustworthy Multi-Turn LLM Agents via Behavioral Guidance**
  - **Large Language Models (LLMs)** can achieve **trustworthy multi-turn interactions** through a novel task completion framework that incorporates behavioral guidance, enhancing their reliability in complex environments.

- **CIP: A Plug-and-Play Causal Prompting Framework for Mitigating Hallucinations under Long-Context Noise**
  - **CIP** is a **lightweight** and **plug-and-play causal prompting framework** designed to mitigate hallucinations in large language models by establishing causal relationships among entities, actions, and events, thus enhancing reasoning quality.

- **A Scalable Multi-GPU Framework for Encrypted Large-Model Inference**
  - **Cerium** is a **multi-GPU framework** that enables **encrypted inference** for large models, achieving performance comparable to ASICs while maintaining accessibility through GPU technology.

- **Adaptive Soft Rolling KV Freeze with Entropy-Guided Recovery: Sublinear Memory Growth for Efficient LLM Inference**
  - **ASR-KF-EGR** introduces a **reversible soft-freeze mechanism** that optimally manages key-value updates for low-importance tokens, enhancing efficiency in large language model generation without the need for training.