# Symbolic Learning Enables Self-Evolving Agents

**Agent symbolic learning** represents a **systematic framework** that propels language agents towards **autonomy** by enabling them to **optimize themselves** using symbolic optimizers, moving beyond the need for extensive manual engineering.

# Claude 3.5 Sonnet

**Claude 3.5 Sonnet** emerges as the **new leading large language model (LLM)**, outperforming its predecessors in speed, cost-efficiency, and intelligence, available on Claude.ai and its iOS app with an API cost of **$3 per million input tokens** and **$15 per million output tokens**.

# Show HN: R2R V2 – An open source RAG engine with prod features

**R2R is an open-source Retrieval-Augmented Generation (RAG) system**, designed to facilitate the transition from local LLM experimentation to scalable, production-ready applications, featuring a RESTful API for ease of integration.

# How to think about creating a dataset for LLM fine-tuning evaluation

Alex Strick van Linschoten emphasizes the **importance of evaluating fine-tuned LLMs** for accuracy by comparing predictions against structured data derived from press releases, aiming to move beyond gut feelings to **quantifiable metrics**.

# AI Revolutionized Protein Science, but Didn't End It

**Google DeepMind's AlphaFold2** significantly advanced protein science by predicting protein structures with over 90% accuracy, yet it did not fully solve the protein folding problem, highlighting the distinction between predicting protein structures and understanding the folding process.

# [R] Are Language Models Actually Useful for Time Series Forecasting?

**Removing or replacing the LLM component** in time series forecasting methods **does not degrade results**, often **improving them** instead, challenging the utility of LLMs in this domain.

# Compressing graphs and indexes with recursive graph bisection (2016)

**Recursive graph bisection** significantly **improves compression** of graphs and indexes by enhancing data locality, extending the theoretical model of Chierichetti et al. from KDD 2009.

# [R] Interpretability research in LLMs

**Most interpretability research in LLMs** has pivoted towards **mechanistic interpretability**, diverging from traditional methods like counterfactuals and saliency maps.

# Optimized Nonlinear Regression Using Data Clustering [P]

**Clustering data into smaller groups** before forming a regression significantly **reduces computation time**, offering a **novel approach** to nonlinear regression optimization.

# YannicKilcher - Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools (Paper Explained)

**Researchers from Stanford and Yale** have critically evaluated the **accuracy of AI legal research tools**, focusing on their propensity for **"hallucinations"**—the tendency to generate incorrect or misleading information.

# Into the Omniverse: SyncTwin Helps Democratize Industrial Digital Twins With Generative AI, OpenUSD

**SyncTwin GmbH** leverages **OpenUSD** and **NVIDIA's technologies** to create digital twins that optimize industrial efficiency and sustainability.

# 🤗Welcome Gemma 2 - Google's new open LLM

**Google's Gemma 2** introduces **two model sizes** with **9 billion and 27 billion parameters**, both available in **base and instruction-tuned versions**, showcasing advancements in **data training volume** and **model performance**. [Models on the Hub](https://huggingface.co/collections/google/g-667d6600fd5220e7b967f315)

# Research Focus: Week of June 24, 2024

**RENC, a system designed by Microsoft researchers, significantly reduces CPU power consumption in 5G vRAN servers by up to 45%** by dynamically adjusting CPU frequency based on cellular workload variations, demonstrating a blend of innovative techniques for energy efficiency. [Read the paper](https://www.microsoft.com/en-us/research/publication/towards-energy-efficient-5g-vran-servers/)

# Selective Prompting Tuning for Personalized Conversations with LLMs

**Selective Prompt Tuning (SPT)** addresses the challenge of integrating persona profiles into conversations by **softly prompting LLMs**, overcoming the limitations of textual prompting and direct fine-tuning.

# AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning

**AdaZeta framework** enhances **memory efficiency** in fine-tuning large language models (LLMs) by introducing a **low-parameter tensorized adapter** and an **adaptive query number schedule**, addressing the limitations of previous Memory-efficient Zeroth-order (MeZO) methods.