This paper introduces CRAVE (Conversational Recommendation Agents with Collaborative Verbalized Experience), a novel framework designed to enhance Large Language Model (LLM)-based conversational recommender systems (CRSs). The core idea is to improve recommendation accuracy by leveraging implicit, personalized, and agent-specific experiences derived from historical user interactions. CRAVE achieves this by sampling trajectories of LLM agents on past queries and creating "verbalized experience banks" based on user feedback. A collaborative retriever network then helps identify relevant, preference-oriented experiences for new queries, further augmented by a debater-critic agent (DCA) system that encourages diverse recommendations through a structured debate. The research demonstrates that this approach significantly outperforms existing zero-shot LLM methods and other baselines, particularly when augmented with collaborative verbalized experience.

This paper introduces a framework for **evaluating language model benchmarks** by quantifying **signal** and **noise**. The signal measures a benchmark's capacity to differentiate between superior and inferior models, while noise reflects its susceptibility to random fluctuations during training. The authors demonstrate that a **higher signal-to-noise ratio (SNR)** correlates with more reliable small-scale experiments for predicting large model performance and that less noise leads to reduced scaling law prediction error. They propose three **interventions** to enhance SNR: **filtering noisy subtasks**, **averaging model checkpoint scores** to reduce variability, and employing **bits-per-byte (BPB)** as a more consistent evaluation metric. The research emphasizes that considering SNR is crucial for designing and selecting benchmarks that accurately guide language model development, rather than relying solely on benchmark size.

This academic paper introduces **causal adjustment for feedback loops (cafl)**, an innovative algorithm designed to mitigate the detrimental effects of feedback loops in **recommender systems**. It highlights how these systems, by influencing user behavior and then retraining on that data, can **compromise recommendation quality and homogenize user preferences**. The authors propose that reasoning about **causal quantities**—specifically, intervention distributions of recommendations on user ratings—can break these loops without resorting to random recommendations, preserving utility. Through **empirical studies** in simulated environments, cafl is shown to **improve predictive performance** and **reduce homogenization** compared to existing methods, even under conditions where standard causal assumptions like positivity are violated.

Today, instead of discussing a research paper, we review the interview by Jeff Huber, CEO of Chroma, discussing the evolution of AI search and retrieval systems. He champions "context engineering" over the widely used "RAG" (Retrieval-Augmented Generation) concept, arguing that the latter is vague and often misunderstood. Huber highlights the importance of efficiently curating information for Large Language Models (LLMs) to combat "context rot," where model performance degrades with increasing input length. The conversation also touches upon Chroma's distributed database, strategies for code indexing and retrieval, and the significance of generative benchmarking for evaluating AI systems.

We cover the comprehensive survey on the integration of personalization within Large Language Models (LLMs), specifically focusing on the evolution from Retrieval-Augmented Generation (RAG) frameworks to agent-based architectures. It systematically examines how personalization is incorporated across the pre-retrieval, retrieval, and generation stages of RAG, and extends this analysis to the more advanced functionalities of Personalized LLM-based Agents, including user understanding, planning and execution, and dynamic content generation. The survey also highlights key datasets, evaluation metrics, challenges, and future research directions in this rapidly evolving field, providing a valuable resource for researchers.

The research introduces CATE-B, an **open-source co-pilot system** designed to **simplify causal inference** for non-experts. This system **leverages large language models (LLMs)** to guide users through the complex process of estimating treatment effects from observational data. CATE-B assists in **constructing structural causal models**, **identifying robust adjustment sets** using a novel "Minimal Uncertainty Adjustment Set" criterion, and **selecting appropriate regression methods**. By integrating LLMs and causal discovery algorithms, CATE-B aims to **lower the barrier to rigorous causal analysis** and promote the widespread adoption of advanced causal inference techniques. The authors also provide a **benchmark suite** to encourage reproducibility and evaluation of LLM-augmented causal inference pipelines.

This paper introduces **text-to-text regression (RLM)** as a novel approach for **predicting system performance metrics**, particularly in complex industrial environments like **Google's Borg compute cluster**. Unlike traditional methods that struggle with non-tabular data, RLMs **directly process raw text inputs** from system logs and configuration files to deliver highly accurate floating-point predictions. The research highlights the **importance of maximizing feature observability** and **large-scale pretraining** for superior performance and **efficient adaptation to new tasks** with minimal additional data. Ultimately, this work positions RLMs as **versatile and scalable tools** for creating **universal simulators of real-world outcomes**.

This paper focuses on "**Sample More to Think Less: Group Filtered Policy Optimization for Concise Reasoning**," authored by Vaishnavi Shrivastava and five other researchers. The paper introduces **GFPO**, a method to mitigate the issue of large language models generating excessively long and verbose responses while maintaining accuracy, especially in demanding **STEM and coding tasks**. It achieves this by strategically **filtering training data based on response length and token efficiency**, demonstrating a trade-off where **increased training computation leads to reduced inference-time computation**. The page also provides various **bibliographic tools, code links, and experimental project information** related to the paper and the arXiv platform.

This academic paper introduces **DINOv3**, a significant advancement in **self-supervised learning (SSL)** for computer vision models. It highlights how **SSL enables training on vast raw image datasets**, leading to versatile and robust "foundation models" that generalize across diverse tasks without extensive fine-tuning. A key innovation is **Gram anchoring**, a novel training strategy that addresses the degradation of dense feature maps often seen in large-scale models, ensuring DINOv3 excels in both high-level semantic and precise geometric tasks. The paper also explores **architectural scaling to a 7-billion parameter model**, data curation techniques, and post-training stages like **resolution adaptation, model distillation**, and **text alignment**, showcasing DINOv3's superior performance across various benchmarks, including object detection, semantic segmentation, and even geospatial applications.

This paper introduces **Agent Lightning**, a novel framework designed to enhance the training of **Large Language Models (LLMs)** within **AI agents** using **Reinforcement Learning (RL)**. A key innovation is the **complete decoupling** of agent execution from the RL training process, allowing for seamless integration with existing agents without significant code changes. This is achieved by formulating agent execution as a **Markov Decision Process (MDP)**, which defines a **unified data interface** to transform agent trajectories into training transitions. The framework also proposes **LightningRL**, a hierarchical RL algorithm, and a **Training-Agent Disaggregation architecture** to standardize the training service, proving its efficacy across various tasks like text-to-SQL and retrieval-augmented generation.