RAG

In the world of Retrieval-Augmented Generation (RAG), most systems still treat document retrieval like a popularity contest — fetch the most relevant-looking text and hope the generator can stitch the answer together. But as any manager who has tried to merge three half-baked reports knows, relevance without completeness is a recipe for failure. A new framework, Compositional Answer Retrieval (CAR), aims to fix that. Instead of asking a retrieval model to find a single “best” set of documents, CAR teaches it to think like a strategist: break the question into its components, retrieve for each, and then assemble the pieces into a coherent whole. ...

Most “smart” RAG stacks are actually compulsive googlers: they fetch first and think later. UR² (“Unified RAG and Reasoning”) flips that reflex. It trains a model to reason by default and retrieve only when necessary, using reinforcement learning (RL) to orchestrate the dance between internal knowledge and external evidence. Why this matters for builders: indiscriminate retrieval is the silent cost center of LLM systems—extra latency, bigger bills, brittle answers. UR² shows a way to make retrieval selective, structured, and rewarded, yielding better accuracy on exams (MMLU‑Pro, MedQA), real‑world QA (HotpotQA, Bamboogle, MuSiQue), and even math. ...

When you chat with a VTuber’s AI twin or a game NPC that remembers your past adventures, breaking character can ruin the magic. Large language models (LLMs) have the raw conversational talent, but keeping them in character—especially when faced with questions outside their scripted knowledge—is notoriously difficult. AMADEUS, a new RAG-based framework, aims to fix that. The Problem with Persona Drift Most role-playing agents (RPAs) rely on a static “persona paragraph” to define who they are. Retrieval-Augmented Generation (RAG) can pull relevant persona chunks into context, but three problems persist: ...

In the world of financial AI, where speed meets complexity, most systems are either too slow to adapt or too brittle to interpret the nuanced messiness of real-world finance. Enter FinKario, a new system that combines event-enhanced financial knowledge graphs with a graph-aware retrieval strategy — and outperforms both specialized financial LLMs and institutional strategies in real-world backtests. The Retail Investor’s Dilemma While retail traders drown in information overload, professional research reports contain rich insights — but they’re long, unstructured, and hard to parse. Most LLM-based tools don’t fully exploit these reports. They either extract static attributes (e.g., stock ticker, sector, valuation) or respond to isolated queries without contextual awareness. ...

One of the most sacred assumptions in financial modeling is the existence of a traded risk-free asset. It anchors discounting, defines arbitrage boundaries, and supports the edifice of Black–Scholes. But what happens when you remove this pillar? Can we still price options, hedge risk, or extract information about funding conditions? In a striking extension of the Lindquist–Rachev (LR) framework, Ziyao Wang shows that not only is it possible — it may reveal financial dynamics that conventional models obscure. ...

The cryptocurrency market is infamous for its volatility, fragmented data, and narrative-driven swings. While traditional deep learning systems crunch historical charts in search of patterns, they often do so blindly—ignoring the social, regulatory, and macroeconomic tides that move crypto prices. Enter MountainLion, a bold new multi-agent system that doesn’t just react to market signals—it reasons, reflects, and explains. Built on a foundation of specialized large language model (LLM) agents, MountainLion offers an interpretable, adaptive, and genuinely multimodal approach to financial trading. ...

Scientific visualization has long been caught in a bind: the more complex the dataset, the more domain-specific the visualization, and the harder it is to automate. From MRI scans to hurricane simulations, modern scientific data is massive, high-dimensional, and notoriously messy. While dashboards and 2D plots have benefitted from LLM-driven automation, 3D volumetric visualization—especially in high-performance computing (HPC) settings—has remained stubbornly manual. VizGenie changes that. Developed at Los Alamos National Laboratory, VizGenie is a hybrid agentic system that doesn’t just automate visualization tasks—it refines itself through them. It blends traditional visualization tools (like VTK) with dynamically generated Python modules and augments this with vision-language models fine-tuned on domain-specific images. The result: a system that can answer questions like “highlight the tissue boundaries” and actually improve its answers over time. ...

Modern oncology is an overwhelming cognitive battlefield: clinicians face decades of fragmented notes, tests, and treatment episodes, scattered across multiple languages and formats. Large Language Models (LLMs) promise relief—but without careful design, they often collapse under the weight of these chaotic Electronic Health Records (EHRs), hallucinate unsafe recommendations, or fail to reason over time. Enter CliCARE: a meticulously designed framework that not only tames this complexity but grounds the entire decision process in clinical guidelines. Rather than stuffing raw records into long-context transformers or bolting on retrieval-augmented generation (RAG), CliCARE introduces a radically more structured approach. ...

When ChatGPT makes up a statistic or misstates a date, it’s annoying. But when a financial assistant claims the wrong interest expense or misattributes a revenue source, it could move markets or mislead clients. This is the stark reality FRED confronts head-on. FRED—short for Financial Retrieval-Enhanced Detection and Editing—is a framework fine-tuned to spot and fix factual errors in financial LLM outputs. Developed by researchers at Pegasi AI, it isn’t just another hallucination detection scheme. It’s an auditor with a domain-specific brain. ...

Retrieval-Augmented Generation (RAG) is often hailed as a cure-all for domain adaptation and factual accuracy in large language models (LLMs). By injecting external context at inference time, RAG systems promise to boost performance on knowledge-intensive tasks. But a new paper, RAG in the Wild (Xu et al., 2025), reveals that this promise is brittle when we leave the sanitized lab environment and enter the real world of messy, multi-source knowledge. ...