LLM

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. ...

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. ...

In quantitative finance, sparse portfolio optimization is a famously unforgiving problem. Selecting the top m assets from a universe of n under budget and risk constraints is NP-hard, highly sensitive to hyperparameters, and often brittle in volatile markets. Traditional solutions—from greedy algorithms to convex relaxations—either crumble under market shifts or produce opaque, overfitted outputs. But what if we reframed the problem entirely? Enter EFS (Evolutionary Factor Search), a radical new framework that turns sparse portfolio construction into an LLM-guided ranking game. Instead of laboriously tuning machine learning models or relying on rigid heuristics, EFS lets large language models generate, evolve, and select alpha factors—and it does so in a way that is not just automated, but interpretable, adaptive, and surprisingly effective. ...

Financial markets don’t reward the loudest opinions. They reward the most timely, well-calibrated ones. FinDPO, a new framework by researchers from Imperial College London, takes this lesson seriously. It proposes a bold shift in how we train language models to read market sentiment. Rather than relying on traditional supervised fine-tuning (SFT), FinDPO uses Direct Preference Optimization (DPO) to align a large language model with how a human trader might weigh sentiment signals in context. And the results are not just academic — they translate into real money. ...

In the age of Digital Twins and hyper-automated warehouses, simulations are everywhere—but insights are not. Discrete Event Simulations (DES) generate rich, micro-level data on logistics flows, delays, and resource utilization, yet interpreting these data remains painfully manual, fragile, and siloed. This paper from Quantiphi introduces a compelling solution: transforming raw simulation outputs into a Knowledge Graph (KG) and querying it via an LLM agent that mimics human investigative reasoning. It’s a shift from spreadsheet-style summaries to an interactive AI assistant that explains why something is slow, where the bottleneck is, and what needs attention. ...

Imagine asking, “Why wasn’t Order A scheduled for production yesterday?” and getting not just an answer, but a causal breakdown, an alternative plan, and a visual comparison — all without involving your operations research (OR) consultant. That’s exactly what SMARTAPS delivers. Built by Huawei researchers, SMARTAPS is a tool-augmented LLM interface for interacting with Advanced Planning Systems (APS) using natural language. It doesn’t try to replace optimization solvers — it simply makes them accessible. In doing so, it redefines how planners interact with complex decision-making models. ...

In the age of natural language interfaces to databases (NLIDBs), asking the right question has never been easier—or more perilous. While systems like ChatGPT or SQL-Palm can convert everyday English into valid SQL, they often do so without interrogating the quality of the question itself. And as Peter Drucker warned, “The most dangerous thing is asking the wrong question.” Enter VeriMinder, a system built not to improve SQL syntax or execution accuracy—but to diagnose and refine the analytical intent behind the user’s query. It tackles a deceptively simple yet far-reaching problem: a well-formed SQL query that answers a poorly formed question can yield confident but misleading insights. This is particularly problematic in enterprise settings where non-technical users rely on LLM-based BI assistants. ...

When a large language model (LLM) answers your question with a high degree of confidence, do you trust it? What if it’s wrong—but still confident? The stakes are high in real-world applications, from legal guidance to enterprise decision support. Yet today’s LLMs remain notoriously unreliable in aligning their confidence with correctness. The paper Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with Constraints (Yin et al., 2025) offers a bold response: rewire LLMs to be reasoning-primary and information-secondary. Instead of front-loading search and passively absorbing evidence, Deliberative Searcher acts more like a prudent investigator: it thinks, self-assesses, retrieves external information only when needed, and calibrates its confidence step-by-step. Crucially, it learns this behavior through a custom constrained reinforcement learning regime. ...

What if we stopped asking language models to “be creative”—and instead let them explore creativity the way humans brainstorm: by remixing ideas, nudging boundaries, and iterating through meaningful variations? That’s exactly what Large Language Models as Innovators proposes: a novel framework that leverages the latent embedding space of ideas—not prompts—to drive controlled, domain-agnostic creativity. Rather than relying on handcrafted rules or complex prompting tricks, the authors show how LLMs can generate original and relevant ideas by interpolating between known concepts, evaluating results, and refining outputs over time. ...

Most real-time financial systems rely on deep stacks of infrastructure, from custom APIs to cloud VMs and high-frequency data ingestion pipelines. But what if a single developer could deploy a daily-updating, AI-powered stock analysis engine without a single server? That’s exactly what Taniv Ashraf set out to do — and accomplished — in his recent case study on a fully serverless architecture using Google Gemini, GitHub Actions, and static web hosting. The result is an elegantly simple yet conceptually powerful demonstration of how qualitative LLM analysis and automation tools can replace entire categories of financial tooling — if wielded strategically. ...