LLM

The GUI Agent Bottleneck: Stuck in Sparse Feedback Training LLM-based GUI agents to complete digital tasks—such as navigating mobile apps or automating workflows—faces a fundamental limitation: reward sparsity. Traditional reward formulations (Outcome Reward Models, or ORMs) provide feedback only at the end of a trajectory. If the task fails, the agent receives zero signal, regardless of how many useful intermediate steps it took. This severely limits credit assignment and slows learning, especially in environments with long action horizons. ...

When it comes to real-world problem solving, today’s LLMs face a critical dilemma: they can solve textbook problems well, but stumble when confronted with messy, open-ended challenges—like optimizing traffic in a growing city or managing fisheries under uncertain climate shifts. Enter ModelingAgent, an ambitious new framework that turns this complexity into opportunity. What Makes Real-World Modeling So Challenging? Unlike standard math problems, real-world tasks involve ambiguity, multiple valid solutions, noisy data, and cross-domain reasoning. They often require: ...

In the quest to make AI more trustworthy, few challenges loom larger than explaining sequential decision-making algorithms like Monte Carlo Tree Search (MCTS). Despite its success in domains from transit scheduling to game playing, MCTS remains a black box to most practitioners, generating decisions from expansive trees of sampled possibilities without accessible rationale. A new framework proposes to change that by fusing LLMs with formal logic to bring transparency and dialogue to this crucial planning tool1. ...

Generative AI is often praised for its creativity—composing symphonies, painting surreal scenes, or offering quirky new business ideas. But in some contexts, especially research and data processing, consistency and accuracy are far more valuable than imagination. A recent exploratory study by Utrecht University demonstrates exactly where Large Language Models (LLMs) like Claude 3 Opus shine—not as muses, but as meticulous clerks. When AI Becomes the Analyst The research project explores three different use cases in which generative AI was employed to perform highly structured research data tasks: ...

Traces of War: Surviving the LLM Arms Race The AI frontier is heating up—not just in innovation, but in protectionism. As open-source large language models (LLMs) flood the field, a parallel move is underway: foundation model providers are fortifying their most powerful models behind proprietary walls. A new tactic in this defensive strategy is antidistillation sampling—a method to make reasoning traces unlearnable for student models without compromising their usefulness to humans. It works by subtly modifying the model’s next-token sampling process so that each generated token is still probable under the original model but would lead to higher loss if used to fine-tune a student model. This is done by incorporating gradients from a proxy student model and penalizing tokens that improve the student’s learning. In practice, this significantly reduces the effectiveness of distillation. For example, in benchmarks like GSM8K and MATH, models distilled from antidistilled traces performed 40–60% worse than those trained on regular traces—without harming the original teacher’s performance. ...

The next generation of quantitative investment agents must be more than data-driven—they must be logic-aware and structurally adaptive. Two recently published research efforts provide important insights into how reasoning patterns and evolving workflows can be integrated to create intelligent, verticalized financial agents. Kimina-Prover explores how reinforcement learning can embed formal reasoning capabilities within a language model for theorem proving. Learning to Be a Doctor shows how workflows can evolve dynamically based on diagnostic feedback, creating adaptable multi-agent frameworks. While each stems from distinct domains—formal logic and medical diagnostics—their approaches are deeply relevant to two classic quant strategies: the Black-Litterman portfolio optimizer and a sentiment/technical-driven Bitcoin perpetual futures trader. ...

What if your business processes could think like your most experienced employee—recalling similar past cases, adapting on the fly, and explaining every decision? Welcome to the world of CBR-augmented LLMs: where Large Language Models meet Case-Based Reasoning to bring Business Process Automation (BPA) to a new cognitive level. From Black Box to Playbook Traditional LLM agents often act like black boxes: smart, fast, but hard to explain. Meanwhile, legacy automation tools follow strict, rule-based scripts that struggle when exceptions pop up. ...

The AI world has always been unusual, but starting in early 2025, it became increasingly so. LLM developers began releasing and updating models at unprecedented paces, while more giants and startups joined the AI rush—from foundational generative models (text, image, audio, video) to specific applications. It’s a new kind of gold rush, but fueled by GPUs and transformer architectures. On February 1st, DeepSeek released its open-source model DeepSeek R1, quickly recognized for rivaling—or even exceeding—the reasoning power of ChatGPT-o1. The impact was immediate. Just days later, a screenshot from Reddit showed Sam Altman, CEO of OpenAI, admitting: ...

📺 Watch this first: Jimmy O. Yang on “Guess How Much” “Because the art is in the savings — you never pay full price.” 💬 “Guess How Much?” — A Philosophy for AI Developers In his stand-up comedy, Jimmy O. Yang jokes about how Asian families brag not about how much they spend, but how little: “Guess how much?” “No — it was $200!” It’s not just a punchline. It’s a philosophy. And for developers building LLM-powered applications for small businesses or individual users, it’s the right mindset. ...

A multilingual large language model developed by the BigScience initiative, capable of generating text in 46 languages and 13 programming languages.