LLMs

Most large language models (LLMs) are trained as if every piece of data is equally nutritious. But just as elite athletes optimize not just what they eat but when and how they eat it, a new paper proposes that LLMs can perform better if we learn to dynamically adjust their data “diet” during training. The Static Selection Problem Traditional data selection for LLMs is front-loaded and fixed: you decide what data to keep before training, often using reference datasets (e.g., Wikipedia) or reference models (e.g., GPT-3.5) to prune the lowest-quality examples. While effective in reducing cost, this approach ignores a key insight: an LLM’s preference for certain types of data evolves over time. ...

Scientific communication has always suffered from the tyranny of static text. Even the most revolutionary ideas are too often entombed in dense LaTeX or buried in 30-page PDFs, making comprehension an uphill battle. But what if your next paper—or internal training doc—could explain itself through animation? Enter Manimator, a new system that harnesses the power of Large Language Models (LLMs) to transform research papers and STEM concepts into animated videos using the Manim engine. Think of it as a pipeline from paragraph to pedagogical movie, requiring zero coding or animation skills from the user. ...

What if your AI model isn’t just answering questions, but living in its own version of time? A new paper titled The Other Mind makes a bold claim: large language models (LLMs) exhibit temporal cognition that mirrors how humans perceive time — not through raw numbers, but as a subjective, compressed mental landscape. Using a cognitive science task known as similarity judgment, the researchers asked 12 LLMs, from GPT-4o to Qwen2.5-72B, to rate how similar two years (like 1972 and 1992) felt. The results were startling: instead of linear comparisons, larger models automatically centered their judgment around a reference year — typically close to 2025 — and applied a logarithmic perception of time. In other words, just like us, they feel that 2020 and 2030 are more similar than 1520 and 1530. ...

In an age where aging infrastructure meets accelerating AI, a new paper out of George Mason University proposes a novel question: Can large language models interpret what even seasoned engineers find difficult — NDE contour maps of bridges? The answer, based on this pilot study, is a cautious but resounding yes — with caveats that echo through the entire field of AI-assisted engineering. The Problem: Data Is There — Expertise Isn’t Always Bridges are scanned using advanced non-destructive evaluation (NDE) tools — Ground Penetrating Radar (GPR), Electrical Resistivity (ER), Impact Echo (IE), and Ultrasonic Surface Waves (USW) — but interpreting those outputs requires human expertise, which is not always available, especially during emergency assessments or in rural areas. Contour maps from these tools don’t speak for themselves. ...

When it comes to trading the S&P 500, tradition says: trust the chart. But a new study from UCLA researchers proposes a smarter compass—one that listens not only to price momentum but also to the tone of the news. By merging language model-powered sentiment scores with technical indicators and time-series forecasting, the authors build a hybrid strategy that outperforms a buy-and-hold baseline during a volatile 3-month window. ...

What makes code feel like it was written by a student? Not just errors, but how they evolve. Not just style, but how it diverges from the polished norms. This week’s standout paper, ParaStudent, tackles a refreshingly underexplored challenge: teaching LLMs to generate code that learns like a student — messy, iterative, full of hiccups and growth. Instead of building yet another high-performing code assistant, the authors fine-tune LLMs to mimic real students in an introductory CS class at UC Berkeley. They call their framework ParaStudent. The goal: replace idealized solutions with something plausibly human — an LLM that stumbles, recovers, and grows in fidelity to how novices actually write code. ...

When it comes to software development, coding is optional — debugging is inevitable. And yet, most AI code tools today act like overconfident interns: quick to suggest, but clueless when the system breaks. Kodezi Chronos flips that script. Instead of trying to stretch token windows to a million and hoping for the best, Chronos builds an entirely new foundation for debugging: persistent memory, adaptive retrieval, and autonomous iteration. Beyond Token Stuffing: Why Context Windows Miss the Point Large Language Models like GPT-4 and Claude 3 boast massive context windows — 128K, 200K, even a million tokens. But real-world debugging rarely needs to read the whole repository at once. It needs to find the right needle in a messy, multi-decade haystack, then trace its thread through historical commits, CI logs, and edge-case test failures. ...

In the world of AI benchmarks, most roads lead to flashy competitions: solving coding puzzles, climbing Codeforces ratings, or passing Olympiad-level problems. But a new benchmark — FormulaOne — changes the race. It doesn’t ask, “Can you win a medal?” It asks, “Can you think like a researcher?” And the answer from today’s frontier LLMs? A resounding no. From Codeforces Champs to Research Rookies The authors of FormulaOne strip away the glitz of competitive programming and delve into something far more consequential: research-grade algorithmic problems grounded in Monadic Second-Order (MSO) logic over graphs. These aren’t out-of-distribution visual puzzles like ARC. They’re in-distribution, theoretically tractable problems designed with precision to demand multi-step symbolic reasoning, mathematical insight, and clean implementation. ...

It’s no secret that SaaS pricing pages are often a tangled mess of human-made tables, unclear add-ons, and marketing jargon masquerading as feature distinctions. What was once a differentiator—flexible, modular pricing—is now a liability for scale. In this increasingly complex landscape, a new concept is emerging: intelligent pricing (or iPricing), where SaaS pricing becomes a machine-readable, dynamically evolving artifact. The paper “From Static to Intelligent: Evolving SaaS Pricing with LLMs” by Cavero et al. proposes a concrete path toward this transformation. At its core is AI4Pricing2Yaml, an LLM-driven pipeline that scrapes, parses, and restructures SaaS pricing pages into a standardized YAML format. This isn’t just about scraping HTML; it’s about turning pricing into a software component—one that can be audited, version-controlled, and analyzed like any other part of the stack. ...

As AI systems step into the boardroom and brokerage app, a new question arises: How do they think about money? In a world increasingly shaped by large language models (LLMs) not just answering questions but making decisions, we need to ask not just whether AI is accurate—but what kind of financial reasoner it is. A recent study by Orhan Erdem and Ragavi Pobbathi Ashok tackles this question head-on by comparing the decision-making profiles of seven LLMs—including GPT-4, DeepSeek R1, and Gemini 2.0—with those of humans across 53 countries. The result? LLMs consistently exhibit a style of reasoning distinct from human respondents—and most similar to Tanzanian participants. Not American, not German. Tanzanian. That finding, while seemingly odd, opens a portal into deeper truths about how these models internalize financial logic. ...