LLMs

In the quest for truly intelligent systems, reasoning has always stood as the ultimate benchmark. But a new paper titled “Towards a Deeper Understanding of Reasoning Capabilities in Large Language Models” by Annie Wong et al. delivers a sobering message: even the most advanced LLMs still stumble in dynamic, high-stakes environments when asked to reason, plan, and act with stability. Beyond the Benchmark Mirage Static benchmarks like math word problems or QA datasets have long given the illusion of emergent intelligence. Yet this paper dives into SmartPlay, a suite of interactive environments, to show that LLMs exhibit brittle reasoning when faced with real-time adaptation. SmartPlay is a collection of dynamic decision-making tasks designed to test planning, adaptation, and coordination under uncertainty. The team evaluates open-source models such as LLAMA3-8B, DEEPSEEK-R1-14B, and LLAMA3.3-70B on tasks involving spatial coordination, opponent modeling, and planning. The result? Larger models perform better—but only to a point. Strategic prompting can help smaller models, but also introduces volatility. ...

Cognaptus Insights introduces Retrieval-Augmented Learning (RAL), a new approach proposed by Zongyuan Li et al.¹, allowing large language models (LLMs) to autonomously enhance their decision-making capabilities without adjusting model parameters through gradient updates or fine-tuning. Understanding Retrieval-Augmented Learning (RAL) RAL is designed for situations where fine-tuning large models like GPT-3.5 or GPT-4 is impractical. It leverages structured memory and dynamic prompt engineering, enabling models to autonomously refine their responses based on previous interactions and validations. ...

Rules of Engagement: Why LLMs Need Logic to Plan When it comes to language generation, large language models (LLMs) like GPT-4o are top of the class. But ask them to reason through a complex plan — such as reorganizing a logistics network or optimizing staff scheduling — and their performance becomes unreliable. That’s the central finding from ACPBench Hard (Kokel et al., 2025), a new benchmark from IBM Research that tests unrestrained reasoning about action, change, and planning. ...

Gemini 2.5 and the Rise of the 2 Million Token Era In March 2025, Google introduced Gemini 2.5 Pro with a 2 million token context window, marking a major milestone in the capabilities of language models. While this remains an experimental and high-cost frontier, it opens the door to new possibilities. To put this in perspective (approximate values, depending on tokenizer): 📖 The entire King James Bible: ~785,000 tokens 🎭 All of Shakespeare’s plays: ~900,000 tokens 📚 A full college textbook: ~500,000–800,000 tokens This means Gemini 2.5 could, in theory, process multiple entire books or large document repositories in one go—though with substantial compute and memory costs that make practical deployment currently limited. ...