LLMs

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. ...

Imagine debugging a black box. Now imagine that black box occasionally narrates its thoughts aloud. That’s the opportunity—and the fragility—presented by Chain-of-Thought (CoT) monitoring, a newly emergent safety paradigm for large language models (LLMs). In their recent landmark paper, Korbak et al. argue that reasoning traces generated by LLMs—especially those trained for explicit multi-step planning—offer a fleeting yet powerful handle on model alignment. But this visibility, they warn, is contingent, brittle, and already under threat. ...

If GPT-4 was the apex of pretraining, DeepSeek might be the blueprint for what comes next. Released in two families—DeepSeek-V3 and DeepSeek-R1—this Chinese open-source model series isn’t just catching up to frontier LLMs. It’s reshaping the paradigm entirely. By sidestepping traditional supervised fine-tuning in favor of reinforcement learning (RL), and coupling it with memory-efficient innovations like Multi-head Latent Attention (MLA) and cost-efficient training techniques like FP8 mixed precision and fine-grained MoE, DeepSeek models demonstrate how strategic architectural bets can outpace brute-force scale. ...

Retrieval-Augmented Generation (RAG) has become the go-to technique for grounding large language models (LLMs) in external data. But as anyone building real-world RAG pipelines knows, there’s a growing tension between accuracy and cost. Existing graph-based RAG solutions promise richer semantics than vanilla vector stores, but suffer from two persistent issues: incomplete graphs and retrieval misalignment. The paper “CUE-RAG: Towards Accurate and Cost-Efficient Graph-Based RAG” proposes a structural rethinking. By integrating a multi-partite graph, hybrid extraction, and a query-driven iterative retriever, CUE-RAG achieves state-of-the-art accuracy while cutting indexing costs by up to 72.58% and even outperforming other methods without using any LLM tokens at all. ...

In the world of physics, when particles in a system become so densely packed or cooled that they lock into place, we call this phenomenon jamming. Sand becoming rigid under pressure, traffic freezing on a highway, or even glass transitioning from fluid to solid—all are governed by this principle. What if the same laws applied to intelligence? A provocative new paper, Consciousness as a Jamming Phase by Kaichen Ouyang, suggests just that: large language models (LLMs) exhibit consciousness-like properties not as a software quirk but as a physical phase transition, mirroring the jamming of particles in disordered systems. ...

When AI agents begin to talk to themselves—really talk to themselves—we might just witness a shift in how machine reasoning is conceived. A new paper, “Introspection of Thought Helps AI Agents”, proposes a reasoning framework (INoT) that takes inspiration not from more advanced outputs or faster APIs, but from an old philosophical skill: inner reflection. Rather than chaining external prompts or simulating collaborative agents outside the model, INoT introduces PromptCode—a code-integrated prompt system that embeds a virtual multi-agent debate directly inside the LLM. The result? A substantial increase in reasoning quality (average +7.95%) and a dramatic reduction in token cost (–58.3%) compared to state-of-the-art baselines. Let’s unpack how this works, and why it could redefine our mental model of what it means for an LLM to “think.” ...

What makes a large language model (LLM) biased? Is it the instruction tuning data, the randomness of training, or something more deeply embedded? A new paper from Itzhak, Belinkov, and Stanovsky, presented at COLM 2025, delivers a clear verdict: pretraining is the primary source of cognitive biases in LLMs. The implications of this are far-reaching — and perhaps more uncomfortable than many developers would like to admit. The Setup: Two Steps, One Core Question The authors dissected the origins of 32 cognitive biases in LLMs using a controlled two-step causal framework: ...