OpenAI's latest machine learning mode has arrived. On Friday, the company released o3-mini and it's available to try now. What's more, for the first time OpenAI is making one of its "reasoning" models available to free users of ChatGPT. If you want to try it yourself, select the "Reason" button under the message composer to get started. According to OpenAI, o3-mini is faster and more accurate than its predecessor, o1-mini.

These types of models are most effective at solving complex problems, so if you have any PhD-level math problems you're cracking away at, you can try them out. Alternatively, if you've had issues with getting previous models to respond properly to your most advanced prompts, you may want to try out this new reasoning model on them. To try out o3-mini, simply select "Reason" when you start a new prompt on ChatGPT. Although reasoning models possess new capabilities, they come at a cost. OpenAI's o1-mini is 20 times more expensive to run than its equivalent non-reasoning model, GPT-4o mini. The company says its new model, o3-mini, costs 63% less than o1-mini per input token However, at 1.10 per million input tokens, it is still about seven times more expensive to run than GPT-4o mini.

I once saw an episode of America's Dumbest Criminals where a man called the cops to report his car stolen, only for it to turn out he'd stolen it from someone else in the first place. I couldn't help thinking of him this week while watching OpenAI's Sam Altman wet his pants about the fact that a Chinese hedge fund might have made unauthorised use of his own chatbot models, including ChatGPT, to train its new little side project. This is the cheaper, more open, extremely share-price-slashing DeepSeek. As news of DeepSeek played havoc with the tech stock market, OpenAI pressed its hanky to its nose and released a statement: "We are aware of and reviewing indications that DeepSeek may have inappropriately distilled our models, and will share information as we know more," this ran. "We take aggressive, proactive countermeasures to protect our technology."

We evaluate the ability of the current generation of large language models (LLMs) to help a decision-making agent facing an exploration-exploitation tradeoff. We use LLMs to explore and exploit in silos in various (contextual) bandit tasks. We find that while the current LLMs often struggle to exploit, in-context mitigations may be used to substantially improve performance for small-scale tasks. However even then, LLMs perform worse than a simple linear regression. On the other hand, we find that LLMs do help at exploring large action spaces with inherent semantics, by suggesting suitable candidates to explore.

The accelerating development and deployment of AI technologies depend on the continued ability to scale their infrastructure. This has implied increasing amounts of monetary investment and natural resources. Frontier AI applications have thus resulted in rising financial, environmental, and social costs. While the factors that AI scaling depends on reach its limits, the push for its accelerated advancement and entrenchment continues. In this paper, we provide a holistic review of AI scaling using four lenses (technical, economic, ecological, and social) and review the relationships between these lenses to explore the dynamics of AI growth. We do so by drawing on system dynamics concepts including archetypes such as "limits to growth" to model the dynamic complexity of AI scaling and synthesize several perspectives. Our work maps out the entangled relationships between the technical, economic, ecological and social perspectives and the apparent limits to growth. The analysis explains how industry's responses to external limits enables continued (but temporary) scaling and how this benefits Big Tech while externalizing social and environmental damages. To avoid an "overshoot and collapse" trajectory, we advocate for realigning priorities and norms around scaling to prioritize sustainable and mindful advancements.

Solving mathematics problems has been an intriguing capability of large language models, and many efforts have been made to improve reasoning by extending reasoning length, such as through self-correction and extensive long chain-of-thoughts. While promising in problem-solving, advanced long reasoning chain models exhibit an undesired single-modal behavior, where trivial questions require unnecessarily tedious long chains of thought. In this work, we propose a way to allow models to be aware of inference budgets by formulating it as utility maximization with respect to an inference budget constraint, hence naming our algorithm Inference Budget-Constrained Policy Optimization (IBPO). In a nutshell, models fine-tuned through IBPO learn to ``understand'' the difficulty of queries and allocate inference budgets to harder ones. With different inference budgets, our best models are able to have a $4.14$\% and $5.74$\% absolute improvement ($8.08$\% and $11.2$\% relative improvement) on MATH500 using $2.16$x and $4.32$x inference budgets respectively, relative to LLaMA3.1 8B Instruct. These improvements are approximately $2$x those of self-consistency under the same budgets.

Context propagation remains a central challenge in language model architectures, particularly in tasks requiring the retention of long-range dependencies. Conventional attention mechanisms, while effective in many applications, exhibit limitations in maintaining coherent contextual representations over extended sequences due to their reliance on discrete token interactions. A novel approach is introduced through the formulation of Intrinsic Tensor Field Propagation (ITFP), which models contextual relationships as continuous tensor fields distributed across token embeddings. The propagation dynamics are governed through differential equations that enable a structured flow of contextual information, augmenting the standard attention mechanism to enhance coherence and recall. A series of experiments conducted on an open-source transformer-based model demonstrate that ITFP provides measurable improvements in contextual retention, dependency resolution, and inference stability across various linguistic structures. Comparisons with baseline models reveal a reduction in syntactic inconsistencies and factual errors, while ablation studies indicate that the choice of propagation depth and integration strength significantly impacts model performance. Additional evaluations assessing domain generalization suggest that ITFP effectively adapts across different text genres, reinforcing its applicability beyond conventional language modeling tasks. Although computational trade-offs are introduced through the inclusion of tensor field computations, empirical findings suggest that the benefits in accuracy and coherence outweigh the increased processing demands.

This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation. The adversarial DIKE-ERIS duality enables adaptation to diverse cultural contexts while upholding consistent ethical principles. This architecture addresses limitations of reinforcement learning with human feedback (RLHF) by providing interpretable, adaptable, and culturally-aware ethical reasoning. Through self-supervised learning and adversarial testing, our framework demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation.

While large language models (LLMs) have shown promising capabilities as zero-shot planners for embodied agents, their inability to learn from experience and build persistent mental models limits their robustness in complex open-world environments like Minecraft. We introduce MINDSTORES, an experience-augmented planning framework that enables embodied agents to build and leverage mental models through natural interaction with their environment. Drawing inspiration from how humans construct and refine cognitive mental models, our approach extends existing zero-shot LLM planning by maintaining a database of past experiences that informs future planning iterations. The key innovation is representing accumulated experiences as natural language embeddings of (state, task, plan, outcome) tuples, which can then be efficiently retrieved and reasoned over by an LLM planner to generate insights and guide plan refinement for novel states and tasks. Through extensive experiments in the MineDojo environment, a simulation environment for agents in Minecraft that provides low-level controls for Minecraft, we find that MINDSTORES learns and applies its knowledge significantly better than existing memory-based LLM planners while maintaining the flexibility and generalization benefits of zero-shot approaches, representing an important step toward more capable embodied AI systems that can learn continuously through natural experience.

Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities through chain-of-thought prompting, yet discovering effective reasoning methods for complex problems remains challenging due to the vast space of possible intermediate steps. We introduce Ant Colony Optimization-guided Tree of Thought (ACO-ToT), a novel algorithm that combines ACO with LLMs to discover optimal reasoning paths for complex problems efficiently. Drawing inspiration from Hebbian learning in neurological systems, our method employs a collection of distinctly fine-tuned LLM "ants" to traverse and lay pheromone trails through a centralized tree of thought, with each ant's movement governed by a weighted combination of existing pheromone trails and its own specialized expertise. The algorithm evaluates complete reasoning paths using a mixture-of-experts-based scoring function, with pheromones reinforcing productive reasoning paths across iterations. Experiments on three challenging reasoning tasks (GSM8K, ARC-Challenge, and MATH) demonstrate that ACO-ToT performs significantly better than existing chain-of-thought optimization approaches, suggesting that incorporating biologically inspired collective search mechanisms into LLM inference can substantially enhance reasoning capabilities.