Large Language Models (LLMs), such as OpenAI's o1 and DeepSeek's R1, excel at advanced reasoning tasks like math and coding via Reinforcement Learning with Verifiable Rewards (RLVR), but still struggle with puzzles solvable by humans without domain knowledge. We introduce ENIGMATA, the first comprehensive suite tailored for improving LLMs with puzzle reasoning skills. It includes 36 tasks across 7 categories, each with: 1) a generator that produces unlimited examples with controllable difficulty, and 2) a rule-based verifier for automatic evaluation. This generator-verifier design supports scalable, multi-task RL training, fine-grained analysis, and seamless RLVR integration. We further propose ENIGMATA-Eval, a rigorous benchmark, and develop optimized multi-task RLVR strategies.

Apple has announced the latest version of macOS. It's all about the reintroduction of Siri, which is now accessible from anywhere on the Mac desktop. The official name of the Mac's operating system is macOS 27 Golden Gate, keeping the California naming scheme around. This year's update is focused on the relaunched Siri (now known as Siri AI), which really strives to transform into a proper AI chatbot along the lines of ChatGPT or Google Gemini--with a unique Apple twist. Is Your Mac Compatible With macOS Golden Gate?

Anthropic Offers Mythos Upgrade for Cyber Partners and a'Safe' Version for the Rest of You Anthropic is releasing Claude Mythos 5 to trusted organizations and Claude Fable 5 to the public, a version it says can't be used for cyberattacks. Anthropic released two new AI models called Claude Fable 5 and Claude Mythos 5 on Tuesday, which the company says have greater capabilities than the Mythos Preview model it released in April to a limited set of tech industry partners. Anthropic has said the initial, limited release stemmed from concerns that the model's capabilities could be exploited by bad actors to develop hacking tools that could catch defenders off guard. Anthropic is currently only releasing Claude Mythos 5 to a limited set of industry partners, many of which received access to Mythos Preview, and the company says it is collaborating with the US government on the rollout. Claude Fable 5, which is being publicly released, uses the same underlying model as Mythos 5, but will have "guardrails" in place at launch, the company said Tuesday, that will block the model from answering many user questions related to cybersecurity, biology, and chemistry.

PCWorld reports that OpenAI launched Lockdown Mode for ChatGPT to combat prompt injection attacks that can hijack AI systems and steal personal information. These attacks have previously compromised AI browsers like Perplexity and controlled smart home devices through Google Gemini by tricking systems with malicious instructions. Lockdown Mode restricts features like live web browsing and Deep Research across all ChatGPT plans, though OpenAI acknowledges risks from uploaded files remain. OpenAI has launched a new security feature in ChatGPT called Lockdown Mode, designed to provide additional protection against so-called "prompt injection attacks." A prompt injection attack is when someone crafts a deceptive prompt in an attempt to trick the LLM into following malicious instructions and/or revealing sensitive information.

Large reasoning models (LRMs), such as OpenAI o1 and DeepSeek-R1, have significantly enhanced their reasoning capabilities by generating longer chains of thought, demonstrating outstanding performance across a variety of tasks. However, this performance gain comes at the cost of a substantial increase in redundant reasoning during the generation process, leading to high computational overhead and exacerbating the issue of overthinking. Although numerous existing approaches aim to address the problem of overthinking, they often rely on external interventions.

Event cameras offer a promising avenue for multi-view stereo depth estimation and Simultaneous Localization And Mapping (SLAM) due to their ability to detect blur-free 3D edges at high-speed and over broad illumination conditions. However, traditional deep learning frameworks designed for conventional cameras struggle with the asynchronous, stream-like nature of event data, as their architectures are optimized for discrete, image-like inputs. We propose a scalable, flexible and adaptable framework for pixel-wise depth estimation with event cameras in both monocular and stereo setups. The 3D scene structure is encoded into disparity space images (DSIs), representing spatial densities of rays obtained by back-projecting events into space via known camera poses. Our neural network processes local subregions of the DSIs combining 3D convolutions and a recurrent structure to recognize valuable patterns for depth prediction. Local processing enables fast inference with full parallelization and ensures constant ultra-low model complexity and memory costs, regardless of camera resolution. Experiments on standard benchmarks (MVSEC and DSEC datasets) demonstrate unprecedented effectiveness: (i) using purely monocular data, our method achieves comparable results to existing stereo methods; (ii) when applied to stereo data, it strongly outperforms all state-of-the-art (SOTA) approaches, reducing the mean absolute error by at least 42\%; (iii) our method also allows for increases in depth completeness by more than 3-fold while still yielding a reduction in median absolute error of at least 30\%. Given its remarkable performance and effective processing of event-data, our framework holds strong potential to become a standard approach for using deep learning for event-based depth estimation and SLAM.

When serving a single base LLM with several different LoRA adapters simultaneously, the adapters cannot simply be merged with the base model's weights as the adapter swapping would create overhead and requests using different adapters could not be batched. Rather, the LoRA computations have to be separated from the base LLM computations, and in a multi-device setup the LoRA adapters can be sharded in a way that is well aligned with the base model's tensor parallel execution, as proposed in S-LoRA. However, the S-LoRA sharding strategy encounters some communication overhead, which may be small in theory, but can be large in practice. In this paper, we propose to constrain certain LoRA factors to be block-diagonal, which allows for an alternative way of sharding LoRA adapters that does not require any additional communication for the LoRA computations. We demonstrate in extensive experiments that our block-diagonal LoRA approach is similarly parameter efficient as standard LoRA (i.e., for a similar number of parameters it achieves similar downstream performance) and that it leads to significant end-to-end speed-up over S-LoRA. For example, when serving on eight A100 GPUs, we observe up to 1.79x (1.23x) end-to-end speed-up with 0.87x (1.74x) the number of adapter parameters for Llama-3.1-70B, and up to 1.63x (1.3x) end-to-end speed-up with 0.86x (1.73x) the number of adapter parameters for Llama-3.1-8B.

Last week, OpenAI shocked the mathematical community by revealing that one of its internal artificial intelligence (AI) models had found a counterexample to a famous conjecture made by legendary Hungarian mathematician Paul Erdős in 1946. The planar unit distance problem, or Erdős problem 90, has intrigued mathematicians for decades. The new result is no mere curiosity. Canadian mathematician Daniel Litt described it as "the first result produced autonomously by an AI that I find interesting in itself". The breakthrough, produced with a general-purpose AI model rather than one specialised for mathematics, also highlights how AI is changing mathematical research itself.

Plus: OpenAI has confidentially filed for a US IPO. The outspoken longevity scientist David Sinclair has predicted that, one day, you'll go to the doctor and get a prescription that will make you 10 years younger. MIT Technology Review has learned of his latest step toward this: human tests of a "reprogramming" drug. Sinclair, a biologist at Harvard Medical School, plans to launch the tests in a $101 million competition organized by the XPrize Foundation. The winners will "restore" a person to an earlier apparent age, as measured by improvements in immune, cognitive, and muscle function. The grand prize goes to any team able to show a 10-year (or greater) relative improvement after one year of treatment.

At SXSW London last week I gave a talk called "Five things you need to know about AI," in which I shared what I think are the biggest themes in AI right now. I pulled a few things from our first AI10 list, an annual guide to the most important trends in this buzzy world, but I also veered off on a number of tangents. In my half-hour slot, I tried to cover the key talking points that I think help to make sense of what's going on in tech--and thus the economy--today.