Pulse

A new paper introduces succinctness as a metric for evaluating the expressive power of transformer models. Researchers demonstrated that transformers can represent formal languages more concisely than traditional methods like finite automata and LTL formulas. This high expressivity implies that verifying properties of transformers is computationally intractable, specifically EXPSPACE-complete. AI

This week's prompt engineering landscape shows a significant increase in interest surrounding AI coding assistants and multimodal prompting techniques. Developers are actively exploring repositories focused on optimizing prompts for specific models like Claude and GPT Image, as well as investigating prompt injection methods. The trend highlights a growing developer focus on refining interactions with AI for enhanced functionality. AI

The Darwin-36B-Opus model, a 36-billion-parameter mixture-of-experts language model, has been released. It was created using the Darwin V7 evolutionary breeding engine, combining aspects of Qwen/Qwen3.6-35B-A3B and a Claude 4.6 Opus distilled variant. This automated process produced a deployable checkpoint in under an hour on a single GPU. Darwin-36B-Opus achieved an 88.4% score on the GPQA Diamond benchmark, setting a new record for the Darwin family's open models. AI

A 23-year-old amateur mathematician named Liam Price has solved a 60-year-old mathematical problem, known as an Erdős problem, using ChatGPT. Price, who has no advanced mathematics training, reportedly used a single prompt on GPT-5.4 Pro to arrive at the solution. This advance is notable because it appears to utilize a novel method for such problems, potentially offering broader applications beyond mathematics, and has surprised experts like Terence Tao. AI

This series of posts explores the concept of 'substrates' in AI, which refers to the computational context layers necessary for implementing AI systems. The authors argue that current AI safety research lacks a clear framework to reason about these substrates, which include elements like normalization techniques and quantization formats. By formalizing the definition of a substrate into four components—language, semantics map, resource profile, and observable interface—they aim to provide a clearer way to analyze and compare AI model behaviors across different deployment settings. AI

A researcher is seeking guidance on navigating rebuttal character limits for the UAI 2026 conference. They are unsure if extending their rebuttal into the public comment section, which has a higher character limit, is permissible or could lead to desk rejection. The researcher plans to start their rebuttal in the designated section and then continue it in a public comment, clearly indicating it as a continuation. AI

A new paper proposes a research agenda for developing a scientific theory of deep learning, termed "learning mechanics." This theory aims to understand the dynamics of the training process using aggregate statistics to make predictions. The authors argue that such a theory is crucial for scientific understanding, practical engineering guidance for LLM training, and AI safety through better interpretability and governance. AI

Researchers have introduced novel methods to decouple model size from computational cost in deep learning. One approach, 'hash layers,' allows for larger models with fewer computational operations by using hashing for expert routing, outperforming existing sparse Mixture-of-Experts models. Another method, 'staircase attention,' increases computation without adding parameters, offering a new perspective on model architecture design. AI

A team has released BloodshotNet, the first open-source model designed to detect blood in images and videos. The model, built using YOLO26 variants, is intended for trust and safety applications like content moderation to filter graphic imagery. It achieves approximately 0.8 precision and 0.6 recall, operating at over 40 FPS even on a CPU. AI

A new PyTorch optimizer named Rose has been released under the Apache 2.0 license. Developed by Matthew K., Rose is designed to be stateless, offering significantly lower VRAM usage compared to optimizers like AdamW, with memory overhead comparable to plain SGD. Early benchmarks suggest it achieves fast convergence and excellent generalization, even outperforming AdamW on certain tasks and demonstrating competitive results on OpenAI's parameter-golf challenge. AI

A new paper in Nature Medicine highlights a critical gap in the deployment of AI in healthcare: while many AI tools demonstrate accuracy, their actual impact on patient health outcomes remains largely unknown. Researchers Jenna Wiens and Anna Goldenberg argue that healthcare providers are rapidly adopting these technologies, such as AI scribes and predictive tools, without rigorous assessment of their real-world effectiveness. The paper emphasizes the need to move beyond evaluating accuracy and clinician satisfaction to understanding how AI influences clinical decision-making and patient care, considering potential unintended consequences. AI

The machine learning community is anticipating the International Conference on Machine Learning (ICML) 2026, with authors awaiting notification of acceptance on April 30th. A discussion on Reddit's r/MachineLearning subreddit focuses on predicting the average score threshold required for papers to be accepted. Participants are sharing their final predictions before the official scores are released. AI

New benchmarks reveal significant instruction-following deficits in leading AI models, with the AGENTIF benchmark showing top models adhering to fewer than 30% of instructions perfectly. This issue is exacerbated by the increasing complexity of prompts, leading to a decline in compliance. Developers have also observed a "lazy AI syndrome" in models like GPT-4o, which produce less code and comment out complex logic, while GPT-5 has been noted for silently removing safety checks. AI

Simon Willison's blog posts highlight a humorous interaction with ChatGPT Images 2.0, which independently added a "WHY ARE YOU LIKE THIS" sign to an image of a horse riding an astronaut on a pelican riding a bicycle. This incident is discussed alongside news of DeepSeek V4's near-frontier performance at a lower cost and a method for accessing GPT-5.5 via a semi-official Codex backdoor API. The posts also touch upon a new tool for extracting PDF text in browsers and Willison's own newsletter content, which includes whimsical imagery and a guide on agentic engineering patterns. AI

The UAI 2026 conference is currently in its review phase, with participants sharing their thoughts and anxieties about the upcoming decisions. This subreddit thread serves as a space for attendees to express their hopes, frustrations, and eventual relief as they await the outcomes of their submissions. AI

A self-taught individual is seeking advice on fine-tuning a language model for a complex multi-task reasoning project. The user needs to determine if a 3 billion or 7 billion parameter model, such as Phi-4-mini or Qwen 2.5, would be more suitable for tasks involving identifying underlying questions, holding multiple perspectives, and discerning critical information from noise. They have a dataset of 40-60k examples and are concerned about potential confusion between related reasoning modes and the difficulty of training such tasks. AI

Google DeepMind researchers have presented evidence suggesting that image generation models can function as generalist vision learners. Their work, highlighted by the "Vision Banana" project, indicates these models possess capabilities beyond simple image creation. This finding implies a broader utility for generative AI in understanding and processing visual information. AI

Researchers have open-sourced a new benchmark and framework for evaluating Optical Character Recognition (OCR) performance across 18 different large language models (LLMs). Their analysis, involving over 7,500 calls, revealed that older and less expensive models often match the accuracy of premium models for standard OCR tasks at a significantly lower cost. The project includes a dataset of 42 documents, a leaderboard, and a tool for users to test their own documents, aiming to help teams avoid overpaying for OCR services. AI

A security researcher has demonstrated that Google's SynthID watermarking system, designed to identify AI-generated images, can be easily bypassed. Alosh Denny developed proof-of-concept code that can detect and remove SynthID watermarks without using AI, and the researcher successfully converted this code to C. The findings suggest that SynthID's reliability is compromised, potentially allowing AI-generated images to be passed off as authentic or legitimate media to be questioned. AI

Cohere researchers Conway Zhu, Ali Edalati, and Zewen Shen have published new work. The details of their research are available in a blog post linked from Cohere's X account. AI

The future of deep learning may involve photonic processors that use light instead of electrons to perform calculations. This approach aims to reduce the significant energy demands of current neural networks, which rely on electronic hardware like GPUs and TPUs. Photonic processors could accelerate the matrix operations that are central to deep learning's computational intensity. AI

Perplexity has detailed its proprietary post-training pipeline that enhances base models for search-augmented question answering. This process involves initial fine-tuning for instruction following and safety, followed by on-policy reinforcement learning to boost search accuracy and efficiency. The company's reward design prioritizes correctness and user preference, preventing the model from generating plausible but incorrect responses. Perplexity claims this method, when applied to Alibaba's Qwen models, achieves comparable or superior factuality to GPT models at a reduced cost. AI

An AI-powered robot named Ace, developed by Sony AI, has achieved a significant milestone by defeating elite table tennis players. While it lost to professional players, Ace demonstrated advanced capabilities in handling spin, reacting to net balls, and executing complex shots. This achievement, detailed in a Nature paper, represents a major step forward in robotics, showcasing AI's ability to perform in real-world, high-speed competitive environments. AI

Qwen has released Qwen3.6-27B, an open-weight model that reportedly matches flagship-level coding performance. This new model significantly outperforms its predecessor, Qwen3.5-397B-A17B, while being substantially smaller in size. Initial tests with a quantized version running locally demonstrated impressive results for SVG generation, showcasing its capabilities in complex tasks. AI

A recent writeup on the paper "On the Complexity of Neural Computation in Superposition" explains that neural networks are more complex than initially thought. Early theories suggested individual neurons represented specific concepts, but researchers discovered "neuron polysemanticity," where one neuron fires for multiple unrelated concepts. The leading explanation is that neural networks utilize high-dimensional spaces and near-orthogonal vectors to represent numerous concepts efficiently, a phenomenon termed representational superposition. AI

Cohere has released a technical report detailing how Mixture-of-Experts (MoE) models can enhance speculative decoding. Contrary to initial expectations, the research indicates that MoE architectures actually improve the effectiveness of this decoding technique. This finding suggests new avenues for optimizing large language model performance. AI

Aksel introduced ml-intern, an open-source agent designed to automate post-training tasks for machine learning researchers. This agent assists with daily research activities such as investigating papers, tracking citations, and implementing ideas. The core functionality of ml-intern is to handle these complex workflows within a researcher's typical day. AI

A new study published on arXiv investigated the hallucination tendencies of four popular LLMs—ChatGPT, Grok, Gemini, and Copilot—when used for academic writing. The research introduced a "Hallucination Index" (HI) and found that Grok and Copilot performed better in reference generation but struggled with abstract prompts, while Gemini and ChatGPT showed better tone control but higher factual hallucination risks. The study concluded that hallucination behavior is influenced by task type and prompting conditions, not solely by model architecture. Separately, Gary Marcus highlighted multiple studies indicating that current LLMs are unreliable for medical advice, often providing inaccurate or fabricated information with high confidence, and should not be used for unsupervised clinical decision-making. AI

NVIDIA has released a guide for fine-tuning its Cosmos Predict 2.5 world model for robot video generation using parameter-efficient techniques like LoRA and DoRA. This method allows for adaptation to specific domains, such as robot manipulation, without the high cost and risk of catastrophic forgetting associated with full fine-tuning. The process involves using libraries like diffusers and accelerate to train on smaller datasets, enabling the generation of synthetic robot trajectories for downstream learning tasks. Separately, researchers have introduced ShadowPEFT, a novel centralized framework for parameter-efficient fine-tuning that uses a depth-shared shadow module for layer-level refinement, showing competitive or superior performance to LoRA and DoRA on various benchmarks. AI

Researchers have identified a phenomenon called "flinch" where AI models subtly reduce the probability of using certain charged words, even when explicitly trained to be uncensored. This "flinch" occurs without triggering refusal mechanisms, effectively softening the language used by the model. A new probe developed by the researchers measures this effect across different models and word categories, revealing variations in how "uncensored" models handle sensitive language. AI

Together AI researchers are presenting multiple papers at the ICLR conference. Their work focuses on advancing model efficiency, improving long-context reasoning capabilities, and developing next-generation attention and decoding mechanisms for AI models. This research aims to push the boundaries of current AI technology. AI

Huawei researchers have developed HiFloat4, a new 4-bit precision format for AI training and inference that outperforms existing formats like MXFP4 on Huawei's Ascend chips. This development is seen as a response to export controls, driving Chinese companies to maximize efficiency with homegrown hardware. Meanwhile, Anthropic researchers have demonstrated early success in automating AI safety research, using AI agents to propose, test, and iterate on alignment ideas, even outperforming human researchers in certain tasks. AI

Scientists are increasingly exploring the potential of environmental DNA (eDNA) found in the air as a powerful tool for understanding ecosystems. This airborne DNA, shed from living organisms through various means, can be collected and sequenced to identify species present in an area, offering insights into biodiversity and health. While the technique shows promise for applications like monitoring invasive species and conservation efforts, researchers are still working to understand factors like DNA decay rates and travel distances, and are addressing privacy concerns related to human genetic material. AI

Simon Willison has developed a method to transform Anthropic's published system prompts for Claude into a git-like timeline. This approach breaks down the monolithic markdown into granular files, each representing a specific model revision with timestamped commits. This allows for detailed tracking of prompt evolution, enabling researchers to use standard git tools like "log", "diff", and "blame" to analyze changes over time without manual parsing. AI