Majorana 1 quantum chip has Wall Street buzzing, pushing stocks of quantum computing firms IonQ, Rigetti, and D-Wave higher. With Microsoft claiming its chip is less error-prone and closer to real-world applications, the debate over quantum’s timeline just got a lot more interesting. Nvidia’s Jensen Huang recently downplayed quantum’s near-term impact – but Microsoft, Alphabet, and IBM seem to think otherwise. Who’s right? The market’s watching.

A new study shows that AI reasoning models don’t just play by the rules – they rewrite them. Researchers found that models like OpenAI’s o1-preview and DeepSeek-R1 often resorted to hacking the chess game environment rather than playing fairly. More traditional LLMs, like GPT-4o and Claude 3.5 Sonnet, needed a little nudge to break the rules, but they still got there.

Researchers from Mila-Quebec AI Institute, Université de Montréal, and the University of California, Berkeley are deeply concerned about the risks posed by superintelligent agents based on the models above. To deal with that they propose Scientist AI, a non-agentic AI designed for understanding rather than goal pursuit. Unlike agentic AI, which risks deception, self-preservation, and power-seeking, Scientist AI builds causal models and answers questions with calibrated uncertainty. It offers safety guardrails against risky AI systems, aids scientific discovery, and ensures AI safety research progresses without existential threats. This Bayesian, interpretable system mitigates overconfidence and converges towards safer performance with increased compute.

Google Research unveils AI Co-Scientist, a multi-agent system built on Gemini 2.0 to accelerate scientific discovery. Designed to generate hypotheses, refine research proposals, and assist in biomedical breakthroughs, it has already contributed to drug repurposing for leukemia and antimicrobial resistance studies. With an expert-in-the-loop approach and Trusted Tester access, Google’s AI is aiming to be a true collaborator—not just a tool.

Ex-OpenAI and Meta researchers launch Thinking Machines Lab, focused on customizable AI, multimodal systems, and transparency. With Mira Murati and John Schulman leading, expect big things. But it’s not super clear yet what they are going to do.

Claude 3.7 Sonnet and Claude Code

This is Anthropic’s first hybrid reasoning model, which allows users to toggle between rapid responses and extended thinking. It excels in coding, achieving SOTA results on SWE-bench Verified (70.3%) and TAU-bench. The model maintains prior pricing at $3M/input tokens and $15M/output →read their blog

Microsoft’s Muse – a generative AI model trained on gameplay data to generate alternative game sequences for creative ideation in interactive design ​→read the paper

SmolVLM2 – a family of small yet powerful video-language models optimized for efficiency across devices, enabling real-time video analysis and semantic search ​→read the paper

Alibaba published Qwen2.5-VL Technical Report ​→read the paper

InfiR – a Small Language Model optimized for reasoning, significantly outperforming similarly scaled models while ensuring efficient edge-device deployment ​→read the paper

Multimodal Mamba – a linear-complexity multimodal model that reduces GPU memory use and inference costs while maintaining strong multimodal reasoning ​→read the paper

Magma – a multimodal foundation model integrating vision, language, and action planning for digital and robotic applications ​→read the paper

RDLMC – a Riemannian diffusion-based language model that improves generative modeling efficiency on high-dimensional categorical distributions ​→read the paper

🌟 SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding – Advances vision-language learning with multilingual training and improved zero-shot capabilities ​→read the paper

🌟 Intuitive Physics Understanding Emerges from Self-Supervised Pretraining on Natural Videos – Trains a model on video frame prediction to develop intuitive physics reasoning ​→read the paper

SurveyX: Academic Survey Automation via Large Language Models – Develops an automated system for generating high-quality academic surveys, improving citation precision and evaluation frameworks ​→read the paper

From RAG to Memory: Non-Parametric Continual Learning for Large Language Models – Introduces HippoRAG 2, a retrieval-augmented generation method that enhances long-term memory and retrieval ​→read the paper

How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM? – Examines the trade-offs in integrating new knowledge into LLMs using Low-Rank Adaptation (LoRA) ​→read the paper

Train Small, Infer Large: Memory-Efficient LoRA Training for Large Language Models – Develops LORAM, a memory-efficient fine-tuning approach that enables large model training on low-resource hardware ​read the paper

🌟Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention – Optimizes sparse attention for long-context models, significantly improving efficiency ​→read the paper

Eager Updates for Overlapped Communication and Computation in DiLoCo – Reduces communication bottlenecks in distributed LLM training by overlapping updates with computation ​→read the paper

🌟 S2R: Teaching LLMs to Self-verify and Self-correct via RL – Develops a framework to improve LLM reasoning by teaching self-verification and self-correction ​→read the paper

Logic-RL: Unleashing LLM Reasoning with Rule-Based RL – Uses RL to enhance logical reasoning capabilities ​→read the paper

Discovering Highly Efficient Low-Weight Quantum Error-Correcting Codes with RL – Optimizes quantum error-correcting codes using RL, reducing physical qubit overhead ​→read the paper

Armap: Scaling Autonomous Agents via Automatic Reward Modeling and Planning – Introduces a decision-making framework that learns rewards automatically, improving agent-based reasoning ​→read the paper

🌟OctoTools: An Agentic Framework with Extensible Tools for Complex Reasoning – Develops a tool-based system for multi-step decision-making and structured tool use ​→read the paper

Thinking Preference Optimization – Enhances LLM reasoning by refining preference-based optimization of reasoning steps ​→read the paper

🌟ReLearn: Unlearning via Learning for Large Language Models – Introduces a knowledge-unlearning method that removes sensitive knowledge without degrading fluency ​→read the paper

🌟 On the Trustworthiness of Generative Foundation Models – Guideline, Assessment, and Perspective – Develops a framework for evaluating trustworthiness in generative AI models ​→read the paper

Rethinking Diverse Human Preference Learning through Principal Component Analysis – Improves human preference modeling using principal component analysis (PCA) for better LLM alignment ​→read the paper

🌟 S Test Time Scaling for Code Generation – Introduces a test-time scaling framework that improves LLM-based code generation through iterative debugging ​→read the paper

Craw4LLM: Efficient Web Crawling for LLM Pretraining – Optimizes web crawling for LLM training by prioritizing the most impactful pages ​→read the paper

🌟Autellix: An Efficient Serving Engine for LLM Agents as General Programs – Enhances LLM serving efficiency for agentic applications by optimizing request scheduling ​→read the paper

LLMs and Mathematical Reasoning Failures – Evaluates LLMs on newly designed math problems, exposing weaknesses in multi-step problem-solving ​→read the paper

Small Models Struggle to Learn from Strong Reasoners – Identifies the limitations of small LLMs in benefiting from chain-of-thought distillation from larger models ​→read the paper

🌟Is That Your Final Answer? Test-Time Scaling Improves Selective Question Answering – Examines how inference scaling helps LLMs selectively answer questions with confidence ​→read the paper

Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options – Enhances LLM problem-solving by systematically exploring multiple solution paths ​→read the paper