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NVIDIA Research has released SpatialClaw, a training-free framework for spatial reasoning. It targets a persistent weakness in vision-language models (VLMs). These models still struggle to judge where objects are, how they relate, and how they move in 3D. SpatialClaw does not retrain the model. Instead, it changes the action interface the agent uses to call […]
While recent breakthroughs in AI reasoning have largely been driven by massive scale, pouring in billions of parameters to cross complex cognitive thresholds—VibeThinker-3B is charting a completely different path. Created by researchers from Sina Weibo Inc (China), this 3-billion-parameter model proves that efficiency can punch far above its weight class. Released under an open-source MIT
Most biology benchmarks ask narrow, fact-based questions with clean answers. Scientists weigh imperfect evidence and make decisions. OpenAI released LifeSciBench and it targets that gap directly. Even the strongest model passes roughly one task in three. The benchmark is far from saturated. What is LifeSciBench LifeSciBench contains 750 expert-authored tasks. They span seven workflows and
MiniMax released MSA (MiniMax Sparse Attention), a sparse attention method built directly on Grouped Query Attention (GQA). It targets one bottleneck: the quadratic cost of softmax attention at long context. The MiniMax research team tested it inside a 109B-parameter Mixture-of-Experts model trained with native multimodal data. They also open-sourced an inference kernel and shipped a
k-means has been an offline tool for decades. You run it once to preprocess data, then move on. A team of researchers from UC Berkeley and UT Austin released Flash-KMeans, a new open-source library that targets a different setting. Modern AI pipelines now call k-means inside training and inference loops. At that frequency, latency per
A new working research from Perplexity and Harvard offers field evidence on what AI agents do to knowledge work. It draws on production data from two Perplexity products: Search and Computer. The setup is a natural comparison. Search is a conversational answer engine. Computer is an agent that plans and executes tasks end to end.
Most search agents are trained as policies over a growing transcript. The model decides how to search. It must also remember what it saw, which evidence matters, and which claims it checked. A team of researchers from University of Illinois Urbana-Champaign, UC Berkeley, and Chroma argues this asks too much. Reinforcement learning ends up optimizing
NVIDIA has released Nemotron 3 Ultra, the largest model in its Nemotron 3 family. It targets a specific problem: long-running agents that plan, call tools, and reason across many turns. As agents run longer, token counts grow and inference cost climbs. Nemotron 3 Ultra is designed to keep accuracy high while making that inference faster
Researchers at Stanford University and Lambda Labs, have published the research paper for OpenJarvis, an open-source framework that runs inference, agents, memory, and learning entirely on-device. The open-weight models configured through OpenJarvis land within 3.2 percentage points of the best cloud model on average, at roughly 800× lower marginal API cost per query and roughly
The Transformer’s attention mechanism has barely changed since 2017. Most efficiency work has tried to replace softmax attention outright. A new paper takes a different route. It keeps softmax attention and bolts on a correction branch. A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention
