Using thousands of AI agents to uncover new leads in biological data
A research overview of distributed agents linking large biological datasets with scientific literature. Proposed connections still require experimental follow-up.
THE RESEARCH ATLAS
Foundation model papers
Architectures, pretraining and general language capabilities. Connect model reports to foundational reading and evaluate what each design establishes.
Advancing coding in low-resource programming languages
Official research discussion of improving coding capabilities in languages with less available training material.
Gemini 4 Argon — Model Card
Official model documentation covering evaluation, limitations and safety considerations. The date records the listed model-card update.
Advancing computer use with Ironclad
An official account of progress in computer-use agents. Read the original methods and evaluations separately from deployment claims.
Qwen-Audio-Agent Technical Report
A foreground-background architecture separates voice interaction from asynchronous task execution, coordinating authorization, state and delivery.
DeepSeek-V4.1-Flash: smarter, faster, more efficient
Official release introducing an asymmetric causal encoder-decoder architecture and linking its technical report and model resources.
Introducing Grok 4.6
An official technical release describing supplemental training, regenerated SFT trajectories and agentic reinforcement learning.
Qwen3.5-Omni Technical Report
The report explores an omni-modal model family and training over heterogeneous visual, textual and audio data.
Qwen3-Coder-Next Technical Report
An open-weight model for coding agents explores strong training recipes with a small active parameter footprint.
A “diff” tool for AI: Finding behavioral differences in new models
An interpretability research overview explores ways to identify behavior differences between AI models beyond predefined tests.
Kimi K2.5: Visual Agentic Intelligence
Studies joint visual-language training and multi-agent task decomposition in an open multimodal model.
DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models
Sparse attention and post-training methods are combined to improve efficiency, reasoning and agent-oriented behavior.
Qwen3-VL Technical Report
The Qwen vision-language family integrates text, image and video understanding across several model sizes.
gpt-oss-120b & gpt-oss-20b Model Card
Open-weight reasoning models are described through their architecture, training, capabilities and safety evaluations.
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
An industrial model-family report covering reasoning, multimodal input, long contexts and agent-related evaluations.
Qwen3 Technical Report
Dense and mixture-of-experts models integrate thinking and non-thinking behavior, with configurable inference-time reasoning.
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Reinforcement learning is used to develop reasoning behavior, with additional pipelines for usable models and distillation.
DeepSeek-V3 Technical Report
A mixture-of-experts language model combines architectural and training-efficiency techniques, including latent attention and multi-token prediction.
SAM 2: Segment Anything in Images and Videos
Extends promptable segmentation from images to video using streaming memory and interactive data collection.
Mixtral of Experts
Introduces a sparse mixture-of-experts model exploring model capacity and activated computation.
GPT-4 Technical Report
The report describes GPT-4’s capabilities, evaluation results and limitations while leaving important training details undisclosed.
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
An IO-aware attention algorithm reduces memory transfers while computing exact attention.
Training language models to follow instructions with human feedback
Human demonstrations and preference feedback are used to improve how a language model follows instructions.
Training Compute-Optimal Large Language Models
The study examines how to allocate a training-compute budget between model size and training data.
LoRA: Low-Rank Adaptation of Large Language Models
Low-rank trainable updates adapt a frozen pretrained model without updating all of its parameters.
Language Models are Few-Shot Learners
GPT-3 studies how larger autoregressive language models perform new tasks through examples in their input context.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Masked-language pretraining learns bidirectional representations that can be adapted to language-understanding tasks.
Attention Is All You Need
The Transformer replaces recurrent sequence processing with attention, creating a foundation for many later language and multimodal models.
ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
Institutions working on this topic
Counts are records from that institution that carry this topic in this index.