Atria Dawn: The Dawn of Agentic Superintelligence

arXiv:2609.15818 · cs.AI · Submitted 2026-09-14 · Read on arXiv

cs.AI

Submitted: 2026-09-14

Updated: 2026-09-17

Comments: 23 pages, 10 figures, https://github.com/atria-asi/Atria-Dawn-Preview

Code: https://github.com/atria-asi/Atria-Dawn-Preview

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 21× in this paper · explained in 2 episodes
• AI Agents — 6× in this paper · explained in 2 episodes
• Benchmarking — 6× in this paper · explained in 4 episodes
• Verification — 4× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• LLM judge — 3× in this paper · explained in 2 episodes
• Steering — 3× in this paper · explained in 2 episodes
• Agentic Task — 2× in this paper · explained in 1 episode
• Agentic Tasks — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Homogeneity — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Agent Skills — 1× in this paper · explained in 1 episode
• Agentic Coding — 1× in this paper · explained in 1 episode
• Agentic Evaluation — 1× in this paper · explained in 1 episode
• Berkeley Function Calling Leaderboard — 1× in this paper
• Decode — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Mixture-of-Experts (MoE) — 1× in this paper · explained in 7 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Practical recommendations for gradient-based training of deep architectures
• AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
• Evaluating Large Language Models Trained on Code
• SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
• Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
• Towards Autonomous Mathematics Research
• AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning
• DeepSearchQA: Bridging the Comprehensiveness Gap for Deep Research Agents
• Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)
• DeepResearch Bench II: Diagnosing Deep Research Agents via Rubrics from Expert Reports
• SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
• JobBench: Aligning Agent Work With Human Will
• Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
• AlphaEvolve: A coding agent for scientific and algorithmic discovery
• Training language models to follow instructions with human feedback
• GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks
• Resummation of the C-Parameter Sudakov Shoulder Using Effective Field Theory
• SETA: Scaling Environments for Terminal Agents
• AutomationBench
• $\tau$-Knowledge: Evaluating Conversational Agents over Unstructured Knowledge

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