AI RESEARCH ANALYSIS

Scaling Intelligence: The X-Masters Workflow

To unlock the full potential of X-Master, the research introduces X-Masters, a scattered-and-stacked agentic workflow. This multi-agent cognitive process systematically enhances both the breadth and depth of reasoning at inference time. It orchestrates instances of X-Master into specialized roles:

• Solver: Generates five diverse initial solutions in parallel, fostering broad exploration.
• Critic: Evaluates and refines each initial solution, identifying flaws and providing corrections.
• Rewriter: Synthesizes all preceding outputs into five new, superior solutions, enhancing depth and quality.
• Selector: Adjudicates and selects the single best answer from the rewritten solutions.

This "scattered" phase explores diverse solution paths by leveraging stochastic decoding, while the "stacked" phase aggregates insights and iteratively refines solutions, ensuring a robust and high-quality final output. This structured thinking significantly scales the agent's intelligence for complex problem-solving.

Dynamic Tool Integration & Web Interaction

X-Master's flexible tool integration is a cornerstone of its intelligence. By expressing interactions as Python code, the agent can leverage a wide spectrum of tools, from existing Python libraries to custom-built modules, and even dynamically generate new tools as needed.

For information-seeking tasks, two core custom-built tools are emphasized:

• Web Search: Leverages search engines to identify relevant webpages. It provides concise summaries, related search queries, and can extract entity-related facts from knowledge graphs, allowing the agent to rapidly comprehend context and prioritize deeper exploration.
• Web Parse: Offers two strategies: General webpage parsing to extract primary content and identify relevant segments, including subpage links for deeper exploration; and Scientific paper parsing, which retrieves HTML versions from arXiv or downloads PDF documents to extract information directly related to the query.

These tools enable X-Master to emulate human-like online information-seeking behavior, iteratively acquiring novel information or validating existing conclusions, thereby transcending the limitations of its inherent knowledge base.

Benchmark Performance: Leading Humanity's Last Exam

X-Masters achieves a new state-of-the-art on Humanity's Last Exam (HLE), a critical touchstone for evaluating scientific AI agents. Its performance demonstrates superior reasoning capabilities and the effectiveness of its tool-augmented agentic workflow.

• HLE Score: Achieves an unprecedented 32.1%, surpassing leading closed-source models from OpenAI (26.6%) and Google DeepMind (26.9%) and becoming the first system to cross the 30% threshold.
• Biology/Medicine Category (HLE): Attains 27.6% accuracy, outperforming specialized scientific agents like STELLA (26%) and Biomni (17.3%) on text-only questions, highlighting its advanced capabilities in complex biomedical problems.
• TRQA-lit (choice): Achieves 67.4% on this specialized biology benchmark, outperforming other models like DeepSeek-R1-0528 (62.1%) and OriGene (60.1%), further cementing its efficacy in research tasks involving therapeutic target identification and biomedical mechanisms.

This leading performance underscores the potential of X-Masters to accelerate scientific discovery and provides valuable insights for future AI advancements.