Introducing ARC Agent

The bottleneck in modern biology isn't reading papers faster. It's that most functional relationships between genes have never been published.

Researchers face a choice: spend months manually analyzing data, or use AI tools that synthesize existing literature at scale. The problem with literature synthesis is fundamental. You can only find what's already been documented. Literature tools can infer possible connections, but they can't provide the quantitative, experimental evidence of functional coupling that comes from real data.

We built ARC Agent to solve a different problem entirely.

Beyond Literature Synthesis

Recent AI research tools focus on reading more papers faster. They process thousands of abstracts and claim to compress months into hours. But this approach has a ceiling: you can only discover what someone else has already published.

ARC Agent takes a different path. Instead of synthesizing existing knowledge at scale, it queries proprietary experimental datasets containing functional relationships that don't exist anywhere in the scientific literature.

Heureka's proprietary functional genomics data reveals which genes are functionally coupled based on how cells actually behave, not based on what researchers have chosen to study. This includes gene-gene relationships derived from large-scale perturbation screens, drug sensitivity data, pathway associations computed from expression patterns, and AI-predicted protein functions for understudied genes.

This data doesn't exist in PubMed. GO and KEGG don't have it. Literature synthesis tools can't find it, because it was never written down.

Scientific Reasoning, Not Brute Force

Some AI research tools treat scale as intelligence: running thousands of iterations, generating massive codebases, processing millions of tokens. But real scientific discovery isn't about brute-force exploration. It's about asking the right question, designing the right analysis, and interpreting results with domain expertise.

ARC Agent is designed to reason like a scientist. It decides which analyses matter, which results warrant follow-up, and which findings are biologically meaningful. When ARC Agent queries a database or runs a statistical test, there's a documented reason, not just iteration through possibilities.

The outputs are reviewable. The methodology is traceable. You can follow every conclusion back to specific data and specific reasoning. Science requires transparency: understanding not just what was found, but why the analysis looked there.

The Data Advantage

Traditional enrichment analysis asks: "What pathways contain my genes?"

ARC Agent asks: "What genes behave like my genes across thousands of experimental conditions, and what biology explains that behavior?"

This is the difference between summarizing known science and discovering new connections.

Most AI research tools compete on the same public information: papers, databases, preprints. They differentiate on processing speed or context length. ARC Agent has access to data others don't:

These aren't curated from papers. They're computed from experimental measurements. When you ask ARC Agent about a gene, you get answers derived from experimental data, not just from what's been published about it.

Architecture

ARC Agent coordinates 17 specialized subagents, each optimized for different research tasks. When you describe a research question, the system allocates the right combination of capabilities.

Resource allocation scales with task complexity. A quick gene lookup runs lightweight. A full multi-omics integration with literature validation and manuscript drafting deploys more compute. You describe what you need; the system handles orchestration.

Core capabilities:

All outputs include documented methodology and reproducible code.

How It Works

1. Describe your research question

Be specific about what you want to discover:

• "Run differential expression on treated vs control, find functionally coupled partners for upregulated genes, identify druggable targets, and draft a methods section"
• "What functional relationships exist between these GWAS hits? Search literature for validation and create a network figure"
• "Screen these 500 abstracts for RCTs in Type 2 diabetes, extract outcomes, assess risk of bias"
• "Generate a conference poster summarizing this RNA-seq analysis with publication-quality figures"

2. ARC Agent executes autonomously

No babysitting required. The system plans multi-step analyses, coordinates subagents, handles errors, adapts based on intermediate results, and documents methodology throughout. It does this through scientific reasoning, not through exhaustive iteration.

3. Review and iterate

All outputs sync to your project: analysis reports, figures, processed data, reproducible code, presentation materials. Continue refining in ARC chat. Ask follow-up questions, request alternative visualizations, or extend the analysis.

Research Applications

Gene list interpretation

Upload a list from RNA-seq, CRISPR screens, or GWAS. ARC Agent queries proprietary functional data, identifies shared functional modules, reveals pathway convergence, and maps therapeutic opportunities. These are insights that literature review cannot provide because the relationships were never published.

Multi-omics integration

Combine transcriptomics, proteomics, metabolomics from the same samples. ARC Agent matches identifiers across platforms, calculates cross-layer correlations, identifies concordant and discordant changes, performs integrated pathway analysis, and generates multi-panel figures.

Hypothesis generation

Explore unexpected gene-disease connections, pathway relationships, and testable predictions. ARC Agent queries functional genomics data to surface relationships that haven't been studied. These are candidates for your next experiment, not summaries of everyone else's.

Scientific communication

Draft manuscript sections, generate figure legends, create conference posters, build presentation slides. All grounded in your actual analysis, with proper citations and reproducible methods.

ARC (chat) + ARC Agent

ARC operates as two complementary systems:

• ARC (chat): Fast, interactive conversations for quick questions, brainstorming, and interpretation
• ARC Agent: Autonomous background processing for deep computational work

When research requires serious computation, ARC deploys ARC Agent automatically. Start with a question in chat, let ARC Agent handle multi-step analysis, then return to chat for interpretation and follow-up. The handoff is seamless.

Getting Started

1. Create a project and upload your data
2. Describe your research question in detail
3. Let ARC Agent work while you continue other research
4. Review results in your project folder
5. Iterate with follow-up analyses or extend to presentation materials