Bioinfoysis Technical Report
Large language model agents have shown promise in bioinformatics, but most existing systems focus primarily on producing final answers, treating planning, tool use, and code execution as transient interactions. This design is poorly suited to long-horizon bioinformatics tasks, where conclusions must remain connected to the data, computations, and intermediate evidence that support them. We introduce \textbf{Bioinfoysis}, a multi-agent harness that represents each request as a persistent, artifact-grounded analysis run. Bioinfoysis combines global planning with step-wise, evidence-driven replan
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 60%Toward Principled Knowledge Editing for Large Language Model Reasoning - Bioengineer.org →
- PossiblePossibly related (embedding) · 47%New dual-expert model detects stance across languages and targets - Bioengineer.org →
- PossiblePossibly related (embedding) · 57%A collaborative agent with two lightweight synergistic models for autonomous crystal materials research →
- PossiblePossibly related (embedding) · 52%WGS2IBI: Cloud Workflow Enables Personalized Bayesian Analysis of Genome Sequences - Bioengineer.org →
