Paper Agent Network: Literature-Grounded Multi-Agent Program Discovery

Using a research paper in program search requires turning its methods into executable candidates and adapting them to a target task. A paper can initialise an approach whose implementation continues to develop through evaluation and findings from other approaches. We introduce the Paper Agent Network (PAN), a literature-grounded multi-agent system that organises this process around persistent coding agents. Each agent starts from a selected paper and develops a revisable method route through tool use, evaluator feedback, and accumulated implementation history. A shared result board and queued reports connect these routes; a progress-based controller allocates work and introduces reserve agents. The design separates admitting an improved artifact from sharing a finding that may inform later work. We evaluate the complete system on 23 tasks spanning mathematics, systems, engineering, planning, scientific code, and prediction against four open program-evolution frameworks. The reported comparison contains twelve best or tied-best results. Two process analyses make method and information use concrete. A denoising construction appears in executable artifacts, and a peer’s scheduling report motivates a change to a route that improves through later work. The 17-task literature comparison shows that the positive denoising case does not imply a general benefit from literature access. These observations establish method and information use within the implemented system; interaction’s independent performance contribution remains unresolved. Persistent paper-informed routes provide a concrete setting for studying how published methods are implemented, exchanged, and adapted during computational problem solving.