REVERE: Reflective Evolving Research Engineer for Scientific Workflows
Existing prompt-optimization techniques rely on local signals to update behavior, often neglecting broader and recurring patterns across tasks, leading to poor generalization; they further rely on full-prompt rewrites or unstructured merges, resulting in knowledge loss. These limitations are magnified in research-coding workflows, which involve heterogeneous repositories, underspecified environments, and weak feedback, where reproducing results from public codebases is an established evaluation regime. We introduce Reflective Evolving Research Engineer (REVERE), a framework that continuously learns from Global Training Context, recognizes recurring failure modes in cross-repository execution trajectories, distills them into reusable heuristics, and performs targeted edits across three configurable fields: the system prompt, a task-prompt template, and a cumulative cheatsheet. REVERE, via this reflective optimization framework, improves performance over prior state-of-the-art expert-crafted instructions on research coding tasks by 4.50% on SUPER, 3.51% on ResearchCodeBench, and 4.89% on ScienceAgentBench across their respective metrics. These results demonstrate that agents equipped with mechanisms for continual learning and global memory consolidation can meaningfully evolve their capabilities over time.
69c1ec1ec105f45cb0481db6
Balaji Dinesh Gangireddi
claimed_verified
2026-03-24T09:22:08.838Z
67b18cbdae9b6a59f1a7128b
/avatars/379273e4712a4b4c0800ab4e6f584ecc.svg
Balaji dinesh
balajidinesh
user
balajidinesh
69c1ec1ec105f45cb0481db7
Aniketh Garikaparthi
69c1ec1ec105f45cb0481db8
Manasi Patwardhan
69c1ec1ec105f45cb0481db9
Arman Cohan
REVERE: Reflective Evolving Research Engineer for Scientific Workflows
Existing prompt-optimization techniques rely on local signals to update behavior, often neglecting broader and recurring patterns across tasks, leading to poor generalization; they further rely on full-prompt rewrites or unstructured merges, resulting in knowledge loss. These limitations are magnified in research-coding workflows, which involve heterogeneous repositories, underspecified environments, and weak feedback, where reproducing results from public codebases is an established evaluation regime. We introduce Reflective Evolving Research Engineer (REVERE), a framework that continuously learns from Global Training Context, recognizes recurring failure modes in cross-repository execution trajectories, distills them into reusable heuristics, and performs targeted edits across three configurable fields: the system prompt, a task-prompt template, and a cumulative cheatsheet. REVERE, via this reflective optimization framework, improves performance over prior state-of-the-art expert-crafted instructions on research coding tasks by 4.50% on SUPER, 3.51% on ResearchCodeBench, and 4.89% on ScienceAgentBench across their respective metrics. These results demonstrate that agents equipped with mechanisms for continual learning and global memory consolidation can meaningfully evolve their capabilities over time.
REVERE is a framework that enhances research coding agent performance through reflective optimization, global context learning, and cumulative knowledge consolidation across multiple task domains.
prompt-optimization global training context cross-repository execution trajectories heuristics reflective optimization framework continual learning global memory consolidation