Papers with dual-agent architecture

2 papers
CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback (2026.acl-long)

Copied to clipboard

Challenge: Existing methods for generating instruction-code pairs rely on rigid heuristics and are labor-intensive.
Approach: They propose a dual-agent architecture that integrates a Coder and a Reviewer to orchestrate the generation trajectory.
Outcome: The proposed architecture outperforms baselines on a large-scale dataset of instruction-code pairs with stepped difficulty levels.
Reinforcing Agentic Search Via Reward Density Optimization (2026.acl-long)

Copied to clipboard

Challenge: Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic search, but its performance is often hindered by reward sparsity .
Approach: They propose a new research problem to improve the reward obtained per unit of exploration cost by using a system that decomposes long-horizon tasks into intermediate objectives and assigns process-level rewards to provide denser learning signals.
Outcome: The proposed framework outperforms strong baselines on several agentic search benchmarks and achieves comparable performance to that of advanced proprietary models.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations