# Multi-Agent Framework

RIVER is a training-free multi-agent retrieval framework that organizes large language models, biochemical computational tools and external knowledge resources into traceable evidence chains for bidirectional enzyme-to-reaction and reaction-to-enzyme retrieval. In four experimentally supported enzyme–reaction cases drawn from an E. coli metabolomics study, it ranked among 667 candidate proteins with a mean reciprocal rank of 0.833 using Qwen3.6-35B-A3B and 0.750 using GPT-5.6 Luna, above the general-purpose coding-agent combinations and domain methods listed in the comparison table. On public benchmarks it reached Top-1 query success rates of 82.3% on Enzyme-405 and 72.8% on Orphan-335.

RIVER is a training-free multi-agent retrieval framework that organizes large language models, biochemical computational tools and external knowledge resources into traceable evidence chains for bidirectional enzyme-to-reaction and reaction-to-enzyme retrieval. In four experimentally supported enzyme–reaction cases drawn from an E. coli metabolomics study, it ranked among 667 candidate proteins with a mean reciprocal rank of 0.833 using Qwen3.6-35B-A3B and 0.750 using GPT-5.6 Luna, above the general-purpose coding-agent combinations and domain methods listed in the comparison table. On public benchmarks it reached Top-1 query success rates of 82.3% on Enzyme-405 and 72.8% on Orphan-335.

Optimizing register-transfer-level (RTL) code has long relied on manual rewriting, and existing evaluations mostly measure gains on manually degraded designs under weak synthesis flows, so they cannot show whether a rewrite beats a commercial synthesis tool. Dr. RTL closes a loop of timing analysis, parallel rewriting, commercial synthesis and sequential equivalence checking, and distills successful and failed rewrites into reusable pattern-strategy skills. On 20 original human-written RTL designs it improves worst negative slack (WNS) by 21.3% and total negative slack (TNS) by 16.9% on average over commercial synthesis, with a 5.8% area reduction and an average sequential equivalence check pass rate of 86%.

Optimizing register-transfer-level (RTL) code has long relied on manual rewriting, and existing evaluations mostly measure gains on manually degraded designs under weak synthesis flows, so they cannot show whether a rewrite beats a commercial synthesis tool. Dr. RTL closes a loop of timing analysis, parallel rewriting, commercial synthesis and sequential equivalence checking, and distills successful and failed rewrites into reusable pattern-strategy skills. On 20 original human-written RTL designs it improves worst negative slack (WNS) by 21.3% and total negative slack (TNS) by 16.9% on average over commercial synthesis, with a 5.8% area reduction and an average sequential equivalence check pass rate of 86%.