HF Space - self-improving scientific agents

Self-Improving Open Scientific Agents

A research demo for scientific AI agents that route tasks through an OpenClaw-style gateway, test Hermes-compatible reasoning lanes, critique their own outputs, and improve through benchmarks, memory, and reproducibility checks.
OpenClaw-style orchestration Hermes-compatible backend lane Self-critique + benchmark gate Statistics + numerical analysis + decision agents
About this multi-agent system
A self-improving research loop for scientific AI agents.
This agent is a showcase for AI systems that do not just answer once and stop. It routes a scientific request through specialist agents, critiques the output, measures improvement against benchmark gates, stages human-approved memory, and prepares the next run with a clearer plan.
Critique loopGenerates a draft, challenges it, tracks failure modes, and records why the next version should improve.
Benchmark gateUses quality, risk, reproducibility, and critic signals before any lesson becomes reusable memory.
Open AI architectureDemonstrates OpenClaw-style orchestration and Hermes-compatible backend lanes in a public research sandbox.
AI Lab connectorFeeds lessons back into Statistics, Numerical Analysis, and Optimal Control/OR companion agents.
Fast proof of value
The public Space now foregrounds the self-improvement loop: scenario presets, visible agent-bus handoffs, critic decisions, benchmark gates, memory candidates, portable adapter sketches, and downloadable audit artifacts.
Visible teamGateway, planner, specialist, critic, benchmark, memory, reproducibility, and report agents expose the handoff trail.
Validation gateCritic findings, score trajectories, judge rows, and human-gated memory separate learning from unchecked drift.
Three code lanesPython, R, and MATLAB/Octave adapter sketches describe equivalent backend and payload routes.
Export bundleMemo, notebook, audit CSV, payload, trace, and artifact bundle preserve each improvement cycle.
Statistics critiqueDiagnose a statistical workflow, score assumptions, and stage reusable lessons.
Solver benchmarkRoute numerical convergence evidence through critic and reproducibility gates.
Decision auditReview a control/OR policy, identify risk, and prepare the next run.
Self-Improving Agents->Statistics->Numerical Analysis->Optimal Control / OR

Control Room
Agent team
Reasoning backend lane
Orchestration layer
1 5
Memory mode
Benchmark gate
Safety mode

When off, benchmark-approved lessons are staged but not promoted into next-run memory.
Research mode

Quick starts
Deployment note: this demo is deterministic and sandboxed. It demonstrates workflow, critique, benchmarking, and product direction without claiming unrestricted autonomous external-system access.
About this multi-agent system
A self-improving research loop for scientific AI agents.
This agent is a showcase for AI systems that do not just answer once and stop. It routes a scientific request through specialist agents, critiques the output, measures improvement against benchmark gates, stages human-approved memory, and prepares the next run with a clearer plan.
Critique loopGenerates a draft, challenges it, tracks failure modes, and records why the next version should improve.
Benchmark gateUses quality, risk, reproducibility, and critic signals before any lesson becomes reusable memory.
Open AI architectureDemonstrates OpenClaw-style orchestration and Hermes-compatible backend lanes in a public research sandbox.
AI Lab connectorFeeds lessons back into Statistics, Numerical Analysis, and Optimal Control/OR companion agents.
Fast proof of value
The public Space now foregrounds the self-improvement loop: scenario presets, visible agent-bus handoffs, critic decisions, benchmark gates, memory candidates, portable adapter sketches, and downloadable audit artifacts.
Visible teamGateway, planner, specialist, critic, benchmark, memory, reproducibility, and report agents expose the handoff trail.
Validation gateCritic findings, score trajectories, judge rows, and human-gated memory separate learning from unchecked drift.
Three code lanesPython, R, and MATLAB/Octave adapter sketches describe equivalent backend and payload routes.
Export bundleMemo, notebook, audit CSV, payload, trace, and artifact bundle preserve each improvement cycle.
Statistics critiqueDiagnose a statistical workflow, score assumptions, and stage reusable lessons.
Solver benchmarkRoute numerical convergence evidence through critic and reproducibility gates.
Decision auditReview a control/OR policy, identify risk, and prepare the next run.
Self-Improving Agents->Statistics->Numerical Analysis->Optimal Control / OR