I use current systems and benchmarks as probes, not proof. OpenCode shows controlled tool use. AI Scientist-v2 shows experimental tree search. ARC-AGI-3 tests interactive adaptation. Quantum computing is considered only for formally specified optimisation subproblems.

Exploration · OpenCode

Reasoning becomes useful when action boundaries are explicit.

OpenCode separates agents, tools and permissions. That makes it a useful case study in how a reasoning system can act without treating every capability as equally available.

What I am learning

The interesting mechanism is not the chat interface. It is the combination of specialised agents, tool routing and allow / ask / deny boundaries.

Experiment I would run

Give two agents the same planning task but different tool permissions. Measure completion, unnecessary calls, reversals and approval burden.

Important limitation

A permission system constrains action. It does not guarantee that the plan is correct or safe.

Interactive reasoning loop

Correction needs evidence.

Click a stage. The research question is how to make the loop more reliable without spending unlimited computation.

Observe

Collect the state, constraints, available evidence and failure conditions.

Quantum is an optional tool path

Four gates before a call.

MCP could expose a specialised solver. It would not create the reasoning, the safety case or the permission to execute.

  1. A bounded optimisation problem is formally specified.
  2. A strong classical baseline has already been run.
  3. Routing criteria justify the extra cost and latency.
  4. The returned candidate can be independently verified.

Primary sources

Read the systems, papers and benchmark.

Original documentation and research behind these explorations. ARC-AGI-3 competition results can change; the Kaggle page is the current record.

View the full source library
OpenCodeAccessed 30 July 2026

Agents and permissions documentation

Original source
Sakana AI and research collaborators10 April 2025

The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search

Original source
ARC Prize Foundation24 March 2026

ARC-AGI-3: A New Challenge for Frontier Agentic Intelligence

Original source
Kaggle and ARC Prize Foundation2026 competition

ARC Prize 2026 — ARC-AGI-3 competition

Original source
Nature Reviews Physics28 October 2024

Challenges and opportunities in quantum optimization

Original source