chrisbanes / chrisbanes/ensemble

Cross-examination pipeline pattern (propose → attack → rebut)

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#178 3 comments 0 reactions 0 assignees View on GitHub

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enhancement ready-for-human
Dominant language
Rust
Stars
11
Forks
0
Avg merge
23h 12m
Merged PRs (30d)
84

Description

Support a workflow where one agent proposes a solution and another attacks it, with configurable rounds of rebuttal. The attacker tries to refute the proposer's work; a judge rules on what survived.

Why

Self-review is weak. A model is measurably worse at finding holes in its own reasoning than at attacking another's. Cross-provider examination (e.g. one model proposes, a different model attacks) decorrelates errors further.

Status

Unblocked by #174 for fixed-round implementations: agents can now receive each other's prior outputs through downstream step context. DAG loops are still not supported, so the practical first version should model a fixed number of rounds as sequential steps.

Example flow

  1. Proposer agent produces a solution
  2. Attacker agent receives the proposal and tries to refute it
  3. Proposer receives the attack and rebuts
  4. Repeat for a configured fixed number of rounds
  5. Judge agent rules on survived/conceded/open points

Initial scope

Start with fixed-round sequential steps. A later dynamic-loop design can be tracked separately if repeated rounds need to continue until convergence.

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by tracing the downstream step context described in the issue and the existing sequential-step execution path enabled by #174. Model the proposer, attacker, rebuttal, and judge as a fixed sequence of steps with configurable rounds; done means the workflow runs without DAG loops and passes the project's relevant tests.

Written by the indexing model from the issue text.

Assessment

Tech stack
rust
Domain
ai
Issue type
Feature
Difficulty
4/5
Estimated time
3-5 days
Activity status
Quiet
Clarity
Mostly clear
Newbie friendliness
48/100

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