Agentic Systems as Boosting Weak Reasoning Models
Quick Answer
The study demonstrates that a committee of weak reasoning models can outperform stronger models, achieving 76.4% task completion on SWE-bench Verified with a critic-comparator orchestration of GPT-5.4 nano, matching Gemini 3 Pro and Claude Opus 4.5.
Quick Take
The study demonstrates that a committee of weak reasoning models can outperform stronger models, achieving 76.4% task completion on Verified with a critic-comparator orchestration of GPT-5.4 nano, matching Gemini 3 Pro and Claude Opus 4.5. The key challenge lies in effectively selecting correct solutions from weak model proposals.
Key Points
- A single GPT-5.4 nano proposal solves 67.0% of tasks on SWE-bench Verified.
- Critic-comparator orchestration with k=8 proposals achieves 76.4% performance.
- Performance matches standalone models Gemini 3 Pro and Claude Opus 4.5.
- Coverage amplification requires reliable local soundness signals for effective selection.
- Remaining failures indicate shared blind spots in proposal coverage.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2605. 14163v1 Announce Type: new Abstract: Can a committee of weak reasoning-model calls reach the performance of much stronger models? We study verifier-backed committee search as inference-time boosting for reasoning language models. The mechanism is not simply that ``more agents help'': samples expose latent correct solutions, while critics and comparators must recover them without access to the hidden verifier.
We formalize this view by separating proposal coverage, local identifiability, progress, and diversity. We prove that coverage can be amplified by repeated sampling, but cannot by itself create useful critics or comparators; reliable amplification requires an additional local soundness signal, such as execution, proof checking, type checking, tests, or constraint solving.
We give rank-based bounds showing when local selection errors compose into reliable trajectories, and characterize the proposer-side ceiling: oracle best-of-\(k\) converges only to the mass of task slices on which the proposal system assigns nonzero useful probability. Empirically, on Verified, a single \texttt{GPT-5. 4 nano} proposal solves \(67. 0\%\) of tasks. Using the same nano model, our critic--comparator orchestration reaches \(76.
4\%\) with \(k=8\) proposals, matching the standalone performance of \texttt{Gemini 3 Pro} and \texttt{Claude Opus 4. 5} Thinking and approaching the \(79. 0\%\) oracle best-of-\(8\) upper bound. Thus, many correct patches are already present in weak-model proposal pools; the main challenge is selecting them. The remaining failures are mostly proposal-coverage failures, indicating shared blind spots that stronger selection alone cannot close.
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