four-diagram-grounded-geometry-benchmarks-multi-agent-pipeline-consistently-helps-open-source CLM-0302observationsingle paperReviewed by AI
On four diagram-grounded geometry benchmarks a multi-agent pipeline consistently helps open-source vision-language models (Qwen-2.5-VL 7B +6.8 and 32B +3.3 points on Geometry3K, with further gains on OlympiadBench and We-Math) but a strong closed model (Gemini 2.0 Flash) generally did better as a single agent on the classic benchmarks, gaining only modestly on the newest one, so agentic decomposition pays for weaker models and less familiar tasks rather than universally.
Capability: Getting more from several models than from one
Observed on
Geometry3K, MathVerse, OlympiadBench, We-Math; Qwen-2.5-VL 7B/32B and Gemini 2.0 Flash, 2025; single-agent versus interpreter-solver-verifier pipeline..
Sources
- One domain (visual math) and one pipeline design; the weaker-model-gains pattern is consistent with other multi-agent evaluations.
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Related claims
- On knowledge, math and medical QA benchmarks with 8B open backbones (Llama-3.1-8B-Instruct, Qwen3-8B), the accuracy advantage of multi-agent debate pipelines over a single agent can be matched by a single-turn single agent whose hidden state is steered by a query-composed mixture of role vectors, at roughly a twentieth of the token cost, so the benefit of these pipelines comes from multi-perspective specialization rather than from inter-agent text exchange.Getting more from several models than from one · reviewed by AI
- Giving an agent a parsed code graph (files, classes, functions with import, call and inheritance edges) and tools to search and traverse it lets a fine-tuned 32B open model localize issues about as well as proprietary agents at roughly an eighth of the cost, reaching about 93% file-level accuracy, and better localization carried through to about 12 points more resolved issues at Pass@10 when plugged into a fixed repair pipeline.Finding what matters in a large codebase · reviewed by AI
- For small open-weight backbones (Qwen3-4B) driving long-horizon remote-sensing tool chains, inserting a step-level verifier that checks each intermediate tool output against schema, sub-goal and domain constraints and triggers replanning is the single largest architectural gain, while hierarchical decomposition without domain-aligned tool grouping adds little and can lower final accuracy relative to a flat agent with the same verifier.Completing tasks that take many steps or many hours · reviewed by AI
- When RL-training a central-agent multi-agent harness with 1.5B-3B Qwen2.5 backbones on multi-hop QA, optimizing only the coordinating central policy and leaving sub-agents frozen scores higher than jointly training central and sub-agent policies (average F1 47.79 vs 45.36, accuracy 42.88 vs 40.49 at 3B).Getting more from several models than from one · reviewed by AI
- A large share of an agent benchmark score is attributable to the harness around the model rather than the model itself: holding the model fixed and changing only guides, sensors, retries and state management is reported to move the same model from 30.9% to 74.6% on GAIA and 30th to 5th on Terminal Bench — so a score reported for "a model" on an agentic benchmark is a score for a model-plus-harness, and comparisons across labs with different harnesses are not comparisons of models.Whether the measurement made the finding · reviewed by AI
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: A frontier model gaining as much from the same pipeline as the open models on the classic benchmarks.