Huishuai Zhang, Rui Yang, Jinwoong Kim
We lifted 11 functions out of this paper's own repositories and ran 10 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ooongs/GeoBuildBench | canonical | 10 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| a_to_cpx | Ran | ooongs/GeoBuildBench/src/core/geo_types.py pointer only (licence: NONE) · get_code("157ccb58eb7a99cc") |
| cpx_to_a | Ran | ooongs/GeoBuildBench/src/core/geo_types.py pointer only (licence: NONE) · get_code("666b801e4beb1eb3") |
| ensure_dir | Ran | ooongs/GeoBuildBench/src/utils.py pointer only (licence: NONE) · get_code("cd13711a78eb95fe") |
| evaluate_math_expression | Ran | ooongs/GeoBuildBench/src/core/random_constr.py pointer only (licence: NONE) · get_code("5de4717df9f3d151") |
| find_failed_problems | Ran | ooongs/GeoBuildBench/resume_benchmark.py pointer only (licence: NONE) · get_code("85b8769cd25af23e") |
| find_incomplete_problems | Ran | ooongs/GeoBuildBench/resume_benchmark.py pointer only (licence: NONE) · get_code("c296574d8c8a2fae") |
| get_completed_problems | Ran | ooongs/GeoBuildBench/resume_benchmark.py pointer only (licence: NONE) · get_code("f9d0b660e2aac910") |
| interpolate | Ran | ooongs/GeoBuildBench/src/core/geo_types.py pointer only (licence: NONE) · get_code("302aa95d6b92f423") |
| parse_trig_function | Ran | ooongs/GeoBuildBench/src/core/random_constr.py pointer only (licence: NONE) · get_code("8799dfa5f983c72d") |
| resolve_path | Ran | ooongs/GeoBuildBench/src/utils.py pointer only (licence: NONE) · get_code("2e2818788a6e2c14") |
| get_output_dir | Not yet run | ooongs/GeoBuildBench/src/utils.py pointer only (licence: NONE) · get_code("9b0c75ec4659dbad") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We introduce GeoBuildBench, a benchmark designed to evaluate whether large language models and multimodal agents can ground informal natural-language plane geometry problems into executable geometric constructions. Unlike existing geometry benchmarks that focus on answer correctness or static diagram interpretation, GeoBuildBench treats geometry diagram as an interactive construction task: given a textual problem, an agent must generate a domain-specific language (DSL) program to produce a diagram satisfying explicitly specified geometric objects and verifiable constraints. The benchmark features 489 Chinese textbook-style problems, curated through automated filtering and human validation to ensure text-complete, constructible problem specifications. We evaluate several state-ofthe-art multimodal models in a bounded iterative setting and show that, despite reasonable success rates, models frequently exhibit structural hallucinations, missing objects, and failures to satisfy geometric constraints, with limited ability to exploit visual and constraintbased feedback for self-correction. These results highlight geometry construction as a rigorous testbed for grounded, executable reasoning beyond textual or visual plausibility. Our benchmark and code are released at https: //github.com/ooongs/GeoBuildBench.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2605.13167")
get_code_for_paper("2605.13167")
have("2605.13167")
Connect an agent — have() is free.