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README.md CHANGED
@@ -1,3 +1,116 @@
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ language:
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+ - en
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+ tags:
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+ - structural-engineering
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+ - truss-optimization
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+ - reinforcement-learning
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+ - reasoning
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+ - design-benchmark
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+ license: mit
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+ ---
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+
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+ # DesignBench-Truss
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+
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+ ## Overview
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+
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+ DesignBench-Truss is the benchmark split (problems 090–099) of DesignBench, a
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+ dataset for teaching and evaluating large language models on iterative
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+ structural engineering design via Tree-of-Thought reasoning.
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+
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+ Each problem requires minimising the mass of a 2-D pin-jointed truss structure
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+ while satisfying structural safety constraints (factor of safety ≥ 1.5 for both
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+ buckling and yielding failure modes). A finite-element analysis oracle
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+ (trussme) provides deterministic feedback after every design modification.
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+
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+ ## Task Description
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+
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+ Given an initial truss design with known joint positions, member cross-sections,
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+ material properties, and loading conditions, a model must produce a sequence of
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+ discrete grammar actions that transform the design into a feasible,
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+ mass-minimised structure.
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+
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+ ### Grammar Actions
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+
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+ | Action | Signature | Description |
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+ |--------|-----------|-------------|
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+ | SCALE_PARAM | `SCALE_PARAM(member_id_or_all, param, factor)` | Scale one parameter of one (or all) members |
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+ | SCALE_MULTI_PARAM | `SCALE_MULTI_PARAM([member_ids], [param:factor, ...])` | Scale multiple parameters simultaneously |
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+ | ADD_MEMBER | `ADD_MEMBER(joint1, joint2, material, shape_type, r, t)` | Add a new pipe member between two joints |
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+
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+ ## Data Format
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+
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+ ### `benchmark.json`
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+
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+ Flat list of examples — one per gold trace — with the following fields:
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+
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+ ```json
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+ {
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+ "problem_id": "auto_problem_090",
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+ "trace_id": "auto_problem_090_trace_0",
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+ "problem_text": "PROBLEM: Truss optimization ...",
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+ "initial_state": {
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+ "mass": 142.5,
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+ "fos_buckling": 0.31,
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+ "fos_yielding": 1.09,
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+ "deflection": 0.038,
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+ "is_feasible": false
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+ },
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+ "gold_action_sequence": [
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+ "ADD_MEMBER(5, 6, 6061_T6_Aluminum, Pipe, 0.0497, 0.0056)",
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+ "SCALE_PARAM(all_members, thickness, 1.28)"
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+ ],
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+ "reaches_solution": true,
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+ "trace_quality": 0.98
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+ }
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+ ```
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+
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+ ### `problems/`
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+
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+ Raw problem JSON files containing joint positions, member topology, loads, and
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+ design constraints.
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+
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+ ### `trees/`
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+
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+ Full modification trees as JSON. Each tree contains all explored design states
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+ (nodes) and the grammar actions connecting them (edges).
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+
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+ ### `traces/`
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+
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+ Serialized gold traces — lists of node-sequences extracted from each tree.
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+
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+ ## Metrics
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+
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+ | Metric | Description |
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+ |--------|-------------|
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+ | feasibility_rate | Fraction of episodes reaching `is_feasible=True` |
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+ | mass_reduction | `(initial_mass - final_mass) / initial_mass` |
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+ | grammar_validity_rate | Fraction of steps with parseable grammar actions |
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+ | avg_steps_to_feasibility | Mean episode length when a feasible design is found |
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+ | overall_score | Weighted composite: design_correctness (0.5) + structural_validity (0.25) + reasoning_quality (0.15) + grammar_compliance (0.10) |
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+
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+ ## Example Usage
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+
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+ ```python
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+ import json
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+
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+ with open("benchmark.json") as f:
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+ examples = json.load(f)
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+
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+ print(f"Loaded {len(examples)} benchmark examples")
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+
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+ for ex in examples[:3]:
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+ print(ex["problem_id"], "->", ex["reaches_solution"],
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+ "actions:", len(ex["gold_action_sequence"]))
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+ ```
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @misc{designbench2025,
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+ title = {DesignBench: A Benchmark for Iterative Structural Engineering Design},
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+ author = {DesignBench Authors},
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+ year = {2025},
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+ note = {https://huggingface.co/datasets/DesignBench-Truss}
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+ }
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+ ```
benchmark.json ADDED
The diff for this file is too large to render. See raw diff
 
dataset_info.json ADDED
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+ {
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+ "dataset_name": "DesignBench-Truss",
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+ "version": "1.0",
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+ "description": "DesignBench is a benchmark for evaluating LLM capabilities in iterative structural engineering design. The truss domain uses finite element analysis (FEA) via trussme as the reward oracle. Each problem asks a model to minimize truss mass while satisfying buckling and yielding factor-of-safety constraints.",
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+ "num_problems": 10,
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+ "num_examples": 5000,
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+ "splits": {
8
+ "benchmark": {
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+ "num_problems": 10,
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+ "num_examples": 5000,
11
+ "purpose": "HuggingFace public benchmark — NEVER used for training"
12
+ }
13
+ },
14
+ "grammar_actions": [
15
+ "SCALE_PARAM",
16
+ "SCALE_MULTI_PARAM",
17
+ "ADD_MEMBER"
18
+ ],
19
+ "metrics": {
20
+ "feasibility_rate": "fraction of episodes reaching is_feasible=True",
21
+ "mass_reduction": "(initial_mass - final_mass) / initial_mass",
22
+ "grammar_validity_rate": "fraction of steps with parseable grammar actions",
23
+ "avg_steps_to_feasibility": "mean episode length when feasible",
24
+ "overall_score": "weighted composite of design_correctness(0.5) + structural_validity(0.25) + reasoning_quality(0.15) + grammar_compliance(0.10)"
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+ }
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+ }
problems/auto_problem_090.json ADDED
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+ "description": "Truss optimization problem (ground structure variant 90)",
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problems/auto_problem_091.json ADDED
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