Restore VLM prompts, defect vocabulary, scoring manifest, and split files (R2 Sec 7 reproducibility) after author review

reproducibility/llm_keywords.txt

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+Complexity
+Cost increase
+Time consumption
+Tooling difficulty
+Material incompatibility
+Process variability
+Yield loss
+Defect rate
+Rework requirement
+Scrap volume
+Lead time extension
+Supply chain disruption
+Regulatory non-compliance
+Design-for-manufacturing challenge
+Insufficient clearance
+Interference fit
+Misalignment
+Tolerance stack‑up
+Dimensional drift
+Wear
+Corrosion
+Thermal expansion mismatch
+Assembly difficulty
+Contact pressure
+Stress concentration
+Undercutting
+Burr formation
+Edge damage
+Roughness
+Waviness
+Unevenness
+Scratches
+Pits
+Contamination
+Oxidation
+Microcracks
+Adhesion failure
+Coating delamination
+Abrasive particles
+Dust accumulation
+Debris presence
+Foreign matter inclusion
+Surface fatigue
+Structural weakness
+Single-point failure
+Limited repairability
+Weight imbalance
+Stiffness reduction
+Vibration sensitivity
+Heat dissipation issue
+Integration constraint
+Compatibility problem
+Scalability limitation
+Slip hazard
+Low grip
+Increased wear from sliding
+Reduced bonding area
+Poor adhesion
+Susceptible to contaminants
+Static charge build‑up
+Decreased friction causing slippage
+Bearing galloping
+Noise generation
+Lubrication film breakdown
+Brittleness
+Fatigue crack initiation
+Creep tendency
+Plastic deformation
+Overload condition
+Load path inefficiency
+Anisotropic behavior
+Residual stresses
+Weld cracking
+Galvanic corrosion
+Temperature-induced weakening
+Aging degradation
+Inadequate reinforcement
+Improper loading
+Environmental exposure
+Humidity ingress
+UV radiation damage
+Chemical attack
+Mechanical shock
+Vibrations
+Thermal cycling
+Pitting corrosion
+Erosion
+Fouling
+Biofouling
+Scaling
+Rust
+Oxidative deterioration
+Embrittlement
+Warping
+Distortion
+Out-of-roundness
+Misfit
+Packaging damage
+Shipping damage
+Storage instability
+Handling mishap
+Operator error
+Training deficit
+Documentation gap
+Quality lapse
+Inspection failure
+Measurement inaccuracy
+Calibration drift
+Statistical variance
+Nonconformance
+Audit finding
+Pending corrective action

reproducibility/scored_results-Manifest.json

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+{
+  "datasets": [
+    {
+      "manifests": "stp_Manifest-LHS",
+      "scores": [
+        { 
+          "name": "scored_results_p1.csv", 
+          "critic" : [
+            "Manufacturability",
+            "Surface Clearence",
+            "Surface Texture",
+            "mono-body",
+            "uni-body",
+            "Smooth Surface",
+            "Good Strength"
+            ], 
+          "n_keywords_set" : "llm_keywords.txt", 
+          "prompt": "Could you describe this red object's surface condition as detail as possible and some Geometry Complexity? Answer:" 
+        },
+        { 
+          "name": "scored_results_p11l.csv", 
+          "critic" : [
+            "Manufacturability",
+            "Surface Clearence",
+            "Surface Texture",
+            "mono-body",
+            "uni-body",
+            "Smooth Surface",
+            "Good Strength"
+            ], 
+          "n_keywords_set" : "llm_keywords.txt", 
+          "prompt": "[llava]Question: Do not use word \"no\". Describe the red object's surface roughness as details. Do not use \"no\". Answer:" 
+        },
+        { 
+          "name": "scored_results_P0.csv", 
+          "critic" : [
+            "Manufacturability",
+            "Surface Clearence",
+            "Surface Texture",
+            "mono-body",
+            "uni-body",
+            "Smooth Surface",
+            "Good Strength"
+            ], 
+          "n_keywords_set" : "llm_keywords.txt", 
+          "prompt": "Question: Could you descrive this red object's surface condition and holes over surface? Answer:" 
+        }
+      ]
+    }    
+  ]
+}

reproducibility/vlm_prompts.md

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+# VLM prompts used in the DeepJEB++ quality filter
+
+<!-- ============================================================================
+ PUBLIC FILE - this is what gets uploaded to Hugging Face as reproducibility/vlm_prompts.md
+ The annotated working copy, with provenance notes, is prompts_extracted.md in this same
+ folder and is NOT for release.
+
+ Cleared for upload: the scoring-criteria question was answered by Jinsu Ra on 2026-08-22.
+ ============================================================================ -->
+
+Stage 1 of the pipeline screens candidate images with a vision-language model. The model is
+asked to describe a rendered bracket, and the description is scored against a fixed vocabulary
+of defect words; images whose score falls in the most defect-like top-*p*% are removed. This
+file gives the exact prompt strings behind every filter variant reported in the paper, so that
+the numbers in Table 2 can be reproduced.
+
+The strings are reproduced verbatim from the generation notebooks and from the scoring manifest
+on our lab server. Nothing here is retyped.
+
+## The adopted filter — LLaVA with negative-word negation (NWN)
+
+```
+Question: Do not use word "no". Describe the red object's surface roughness as details. Do not use "no". Answer:
+```
+
+This is the filter used to build the released dataset. It reaches **72.17%** accuracy on the
+labeled benchmark at the default threshold and **76.10%** at the tuned operating point
+*p* = 29% (false-positive rate 40.7%, false-negative rate 14.0%).
+
+The doubled negation is deliberate. Asking the model not to use the word "no" suppresses the
+negated descriptions ("no visible cracks", "not rough") that otherwise dominate the output and
+collide with the defect vocabulary, since a negated defect word and a present defect word score
+alike under embedding similarity.
+
+## The no-correction baseline — same backbone, no negation instruction
+
+```
+Could you describe this red object's surface condition as detail as possible and some Geometry Complexity? Answer:
+```
+
+This is Table 2's "Vanilla prompt (no NWN)" row at **56.45%**. The gap between this and the
+adopted prompt is the effect of the negation instruction alone: the backbone, the vocabulary and
+the scoring are identical.
+
+## The alternative backbone — BLIP with the same NWN prompt
+
+```
+Question: Do not use word "no". Describe the red object's surface roughness as details. Do not use "no". Answer:
+```
+
+Identical prompt text; only the vision-language backbone differs. This is Table 2's
+"BLIP + NWN prompt" row at **62.67%**, where the filter degenerates to near keep-all.
+
+## Which result file used which prompt
+
+| Reported result | Backbone | Prompt above | Vocabulary |
+|---|---|---|---|
+| 76.10% / 72.17% (adopted) | LLaVA | adopted NWN | `llm_keywords.txt` |
+| 56.45% | LLaVA | no-correction baseline | `llm_keywords.txt` |
+| 62.67% | BLIP | adopted NWN | `llm_keywords.txt` |
+
+All three scoring runs use the same 112-word vocabulary, released here as
+`reproducibility/llm_keywords.txt`.
+
+## Vocabulary
+
+`llm_keywords.txt` holds the **112** defect and manufacturability terms, one per line, that form
+the negative-word set. The description embedding is compared against each term and the *mean*
+cosine similarity is the image's defect score, so the file's length is also the dimension of the
+similarity vector referred to in the paper as the 112-dimensional negative-word similarities.
+
+The file has no trailing newline, so `wc -l` reports 111; the count is 112.
+
+## Scoring criteria
+
+The 112 defect words were generated by prompting a language model with seven engineering
+criteria. All three scoring runs use the same list:
+
+```
+Manufacturability
+Surface Clearence
+Surface Texture
+mono-body
+uni-body
+Smooth Surface
+Good Strength
+```
+
+These are reproduced exactly as the scoring configuration records them, including the spelling
+of "Clearence". Section 3.2.4 of the paper and Figure 3 list the same seven; both write
+"surface roughness" for the criterion the configuration labels "Surface Texture", which is the
+same criterion under two names.
+
+The criteria are the prompt given to the language model that produces the vocabulary. They are
+not applied to images as separate tests: the only quantity computed per image is the mean
+similarity to the 112 words.
+
+## A limitation of this release
+
+The prompts and the vocabulary are fully determined, and the diffusion sampler is deterministic.
+The selection of *which* images condition a given 3D generation is not recoverable: each design
+is conditioned on eight images drawn from its interpolation pair's retained pool, and that
+sampler was seeded with Python's string hash, which is randomized per process and was not fixed.
+The dataset itself is unaffected — the meshes and labels are what they are — but a bit-exact
+replay of the image-to-mesh step is not possible.