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flat/objects/30/03/3003eb23-a59a-4085-8bfa-818de99f4480.json

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flat/objects/30/51/3051fdf1-c3c6-458d-88c6-e9cbb4233431.json

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flat/objects/30/58/3058440f-acc5-4623-8e8e-443cd054e8ff.json

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flat/objects/30/58/30589df9-0808-4773-b5ff-39a022475170.json

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flat/objects/30/59/305901bf-57de-43a4-ae16-858a0a7fce58.json

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flat/objects/30/5a/305acd2f-7516-4e20-a94a-4bf6e8f16a77.json

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flat/objects/30/60/3060c809-a916-4d81-a580-cb91d5de75b7.json

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flat/objects/30/61/3061ba2b-28ee-4feb-8522-7e4a2a0875e6.json

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flat/objects/30/61/3061fc5f-24ae-49ef-bd9a-bcf7a7bfccb8.json

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flat/objects/30/66/3066be27-df9c-4ccc-9b8a-1ba061f95171.json

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flat/objects/30/67/306776bc-f7b5-4126-9c76-dc93de3467cf.json

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flat/objects/30/68/3068c2d2-b3bd-460e-83d6-4154e897a4fa.json

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flat/objects/30/92/30927b6d-ed7c-46ea-b2a7-6eade3495150.json

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flat/objects/30/92/3092e060-bc07-4101-869d-3dd9834c40ae.json

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flat/objects/30/a3/30a3fb6f-9f34-441c-895b-0cf401d0fa07.json

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flat/objects/30/b3/30b3d253-0b4f-4b34-a1d7-595aa815d09f.json

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flat/objects/30/b3/30b3f237-1da5-4163-b170-43e4eda5cbf7.json

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+        "max_score": 100.0,
+        "evaluation_description": "Measures the functional correctness of LLM-generated Java code. It is the percentage of problems for which the first generated solution passes all test cases. A higher score is better.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Pass@1 (%) - Java",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Java Code Correctness (Pass@1) on EFFIBENCH-X"
+        },
+        "metric_id": "java_code_correctness_pass_1_on_effibench_x",
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+    },
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+        "score_type": "continuous",
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+        "max_score": 100.0,
+        "evaluation_description": "Measures the functional correctness of LLM-generated JavaScript code. It is the percentage of problems for which the first generated solution passes all test cases. A higher score is better.",
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+          "alphaxiv_y_axis": "Pass@1 (%) - JavaScript",
+          "alphaxiv_is_primary": "False",
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+        },
+        "metric_id": "javascript_code_correctness_pass_1_on_effibench_x",
+        "metric_name": "JavaScript Code Correctness (Pass@1) on EFFIBENCH-X",
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+    },
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+        "source_type": "url",
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+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the functional correctness of LLM-generated Ruby code. It is the percentage of problems for which the first generated solution passes all test cases. A higher score is better.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Pass@1 (%) - Ruby",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Ruby Code Correctness (Pass@1) on EFFIBENCH-X"
+        },
+        "metric_id": "ruby_code_correctness_pass_1_on_effibench_x",
+        "metric_name": "Ruby Code Correctness (Pass@1) on EFFIBENCH-X",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 53.77
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+      "evaluation_result_id": "EffiBench-X/Gemini-2.0-Flash-Thinking/1771591481.616601#effibench_x#ruby_code_correctness_pass_1_on_effibench_x"
+    },
+    {
+      "evaluation_name": "EffiBench-X",
+      "source_data": {
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+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/EffiBench/effibench-x"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures functional correctness (Pass@1) on the subset of 'standard I/O' problems, where the model implements a complete program reading from stdin and writing to stdout. Models show a significant drop in performance on this subset.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Pass@1 (%) - Standard I/O Subset",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Code Correctness (Pass@1) on Standard I/O Problems"
+        },
+        "metric_id": "code_correctness_pass_1_on_standard_i_o_problems",
+        "metric_name": "Code Correctness (Pass@1) on Standard I/O Problems",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 44.33
+      },
+      "evaluation_result_id": "EffiBench-X/Gemini-2.0-Flash-Thinking/1771591481.616601#effibench_x#code_correctness_pass_1_on_standard_i_o_problems"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/30/b4/30b44854-6c23-4c8a-a143-017c15b73d9c.json

@@ -0,0 +1,328 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "ParEval/CodeLlama-13B/1771591481.616601",
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+    "source_type": "documentation",
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+    }
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+    "name": "CodeLlama-13B",
+    "developer": "unknown"
+  },
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+          "https://www.alphaxiv.org/abs/2401.12554"
+        ]
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+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "This metric, ParEval Parallel pass@1, measures the percentage of parallel code generation problems solved correctly on the first attempt. It is the primary metric for the ParEval benchmark, evaluating a model's ability to generate correct code for complex parallel computing tasks across various programming models (OpenMP, MPI, CUDA, etc.). These results are from the official, up-to-date ParEval leaderboard.",
+        "additional_details": {
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+        },
+        "metric_id": "pareval_parallel_code_generation_correctness",
+        "metric_name": "ParEval: Parallel Code Generation Correctness",
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+        "metric_unit": "points"
+      },
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+        "score": 17.4
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+      "evaluation_result_id": "ParEval/CodeLlama-13B/1771591481.616601#pareval#pareval_parallel_code_generation_correctness"
+    },
+    {
+      "evaluation_name": "ParEval",
+      "source_data": {
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+        "source_type": "url",
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+          "https://www.alphaxiv.org/abs/2401.12554"
+        ]
+      },
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+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
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+        "evaluation_description": "The `efficiency_n@1` metric measures the expected best performance efficiency (speedup per resource) of the generated parallel code, given one attempt. It indicates how well the code utilizes the available parallel resources (e.g., CPU cores). A value of 1.0 indicates perfect scaling, while lower values suggest poor resource utilization. This metric highlights that even when LLM-generated code is correct, it is often inefficient.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Efficiency (0-1)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "ParEval: Parallel Code Efficiency"
+        },
+        "metric_id": "pareval_parallel_code_efficiency",
+        "metric_name": "ParEval: Parallel Code Efficiency",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.09
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+      "evaluation_result_id": "ParEval/CodeLlama-13B/1771591481.616601#pareval#pareval_parallel_code_efficiency"
+    },
+    {
+      "evaluation_name": "ParEval",
+      "source_data": {
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+        "source_type": "url",
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+        ]
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+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "This metric, ParEval Serial pass@1, measures the percentage of sequential (non-parallel) C++ code generation problems solved correctly on the first attempt. It serves as a crucial baseline to contrast with parallel code generation performance, highlighting the specific difficulty of parallelism. These results are from the official, up-to-date ParEval leaderboard.",
+        "additional_details": {
+          "alphaxiv_y_axis": "pass@1 (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "ParEval: Serial Code Generation Correctness"
+        },
+        "metric_id": "pareval_serial_code_generation_correctness",
+        "metric_name": "ParEval: Serial Code Generation Correctness",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 52.8
+      },
+      "evaluation_result_id": "ParEval/CodeLlama-13B/1771591481.616601#pareval#pareval_serial_code_generation_correctness"
+    },
+    {
+      "evaluation_name": "ParEval",
+      "source_data": {
+        "dataset_name": "ParEval",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2401.12554"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "The `speedup_n@1` metric measures the expected best performance speedup of the generated parallel code relative to a sequential baseline, given one attempt. This novel metric, introduced by ParEval, quantifies not just correctness but also the runtime performance of the code. A higher value indicates the generated code runs faster.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Speedup (x)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "ParEval: Parallel Code Speedup"
+        },
+        "metric_id": "pareval_parallel_code_speedup",
+        "metric_name": "ParEval: Parallel Code Speedup",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 10.28
+      },
+      "evaluation_result_id": "ParEval/CodeLlama-13B/1771591481.616601#pareval#pareval_parallel_code_speedup"
+    },
+    {
+      "evaluation_name": "ParEval",
+      "source_data": {
+        "dataset_name": "ParEval",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2401.12554"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "This metric shows the pass@1 correctness score specifically for generating code using the CUDA programming model for NVIDIA GPUs. This task requires knowledge of GPU architecture concepts like kernels, threads, and memory spaces.",
+        "additional_details": {
+          "alphaxiv_y_axis": "pass@1 (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "ParEval: Correctness on CUDA Generation"
+        },
+        "metric_id": "pareval_correctness_on_cuda_generation",
+        "metric_name": "ParEval: Correctness on CUDA Generation",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 16
+      },
+      "evaluation_result_id": "ParEval/CodeLlama-13B/1771591481.616601#pareval#pareval_correctness_on_cuda_generation"
+    },
+    {
+      "evaluation_name": "ParEval",
+      "source_data": {
+        "dataset_name": "ParEval",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2401.12554"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "This metric shows the pass@1 correctness score specifically for generating code using the Kokkos performance portability framework. Kokkos abstracts over different parallel hardware (CPU, GPU), presenting a moderate level of difficulty for LLMs.",
+        "additional_details": {
+          "alphaxiv_y_axis": "pass@1 (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "ParEval: Correctness on Kokkos Generation"
+        },
+        "metric_id": "pareval_correctness_on_kokkos_generation",
+        "metric_name": "ParEval: Correctness on Kokkos Generation",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 16
+      },
+      "evaluation_result_id": "ParEval/CodeLlama-13B/1771591481.616601#pareval#pareval_correctness_on_kokkos_generation"
+    },
+    {
+      "evaluation_name": "ParEval",
+      "source_data": {
+        "dataset_name": "ParEval",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2401.12554"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "This metric shows the pass@1 correctness score specifically for generating code using the Message Passing Interface (MPI) execution model. MPI is a distributed-memory programming model that often requires significant algorithmic changes and complex communication patterns, making it one of the most challenging parallel paradigms for LLMs.",
+        "additional_details": {
+          "alphaxiv_y_axis": "pass@1 (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "ParEval: Correctness on MPI Generation"
+        },
+        "metric_id": "pareval_correctness_on_mpi_generation",
+        "metric_name": "ParEval: Correctness on MPI Generation",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 11
+      },
+      "evaluation_result_id": "ParEval/CodeLlama-13B/1771591481.616601#pareval#pareval_correctness_on_mpi_generation"
+    },
+    {
+      "evaluation_name": "ParEval",
+      "source_data": {
+        "dataset_name": "ParEval",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2401.12554"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "This metric shows the pass@1 correctness score specifically for generating code using the OpenMP execution model. OpenMP is a shared-memory programming model that often involves adding directives (pragmas) to sequential code, making it one of the simpler parallel paradigms. Models generally perform better on OpenMP compared to other parallel models.",
+        "additional_details": {
+          "alphaxiv_y_axis": "pass@1 (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "ParEval: Correctness on OpenMP Generation"
+        },
+        "metric_id": "pareval_correctness_on_openmp_generation",
+        "metric_name": "ParEval: Correctness on OpenMP Generation",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 24
+      },
+      "evaluation_result_id": "ParEval/CodeLlama-13B/1771591481.616601#pareval#pareval_correctness_on_openmp_generation"
+    },
+    {
+      "evaluation_name": "ParEval",
+      "source_data": {
+        "dataset_name": "ParEval",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2401.12554"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "This metric measures the percentage of problems solved correctly on the first attempt on the standard HumanEval benchmark, which evaluates general-purpose Python code generation. It is included to provide context on the models' overall coding capabilities outside of the specialized ParEval tasks. These results are from the official ParEval leaderboard.",
+        "additional_details": {
+          "alphaxiv_y_axis": "pass@1 (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "HumanEval: General Code Generation Correctness"
+        },
+        "metric_id": "humaneval_general_code_generation_correctness",
+        "metric_name": "HumanEval: General Code Generation Correctness",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 35
+      },
+      "evaluation_result_id": "ParEval/CodeLlama-13B/1771591481.616601#pareval#humaneval_general_code_generation_correctness"
+    },
+    {
+      "evaluation_name": "ParEval",
+      "source_data": {
+        "dataset_name": "ParEval",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2401.12554"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "This metric evaluates a model's ability to translate a correct serial C++ implementation into a parallel OpenMP version. This task assesses whether providing a correct example improves performance over generating code from scratch. Smaller models show significant improvement on translation tasks.",
+        "additional_details": {
+          "alphaxiv_y_axis": "pass@1 (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "ParEval: Correctness on Serial to OpenMP Translation"
+        },
+        "metric_id": "pareval_correctness_on_serial_to_openmp_translation",
+        "metric_name": "ParEval: Correctness on Serial to OpenMP Translation",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 56
+      },
+      "evaluation_result_id": "ParEval/CodeLlama-13B/1771591481.616601#pareval#pareval_correctness_on_serial_to_openmp_translation"
+    }
+  ],
+  "eval_library": {
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+    "version": "unknown"
+  }
+}

flat/objects/30/b5/30b58cff-6d8f-46a8-84f3-c22f88a948c1.json

@@ -0,0 +1,358 @@
+{
+  "schema_version": "0.2.2",
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+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Carnegie Mellon University",
+      "alphaxiv_dataset_type": "image",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Openjourney v1",
+    "name": "Openjourney v1",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
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+      "evaluation_name": "Holistic Evaluation of Text-to-Image Models",
+      "source_data": {
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+        "source_type": "url",
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+          "https://www.alphaxiv.org/abs/2311.04287"
+        ]
+      },
+      "metric_config": {
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+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures how semantically correct a generated image is with respect to its text prompt. The score is a 'win rate,' defined as the probability that the model outperforms another model selected uniformly at random in a head-to-head comparison on this aspect. This is evaluated using both human ratings and automated metrics like CLIPScore across datasets like MS-COCO, CUB-200-2011, DrawBench, and PartiPrompts.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Win Rate (Alignment)",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "HEIM: Text-Image Alignment Win Rate"
+        },
+        "metric_id": "heim_text_image_alignment_win_rate",
+        "metric_name": "HEIM: Text-Image Alignment Win Rate",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.617
+      },
+      "evaluation_result_id": "Holistic Evaluation of Text-to-Image Models/Openjourney v1/1771591481.616601#holistic_evaluation_of_text_to_image_models#heim_text_image_alignment_win_rate"
+    },
+    {
+      "evaluation_name": "Holistic Evaluation of Text-to-Image Models",
+      "source_data": {
+        "dataset_name": "Holistic Evaluation of Text-to-Image Models",
+        "source_type": "url",
+        "url": [
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+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the model's performance in generating images in various specified art styles (e.g., oil painting, watercolor, pixel art). The score is a 'win rate,' which is the probability of outperforming a random competitor. This evaluation likely combines the alignment (correctly applying the style) and aesthetics (visual appeal of the styled image) aspects for prompts from modified MS-COCO scenarios.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Win Rate (Art Styles)",
+          "alphaxiv_is_primary": "False",
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+        },
+        "metric_id": "heim_art_style_generation_win_rate",
+        "metric_name": "HEIM: Art Style Generation Win Rate",
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+        "metric_unit": "points"
+      },
+      "score_details": {
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+        "additional_details": {
+          "alphaxiv_y_axis": "Win Rate (Efficiency)",
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+      },
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+    },
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+      "evaluation_name": "Holistic Evaluation of Text-to-Image Models",
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+        "additional_details": {
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+    },
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+      "evaluation_name": "Holistic Evaluation of Text-to-Image Models",
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+        "source_type": "url",
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+        "score_type": "continuous",
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+        "additional_details": {
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+          "alphaxiv_is_primary": "False",
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+        "metric_name": "HEIM: Originality Win Rate",
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+        "additional_details": {
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+          "raw_evaluation_name": "HEIM: Aesthetics Win Rate"
+        },
+        "metric_id": "heim_aesthetics_win_rate",
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+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "HEIM: Toxicity Avoidance Win Rate"
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+}

flat/objects/30/b5/30b5ebb7-6c45-4205-92de-c490c118a1ca.json

@@ -0,0 +1,238 @@
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flat/objects/30/be/30be5cb1-13b6-4d5f-a5e0-ad24fe6d0a41.json

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flat/objects/30/be/30beb7b9-ebe6-47f8-8e3b-60842cf25fde.json

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flat/objects/30/d9/30d914dc-4344-4baa-8d21-b9064cba37fe.json

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+    {
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+        "metric_id": "summarization_performance_on_perspectrumx_with_gritlm_retriever_k_20",
+        "metric_name": "Summarization Performance on Perspectrumx with GritLM Retriever (k=20)",
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+}

flat/objects/30/df/30df32b2-805d-45a9-9fd9-057d3205b9a1.json

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flat/objects/3a/04/3a043a02-be37-44e6-9c43-5e4c8e3e5c23.json

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flat/objects/3a/05/3a05cc46-edd0-4e2e-b9fb-4cc2a5610b26.json

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flat/objects/3a/06/3a06ae8d-2a6f-4f32-bc00-6bef6f822a60.json

@@ -0,0 +1,268 @@
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flat/objects/3a/06/3a06e59d-662e-4cea-8e4b-9e43ef8c0f9d.json

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flat/objects/3a/07/3a07c665-83af-4e35-9289-32a967269763.json

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flat/objects/3a/07/3a07f6d5-8f55-494b-8152-327f0441a202.json

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flat/objects/3a/08/3a08002c-a48b-4d5f-ad95-0ed5f0b0a813.json

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