diff --git a/.gitignore b/.gitignore
index f26d9d1..bda1609 100644
--- a/.gitignore
+++ b/.gitignore
@@ -50,4 +50,3 @@ __pycache__
 
 docs/build
 site/
-uv.lock
diff --git a/oe_eval/configs/task_suites.py b/oe_eval/configs/task_suites.py
index 1222a8b..0b33c5f 100644
--- a/oe_eval/configs/task_suites.py
+++ b/oe_eval/configs/task_suites.py
@@ -399,6 +399,10 @@
     "tasks": [f"mmlu_pro_{cat}:cot::none" for cat in MMLU_PRO_CATEGORIES],
     "primary_metric": "micro",
 }
+TASK_SUITE_CONFIGS["mmlu_pro:cot::qwen3_stopfix"] = {
+    "tasks": [f"mmlu_pro_{cat}:cot::qwen3_stopfix" for cat in MMLU_PRO_CATEGORIES],
+    "primary_metric": "micro",
+}
 TASK_SUITE_CONFIGS["mmlu_pro:cot::llama3.1"] = {
     "tasks": [f"mmlu_pro_{cat}:cot::llama3.1" for cat in MMLU_PRO_CATEGORIES],
     "primary_metric": "micro",
diff --git a/oe_eval/configs/tasks.py b/oe_eval/configs/tasks.py
index 658df96..ba7c404 100644
--- a/oe_eval/configs/tasks.py
+++ b/oe_eval/configs/tasks.py
@@ -1226,6 +1226,68 @@
                 "regimes": ["Llama-3"],
             },
         },
+        "gpqa:5shot_cot::qwen3_official_prompt": {
+            "task_name": "gpqa",
+            "dataset_name": "gpqa_main",
+            "split": "train",
+            "primary_metric": "exact_match",
+            "num_shots": 5,
+            "fewshot_source": "Original:GPQA",
+            "generation_kwargs": {
+                "max_gen_toks": 2048,
+                "do_sample": False,
+                "temperature": 0.0,
+                "stop_sequences": [
+                    "\n\nQuestion:",
+                    "\nQuestion:",
+                    "You are an AI assistant",
+                    "</s>",
+                    "<|im_end|>",
+                ],
+            },
+            "context_kwargs": {
+                "answer_shuffling_seed": 111,
+                "description": "Here are some example questions from experts. An explanation is given before the final answer. Answer the final question yourself, giving your reasoning beforehand.\n",
+                "final_description": "\nGive step by step reasoning before you answer, and when you're ready to answer, please use the format \"The correct answer is (insert answer here)\":\n",
+            },
+            "metric_kwargs": {
+                "answer_format_regex": "The correct answer is \\(([A-D])\\)",
+            },
+            "metadata": {
+                "regimes": [],
+            },
+        },
+        "gpqa_diamond:5shot_cot::qwen3_official_prompt": {
+            "task_name": "gpqa",
+            "dataset_name": "gpqa_diamond",
+            "split": "train",
+            "primary_metric": "exact_match",
+            "num_shots": 5,
+            "fewshot_source": "Original:GPQA",
+            "generation_kwargs": {
+                "max_gen_toks": 2048,
+                "do_sample": False,
+                "temperature": 0.0,
+                "stop_sequences": [
+                    "\n\nQuestion:",
+                    "\nQuestion:",
+                    "You are an AI assistant",
+                    "</s>",
+                    "<|im_end|>",
+                ],
+            },
+            "context_kwargs": {
+                "answer_shuffling_seed": 111,
+                "description": "Here are some example questions from experts. An explanation is given before the final answer. Answer the final question yourself, giving your reasoning beforehand.\n",
+                "final_description": "\nGive step by step reasoning before you answer, and when you're ready to answer, please use the format \"The correct answer is (insert answer here)\":\n",
+            },
+            "metric_kwargs": {
+                "answer_format_regex": "The correct answer is \\(([A-D])\\)",
+            },
+            "metadata": {
+                "regimes": [],
+            },
+        },
         "gpqa:0shot_cot::tulu3": {
             "task_name": "gpqa",
             "split": "train",
@@ -1653,6 +1715,33 @@
             "num_shots": 3,  # like deepseek
             "limit": 500,
         },
+        "mbpp:3shot::qwen3_signature_tests": {
+            "task_name": "mbpp",
+            "primary_metric": "pass_at_1",
+            "generation_kwargs": {
+                "stop_sequences": ["```", '\n"""', "\nassert", "\n#", "\n<|/", "<|eot_id|>"],
+                "do_sample": True,
+                "top_p": 0.95,
+                "temperature": 0.2,
+                "repeats": 20,
+            },
+            "use_chat_format": False,
+            "context_kwargs": {
+                "prompt_variant": "qwen3_signature_tests",
+                "assistant_prefix": None,
+            },
+            "metric_kwargs": {
+                "pass_at_ks": [1],
+                "n_exe_workers": 20,
+                "rich_exec_info": True,
+            },
+            "compute_gold_bpb": False,
+            "num_shots": 3,
+            "limit": 500,
+            "metadata": {
+                "regimes": [],
+            },
+        },
         "mbpp:3shot::olmo3": {
             "task_name": "mbpp",
             "primary_metric": "pass_at_1",
@@ -4942,6 +5031,20 @@
             "regimes": [],
         },
     }
+    TASK_CONFIGS[f"mmlu_pro_{cat}:cot::qwen3_stopfix"] = {
+        "task_name": f"mmlu_pro_{cat}:cot",
+        "split": "test",
+        "num_shots": 5,
+        "generation_kwargs": {
+            "max_gen_toks": 1024,
+            "do_sample": False,
+            "temperature": 0.0,
+            "stop_sequences": ["Question:"],
+        },
+        "metadata": {
+            "regimes": [],
+        },
+    }
     TASK_CONFIGS[f"mmlu_pro_{cat}:cot::llama3.1"] = {
         "task_name": f"mmlu_pro_{cat}:cot",
         "split": "test",
@@ -7092,6 +7195,127 @@
                 "regimes": [],
             },
         },
+        "cruxeval_output::qwen3_greedy": {
+            "task_name": "cruxeval_output",
+            "primary_metric": "pass_at_1",
+            "generation_kwargs": {
+                "do_sample": False,
+                "temperature": 0.0,
+                "repeats": 1,
+                "stop_sequences": ["\nassert", "\ndef", "\n```", '\n"""', "\n#", "\n\n"],
+            },
+            "metric_kwargs": {
+                "pass_at_ks": [1],
+                "n_exe_workers": 20,
+                "rich_exec_info": True,
+            },
+            "metadata": {
+                "regimes": [],
+            },
+        },
+        "cruxeval_output::official_direct": {
+            "task_name": "cruxeval_output",
+            "primary_metric": "pass_at_1",
+            "num_shots": 0,
+            "generation_kwargs": {
+                "max_gen_toks": 512,
+                "do_sample": False,
+                "temperature": 0.0,
+                "repeats": 1,
+                "stop_sequences": ["[/ANSWER]", "</s>", "<|im_end|>"],
+            },
+            "context_kwargs": {
+                "description": "",
+                "prompt_variant": "official_direct",
+            },
+            "metric_kwargs": {
+                "pass_at_ks": [1],
+                "n_exe_workers": 20,
+                "rich_exec_info": True,
+            },
+            "metadata": {
+                "regimes": [],
+            },
+        },
+        "cruxeval_output::official_cot_1shot": {
+            "task_name": "cruxeval_output",
+            "primary_metric": "pass_at_1",
+            "num_shots": 0,
+            "generation_kwargs": {
+                "max_gen_toks": 2048,
+                "do_sample": True,
+                "temperature": 0.2,
+                "top_p": 0.95,
+                "repeats": 10,
+                "stop_sequences": ["[/ANSWER]", "</s>", "<|im_end|>"],
+            },
+            "context_kwargs": {
+                "description": "",
+                "prompt_variant": "official_cot_output",
+            },
+            "metric_kwargs": {
+                "pass_at_ks": [1, 5],
+                "n_exe_workers": 20,
+                "rich_exec_info": True,
+            },
+            "metadata": {
+                "regimes": [],
+            },
+        },
+        "cruxeval_output::lm_eval_2shot": {
+            "task_name": "cruxeval_output",
+            "primary_metric": "pass_at_1",
+            "num_shots": 2,
+            "fewshot_source": "Original:CruxEvalOutput:lm_eval",
+            "generation_kwargs": {
+                "max_gen_toks": 1024,
+                "do_sample": True,
+                "temperature": 0.2,
+                "top_p": 0.95,
+                "repeats": 10,
+                "stop_sequences": ["[/ANSWER]", "</seed:think>[ANSWER]", "</s>", "<|im_end|>"],
+            },
+            "context_kwargs": {
+                "description": "You are given a Python function and an assertion containing an input to the function. Complete the assertion with a literal (no unsimplified expressions, no function calls) containing the output when executing the provided code on the given input, even if the function is incorrect or incomplete. Do NOT output any extra information. Provide the full assertion with the correct output in [ANSWER] and [/ANSWER] tags, following the examples.\n",
+                "final_description": "\n[ANSWER]\n",
+                "prompt_variant": "lm_eval_direct",
+            },
+            "metric_kwargs": {
+                "pass_at_ks": [1],
+                "n_exe_workers": 20,
+                "rich_exec_info": True,
+            },
+            "metadata": {
+                "regimes": [],
+            },
+        },
+        "cruxeval_output::lm_eval_1shot": {
+            "task_name": "cruxeval_output",
+            "primary_metric": "pass_at_1",
+            "num_shots": 1,
+            "fewshot_source": "Original:CruxEvalOutput:lm_eval",
+            "generation_kwargs": {
+                "max_gen_toks": 1024,
+                "do_sample": True,
+                "temperature": 0.2,
+                "top_p": 0.95,
+                "repeats": 10,
+                "stop_sequences": ["[/ANSWER]", "</seed:think>[ANSWER]", "</s>", "<|im_end|>"],
+            },
+            "context_kwargs": {
+                "description": "You are given a Python function and an assertion containing an input to the function. Complete the assertion with a literal (no unsimplified expressions, no function calls) containing the output when executing the provided code on the given input, even if the function is incorrect or incomplete. Do NOT output any extra information. Provide the full assertion with the correct output in [ANSWER] and [/ANSWER] tags, following the examples.\n",
+                "final_description": "\n[ANSWER]\n",
+                "prompt_variant": "lm_eval_direct",
+            },
+            "metric_kwargs": {
+                "pass_at_ks": [1],
+                "n_exe_workers": 20,
+                "rich_exec_info": True,
+            },
+            "metadata": {
+                "regimes": [],
+            },
+        },
         "cruxeval_output:pass@5": {
             "task_name": "cruxeval_output",
             "primary_metric": "pass_at_5",
diff --git a/oe_eval/models/eleuther_vllm_causallms.py b/oe_eval/models/eleuther_vllm_causallms.py
index cca0a39..794666e 100644
--- a/oe_eval/models/eleuther_vllm_causallms.py
+++ b/oe_eval/models/eleuther_vllm_causallms.py
@@ -7,7 +7,9 @@
 import importlib_metadata
 import torch
 import transformers
+from more_itertools import distribute
 from lm_eval.models.utils import Collator
+from lm_eval.models.utils import undistribute
 from lm_eval.models.vllm_causallms import VLLM
 from packaging import version
 from tqdm import tqdm
@@ -111,6 +113,61 @@ def unload_model(self):
         except Exception:
             pass
 
+    @staticmethod
+    def _as_vllm_token_prompts(requests: List[List[int]]):
+        from vllm import TokensPrompt
+
+        return [TokensPrompt(prompt_token_ids=list(request)) for request in requests]
+
+    def _model_generate(
+        self,
+        requests: List[List[int]] = None,
+        generate: bool = False,
+        max_tokens: int = None,
+        stop: Optional[List[str]] = None,
+        **kwargs,
+    ):
+        from vllm import LLM
+        from vllm import SamplingParams
+
+        requests = requests or []
+        if generate:
+            kwargs = self.modify_gen_kwargs(kwargs)
+            sampling_params = SamplingParams(max_tokens=max_tokens, stop=stop, **kwargs)
+        else:
+            sampling_params = SamplingParams(
+                temperature=0, prompt_logprobs=1, max_tokens=1, detokenize=False
+            )
+
+        if self.data_parallel_size > 1:
+            import ray
+
+            @ray.remote
+            def run_inference_one_model(
+                model_args: dict, sampling_params, requests: List[List[int]]
+            ):
+                llm = LLM(**model_args)
+                return llm.generate(
+                    prompts=VLLM_Verbose._as_vllm_token_prompts(requests),
+                    sampling_params=sampling_params,
+                )
+
+            requests = [list(x) for x in distribute(self.data_parallel_size, requests)]
+            inputs = ((self.model_args, sampling_params, req) for req in requests)
+            object_refs = [run_inference_one_model.remote(*x) for x in inputs]
+            results = ray.get(object_refs)
+            ray.shutdown()
+            return undistribute(results)
+
+        generate_kwargs = {
+            "prompts": self._as_vllm_token_prompts(requests),
+            "sampling_params": sampling_params,
+            "use_tqdm": True if self.batch_size == "auto" else False,
+        }
+        if self.lora_request is not None:
+            generate_kwargs["lora_request"] = self.lora_request
+        return self.model.generate(**generate_kwargs)
+
     def generate_until_verbose(
         self, requests: List[GenerateUntilRequest], disable_tqdm: bool = False
     ) -> List[dict]:
diff --git a/oe_eval/tasks/fewshot_sources.py b/oe_eval/tasks/fewshot_sources.py
index 39a48d2..9ba6a72 100644
--- a/oe_eval/tasks/fewshot_sources.py
+++ b/oe_eval/tasks/fewshot_sources.py
@@ -2498,6 +2498,21 @@
     },
 ]
 
+FEWSHOT_SOURCES["Original:CruxEvalOutput:lm_eval"] = [
+    {
+        "id": "1",
+        "code": "def f(n):\n    return n",
+        "input": "17",
+        "output": "17",
+    },
+    {
+        "id": "2",
+        "code": 'def f(s):\n    return s + "a"',
+        "input": '"x9j"',
+        "output": '"x9ja"',
+    },
+]
+
 FEWSHOT_SOURCES["Llama3:squad2"] = [
     {
         "id": "56d43ce42ccc5a1400d830b5",
diff --git a/oe_eval/tasks/oe_eval_tasks/codex_mbpp.py b/oe_eval/tasks/oe_eval_tasks/codex_mbpp.py
index df36c12..1a59bab 100644
--- a/oe_eval/tasks/oe_eval_tasks/codex_mbpp.py
+++ b/oe_eval/tasks/oe_eval_tasks/codex_mbpp.py
@@ -11,6 +11,7 @@
 """
 
 import random
+import re
 from typing import List, Optional, Union
 
 from oe_eval.components.instances import RequestInstance
@@ -182,6 +183,13 @@ def _process_doc(self, doc):
                 + doc["code"].split(":")[0]
             )
             out_doc["query"] = text_prefix
+        elif self.task_config["context_kwargs"].get("prompt_variant") == "qwen3_signature_tests":
+            text_prefix = doc[self.PROMPT_FIELD].strip()
+            unittest_prefix = "\nYour code should pass these tests:\n" + "\n".join(
+                doc["test_list"][:3]
+            )
+            signature = doc["code"].split(":")[0] + ":"
+            out_doc["query"] = text_prefix + unittest_prefix + "\n```python\n" + signature
         elif self.task_config["context_kwargs"].get("prompt_variant") == "inloop_bpb":
             text_prefix = doc[self.PROMPT_FIELD].strip()
             out_doc["query"] = text_prefix + "\n```python\n"
@@ -198,6 +206,12 @@ def _process_doc(self, doc):
     def doc_to_target(self, doc):
         if self.task_config["context_kwargs"].get("prompt_variant") == "deepseek":
             return "\n[BEGIN]\n" + doc["code"] + "\n[DONE]"
+        if self.task_config["context_kwargs"].get("prompt_variant") == "qwen3_signature_tests":
+            signature = doc["code"].split(":")[0] + ":"
+            code = doc["code"].rstrip("\n").rstrip().replace("\r", "")
+            if code.startswith(signature):
+                return code[len(signature) :] + "\n```"
+            return "\n" + code + "\n```"
         if self.task_config["context_kwargs"].get("prompt_variant") == "inloop_bpb":
             return doc["code"].rstrip("\n").rstrip().replace("\r", "") + "\n```"
         else:
@@ -231,7 +245,8 @@ def fewshot_context(
         use_chat_format: bool = False,
         fewshot_as_multiturn: bool = False,
     ) -> Union[str, list, dict]:
-        if self.task_config["context_kwargs"].get("prompt_variant") == "inloop_bpb":
+        prompt_variant = self.task_config["context_kwargs"].get("prompt_variant")
+        if prompt_variant in {"inloop_bpb", "qwen3_signature_tests"}:
             assert num_fewshot <= len(
                 self.dataset["prompt"]
             ), f"num_fewshot must be <= {len(self.dataset['prompt'])}"
@@ -245,16 +260,12 @@ def fewshot_context(
                 for query, answer in fewshot_docs:
                     messages.append({"role": "user", "content": query.strip()})
                     messages.append({"role": "assistant", "content": answer.strip()})
-                messages.append({"role": "user", "content": doc["text"]})
+                messages.append({"role": "user", "content": self.doc_to_text(doc).strip()})
                 return {"messages": messages}
             else:
-                text = (
-                    "\n\n".join([query + answer for query, answer in fewshot_docs])
-                    + "\n\n"
-                    + doc["text"].strip()
-                    + "\n```python\n"
+                return "\n\n".join([query + answer for query, answer in fewshot_docs]) + (
+                    "\n\n" + self.doc_to_text(doc)
                 )
-                return text
         else:
             return super().fewshot_context(
                 doc=doc,
@@ -277,7 +288,18 @@ def _process_code_results(self, results: List[dict]) -> List[dict]:
         """
         output = []
         for res in results:
+            res_id = res.get("res_id", f"{res['doc_id']}-{res['idx']}")
+            res["res_id"] = res_id
             completion = res["model_resps"]["continuation"]
+            if self.task_config["context_kwargs"].get("prompt_variant") == "qwen3_signature_tests":
+                stripped_completion = completion.lstrip()
+                if re.match(r"^(def|import|from|class)\b", stripped_completion):
+                    completion = stripped_completion
+                else:
+                    body = re.sub(r"^[ \t]*\r?\n", "", completion, count=1)
+                    if body and not body[0].isspace():
+                        body = "    " + body.replace("\n", "\n    ")
+                    completion = res["doc"]["code"].split(":")[0] + ":\n" + body
 
             if self.task_config["metric_kwargs"].get("answer_regexes"):
                 model_answer = extract_answer(completion, task_config=self.task_config)["answer"]
@@ -298,10 +320,13 @@ def _process_code_results(self, results: List[dict]) -> List[dict]:
                 raise ValueError(
                     "Invalid unittest_list value. Use 'test_list' for original MBPP tests or 'test' for MBPPPlus tests."
                 )
+            test_setup_code = res["doc"].get("test_setup_code", "")
+            if test_setup_code:
+                unittests = test_setup_code.rstrip() + "\n" + unittests
 
             output.append(
                 {
-                    "res_id": res["res_id"],
+                    "res_id": res_id,
                     "completion": completion,
                     "test": unittests,
                 }
diff --git a/oe_eval/tasks/oe_eval_tasks/cruxeval.py b/oe_eval/tasks/oe_eval_tasks/cruxeval.py
index 9cf9a88..a38413f 100644
--- a/oe_eval/tasks/oe_eval_tasks/cruxeval.py
+++ b/oe_eval/tasks/oe_eval_tasks/cruxeval.py
@@ -8,6 +8,7 @@
 Huggingface dataset: https://huggingface.co/datasets/cruxeval-org/cruxeval
 """
 
+import re
 from typing import List, Union
 
 from oe_eval.components.instances import RequestInstance
@@ -165,7 +166,7 @@ class CruxEvalOutput(CruxEvalInput):
                 "description": "Based on the given Python code, which may contain errors, complete the assert statement with the output when executing the code on the given test case. Do NOT output any extra information, even if the function is incorrect or incomplete. Do NOT output a description for the assert.\n"
             },
             "generation_kwargs": {
-                "stop_sequences": ["\nassert", "\ndef" "```", '\n"""', "\n#", "\n\n"],
+                "stop_sequences": ["\nassert", "\ndef", "\n```", '\n"""', "\n#", "\n\n"],
             },
         },
         CruxEvalInput.TASK_CONFIG_DEFAULTS,
@@ -175,8 +176,46 @@ def _process_doc(self, doc, index=1):
         code = doc["code"]
         input = doc["input"]
         output = doc["output"]
-
-        query = f"\n{code}\nassert f({input}) =="
+        prompt_variant = self.task_config["context_kwargs"].get("prompt_variant")
+
+        if prompt_variant == "official_direct":
+            query = (
+                "You are given a Python function and an assertion containing an input to the "
+                "function. Complete the assertion with a literal (no unsimplified expressions, "
+                "no function calls) containing the output when executing the provided code on "
+                "the given input, even if the function is incorrect or incomplete.\n"
+                "Do NOT output any extra information. Provide the full assertion with the "
+                "correct output in [ANSWER] and [/ANSWER] tags, following the examples.\n"
+                '[PYTHON] def f(n): return n assert f(17) == ?? [/PYTHON] '
+                "[ANSWER] assert f(17) == 17 [/ANSWER] "
+                '[PYTHON] def f(s): return s + "a" assert f("x9j") == ?? [/PYTHON] '
+                '[ANSWER] assert f("x9j") == "x9ja" [/ANSWER] '
+                f"[PYTHON] {code} assert f({input}) == ?? [/PYTHON] [ANSWER] "
+            )
+        elif prompt_variant == "official_cot_output":
+            query = (
+                "You are given a Python function and an assertion containing an input to the "
+                "function. Complete the assertion with a literal (no unsimplified expressions, "
+                "no function calls) containing the output when executing the provided code on "
+                "the given input, even if the function is incorrect or incomplete.\n"
+                "Do NOT output any extra information. Execute the program step by step before "
+                "arriving at an answer, and provide the full assertion with the correct output "
+                "in [ANSWER] and [/ANSWER] tags, following the examples. "
+                '[PYTHON] def f(s): s = s + s return "b" + s + "a" assert f("hi") == ?? [/PYTHON] '
+                "[THOUGHT] Let's execute the code step by step: 1. The function f is defined, "
+                'which takes a single argument s. 2. The function is called with the argument "hi", '
+                'so within the function, s is initially "hi". 3.\n'
+                'Inside the function, s is concatenated with itself, so s becomes "hihi". '
+                '4. The function then returns a new string that starts with "b", followed by '
+                'the value of s (which is now "hihi"), and ends with "a". 5. The return value '
+                'of the function is therefore "bhihia".\n'
+                '[/THOUGHT] [ANSWER] assert f("hi") == "bhihia" [/ANSWER] '
+                f"[PYTHON] {code} assert f({input}) == ?? [/PYTHON] [THOUGHT] "
+            )
+        elif prompt_variant == "lm_eval_direct":
+            query = f"[PYTHON]\n{code}\nassert f({input}) == ??\n[/PYTHON]"
+        else:
+            query = f"\n{code}\nassert f({input}) =="
 
         out_doc = {
             "index": index,
@@ -192,8 +231,33 @@ def _process_doc(self, doc, index=1):
         return out_doc
 
     def doc_to_target(self, doc):
+        prompt_variant = self.task_config["context_kwargs"].get("prompt_variant")
+        if prompt_variant == "official_direct":
+            return f'assert f({doc["input"]}) == {doc["output"]} [/ANSWER]'
+        if prompt_variant == "official_cot_output":
+            return f'[/THOUGHT] [ANSWER] assert f({doc["input"]}) == {doc["output"]} [/ANSWER]'
+        if prompt_variant == "lm_eval_direct":
+            return f'\n[ANSWER]\nassert f({doc["input"]}) == {doc["output"]}\n[/ANSWER]'
         return " " + doc["output"]
 
+    @staticmethod
+    def _extract_official_direct_output(continuation: str) -> str:
+        answer = continuation.strip()
+        if "[ANSWER]" in answer:
+            answer = answer.rsplit("[ANSWER]", 1)[-1].strip()
+        elif "[/THOUGHT]" in answer:
+            answer = answer.rsplit("[/THOUGHT]", 1)[-1].strip()
+        if "[/ANSWER]" in answer:
+            answer = answer.split("[/ANSWER]", 1)[0].strip()
+        if "assert" in answer and "==" in answer:
+            answer = answer.rsplit("==", 1)[-1].strip()
+        answer = re.split(
+            r"\n\s*(?:\[/?(?:PYTHON|THOUGHT|ANSWER)\]|assert\s+|def\s+)",
+            answer,
+            maxsplit=1,
+        )[0].strip()
+        return answer
+
     def _process_code_results(self, results: List[dict]) -> List[dict]:
         """
         assert that the provided function and input produce the generated output
@@ -203,6 +267,12 @@ def _process_code_results(self, results: List[dict]) -> List[dict]:
             function = res["doc"]["code"]
             input = res["doc"]["input"]
             predicted_output = res["model_resps"]["continuation"]
+            if self.task_config["context_kwargs"].get("prompt_variant") in {
+                "official_direct",
+                "official_cot_output",
+                "lm_eval_direct",
+            }:
+                predicted_output = self._extract_official_direct_output(predicted_output)
 
             unittests = f"assert f({input}) == {predicted_output}"
 
diff --git a/oe_eval/tasks/oe_eval_tasks/gpqa.py b/oe_eval/tasks/oe_eval_tasks/gpqa.py
index f4cc7cd..ad59994 100644
--- a/oe_eval/tasks/oe_eval_tasks/gpqa.py
+++ b/oe_eval/tasks/oe_eval_tasks/gpqa.py
@@ -175,6 +175,11 @@ def construct_requests(
         )
 
     def _extract_answer(self, continuation: str):
+        # Some base models keep generating the next few-shot/example prompt after
+        # answering. Score only the first response span so fallback regexes don't
+        # pick up an answer from a hallucinated follow-on question.
+        continuation = re.split(r"\n\s*Question:", continuation, maxsplit=1)[0]
+
         if self.task_config["metric_kwargs"].get("answer_regexes"):
             res = extract_answer(continuation, task_config=self.task_config)
             res = self._clean_answer(res)
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..3600c9d
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,6627 @@
+version = 1
+revision = 3
+requires-python = ">=3.10, <3.13"
+resolution-markers = [
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and sys_platform == 'win32'",
+    "python_full_version >= '3.12' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.12' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.11.*' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux'",
+]
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+[package.dev-dependencies]
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