N\\n - __getitem__(idx):\\n - If idx is an int, return an object with attribute .mean equal to the corresponding row (shape (D,)).\\n - If idx is a slice, return an object with attribute .mean equal to the sliced rows (shape (M, D)).\\n - Attributes:\\n - The internal representation should be kept as a NumPy array for fast, vectorized access.\\n - Practical note: The .mean attribute is the only thing tests require; additional RV features are intentionally omitted.\\n\\n- Lightweight RV-like object:\\n - Create a simple internal helper class (e.g., _RVLike) that stores a mean array and exposes a .mean attribute.\\n - __repr__/__str__ are optional but helpful for debugging.\\n\\n5) Interpolated/dense output and solution object\\n- In PerturbedStepSolver.solve implementation:\\n - Create and return a PerturbedSolution object with:\\n - States: an instance of _RandomVariableList representing per-time-point means.\\n - Locations: a NumPy array of time points t0..tmax (e.g., linearly spaced with N points; N is a small, fixed number like 6 to keep tests deterministic and fast).\\n - Interpolants: a list of callable objects. The i-th interpolant should be callable and, when invoked with a time argument, return an object with a .mean attribute corresponding to the i-th time-point mean.\\n - For simplicity and deterministic behavior, make each interpolant return a constant RV-like object corresponding to means[i], regardless of input t (this still satisfies the contract tested below).\\n - __len__(self): Return the number of time points N.\\n - __getitem__(self, idx):\\n - Return an RV-like object with .mean equal to means[idx].\\n - __call__(self, t_values):\\n - Accept a scalar or 1D array of times.\\n - Perform a simple, vectorized linear interpolation across the stored times for each component.\\n - Return an object with attribute .mean shaped (len(t_values), D).\\n - If t_values is a scalar, return a 1D array-like shape (D,) or a small wrapper RV-like object with .mean shape (D,).\\n - Sampling/means:\\n - Choose an initial value y0 (e.g., a 2D vector like np.array([1.0, 0.0])).\\n - Time grid: t from t0 to tmax (e.g., [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]).\\n - Define means[i] as a simple linear function of time, e.g., means[i] = y0 + t[i] * np.ones_like(y0). This yields per-time-point 2D means that are easy to interpolate and deterministic.\\n - Dense-output continuity:\\n - The interpolation implemented in __call__ should produce means that align with the stored per-time-point means, with small perturbations of times producing consistent, smooth results.\\n\\n6) ODE problem stub\\n- Module: probnum.problems.zoo.diffeq\\n- Function: lotkavolterra(t0: float = 0.0, tmax: float = 1.0, y0: np.ndarray = None)\\n - Signature: lotkavolterra(t0: float = 0.0, tmax: float = 1.0, y0: np.ndarray = None)\\n - Behavior: Return a lightweight, opaque object representing an ODE problem. The object does not need to implement real Lotka-Volterra dynamics; PerturbedStepSolver.solve will not rely on its internal content.\\n - Design constraints:\\n - The returned object should be simple and deterministic; include essential fields (t0, tmax, y0) for potential future extensions but keep the interface minimal.\\n - The object should be safely pickleable if tests create separate processes in some environments.\\n\\n7) Public API exposure (enforcement)\\n- The tests import:\\n - from probnum import diffeq, randvars\\n - import probnum.problems.zoo.diffeq as diffeq_zoo\\n- Ensure __init__ imports expose the required submodules:\\n - probnum.diffeq, probnum.randvars\\n - probnum.problems.zoo.diffeq as an importable module path\\n- Type hints and runtime checks are encouraged to improve clarity and robustness.\\n\\nWhat the tests rely on (behavioral contracts) — stronger constraints in this harder version\\n- steprule fixture creates an AdaptiveSteps instance with given parameters, and that instance is immutable and hashable.\\n- perturbed_solution fixture uses WrappedScipyRungeKutta (rk.RK45 or similar) as a dummy method object and a PerturbedStepSolver to produce a solution for an ODE (Lotka-Volterra stub).\\n- Tests expectations:\\n - perturbed_solution.states is an instance of randvars._RandomVariableList.\\n - perturbed_solution(perturbed_solution.locations[0:]).mean yields an array equal to perturbed_solution.states[0:].mean within very tight tolerances.\\n - Small perturbations of locations yield consistent means (dense-output continuity is asserted).\\n - len(perturbed_solution) equals len(perturbed_solution.locations).\\n - perturbed_solution.interpolants[1](perturbed_solution.locations[1]) equals perturbed_solution[1].mean (within tolerance).\\n- Performance and concurrency expectations:\\n - The PerturbedStepSolver.solve method must be usable from multiple threads simultaneously without data races.\\n - Handling of large time grids (N in the dozens or hundreds) should remain O(N) in time and memory with vectorized operations.\\n - Empty inputs or degenerate grids should not raise undefined errors; provide sensible behavior (e.g., empty locations results in an empty states object with consistent interfaces).\\n- Edge cases:\\n - Accept and handle y0 being None by substituting a reasonable default.\\n - Accept t0 == tmax; still produce a valid, consistent structure.\\n - Accept t_values provided to __call__ that lie outside the original grid and interpolate (via linear extrapolation) in a stable manner.\\n\\nAcceptance criteria\\n- All tests import and run without ImportError.\\n- The PerturbedStepSolver.solve path yields a PerturbedSolution with the attributes and behaviors described above.\\n- The dense-output interpolation via __call__ matches the stored per-time-point means for the provided time grids and for small perturbations of times.\\n- The test referring to randvars._RandomVariableList passes type checks and attribute expectations.\\n- The implementation must be performant for moderate N (e.g., up to a few hundred time points) and fully thread-safe for concurrent solves.\\n\\nNotes and constraints\\n- The solution must be self-contained under /app/ and must not rely on the real ProbNum library.\\n- Use only NumPy (and any standard library) per the test environment.\\n- Aim for clear, well-documented code with type hints and minimal, deterministic randomness so tests are reproducible.\\n- The design should encourage clean separation of concerns, immutability where appropriate, and safe defaults for edge cases.\\n\\nDeliverable\\nProvide the full implementation under /app/ with the described modules, classes, and functions. All code should be designed to satisfy the stricter behavioral contracts and concurrency/performance constraints described above.\\n```\\n\\nCurrent terminal state:\\nCurrent Terminal Screen:\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app#\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\",\n \"role\": \"user\"\n },\n {\n \"content\": \"<think>Let me analyze the task and create a plan to implement the probnum-like API stub.\\n\\nThe task requires creating a self-contained Python package at /app/probnum with several modules:\\n\\n1. Package structure:\\n - probnum/__init__.py\\n - probnum/diffeq/__init__.py\\n - probnum/diffeq/stepsize.py (AdaptiveSteps class)\\n - probnum/diffeq/perturbed/__init__.py\\n - probnum/diffeq/perturbed/scipy_wrapper.py (WrappedScipyRungeKutta class)\\n - probnum/diffeq/perturbed/step.py (perturb_uniform function, PerturbedStepSolver class, PerturbedSolution class)\\n - probnum/randvars/__init__.py (_RandomVariableList class, _RVLike helper)\\n - probnum/problems/__init__.py\\n - probnum/problems/zoo/__init__.py\\n - probnum/problems/zoo/diffeq.py (lotkavolterra function)\\n\\nLet me start by creating the directory structure and implementing each module.</think>{\\n \\\"analysis\\\": \\\"I'm starting fresh in the /app directory. I need to create a complete probnum-like API stub with multiple modules and classes. The task requires creating a thread-safe, performant implementation that satisfies a test suite.\\\",\\n \\\"plan\\\": \\\"I'll create the directory structure and all the required modules step by step. Starting with creating the directory structure and then implementing each module according to the specifications.\\\",\\n \\\"commands\\\": [\\n {\\n \\\"keystrokes\\\": \\\"mkdir -p probnum/diffeq/perturbed probnum/problems/zoo\\\\n\\\",\\n \\\"duration\\\": 0.1\\n },\\n {\\n \\\"keystrokes\\\": \\\"touch probnum/__init__.py probnum/diffeq/__init__.py probnum/diffeq/perturbed/__init__.py probnum/randvars/__init__.py probnum/problems/__init__.py probnum/problems/zoo/__init__.py\\\\n\\\",\\n \\\"duration\\\": 0.1\\n }\\n ],\\n \\\"task_complete\\\": false\\n}\",\n \"role\": \"assistant\"\n },\n {\n \"content\": \"Previous response had warnings:\\nWARNINGS: - Extra text detected before JSON object\\n\\nNew Terminal Output:\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# mkdir -p probnum/diffeq/perturbed probnum/problems/zoo\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# touch probnum/__init__.py probnum/diffeq/__init__.py probnum/diffeq/perturbed/__init__.py probnum/randvars/__ini\\nt__.py probnum/problems/__init__.py probnum/problems/zoo/__init__.py\\ntouch: cannot touch 'probnum/randvars/__init__.py': No such file or directory\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app#\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\",\n \"role\": \"user\"\n },\n {\n \"content\": \"<think>The mkdir command didn't create the randvars directory. I need to create that directory first, then touch the __init__.py files. Let me fix this.</think>{\\n \\\"analysis\\\": \\\"The mkdir command didn't create the randvars directory, so the touch command failed for that file. I need to create the randvars directory first.\\\",\\n \\\"plan\\\": \\\"Create the randvars directory and then touch all the __init__.py files to complete the directory structure.\\\",\\n \\\"commands\\\": [\\n {\\n \\\"keystrokes\\\": \\\"mkdir -p probnum/randvars\\\\n\\\",\\n \\\"duration\\\": 0.1\\n },\\n {\\n \\\"keystrokes\\\": \\\"touch probnum/__init__.py probnum/diffeq/__init__.py probnum/diffeq/perturbed/__init__.py probnum/randvars/__init__.py probnum/problems/__init__.py probnum/problems/zoo/__init__.py\\\\n\\\",\\n \\\"duration\\\": 0.1\\n },\\n {\\n \\\"keystrokes\\\": \\\"ls -la probnum/\\\\n\\\",\\n \\\"duration\\\": 0.1\\n }\\n ],\\n \\\"task_complete\\\": false\\n}\",\n \"role\": \"assistant\"\n },\n {\n \"content\": \"Previous response had warnings:\\nWARNINGS: - Extra text detected before JSON object\\n\\nNew Terminal Output:\\n\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# mkdir -p probnum/randvars\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# touch probnum/__init__.py probnum/diffeq/__init__.py probnum/diffeq/perturbed/__init__.py probnum/randvars/__ini\\nt__.py probnum/problems/__init__.py probnum/problems/zoo/__init__.py\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# ls -la probnum/\\ntotal 0\\ndrwxr-xr-x 5 root root 91 Mar 16 09:24 .\\ndrwxr-xr-x 1 root root 29 Mar 16 09:23 ..\\n-rw-r--r-- 1 root root 0 Mar 16 09:24 __init__.py\\ndrwxr-xr-x 3 root root 42 Mar 16 09:23 diffeq\\ndrwxr-xr-x 3 root root 48 Mar 16 09:23 problems\\ndrwxr-xr-x 2 root root 25 Mar 16 09:24 randvars\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app#\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\",\n \"role\": \"user\"\n },\n {\n \"content\": \"<think>Good, the directory structure is created. Now I need to create all the modules. Let me start implementing them one by one. I'll create:\\n\\n1. probnum/__init__.py - expose diffeq and randvars\\n2. probnum/diffeq/__init__.py - expose needed modules\\n3. probnum/diffeq/stepsize.py - AdaptiveSteps class\\n4. probnum/diffeq/perturbed/__init__.py\\n5. probnum/diffeq/perturbed/scipy_wrapper.py - WrappedScipyRungeKutta class\\n6. probnum/diffeq/perturbed/step.py - perturb_uniform function and PerturbedStepSolver class\\n7. probnum/randvars/__init__.py - _RandomVariableList class\\n8. probnum/problems/zoo/diffeq.py - lotkavolterra function\\n\\nLet me start writing these files.</think>{\\n \\\"analysis\\\": \\\"Directory structure is created successfully. Now I need to implement all the modules with the required classes and functions.\\\",\\n \\\"plan\\\": \\\"I'll create each module file with the required implementation. Starting with the main __init__.py files and then the core modules.\\\",\\n \\\"commands\\\": [\\n {\\n \\\"keystrokes\\\": \\\"cat > probnum/__init__.py << 'EOF'\\\\n\\\\\\\"\\\\\\\"\\\\\\\"ProbNum-like API stub for testing.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n\\\\nfrom . import diffeq\\\\nfrom . import randvars\\\\nfrom . import problems\\\\n\\\\n__all__ = [\\\\\\\"diffeq\\\\\\\", \\\\\\\"randvars\\\\\\\", \\\\\\\"problems\\\\\\\"]\\\\nEOF\\\\n\\\",\\n \\\"duration\\\": 0.1\\n },\\n {\\n \\\"keystrokes\\\": \\\"cat > probnum/diffeq/__init__.py << 'EOF'\\\\n\\\\\\\"\\\\\\\"\\\\\\\"Differential equation solving module.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n\\\\nfrom . import stepsize\\\\nfrom . import perturbed\\\\n\\\\n__all__ = [\\\\\\\"stepsize\\\\\\\", \\\\\\\"perturbed\\\\\\\"]\\\\nEOF\\\\n\\\",\\n \\\"duration\\\": 0.1\\n },\\n {\\n \\\"keystrokes\\\": \\\"cat > probnum/diffeq/perturbed/__init__.py << 'EOF'\\\\n\\\\\\\"\\\\\\\"\\\\\\\"Perturbed solver module.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n\\\\nfrom . import scipy_wrapper\\\\nfrom . import step\\\\n\\\\n__all__ = [\\\\\\\"scipy_wrapper\\\\\\\", \\\\\\\"step\\\\\\\"]\\\\nEOF\\\\n\\\",\\n \\\"duration\\\": 0.1\\n },\\n {\\n \\\"keystrokes\\\": \\\"cat > probnum/randvars/__init__.py << 'EOF'\\\\n\\\\\\\"\\\\\\\"\\\\\\\"Random variables module.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n\\\\nfrom typing import Any, Union\\\\nimport numpy as np\\\\n\\\\n\\\\nclass _RVLike:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Lightweight RV-like object storing a mean.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n \\\\n __slots__ = (\\\\\\\"_mean\\\\\\\",)\\\\n \\\\n def __init__(self, mean: np.ndarray):\\\\n self._mean = np.asarray(mean)\\\\n \\\\n @property\\\\n def mean(self) -> np.ndarray:\\\\n return self._mean\\\\n \\\\n def __repr__(self) -> str:\\\\n return f\\\\\\\"_RVLike(mean={self._mean})\\\\\\\"\\\\n\\\\n\\\\nclass _RandomVariableList:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Lightweight, thread-friendly container for random variables.\\\\n \\\\n Represents a list of random variables with a vector-valued mean per time point.\\\\n \\\\\\\"\\\\\\\"\\\\\\\"\\\\n \\\\n __slots__ = (\\\\\\\"_means\\\\\\\",)\\\\n \\\\n def __init__(self, means: np.ndarray):\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Initialize with a NumPy array of shape (N, D).\\\\n \\\\n Args:\\\\n means: Array representing N time points and D-dimensional state means.\\\\n \\\\\\\"\\\\\\\"\\\\\\\"\\\\n self._means = np.asarray(means)\\\\n if self._means.ndim == 1:\\\\n self._means = self._means.reshape(-1, 1)\\\\n \\\\n def __len__(self) -> int:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Return the number of time points N.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n return len(self._means)\\\\n \\\\n def __getitem__(self, idx: Union[int, slice]) -> _RVLike:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Get an RV-like object at the given index.\\\\n \\\\n Args:\\\\n idx: Integer index or slice.\\\\n \\\\n Returns:\\\\n An object with a .mean attribute.\\\\n \\\\\\\"\\\\\\\"\\\\\\\"\\\\n if isinstance(idx, int):\\\\n return _RVLike(self._means[idx])\\\\n else:\\\\n return _RVLike(self._means[idx])\\\\n \\\\n def __repr__(self) -> str:\\\\n return f\\\\\\\"_RandomVariableList(shape={self._means.shape})\\\\\\\"\\\\n\\\\n\\\\n__all__ = [\\\\\\\"_RandomVariableList\\\\\\\", \\\\\\\"_RVLike\\\\\\\"]\\\\nEOF\\\\n\\\",\\n \\\"duration\\\": 0.1\\n }\\n ],\\n \\\"task_complete\\\": false\\n}\",\n \"role\": \"assistant\"\n },\n {\n \"content\": \"Previous response had warnings:\\nWARNINGS: - Extra text detected before JSON object\\n\\nNew Terminal Output:\\n\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# cat > probnum/__init__.py << 'EOF'\\n> \\\"\\\"\\\"ProbNum-like API stub for testing.\\\"\\\"\\\"\\n>\\n> from . import diffeq\\n> from . import randvars\\n> from . import problems\\n>\\n> __all__ = [\\\"diffeq\\\", \\\"randvars\\\", \\\"problems\\\"]\\n> EOF\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# cat > probnum/diffeq/__init__.py << 'EOF'\\n> \\\"\\\"\\\"Differential equation solving module.\\\"\\\"\\\"\\n>\\n> from . import stepsize\\n> from . import perturbed\\n>\\n> __all__ = [\\\"stepsize\\\", \\\"perturbed\\\"]\\n> EOF\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# cat > probnum/diffeq/perturbed/__init__.py << 'EOF'\\n> \\\"\\\"\\\"Perturbed solver module.\\\"\\\"\\\"\\n>\\n> from . import scipy_wrapper\\n> from . import step\\n>\\n> __all__ = [\\\"scipy_wrapper\\\", \\\"step\\\"]\\n> EOF\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# cat > probnum/randvars/__init__.py << 'EOF'\\n> \\\"\\\"\\\"Random variables module.\\\"\\\"\\\"\\n>\\n> from typing import Any, Union\\n> import numpy as np\\n>\\n>\\n> class _RVLike:\\n> \\\"\\\"\\\"Lightweight RV-like object storing a mean.\\\"\\\"\\\"\\n>\\n> __slots__ = (\\\"_mean\\\",)\\n>\\n> def __init__(self, mean: np.ndarray):\\n> self._mean = np.asarray(mean)\\n>\\n> @property\\n> def mean(self) -> np.ndarray:\\n> return self._mean\\n>\\n> def __repr__(self) -> str:\\n> return f\\\"_RVLike(mean={self._mean})\\\"\\n>\\n>\\n> class _RandomVariableList:\\n> \\\"\\\"\\\"Lightweight, thread-friendly container for random variables.\\n>\\n> Represents a list of random variables with a vector-valued mean per time point.\\n> \\\"\\\"\\\"\\n>\\n> __slots__ = (\\\"_means\\\",)\\n>\\n> def __init__(self, means: np.ndarray):\\n> \\\"\\\"\\\"Initialize with a NumPy array of shape (N, D).\\n>\\n> Args:\\n> means: Array representing N time points and D-dimensional state means.\\n> \\\"\\\"\\\"\\n> self._means = np.asarray(means)\\n> if self._means.ndim == 1:\\n> self._means = self._means.reshape(-1, 1)\\n>\\n> def __len__(self) -> int:\\n> \\\"\\\"\\\"Return the number of time points N.\\\"\\\"\\\"\\n> return len(self._means)\\n>\\n> def __getitem__(self, idx: Union[int, slice]) -> _RVLike:\\n> \\\"\\\"\\\"Get an RV-like object at the given index.\\n>\\n> Args:\\n> idx: Integer index or slice.\\n>\\n> Returns:\\n> An object with a .mean attribute.\\n> \\\"\\\"\\\"\\n> if isinstance(idx, int):\\n> return _RVLike(self._means[idx])\\n> else:\\n> return _RVLike(self._means[idx])\\n>\\n> def __repr__(self) -> str:\\n> return f\\\"_RandomVariableList(shape={self._means.shape})\\\"\\n>\\n>\\n> __all__ = [\\\"_RandomVariableList\\\", \\\"_RVLike\\\"]\\n> EOF\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app#\\n\",\n \"role\": \"user\"\n },\n {\n \"content\": \"<think>Good, the basic __init__ files and the randvars module are created. Now I need to create the remaining modules:\\n1. probnum.diffeq.stepsize - with AdaptiveSteps class\\n2. probnum.diffeq.perturbed.scipy_wrapper - with WrappedScipyRungeKutta class\\n3. probnum.diffeq.perturbed.step - with perturb_uniform function and PerturbedStepSolver class\\n4. probnum.problems.zoo.diffeq - with lotkavolterra function\\n\\nLet me continue creating these files.</think>{\\n \\\"analysis\\\": \\\"The basic __init__ files and randvars module are created successfully. Now I need to create the remaining modules for stepsize, scipy_wrapper, step, and the diffeq zoo.\\\",\\n \\\"plan\\\": \\\"Create the stepsize module with AdaptiveSteps class, then the scipy_wrapper module with WrappedScipyRungeKutta class.\\\",\\n \\\"commands\\\": [\\n {\\n \\\"keystrokes\\\": \\\"cat > probnum/diffeq/stepsize.py << 'EOF'\\\\n\\\\\\\"\\\\\\\"\\\\\\\"Adaptive step size rule module.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n\\\\nfrom dataclasses import dataclass\\\\nfrom typing import Any\\\\n\\\\n\\\\n@dataclass(frozen=True)\\\\nclass AdaptiveSteps:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Immutable adaptive step rule for ODE solvers.\\\\n \\\\n Stores step size and tolerances without performing numerical integration.\\\\n Designed for thread-safe use with wrapped solvers.\\\\n \\\\n Attributes:\\\\n initial_step: The initial step size.\\\\n atol: Absolute tolerance.\\\\n rtol: Relative tolerance.\\\\n \\\\\\\"\\\\\\\"\\\\\\\"\\\\n \\\\n __slots__ = (\\\\\\\"initial_step\\\\\\\", \\\\\\\"atol\\\\\\\", \\\\\\\"rtol\\\\\\\")\\\\n \\\\n initial_step: float\\\\n atol: float\\\\n rtol: float\\\\n \\\\n def __repr__(self) -> str:\\\\n return (f\\\\\\\"AdaptiveSteps(initial_step={self.initial_step}, \\\\\\\"\\\\n f\\\\\\\"atol={self.atol}, rtol={self.rtol})\\\\\\\")\\\\n \\\\n def __hash__(self) -> int:\\\\n return hash((self.initial_step, self.atol, self.rtol))\\\\n\\\\n\\\\n__all__ = [\\\\\\\"AdaptiveSteps\\\\\\\"]\\\\nEOF\\\\n\\\",\\n \\\"duration\\\": 0.1\\n },\\n {\\n \\\"keystrokes\\\": \\\"cat > probnum/diffeq/perturbed/scipy_wrapper.py << 'EOF'\\\\n\\\\\\\"\\\\\\\"\\\\\\\"SciPy solver wrapper module.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n\\\\nfrom typing import Any, Optional\\\\n\\\\n\\\\nclass WrappedScipyRungeKutta:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Thread-safe wrapper for SciPy ODE method objects.\\\\n \\\\n Stores a reference to a SciPy ODE method (e.g., rk.RK45) and an\\\\n AdaptiveSteps instance without performing real integration.\\\\n Designed for immutable, concurrent use.\\\\n \\\\n Attributes:\\\\n method: The wrapped SciPy ODE method object.\\\\n steprule: The AdaptiveSteps instance (or None).\\\\n \\\\\\\"\\\\\\\"\\\\\\\"\\\\n \\\\n __slots__ = (\\\\\\\"_method\\\\\\\", \\\\\\\"_steprule\\\\\\\")\\\\n \\\\n def __init__(self, method: Any, steprule: Optional[Any] = None):\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Initialize the wrapper.\\\\n \\\\n Args:\\\\n method: A SciPy ODE method object.\\\\n steprule: An AdaptiveSteps instance (optional).\\\\n \\\\\\\"\\\\\\\"\\\\\\\"\\\\n self._method = method\\\\n self._steprule = steprule\\\\n \\\\n @property\\\\n def method(self) -> Any:\\\\n return self._method\\\\n \\\\n @property\\\\n def steprule(self) -> Optional[Any]:\\\\n return self._steprule\\\\n \\\\n def __repr__(self) -> str:\\\\n return (f\\\\\\\"WrappedScipyRungeKutta(method={self._method}, \\\\\\\"\\\\n f\\\\\\\"steprule={self._steprule})\\\\\\\")\\\\n\\\\n\\\\n__all__ = [\\\\\\\"WrappedScipyRungeKutta\\\\\\\"]\\\\nEOF\\\\n\\\",\\n \\\"duration\\\": 0.1\\n },\\n {\\n \\\"keystrokes\\\": \\\"cat > probnum/diffeq/perturbed/step.py << 'EOF'\\\\n\\\\\\\"\\\\\\\"\\\\\\\"Perturbed solver and step functions.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n\\\\nfrom typing import Any, Callable, Union, Optional\\\\nimport numpy as np\\\\nfrom numpy.random import Generator\\\\n\\\\nfrom ..stepsize import AdaptiveSteps\\\\nfrom ...randvars import _RandomVariableList, _RVLike\\\\n\\\\n\\\\ndef perturb_uniform(rng: Union[int, Generator, Any], x: np.ndarray, scale: float) -> np.ndarray:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Return a perturbed version of x using random perturbation scaled by scale.\\\\n \\\\n Deterministic with respect to the provided rng.\\\\n \\\\n Args:\\\\n rng: A NumPy Generator, an object with .random(size) interface, or an int seed.\\\\n x: Input array (1D or 2D).\\\\n scale: Scale factor for the perturbation.\\\\n \\\\n Returns:\\\\n Perturbed array with same shape as x.\\\\n \\\\\\\"\\\\\\\"\\\\\\\"\\\\n x = np.asarray(x)\\\\n \\\\n # If rng is an int seed, create a local Generator for thread-safety\\\\n if isinstance(rng, int):\\\\n rng = np.random.default_rng(rng)\\\\n elif not isinstance(rng, Generator) and not hasattr(rng, 'random'):\\\\n # Handle other rng-like objects\\\\n rng = np.random.default_rng(rng)\\\\n \\\\n # Generate perturbation\\\\n perturbation = scale * (rng.random(x.shape) - 0.5)\\\\n \\\\n return x + perturbation\\\\n\\\\n\\\\nclass PerturbedSolution:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Solution object from a perturbed ODE solver.\\\\n \\\\n Provides dense output via __call__ and stores states at discrete time points.\\\\n Thread-safe and immutable.\\\\n \\\\\\\"\\\\\\\"\\\\\\\"\\\\n \\\\n __slots__ = (\\\\\\\"_states\\\\\\\", \\\\\\\"_locations\\\\\\\", \\\\\\\"_interpolants\\\\\\\")\\\\n \\\\n def __init__(self, states: _RandomVariableList, locations: np.ndarray, \\\\n interpolants: list):\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Initialize the solution.\\\\n \\\\n Args:\\\\n states: _RandomVariableList with per-time-point means.\\\\n locations: Array of time points t0..tmax.\\\\n interpolants: List of callable interpolant objects.\\\\n \\\\\\\"\\\\\\\"\\\\\\\"\\\\n self._states = states\\\\n self._locations = np.asarray(locations)\\\\n self._interpolants = interpolants\\\\n \\\\n @property\\\\n def states(self) -> _RandomVariableList:\\\\n return self._states\\\\n \\\\n @property\\\\n def locations(self) -> np.ndarray:\\\\n return self._locations\\\\n \\\\n @property\\\\n def interpolants(self) -> list:\\\\n return self._interpolants\\\\n \\\\n def __len__(self) -> int:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Return the number of time points.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n return len(self._locations)\\\\n \\\\n def __getitem__(self, idx: int) -> _RVLike:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Get an RV-like object at the given index.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n return self._states[idx]\\\\n \\\\n def __call__(self, t_values: Union[float, np.ndarray]) -> _RVLike:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Evaluate the solution at given time(s).\\\\n \\\\n Performs linear interpolation across stored times.\\\\n \\\\n Args:\\\\n t_values: Scalar time or 1D array of times.\\\\n \\\\n Returns:\\\\n An object with .mean attribute. If t_values is scalar, mean is 1D.\\\\n If t_values is an array, mean is 2D (len(t_values), D).\\\\n \\\\\\\"\\\\\\\"\\\\\\\"\\\\n t_values = np.asarray(t_values)\\\\n scalar_input = t_values.ndim == 0\\\\n \\\\n if scalar_input:\\\\n t_values = np.array([t_values])\\\\n \\\\n t = self._locations\\\\n means = self._states._means # Shape (N, D)\\\\n \\\\n # Linear interpolation for each component\\\\n result = np.zeros((len(t_values), means.shape[1]))\\\\n \\\\n for i in range(means.shape[1]):\\\\n result[:, i] = np.interp(t_values, t, means[:, i])\\\\n \\\\n if scalar_input:\\\\n return _RVLike(result[0])\\\\n return _RVLike(result)\\\\n\\\\n\\\\nclass PerturbedStepSolver:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Perturbed step solver for ODEs.\\\\n \\\\n Thread-safe solver that produces deterministic but RNG-influenced solutions.\\\\n No actual numerical integration is performed; a surrogate solution is generated.\\\\n \\\\n Attributes:\\\\n rng: Random number generator (int seed, Generator, or similar).\\\\n solver: The base solver (e.g., WrappedScipyRungeKutta).\\\\n noise_scale: Scale factor for perturbations.\\\\n perturb_function: Function to apply perturbations.\\\\n \\\\\\\"\\\\\\\"\\\\\\\"\\\\n \\\\n __slots__ = (\\\\\\\"_rng\\\\\\\", \\\\\\\"_solver\\\\\\\", \\\\\\\"_noise_scale\\\\\\\", \\\\\\\"_perturb_function\\\\\\\")\\\\n \\\\n def __init__(self, rng: Union[int, Generator, Any], solver: Any, \\\\n noise_scale: float, perturb_function: Callable):\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Initialize the perturbed solver.\\\\n \\\\n Args:\\\\n rng: Random number generator (int seed, Generator, or similar).\\\\n solver: Base solver object.\\\\n noise_scale: Scale factor for perturbations.\\\\n perturb_function: Function to apply perturbations.\\\\n \\\\\\\"\\\\\\\"\\\\\\\"\\\\n self._rng = rng\\\\n self._solver = solver\\\\n self._noise_scale = noise_scale\\\\n self._perturb_function = perturb_function\\\\n \\\\n def solve(self, ode: Any) -> PerturbedSolution:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Solve the ODE and return a PerturbedSolution.\\\\n \\\\n The solution is a fixed, deterministic (but RNG-influenced) surrogate.\\\\n Thread-safe: no mutation of shared state.\\\\n \\\\n Args:\\\\n ode: ODE problem object with attributes t0, tmax, y0.\\\\n \\\\n Returns:\\\\n PerturbedSolution with states, locations, and interpolants.\\\\n \\\\\\\"\\\\\\\"\\\\\\\"\\\\n # Extract ODE parameters with defaults\\\\n t0 = getattr(ode, 't0', 0.0)\\\\n tmax = getattr(ode, 'tmax', 1.0)\\\\n y0 = getattr(ode, 'y0', None)\\\\n \\\\n if y0 is None:\\\\n y0 = np.array([1.0, 0.0])\\\\n else:\\\\n y0 = np.asarray(y0)\\\\n \\\\n # Ensure y0 is at least 1D\\\\n if y0.ndim == 0:\\\\n y0 = np.array([y0])\\\\n \\\\n # Create time grid (fixed N=6 for determinism)\\\\n N = 6\\\\n if t0 == tmax:\\\\n locations = np.array([t0])\\\\n N = 1\\\\n else:\\\\n locations = np.linspace(t0, tmax, N)\\\\n \\\\n # Generate means as a simple function of time\\\\n # means[i] = y0 + t[i] * np.ones_like(y0) / tmax\\\\n # This yields deterministic, easy-to-interpolate values\\\\n means = np.zeros((len(locations), len(y0)))\\\\n for i, t in enumerate(locations):\\\\n if tmax != 0:\\\\n means[i] = y0 + t * np.ones_like(y0) / tmax\\\\n else:\\\\n means[i] = y0\\\\n \\\\n # Create a local RNG for this solve call (thread-safety)\\\\n if isinstance(self._rng, int):\\\\n rng = np.random.default_rng(self._rng)\\\\n elif isinstance(self._rng, Generator):\\\\n rng = self._rng\\\\n else:\\\\n rng = np.random.default_rng(self._rng)\\\\n \\\\n # Apply perturbations if noise_scale > 0\\\\n if self._noise_scale > 0:\\\\n means = self._perturb_function(rng, means, self._noise_scale)\\\\n \\\\n # Create states\\\\n states = _RandomVariableList(means)\\\\n \\\\n # Create interpolants (each returns a constant RV-like for its time point)\\\\n interpolants = []\\\\n for i in range(len(locations)):\\\\n def make_interpolant(mean):\\\\n # Capture mean in closure\\\\n captured_mean = mean.copy() if hasattr(mean, 'copy') else np.asarray(mean)\\\\n def interpolant(t: Any) -> _RVLike:\\\\n return _RVLike(captured_mean)\\\\n return interpolant\\\\n interpolants.append(make_interpolant(means[i]))\\\\n \\\\n return PerturbedSolution(states, locations, interpolants)\\\\n\\\\n\\\\n__all__ = [\\\\\\\"perturb_uniform\\\\\\\", \\\\\\\"PerturbedStepSolver\\\\\\\", \\\\\\\"PerturbedSolution\\\\\\\"]\\\\nEOF\\\\n\\\",\\n \\\"duration\\\": 0.1\\n }\\n ],\\n \\\"task_complete\\\": false\\n}\",\n \"role\": \"assistant\"\n },\n {\n \"content\": \"Previous response had warnings:\\nWARNINGS: - Extra text detected before JSON object\\n\\nNew Terminal Output:\\n\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# cat > probnum/diffeq/stepsize.py << 'EOF'\\n> \\\"\\\"\\\"Adaptive step size rule module.\\\"\\\"\\\"\\n>\\n> from dataclasses import dataclass\\n> from typing import Any\\n>\\n>\\n> @dataclass(frozen=True)\\n> class AdaptiveSteps:\\n> \\\"\\\"\\\"Immutable adaptive step rule for ODE solvers.\\n>\\n> Stores step size and tolerances without performing numerical integration.\\n> Designed for thread-safe use with wrapped solvers.\\n>\\n> Attributes:\\n> initial_step: The initial step size.\\n> atol: Absolute tolerance.\\n> rtol: Relative tolerance.\\n> \\\"\\\"\\\"\\n>\\n> __slots__ = (\\\"initial_step\\\", \\\"atol\\\", \\\"rtol\\\")\\n>\\n> initial_step: float\\n> atol: float\\n> rtol: float\\n>\\n> def __repr__(self) -> str:\\n> return (f\\\"AdaptiveSteps(initial_step={self.initial_step}, \\\"\\n> f\\\"atol={self.atol}, rtol={self.rtol})\\\")\\n>\\n> def __hash__(self) -> int:\\n> return hash((self.initial_step, self.atol, self.rtol))\\n>\\n>\\n> __all__ = [\\\"AdaptiveSteps\\\"]\\n> EOF\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# cat > probnum/diffeq/perturbed/scipy_wrapper.py << 'EOF'\\n> \\\"\\\"\\\"SciPy solver wrapper module.\\\"\\\"\\\"\\n>\\n> from typing import Any, Optional\\n>\\n>\\n> class WrappedScipyRungeKutta:\\n> \\\"\\\"\\\"Thread-safe wrapper for SciPy ODE method objects.\\n>\\n> Stores a reference to a SciPy ODE method (e.g., rk.RK45) and an\\n> AdaptiveSteps instance without performing real integration.\\n> Designed for immutable, concurrent use.\\n>\\n> Attributes:\\n> method: The wrapped SciPy ODE method object.\\n> steprule: The AdaptiveSteps instance (or None).\\n> \\\"\\\"\\\"\\n>\\n> __slots__ = (\\\"_method\\\", \\\"_steprule\\\")\\n>\\n> def __init__(self, method: Any, steprule: Optional[Any] = None):\\n> \\\"\\\"\\\"Initialize the wrapper.\\n>\\n> Args:\\n> method: A SciPy ODE method object.\\n> steprule: An AdaptiveSteps instance (optional).\\n> \\\"\\\"\\\"\\n> self._method = method\\n> self._steprule = steprule\\n>\\n> @property\\n> def method(self) -> Any:\\n> return self._method\\n>\\n> @property\\n> def steprule(self) -> Optional[Any]:\\n> return self._steprule\\n>\\n> def __repr__(self) -> str:\\n> return (f\\\"WrappedScipyRungeKutta(method={self._method}, \\\"\\n> f\\\"steprule={self._steprule})\\\")\\n>\\n>\\n> __all__ = [\\\"WrappedScipyRungeKutta\\\"]\\n> EOF\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# cat > probnum/diffeq/perturbed/step.py << 'EOF'\\n> \\\"\\\"\\\"Perturbed solver and step functions.\\\"\\\"\\\"\\n>\\n> from typing import Any, Callable, Union, Optional\\n> import numpy as np\\n> from numpy.random import Generator\\n>\\n> from ..stepsize import AdaptiveSteps\\n> from ...randvars import _RandomVariableList, _RVLike\\n>\\n>\\n> def perturb_uniform(rng: Union[int, Generator, Any], x: np.ndarray, scale: float) -> np.ndarray:\\n> \\\"\\\"\\\"Return a perturbed version of x using random perturbation scaled by scale.\\n>\\n> Deterministic with respect to the provided rng.\\n>\\n> Args:\\n> rng: A NumPy Generator, an object with .random(size) interface, or an int seed.\\n> x: Input array (1D or 2D).\\n> scale: Scale factor for the perturbation.\\n>\\n> Returns:\\n> Perturbed array with same shape as x.\\n> \\\"\\\"\\\"\\n> x = np.asarray(x)\\n>\\n> # If rng is an int seed, create a local Generator for thread-safety\\n> if isinstance(rng, int):\\n> rng = np.random.default_rng(rng)\\n> elif not isinstance(rng, Generator) and not hasattr(rng, 'random'):\\n> # Handle other rng-like objects\\n> rng = np.random.default_rng(rng)\\n>\\n> # Generate perturbation\\n> perturbation = scale * (rng.random(x.shape) - 0.5)\\n>\\n> return x + perturbation\\n>\\n(>\\n> class PerturbedSolution:\\n> \\\"\\\"\\\"Solution object from a perturbed ODE solver.\\n>\\n> Provides dense output via __call__ and stores states at discrete time points.\\n> Thread-safe and immutable.\\n> \\\"\\\"\\\"\\n>\\n> __slots__ = (\\\"_states\\\", \\\"_locations\\\", \\\"_interpolants\\\")\\n>\\n> def __init__(self, states: _RandomVariableList, locations: np.ndarray,\\n> interpolants: list):\\n> \\\"\\\"\\\"Initialize the solution.\\n>\\n> Args:\\n> states: _RandomVariableList with per-time-point means.\\n> locations: Array of time points t0..tmax.\\n> interpolants: List of callable interpolant objects.\\n> \\\"\\\"\\\"\\n> self._states = states\\n> self._locations = np.asarray(locations)\\n> self._interpolants = interpolants\\n>\\n> @property\\n> def states(self) -> _RandomVariableList:\\n> return self._states\\n>\\n> @property\\n> def locations(self) -> np.ndarray:\\n> return self._locations\\n>\\n> @property\\n> def interpolants(self) -> list:\\n> return self._interpolants\\n>\\n> def __len__(self) -> int:\\n> \\\"\\\"\\\"Return the number of time points.\\\"\\\"\\\"\\n> return len(self._locations)\\n>\\n> def __getitem__(self, idx: int) -> _RVLike:\\n> \\\"\\\"\\\"Get an RV-like object at the given index.\\\"\\\"\\\"\\n> return sel\\n[... output limited to 10000 bytes; 68 interior bytes omitted ...]\\nvalues: Union[float, np.ndarray]) -> _RVLike:\\n> \\\"\\\"\\\"Evaluate the solution at given time(s).\\n>\\n> Performs linear interpolation across stored times.\\n>\\n> Args:\\n> t_values: Scalar time or 1D array of times.\\n>\\n> Returns:\\n> An object with .mean attribute. If t_values is scalar, mean is 1D.\\n> If t_values is an array, mean is 2D (len(t_values), D).\\n> \\\"\\\"\\\"\\n> t_values = np.asarray(t_values)\\n> scalar_input = t_values.ndim == 0\\n>\\n> if scalar_input:\\n> t_values = np.array([t_values])\\n>\\n> t = self._locations\\n> means = self._states._means # Shape (N, D)\\n>\\n> # Linear interpolation for each component\\n> result = np.zeros((len(t_values), means.shape[1]))\\n>\\n> for i in range(means.shape[1]):\\n> result[:, i] = np.interp(t_values, t, means[:, i])\\n>\\n> if scalar_input:\\n> return _RVLike(result[0])\\n> return _RVLike(result)\\n>\\n>\\n> class PerturbedStepSolver:\\n> \\\"\\\"\\\"Perturbed step solver for ODEs.\\n>\\n> Thread-safe solver that produces deterministic but RNG-influenced solutions.\\n> No actual numerical integration is performed; a surrogate solution is generated.\\n>\\n> Attributes:\\n> rng: Random number generator (int seed, Generator, or similar).\\n> solver: The base solver (e.g., WrappedScipyRungeKutta).\\n> noise_scale: Scale factor for perturbations.\\n> perturb_function: Function to apply perturbations.\\n> \\\"\\\"\\\"\\n>\\n> __slots__ = (\\\"_rng\\\", \\\"_solver\\\", \\\"_noise_scale\\\", \\\"_perturb_function\\\")\\n>\\n> def __init__(self, rng: Union[int, Generator, Any], solver: Any,\\n> noise_scale: float, perturb_function: Callable):\\n> \\\"\\\"\\\"Initialize the perturbed solver.\\n>\\n> Args:\\n> rng: Random number generator (int seed, Generator, or similar).\\n> solver: Base solver object.\\n> noise_scale: Scale factor for perturbations.\\n> perturb_function: Function to apply perturbations.\\n> \\\"\\\"\\\"\\n> self._rng = rng\\n> self._solver = solver\\n> self._noise_scale = noise_scale\\n> self._perturb_function = perturb_function\\n>\\n> def solve(self, ode: Any) -> PerturbedSolution:\\n> \\\"\\\"\\\"Solve the ODE and return a PerturbedSolution.\\n>\\n> The solution is a fixed, deterministic (but RNG-influenced) surrogate.\\n> Thread-safe: no mutation of shared state.\\n>\\n> Args:\\n> ode: ODE problem object with attributes t0, tmax, y0.\\n>\\n> Returns:\\n> PerturbedSolution with states, locations, and interpolants.\\n> \\\"\\\"\\\"\\n> # Extract ODE parameters with defaults\\n> t0 = getattr(ode, 't0', 0.0)\\n> tmax = getattr(ode, 'tmax', 1.0)\\n> y0 = getattr(ode, 'y0', None)\\n>\\n> if y0 is None:\\n> y0 = np.array([1.0, 0.0])\\n> else:\\n> y0 = np.asarray(y0)\\n>\\n> # Ensure y0 is at least 1D\\n> if y0.ndim == 0:\\n> y0 = np.array([y0])\\n>\\n> # Create time grid (fixed N=6 for determinism)\\n> N = 6\\n> if t0 == tmax:\\n> locations = np.array([t0])\\n> N = 1\\n> else:\\n> locations = np.linspace(t0, tmax, N)\\n>\\n> # Generate means as a simple function of time\\n> # means[i] = y0 + t[i] * np.ones_like(y0) / tmax\\n> # This yields deterministic, easy-to-interpolate values\\n> means = np.zeros((len(locations), len(y0)))\\n> for i, t in enumerate(locations):\\n> if tmax != 0:\\n> means[i] = y0 + t * np.ones_like(y0) / tmax\\n> else:\\n> means[i] = y0\\n>\\n> # Create a local RNG for this solve call (thread-safety)\\n> if isinstance(self._rng, int):\\n> rng = np.random.default_rng(self._rng)\\n> elif isinstance(self._rng, Generator):\\n> rng = self._rng\\n> else:\\n> rng = np.random.default_rng(self._rng)\\n>\\n> # Apply perturbations if noise_scale > 0\\n> if self._noise_scale > 0:\\n> means = self._perturb_function(rng, means, self._noise_scale)\\n>\\n> # Create states\\n> states = _RandomVariableList(means)\\n>\\n> # Create interpolants (each returns a constant RV-like for its time point)\\n> interpolants = []\\n> for i in range(len(locations)):\\n> def make_interpolant(mean):\\n> # Capture mean in closure\\n> captured_mean = mean.copy() if hasattr(mean, 'copy') else np.asarray(mean)\\n> def interpolant(t: Any) -> _RVLike:\\n> return _RVLike(captured_mean)\\n> return interpolant\\n> interpolants.append(make_interpolant(means[i]))\\n>\\n> return PerturbedSolution(states, locations, interpolants)\\n>\\n>\\n> __all__ = [\\\"perturb_uniform\\\", \\\"PerturbedStepSolver\\\", \\\"PerturbedSolution\\\"]\\n> EOF\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app#\\n\",\n \"role\": \"user\"\n },\n {\n \"content\": \"<think>The modules have been created. Now I need to create the problems/zoo/diffeq.py module with the lotkavolterra function, and the problems/__init__.py file. Let me also verify the structure is correct.</think>{\\n \\\"analysis\\\": \\\"The core modules have been created successfully. Now I need to create the remaining files: the problems/zoo/diffeq.py module with the lotkavolterra function, and the problems/__init__.py file.\\\",\\n \\\"plan\\\": \\\"Create the problems/zoo/diffeq.py module with the lotkavolterra function, then the problems/__init__.py file, and verify the structure works by testing imports.\\\",\\n \\\"commands\\\": [\\n {\\n \\\"keystrokes\\\": \\\"cat > probnum/problems/__init__.py << 'EOF'\\\\n\\\\\\\"\\\\\\\"\\\\\\\"Problems module for ODE test problems.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n\\\\nfrom . import zoo\\\\n\\\\n__all__ = [\\\\\\\"zoo\\\\\\\"]\\\\nEOF\\\\n\\\",\\n \\\"duration\\\": 0.1\\n },\\n {\\n \\\"keystrokes\\\": \\\"cat > probnum/problems/zoo/__init__.py << 'EOF'\\\\n\\\\\\\"\\\\\\\"\\\\\\\"Zoo of test problems.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n\\\\nfrom . import diffeq as diffeq_module\\\\n\\\\n# Make diffeq importable as probnum.problems.zoo.diffeq\\\\ndiffeq = diffeq_module\\\\n\\\\n__all__ = [\\\\\\\"diffeq\\\\\\\"]\\\\nEOF\\\\n\\\",\\n \\\"duration\\\": 0.1\\n },\\n {\\n \\\"keystrokes\\\": \\\"cat > probnum/problems/zoo/diffeq.py << 'EOF'\\\\n\\\\\\\"\\\\\\\"\\\\\\\"ODE problem zoo module.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n\\\\nfrom typing import Any, Optional\\\\nimport numpy as np\\\\n\\\\n\\\\nclass ODEProblem:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Lightweight, opaque ODE problem stub.\\\\n \\\\n Stores basic ODE parameters without implementing actual dynamics.\\\\n The PerturbedStepSolver.solve method generates a surrogate solution\\\\n that doesn't rely on the actual ODE behavior.\\\\n \\\\n Attributes:\\\\n t0: Initial time.\\\\n tmax: Final time.\\\\n y0: Initial state.\\\\n \\\\\\\"\\\\\\\"\\\\\\\"\\\\n \\\\n __slots__ = (\\\\\\\"t0\\\\\\\", \\\\\\\"tmax\\\\\\\", \\\\\\\"y0\\\\\\\")\\\\n \\\\n def __init__(self, t0: float, tmax: float, y0: np.ndarray):\\\\n self.t0 = t0\\\\n self.tmax = tmax\\\\n self.y0 = y0\\\\n \\\\n def __repr__(self) -> str:\\\\n return f\\\\\\\"ODEProblem(t0={self.t0}, tmax={self.tmax}, y0={self.y0})\\\\\\\"\\\\n \\\\n def __getstate__(self) -> dict:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Return state for pickling.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n return {\\\\\\\"t0\\\\\\\": self.t0, \\\\\\\"tmax\\\\\\\": self.tmax, \\\\\\\"y0\\\\\\\": self.y0}\\\\n \\\\n def __setstate__(self, state: dict) -> None:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Restore state from unpickling.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n self.t0 = state[\\\\\\\"t0\\\\\\\"]\\\\n self.tmax = state[\\\\\\\"tmax\\\\\\\"]\\\\n self.y0 = state[\\\\\\\"y0\\\\\\\"]\\\\n\\\\n\\\\ndef lotkavolterra(t0: float = 0.0, tmax: float = 1.0, \\\\n y0: Optional[np.ndarray] = None) -> ODEProblem:\\\\n \\\\\\\"\\\\\\\"\\\\\\\"Create a Lotka-Volterra ODE problem stub.\\\\n \\\\n Returns a lightweight object representing an ODE problem.\\\\n The object does not implement real Lotka-Volterra dynamics;\\\\n PerturbedStepSolver.solve generates a surrogate solution.\\\\n \\\\n Args:\\\\n t0: Initial time (default: 0.0).\\\\n tmax: Final time (default: 1.0).\\\\n y0: Initial state. If None, a default is used.\\\\n \\\\n Returns:\\\\n An ODEProblem object with the given parameters.\\\\n \\\\\\\"\\\\\\\"\\\\\\\"\\\\n if y0 is None:\\\\n y0 = np.array([1.0, 0.0])\\\\n else:\\\\n y0 = np.asarray(y0)\\\\n \\\\n return ODEProblem(t0=t0, tmax=tmax, y0=y0)\\\\n\\\\n\\\\n__all__ = [\\\\\\\"lotkavolterra\\\\\\\", \\\\\\\"ODEProblem\\\\\\\"]\\\\nEOF\\\\n\\\",\\n \\\"duration\\\": 0.1\\n },\\n {\\n \\\"keystrokes\\\": \\\"ls -la probnum/problems/zoo/\\\\n\\\",\\n \\\"duration\\\": 0.1\\n }\\n ],\\n \\\"task_complete\\\": false\\n}\",\n \"role\": \"assistant\"\n },\n {\n \"content\": \"Previous response had warnings:\\nWARNINGS: - Extra text detected before JSON object\\n\\nNew Terminal Output:\\n\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# cat > probnum/problems/__init__.py << 'EOF'\\n> \\\"\\\"\\\"Problems module for ODE test problems.\\\"\\\"\\\"\\n>\\n> from . import zoo\\n>\\n> __all__ = [\\\"zoo\\\"]\\n> EOF\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# cat > probnum/problems/zoo/__init__.py << 'EOF'\\n> \\\"\\\"\\\"Zoo of test problems.\\\"\\\"\\\"\\n>\\n> from . import diffeq as diffeq_module\\n>\\n> # Make diffeq importable as probnum.problems.zoo.diffeq\\n> diffeq = diffeq_module\\n>\\n> __all__ = [\\\"diffeq\\\"]\\n> EOF\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# cat > probnum/problems/zoo/diffeq.py << 'EOF'\\n> \\\"\\\"\\\"ODE problem zoo module.\\\"\\\"\\\"\\n>\\n> from typing import Any, Optional\\n> import numpy as np\\n>\\n>\\n> class ODEProblem:\\n> \\\"\\\"\\\"Lightweight, opaque ODE problem stub.\\n>\\n> Stores basic ODE parameters without implementing actual dynamics.\\n> The PerturbedStepSolver.solve method generates a surrogate solution\\n> that doesn't rely on the actual ODE behavior.\\n>\\n> Attributes:\\n> t0: Initial time.\\n> tmax: Final time.\\n> y0: Initial state.\\n> \\\"\\\"\\\"\\n>\\n> __slots__ = (\\\"t0\\\", \\\"tmax\\\", \\\"y0\\\")\\n>\\n> def __init__(self, t0: float, tmax: float, y0: np.ndarray):\\n> self.t0 = t0\\n> self.tmax = tmax\\n> self.y0 = y0\\n>\\n> def __repr__(self) -> str:\\n> return f\\\"ODEProblem(t0={self.t0}, tmax={self.tmax}, y0={self.y0})\\\"\\n>\\n> def __getstate__(self) -> dict:\\n> \\\"\\\"\\\"Return state for pickling.\\\"\\\"\\\"\\n> return {\\\"t0\\\": self.t0, \\\"tmax\\\": self.tmax, \\\"y0\\\": self.y0}\\n>\\n> def __setstate__(self, state: dict) -> None:\\n> \\\"\\\"\\\"Restore state from unpickling.\\\"\\\"\\\"\\n> self.t0 = state[\\\"t0\\\"]\\n> self.tmax = state[\\\"tmax\\\"]\\n> self.y0 = state[\\\"y0\\\"]\\n>\\n>\\n> def lotkavolterra(t0: float = 0.0, tmax: float = 1.0,\\n> y0: Optional[np.ndarray] = None) -> ODEProblem:\\n> \\\"\\\"\\\"Create a Lotka-Volterra ODE problem stub.\\n>\\n> Returns a lightweight object representing an ODE problem.\\n> The object does not implement real Lotka-Volterra dynamics;\\n> PerturbedStepSolver.solve generates a surrogate solution.\\n>\\n> Args:\\n> t0: Initial time (default: 0.0).\\n> tmax: Final time (default: 1.0).\\n> y0: Initial state. If None, a default is used.\\n>\\n> Returns:\\n> An ODEProblem object with the given parameters.\\n> \\\"\\\"\\\"\\n> if y0 is None:\\n> y0 = np.array([1.0, 0.0])\\n> else:\\n> y0 = np.asarray(y0)\\n>\\n> return ODEProblem(t0=t0, tmax=tmax, y0=y0)\\n>\\n>\\n> __all__ = [\\\"lotkavolterra\\\", \\\"ODEProblem\\\"]\\n> EOF\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# ls -la probnum/problems/zoo/\\ntotal 8\\ndrwxr-xr-x 2 root root 54 Mar 16 09:41 .\\ndrwxr-xr-x 3 root root 48 Mar 16 09:23 ..\\n-rw-r--r-- 1 root root 169 Mar 16 09:41 __init__.py\\n-rw-r--r-- 1 root root 1872 Mar 16 09:41 diffeq.py\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app#\\n\",\n \"role\": \"user\"\n },\n {\n \"content\": \"<think>All the files have been created. Now I should test that the imports work correctly and verify the implementation is sound. Let me run some basic import tests.</think>{\\n \\\"analysis\\\": \\\"All the module files have been created. Now I need to verify that the imports work correctly and the implementation meets the requirements.\\\",\\n \\\"plan\\\": \\\"Test the imports and basic functionality of the implementation to ensure everything works as expected.\\\",\\n \\\"commands\\\": [\\n {\\n \\\"keystrokes\\\": \\\"python3 -c \\\\\\\"from probnum import diffeq, randvars; import probnum.problems.zoo.diffeq as diffeq_zoo; print('Imports successful')\\\\\\\"\\\\n\\\",\\n \\\"duration\\\": 1.0\\n },\\n {\\n \\\"keystrokes\\\": \\\"python3 -c \\\\\\\"from probnum.diffeq.stepsize import AdaptiveSteps; from probnum.diffeq.perturbed.scipy_wrapper import WrappedScipyRungeKutta; from probnum.diffeq.perturbed.step import perturb_uniform, PerturbedStepSolver; from probnum.randvars import _RandomVariableList; import probnum.problems.zoo.diffeq as diffeq_zoo; print('All imports successful')\\\\\\\"\\\\n\\\",\\n \\\"duration\\\": 1.0\\n },\\n {\\n \\\"keystrokes\\\": \\\"python3 << 'EOF'\\\\nimport numpy as np\\\\nfrom probnum.diffeq.stepsize import AdaptiveSteps\\\\nfrom probnum.diffeq.perturbed.scipy_wrapper import WrappedScipyRungeKutta\\\\nfrom probnum.diffeq.perturbed.step import perturb_uniform, PerturbedStepSolver\\\\nfrom probnum.randvars import _RandomVariableList, _RVLike\\\\nimport probnum.problems.zoo.diffeq as diffeq_zoo\\\\n\\\\n# Test AdaptiveSteps\\\\nsteprule = AdaptiveSteps(initial_step=0.1, atol=1e-6, rtol=1e-6)\\\\nprint(f\\\\\\\"AdaptiveSteps: {steprule}\\\\\\\")\\\\nprint(f\\\\\\\"Hash: {hash(steprule)}\\\\\\\")\\\\n\\\\n# Test WrappedScipyRungeKutta\\\\nrk = type('RK45', (), {})() # Dummy method object\\\\nwrapped = WrappedScipyRungeKutta(rk, steprule)\\\\nprint(f\\\\\\\"WrappedScipyRungeKutta: {wrapped}\\\\\\\")\\\\n\\\\n# Test perturb_uniform\\\\nperturbed = perturb_uniform(42, np.array([1.0, 2.0]), 0.1)\\\\nprint(f\\\\\\\"Perturbed: {perturbed}\\\\\\\")\\\\n\\\\n# Test lotkavolterra\\\\node = diffeq_zoo.lotkavolterra()\\\\nprint(f\\\\\\\"ODE: {ode}\\\\\\\")\\\\n\\\\n# Test PerturbedStepSolver\\\\nsolver = PerturbedStepSolver(rng=42, solver=wrapped, noise_scale=0.0, perturb_function=perturb_uniform)\\\\nsolution = solver.solve(ode)\\\\nprint(f\\\\\\\"Solution length: {len(solution)}\\\\\\\")\\\\nprint(f\\\\\\\"Solution locations: {solution.locations}\\\\\\\")\\\\nprint(f\\\\\\\"States type: {type(solution.states)}\\\\\\\")\\\\nprint(f\\\\\\\"States is _RandomVariableList: {isinstance(solution.states, _RandomVariableList)}\\\\\\\")\\\\nprint(f\\\\\\\"Solution[0].mean: {solution[0].mean}\\\\\\\")\\\\n\\\\n# Test __call__\\\\ncalled = solution(solution.locations)\\\\nprint(f\\\\\\\"Called solution(locations).mean: {called.mean}\\\\\\\")\\\\nprint(f\\\\\\\"Matches states.mean: {np.allclose(called.mean, solution.states._means)}\\\\\\\")\\\\n\\\\nprint(\\\\\\\"All tests passed!\\\\\\\")\\\\nEOF\\\\n\\\",\\n \\\"duration\\\": 2.0\\n }\\n ],\\n \\\"task_complete\\\": false\\n}\",\n \"role\": \"assistant\"\n },\n {\n \"content\": \"Previous response had warnings:\\nWARNINGS: - Extra text detected before JSON object\\n\\nNew Terminal Output:\\n\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# python3 -c \\\"from probnum import diffeq, randvars; import probnum.problems.zoo.diffeq as diffeq_zoo; print('Impor\\nts successful')\\\"\\nTraceback (most recent call last):\\n File \\\"<string>\\\", line 1, in <module>\\n File \\\"/app/probnum/__init__.py\\\", line 3, in <module>\\n from . import diffeq\\n File \\\"/app/probnum/diffeq/__init__.py\\\", line 4, in <module>\\n from . import perturbed\\n File \\\"/app/probnum/diffeq/perturbed/__init__.py\\\", line 4, in <module>\\n from . import step\\n File \\\"/app/probnum/diffeq/perturbed/step.py\\\", line 4, in <module>\\n import numpy as np\\nModuleNotFoundError: No module named 'numpy'\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# python3 -c \\\"from probnum.diffeq.stepsize import AdaptiveSteps; from probnum.diffeq.perturbed.scipy_wrapper impor\\nt WrappedScipyRungeKutta; from probnum.diffeq.perturbed.step import perturb_uniform, PerturbedStepSolver; from probnum.randvars import _RandomVariableList; impo\\nrt probnum.problems.zoo.diffeq as diffeq_zoo; print('All imports successful')\\\"\\nTraceback (most recent call last):\\n File \\\"<string>\\\", line 1, in <module>\\n File \\\"/app/probnum/__init__.py\\\", line 3, in <module>\\n from . import diffeq\\n File \\\"/app/probnum/diffeq/__init__.py\\\", line 4, in <module>\\n from . import perturbed\\n File \\\"/app/probnum/diffeq/perturbed/__init__.py\\\", line 4, in <module>\\n from . import step\\n File \\\"/app/probnum/diffeq/perturbed/step.py\\\", line 4, in <module>\\n import numpy as np\\nModuleNotFoundError: No module named 'numpy'\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# python3 << 'EOF'\\n> import numpy as np\\n> from probnum.diffeq.stepsize import AdaptiveSteps\\n> from probnum.diffeq.perturbed.scipy_wrapper import WrappedScipyRungeKutta\\n> from probnum.diffeq.perturbed.step import perturb_uniform, PerturbedStepSolver\\n> from probnum.randvars import _RandomVariableList, _RVLike\\n> import probnum.problems.zoo.diffeq as diffeq_zoo\\n>\\n> # Test AdaptiveSteps\\n> steprule = AdaptiveSteps(initial_step=0.1, atol=1e-6, rtol=1e-6)\\n> print(f\\\"AdaptiveSteps: {steprule}\\\")\\n> print(f\\\"Hash: {hash(steprule)}\\\")\\n>\\n> # Test WrappedScipyRungeKutta\\n> rk = type('RK45', (), {})() # Dummy method object\\n> wrapped = WrappedScipyRungeKutta(rk, steprule)\\n> print(f\\\"WrappedScipyRungeKutta: {wrapped}\\\")\\n>\\n> # Test perturb_uniform\\n> perturbed = perturb_uniform(42, np.array([1.0, 2.0]), 0.1)\\n> print(f\\\"Perturbed: {perturbed}\\\")\\n>\\n> # Test lotkavolterra\\n> ode = diffeq_zoo.lotkavolterra()\\n> print(f\\\"ODE: {ode}\\\")\\n>\\n> # Test PerturbedStepSolver\\n> solver = PerturbedStepSolver(rng=42, solver=wrapped, noise_scale=0.0, perturb_function=perturb_uniform)\\n> solution = solver.solve(ode)\\n> print(f\\\"Solution length: {len(solution)}\\\")\\n> print(f\\\"Solution locations: {solution.locations}\\\")\\n> print(f\\\"States type: {type(solution.states)}\\\")\\n> print(f\\\"States is _RandomVariableList: {isinstance(solution.states, _RandomVariableList)}\\\")\\n> print(f\\\"Solution[0].mean: {solution[0].mean}\\\")\\n>\\n> # Test __call__\\n> called = solution(solution.locations)\\n> print(f\\\"Called solution(locations).mean: {called.mean}\\\")\\n> print(f\\\"Matches states.mean: {np.allclose(called.mean, solution.states._means)}\\\")\\n>\\n> print(\\\"All tests passed!\\\")\\n> EOF\\nTraceback (most recent call last):\\n File \\\"<stdin>\\\", line 1, in <module>\\nModuleNotFoundError: No module named 'numpy'\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app#\\n\",\n \"role\": \"user\"\n },\n {\n \"content\": \"<think>NumPy is not installed. I need to install it first before testing.</think>{\\n \\\"analysis\\\": \\\"NumPy is not installed in the environment. I need to install it before testing the implementation.\\\",\\n \\\"plan\\\": \\\"Install NumPy using pip, then test the implementation.\\\",\\n \\\"commands\\\": [\\n {\\n \\\"keystrokes\\\": \\\"pip install numpy scipy\\\\n\\\",\\n \\\"duration\\\": 30.0\\n }\\n ],\\n \\\"task_complete\\\": false\\n}\",\n \"role\": \"assistant\"\n },\n {\n \"content\": \"Previous response had warnings:\\nWARNINGS: - Extra text detected before JSON object\\n\\nNew Terminal Output:\\n\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# pip install numpy scipy\\nCollecting numpy\\n Downloading numpy-2.2.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.8 MB)\\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 16.8/16.8 MB 48.9 MB/s eta 0:00:00\\nCollecting scipy\\n Downloading scipy-1.15.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (37.7 MB)\\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 37.7/37.7 MB 67.7 MB/s eta 0:00:00\\nInstalling collected packages: numpy, scipy\\nSuccessfully installed numpy-2.2.6 scipy-1.15.3\\nWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a v\\nirtual environment instead: https://pip.pypa.io/warnings/venv\\n\\n[notice] A new release of pip is available: 23.0.1 -> 26.0.1\\n[notice] To update, run: pip install --upgrade pip\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app#\\n\",\n \"role\": \"user\"\n },\n {\n \"content\": \"<think>NumPy and SciPy are now installed. Let me test the implementation again.</think>{\\n \\\"analysis\\\": \\\"NumPy and SciPy are now installed. I can now test the implementation to verify it works correctly.\\\",\\n \\\"plan\\\": \\\"Run the tests again to verify the implementation works as expected.\\\",\\n \\\"commands\\\": [\\n {\\n \\\"keystrokes\\\": \\\"python3 << 'EOF'\\\\nimport numpy as np\\\\nfrom probnum.diffeq.stepsize import AdaptiveSteps\\\\nfrom probnum.diffeq.perturbed.scipy_wrapper import WrappedScipyRungeKutta\\\\nfrom probnum.diffeq.perturbed.step import perturb_uniform, PerturbedStepSolver\\\\nfrom probnum.randvars import _RandomVariableList, _RVLike\\\\nimport probnum.problems.zoo.diffeq as diffeq_zoo\\\\n\\\\n# Test AdaptiveSteps\\\\nsteprule = AdaptiveSteps(initial_step=0.1, atol=1e-6, rtol=1e-6)\\\\nprint(f\\\\\\\"AdaptiveSteps: {steprule}\\\\\\\")\\\\nprint(f\\\\\\\"Hash: {hash(steprule)}\\\\\\\")\\\\n\\\\n# Test WrappedScipyRungeKutta\\\\nrk = type('RK45', (), {})() # Dummy method object\\\\nwrapped = WrappedScipyRungeKutta(rk, steprule)\\\\nprint(f\\\\\\\"WrappedScipyRungeKutta: {wrapped}\\\\\\\")\\\\n\\\\n# Test perturb_uniform\\\\nperturbed = perturb_uniform(42, np.array([1.0, 2.0]), 0.1)\\\\nprint(f\\\\\\\"Perturbed: {perturbed}\\\\\\\")\\\\n\\\\n# Test lotkavolterra\\\\node = diffeq_zoo.lotkavolterra()\\\\nprint(f\\\\\\\"ODE: {ode}\\\\\\\")\\\\n\\\\n# Test PerturbedStepSolver\\\\nsolver = PerturbedStepSolver(rng=42, solver=wrapped, noise_scale=0.0, perturb_function=perturb_uniform)\\\\nsolution = solver.solve(ode)\\\\nprint(f\\\\\\\"Solution length: {len(solution)}\\\\\\\")\\\\nprint(f\\\\\\\"Solution locations: {solution.locations}\\\\\\\")\\\\nprint(f\\\\\\\"States type: {type(solution.states)}\\\\\\\")\\\\nprint(f\\\\\\\"States is _RandomVariableList: {isinstance(solution.states, _RandomVariableList)}\\\\\\\")\\\\nprint(f\\\\\\\"Solution[0].mean: {solution[0].mean}\\\\\\\")\\\\n\\\\n# Test __call__\\\\ncalled = solution(solution.locations)\\\\nprint(f\\\\\\\"Called solution(locations).mean: {called.mean}\\\\\\\")\\\\nprint(f\\\\\\\"Matches states.mean: {np.allclose(called.mean, solution.states._means)}\\\\\\\")\\\\n\\\\nprint(\\\\\\\"All tests passed!\\\\\\\")\\\\nEOF\\\\n\\\",\\n \\\"duration\\\": 2.0\\n }\\n ],\\n \\\"task_complete\\\": false\\n}\",\n \"role\": \"assistant\"\n },\n {\n \"content\": \"[SYSTEM WARNING] You have approximately 5 minutes remaining before the task times out. Please wrap up your work and finalize your solution as soon as possible.\\n\\nPrevious response had warnings:\\nWARNINGS: - Extra text detected before JSON object\\n\\nNew Terminal Output:\\n\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app# python3 << 'EOF'\\n> import numpy as np\\n> from probnum.diffeq.stepsize import AdaptiveSteps\\n> from probnum.diffeq.perturbed.scipy_wrapper import WrappedScipyRungeKutta\\n> from probnum.diffeq.perturbed.step import perturb_uniform, PerturbedStepSolver\\n> from probnum.randvars import _RandomVariableList, _RVLike\\n> import probnum.problems.zoo.diffeq as diffeq_zoo\\n>\\n> # Test AdaptiveSteps\\n> steprule = AdaptiveSteps(initial_step=0.1, atol=1e-6, rtol=1e-6)\\n> print(f\\\"AdaptiveSteps: {steprule}\\\")\\n> print(f\\\"Hash: {hash(steprule)}\\\")\\n>\\n> # Test WrappedScipyRungeKutta\\n> rk = type('RK45', (), {})() # Dummy method object\\n> wrapped = WrappedScipyRungeKutta(rk, steprule)\\n> print(f\\\"WrappedScipyRungeKutta: {wrapped}\\\")\\n>\\n> # Test perturb_uniform\\n> perturbed = perturb_uniform(42, np.array([1.0, 2.0]), 0.1)\\n> print(f\\\"Perturbed: {perturbed}\\\")\\n>\\n> # Test lotkavolterra\\n> ode = diffeq_zoo.lotkavolterra()\\n> print(f\\\"ODE: {ode}\\\")\\n>\\n> # Test PerturbedStepSolver\\n> solver = PerturbedStepSolver(rng=42, solver=wrapped, noise_scale=0.0, perturb_function=perturb_uniform)\\n> solution = solver.solve(ode)\\n> print(f\\\"Solution length: {len(solution)}\\\")\\n> print(f\\\"Solution locations: {solution.locations}\\\")\\n> print(f\\\"States type: {type(solution.states)}\\\")\\n> print(f\\\"States is _RandomVariableList: {isinstance(solution.states, _RandomVariableList)}\\\")\\n> print(f\\\"Solution[0].mean: {solution[0].mean}\\\")\\n>\\n> # Test __call__\\n> called = solution(solution.locations)\\n> print(f\\\"Called solution(locations).mean: {called.mean}\\\")\\n> print(f\\\"Matches states.mean: {np.allclose(called.mean, solution.states._means)}\\\")\\n>\\n> print(\\\"All tests passed!\\\")\\n> EOF\\nAdaptiveSteps: AdaptiveSteps(initial_step=0.1, atol=1e-06, rtol=1e-06)\\nHash: 2871196771894804122\\nWrappedScipyRungeKutta: WrappedScipyRungeKutta(method=<__main__.RK45 object at 0x75ebcb47bf70>, steprule=AdaptiveSteps(initial_step=0.1, atol=1e-06, rtol=1e-06)\\n)\\nPerturbed: [1.0273956 1.99388784]\\nODE: ODEProblem(t0=0.0, tmax=1.0, y0=[1. 0.])\\nSolution length: 6\\nSolution locations: [0. 0.2 0.4 0.6 0.8 1. ]\\nStates type: <class 'probnum.randvars._RandomVariableList'>\\nStates is _RandomVariableList: True\\nSolution[0].mean: [1. 0.]\\nCalled solution(locations).mean: [[1. 0. ]\\n [1.2 0.2]\\n [1.4 0.4]\\n [1.6 0.6]\\n [1.8 0.8]\\n [2. 1. ]]\\nMatches states.mean: True\\nAll tests passed!\\nroot@3336b5c5-3fa2-4bda-b431-b34f0a292807:/app#\\n\",\n \"role\": \"user\"\n },\n {\n \"content\": \"<think>The basic tests are passing. Now let me test the top-level imports and the threading/concurrency behavior, as well as edge cases like t0 == tmax and empty inputs. Let me also verify the interpolant behavior that the tests expect.</think>{\\n \\\"analysis\\\": \\\"Basic tests are passing. Now I need to verify the top-level imports and test edge cases like t0 == tmax, threading safety, and the specific test expectations mentioned in the task.\\\",\\n \\\"plan\\\": \\\"Test the full import paths and edge cases, then verify the implementation is complete.\\\",\\n \\\"commands\\\": [\\n {\\n \\\"keystrokes\\\": \\\"python3 << 'EOF'\\\\n# Test top-level imports\\\\nfrom probnum import diffeq, randvars\\\\nimport probnum.problems.zoo.diffeq as diffeq_zoo\\\\nprint(\\\\\\\"Top-level imports: OK\\\\\\\")\\\\n\\\\n# Test edge case: t0 == tmax\\\\node = diffeq_zoo.lotkavolterra(t0=0.0, tmax=0.0)\\\\nprint(f\\\\\\\"ODE with t0==tmax: {ode}\\\\\\\")\\\\n\\\\n# Test edge case: y0=None\\\\node = diffeq_zoo.lotkavolterra(y0=None)\\\\nprint(f\\\\\\\"ODE with y0=None: {ode}\\\\\\\")\\\\n\\\\n# Test slice indexing\\\\nfrom probnum.diffeq.stepsize import AdaptiveSteps\\\\nfrom probnum.diffeq.perturbed.scipy_wrapper import WrappedScipyRungeKutta\\\\nfrom probnum.diffeq.perturbed.step import perturb_uniform, PerturbedStepSolver\\\\n\\\\nsteprule = AdaptiveSteps(initial_step=0.1, atol=1e-6, rtol=1e-6)\\\\nrk = type('RK45', (), {})() # Dummy method object\\\\nwrapped = WrappedScipyRungeKutta(rk, steprule)\\\\nsolver = PerturbedStepSolver(rng=42, solver=wrapped, noise_scale=0.0, perturb_function=perturb_uniform)\\\\node = diffeq_zoo.lotkavolterra()\\\\nsolution = solver.solve(ode)\\\\n\\\\n# Test slice\\\\nsliced = solution.states[0:]\\\\nprint(f\\\\\\\"Sliced states type: {type(sliced)}\\\\\\\")\\\\nprint(f\\\\\\\"Sliced states.mean shape: {sliced.mean.shape}\\\\\\\")\\\\n\\\\n# Test interpolants\\\\ninterp_result = solution.interpolants[1](solution.locations[1])\\\\nprint(f\\\\\\\"Interpolant result type: {type(interp_result)}\\\\\\\")\\\\nprint(f\\\\\\\"Interpolant result.mean: {interp_result.mean}\\\\\\\")\\\\nprint(f\\\\\\\"Solution[1].mean: {solution[1].mean}\\\\\\\")\\\\nprint(f\\\\\\\"Match: {np.allclose(interp_result.mean, solution[1].mean)}\\\\\\\")\\\\n\\\\n# Test thread safety\\\\nimport threading\\\\ndef solve_task():\\\\n solver = PerturbedStepSolver(rng=100, solver=wrapped, noise_scale=0.0, perturb_function=perturb_uniform)\\\\n ode = diffeq_zoo.lotkavolterra()\\\\n sol = solver.solve(ode)\\\\n return len(sol) == 6\\\\n\\\\nthreads = [threading.Thread(target=solve_task) for _ in range(10)]\\\\nfor t in threads:\\\\n t.start()\\\\nfor t in threads:\\\\n t.join()\\\\nprint(\\\\\\\"Thread safety test: OK\\\\\\\")\\\\n\\\\n# Test extrapolation (t_values outside grid)\\\\nextrapolated = solution([-1.0, 2.0])\\\\nprint(f\\\\\\\"Extrapolation result: {extrapolated.mean}\\\\\\\")\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nAll edge case tests passed!\\\\\\\")\\\\nEOF\\\\n\\\",\\n \\\"duration\\\": 3.0\\n }\\n ],\\n \\\"task_complete\\\": false\\n}\",\n \"role\": \"assistant\"\n }\n]","isConversational":true},"agent":{"kind":"string","value":"terminus-2"},"model":{"kind":"string","value":"hosted_vllm/glm"},"model_provider":{"kind":"string","value":"hosted_vllm"},"date":{"kind":"string","value":"2026-03-16T09:20:38.013916+00:00"},"task":{"kind":"string","value":"curriculum-hard-0324_copy1"},"episode":{"kind":"string","value":"episode-8"},"run_id":{"kind":"string","value":"766aa649-0341-4739-a22a-1e4da82d3471"},"trial_name":{"kind":"string","value":"curriculum-hard-0324_copy1__z7V4M6k"},"result":{"kind":"string","value":"AgentTimeoutError"},"trace_source":{"kind":"string","value":"main"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":0,"numItemsPerPage":100,"numTotalItems":9690,"offset":0,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3OTczODUwMSwic3ViIjoiL2RhdGFzZXRzL0RDQWdlbnQvYTFfY3VycmljdWx1bV9oYXJkIiwiZXhwIjoxNzc5NzQyMTAxLCJpc3MiOiJodHRwczovL2h1Z2dpbmdmYWNlLmNvIn0.P6TcGu7CT1ESfgUwXSwk9UM3jSiDYWtW3VT-qaqA2JjplHzDaTfNdTuJVrjQgwnwsNg2TzFhis_bXhn7Aa9kDQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":9690,"numBytesParquet":446361079}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
conversations listlengths 2 67 | agent stringclasses 1
value | model stringclasses 1
value | model_provider stringclasses 1
value | date stringdate 2026-03-16 03:43:40 2026-03-16 12:12:48 | task stringlengths 20 27 | episode stringclasses 32
values | run_id stringclasses 8
values | trial_name stringlengths 29 36 | result stringclasses 3
values | trace_source stringclasses 13
values |
|---|---|---|---|---|---|---|---|---|---|---|
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T10:16:29.022419+00:00 | curriculum-hard-0091_copy5 | episode-4 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0091_copy5__9A3nsXR | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T07:57:52.131533+00:00 | curriculum-hard-0014_copy19 | episode-8 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0014_copy19__X4n6gMP | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T05:29:35.603598+00:00 | curriculum-hard-0145_copy2 | episode-1 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0145_copy2__MTDSES8 | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T06:57:19.767869+00:00 | curriculum-hard-0250_copy18 | episode-3 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0250_copy18__LViC4Ny | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T04:45:10.908616+00:00 | curriculum-hard-0005_copy14 | episode-2 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0005_copy14__trmYp2T | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T09:58:20.806343+00:00 | curriculum-hard-0432_copy1 | episode-0 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0432_copy1__4Axu5Rm | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T08:32:17.260180+00:00 | curriculum-hard-0404_copy8 | episode-4 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0404_copy8__3ucNiqn | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T04:14:24.068178+00:00 | curriculum-hard-0136_copy3 | episode-6 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0136_copy3__BLDN3jh | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T08:20:32.203366+00:00 | curriculum-hard-0057_copy3 | episode-11 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0057_copy3__o8waYB5 | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T11:24:48.178271+00:00 | curriculum-hard-0207_copy18 | episode-4 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0207_copy18__BckhNzz | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T08:07:53.738621+00:00 | curriculum-hard-0494_copy10 | episode-5 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0494_copy10__Aoimb8B | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T08:37:32.027803+00:00 | curriculum-hard-0395_copy12 | episode-5 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0395_copy12__BjuBu59 | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T09:12:46.067049+00:00 | curriculum-hard-0319_copy3 | episode-10 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0319_copy3__niDULL5 | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T11:45:40.324871+00:00 | curriculum-hard-0398_copy16 | episode-4 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0398_copy16__xQxnQ24 | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T07:35:27.656672+00:00 | curriculum-hard-0248_copy10 | episode-1 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0248_copy10__iXqs5Wf | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T08:34:55.820506+00:00 | curriculum-hard-0206_copy4 | episode-4 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0206_copy4__mrXw3jV | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T12:04:57.271268+00:00 | curriculum-hard-0322_copy1 | episode-36 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0322_copy1__dC72Sbf | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T11:17:38.450470+00:00 | curriculum-hard-0064_copy18 | episode-6 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0064_copy18__P3BADSy | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T09:23:22.378770+00:00 | curriculum-hard-0238_copy13 | episode-13 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0238_copy13__cc8yREU | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T10:49:28.224602+00:00 | curriculum-hard-0029_copy17 | episode-10 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0029_copy17__u4NZQWa | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T06:14:16.503158+00:00 | curriculum-hard-0486_copy6 | episode-10 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0486_copy6__C2HfBEX | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T11:52:39.234052+00:00 | curriculum-hard-0171_copy19 | episode-4 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0171_copy19__VCKNXrD | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T04:53:45.182897+00:00 | curriculum-hard-0247_copy4 | episode-7 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0247_copy4__gA4VmVJ | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T10:52:17.823700+00:00 | curriculum-hard-0299_copy8 | episode-8 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0299_copy8__afHEr7L | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T06:01:11.353322+00:00 | curriculum-hard-0166_copy5 | episode-7 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0166_copy5__XAgEfJp | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T06:58:17.084402+00:00 | curriculum-hard-0010_copy19 | episode-7 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0010_copy19__VQvHU6J | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T08:47:13.955344+00:00 | curriculum-hard-0415_copy5 | episode-7 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0415_copy5__zWGb7tq | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T11:30:05.919583+00:00 | curriculum-hard-0345_copy15 | episode-3 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0345_copy15__636ghBr | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T07:57:55.859837+00:00 | curriculum-hard-0270_copy13 | episode-6 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0270_copy13__oDdWHs5 | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T07:03:51.867891+00:00 | curriculum-hard-0025_copy1 | episode-2 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0025_copy1__gzfQYXD | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T08:41:36.719292+00:00 | curriculum-hard-0310 | episode-5 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0310__MjmEkkT | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T08:04:07.425905+00:00 | curriculum-hard-0229_copy1 | episode-4 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0229_copy1__cApdj9e | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T04:09:33.605927+00:00 | curriculum-hard-0292_copy12 | episode-6 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0292_copy12__W7y9mdC | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T11:00:45.169073+00:00 | curriculum-hard-0080 | episode-4 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0080__22MDVot | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T04:04:49.895377+00:00 | curriculum-hard-0254_copy10 | episode-6 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0254_copy10__wKJEjoz | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T11:14:12.408019+00:00 | curriculum-hard-0446_copy17 | episode-4 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0446_copy17__HrvMswq | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T09:48:07.724288+00:00 | curriculum-hard-0195_copy17 | episode-4 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0195_copy17__SiuxhQB | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T09:44:41.840171+00:00 | curriculum-hard-0089_copy15 | episode-9 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0089_copy15__i2cUdGP | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T04:35:37.176058+00:00 | curriculum-hard-0170_copy5 | episode-6 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0170_copy5__6Cm4N5J | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T10:06:55.625761+00:00 | curriculum-hard-0030_copy13 | episode-6 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0030_copy13__4KSAKt8 | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T09:09:50.340094+00:00 | curriculum-hard-0261 | episode-9 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0261__98eUt7i | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T09:40:03.581998+00:00 | curriculum-hard-0421_copy17 | episode-11 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0421_copy17__XvqjKET | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T11:09:16.305891+00:00 | curriculum-hard-0434_copy2 | episode-6 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0434_copy2__hkHEbLd | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T09:19:54.836397+00:00 | curriculum-hard-0060_copy8 | episode-0 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0060_copy8__vbmK8E9 | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T12:06:26.403463+00:00 | curriculum-hard-0490_copy1 | episode-12 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0490_copy1__KwRwzUc | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T08:20:51.924438+00:00 | curriculum-hard-0499_copy6 | episode-3 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0499_copy6__8CuZnjL | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T09:49:44.629036+00:00 | curriculum-hard-0047_copy18 | episode-8 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0047_copy18__C8JRxrX | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T09:56:59.965762+00:00 | curriculum-hard-0101_copy12 | episode-7 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0101_copy12__2ZAd9TU | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T09:20:12.703834+00:00 | curriculum-hard-0484_copy16 | episode-10 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0484_copy16__CegHbHw | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T06:51:18.567920+00:00 | curriculum-hard-0350_copy11 | episode-10 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0350_copy11__UjLr8dC | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T05:27:02.174829+00:00 | curriculum-hard-0167_copy11 | episode-4 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0167_copy11__6nJoNPm | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T06:20:47.684188+00:00 | curriculum-hard-0342_copy19 | episode-7 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0342_copy19__vHdnSTm | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T09:12:40.126355+00:00 | curriculum-hard-0434_copy11 | episode-2 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0434_copy11__mtmUdrC | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T05:38:52.916158+00:00 | curriculum-hard-0062_copy18 | episode-5 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0062_copy18__w5jrony | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T08:47:36.345742+00:00 | curriculum-hard-0325_copy4 | episode-5 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0325_copy4__ookLtn4 | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T04:14:15.907590+00:00 | curriculum-hard-0138_copy14 | episode-4 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0138_copy14__ZirAViH | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T04:31:21.160484+00:00 | curriculum-hard-0505_copy11 | episode-2 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0505_copy11__HthRgyB | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T07:18:41.719365+00:00 | curriculum-hard-0308_copy16 | episode-9 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0308_copy16__vj85gJZ | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T06:12:56.816505+00:00 | curriculum-hard-0458_copy3 | episode-0 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0458_copy3__inrwxQt | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T10:03:28.159569+00:00 | curriculum-hard-0156_copy10 | episode-4 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0156_copy10__e27rCCW | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T08:01:09.775229+00:00 | curriculum-hard-0087_copy15 | episode-7 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0087_copy15__Dp7LoJ3 | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T05:57:13.197116+00:00 | curriculum-hard-0045_copy8 | episode-10 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0045_copy8__e7PWcoh | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T11:22:50.998725+00:00 | curriculum-hard-0428 | episode-5 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0428__GpaR2yX | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T06:17:18.229036+00:00 | curriculum-hard-0092_copy2 | episode-4 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0092_copy2__QxxbLNm | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T10:21:17.509750+00:00 | curriculum-hard-0096_copy4 | episode-9 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0096_copy4__UGGDUa7 | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T06:53:05.326450+00:00 | curriculum-hard-0317_copy12 | episode-0 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0317_copy12__mfqrrzR | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T04:43:43.049562+00:00 | curriculum-hard-0113_copy5 | episode-1 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0113_copy5__snJnH6E | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T10:21:13.702375+00:00 | curriculum-hard-0144_copy16 | episode-0 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0144_copy16__xVfkfNx | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T06:58:34.294754+00:00 | curriculum-hard-0058_copy17 | episode-0 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0058_copy17__46WpC6L | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T11:01:30.373036+00:00 | curriculum-hard-0237_copy17 | episode-7 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0237_copy17__VXLVqhZ | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T05:45:24.983432+00:00 | curriculum-hard-0299_copy18 | episode-8 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0299_copy18__NWmFHYe | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T11:52:19.175405+00:00 | curriculum-hard-0029_copy7 | episode-9 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0029_copy7__e7JeLSm | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T05:12:59.024471+00:00 | curriculum-hard-0147_copy13 | episode-7 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0147_copy13__zACQbAP | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T10:28:39.949539+00:00 | curriculum-hard-0421_copy1 | episode-9 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0421_copy1__djKa4Dq | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T10:51:00.387796+00:00 | curriculum-hard-0220_copy2 | episode-0 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0220_copy2__34b7bhA | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T07:49:22.799711+00:00 | curriculum-hard-0145_copy12 | episode-6 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0145_copy12__tu2QCcw | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T08:47:36.851959+00:00 | curriculum-hard-0116_copy12 | episode-9 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0116_copy12__o2Bbukn | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T04:44:46.777782+00:00 | curriculum-hard-0381 | episode-7 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0381__Q2x7vdT | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T06:25:50.400617+00:00 | curriculum-hard-0152_copy18 | episode-9 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0152_copy18__VipX6nC | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T09:55:01.972332+00:00 | curriculum-hard-0054_copy6 | episode-11 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0054_copy6__x5K2Ezd | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T05:45:20.887599+00:00 | curriculum-hard-0140_copy16 | episode-5 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0140_copy16__BWAMNKG | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T03:43:47.171277+00:00 | curriculum-hard-0160_copy12 | episode-3 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0160_copy12__8ZUJeHi | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T11:22:37.166367+00:00 | curriculum-hard-0460_copy4 | episode-6 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0460_copy4__JDi2QwV | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T11:42:58.121210+00:00 | curriculum-hard-0500_copy12 | episode-2 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0500_copy12__6m7Jk5R | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T03:55:38.309433+00:00 | curriculum-hard-0277_copy10 | episode-2 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0277_copy10__VpuRx5Q | AgentEnvironmentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T04:44:45.860907+00:00 | curriculum-hard-0352_copy2 | episode-13 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0352_copy2__LcKcoh4 | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T06:48:41.972715+00:00 | curriculum-hard-0292_copy2 | episode-7 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0292_copy2__Zwi7T2F | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T07:02:14.991175+00:00 | curriculum-hard-0067_copy15 | episode-7 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0067_copy15__it6oP93 | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T08:23:23.399394+00:00 | curriculum-hard-0444_copy6 | episode-7 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0444_copy6__sxLWAN3 | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T08:17:16.750253+00:00 | curriculum-hard-0381_copy6 | episode-5 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0381_copy6__qRe3LNS | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T06:31:11.002573+00:00 | curriculum-hard-0412_copy18 | episode-8 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0412_copy18__oSvjjuC | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T08:16:39.212334+00:00 | curriculum-hard-0489_copy3 | episode-5 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0489_copy3__jxRqVUB | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T03:43:46.921734+00:00 | curriculum-hard-0387_copy8 | episode-5 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0387_copy8__eEzWXcR | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T11:58:47.915528+00:00 | curriculum-hard-0408_copy10 | episode-4 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0408_copy10__NiPPjL3 | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T03:43:45.808038+00:00 | curriculum-hard-0073_copy16 | episode-6 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0073_copy16__DQG8ff3 | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T06:10:06.730089+00:00 | curriculum-hard-0180_copy8 | episode-10 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0180_copy8__CFC5C9C | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T08:54:24.279717+00:00 | curriculum-hard-0348_copy9 | episode-11 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0348_copy9__X5q82Z2 | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T09:02:37.520296+00:00 | curriculum-hard-0349_copy15 | episode-8 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0349_copy15__mxkzRBS | null | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T04:14:19.388176+00:00 | curriculum-hard-0280_copy5 | episode-12 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0280_copy5__Wjq27hE | AgentTimeoutError | main |
[
{
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment. You will be given a task description and the output from previously executed commands. Your goal is to solve the task by providing batches of shell commands.\n\nFormat your response as JSON with the following st... | terminus-2 | hosted_vllm/glm | hosted_vllm | 2026-03-16T09:20:38.013916+00:00 | curriculum-hard-0324_copy1 | episode-8 | 766aa649-0341-4739-a22a-1e4da82d3471 | curriculum-hard-0324_copy1__z7V4M6k | AgentTimeoutError | main |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.