diff --git a/.github/workflows/math-execution.yml b/.github/workflows/math-execution.yml
new file mode 100644
index 0000000..c474472
--- /dev/null
+++ b/.github/workflows/math-execution.yml
@@ -0,0 +1,30 @@
+name: Restricted math execution
+
+on:
+ push:
+ pull_request:
+
+permissions:
+ contents: read
+
+jobs:
+ regression:
+ runs-on: ubuntu-latest
+ timeout-minutes: 15
+ strategy:
+ matrix:
+ python-version: ['3.11', '3.12']
+ steps:
+ - uses: actions/checkout@v4
+ with:
+ persist-credentials: false
+ - uses: actions/setup-python@v5
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Install CPU-only regression dependencies
+ run: |
+ python -m pip install torch --index-url https://download.pytorch.org/whl/cpu
+ python -m pip install sympy==1.14.0 smolagents==1.26.0 transformers==5.10.4
+ python -m pip check
+ - name: Test real Linux worker limits and agent integration
+ run: python -B -m unittest discover -s tests -v
\ No newline at end of file
diff --git a/README.md b/README.md
index 8edce9e..0b5d971 100644
--- a/README.md
+++ b/README.md
@@ -9,11 +9,9 @@ Large language models (LLMs) have made impressive strides in reasoning tasks, ye
## DeepMath
-DeepMath is built on **Qwen3-4B Thinking** and fine-tuned with **GRPO (Group Relative Policy Optimization)**. Instead of verbose text, the model emits **tiny Python snippets** for intermediate steps, runs them in a secure sandbox, and folds the results back into its reasoning, reducing errors and output length.
+DeepMath is built on **Qwen3-4B Thinking** and fine-tuned with **GRPO (Group Relative Policy Optimization)**. Instead of verbose text, the model emits short math snippets in a restricted Python-like language. A bounded interpreter evaluates approved mathematical operations and folds the results back into its reasoning.
-✅ No file I/O, no network calls, strict timeouts.
-
-✅ Safe, deterministic, and auditable.
+The interpreter does not expose general Python imports, arbitrary file/network operations, or SymPy's string-expression evaluators. Its worker is **not an OS sandbox**: it runs with the service account's operating-system privileges unless deployment-level isolation is configured. The inference server also does not authenticate requests by default; do not expose it to untrusted networks without adding access controls and isolation.
We evaluate DeepMath on four math datasets: **MATH500, AIME, HMMT, and HLE,** and show that:
@@ -33,16 +31,16 @@ Check out:
LLMs often struggle with numeric precision and produce unnecessarily long reasoning chains. Two opportunities stand out:
-1. **Offload deterministic computation** to a safe executor.
+1. **Offload deterministic computation** to a restricted math executor.
2. **Train models to prefer concise, computation-oriented traces** over verbose text.
-DeepMath implements both. The model learns to generate short Python snippets, which are executed in a sandbox and reintegrated into the context. GRPO fine-tuning encourages this behavior by rewarding correctness and encouraging shorter outputs.
+DeepMath implements both. The model learns to generate short math snippets, which are interpreted by a restricted math executor and reintegrated into the context. GRPO fine-tuning encourages this behavior by rewarding correctness and encouraging shorter outputs.
## How It Works
- Base model: [Qwen3-4B Thinking](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507).
-- Executor constraints: sandboxed environment, allow-list of imported modules, per-snippet timeout.
+- Executor constraints: restricted math syntax and fixed operation registry, bounded inputs/outputs and per-call worker deadline. This is not an OS sandbox; production deployments need OS-level isolation.
- Inference: based on [SmolAgents](https://github.com/huggingface/smolagents/), a math agent was created. vLLM is used as the inference engine.
- Training: based on the GRPO trainer in [TRL](https://github.com/huggingface/trl), we modified TRL's vLLM client and server to generate GRPO completions using our DeepMath agent.
@@ -52,15 +50,15 @@ DeepMath implements both. The model learns to generate short Python snippets, wh
Figure 1: The vLLM client and server were modified to use the DeepMath agent in generating the candidates, while using the vLLM backend.
-- **Agent Interface:** During inference, the model can output normal tokens or special agent calls containing Python snippets.
+- **Agent Interface:** During inference, the model can output normal tokens or special agent calls containing restricted math snippets.
-- **Execution:** Snippets run in a sandboxed environment with strict safety constraints (no file I/O, no network, timeouts).
+- **Execution:** Snippets run in a restricted interpreter. The worker retains its service account's OS privileges; filesystem, network, credentials, and deployment isolation must be controlled outside the interpreter.
- **Design Goals:**
- **Concision:** Replace multi-line textual calculations with short, focused snippets.
- - **Determinism & Safety:** Enforce strict execution limits.
+ - **Bounded computation:** Enforce interpreter-level source, operation, collection, output, and execution limits.
- **Interpretability:** Snippets are readable and auditable.
@@ -108,7 +106,7 @@ We benchmarked DeepMath against baselines on four datasets. Metrics include:
- **Efficiency:** Shorter outputs mean faster inference and easier interpretability.
-- **Safety:** Sandbox execution mitigates risks of running arbitrary code.
+ - **Safety:** Restrict the supported math language; use deployment-level OS isolation for defense in depth.
## Conclusion
@@ -116,32 +114,67 @@ DeepMath demonstrates a practical and lightweight way to combine a small executo
## Try It Yourself
-Check out the GitHub repo and share your feedback! Contributions welcome. 🚀
+Check out the GitHub repository and give DeepMath a try. Feedback and contributions are welcome. 🚀
## Installation
-DeepMath uses [uv](https://github.com/astral-sh/uv):
+DeepMath uses [uv](https://docs.astral.sh/uv/) for Python environment and dependency management.
+
+### 1. Install uv
-1. **Install uv** (if not already installed):
+If `uv` is not already installed:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
-2. **Clone the repository**:
+### 2. Clone the repository
```bash
git clone https://github.com/intellabs/DeepMath.git
cd DeepMath
```
-3. **Install dependencies**:
+### 3. Install DeepMath
+
+Create the project's virtual environment and install the standard dependencies:
+
+```bash
+uv sync
+```
+
+`uv` will automatically create a `.venv` virtual environment, install the locked dependencies, and install DeepMath in editable mode.
+
+You can run commands directly in the project environment with `uv run`:
+
+```bash
+uv run python --version
+```
+
+Or activate the virtual environment:
```bash
-uv pip install -r requirements.txt
-uv pip install -e .
+source .venv/bin/activate
```
+### GPU / CUDA Support
+
+DeepMath's GPU training and vLLM dependencies are optional because they require a supported GPU environment and are not available on all platforms.
+
+For a Linux x86-64 NVIDIA/CUDA environment with Python 3.12, install the GPU dependencies with:
+
+```bash
+uv sync --extra gpu
+```
+
+The `gpu` extra includes dependencies such as vLLM, FlashAttention, DeepSpeed, and bitsandbytes.
+
+A standard `uv sync` is sufficient for development environments that do not require these GPU-specific features, including macOS.
+
+Standard development still installs PyTorch because the training libraries depend on it. On macOS, PyTorch comes from PyPI and does not require CUDA; CPU and supported Apple MPS operations remain available. The CUDA 13.0 PyTorch source and the pinned FlashAttention wheel are restricted to the Linux x86-64 `gpu` extra. This configuration does not guarantee a CPU-only PyTorch installation on Linux.
+
+`uv` resolves all extras when updating the lockfile, so GPU packages can appear in the lockfile without being installed by a standard macOS sync.
+
## Usage
The `example.sh` script provides complete examples for training, inference, and evaluation. Below are the main workflows:
@@ -251,4 +284,4 @@ If you use DeepMath in your research, please cite:
- **Generalization**: evaluated on contest-style math; results may not transfer to open-ended mathematical creativity or formal proofs.
-- Executing generated code is inherently risky. DeepMath uses strict sandboxing and resource limits, but any deployment should carefully manage attack surfaces and enforce rate limits.
+- Generated snippets are interpreted by a restricted math executor, not run as arbitrary Python. The worker is not an OS sandbox and the server routes are unauthenticated by default; do not expose the service to untrusted networks. Production operators must add authentication, OS-level isolation, resource controls, and rate limits. See [restricted math execution](docs/math-execution.md).
diff --git a/deep_math/agent.py b/deep_math/agent.py
index 9517e6e..3a80826 100644
--- a/deep_math/agent.py
+++ b/deep_math/agent.py
@@ -1,9 +1,4 @@
-import multiprocessing as mp
-import pickle
-import re
-import threading
import time
-import traceback
from collections.abc import Generator
from dataclasses import dataclass
from enum import IntEnum
@@ -18,21 +13,25 @@
ChatMessage,
CodeAgent,
FinalAnswerStep,
- Message,
MessageRole,
Model,
PlanningStep,
Tool,
VLLMModel,
- fix_final_answer_code,
handle_agent_output_types,
- parse_code_blobs,
truncate_content,
)
from smolagents.memory import MemoryStep
+from smolagents.monitoring import Timing
from transformers.generation import StoppingCriteriaList, StopStringCriteria
from vllm import SamplingParams
+from deep_math.math_executor import (
+ RestrictedMathExecutor,
+ extract_math_code,
+ validate_model_output,
+)
+
class LogLevel(IntEnum):
OFF = -1 # No output
@@ -44,94 +43,6 @@ class LogLevel(IntEnum):
YELLOW_HEX = "#d4b702"
-class CodeExecutionTimeout:
- """Run a callable with a hard timeout using a separate process.
-
- If the callable and its arguments are picklable, the callable is executed in
- a child process which is terminated on timeout (strong enforcement).
-
- If pickling fails (common for bound methods / complex objects), we fall
- back to a thread-based wait that raises TimeoutError but cannot kill the
- background thread; this preserves previous behavior but is not a hard kill.
- """
-
- def __init__(self, timeout_seconds: int = 30):
- self.timeout_seconds = timeout_seconds
- self.result = None
- self.exception = None
- self.completed = threading.Event()
-
- @staticmethod
- def _process_wrapper(queue: "mp.Queue", func, args, kwargs):
- try:
- res = func(*args, **kwargs)
- queue.put(("OK", res))
- except Exception:
- tb = traceback.format_exc()
- queue.put(("EXC", tb))
-
- def run_with_timeout(self, func, *args, **kwargs):
- """Run `func(*args, **kwargs)` with hard timeout enforcement where possible.
-
- Returns whatever the callable returns. Raises TimeoutError on timeout.
- Reraises exceptions from the callable (wrapped from subprocess traceback
- into a RuntimeError) when running in a subprocess.
- """
- # First attempt: run in a subprocess so we can terminate it on timeout.
- try:
- # Validate picklability early to provide clearer fallback behavior
- pickle.dumps((func, args, kwargs))
-
- q: "mp.Queue" = mp.Queue()
- p = mp.Process(target=self._process_wrapper, args=(q, func, args, kwargs), daemon=True)
- p.start()
- p.join(self.timeout_seconds)
- if p.is_alive():
- p.terminate()
- p.join(1.0)
- if p.is_alive():
- p.kill()
- p.join()
- raise RuntimeError(f"Code execution timed out after {self.timeout_seconds} seconds")
-
- if not q.empty():
- tag, payload = q.get()
- if tag == "OK":
- return payload
- else:
- # Exception occurred in subprocess; present traceback
- raise RuntimeError(f"Exception in subprocess:\n{payload}")
- else:
- raise RuntimeError("Worker process exited without returning a result.")
-
- except (pickle.PicklingError, AttributeError, TypeError) as e:
- # Fallback: not picklable — run in a thread and wait (cannot kill thread)
- # Preserve previous behavior: start thread, wait, raise TimeoutError on timeout
- thread_exc = None
-
- def _target_wrapper(func, args, kwargs):
- nonlocal thread_exc
- try:
- self.result = func(*args, **kwargs)
- except Exception as ex:
- thread_exc = ex
- finally:
- self.completed.set()
-
- thread = threading.Thread(
- target=_target_wrapper, args=(func, args, kwargs), daemon=True
- )
- thread.start()
-
- if self.completed.wait(timeout=self.timeout_seconds):
- if thread_exc:
- raise thread_exc
- return self.result
- else:
- # We cannot forcefully terminate the thread — raise to caller
- raise RuntimeError(f"Code execution timed out after {self.timeout_seconds} seconds")
-
-
def substring_stopping_criteria(
self, stop_sequences: list[str], tokenizer
) -> "StoppingCriteriaList":
@@ -234,7 +145,7 @@ class QueryWithInstructionsStep(MemoryStep):
prompt: str
- def to_messages(self, summary_mode: bool = False) -> list[Message]:
+ def to_messages(self, summary_mode: bool = False) -> list[str]:
return [self.prompt]
@@ -255,35 +166,37 @@ def __init__(
planning_interval: int | None = None,
stream_outputs: bool = False,
code_execution_timeout: int = 20, # Timeout in seconds for Python code execution
- additional_authorized_imports: list[str] = [
- "cmath",
- "numpy",
- "numpy.*",
- "scipy",
- "scipy.*",
- "sympy",
- "sympy.*",
- ],
+ additional_authorized_imports: list[str] | None = None,
**kwargs,
):
# NOTE!: we now assume the "task" is a complete processed chat template, maybe including system instruction
+ if tools or additional_authorized_imports or kwargs.get("managed_agents"):
+ raise ValueError("MathAgent uses a fixed math policy; extra tools/imports are disabled")
+ if any(key in kwargs for key in ("executor", "executor_type", "executor_kwargs")):
+ raise ValueError("MathAgent's restricted executor cannot be overridden")
+ if kwargs.get("add_base_tools"):
+ raise ValueError("Additional base tools are disabled for MathAgent")
self.code_execution_timeout = code_execution_timeout
+ self.grammar = grammar
super().__init__(
tools=tools,
model=model,
max_steps=max_steps,
- grammar=grammar,
planning_interval=planning_interval,
stream_outputs=stream_outputs,
- additional_authorized_imports=additional_authorized_imports,
+ additional_authorized_imports=[],
**kwargs,
)
+ self.authorized_imports = ["sympy", "numpy"]
self._max_agent_output = max_agent_output
self._output_tokens = 0 # we'll manage total output tokens per run as a global constraint instead of #steps x vllm_step_max_length
+ def create_python_executor(self):
+ return RestrictedMathExecutor(self.code_execution_timeout)
+
def initialize_system_prompt(self) -> str:
return ""
@@ -326,9 +239,13 @@ def run(
If `False`, executes all steps internally and returns only the final answer after completion.
reset (`bool`): Whether to reset the conversation or keep it going from previous run.
images (`list[PIL.Image.Image]`, *optional*): Image(s) objects.
- additional_args (`dict`, *optional*): Any other variables that you want to pass to the agent run, for instance images or dataframes. Give them clear names!
+ additional_args (`dict`, *optional*): Nonempty injected arguments are prohibited by the math execution policy.
max_steps (`int`, *optional*): Maximum number of steps the agent can take to solve the task. if not provided, will use the agent's default value.
"""
+ # Conversation memory may be retained, but execution state never crosses tasks.
+ self.python_executor.reset()
+ if additional_args:
+ raise ValueError("Host objects cannot be injected into restricted math execution")
max_steps = max_steps or self.max_steps
self.task = task
self.sampling_params = (
@@ -338,11 +255,6 @@ def run(
self._typical_step = self.sampling_params.max_tokens if self.sampling_params else 512
self.interrupt_switch = False
- if additional_args is not None:
- self.state.update(additional_args)
- self.task += f"""
-You have been provided with these additional arguments, that you can access using the keys as variables in your python code:
-{str(additional_args)}."""
self.memory.system_prompt = QueryWithInstructionsStep(prompt=self.task)
if reset:
@@ -356,17 +268,18 @@ def run(
title=self.name if hasattr(self, "name") else None,
)
- if getattr(self, "python_executor", None):
- self.python_executor.send_variables(variables=self.state)
- self.python_executor.send_tools({**self.tools, **self.managed_agents})
-
+ steps = self._run_with_math_cleanup(task=self.task, max_steps=max_steps, images=images)
if stream:
# The steps are returned as they are executed through a generator to iterate on.
- return self._run_stream(task=self.task, max_steps=max_steps, images=images)
+ return steps
# Outputs are returned only at the end. We only look at the last step.
- return list(self._run_stream(task=self.task, max_steps=max_steps, images=images))[
- -1
- ].final_answer
+ return list(steps)[-1].output
+
+ def _run_with_math_cleanup(self, **kwargs):
+ try:
+ yield from self._run_stream(**kwargs)
+ finally:
+ self.python_executor.close()
def _run_stream(
self, task: str, max_steps: int, images: list["PIL.Image.Image"] | None = None
@@ -386,7 +299,9 @@ def _run_stream(
yield element
self.memory.steps.append(element)
action_step = ActionStep(
- step_number=self.step_number, start_time=step_start_time, observations_images=images
+ step_number=self.step_number,
+ timing=Timing(start_time=step_start_time),
+ observations_images=images,
)
try:
for el in self._execute_step(action_step):
@@ -398,11 +313,13 @@ def _run_stream(
except AgentError as e:
# Other AgentError types are caused by the Model, so we should log them and iterate.
action_step.error = e
+ action_step.is_final_answer = False
+ final_answer = None
finally:
- self._finalize_step(action_step, step_start_time)
+ self._finalize_step(action_step)
self.memory.steps.append(action_step)
- yield FinalAnswerStep(handle_agent_output_types(self.collect_all_outputs()))
self.step_number += 1
+ yield action_step
yield FinalAnswerStep(handle_agent_output_types(self.collect_all_outputs()))
@@ -472,6 +389,7 @@ def _step_stream(self, memory_step: ActionStep) -> Generator[Any]:
title="Output message of the LLM:",
level=LogLevel.DEBUG,
)
+ validate_model_output(output_text)
model_output = output_text
chat_message = ChatMessage(role="assistant", content=model_output)
@@ -490,6 +408,7 @@ def _step_stream(self, memory_step: ActionStep) -> Generator[Any]:
sampling_params=self.sampling_params,
**additional_args,
)
+ validate_model_output(chat_message.content)
memory_step.model_output_message = chat_message
model_output = chat_message.content
self.logger.log_markdown(
@@ -535,30 +454,18 @@ def _step_stream(self, memory_step: ActionStep) -> Generator[Any]:
if self.sampling_params.max_tokens < 50:
out_of_tokens = True
- # Replace ... with ```python...\n```
- if model_output is not None:
- model_output_to_md = re.sub(
- r"(.*?)",
- r"```python\n\1\n```\n",
- model_output,
- flags=re.DOTALL,
- )
-
- correct_mode_output = re.sub(
- r"```python\n(.*?)\n```",
- r"\n\1\n\n",
- model_output,
- flags=re.DOTALL,
- )
- if correct_mode_output != memory_step.model_output:
- memory_step.model_output = correct_mode_output
-
try:
- code_action = fix_final_answer_code(parse_code_blobs(model_output_to_md))
+ code_action = extract_math_code(model_output)
except Exception as e:
+ self.python_executor.reset()
+ memory_step.observations = f"Exception: {e} (task state discarded)"
+ yield out_of_tokens
+ return
+ if code_action is None:
self.logger.log("NO CODE REQUESTS THIS STEP", level=LogLevel.INFO)
yield out_of_tokens
return
+ memory_step.code_action = code_action
### Execute action ###
self.logger.log_code(
@@ -567,9 +474,11 @@ def _step_stream(self, memory_step: ActionStep) -> Generator[Any]:
is_final_answer = False
try:
execution_outputs_console = []
- timeout_manager = CodeExecutionTimeout(self.code_execution_timeout)
- output, execution_logs, is_final_answer = timeout_manager.run_with_timeout(
- self.python_executor, code_action
+ result = self.python_executor(code_action)
+ output, execution_logs, is_final_answer = (
+ result.output,
+ result.logs,
+ result.is_final_answer,
)
if len(execution_logs) > 0:
execution_outputs_console += [
@@ -580,28 +489,7 @@ def _step_stream(self, memory_step: ActionStep) -> Generator[Any]:
except Exception as e:
output = ""
- execution_logs = ""
- if (
- hasattr(self.python_executor, "state")
- and "_print_outputs" in self.python_executor.state
- ):
- execution_logs = str(self.python_executor.state["_print_outputs"])
- if len(execution_logs) > 0:
- execution_outputs_console = [
- Text("Execution logs:", style="bold"),
- Text(execution_logs),
- ]
- self.logger.log(Group(*execution_outputs_console), level=LogLevel.INFO)
-
- error_msg = str(e)
- if execution_logs == "":
- execution_logs = f"Exception: {error_msg}"
-
- if "Import of " in error_msg and " is not allowed" in error_msg:
- self.logger.log(
- "[bold red]Warning to user: Code execution failed due to an unauthorized import - Consider passing said import under `additional_authorized_imports` when initializing your CodeAgent.",
- level=LogLevel.INFO,
- )
+ execution_logs = f"Exception: {e}"
truncated_output = truncate_content(str(output))
execution_logs = truncate_content(execution_logs, max_length=500)
@@ -617,5 +505,6 @@ def _step_stream(self, memory_step: ActionStep) -> Generator[Any]:
]
self.logger.log(Group(*execution_outputs_console), level=LogLevel.INFO)
memory_step.action_output = output
+ memory_step.is_final_answer = is_final_answer
- yield out_of_tokens
+ yield output if is_final_answer else out_of_tokens
diff --git a/deep_math/math_executor.py b/deep_math/math_executor.py
new file mode 100644
index 0000000..a64f6fc
--- /dev/null
+++ b/deep_math/math_executor.py
@@ -0,0 +1,196 @@
+"""Killable math worker with bounded JSON IPC; no pickle or host-thread fallback.
+
+This is resource containment, NOT filesystem/network isolation. Run the service
+under the OS/container restrictions described in docs/math-execution.md.
+"""
+
+import json
+import math
+import os
+import re
+import selectors
+import subprocess
+import sys
+import tempfile
+import time
+from dataclasses import dataclass
+from pathlib import Path
+
+MAX_SOURCE = 32_768
+MAX_MESSAGE = 65_536
+MAX_OUTPUT = 8_192
+MAX_MODEL_OUTPUT = 131_072
+_CODE_MARKERS = re.compile(r"||||```python[ \t]*\r?\n|```")
+
+
+def validate_model_output(text):
+ if (
+ type(text) is not str
+ or len(text) > MAX_MODEL_OUTPUT
+ or len(text.encode("utf-8")) > MAX_MODEL_OUTPUT
+ ):
+ raise ValueError("Model output must be bounded text")
+
+
+def extract_math_code(text):
+ """Linear, bounded fenced-code extraction with no host-side Python parsing."""
+ validate_model_output(text)
+ blocks = []
+ start = None
+ closing = None
+ for marker in _CODE_MARKERS.finditer(text):
+ token = marker.group()
+ if start is None:
+ if token in ("", "") or token.startswith("```python"):
+ closing = {"": "", "": ""}.get(token, "```")
+ start = marker.end()
+ elif token == closing:
+ blocks.append(text[start : marker.start()])
+ start = None
+ else:
+ raise ValueError("Nested or mismatched code markers are not supported")
+ if start is not None:
+ # Generation can stop at the closing marker without returning it.
+ blocks.append(text[start:])
+ if not blocks:
+ return None
+ source = "\n".join(blocks)
+ if len(source) > MAX_SOURCE or len(source.encode("utf-8")) > MAX_SOURCE:
+ raise ValueError("Math source size limit exceeded")
+ return source
+
+
+@dataclass(frozen=True)
+class MathResult:
+ output: str
+ logs: str
+ is_final_answer: bool
+
+
+class RestrictedMathExecutor:
+ """One serial worker per task. Errors and reset destroy all task state."""
+
+ def __init__(self, timeout_seconds=20):
+ if (
+ type(timeout_seconds) not in (int, float)
+ or not math.isfinite(timeout_seconds)
+ or timeout_seconds <= 0
+ ):
+ raise ValueError("Math execution timeout must be positive and finite")
+ self.timeout_seconds = timeout_seconds
+ self._process = None
+ self._directory = None
+
+ def _start(self):
+ if os.name != "posix":
+ raise RuntimeError("Restricted math execution requires POSIX resource limits")
+ self._directory = tempfile.TemporaryDirectory(prefix="deep-math-")
+ worker = Path(__file__).with_name("math_interpreter.py").resolve()
+ self._process = subprocess.Popen(
+ [sys.executable, "-I", "-B", str(worker)],
+ stdin=subprocess.PIPE,
+ stdout=subprocess.PIPE,
+ stderr=subprocess.DEVNULL,
+ cwd=self._directory.name,
+ env={"PYTHONHASHSEED": "0"},
+ close_fds=True,
+ start_new_session=True,
+ bufsize=0,
+ )
+ os.set_blocking(self._process.stdin.fileno(), False)
+ os.set_blocking(self._process.stdout.fileno(), False)
+
+ def __call__(self, source):
+ try:
+ if (
+ type(source) is not str
+ or len(source) > MAX_SOURCE
+ or len(source.encode("utf-8")) > MAX_SOURCE
+ ):
+ raise ValueError("Math source size limit exceeded")
+ request = json.dumps({"source": source}, ensure_ascii=False).encode("utf-8") + b"\n"
+ if len(request) > MAX_MESSAGE:
+ raise ValueError("Math request size limit exceeded")
+ deadline = time.monotonic() + self.timeout_seconds
+ if self._process is None:
+ self._start()
+ response = self._exchange(request, deadline)
+ return self._decode(response)
+ except BaseException:
+ self.close()
+ raise
+
+ def _exchange(self, request, deadline):
+ process = self._process
+ response = bytearray()
+ sent = 0
+ with selectors.DefaultSelector() as selector:
+ selector.register(process.stdin, selectors.EVENT_WRITE)
+ selector.register(process.stdout, selectors.EVENT_READ)
+ while True:
+ remaining = deadline - time.monotonic()
+ if remaining <= 0:
+ raise TimeoutError("Math execution timed out; task state was discarded")
+ events = selector.select(remaining)
+ if not events:
+ raise TimeoutError("Math execution timed out; task state was discarded")
+ for key, _ in events:
+ if key.fileobj is process.stdin:
+ sent += os.write(process.stdin.fileno(), request[sent:])
+ if sent == len(request):
+ selector.unregister(process.stdin)
+ else:
+ chunk = os.read(process.stdout.fileno(), 4096)
+ if not chunk:
+ raise RuntimeError("Math worker exited without a response")
+ response.extend(chunk)
+ if len(response) > MAX_MESSAGE:
+ raise ValueError("Math response size limit exceeded")
+ if b"\n" in response:
+ if not response.endswith(b"\n") or response.count(b"\n") != 1:
+ raise ValueError("Malformed math response framing")
+ return bytes(response)
+
+ @staticmethod
+ def _decode(response):
+ data = json.loads(response)
+ if type(data) is not dict or type(data.get("ok")) is not bool:
+ raise ValueError("Malformed math response")
+ if not data["ok"]:
+ if set(data) != {"ok", "error"} or type(data["error"]) is not str:
+ raise ValueError("Malformed math error response")
+ raise ValueError(data["error"][:1000] + " (task state discarded)")
+ if (
+ set(data) != {"ok", "output", "logs", "final"}
+ or type(data["final"]) is not bool
+ or any(
+ type(data[key]) is not str or len(data[key].encode("utf-8")) > MAX_OUTPUT
+ for key in ("output", "logs")
+ )
+ ):
+ raise ValueError("Malformed math result")
+ return MathResult(data["output"], data["logs"], data["final"])
+
+ def reset(self):
+ self.close()
+
+ def cleanup(self):
+ self.close()
+
+ def close(self):
+ process, self._process = self._process, None
+ try:
+ if process is not None:
+ if process.poll() is None:
+ process.kill()
+ process.wait()
+ process.stdin.close()
+ process.stdout.close()
+ finally:
+ if self._directory is not None:
+ self._directory.cleanup()
+ self._directory = None
+
+ def __del__(self):
+ if hasattr(self, "_process"):
+ self.close()
diff --git a/deep_math/math_interpreter.py b/deep_math/math_interpreter.py
new file mode 100644
index 0000000..c72b69f
--- /dev/null
+++ b/deep_math/math_interpreter.py
@@ -0,0 +1,557 @@
+"""A deliberately small math language, not a general Python sandbox.
+
+Model source is interpreted, never compiled or evaluated. Library objects and
+Python callables are not language values. Only the wrappers below can call SymPy;
+they reject strings (including nested strings) before mathematical coercion.
+This file is also the private, JSON-only worker entry point.
+"""
+
+import ast
+import json
+import math
+import operator
+import re
+import sys
+from dataclasses import dataclass
+from types import MappingProxyType
+
+import sympy as sp
+
+MAX_SOURCE = 32_768
+MAX_MESSAGE = 65_536
+MAX_OUTPUT = 8_192
+MAX_ITEMS = 4_096
+MAX_STEPS = 100_000
+MAX_ITERATIONS = 5_000
+MAX_BITS = 4_096
+MAX_POWER = 2_048
+MAX_DEPTH = 64
+MAX_NAMES = 256
+MAX_SYMBOLS = 64
+IDENTIFIER = re.compile(r"[A-Za-z][A-Za-z0-9_]{0,63}\Z")
+
+
+class MathPolicyError(ValueError):
+ """Source or values fall outside the supported mathematical language."""
+
+
+class _FinishedError(Exception):
+ def __init__(self, value):
+ self.value = value
+
+
+@dataclass(frozen=True)
+class _Namespace:
+ name: str
+
+
+@dataclass(frozen=True)
+class _Operation:
+ name: str
+ receiver: object = None
+
+
+# These are language tokens, not module/function references.
+CAPABILITIES = MappingProxyType(
+ {
+ "sympy": MappingProxyType(
+ {
+ **{
+ name: _Operation(name)
+ for name in (
+ "symbols",
+ "Symbol",
+ "Rational",
+ "Eq",
+ "Abs",
+ "solve",
+ "exp",
+ "sqrt",
+ )
+ },
+ "I": sp.I,
+ "pi": sp.pi,
+ }
+ ),
+ "numpy": MappingProxyType({"argmin": _Operation("argmin")}),
+ }
+)
+BUILTINS = MappingProxyType(
+ {name: _Operation(name) for name in ("range", "print", "final_answer", "abs", "len")}
+)
+BINARY = MappingProxyType(
+ {
+ ast.Add: operator.add,
+ ast.Sub: operator.sub,
+ ast.Mult: operator.mul,
+ ast.Div: operator.truediv,
+ ast.FloorDiv: operator.floordiv,
+ ast.Mod: operator.mod,
+ ast.Pow: operator.pow,
+ }
+)
+COMPARE = MappingProxyType(
+ {
+ ast.Eq: operator.eq,
+ ast.NotEq: operator.ne,
+ ast.Lt: operator.lt,
+ ast.LtE: operator.le,
+ ast.Gt: operator.gt,
+ ast.GtE: operator.ge,
+ }
+)
+ALLOWED_NODES = frozenset(
+ {
+ ast.Module,
+ ast.Expr,
+ ast.Assign,
+ ast.AugAssign,
+ ast.For,
+ ast.Name,
+ ast.Load,
+ ast.Store,
+ ast.Constant,
+ ast.List,
+ ast.Tuple,
+ ast.Dict,
+ ast.BinOp,
+ ast.UnaryOp,
+ ast.UAdd,
+ ast.USub,
+ ast.Call,
+ ast.keyword,
+ ast.Attribute,
+ ast.Subscript,
+ ast.ListComp,
+ ast.comprehension,
+ ast.Compare,
+ ast.Import,
+ ast.ImportFrom,
+ ast.alias,
+ *BINARY,
+ *COMPARE,
+ }
+)
+
+
+def _name(name):
+ if not IDENTIFIER.fullmatch(name):
+ raise MathPolicyError("Names must be bounded public ASCII identifiers")
+ return name
+
+
+def _scalar(value):
+ if type(value) in (int, float, complex) or isinstance(value, sp.Expr):
+ return value
+ raise MathPolicyError("Expected a number or typed symbolic expression, never a string")
+
+
+def _math_values(value):
+ """Recursively reject strings, capabilities and foreign objects BEFORE SymPy calls."""
+ if type(value) in (list, tuple):
+ for item in value:
+ _math_values(item)
+ elif type(value) is dict:
+ for key, item in value.items():
+ _math_values(key)
+ _math_values(item)
+ elif isinstance(value, sp.Equality):
+ return
+ else:
+ _scalar(value)
+
+
+class MathInterpreter:
+ """Stateful interpreter used only inside the resource-limited worker in production."""
+
+ def __init__(self):
+ self.variables = {}
+ self.symbol_names = set()
+
+ def _check(self, value, depth=0, budget=None):
+ if budget is None:
+ budget = [MAX_ITEMS]
+ budget[0] -= 1
+ if budget[0] < 0:
+ raise MathPolicyError("Aggregate value size limit exceeded")
+ if depth > MAX_DEPTH:
+ raise MathPolicyError("Value nesting limit exceeded")
+ if isinstance(value, (_Namespace, _Operation)) or value is None:
+ return value
+ if type(value) is bool:
+ return value
+ if type(value) is str:
+ if len(value.encode("utf-8")) > MAX_OUTPUT:
+ raise MathPolicyError("String limit exceeded")
+ elif type(value) is int:
+ if value.bit_length() > MAX_BITS:
+ raise MathPolicyError("Integer size limit exceeded")
+ elif type(value) in (float, complex):
+ if not (math.isfinite(value.real) and math.isfinite(value.imag)):
+ raise MathPolicyError("Nonfinite numbers are not supported")
+ elif type(value) in (list, tuple, dict, range):
+ if len(value) > MAX_ITEMS:
+ raise MathPolicyError("Collection size limit exceeded")
+ for item in value:
+ self._check(item, depth + 1, budget)
+ if type(value) is dict:
+ self._check(value[item], depth + 1, budget)
+ elif isinstance(value, sp.Basic):
+ for item in sp.preorder_traversal(value):
+ budget[0] -= 1
+ if budget[0] < 0:
+ raise MathPolicyError("Symbolic complexity limit exceeded")
+ if (
+ isinstance(item, sp.Rational)
+ and max(int(item.p).bit_length(), int(item.q).bit_length()) > MAX_BITS
+ ):
+ raise MathPolicyError("Integer size limit exceeded")
+ if item in (sp.oo, -sp.oo, sp.zoo, sp.nan):
+ raise MathPolicyError("Nonfinite numbers are not supported")
+ if len(value.free_symbols) > MAX_SYMBOLS:
+ raise MathPolicyError("Symbol limit exceeded")
+ else:
+ raise MathPolicyError("Unsupported value type")
+ return value
+
+ def _tick(self):
+ self.steps += 1
+ if self.steps > MAX_STEPS:
+ raise MathPolicyError("Execution step limit exceeded")
+
+ def _iterate(self, values):
+ if type(values) not in (list, tuple, range, dict):
+ raise MathPolicyError("Only bounded collections can be iterated")
+ for value in values:
+ self.iterations += 1
+ if self.iterations > MAX_ITERATIONS:
+ raise MathPolicyError("Iteration limit exceeded")
+ yield value
+
+ def _bind(self, target, value):
+ self._check(value)
+ if isinstance(target, ast.Name):
+ name = _name(target.id)
+ if name in BUILTINS:
+ raise MathPolicyError("Cannot replace a built-in operation")
+ self.variables[name] = value
+ if len(self.variables) > MAX_NAMES:
+ raise MathPolicyError("Variable limit exceeded")
+ elif isinstance(target, (ast.Tuple, ast.List)):
+ if type(value) not in (tuple, list) or len(target.elts) != len(value):
+ raise MathPolicyError("Unpacking requires a matching tuple or list")
+ for child, item in zip(target.elts, value):
+ self._bind(child, item)
+ else:
+ raise MathPolicyError("Only local variable assignment is allowed")
+
+ def execute(self, source):
+ if (
+ type(source) is not str
+ or len(source) > MAX_SOURCE
+ or len(source.encode("utf-8")) > MAX_SOURCE
+ ):
+ raise MathPolicyError("Source size limit exceeded")
+ self.steps = self.iterations = 0
+ self.logs = ""
+ self.final = False
+ tree = ast.parse(source, mode="exec")
+ pending = [(tree, 0)]
+ count = 0
+ while pending:
+ node, depth = pending.pop()
+ count += 1
+ if count > MAX_ITEMS or depth > MAX_DEPTH:
+ raise MathPolicyError("AST size or depth limit exceeded")
+ if type(node) not in ALLOWED_NODES:
+ raise MathPolicyError(f"Unsupported syntax: {type(node).__name__}")
+ if isinstance(node, ast.Name):
+ _name(node.id)
+ pending.extend((child, depth + 1) for child in ast.iter_child_nodes(node))
+ result = None
+ try:
+ for statement in tree.body:
+ result = self._statement(statement)
+ except _FinishedError as finished:
+ result = finished.value
+ self.final = True
+ output = self._render(result) if result is not None else ""
+ return {"output": output, "logs": self.logs, "final": self.final}
+
+ def _statement(self, node):
+ self._tick()
+ if isinstance(node, ast.Expr):
+ return self._expression(node.value)
+ if isinstance(node, ast.Assign):
+ value = self._expression(node.value)
+ for target in node.targets:
+ self._bind(target, value)
+ elif isinstance(node, ast.AugAssign):
+ if not isinstance(node.target, ast.Name):
+ raise MathPolicyError("Only local augmented assignment is allowed")
+ value = self._binary(
+ node.op, self._expression(node.target), self._expression(node.value)
+ )
+ self._bind(node.target, value)
+ elif isinstance(node, ast.For):
+ if node.orelse:
+ raise MathPolicyError("Loop else is not supported")
+ for item in self._iterate(self._expression(node.iter)):
+ self._bind(node.target, item)
+ for statement in node.body:
+ result = self._statement(statement)
+ if self.final:
+ return result
+ elif isinstance(node, ast.Import):
+ for alias in node.names:
+ if alias.name not in CAPABILITIES:
+ raise MathPolicyError("Only restricted sympy and numpy imports are supported")
+ self._bind(ast.Name(id=alias.asname or alias.name), _Namespace(alias.name))
+ elif isinstance(node, ast.ImportFrom):
+ if node.level or node.module not in CAPABILITIES:
+ raise MathPolicyError("Import is not an approved math namespace")
+ for alias in node.names:
+ if alias.name not in CAPABILITIES[node.module]:
+ raise MathPolicyError("Import is not an approved math capability")
+ self._bind(
+ ast.Name(id=alias.asname or alias.name), CAPABILITIES[node.module][alias.name]
+ )
+ else:
+ raise MathPolicyError("Unsupported statement")
+ return None
+
+ def _expression(self, node):
+ self._tick()
+ return self._check(self._evaluate_node(node))
+
+ def _evaluate_node(self, node):
+ if isinstance(node, ast.Constant):
+ return node.value
+ if isinstance(node, ast.Name):
+ if node.id in BUILTINS:
+ return BUILTINS[node.id]
+ if node.id not in self.variables:
+ raise MathPolicyError(f"Undefined variable: {node.id}")
+ return self.variables[node.id]
+ if isinstance(node, (ast.List, ast.Tuple)):
+ values = [self._expression(item) for item in node.elts]
+ return tuple(values) if isinstance(node, ast.Tuple) else values
+ if isinstance(node, ast.Dict):
+ if any(key is None for key in node.keys):
+ raise MathPolicyError("Dictionary expansion is not supported")
+ return {
+ self._expression(k): self._expression(v) for k, v in zip(node.keys, node.values)
+ }
+ if isinstance(node, ast.BinOp):
+ return self._binary(node.op, self._expression(node.left), self._expression(node.right))
+ if isinstance(node, ast.UnaryOp):
+ value = _scalar(self._expression(node.operand))
+ return -value if isinstance(node.op, ast.USub) else +value
+ if isinstance(node, ast.Compare):
+ left = _scalar(self._expression(node.left))
+ for op, child in zip(node.ops, node.comparators):
+ right = _scalar(self._expression(child))
+ if not bool(COMPARE[type(op)](left, right)):
+ return False
+ left = right
+ return True
+ if isinstance(node, ast.Attribute):
+ owner = self._expression(node.value)
+ if isinstance(owner, _Namespace) and node.attr in CAPABILITIES[owner.name]:
+ return CAPABILITIES[owner.name][node.attr]
+ if isinstance(owner, (sp.Expr, sp.Equality)) and node.attr in ("subs", "as_real_imag"):
+ return _Operation(node.attr, owner)
+ raise MathPolicyError("Attribute is not an approved math capability")
+ if isinstance(node, ast.Subscript):
+ owner, key = self._expression(node.value), self._expression(node.slice)
+ if type(owner) in (list, tuple, range) and type(key) is int:
+ return owner[key]
+ if type(owner) is dict:
+ _scalar(key)
+ return owner[key]
+ raise MathPolicyError("Only typed collection indexing is supported")
+ if isinstance(node, ast.ListComp):
+ if len(node.generators) != 1 or node.generators[0].is_async:
+ raise MathPolicyError("Only single bounded comprehensions are supported")
+ generator = node.generators[0]
+ values = self._expression(generator.iter)
+ saved = self.variables.copy()
+ result = []
+ try:
+ for item in self._iterate(values):
+ self._bind(generator.target, item)
+ if all(bool(self._expression(test)) for test in generator.ifs):
+ result.append(self._expression(node.elt))
+ if len(result) > MAX_ITEMS:
+ raise MathPolicyError("Collection size limit exceeded")
+ finally:
+ self.variables = saved
+ return result
+ if isinstance(node, ast.Call):
+ operation = self._expression(node.func)
+ if not isinstance(operation, _Operation):
+ raise MathPolicyError("Only approved operations can be called")
+ if node.keywords:
+ raise MathPolicyError("Keyword arguments and callbacks are not supported")
+ args = [self._expression(arg) for arg in node.args]
+ return self._call(operation, args)
+ raise MathPolicyError("Unsupported expression")
+
+ def _binary(self, op, left, right):
+ _scalar(left)
+ _scalar(right)
+ if isinstance(op, ast.Pow):
+ if type(right) not in (int, float) and not isinstance(right, sp.Rational):
+ raise MathPolicyError("Powers require a bounded numeric exponent")
+ if abs(right) > MAX_POWER:
+ raise MathPolicyError("Exponent limit exceeded")
+ if type(left) is int or isinstance(left, sp.Rational):
+ bits = (
+ int(left).bit_length()
+ if type(left) is int
+ else max(int(left.p).bit_length(), int(left.q).bit_length())
+ )
+ if bits * float(abs(right)) > MAX_BITS:
+ raise MathPolicyError("Power result size limit exceeded")
+ return self._check(BINARY[type(op)](left, right))
+
+ def _render(self, value):
+ pending = [value]
+ while pending:
+ item = pending.pop()
+ if isinstance(item, (_Namespace, _Operation)):
+ raise MathPolicyError("Capabilities cannot be rendered as data")
+ if type(item) in (list, tuple):
+ pending.extend(item)
+ elif type(item) is dict:
+ pending.extend(item.keys())
+ pending.extend(item.values())
+ text = str(value)
+ if len(text.encode("utf-8")) > MAX_OUTPUT:
+ raise MathPolicyError("Output size limit exceeded")
+ return text
+
+ def _call(self, operation, args):
+ name = operation.name
+ if name in ("symbols", "Symbol"):
+ if len(args) != 1 or type(args[0]) is not str:
+ raise MathPolicyError("Symbol declarations require one identifier string")
+ names = args[0].replace(",", " ").split()
+ if not names or (name == "Symbol" and len(names) != 1):
+ raise MathPolicyError("Invalid symbol declaration")
+ for item in names:
+ _name(item)
+ self.symbol_names.update(names)
+ if len(names) > MAX_SYMBOLS or len(self.symbol_names) > MAX_SYMBOLS:
+ raise MathPolicyError("Symbol limit exceeded")
+ # Do not pass model strings to sympify, parse_expr, or symbols' mini-language.
+ symbols = tuple(sp.Symbol(item) for item in names)
+ return symbols[0] if len(symbols) == 1 else symbols
+ if name in ("print", "final_answer"):
+ if name == "final_answer":
+ if len(args) != 1:
+ raise MathPolicyError("final_answer requires one value")
+ raise _FinishedError(args[0])
+ text = " ".join(self._render(arg) for arg in args) + "\n"
+ if len((self.logs + text).encode("utf-8")) > MAX_OUTPUT:
+ raise MathPolicyError("Print limit exceeded")
+ self.logs += text
+ return None
+ if name == "len":
+ if len(args) != 1 or type(args[0]) not in (list, tuple, dict, range):
+ raise MathPolicyError("len requires a collection")
+ return len(args[0])
+ # Every mathematical wrapper must pass this recursive check before invoking a library.
+ for arg in args:
+ _math_values(arg)
+ if name == "range":
+ if not 1 <= len(args) <= 3 or any(type(arg) is not int for arg in args):
+ raise MathPolicyError("range requires one to three integers")
+ return self._check(range(*args))
+ if name == "Rational":
+ if len(args) != 2 or any(type(arg) is not int for arg in args):
+ raise MathPolicyError("Rational requires two integers")
+ if args[1] == 0:
+ raise MathPolicyError("Rational denominator must be nonzero")
+ return sp.Rational(*args)
+ if name == "Eq" and len(args) == 2:
+ return sp.Eq(*[_scalar(arg) for arg in args])
+ if name in ("Abs", "abs", "sqrt", "exp") and len(args) == 1:
+ function = {"Abs": sp.Abs, "abs": sp.Abs, "sqrt": sp.sqrt, "exp": sp.exp}[name]
+ return function(_scalar(args[0]))
+ if name == "solve" and len(args) == 2:
+ equations, symbols = args
+ equations = equations if type(equations) in (list, tuple) else [equations]
+ symbols = symbols if type(symbols) in (list, tuple) else [symbols]
+ if (
+ not 1 <= len(equations) <= 16
+ or not 1 <= len(symbols) <= 16
+ or any(not isinstance(s, sp.Symbol) for s in symbols)
+ or any(not isinstance(e, (sp.Expr, sp.Equality)) for e in equations)
+ ):
+ raise MathPolicyError("solve requires typed equations and symbols (at most 16)")
+ return sp.solve(equations, symbols)
+ if name == "subs" and operation.receiver is not None:
+ if len(args) == 1 and type(args[0]) is dict:
+ for key, value in args[0].items():
+ _scalar(key)
+ _scalar(value)
+ return operation.receiver.subs(args[0])
+ if len(args) == 2:
+ return operation.receiver.subs(*[_scalar(arg) for arg in args])
+ if name == "as_real_imag" and not args and isinstance(operation.receiver, sp.Expr):
+ return operation.receiver.as_real_imag()
+ if name == "argmin" and len(args) == 1 and type(args[0]) in (list, tuple):
+ values = args[0]
+ if not values:
+ raise MathPolicyError("argmin requires a nonempty collection")
+ for value in values:
+ _scalar(value)
+ if isinstance(value, sp.Expr) and (value.free_symbols or value.is_real is not True):
+ raise MathPolicyError("argmin requires real numeric values")
+ return min(range(len(values)), key=values.__getitem__)
+ raise MathPolicyError("Unsupported operation or argument signature")
+
+
+def _worker_main():
+ # A fresh process, not forked model/GPU state. Fail closed if limits cannot be set.
+ try:
+ import resource
+
+ resource.setrlimit(resource.RLIMIT_CORE, (0, 0))
+ resource.setrlimit(resource.RLIMIT_AS, (2 * 1024**3, 2 * 1024**3))
+ resource.setrlimit(resource.RLIMIT_CPU, (120, 120))
+ except (ImportError, OSError, ValueError) as error:
+ print(
+ json.dumps(
+ {"ok": False, "error": f"Required worker resource limits unavailable: {error}"}
+ ),
+ flush=True,
+ )
+ return
+ interpreter = MathInterpreter()
+ while True:
+ request = sys.stdin.buffer.readline(MAX_MESSAGE + 1)
+ if not request:
+ return
+ if len(request) > MAX_MESSAGE or not request.endswith(b"\n"):
+ return
+ try:
+ data = json.loads(request)
+ if type(data) is not dict or set(data) != {"source"}:
+ raise MathPolicyError("Invalid request")
+ result = interpreter.execute(data["source"])
+ reply = {"ok": True, **result}
+ except Exception as error:
+ # Drop partially mutated state on all failures, not just on timeouts.
+ interpreter = MathInterpreter()
+ reply = {"ok": False, "error": f"{type(error).__name__}: {str(error)[:1000]}"}
+ payload = json.dumps(reply, ensure_ascii=False, allow_nan=False).encode("utf-8") + b"\n"
+ if len(payload) > MAX_MESSAGE:
+ return
+ sys.stdout.buffer.write(payload)
+ sys.stdout.buffer.flush()
+
+
+if __name__ == "__main__":
+ _worker_main()
diff --git a/deep_math/prompts.py b/deep_math/prompts.py
index 0ce7bf7..a044a04 100644
--- a/deep_math/prompts.py
+++ b/deep_math/prompts.py
@@ -30,8 +30,13 @@
* Use only variables that you have defined!
-* You can use imports in your code, but only from the following list of modules: cmath, numpy, scipy, math, random, sympy, statistics.
-* Use numpy in your code as much as possible as it's very efficient.
+* Code uses a restricted math subset, NOT arbitrary Python. Imports are aliases into a fixed capability list, not real modules.
+* From sympy you may use symbols, Symbol, Rational, Eq, Abs, solve, exp, sqrt, I, pi; from numpy only argmin.
+* Supported expression methods: subs and as_real_imag. Built-ins: range, print, abs, len, final_answer.
+* Use assignments, arithmetic, lists/tuples/dicts, indexing, bounded for loops and single list comprehensions. Use ** for powers. No functions, classes, arbitrary attributes, callbacks, keyword arguments or other imports.
+* Declare symbols with simple ASCII names, e.g. symbols('x y'). All other math arguments must be numbers or typed symbolic expressions, never expression strings. Rational takes two integers; solve takes equations and symbols.
+* Variables persist between successful tool calls within this question. Any error or timeout clears all variables, so redefine them before retrying. Variables never persist between questions.
+* Keep computations and output small; excessive source, nesting, iterations, exponents, symbolic complexity and output are rejected.
* An `output` block will be inserted with the code evaluation results; don't add it yourself.
* Use the results in your reasoning, no need to repeat them.
* Never say that you don't know, always try to solve the problem!
diff --git a/deep_math/vllm_serve_agent.py b/deep_math/vllm_serve_agent.py
index c7773ca..ea0db15 100644
--- a/deep_math/vllm_serve_agent.py
+++ b/deep_math/vllm_serve_agent.py
@@ -329,24 +329,21 @@ def chunk_list(lst: list, n: int) -> list[list]:
def main(script_args: ScriptArguments):
- if not is_fastapi_available():
- raise ImportError(
- "FastAPI is required to run the vLLM serve script. Please install it using `pip install fastapi`."
- )
-
- if not is_pydantic_available():
- raise ImportError(
- "Pydantic is required to run the vLLM serve script. Please install it using `pip install pydantic`."
- )
-
- if not is_uvicorn_available():
- raise ImportError(
- "Uvicorn is required to run the vLLM serve script. Please install it using `pip install uvicorn`."
+ missing = [
+ name
+ for name, available in (
+ ("FastAPI", is_fastapi_available()),
+ ("Pydantic", is_pydantic_available()),
+ ("Uvicorn", is_uvicorn_available()),
+ ("vLLM", is_vllm_available()),
)
+ if not available
+ ]
- if not is_vllm_available():
+ if missing:
raise ImportError(
- "vLLM is required to run the vLLM serve script. Please install it using `pip install vllm`."
+ f"Missing GPU/vLLM dependencies: {', '.join(missing)}. "
+ "Install them with `uv sync --extra gpu`."
)
if is_vllm_available():
diff --git a/docs/math-execution.md b/docs/math-execution.md
new file mode 100644
index 0000000..efaeee7
--- /dev/null
+++ b/docs/math-execution.md
@@ -0,0 +1,84 @@
+# Restricted math execution
+
+MathAgent executes a deliberately restricted Python-like math language, not
+general Python. This replaces its former broad smolagents import policy and
+process/thread timeout wrapper. There is no fallback to a general executor.
+
+## Supported contract
+
+- `import sympy as sp` and `from sympy import ...` resolve fixed language tokens:
+ `symbols`, `Symbol`, `Rational`, `Eq`, `Abs`, `solve`, `exp`, `sqrt`, `I`, `pi`.
+- `import numpy as np` exposes only `np.argmin`; NumPy is not actually imported.
+- Expression methods: `subs` (dictionary or two expressions), `as_real_imag()`.
+- Built-ins: `range`, `print`, `abs`, `len`, `final_answer`.
+- Numeric literals, Python-style division, arithmetic, assignments/unpacking,
+ lists/tuples/dictionaries, indexing, comparisons, bounded `for` loops and
+ single list comprehensions. No keyword arguments or arbitrary Python syntax.
+- Symbol declarations accept bounded ASCII identifiers separated by spaces or
+ commas. They do not accept SymPy's string expression or symbol-range syntax.
+ `Rational` takes two integers. `solve` takes equations and explicit symbols.
+- Task state persists across successful tool calls, not between `run()` calls
+ (even with `reset=False`). Every execution error/timeout discards task state.
+- Tools, managed agents, nonempty `additional_args`, additional imports and
+ executor overrides are rejected rather than silently weakening the policy.
+- Streaming callers must finish or close the returned generator to release the
+ worker promptly. A MathAgent instance must not be shared by concurrent runs.
+
+The four worked examples remain the compatibility baseline. This is an
+intentional capability reduction; arbitrary existing Python snippets may fail.
+
+## Security boundary
+
+AST nodes are manually dispatched, never compiled/evaluated. Module names and
+operations are opaque application tokens, not Python module/function objects.
+The registry cannot expose `sympify`, `S`, `parse_expr`, `lambdify`, dynamic
+imports, reflection, or arbitrary attributes. Mathematical wrappers recursively
+reject strings and capability objects **before** calling SymPy. Typed SymPy
+expressions may still use internal numeric/symbolic coercion; that is different
+from evaluating model-provided expression strings.
+
+Parsing, interpretation, mathematical operations and string rendering run in a
+fresh isolated-mode Python subprocess, with a minimal environment, private
+temporary working directory, no inherited descriptors, 2 GiB address-space
+limit, 120 seconds cumulative CPU limit, and a per-call wall deadline (20 seconds
+by default, checked after startup and during IPC). OS process creation and cleanup
+are synchronous; external supervision is required for an end-to-end deadline.
+Failure to install resource limits fails closed. Production execution targets
+Linux. The current macOS environment rejects the required address-space limit,
+so production execution is unavailable there rather than weakened. The parent kills and reaps the worker on failure. No daemon-thread
+fallback, pickle deserialization, or join-before-draining queue is used.
+
+Source is limited to 32 KiB; JSON messages to 64 KiB; output/logs to 8 KiB each;
+AST/value depth to 64; AST nodes, collection lengths and symbolic traversal to
+4096; interpreter steps to 100,000; iterations to 5000; variable names to 256;
+symbols to 64; integers to 4096 bits; exponent magnitude to 2048. Solves accept
+at most 16 equations/symbols. Expensive operations remain subject to worker
+deadlines/resource limits. Replies are schema-checked primitive JSON, not live
+symbolic objects. All worker errors are surfaced as observations, including
+errors after print calls.
+
+**This worker is not an OS sandbox.** It retains the service account's file and
+network privileges. Production operators must additionally run the service with
+no credentials available to the worker, an unprivileged identity, read-only
+filesystem with isolated scratch space, denied network egress, and externally
+enforced CPU/memory/process limits (for example, an appropriately configured
+container or sandbox). Python `-I` and resource limits do not provide those
+protections. No deployment configuration is installed by this change. Do not
+expose this service as a general public code-execution endpoint.
+
+The separate `math_verify` parsing paths in rewards/metrics are outside this
+remediation and require their own review. Model/GPU inference and the original
+security scanner must also be validated in the deployment environment.
+
+## CPU-only regression tests
+
+The tests use the standard library `unittest`, the declared SymPy and smolagents
+versions, and stub only the optional vLLM import for agent integration tests.
+They can be run with `python -m unittest discover -s tests -v`, without loading
+model weights or a GPU. No exploit test invokes an OS command from model source.
+On Linux these tests use the real production worker with resource limits. On
+macOS, transport/integration tests explicitly substitute a **test-only** worker
+fixture that omits the unsupported address-space limit. A separate test checks
+that the unmodified production worker fails closed. Passing macOS tests does not
+establish Linux resource-limit enforcement. The Linux CI workflow runs the real
+worker on Python 3.11 and 3.12; CI results must be checked before deployment.
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 7e9aadd..b625b38 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,7 +3,7 @@ name = "deep-math"
version = "1.0.0"
description = "Library for training and evaluating Math agent."
readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.12,<3.13"
license = "Apache-2.0"
authors = [
{name = "Daniel Fleischer", email = "daniel.fleischer@intel.com"},
@@ -13,33 +13,30 @@ classifiers = [
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"Programming Language :: Python :: 3",
- "Programming Language :: Python :: 3.10",
- "Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
- "Programming Language :: Python :: 3.13",
- "Programming Language :: Python :: 3.14",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Software Development :: Libraries :: Python Modules",
]
dependencies = [
"accelerate==1.6.0",
- "datasets==3.5.0",
- "deepspeed==0.16.7",
- "flash-attn",
- "huggingface-hub==0.30.2",
- "hydra-core==1.3.4",
+ "datasets==5.0.1",
+ "huggingface-hub==1.31.0",
+ "hydra-core==1.3.7",
"math-verify==0.7.0",
- "mistral-common==1.5.4",
+ "mistral-common==1.11.6",
"omegaconf==2.3.0",
- "openai==1.76.0",
+ "openai==2.25.0",
"peft==0.15.2",
- "regex==2024.11.6",
+ "regex==2025.10.22",
"smolagents==1.26.0",
+ "sympy==1.14.0",
"submitit",
- "tiktoken==0.9.0",
- "transformers==5.5.0",
+ "tiktoken==0.12.0",
+ "torch==2.10.0",
+ "transformers==5.10.4",
"trl==0.17.0",
- "vllm==0.24.0",
+ "requests==2.34.2",
+ "pip>=26.2.2",
]
[project.urls]
@@ -49,11 +46,37 @@ Issues = "https://github.com/intellabs/DeepMath/issues"
Documentation = "https://github.com/intellabs/DeepMath#readme"
[project.optional-dependencies]
-dev = ["hydra-submitit-launcher>=1.2.0", "ipykernel", "ipython", "pre-commit"]
-gpu = ["bitsandbytes>=0.45.3", "vllm>=0.7.2"]
+# The pinned FlashAttention wheel targets Linux x86-64 and Python 3.12.
+gpu = [
+ "bitsandbytes>=0.45.3; sys_platform == 'linux' and platform_machine == 'x86_64'",
+ "deepspeed==0.16.7; sys_platform == 'linux' and platform_machine == 'x86_64'",
+ "fastapi",
+ "flash-attn==2.8.3; sys_platform == 'linux' and platform_machine == 'x86_64'",
+ "pydantic",
+ "torchaudio==2.10.0",
+ "torchvision==0.25.0",
+ "uvicorn",
+ "vllm==0.19.1; sys_platform == 'linux' and platform_machine == 'x86_64'",
+]
-[tool.setuptools]
-package-dir = { "" = "deep_math" }
+[tool.uv.sources]
+torch = { index = "pytorch-cu130" }
+torchvision = { index = "pytorch-cu130" }
+torchaudio = { index = "pytorch-cu130" }
+flash-attn = { url = "https://github.com/Dao-AILab/flash-attention/releases/download/v2.8.3/flash_attn-2.8.3%2Bcu13torch2.10cxx11abiTRUE-cp312-cp312-linux_x86_64.whl" }
+
+[[tool.uv.index]]
+name = "pytorch-cu130"
+url = "https://download.pytorch.org/whl/cu130"
+explicit = true
[dependency-groups]
-dev = ["pre-commit>=4.2.0"]
+dev = [
+ "hydra-submitit-launcher>=1.2.0",
+ "ipykernel",
+ "ipython",
+ "pre-commit>=4.2.0",
+]
+
+[tool.setuptools]
+package-dir = { "" = "deep_math" }
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index bc355a8..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,18 +0,0 @@
-accelerate==1.6.0
-datasets==3.5.0
-deepspeed==0.16.7
-flash-attn
-huggingface-hub==0.30.2
-hydra-core==1.3.2
-math-verify==0.7.0
-mistral-common==1.5.4
-omegaconf==2.3.0
-openai==1.76.0
-peft==0.15.2
-regex==2024.11.6
-smolagents==1.22.0
-submitit
-tiktoken==0.9.0
-transformers==5.5.0
-trl==0.17.0
-vllm==0.24.0
diff --git a/tests/math_worker_fixture.py b/tests/math_worker_fixture.py
new file mode 100644
index 0000000..e61194d
--- /dev/null
+++ b/tests/math_worker_fixture.py
@@ -0,0 +1,26 @@
+"""Transport-only fixture for macOS; NEVER used by the production executor.
+
+macOS rejects RLIMIT_AS in this environment. This fixture removes that one
+limit ONLY to test subprocess protocol, state, and cancellation. Linux tests
+use the unmodified production worker and all real limits instead.
+"""
+
+import resource
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from deep_math.math_interpreter import _worker_main # noqa: E402
+
+if __name__ == "__main__":
+ if sys.platform != "darwin":
+ raise RuntimeError("This fixture is only for macOS transport tests")
+ original = resource.setrlimit
+
+ def set_test_limit(kind, limits):
+ if kind != resource.RLIMIT_AS:
+ original(kind, limits)
+
+ resource.setrlimit = set_test_limit
+ _worker_main()
diff --git a/tests/test_math_execution.py b/tests/test_math_execution.py
new file mode 100644
index 0000000..83acd10
--- /dev/null
+++ b/tests/test_math_execution.py
@@ -0,0 +1,466 @@
+import ast
+import importlib.util
+import json
+import re
+import subprocess
+import sys
+import types
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+
+import sympy as sp
+from smolagents import ChatMessage, FinalAnswerStep, Model
+from smolagents.memory import ActionStep
+
+from deep_math.math_executor import (
+ MAX_MODEL_OUTPUT,
+ MAX_SOURCE,
+ RestrictedMathExecutor,
+ extract_math_code,
+)
+from deep_math.math_interpreter import MathInterpreter, MathPolicyError
+
+ROOT = Path(__file__).resolve().parents[1]
+EXAMPLES = re.findall(
+ r"(.*?)",
+ (ROOT / "deep_math/fewshot.txt").read_text(),
+ re.S,
+)
+
+
+class TransportExecutor(RestrictedMathExecutor):
+ """Real production worker on Linux; explicitly transport-only on macOS."""
+
+ def _start(self):
+ if sys.platform != "darwin":
+ return super()._start()
+ popen = subprocess.Popen
+
+ def start_fixture(args, **kwargs):
+ return popen([*args[:-1], str(ROOT / "tests/math_worker_fixture.py")], **kwargs)
+
+ with patch("deep_math.math_executor.subprocess.Popen", side_effect=start_fixture):
+ return super()._start()
+
+
+class InterpreterTests(unittest.TestCase):
+ def test_documented_examples(self):
+ interpreter = MathInterpreter()
+ results = [interpreter.execute(source) for source in EXAMPLES]
+ self.assertEqual(results[0]["output"], "-7")
+ self.assertEqual(results[1]["output"], "[2/25, 3/100, 1/300, 19/700, 9/200]")
+ self.assertEqual(results[2]["logs"], "1/6\n")
+ self.assertEqual(results[3]["output"], "[-10, -10, 10, 10]")
+ self.assertEqual(results[4]["logs"], "sqrt(2)*(1008 + 1008*sqrt(3))\n")
+
+ def test_rejects_evaluation_capabilities_before_invocation(self):
+ attacks = [
+ "sp.sympify('1+1')",
+ "sp.S('1+1')",
+ "sp.parse_expr('1+1')",
+ "sp.lambdify(x, x)",
+ "sp.parsing.sympy_parser.parse_expr('1+1')",
+ "from sympy import sympify as parse\nparse('1+1')",
+ "from sympy import *",
+ "from sympy.core import sympify",
+ "import os",
+ "import scipy",
+ "import sympy.core",
+ "np.load('file')",
+ "np.frompyfunc(sp.sqrt, 1, 1)",
+ "__import__('os')",
+ "eval('1+1')",
+ "exec('1+1')",
+ "getattr(sp, 'sympify')('1+1')",
+ "x.__class__",
+ "sp.__dict__",
+ "sp.sqrt('x+1')",
+ "f = sp.sqrt\nf('x+1')",
+ "sp.Eq('x', 1)",
+ "sp.Rational('1/2', 1)",
+ "sp.solve(['x+1'], [x])",
+ "sp.solve([{x: 'x+1'}], [x])",
+ "x.subs({x: 'x+1'})",
+ "x.subs('x', 1)",
+ "sp.Symbol('x', cls=sp.sqrt)",
+ "sp.Symbol('x+1')",
+ "sp.symbols('x:10')",
+ "sp.sqrt(**{x: 1})",
+ "sp.sqrt(sp.sqrt)",
+ "sp.sqrt = 1",
+ "a = [1]\na[0] = 2",
+ "[1].append(2)",
+ "[sp.sqrt]",
+ "print({1: sp})",
+ "(lambda: 1)()",
+ "def f():\n return 1",
+ "while True:\n pass",
+ ]
+ for source in attacks:
+ with self.subTest(source=source):
+ interpreter = MathInterpreter()
+ interpreter.execute("import sympy as sp\nimport numpy as np\nx = sp.Symbol('x')")
+ with (
+ patch.object(
+ sp, "sympify", side_effect=AssertionError("sympify reached")
+ ) as sympify,
+ patch.object(
+ sp, "lambdify", side_effect=AssertionError("lambdify reached")
+ ) as lambdify,
+ patch(
+ "sympy.parsing.sympy_parser.parse_expr",
+ side_effect=AssertionError("parser reached"),
+ ) as parser,
+ ):
+ with self.assertRaises(MathPolicyError):
+ interpreter.execute(source)
+ sympify.assert_not_called()
+ lambdify.assert_not_called()
+ parser.assert_not_called()
+
+ def test_reported_sympify_escape_never_reaches_eval_capable_sink(self):
+ # Freeze the reported snippet here; do not depend on the optional PoC bundle.
+ source = (
+ "from sympy import sympify\n"
+ "result = sympify(\"__import__('os').popen('id -u; id -g; uname -m; "
+ "echo POC-MARKER > proof_from_payload.txt').read()\")\n"
+ "print(result)\n"
+ "final_answer(result)\n"
+ )
+ # Intercept the dangerous sink even if the policy regresses. This test must
+ # never execute the report's shell command or write its marker file.
+ with (
+ patch.object(sp, "sympify", side_effect=AssertionError("sympify reached")) as sink,
+ patch("os.popen", side_effect=AssertionError("shell reached")) as shell,
+ ):
+ with self.assertRaisesRegex(
+ MathPolicyError, "Import is not an approved math capability"
+ ):
+ MathInterpreter().execute(source)
+ sink.assert_not_called()
+ shell.assert_not_called()
+
+ def test_statement_semantics_and_safe_aliases(self):
+ interpreter = MathInterpreter()
+ self.assertEqual(interpreter.execute("1/2")["output"], "0.5")
+ self.assertEqual(
+ interpreter.execute("from sympy import Rational as R\nf = R\nf(1, 2)")["output"], "1/2"
+ )
+ interpreter.execute("i = 99\na, b = (2, 3)\n[a*i for i in range(b)]")
+ self.assertEqual(interpreter.execute("i")["output"], "99")
+ self.assertEqual(interpreter.execute("[i for i in range(4) if i > 1]")["output"], "[2, 3]")
+
+ def test_final_answer_unwinds_any_statement(self):
+ for source in (
+ "final_answer(42)\nmissing",
+ "answer = final_answer(42)\nmissing",
+ "for i in range(3):\n answer = final_answer(42)\nmissing",
+ ):
+ with self.subTest(source=source):
+ self.assertEqual(
+ MathInterpreter().execute(source),
+ {
+ "output": "42",
+ "logs": "",
+ "final": True,
+ },
+ )
+
+ def test_numeric_and_structural_budgets(self):
+ sources = [
+ "2 ** 2049",
+ "(2 ** 1024) ** 1024",
+ "from sympy import Rational\nr = Rational(1, 2) ** 1024\nr ** 2048",
+ "from sympy import Rational\nRational(1, 2) / 0",
+ "range(4097)",
+ "1e309",
+ "'x' * 1000000",
+ "x = 0\nfor i in range(13):\n x = [x, x]",
+ "for i in range(100):\n for j in range(100):\n x = i + j",
+ "for i in range(3000):\n print('abcd')",
+ "from sympy import symbols\nsymbols('" + " ".join(f"x{i}" for i in range(65)) + "')",
+ "[" * 70 + "1" + "]" * 70,
+ "x = 1\n" * 5000,
+ ]
+ for source in sources:
+ with self.subTest(source=source[:100]):
+ with self.assertRaises(MathPolicyError):
+ MathInterpreter().execute(source)
+ self.assertEqual(MathInterpreter().execute("2 ** 2048 > 0")["output"], "True")
+
+ def test_foreign_objects_never_reach_wrappers(self):
+ interpreter = MathInterpreter()
+ interpreter.execute("import sympy as sp")
+ interpreter.variables["foreign"] = object()
+ with patch.object(sp, "sqrt") as sqrt:
+ with self.assertRaises(MathPolicyError):
+ interpreter.execute("sp.sqrt(foreign)")
+ sqrt.assert_not_called()
+
+
+@unittest.skipUnless(sys.platform in ("darwin", "linux"), "POSIX worker tests")
+class WorkerTransportTests(unittest.TestCase):
+ def setUp(self):
+ self.worker = TransportExecutor(30)
+ self.addCleanup(self.worker.close)
+
+ def test_documented_examples_over_json(self):
+ results = [self.worker(source) for source in EXAMPLES]
+ self.assertEqual(results[0].output, "-7")
+ self.assertEqual(results[2].logs, "1/6\n")
+ self.assertEqual(results[3].output, "[-10, -10, 10, 10]")
+ self.assertEqual(results[4].logs, "sqrt(2)*(1008 + 1008*sqrt(3))\n")
+
+ def test_state_reset_and_errors(self):
+ self.worker("x = 7")
+ self.assertEqual(self.worker("x + 1").output, "8")
+ process = self.worker._process
+ with self.assertRaisesRegex(ValueError, "Undefined variable"):
+ self.worker("print('before error')\nmissing")
+ self.assertIsNotNone(process.poll())
+ with self.assertRaises(ValueError):
+ self.worker("x")
+ self.worker("x = 9")
+ self.worker.reset()
+ with self.assertRaises(ValueError):
+ self.worker("x")
+
+ def test_deadline_kills_and_reaps_worker(self):
+ self.worker("x = 1")
+ process = self.worker._process
+ directory = self.worker._directory.name
+ self.worker.timeout_seconds = 1e-9
+ with self.assertRaises(TimeoutError):
+ self.worker("1 + 1")
+ self.assertIsNotNone(process.poll())
+ self.assertIsNone(self.worker._process)
+ self.assertFalse(Path(directory).exists())
+ self.worker.timeout_seconds = 30
+ with self.assertRaisesRegex(ValueError, "Undefined variable"):
+ self.worker("x")
+
+ def test_cleanup_contract(self):
+ self.worker("1")
+ process = self.worker._process
+ self.worker.cleanup()
+ self.worker.cleanup()
+ self.assertIsNotNone(process.poll())
+
+ def test_oversized_output_kills_worker(self):
+ self.worker("x = 1")
+ with self.assertRaisesRegex(ValueError, "Print limit"):
+ self.worker("for i in range(3000):\n print('abcd')")
+ self.assertIsNone(self.worker._process)
+
+ def test_startup_failure_never_falls_back(self):
+ with patch(
+ "deep_math.math_executor.subprocess.Popen", side_effect=OSError("startup failed")
+ ):
+ with self.assertRaisesRegex(OSError, "startup failed"):
+ RestrictedMathExecutor()("1")
+
+ @unittest.skipUnless(sys.platform == "darwin", "macOS fail-closed regression")
+ def test_production_macos_resource_limits_fail_closed(self):
+ worker = RestrictedMathExecutor(30)
+ self.addCleanup(worker.close)
+ with self.assertRaisesRegex(ValueError, "Required worker resource limits unavailable"):
+ worker("1")
+ self.assertIsNone(worker._process)
+
+
+class ProtocolTests(unittest.TestCase):
+ def test_input_limits_before_encoding_or_startup(self):
+ with patch.object(RestrictedMathExecutor, "_start") as start:
+ for source in ("x" * (MAX_SOURCE + 1), "é" * MAX_SOURCE, object()):
+ with self.assertRaises(ValueError):
+ RestrictedMathExecutor()(source)
+ start.assert_not_called()
+
+ def test_strict_reply_schema(self):
+ for reply in (
+ [],
+ {"ok": 1},
+ {"ok": True},
+ {"ok": False, "error": []},
+ {"ok": True, "output": [], "logs": "", "final": False},
+ {"ok": True, "output": "", "logs": "", "final": 1},
+ {"ok": True, "output": "x" * 8193, "logs": "", "final": False},
+ {"ok": True, "output": "", "logs": "", "final": False, "extra": 1},
+ ):
+ with self.subTest(reply=str(reply)[:100]):
+ with self.assertRaises(ValueError):
+ RestrictedMathExecutor._decode(json.dumps(reply))
+
+ def test_code_extraction_never_parses_python_in_parent(self):
+ with patch.object(ast, "parse", side_effect=AssertionError("host parsed Python")):
+ self.assertIsNone(extract_math_code("1 + 1"))
+ self.assertEqual(extract_math_code("text\n1 + 1"), "1 + 1")
+ self.assertEqual(extract_math_code("```python\n1 + 1\n```"), "1 + 1\n")
+ self.assertEqual(extract_math_code("1 + 1"), "1 + 1")
+ with self.assertRaises(ValueError):
+ extract_math_code("" * 2000)
+ with self.assertRaises(ValueError):
+ extract_math_code("x" * (MAX_MODEL_OUTPUT + 1))
+
+
+# Stub only the optional GPU import; all smolagents classes/lifecycle are real.
+if importlib.util.find_spec("vllm") is None:
+ vllm_stub = types.ModuleType("vllm")
+ vllm_stub.SamplingParams = type("SamplingParams", (), {})
+ with patch.dict(sys.modules, {"vllm": vllm_stub}):
+ from deep_math.agent import MathAgent
+else:
+ from deep_math.agent import MathAgent
+
+
+class StubModel(Model):
+ def __init__(self, outputs):
+ super().__init__()
+ self.outputs = iter(outputs)
+ self.calls = 0
+ self.last_output_token_count = 1
+ self.model_id = "test-model"
+
+ def generate(self, messages, **kwargs):
+ self.calls += 1
+ return ChatMessage(role="assistant", content=next(self.outputs))
+
+
+class TransportMathAgent(MathAgent):
+ def create_python_executor(self):
+ return TransportExecutor(self.code_execution_timeout)
+
+
+class AgentTests(unittest.TestCase):
+ def agent(self, outputs, **kwargs):
+ agent = TransportMathAgent([], StubModel(outputs), verbosity_level=-1, **kwargs)
+ self.addCleanup(agent.cleanup)
+ return agent
+
+ def test_actual_agent_constructor_and_bypass_rejection(self):
+ agent = MathAgent([], StubModel([]), verbosity_level=-1)
+ self.addCleanup(agent.cleanup)
+ self.assertIs(type(agent.python_executor), RestrictedMathExecutor)
+ cases = [
+ {"additional_authorized_imports": ["sympy"]},
+ {"executor": object()},
+ {"executor_type": "local"},
+ {"executor_kwargs": {}},
+ {"managed_agents": [object()]},
+ {"add_base_tools": True},
+ ]
+ for kwargs in cases:
+ with self.subTest(kwargs=kwargs):
+ with self.assertRaises(ValueError):
+ MathAgent([], StubModel([]), **kwargs)
+ with self.assertRaises(ValueError):
+ MathAgent([object()], StubModel([]))
+ with self.assertRaises(ValueError):
+ agent.run("task", additional_args={"host": object()})
+
+ def test_model_emitted_sympify_escape_rejected_then_valid_math_succeeds(self):
+ # Preserve the report's fenced code, import, and nested __import__ pattern,
+ # but use a harmless expression: subprocesses do not inherit parent mocks.
+ probe = (
+ "Here is the computation:\n```python\n"
+ "from sympy import sympify\n"
+ "result = sympify(\"__import__('builtins').str(6 * 7)\")\n"
+ "print(result)\n"
+ "final_answer(result)\n```"
+ )
+ agent = self.agent(
+ [probe, "final_answer(6 * 7)"], max_steps=2
+ )
+ events = list(agent.run("Security regression probe", stream=True))
+ actions = [event for event in events if isinstance(event, ActionStep)]
+ finals = [event for event in events if isinstance(event, FinalAnswerStep)]
+
+ self.assertEqual(agent.model.calls, 2)
+ self.assertEqual(len(actions), 2)
+ # Assert actual execution observations, not the echoed model source.
+ self.assertEqual(actions[0].model_output, probe)
+ self.assertIn(
+ "MathPolicyError: Import is not an approved math capability",
+ actions[0].observations,
+ )
+ self.assertIn("task state discarded", actions[0].observations)
+ self.assertFalse(actions[0].is_final_answer)
+ self.assertEqual(actions[1].action_output, "42")
+ self.assertTrue(actions[1].is_final_answer)
+ self.assertEqual(len(finals), 1)
+ self.assertIsNone(agent.python_executor._process)
+
+ def test_multistep_state_single_final_event_and_cleanup(self):
+ agent = self.agent(
+ [
+ "x = 6",
+ "answer = final_answer(x * 7)",
+ ]
+ )
+ events = list(agent.run("task", stream=True))
+ finals = [event for event in events if isinstance(event, FinalAnswerStep)]
+ actions = [event for event in events if isinstance(event, ActionStep)]
+ self.assertEqual(len(finals), 1)
+ self.assertEqual(len(actions), 2)
+ self.assertTrue(actions[-1].is_final_answer)
+ self.assertEqual(actions[-1].action_output, "42")
+ self.assertIn("42", finals[0].output)
+ self.assertEqual(agent.model.calls, 2)
+ self.assertIsNone(agent.python_executor._process)
+
+ def test_errors_after_print_are_observations_and_clear_state(self):
+ agent = self.agent(
+ [
+ "x = 7\nprint('before')\nmissing",
+ "x",
+ "\\boxed{7}",
+ ]
+ )
+ output = agent.run("task")
+ self.assertIn("Undefined variable: missing", output)
+ self.assertIn("Undefined variable: x", output)
+ self.assertIn("\\boxed{7}", output)
+ self.assertIsNone(agent.python_executor._process)
+
+ def test_stream_cancellation_and_inherited_cleanup(self):
+ agent = self.agent(["x = 1"])
+ stream = agent.run("task", stream=True)
+ next(stream)
+ process = agent.python_executor._process
+ self.assertIsNotNone(process)
+ stream.close()
+ self.assertIsNotNone(process.poll())
+ agent = self.agent(["x = 1"])
+ stream = agent.run("task", stream=True)
+ next(stream)
+ process = agent.python_executor._process
+ agent.cleanup()
+ self.assertIsNotNone(process.poll())
+ stream.close()
+
+ def test_no_task_state_with_retained_conversation(self):
+ agent = self.agent(["x = 1", "x"])
+ agent.run("first", max_steps=1)
+ agent.run("second", max_steps=1, reset=False)
+ self.assertIn("Undefined variable: x", agent.memory.steps[-1].observations)
+
+ def test_final_answer_checks_can_reject_and_retry(self):
+ def check(answer, memory, agent):
+ return answer == "42"
+
+ agent = self.agent(
+ [
+ "final_answer(1)",
+ "final_answer(42)",
+ ],
+ final_answer_checks=[check],
+ )
+ agent.run("task")
+ self.assertEqual(agent.model.calls, 2)
+ self.assertIsNotNone(agent.memory.steps[0].error)
+ self.assertTrue(agent.memory.steps[1].is_final_answer)
+
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..6775e7f
--- /dev/null
+++ b/uv.lock
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