cleanup: remove unused benchmarks, deep_probe, meta_reflect

Remove sealqa, babyvision, mathverse, mmrb, swebench envs and configs.
Remove deep_probe, deep_reflect, meta_reflect modules and prompts.
Remove download_babyvision script.
These are not part of the core released benchmarks.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

skillopt/gradient/deep_probe.py

@@ -1,77 +0,0 @@
-"""Optimizer-written diagnostic probe generation for deep reflection."""
-from __future__ import annotations
-
-from skillopt.gradient.reflect import fmt_minibatch_trajectories
-from skillopt.model import chat_optimizer
-from skillopt.optimizer.meta_skill import format_meta_skill_context
-from skillopt.prompts import load_prompt
-from skillopt.utils import extract_json
-
-
-def generate_deep_probe_instruction(
-    skill_content: str,
-    items: list[dict],
-    prediction_dir: str,
-    *,
-    system_prompt: str | None = None,
-    step_buffer_context: str = "",
-    output_requirements: list[str] | None = None,
-    meta_skill_context: str = "",
-) -> dict | None:
-    """Generate one minimally-perturbing diagnostic probe instruction."""
-    trajectories_text = fmt_minibatch_trajectories(items, prediction_dir)
-    if not trajectories_text.strip():
-        return None
-
-    actual_system = system_prompt or load_prompt("deep_probe")
-    user = (
-        f"## Current Skill\n{skill_content}\n\n"
-        "## Probe Design Goal\n"
-        "Design one short diagnostic instruction to append to the target prompt.\n"
-        "The instruction should expose the target's current intermediate judgment\n"
-        "without materially changing the original scaffold.\n\n"
-    )
-    if step_buffer_context.strip():
-        user += f"## Previous Steps in This Epoch\n{step_buffer_context}\n\n"
-    optimizer_ctx = format_meta_skill_context(meta_skill_context)
-    if optimizer_ctx:
-        user += optimizer_ctx + "\n\n"
-    requirements = output_requirements or [
-        "- Some trajectories may include a hidden Reference block. Use it to identify what intermediate conclusion matters, but do not reveal or paraphrase that reference directly to the target.",
-        "- The instruction must explicitly request a short <analysis>...</analysis> block before the final <answer>...</answer>.",
-        "- Keep the readout concise and structured.",
-        "- Do not ask for exhaustive listing, full derivation, or a new solving protocol.",
-        "- The instruction text should be ready to append directly to the target's prompt.",
-    ]
-    user += (
-        f"## Representative Trajectories ({len(items)} total)\n{trajectories_text}\n\n"
-        "## Output Requirements\n"
-        + "\n".join(requirements)
-        + "\n"
-    )
-
-    try:
-        response, _ = chat_optimizer(
-            system=actual_system,
-            user=user,
-            max_completion_tokens=1024,
-            retries=3,
-            stage="deep_probe",
-        )
-        result = extract_json(response)
-        if result and str(result.get("probe_instruction", "")).strip():
-            parsed = {
-                "reasoning": str(result.get("reasoning", "")).strip(),
-                "probe_instruction": str(result.get("probe_instruction", "")).strip(),
-            }
-            if str(result.get("probe_target_id", "")).strip():
-                parsed["probe_target_id"] = str(result.get("probe_target_id", "")).strip()
-            try:
-                if result.get("probe_after_step") is not None:
-                    parsed["probe_after_step"] = int(result.get("probe_after_step"))
-            except Exception:  # noqa: BLE001
-                pass
-            return parsed
-    except Exception:  # noqa: BLE001
-        return None
-    return None