feat: integrate worldvqa benchmark task

[Luodian]

Summary

• add a new worldvqa task backed by moonshotai/WorldVQA with base64 image decoding and exact-match scoring
• add WorldQA compatibility aliases under lmms_eval/tasks/worldvqa/ (worldvqa_gen, worldvqa_mc, worldvqa_mc_ppl) to keep parity with existing worldqa task flows
• update docs/current_tasks.md to document the new WorldVQA task and compatibility aliases

Verification

• uv run python -c "import subprocess, sys; output = subprocess.check_output([sys.executable, '-m', 'lmms_eval', '--tasks', 'list'], text=True); print('worldvqa_present', 'worldvqa' in output)"
• HF_TOKEN='' HUGGING_FACE_HUB_TOKEN='' uv run python -m lmms_eval --model dummy_video_reader --model_args response=A,fail_on_missing=false --tasks worldvqa --limit 1 --batch_size 1 --output_path /tmp/worldvqa_smoke --log_samples
• lsp_diagnostics clean for lmms_eval/tasks/worldvqa/utils.py (YAML files still emit expected !function unresolved-tag schema warnings)

Closes #1146

Smoke Validation (limit=8)

Status: PASS (LMM-297 / worldvqa)

Output Table

Metric	Value
exact_match	0.25

Sample Output

Sample 1 (doc_id: 0)

• Input: What breed of dog is in the picture? ↵ Answer briefly.
• Model Output: Doberman Pinscher
• Reference: Greek Hound
• Scores: exact_match = 0.0
• Tokens: output=95, reasoning=89

Sample 2 (doc_id: 1)

• Input: What breed of dog is in the picture? ↵ Answer briefly.
• Model Output: Finnish Lapphund.
• Reference: European Russian Laika
• Scores: exact_match = 0.0
• Tokens: output=601, reasoning=594

Test Params

uv run python -m lmms_eval --model openai_compatible --model_args "model_version=bytedance-seed/seed-1.6-flash" --tasks worldvqa --batch_size 1 --limit 8.0 --log_samples