[Benchmark Backfill] Integrate TVBench into lmms-eval

[Luodian]

Summary

• Integrate TVBench as a grouped benchmark task (tvbench) with 10 subset task configs under lmms_eval/tasks/tvbench/.
• Add TVBench task utilities for video path resolution, prompt formatting, answer letter normalization, and accuracy scoring.
• Add task docs mapping in docs/current_tasks.md and unit coverage for registration plus utility behavior.

Validation

• uv run python -m unittest discover -s test/eval -p \"test_tvbench_task.py\"
• uv run python -m lmms_eval --tasks list (includes tvbench and all tvbench_* tasks)
• uv run python -m lmms_eval --model dummy_video_reader --model_args response=A,fail_on_missing=false --tasks tvbench_action_antonym --limit 1 --batch_size 1 --output_path outputs/tvbench_smoke --log_samples

Closes #1138

Smoke Validation (limit=8)

Status: PASS (LMM-289 / tvbench)

Output Table

Metric	Value
tvbench_action_antonym.tvbench_acc	0.0

Note: Smoke run on single subtask tvbench_action_antonym (full group has 10 subtasks × 8 samples = 80 video calls, too slow for single-run smoke).

Sample Output

Sample 1 (doc_id: 0)

• Input: What is the action being performed in the video? ↵ A. Put on a hat/cap. ↵ B. Take off a hat/cap. ↵ Answer with the option letter only.
• Model Output: B
• Reference: A
• Scores: tvbench_acc = 0.0
• Tokens: output=1, reasoning=0

Sample 2 (doc_id: 1)

• Input: What is the action being performed in the video? ↵ A. Put on a hat/cap. ↵ B. Take off a hat/cap. ↵ Answer with the option letter only.
• Model Output: B
• Reference: A
• Scores: tvbench_acc = 0.0
• Tokens: output=1, reasoning=0

Test Params

uv run python -m lmms_eval --model openai --model_args "model_version=google/gemini-2.5-flash-lite-preview-09-2025" --tasks tvbench_action_antonym --batch_size 1 --limit 8 --log_samples