[feat] Add pipeline parallelism and fix batch_run to use ThreadPoolExecutor

[kyegomez]

Summary

Two related issues with AgentRearrange.batch_run():

1. batch_run is not concurrent — The comment says "process batch using concurrent execution" (line 843) but the implementation is a sequential list comprehension. It should use a thread pool.
2. No pipeline parallelism — When processing multiple tasks through a sequential flow, agent 2 could start on task 1 while agent 1 works on task 2. Currently each task must fully complete before the next one starts.

Problem

Current implementation (agent_rearrange.py lines 844-852):

# Process batch using concurrent execution   <-- comment says concurrent
batch_results = [                             # <-- but it's sequential
    self.run(task=task, img=img_path)
    for task, img_path in zip(batch_tasks, batch_imgs)
]

For 5 tasks with 3 agents each taking 2s: current = 30s, with thread pool = ~6s per batch.

Proposed solution

Fix 1: Use ThreadPoolExecutor within batches

with ThreadPoolExecutor(max_workers=min(len(batch_tasks), os.cpu_count())) as executor:
    futures = [
        executor.submit(self.run, task=task, img=img_path)
        for task, img_path in zip(batch_tasks, batch_imgs)
    ]
    batch_results = [f.result() for f in futures]

Fix 2: Pipeline parallelism (future enhancement)

For sequential flows, allow task N+1 to enter the pipeline at agent 1 while task N is at agent 2. This requires decoupling per-task conversation state from the shared self.conversation.

Acceptance criteria

• batch_run uses ThreadPoolExecutor to process tasks within each batch concurrently
• Each task gets its own conversation copy to avoid shared state corruption
• Batch ordering is preserved in results
• Existing tests pass unchanged

[adichaudhary]

Working on this