From discovery,to understanding.

for paper in reading_list:
    print(paper["title"], paper["url"])

# Three papers. Three research questions.

> **Sources:** [When Agents Slow Down](https://arxiv.org/abs/2609.15309) · [WearableQA](https://arxiv.org/abs/2609.05405) · [Conversation Compiler](https://arxiv.org/abs/2603.29678)

## 1. Start with the research question

1. **Agent reasoning**
   - Inspect the test-time strategy and token budget.
   - Follow the decisions recorded in the *trace*.
2. **Wearable data**
   - Check the available health signals.
   - Separate the task definition from the evidence.
3. **Conversation analysis**
   - Follow the views used to inspect an agent trace.
   - Return to the original paper for its full method.

### 1.1 A compact comparison

| Paper | Research focus | Read for |
| :--- | :--- | :--- |
| [When Agents Slow Down](https://arxiv.org/abs/2609.15309) | LLM agent strategies | Compute and progress |
| [WearableQA](https://arxiv.org/abs/2609.05405) | Reasoning over wearable data | Signals and questions |
| [Conversation Compiler](https://arxiv.org/abs/2603.29678) | Agent trace analysis | Views and evidence |

## 2. Trace the evidence

A **trace** records steps in a process. Keep each claim connected to its source.

- Read the research question.
  - Locate the relevant figure or example.
  - Keep source links with your notes.
- Record open questions for the next pass.

## 3. Keep code intact

```python
for paper in reading_list:
    print(paper["title"], paper["url"])
```

Illustrative reading checklist. Open each source for the complete paper.