AI research is advancing faster than any reading list. Organize arXiv papers around your interests, with multidimensional evidence and a traceable reading order.
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
for paper in reading_list:
print(paper["title"], paper["url"])
Illustrative reading checklist. Open each source for the complete paper.
# 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.
Translation Original structure preservedSimplified Chinese
for paper in reading_list:
print(paper["title"], paper["url"])
此处为阅读清单示例;完整内容请查阅各篇原文。
Aa
Your preferred termtrace →
Keep your terminology consistent with the domain and context.
Localized interface preview · Swipe to explore. Papers and scores use public metadata.
PAPER RADAR / MULTIDIMENSIONAL SCORING
Your research. Your ranking.
Topic relevance, author and institutional evidence, recency and your research interests are weighted in layers. Overall value, Taste (quality and recency) and a personal score offer complementary views of each paper.
Combine relevance, author and institutional evidence, and recency through layered weighting.
02
Inspect the evidence
Explore scores, author contributions and source records.
03
Stay with the paper
View annotations on arXiv and save papers for your next read.
GET STARTED
Start here.
Install. Set up. Start reading. Find answers as you go.
1
Install from the storePin Paper Radar to the toolbar for easy access.
2
Choose your interestsStart with 1–3 topics and run an online refresh.
3
Inspect and saveExplore scores and author evidence, then save papers worth reading.
How do I build my first paper cache?+
Choose your topics and run an online refresh. Then use cache-only mode to revisit the same date range and filters. Cache-only mode does not contact arXiv; refresh online for the latest papers.
How do I set up page annotations?+
Author profiles and Scholar search / arXiv details have separate controls. Compact mode keeps the KnowMe button and hides long status text and author coloring. Annotations use local records and a limited bundled index; a missing match does not imply a paper is unimportant.
Does my data sync automatically?+
Saved papers, cache, personal terms and history stay in the current browser and do not sync through your Google account. Export a backup before changing devices, browser profiles or uninstalling. Your chosen model service processes translations and connection tests.