Nemotron-3-Nano analyze results

[juanmichelini]

Let's look into output.jsonl of each of the full archives in https://github.com/OpenHands/openhands-index-results/blob/main/results/Nemotron-3-Nano/scores.json
and analyze the errors.

[juanmichelini]

@OpenHands please do this. do not create PR just analyze and report here

[openhands-ai]

I'm on it! juanmichelini can track my progress at all-hands.dev

[juanmichelini]

Nemotron-3-Nano Error Analysis

I analyzed the output.jsonl files from all 5 full archives. Here are my findings:

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1. SWE-Bench (Score: 34.2%, 500 instances)

• Issue: The output.jsonl contains git_patch fields but no ground truth patches to compare against, so we cannot definitively determine pass/fail from the file alone
• Observations:
  • All 500 instances have a git_patch field populated
  • 106 instances (21%) contain .bak backup files as artifacts in the patch (e.g., *.bak, *.py.bak)
  • 45 instances (9%) only create new files without modifying existing code
  • 199 instances (40%) have relatively small patches (<10k chars)
  • History lengths vary significantly (from ~40 to ~200 turns)

Key Pattern: Many small patches with artifacts suggest the model is not always producing clean, minimal solutions. Some patches include debugging artifacts that should be cleaned up.

---

2. GAIA (Score: 8.5%, 99 instances)

• Passed: 14 (14%)
• Failed: 85 (86%)
• With Errors: 0

Observations:

• Average history length: 68 turns (range: 18-232)
• Many failures appear to be due to:
  1. Incorrect answer format: Model uses wrong output format (e.g., <message> instead of <final_answer>)
  2. Wrong but close answers: Examples:
     • Ground truth: "cloak", Model: "veil"
     • Ground truth: "142", Model: "40"
     • Ground truth: "3.1.3.1; 1.11.1.7", Model: "231-233"
  3. Over-thinking: Some instances have 200+ turns, suggesting the model keeps searching
  • Answer format breakdown:
    • final_answer_tags: 12
    • message_tags: 23
    • plain_text: 48
    • empty: 16

Key Pattern: The model struggles with precise factual recall and numeric answers. Many answers are in the right domain but incorrect.

---

3. Commit0 (Score: 6.2%, 16 repos)

• Passed: 0
• Failed: 16 (100%)
• With Errors: 0

Observations:

• All instances have num_tests: 0 and num_passed: 0
• Very short history (~6 turns), suggesting the model gives up quickly
• The eval result structure suggests no tests were found/ran for any repo

Key Pattern: The model appears unable to properly identify or run the test suites for commit-level tasks. This may be a task complexity issue rather than a model capability issue.

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4. SWT-Bench (Score: 7.6%, 432 instances)

• Passed (has patch): 406
• Failed (no patch): 26 (6%)
• With Errors: 0

Failed instances (no patch generated):

• sympy: 5 instances
• django: 6 instances
• pydata/xarray: 1 instance
• matplotlib: 1 instance
• scikit-learn: 3 instances
• Other: 10 instances

Key Pattern: ~6% of instances produced no patch at all. These are distributed across multiple repositories, suggesting the model struggled with certain types of issues in these repos.

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5. SWE-Bench Multimodal (Score: 20.9%, 102 instances)

• Passed (has patch): 101
• Failed (no patch): 1 (1%)
• With Errors: 0

Failed instance: Automattic__wp-calypso-35531

Key Pattern: Very high success rate (99%) for generating patches. Only 1 instance failed to produce any output.

---

Summary of Main Issues

1. GAIA: Model struggles with precise factual/numeric answers and often uses incorrect output formats
2. Commit0: Model cannot identify or run test suites (100% failure rate)
3. SWE-Bench/SWT-Bench: Many patches contain artifacts (.bak files), suggesting incomplete cleanup
4. SWT-Bench: ~6% of instances produce no patch at all

The model seems most challenged by:

• Multi-step reasoning tasks requiring precise factual recall
• Identifying and running appropriate test suites
• Producing clean, artifact-free patches

[openhands-ai]

Final Summary

I have completed the analysis of Nemotron-3-Nano benchmark results as requested in issue #728.

Work Completed:

1. Downloaded and analyzed all 5 full archives from the URLs in results/Nemotron-3-Nano/scores.json:

   • SWE-bench (500 instances)
   • GAIA (99 instances)
   • Commit0 (16 repos)
   • SWT-bench (432 instances)
   • SWE-bench Multimodal (102 instances)

2. Key findings identified:

   • SWE-bench: 21% of patches contain .bak backup artifacts; 9% only create new files
   • GAIA: 86% failure rate; model struggles with precise factual/numeric answers and uses incorrect output formats
   • Commit0: 100% failure rate; model cannot identify/run test suites (num_tests: 0)
   • SWT-bench: 6% produce no patch at all
   • SWE-bench Multimodal: 99% success rate generating patches

3. Posted comprehensive analysis to GitHub issue Nemotron-3-Nano analyze results #728 as a comment: Nemotron-3-Nano analyze results #728 (comment)

Checklist:

• ✅ Analyzed output.jsonl from all 5 full archives
• ✅ Identified error patterns for each benchmark
• ✅ Posted analysis to GitHub issue (as instructed: "do not create PR just analyze and report here")
• ✅ No code changes made (only analysis requested)
• ✅ Cleaned up temporary downloaded files