@openswe test trace

[SamSpectre]

@openswe say hello

[enterprise-code-agent]

Hello @SamSpectre! 👋

I've analyzed this issue and there are no code changes required — this appears to be a test trace. Let me know if there's anything else you need!

[SamSpectre]

@openswe What further projects can be started based on the learnings and principles from auto-research-master?

[enterprise-code-agent]

Great question @SamSpectre! After reviewing the full repository — including the autoresearch-master source, Project 1's architecture doc, and the core pattern — here are concrete project ideas that build on the same principles:

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Core Principles from autoresearch-master

The transferable pattern is: fixed eval harness + mutable skill files + scalar metric + autonomous keep/discard loop. Any domain where you can define a measurable quality score and have an agent iteratively improve prompts/configs is a candidate.

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Project Ideas

1. Recruitment/HR AutoAgent (already hinted at in your architecture doc as "TalentAI")

• Inner pipeline: JD Parser → CV Extractor → Eligibility Scorer → EP Template Generator
• Ground truth: Recruiter-validated candidate assessments
• Metric: Composite of extraction accuracy (CV fields), eligibility scoring accuracy, and template quality
• Why it works: Direct structural analog — swap SEC filings for resumes, XBRL for recruiter labels

2. Legal Contract Analysis AutoAgent

• Inner pipeline: Clause Extractor → Risk Analyzer → Summary Synthesizer
• Ground truth: Lawyer-annotated contracts (key clauses, risk flags, obligations)
• Metric: Clause extraction F1 + risk classification accuracy + summary quality (LLM-as-judge)
• Mutable skills: clause_extractor.md, risk_analyzer.md, summary_writer.md

3. Medical Literature Review AutoAgent

• Inner pipeline: Paper Extractor (methods, results, conclusions) → Evidence Grader → Systematic Review Synthesizer
• Ground truth: Published systematic reviews / Cochrane summaries
• Metric: Extraction completeness + evidence grading accuracy + synthesis quality
• Interesting twist: Could optimize for different evidence frameworks (GRADE, Oxford CEBM)

4. Code Review AutoAgent

• Inner pipeline: Diff Analyzer → Bug/Smell Detector → Review Comment Generator
• Ground truth: Historical PR reviews from high-quality open-source repos (accepted review comments)
• Metric: Bug detection precision/recall + comment quality (LLM-as-judge) + false positive rate
• Mutable skills: diff_analyzer.md, bug_detector.md, review_writer.md

5. Real Estate Due Diligence AutoAgent

• Inner pipeline: Property Data Extractor → Comparable Analysis Agent → Investment Memo Synthesizer
• Ground truth: Verified property financials (cap rates, NOI, rent rolls from public REITs)
• Metric: Financial extraction accuracy + comp selection quality + memo completeness

6. Compliance/Regulatory AutoAgent

• Inner pipeline: Regulation Parser → Requirement Extractor → Gap Analyzer
• Ground truth: Compliance checklists from known frameworks (SOC2, GDPR, HIPAA)
• Metric: Requirement extraction recall + gap identification accuracy + actionability score
• High enterprise value — compliance is expensive and error-prone

7. Academic Research AutoAgent (meta — autoresearch for research)

• Inner pipeline: Paper Summarizer → Literature Mapper → Research Gap Identifier
• Ground truth: Expert-curated literature reviews in specific fields
• Metric: Summary accuracy + citation coverage + gap identification quality
• Could use Semantic Scholar API as the data source (free, public, like EDGAR)

8. Customer Support AutoAgent

• Inner pipeline: Ticket Classifier → Solution Retriever → Response Drafter
• Ground truth: Historical resolved tickets with CSAT scores
• Metric: Classification accuracy + solution relevance + response quality + resolution rate
• Interesting: Cost efficiency weight matters more here (high volume)

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Meta-Projects (Advancing the Pattern Itself)

9. Multi-Domain AutoAgent Framework

Extract the common infrastructure (eval harness, optimizer loop, git keep/discard, results.tsv tracking) into a reusable framework. Each new domain just provides: skill files, ground truth data, and a scoring function. This is the "autoresearch SDK."

10. Optimizer-of-Optimizers

Apply the autoresearch pattern to optimizer_program.md itself. Have a meta-optimizer that modifies the optimizer's instructions and measures whether the inner optimization loop converges faster. Recursive self-improvement.

11. Cross-Domain Transfer Learning for Prompts

Run autoresearch across multiple domains simultaneously and study whether prompt improvements in one domain (e.g., "be more specific about numeric precision") transfer to others. Build a shared findings.md across projects.

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Highest-Impact Next Project IMO

Project 2 should be the reusable framework (idea #9) — factor out the domain-agnostic pieces from Project 1 into a shared library, then build Project 3 (e.g., Legal or Recruitment) on top of it. This proves the "general framework" thesis from your architecture doc and makes the strongest case for the LinkedIn Post #3 narrative: "The autoresearch pattern is not about model training. It's a general framework for self-improving systems."

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The Phase 2 work you've already planned (parallel optimizers via LangGraph + shared findings.md) would also transfer directly to any of these projects, since the outer optimization loop is domain-agnostic.