Observations on Claude’s Legal Module Release and a Personal Research Note

Today, Claude released a professional-grade module tailored for law firms and legal professionals.
Interestingly, this announcement coincided with a notable downturn in U.S. tech stocks, and was widely interpreted as another signal that large language models are beginning to meaningfully disrupt high-margin, expertise-driven industries.

Against this backdrop, I also spent time experimenting with Claude’s newly released model — Opus 4.6.

First Impressions of Opus 4.6

From a hands-on perspective, Opus 4.6 demonstrates a clear step forward, particularly in research-oriented scenarios:

• Stronger ability to decompose complex research problems
• Improved awareness of practical constraints, such as compute cost and experimental feasibility
• Better logical consistency across long, multi-turn discussions

I used the model to collaboratively explore a research idea, iterating over assumptions, constraints, and evaluation strategies. The result was a concrete and cost-controlled research plan that feels realistic to execute rather than purely speculative.

Overview of the Proposed Research Direction

The proposed research focuses on a critical but often underexplored aspect of code agents and autonomous systems:
the quality of trajectory data.

Research Core

Systematic evaluation and filtering of trajectory data generated by OpenHands.

Research Objectives

• Propose a practical and reproducible method for assessing trajectory quality
• Enable automated scoring and filtering of trajectories
• Construct a high-quality trajectory subset suitable for training or fine-tuning models

Validation Strategy

• Conduct experiments using a 7B-scale model
• Compare performance between:
  • Randomly sampled, unfiltered trajectory data
  • A curated subset filtered by the proposed quality evaluation method
• The key hypothesis is that:
  Under the same model size and training budget, a high-quality trajectory subset outperforms randomly selected, unfiltered data

Next Steps

In future updates, I will also share the detailed training plan generated and iteratively refined with Claude, serving as the concrete execution blueprint for this research.

More broadly, this experience reinforced a growing belief of mine:
Large language models are no longer just tools for implementation — they are increasingly capable collaborators in the research design process itself.