| name | revere-reflective-research-agent |
| title | REVERE: Reflective Evolving Research Engineer for Scientific Workflows |
| version | 0.0.3 |
| engine | skillxiv-v0.0.3-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2603.20667 |
| keywords | ["Agentic AI","Self-Adaptation","Research Code","Prompt Optimization","Reflective Learning"] |
| description | Enable LLM agents to autonomously improve on research-code tasks through reflective learning from execution trajectories. Distill recurring failure patterns into actionable heuristics applied via targeted prompt edits, improving performance 3.51%-4.89% while maintaining 10x better cost-efficiency. |
Capability Gap Addressed
Existing prompt-optimization techniques fail at research-coding tasks because they rely on local signals (single-batch feedback) and full-prompt rewrites, causing poor generalization and knowledge loss. Research-coding workflows demand handling of heterogeneous repositories, underspecified environments, and weak feedback—conditions where static prompts and multi-agent systems struggle.
Core Abstractions
1. Execution Trajectory Reflection - Record failure patterns across repositories: dependency resolution issues, environment configuration errors, data formatting mismatches, incomplete library specifications.
2. Heuristic Distillation - Convert recurring failure patterns into reusable heuristics (e.g., "always check Python version compatibility before importing," "validate data encoding before processing").
3. Prompt Field Edits - Apply heuristics as surgical Python programs rather than full-prompt regeneration, targeting three configurable fields:
- System prompt (agent role and capabilities)
- Task-prompt template (structured task specification)
- Cumulative cheatsheet (repository-specific and cross-repository remedies)
Design Decisions
Global Training Context - Maintains across adaptation iterations:
- Reflection history (what failures were observed and how they were addressed)
- Auxiliary context (environment specs, common libraries, known issues)
- Cumulative cheatsheet (growing repository of solutions)
This enables learning beyond immediate batch feedback and knowledge retention across iterations.
Code-Based Field Updates - Generates Python programs that apply surgical edits to prompts rather than regenerating prompts entirely. Benefits:
- Prevents semantic drift from full rewrites
- Enables precise, targeted modifications
- Preserves coherent system-state understanding
Unified Reflector Module - Single agent diagnoses failures and performs edits rather than splitting responsibilities across multiple agents. Advantages:
- Maintains coherent understanding of system state
- Reduces inter-agent communication overhead
- Enables holistic learning across failure types
Self-Improvement Patterns
REVERE autonomously identifies and accumulates:
- Infrastructure issues - Dependency resolution, environment setup, library version mismatches
- Code-specific patterns - Common error patterns in particular codebases, optimization opportunities
