Competition Task Intelligence

Build and maintain a structured PDE equation registry, analyze competition tasks (difficulty, bottlenecks, score projections), generate strategic recommendations for research focus, and expose this intelligence via CLI and MCP tools.

Overview

System for structured PDE equation management and competition task analysis. Provides:

1. PDE Equation Registry — structured metadata (LaTeX, dimensions, params, datasets) for 11+ PDEs
2. Task Analysis — per-task difficulty assessment, bottleneck identification, proven strategy catalog
3. Score Projection — optimistic/expected/conservative score estimates with confidence levels
4. Strategic Advising — which task to focus on, suggested schedule, rationale
5. CLI + MCP — expflow analyze command group and MCP tools

Installation

pip install expflow-pde

Architecture

expflow_pde/equations.py     ──── PDE equation static registry (11+ equations)
expflow_pde/analyze.py       ──── Analysis engine (task intelligence, strategy)
expflow_pde/cli_analyze.py   ──── CLI: analyze task/equations/status/advise
expflow_pde/mcp_server.py    ──── MCP: exp_compare_scores, exp_list_workers

1. PDE Equation Registry

Each equation entry in EQUATIONS dict includes: full name, LaTeX, dimensions, parameters, competition task mapping, metrics, solver, data samples, and competition info.

API

from expflow_pde.equations import (
    get_equations(),                    # All 11+ equations
    get_equation(name),                 # Single equation
    list_equations_for_task(task_id),   # task1/task2/task3
    get_equation_metrics(name, task),   # Relevant STANDARD_METRICS
    list_equation_names(),              # Sorted names
    list_competition_equations(),       # Only competition equations
)

2. Task-Level Intelligence

CLI

# Strategic advising (primary entry point)
expflow analyze advise

# Per-task analysis
expflow analyze task task1
expflow analyze task task3

# Equation reference
expflow analyze equations --task competition

# Competition overview
expflow analyze status

Example Output

expflow analyze status

Task     Score              Difficulty     Status         Priority
  ────────────────────────────────────────────────────────────────────
  task1    142/150            🟡 medium       🔴 In Progress  high
  task2    -/150              🔴 hard         ⚪ Not Started  low
  task3    -/350              🔥 very_hard    ⚪ Not Started  medium

  总分: 142/650  (508 pts remaining)

Score Estimation

from expflow_pde.analyze import estimate_score_potential, get_strategic_recommendation

estimates = estimate_score_potential("task1")
# Returns: {"optimistic": 148, "expected": 145, "conservative": 140, "confidence": "high"}

rec = get_strategic_recommendation()
# Returns: {"primary_focus": "task1", "remaining_headroom": {...}, "suggested_schedule": {...}}

Difficulty Classification

Label	Icon	Example	Meaning
easy	🟢	Baseline tasks	High confidence, proven methods exist
medium	🟡	Task 1	Known bottlenecks, clear path forward
hard	🔴	Task 2	Multiple unknown challenges
very_hard	🔥	Task 3 (KS)	Chaotic dynamics, exponential error growth

Integration with Other Systems

With experiment-lifecycle-governance

compare-scores gating builds on equation metrics from this system. When adding a new equation, its metrics must exist in STANDARD_METRICS for gating to work.

With analyze-experiment-autoregressive-degradation

Chain: analyze advise → decide task → run experiment → analyze degradation → feed back to _TASK_META.

Pitfalls

1. _TASK_META becomes stale — hardcoded scores must be updated after each submission
2. Competition deadline hardcoded — get_strategic_recommendation() has remaining_days from 2026-05-27
3. Scoring formula duplication — Task 3 formulae are in both equations.py and analyze.py; keep synced
4. No clearml import in analyze — analyze.py uses only pure Python/stdlib for fast CLI startup