Competition Task Intelligence

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