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+# Evaluation Architecture
+
+This document describes how evaluation flows through the codebase and the
+design choices behind the current structure.
+
+## Audience
+
+- **Problem contributors** (see `CONTRIBUTING.md`)
+- **Model submitters** (see `SUBMIT.md`)
+- **General researchers** using Frontier-CS to evaluate solutions
+
+## Goals
+
+- Clear separation between single-problem and batch evaluation.
+- Shared validation and config parsing across research backends.
+- Predictable cleanup to avoid orphaned cloud resources.
+- Explicit naming to avoid backend ambiguity.
+
+## Architecture at a Glance
+
+- **CLI**:
+  - `frontier eval` → `SingleEvaluator`
+  - `frontier batch` → `BatchEvaluator`
+  - `frontier list` / `frontier show` → problem discovery
+- **CI**:
+  - Validate Problems → `scripts/validate_problems.py` → `SingleEvaluator`
+  - Weekly Batch Evaluation → `scripts/run_eval.sh` → `BatchEvaluator`
+
+## Components
+
+### SingleEvaluator
+Unified API for **single-problem evaluation**:
+
+- Selects a runner based on track and backend.
+- Registers SIGINT/atexit hooks to tear down SkyPilot clusters on exit.
+
+### BatchEvaluator
+Orchestrates **batch evaluation** with parallel workers and SkyPilot
+cluster pools:
+
+- Work queues with resumable state and result aggregation.
+- Resource-grouped cluster pools — pairs are grouped by
+  `ResourceSignature` (cloud, accelerators, instance type) so that
+  CPU-only and GPU problems run on separate pools.
+- Hash-based resume — each result stores solution/problem hashes so stale
+  results are automatically re-evaluated when source changes.
+
+### Runners
+Runners execute evaluation. The class hierarchy:
+
+```
+Runner (ABC)
+├── ResearchRunner              # shared: problem validation, config loading, uv install
+│   ├── ResearchDockerRunner    # local container
+│   └── ResearchSkyPilotRunner  # cloud via SkyPilot
+├── AlgorithmicLocalRunner      # go-judge via HTTP
+└── AlgorithmicSkyPilotRunner   # go-judge on SkyPilot
+```
+
+### Solution Generation (`gen/`)
+The `gen/` module generates solutions by calling LLMs. It is independent of
+the evaluation pipeline — `frontier eval` and `frontier batch` do not
+depend on it. It provides an LLM interface, an API key pool for concurrent
+requests, and solution file formatting.
+
+## Design Decisions
+
+- **Single vs Batch**: `SingleEvaluator` stays focused on one-off runs
+  (simple API + cleanup hooks), while `BatchEvaluator` owns scheduling,
+  resumable state, and cluster pools. This keeps single-run paths
+  lightweight and batch runs scalable.
+- **Shared research helpers**: input validation and config parsing live in
+  the `ResearchRunner` base class so Docker and SkyPilot backends stay in
+  sync.
+- **Cleanup strategy**: research evaluations tear down clusters by default
+  unless `keep_cluster` is set. `SingleEvaluator` cleans up via an
+  active-cluster registry; `BatchEvaluator` manages its own pool lifecycle.
+- **Naming**: runner class names encode track + backend
+  (e.g., `ResearchDockerRunner`) to remove ambiguity in logs and docs.
+- **Score semantics**: a score of **0** can mean the evaluator ran
+  successfully; failures are reported via status/metadata rather than score
+  alone.
+- **Reference solutions**: problems ship with `reference.cpp`/`reference.py`
+  so CI can verify end-to-end evaluation without model submissions.
+- **Results separation**: evaluation outputs go to a dedicated results
+  repository to keep the main repo lean and auditable.
+- **Internal vs public**: internal test cases and tooling live in a private
+  repo; public artifacts are kept minimal but compatible.
+- **Weekly vs local**: weekly CI uses `scripts/run_eval.sh` with batch
+  scheduling; local runs use the same script or `frontier eval` for quick
+  iteration.
+- **Resource-grouped cluster pools**: `BatchEvaluator` groups pairs by
+  `ResourceSignature` (cloud × accelerators × instance type) and creates a
+  separate pool per group, avoiding the waste of running CPU-only problems
+  on GPU clusters.
+- **Hash-based resume**: resuming a batch compares solution/problem hashes
+  against stored results. Changed inputs are re-evaluated even when a prior
+  result exists, preventing silently stale scores.
+- **Generation vs evaluation**: solution generation (`gen/`) is fully
+  decoupled from evaluation. Generated files are plain source files with no
+  special metadata; the evaluator has no dependency on the generation
+  pipeline.
+
+## Runner Flow (Research)
+
+Both research runners share the same pre-evaluation steps (via
+`ResearchRunner`):
+
+1. Validate solution file and `.FAILED` marker.
+2. Verify the problem path exists.
+3. Load `config.yaml` and runtime settings.
+4. Build uv install command if `uv_project` is specified.
+
+Execution diverges at the backend:
+
+- **Docker** — launches a local container.
+- **SkyPilot** — provisions a cloud VM and runs remotely.
+
+## Operations (Cleanup + CI)
+
+- **Cleanup**: research evaluations tear down clusters by default unless
+  `keep_cluster=True`. `SingleEvaluator` uses an active-cluster registry to
+  clean up on SIGINT/atexit; `BatchEvaluator` manages its own cluster pool
+  lifecycle.
+
+- **CI**: problem validation runs single evals; the weekly batch job runs
+  full evaluations on SkyPilot (typically GCP).
diff --git a/README.md b/README.md
index 16476f0c..515b7bd2 100644
--- a/README.md
+++ b/README.md
@@ -168,6 +168,9 @@ print(f"Score (bounded): {result.score}")
 print(f"Score (unbounded): {result.score_unbounded}")
 ```
 
+See `ARCHITECTURE.md` for an overview of the evaluation stack
+and runner mapping.
+
 ### Batch Evaluation
 
 For testing your solutions at scale with public test cases.