TL;DR in plain English
- A compact prototype that follows the Compute‑Grounded Reasoning (CGR) pattern from Spatial Atlas (https://arxiv.org/abs/2604.12102) keeps numeric spatial computation separate from language generation.
- Implement a deterministic compute layer that produces auditable facts (distances, safety flags, timestamps). The language model is then given only those facts to generate explanations and next steps (https://arxiv.org/abs/2604.12102).
- This pattern reduces spatial hallucination, improves interpretability, and supports routing/escation policies via a three‑tier model stack as described in the paper (three‑tier frontier model stack; see https://arxiv.org/abs/2604.12102).
Short concrete example: a camera + detector produce boxes; the compute layer reports "distance = 1.1 m, clearance_violation = true"; the LLM consumes those facts and writes a short action note.
Methodology note: this guide follows the high‑level CGR design from the Spatial Atlas abstract and maps its components to a minimal, testable prototype (https://arxiv.org/abs/2604.12102).
What you will build and why it helps
You will build a minimal Agent‑to‑Agent (A2A) style loop that applies Compute‑Grounded Reasoning (CGR) as instantiated in Spatial Atlas (https://arxiv.org/abs/2604.12102).
Core components described in the paper and implemented here:
- Scene graph extractor: turns detector outputs into structured entities and relations (entity id, label, bbox, coordinates, provenance). The Spatial Atlas design uses a scene graph engine for extracting entities and relations (https://arxiv.org/abs/2604.12102).
- Deterministic compute layer: runs numeric calculations (e.g., Euclidean distance) and safety checks; these computed facts are stored as immutable, auditable entries. Spatial Atlas emphasizes deterministic computation of distances and safety violations (https://arxiv.org/abs/2604.12102).
- LLM layer: consumes only the computed facts and generates human‑readable explanations or suggestions. The paper describes feeding computed facts to language models to avoid hallucinated spatial reasoning (https://arxiv.org/abs/2604.12102).
Why this helps (paper summary): Spatial Atlas demonstrates that compute‑first, language‑second avoids hallucination in spatial tasks while maintaining interpretability; it also describes entropy‑guided action selection and a three‑tier model stack for routing (https://arxiv.org/abs/2604.12102).
Before you start (time, cost, prerequisites)
Reference: Spatial Atlas is the architectural baseline for CGR and presents two benchmark suites (FieldWorkArena and MLE‑Bench) and a three‑tier model stack (https://arxiv.org/abs/2604.12102).
Prerequisites (minimal):
- Python 3.9+ or equivalent runtime and a basic project scaffold. See the paper for the CGR architecture (https://arxiv.org/abs/2604.12102).
- A small set of detector outputs or a controlled sample dataset (bounding boxes, labels).
- Secure storage for any hosted LLM API keys and access to at least one LLM for explanations.
Starter checklist:
- [ ] Create project scaffold and virtual environment
- [ ] Add a small sample detector output for repeatable smoke tests
- [ ] Implement deterministic compute functions with unit tests
- [ ] Store computed facts with provenance (sensor_id, model_version, timestamp)
Time and cost (high‑level guidance driven by prototype goals and the paper's multi‑tier idea):
- Prototype scope: minimal loop (scene graph -> compute -> LLM) can be done as a short prototype.
- Spatial Atlas frames a three‑tier frontier model stack and escalation; use that architecture as a guide for cost control (https://arxiv.org/abs/2604.12102).
Step-by-step setup and implementation
Follow these steps to get a minimal CGR loop working. The Spatial Atlas paper describes more advanced routing and a three‑tier model stack; add those later (https://arxiv.org/abs/2604.12102).
- Create the project and environment
# commands to start a project
git init spatial-cgr-proto
cd spatial-cgr-proto
python -m venv .venv && source .venv/bin/activate
pip install --upgrade pip
pip install pytest
- Add a minimal config reflecting model tiers and gating (paper mentions a three‑tier stack and entropy‑guided action selection; see https://arxiv.org/abs/2604.12102):
# config.yaml
model_tiers:
- local: tiny-llm
- mid: openai-gpt
- high: anthropic-claude
entropy_threshold: 0.6
max_prompt_tokens: 1000
-
Implement the scene graph extractor (input: detector JSON; output: entities list with id, label, bbox, coords, provenance). Persist outputs as JSON.
-
Implement deterministic computes and facts log:
- Compute Euclidean distances and boolean safety checks (e.g., clearance violation flags).
- Persist each computed fact as an immutable entry with timestamp + code version; this is the core of CGR as described in Spatial Atlas (https://arxiv.org/abs/2604.12102).
-
Build constrained prompt templates that accept only the computed facts and provenance. The LLM must not receive raw sensor narratives—only the numeric, labeled facts.
-
Add unit tests for compute functions and one end‑to‑end smoke test that uses a saved scene_graph.json.
-
Run a smoke test
# simple smoke run
python run_smoke.py --case samples/fieldwork_arena_sample.json
Decision frame (who should compute what):
| Component | Deterministic compute (math) | LLM (language) | Example output | |---|---:|---|---| | Distance check | Euclidean distance (2D/3D) | Use facts to explain | "distance = 1.1 m" + explanation | | Safety flag | clearance_violation = distance < threshold | Recommend action based on facts | "clearance_violation = true" + remediation | | Provenance | sensor_id, model_version, timestamp | Summarize provenance when asked | Immutable facts log |
(Spatial Atlas uses a scene graph engine and deterministic computations as the trusted ground truth for spatial facts; see https://arxiv.org/abs/2604.12102.)
Common problems and quick fixes
Reference: the Spatial Atlas design motivates deterministic computation to avoid LLM spatial hallucination (https://arxiv.org/abs/2604.12102).
Common problems and fixes:
- LLM still makes spatial errors
- Fix: ensure prompts include only the computed numeric facts and provenance; remove raw sensor prose.
- Escalate: add a verification gate that compares LLM outputs to the facts log; fail if contradictory.
- Scene graph misses objects
- Fix: add detector ensembling or a view fallback. Increase recall carefully by adjusting confidence thresholds.
- API rate limits / cost spikes
- Fix: route noncritical queries to local models and keep an entropy gating threshold before escalation (paper describes entropy‑guided routing; see https://arxiv.org/abs/2604.12102).
- Flaky generated code in pipelines
- Fix: add an automated validation gate and CI tests for any generated code before deployment.
Quick diagnostics to run now:
- Recompute distances from your sample and compare to stored facts.
- Increase provenance logging (sensor_id, model_version, timestamp) and inspect mismatches.
First use case for a small team
Use a narrow, focused pilot that validates the CGR loop end‑to‑end while minimizing scope. The Spatial Atlas paper provides the CGR pattern and benchmark framing (FieldWorkArena and MLE‑Bench) that motivate this scope (https://arxiv.org/abs/2604.12102).
Concrete, actionable starter steps for solo founders / very small teams:
- Focus scope: pick one simple rule to validate (for example: one clearance rule or one proximity check) and implement only that compute first so you can test repeatably.
- Build the compute-first pipeline: implement the scene graph extractor and a single deterministic compute function (distance) and log the fact with provenance; keep the LLM prompt minimal and constrained to those facts.
- Run repeatable smoke tests: create 3–5 saved sample cases; automate a smoke run that asserts the computed facts match expected numbers.
Why these steps work: they implement the core CGR separation (compute → facts → language) the Spatial Atlas paper recommends for avoiding spatial hallucination and improving interpretability (https://arxiv.org/abs/2604.12102).
Minimal decision table for an MVP:
| MVP scope | Task count | Primary verification | |---|---:|---| | Single clearance rule | 3 tasks (extract, compute, explain) | Deterministic fact matches expected value |
Technical notes (optional)
Advanced concepts from Spatial Atlas to consider as you scale (https://arxiv.org/abs/2604.12102):
- Scene graph schema: include entities, relations, coordinates, boxes, provenance fields and make the schema versioned.
- Deterministic computes: prefer exact arithmetic (Euclidean) and persist results immutably in a facts log.
- Routing and escalation: Spatial Atlas describes entropy‑guided action selection and a three‑tier model stack to route queries cost‑effectively (examples include OpenAI and Anthropic; see https://arxiv.org/abs/2604.12102).
- Self‑healing pipeline: the paper mentions strategy‑aware code generation, iterative refinement loops driven by scores, and a prompt leak audit registry.
If you adopt these, add monitoring for correctness and cost over time and version all compute logic with small, audited releases.
What to do next (production checklist)
Use the Spatial Atlas paper as the architecture reference for CGR, scene graph engine, entropy routing, and self‑healing features (https://arxiv.org/abs/2604.12102).
- [ ] Run saved FieldWorkArena sample cases and collect correctness metrics
- [ ] Harden CI and increase unit test coverage
- [ ] Add an immutable facts log and provenance for every computed fact
- [ ] Implement entropy gating and local fallback for cost control
- [ ] Prepare a canary rollout and monitoring for safety rules
Assumptions / Hypotheses
- The CGR pattern (compute before LLM) from Spatial Atlas transfers to practical pipelines and improves interpretability (https://arxiv.org/abs/2604.12102).
- Pilot numeric hypotheses and thresholds for your environment (tune during validation):
- Prototype time (small scope): 4 hours
- Short pilot: 1 week
- Team size for MVP: 3 people
- Example clearance rule to start with: 1.2 m
- Canary rollout steps: 5% → 20% → 100%
- Safety unit test pass gate: 95%
- Entropy escalation threshold: 0.6
- Latency targets: <200 ms local, <800 ms hosted API
- Daily API budget example: $50–$300
- Unit tests: start with ≥10 tests, aim for ≥50 before production
- Prompt token cap example: 1000 tokens
- Paper reference points: Spatial Atlas evaluates two benchmarks and reports an implementation spanning FieldWorkArena and MLE‑Bench, and MLE‑Bench contains 75 Kaggle competitions; the report length is 11 pages (see https://arxiv.org/abs/2604.12102).
Risks / Mitigations
- Risk: missed objects or sensor blind spots.
- Mitigation: detector ensembles, view fallbacks, and strict provenance on facts (sensor_id, model_version, timestamp), as recommended by the Spatial Atlas design (https://arxiv.org/abs/2604.12102).
- Risk: cost overruns from escalations to hosted models.
- Mitigation: daily budget caps, entropy gating before escalation, and a local fallback model.
- Risk: prompt or data leakage.
- Mitigation: maintain a prompt leak audit registry and an immutable computed facts log (paper describes prompt leak auditing and self‑healing pipeline features; see https://arxiv.org/abs/2604.12102).
Next steps
- Run the saved FieldWorkArena samples and capture: correctness rate, percent of outputs justified by deterministic facts, average latency, and cost per run. Spatial Atlas frames the benchmark context for these measures (https://arxiv.org/abs/2604.12102).
- Harden CI and reach ≥50 tests before production.
- Expand scene graph coverage, version schemas, and persist provenance for every fact.
Example reusable snippets to commit to your repo:
# local smoke test
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
python run_smoke.py --case samples/fieldwork_arena_sample.json
{
"model_tiers": ["local", "openai", "anthropic"],
"entropy_threshold": 0.6,
"canary": 0.05,
"safety_pass_rate": 0.95
}
Final reference: use Spatial Atlas as the architectural guide for CGR, the scene‑graph engine, entropy routing, and self‑healing pipeline features (https://arxiv.org/abs/2604.12102).