# DeepSeek Review Process > **Source:** [[Home|Wiki Home]] · `shared-data/artifacts/deepseek_review/` The Research Stack incorporates DeepSeek AI models for formal mathematical review and validation of canonical specifications, Lean kernels, and statistical-interpretation receipts. This page documents the wiki-level view of the review pipeline, the receipt schema, and the canonical example (prime-gap entropy collapse) shipped under `shared-data/artifacts/deepseek_review/`. Important boundary: the canonical artifacts documented on this page are Ollama-compatible review receipts. Use `5-Applications/tools-scripts/llm/ollama_deepseek_review_emitter.py` for this schema. The tracked `5-Applications/tools-scripts/llm/deepseek_review_adapter.py` is a separate Anthropic-compatible DeepSeek adapter with its own `schema_version: "1.0"` receipt format. --- ## AI-Assisted Mathematical Review DeepSeek review receipts provide reproducibility and integrity tracking for AI-assisted mathematical validation. Each review run emits a paired `.md` answer file and a `.receipt.json` sidecar that records the exact model, endpoint, token usage, and SHA-256 hashes needed to re-validate the review without re-running the model. ### Review Artifacts | Path | Purpose | |---|---| | `shared-data/artifacts/deepseek_review/` | Root for all review answers and receipt sidecars | | `*_deepseek-v3.2_*.md` | Primary review answer (markdown, model-authored) | | `*_deepseek-v3.2_*.receipt.json` | Receipt for the primary review (schema `ollama_deepseek_review_receipt_v1`) | | `*_deepseek-v4-flash_continuation_*.md` | Continuation answer when the primary review was truncated | | `*_deepseek-v4-flash_continuation_*.receipt.json` | Receipt for the continuation (schema `ollama_deepseek_review_continuation_receipt_v1`) | Receipt filenames are aligned with their answer files by sharing the same `__` stem. ### Emitter Boundary The existing prime-gap receipts were not emitted by `5-Applications/tools-scripts/llm/deepseek_review_adapter.py`. That adapter targets `https://api.deepseek.com/anthropic`, records `schema_version`, `completed_at`, `tokens.{input,output,cache_creation,cache_read}`, `response_sha256`, and structured `cached_context_files`, and is suitable for Anthropic-compatible DeepSeek reviews. The artifacts in `shared-data/artifacts/deepseek_review/` instead record the Ollama-compatible schema documented below: `schema`, `created_at`, `endpoint`, `usage.{prompt_tokens,completion_tokens,total_tokens}`, plain string `context_files`, and `answer_sha256`. The canonical emitter is `5-Applications/tools-scripts/llm/ollama_deepseek_review_emitter.py`; it writes the answer file first, computes `answer_sha256` from bytes read back from disk, writes the receipt, and immediately verifies the receipt against the answer path before returning success. This write-time verification gate exists because the first manually landed Ollama receipts had stale `answer_sha256` values. ### Receipt Schema Both receipt schemas are versioned JSON records that track every field needed to re-derive the review independently of the model server. | Field | Type | Notes | |---|---|---| | `schema` | string | `ollama_deepseek_review_receipt_v1` (primary) or `ollama_deepseek_review_continuation_receipt_v1` (continuation) | | `created_at` | ISO-8601 timestamp | UTC time at which the review was produced | | `model` | string | Model identifier (e.g. `deepseek-v3.2`, `deepseek-v4-flash`) | | `endpoint` | URL | API endpoint that served the review (e.g. `https://ollama.com/v1/chat/completions`) | | `prompt_sha256` | `sha256:` | SHA-256 of the exact serialized prompt body sent to the endpoint | | `answer_sha256` | `sha256:` | SHA-256 of the answer payload written to `answer_path` | | `usage.prompt_tokens` | int | Tokens consumed by the prompt as reported by the endpoint | | `usage.completion_tokens` | int | Tokens produced by the model | | `usage.total_tokens` | int | Sum of the two above (recorded for cross-checks) | | `context_files` | string[] | Repo-relative paths to every file that participated in the review prompt context (primary receipt only; continuation consumers reconstruct this through `previous_answer_path` and the primary receipt) | | `answer_path` | string | Repo-relative path to the answer markdown | | `previous_answer_path` | string | Repo-relative path to the answer being continued (continuation receipt only) | | `message_keys` | string[] | Field names returned alongside `content` by the continuation endpoint (e.g. `role`, `content`, `reasoning`) (continuation receipt only) | Receipts are committed alongside the answers so that any future agent can: 1. Verify integrity by recomputing the SHA-256 of `answer_path` and comparing to `answer_sha256`. 2. Reconstruct the prompt by joining the listed `context_files` against the commit at which the receipt was authored. 3. Audit token usage and cost without re-querying the endpoint. ### Reconstructing context for continuations Continuation receipts intentionally omit `context_files` — a continuation inherits the prompt context of the primary review it extends. Consumers should not treat the missing field as lost context; they reconstruct it through the previous answer and primary receipt. To reconstruct the full context for a continuation answer: 1. Read `previous_answer_path` from the continuation receipt. 2. Locate the sibling primary receipt by replacing the `.md` suffix on `previous_answer_path` with `.receipt.json` (the primary receipt and its answer share a stem). 3. Read `context_files` from that primary receipt and treat it as the continuation's effective context bundle. 4. The continuation prompt body itself is the primary answer at `previous_answer_path` plus any continuation directive recorded in the continuation answer's preamble; `prompt_sha256` on the continuation receipt covers that combined body. ### Review Process Reviews are executed as a two-stage pipeline: 1. **Primary analysis** with `deepseek-v3.2` against a curated prompt that bundles the canonical spec, statistical receipts, and Lean kernels for the topic under review. 2. **Continuation** with `deepseek-v4-flash` when the primary review is truncated by the per-completion token cap. The continuation receipt references the primary answer via `previous_answer_path` and inherits the review topic via filename stem. Every review answer is structured to include: - A **YES/NO verdict table** answering the specific review questions posed in the prompt. - An **arithmetic recheck** that recomputes every numeric claim in the reviewed material against the canonical specification. - A **statistical interpretation** section that classifies thresholds as `CANONICAL`, `HEURISTIC`, or `DETERMINISTIC WINDOW FEATURE`, and flags null-model mismatches. - A **failure mode analysis** that enumerates the conditions under which the reviewed method would emit false positives or false negatives. - A **canonical spec patches** block of explicit warnings to copy verbatim into the reviewed specification. ### Example Review: Prime Gap Entropy Collapse The canonical example shipped with the initial DeepSeek review tracking is the prime-gap entropy-collapse analysis: | Artifact | Path | |---|---| | Primary answer | `shared-data/artifacts/deepseek_review/prime_gap_entropy_collapse_deepseek_deepseek-v3.2_20260512T033551Z.md` | | Primary receipt | `shared-data/artifacts/deepseek_review/prime_gap_entropy_collapse_deepseek_deepseek-v3.2_20260512T033551Z.receipt.json` | | Continuation answer | `shared-data/artifacts/deepseek_review/prime_gap_entropy_collapse_deepseek_deepseek-v4-flash_continuation_20260512T033849Z.md` | | Continuation receipt | `shared-data/artifacts/deepseek_review/prime_gap_entropy_collapse_deepseek_deepseek-v4-flash_continuation_20260512T033849Z.receipt.json` | Context files cited by the primary receipt: - `6-Documentation/docs/distilled/ArithmeticSpec_Corrected_2026-05-11.md` - `shared-data/data/stack_solidification/prime_gap_k21_rerun_receipt_2026-05-11.md` - `0-Core-Formalism/lean/Semantics/Semantics/HCMMR/Kernels/EntropyCollapseDetector.lean` The review covers: - **Arithmetic verification** of the canonical spec (crossing counts, `D₂`, `σ_q`, Kendall SD, and exact tail probabilities at `W=8`). - **Statistical interpretation** of threshold selection — confirming `K=7` as non-selective (94.57% FPR under random-permutation null) and `K=21` as a heuristic ~5% FPR calibration (strict `>21`: 3.05%, inclusive `>=21`: 5.43%). - **Failure mode analysis** for the random-permutation null model when applied to prime gaps (ties, non-uniform marginal distribution, local dependence make the FPR estimates unreliable). - **Canonical spec patches** that add HEURISTIC warnings, clarify that window-level `σ_q` and `D₂` are deterministic features rather than estimators, document the null-model mismatch for prime gaps, and address multiple-testing and edge-effect considerations. - **Continuation** (deepseek-v4-flash) extending the canonical spec patches with predictive-versus-contemporaneous fusion semantics, ground-truth caveats, parameter-sensitivity reporting requirements, edge-effect handling, reproducibility requirements, and an interpretation caveat for the final verdict surface. --- ## Adding a New Review When emitting new review artifacts: 1. Write the answer to `shared-data/artifacts/deepseek_review/_deepseek__.md`. The literal `_deepseek_` segment is the **provider tag** and is held constant across all DeepSeek-family reviews; `` is the specific model identifier (e.g. `deepseek-v3.2`, `deepseek-v4-flash`). The two segments are kept separate so future provider-level tooling (rate caps, budget accounting, fleet-wide audits) can filter on the provider tag without parsing the `` slug. This is why the canonical example filenames contain `_deepseek_deepseek-v3.2_` — the doubled `deepseek` is intentional and reflects `_`. 2. Write the matching receipt alongside it with the same stem and `.receipt.json` suffix, using `ollama_deepseek_review_receipt_v1` for the primary review and `ollama_deepseek_review_continuation_receipt_v1` for any continuation. Use `5-Applications/tools-scripts/llm/ollama_deepseek_review_emitter.py`; do not generate these receipts with `5-Applications/tools-scripts/llm/deepseek_review_adapter.py`, which emits a different Anthropic-compatible schema. 3. Populate `context_files` with repo-relative paths to every file consumed by the prompt so future agents can reproduce the prompt body. Continuation receipts omit `context_files` and `message_keys` records the alternate shape of the continuation response; consumers reconstruct continuation context via the primary receipt indexed by `previous_answer_path` (see [[#Reconstructing context for continuations]] above). 4. Record `prompt_sha256` and `answer_sha256` for integrity verification. 5. Commit the answer and receipt together — the receipt is meaningless without the answer it indexes, and the answer is unverifiable without the receipt. --- ## Related - [[Build-System]] — pinned Python interpreter for review tooling and prompt-hash reproducibility across runs. - `5-Applications/tools-scripts/llm/ollama_deepseek_review_emitter.py` — canonical Ollama-compatible emitter for this schema; verifies `answer_sha256` after writing. - `5-Applications/tools-scripts/llm/deepseek_review_adapter.py` — Anthropic-compatible DeepSeek adapter with a different receipt schema; useful as related infrastructure, but not the emitter for the current Ollama-style artifacts. - `6-Documentation/docs/distilled/` — canonical specs that reviews validate against (e.g. `ArithmeticSpec_Corrected_2026-05-11.md`). - `0-Core-Formalism/lean/Semantics/Semantics/HCMMR/Kernels/` — Lean kernels cross-referenced as review context.