# ARS v3.4 Design Spec — PRISMA-trAIce + RAISE Compliance + Reading-Check

**Status:** Draft for implementation
**Date:** 2026-04-20
**Author:** Cheng-I Wu
**Target versions:** v3.4.0 (compliance agent) → v3.5.0 (reading-check)
**File path:** `docs/design/2026-04-20-v3.4-prisma-trAIce-raise-readcheck-design.md`
**Supersedes:** the three-part roadmap saved in private planning notes (2026-04-19 "下次優化的三項補強")

---

## 1. Background

ARS v3.3.6 shipped on 2026-04-15. Three gaps flagged during the NYCU EMBA fact-check exercise:

1. **A. PRISMA-trAIce** — 2025 reporting checklist for AI-assisted systematic reviews (Holst et al. 2025, *JMIR AI*, doi:10.2196/80247). 17 items across 6 sections, 4 priority tiers (Mandatory / Highly Recommended / Recommended / Optional). GitHub canonical: `cqh4046/PRISMA-trAIce`.
2. **B. RAISE** — Cochrane / Campbell / JBI / EPPI-Centre / CEE joint framework for Responsible AI in Evidence Synthesis (Thomas et al. 2025, NIHR ESG Working Group). Role-based ecosystem, not an item checklist. 8 roles × 2–3 responsibilities, organised around four principles: human oversight, transparency, reproducibility, fit-for-purpose. Source: `eppi.ioe.ac.uk/CMS/Portals/0/RAISE ESG BPWG.pdf` (accessed 2026-04-20).
3. **C. Reading-check probe** — Socratic-mentor nudge to detect whether the user has actually read the AI-cited material.

v3.3 anti-sycophancy and S2 verification already cover ~80% of EMBA speaker warning points. v3.4 closes the reporting-framework gap.

## 2. Positioning Decisions

### 2.1 Why a single compliance agent (not two)

PRISMA-trAIce and RAISE share the same evidence base (the manuscript + material passport) and the same checkpoint trigger surface (Stage 2.5 / 4.5). Merging reduces token cost by ~50% and keeps compliance logic in one place.

### 2.2 Why mode-aware scope

- **PRISMA-trAIce** is a systematic-review reporting standard. Running it on primary research is a category mistake.
- **RAISE principles** are universal AI ethics for evidence work. Running them on non-SR is a principled extension, not official compliance.
- Decision: the agent accepts `compliance_mode ∈ {systematic_review, primary_research, other_evidence_synthesis}` and dispatches payload accordingly.

### 2.3 Why non-SR warn-only

RAISE's formal scope is evidence synthesis. When ARS runs RAISE principles on `academic-paper full` (primary research), that is principle extension — it should not produce hard blocks that suggest false authority. Warn-only + honest disclaimer is the honest design.

### 2.4 Why C ships separately

Reading-check has zero coupling with the compliance agent. Same bundle = larger blast radius for rollback. Separate minor bump keeps each PR's regression surface small.

## 3. Architecture

### 3.1 File layout

```
shared/
├── agents/
│   └── compliance_agent.md              ← NEW: shared agent, single source
├── prisma_trAIce_protocol.md            ← NEW: 17 items embedded verbatim
├── raise_framework.md                   ← NEW: principles + 8-role matrix + scope disclaimer
├── compliance_checkpoint_protocol.md    ← NEW: 2.5/4.5 gate behaviour spec
└── handoff_schemas.md                   ← CHANGED: add Schema 12 compliance_report

academic-pipeline/SKILL.md               ← CHANGED: Stage 2.5/4.5 payload extended
deep-research/SKILL.md                   ← CHANGED: systematic-review mode hooks compliance
academic-paper/SKILL.md                  ← CHANGED: full mode adds pre-finalize RAISE principles (warn-only)

docs/PERFORMANCE.md + .zh-TW.md          ← CHANGED: new "Long-running session management" section
README.md + .zh-TW.md                    ← CHANGED: v3.4 key additions
CHANGELOG.md                             ← CHANGED: 3.4.0 entry
```

v3.5.0 adds:

```
deep-research/SKILL.md                   ← CHANGED: 8th mode = reading-check
deep-research/agents/
├── reading_check_agent.md               ← NEW
└── socratic_mentor_agent.md             ← CHANGED: optional probe layer
deep-research/references/
└── reading_check_protocol.md            ← NEW
```

### 3.2 Coupling diagram

```
              ┌─────────────────────────┐
              │  academic-pipeline      │
              │  (orchestrator)         │
              └────────────┬────────────┘
                           │
      ┌────────────────────┼────────────────────┐
      │                    │                    │
      ▼                    ▼                    ▼
┌───────────┐      ┌──────────────┐      ┌──────────────┐
│Stage 2.5  │      │Stage 4.5     │      │other stages  │
│Integrity  │      │Integrity     │      │(unchanged)   │
│Gate       │      │Gate          │      └──────────────┘
│+compliance│      │+compliance   │
└─────┬─────┘      └──────┬───────┘
      │                   │
      └─────────┬─────────┘
                ▼
      ┌─────────────────────┐
      │ compliance_agent    │  ← shared/, mode-aware
      └──────────┬──────────┘
                 │
    ┌────────────┴────────────┐
    ▼                         ▼
[SR mode]                [non-SR mode]
PRISMA-trAIce 17 +       RAISE 4 principles
RAISE full               only
(tier-based block)       (warn-only)
```

### 3.3 Design principles

1. **Single compliance agent.** Not two agents. `compliance_mode` parameter dispatches payload.
2. **No new stage.** 2.5 / 4.5 are existing Integrity Gates. Compliance is a payload extension, not a structural change. Fail-loop and resume reuse existing machinery.
3. **Scope honesty.** `raise_framework.md` opens with a disclaimer: RAISE's official scope is evidence synthesis; non-SR use is principle extension, not RAISE compliance.
4. **Reading-check decoupled.** v3.5.0 has zero dependency on compliance_agent; ships and rolls back independently.

## 4. Components

### 4.1 `shared/agents/compliance_agent.md`

**Metadata**

```yaml
name: compliance_agent
version: 1.0.0
data_access_level: verified_only
task_type: open-ended
owner_skill: shared
invoked_by: [academic-pipeline, deep-research, academic-paper]
```

**Input contract**

```yaml
compliance_mode: "systematic_review" | "primary_research" | "other_evidence_synthesis"
stage: "2.5" | "4.5"
materials:
  manuscript_draft: <path or inline>
  methodology_blueprint: <path>
  bibliography: <path>
  material_passport: <Schema 9 payload>
user_metadata:
  intended_venue: <string or null>
  ai_tools_used: [<list of {name, version, stage, purpose}>]
```

**Output contract** — full schema defined in §4.5 Schema 12.

**Scope guardrails**

- Read-only on manuscript. Never edits.
- On missing materials: apply Anti-Leakage Protocol — mark `[MATERIAL GAP: <item_id>]`, do not hallucinate.
- Stage split:
  - 2.5: PRISMA-trAIce M1–M10 (Methods) + RAISE principles (oversight, fit-for-purpose focus)
  - 4.5: PRISMA-trAIce T1, A1, I1, R1, R2, D1, D2 + RAISE principles (transparency, reproducibility focus) + RAISE 8-role matrix (SR mode only)

**Self-checks** (compliance agent's own failure modes)

| Check | What | On fail |
|---|---|---|
| CA-1 | Am I quoting PRISMA-trAIce from memory or from protocol? | Force re-read `shared/prisma_trAIce_protocol.md` |
| CA-2 | Are block decisions anchored to Mandatory tier, not tier-promoted? | Cross-ref protocol tier table |
| CA-3 | Did all 17 items pass? (Sycophancy risk) | Force second pass on 3 Mandatory items; require explicit evidence citation |
| CA-4 | Do RAISE principle passes have evidence citations? | Downgrade no-evidence passes to warn with `[weak evidence]` tag |

### 4.2 `shared/prisma_trAIce_protocol.md`

Full verbatim snapshot of the 17-item checklist from GitHub `cqh4046/PRISMA-trAIce`. Each item contains:

1. **Original description** (quoted from upstream)
2. **ARS check procedure** — what material fields to consult, what evidence counts as pass
3. **Tier behaviour** — Mandatory=block, Highly Recommended=warn, Recommended/Optional=info

Stage-assignment table:

| Stage | Items |
|---|---|
| 2.5 | M1, M2, M3, M4, M5, M6, M7, M8, M9, M10 |
| 4.5 | T1, A1, I1, R1, R2, D1, D2 |

**Header metadata (required):**

```yaml
snapshot_date: "2025-12-10"
upstream_source: "https://github.com/cqh4046/PRISMA-trAIce"
upstream_version_commit: "<commit hash at snapshot time>"
update_policy: "Manual sync. CI freshness check runs weekly; drift emits warning, not merge block."
```

Citation: Holst D, et al. Transparent Reporting of AI in Systematic Literature Reviews: Development of the PRISMA-trAIce Checklist. *JMIR AI.* 2025. doi:10.2196/80247

### 4.3 `shared/raise_framework.md`

**Structure**

1. **Scope disclaimer (must be first, non-removable):**
   > RAISE's official scope is evidence synthesis (systematic review, meta-analysis, scoping review, rapid review, evidence mapping). When ARS applies RAISE principles to non-SR work (`academic-paper full`, primary research, etc.), this is a **principle extension**. ARS does not claim official RAISE compliance for non-SR outputs.

2. **Four principles** — human oversight, transparency, reproducibility, fit-for-purpose. Per-principle: definition, ARS check procedure, pass/warn/fail criteria.

3. **Full 8-role matrix** (SR mode only) — 8 roles × 2–3 responsibilities each. Default ARS-user role assumption: Evidence Synthesists. ARS itself also occupies AI Development Team + Methodologist roles; this dual-role declaration is stated in the doc.

4. **Citation:** Thomas J, Flemyng E, Noel-Storr A, et al. *Responsible Use of AI in Evidence Synthesis (RAISE).* NIHR ESG Best Practice Working Group in AI/Automation, EPPI-Centre, UCL Social Research Institute. 17 July 2025. `https://eppi.ioe.ac.uk/CMS/Portals/0/RAISE%20ESG%20BPWG.pdf`

### 4.4 `shared/compliance_checkpoint_protocol.md`

**Dual-gate behaviour**

| Aspect | Stage 2.5 | Stage 4.5 |
|---|---|---|
| PRISMA-trAIce scope | M1–M10 | T1, A1, I1, R1, R2, D1, D2 |
| RAISE scope | 4 principles (focus: oversight, fit-for-purpose) | 4 principles (focus: transparency, reproducibility) + 8-role matrix (SR only) |
| Block condition | SR mode AND any Mandatory PRISMA-trAIce item = FAIL | Same |
| Warn condition | Highly Recommended fail OR RAISE principle warn | Same |
| Info condition | Recommended / Optional fail | Same |
| Non-SR mode | RAISE principles only, warn-only (never block) | Same |
| Checkpoint display | FULL checkpoint + compliance section | FULL checkpoint + compliance section |

**User action UX**

- Block → list Mandatory gaps → user choice: [backfill / return to Stage 2 / acknowledge limitation (own responsibility)]
- Warn → list HR/RAISE warns → user choice: [fix / skip]
- Info → dashboard bullet only, no flow interruption

**Fail loop** reuses `pipeline_state_machine.md §Integrity Check FAIL Loop`: after 3 unresolved rounds, user picks [manual handling / remove AI usage / continue with "partially non-compliant" warning]. See §6.3.

### 4.5 `shared/handoff_schemas.md` — Schema 12

```yaml
compliance_report:
  mode: "systematic_review" | "primary_research" | "other_evidence_synthesis"
  stage: "2.5" | "4.5"
  generated_at: <ISO-8601>
  prisma_trAIce:        # null when mode != systematic_review
    items_total: 17
    by_tier:
      mandatory:
        total: 9
        pass: <int>
        fail: [<item_id>, ...]
        gaps: [{item_id, reason, evidence_path}, ...]
      highly_recommended: {...}
      recommended: {...}
      optional: {...}
    block_decision: "block" | "warn" | "pass"
  raise:
    mode: "full" | "principles_only"
    principles:
      human_oversight: "pass" | "warn" | "fail"
      transparency: ...
      reproducibility: ...
      fit_for_purpose: ...
    principle_evidence:
      human_oversight: [<path into manuscript / methodology>, ...]
      ...
    roles:                # only when raise.mode == "full"
      evidence_synthesists: [<responsibility fulfilment notes>, ...]
      ai_development_teams: [...]
      methodologists: [...]
      publishers: [...]
      users: [...]
      trainers: [...]
      organisations: [...]
      funders: [...]
    block_decision: "block" | "warn" | "pass"
  overall_decision: "block" | "warn" | "pass"
  user_action_required: <boolean>
  evidence: [<path into manuscript / methodology>, ...]
  user_override:        # only present when user overrode a block
    decision: true
    timestamp: <ISO-8601>
    rationale: <string, min 100 chars on 3rd-round override>
    scope: [<item_ids or principle_names>]
  upstream_sync_status: "current" | "stale"   # from freshness check
```

**Material Passport extension (Schema 9):**

```yaml
compliance_history: [<compliance_report>, ...]   # append-only
```

### 4.6 SKILL.md changes

**academic-pipeline/SKILL.md**
- Stage 2.5 section: add "compliance payload" bullet listing what compliance_agent contributes
- Stage 4.5 section: same + note AI Self-Reflection Report includes compliance summary
- Checkpoint dashboard example: add compliance row

**deep-research/SKILL.md**
- `systematic-review` mode flow: add compliance entry at Stage 2.5 and 4.5 positions
- Mode selection guide: note PRISMA-trAIce coverage for SR mode

**academic-paper/SKILL.md**
- `full` mode: add pre-finalize RAISE principles check (warn-only, mode=primary_research)
- `disclosure` mode: unchanged; stated as complementary (venue policy ≠ RAISE)

### 4.7 `docs/PERFORMANCE.md` — new section

**Long-running session management**

- Anthropic prompt cache TTL = 5 minutes. Checkpoints that pause > 5 min incur cache miss on resume.
- Material Passport is the cross-session state carrier. To resume in a new session: paste passport YAML, orchestrator reads `compliance_history[]` and stage completion, continues from breakpoint.
- compliance_agent adds approximately +10–15K input / +5–8K output tokens per full-pipeline SR run (both gates combined). New entry in the token budget table.

## 5. Data Flow

### 5.1 SR pipeline with dual-gate compliance (primary scenario)

```
Stage 1 Scoping → Stage 2 Investigation
        │
        ▼  Material Passport v1
        │
Stage 2.5 INTEGRITY GATE
  1) integrity_verification_agent (existing)
  2) failure_mode_checklist (v3.2)
  3) compliance_agent (NEW)
       mode=systematic_review, stage=2.5
       → PRISMA-trAIce M1–M10 check
       → RAISE principles (oversight, fit-for-purpose)
       → compliance_report_v1
  overall decision: block / warn / pass
    block → FAIL loop (max 3 rounds)
    warn/pass → FULL checkpoint
  user confirms → append to compliance_history
        │
        ▼
Stage 3/4/4' Write + Revise (compliance_agent not invoked)
        │
        ▼  Material Passport v2
        │
Stage 4.5 INTEGRITY GATE
  1) integrity_verification_agent
  2) failure_mode_checklist
  3) compliance_agent
       mode=systematic_review, stage=4.5
       → PRISMA-trAIce T1/A1/I1/R1/R2/D1/D2
       → RAISE principles + 8-role matrix
       → compliance_report_v2
  same block/warn/pass logic
  user confirms → append to compliance_history
        │
        ▼
Stage 5 Review → Stage 6 Finalize
                  AI Self-Reflection Report
                    + compliance summary section
                      - pass/warn/fail counts across history
                      - full compliance_report JSON in appendix
```

### 5.2 Primary research (non-SR)

```
academic-paper full mode
  Stage A Plan → Stage B Draft → Stage C Revision
        │
        ▼  Material Passport (primary research variant)
        │
  pre-finalize compliance check (NEW)
    compliance_agent
      mode=primary_research
      → RAISE 4 principles only (principles_only payload)
      → PRISMA-trAIce section null
      → block_decision always ≤ warn
        │
        ▼
  Finalize (warns do not block; listed in disclosure)
```

### 5.3 Other evidence synthesis (scoping / rapid review, etc.)

```
compliance_mode = "other_evidence_synthesis"
  → RAISE full suite (4 principles + 8-role matrix)
  → PRISMA-trAIce runs in "adaptation" mode: items marked as informational, not Mandatory
  → block condition relaxed to warn (flagged "not formal SR")
```

### 5.4 Mode × skill × stage matrix

| Skill / entry | Stage 2.5 | Stage 4.5 | compliance_mode |
|---|---|---|---|
| academic-pipeline, SR route | full | full | systematic_review |
| academic-pipeline, primary research route | n/a | pre-finalize hook | primary_research |
| deep-research systematic-review (standalone) | full | terminal-hook | systematic_review |
| deep-research full / lit-review | n/a | pre-output hook | other_evidence_synthesis |
| academic-paper full (standalone) | n/a | pre-finalize hook | primary_research |
| academic-paper-reviewer (any mode) | n/a | n/a | not invoked |

### 5.5 Resume flow

```
session ends → Material Passport + compliance_history persisted to local file
new session starts → user pastes passport YAML
  orchestrator reads compliance_history[]
  locates last completed stage
  resumes from next stage
  already-run compliance is NOT re-run (passport already has compliance_report_v1)
```

**Append-only invariant:** a Stage 2.5 FAIL → backfill → retry pass produces `compliance_history = [v1_fail, v1_retry_pass, ...]`. Full audit trail preserved; AI Self-Reflection Report can cite correction trajectory. This implements RAISE's transparency principle on the pipeline itself.

## 6. Error Handling

### 6.1 Error taxonomy

| # | Error | Trigger | Handling |
|---|---|---|---|
| E1 | Missing material | compliance_agent needs M4 (Input Data) but passport.bibliography is empty | Apply Anti-Leakage Protocol: mark `[MATERIAL GAP: M4 — bibliography not provided]`, no hallucination. Item auto-FAILs, tier dictates block/warn |
| E2 | Mode mismatch | `academic-paper full` passes `compliance_mode=systematic_review` by mistake | compliance_agent entry-point validates mode vs skill context; on mismatch returns `{decision: abort, reason: "mode/context mismatch"}`, orchestrator re-evaluates mode |
| E3 | Block loop > 3 rounds | Stage 2.5 FAIL → backfill → FAIL → … exceeds 3 | Existing `pipeline_state_machine §Integrity Check FAIL Loop`: list unresolvable items; user picks [manual handling / remove AI usage / continue with "partially non-compliant" warning] |
| E4 | User override block | User chooses "acknowledge limitation" after a block | Allowed with audit: `compliance_report.user_override.decision=true`; AI Self-Reflection Report must record override; disclosure statement auto-adds line "PRISMA-trAIce Mandatory item X not met; authors acknowledge limitation" |
| E5 | Schema validation fail | compliance_report does not match Schema 12 | Pipeline halts; orchestrator reports internal error; NOT appended to compliance_history (avoid polluting audit trail) |
| E6 | Upstream reference drift | `prisma_trAIce_protocol.md` snapshot_date diverges from GitHub canonical | No pipeline block; compliance_agent adds `upstream_sync_status: "stale"` to report; weekly CI `check_prisma_trAIce_freshness.py` emits warning annotation |

### 6.2 E4 user override design

Override is not "skip" — it is "leave a trace." 3-round friction ladder:

| Round | Behaviour |
|---|---|
| 1st block | User sees warning, must acknowledge; rationale string optional |
| 2nd FAIL | User must provide rationale (any length); system notes "second override attempt" |
| 3rd FAIL | User must provide rationale ≥ 100 chars; only then released |

Generated `disclosure_addendum` is **auto-injected** into final manuscript's AI disclosure section. Non-removable. This is the concrete form of the `no detection evasion` iron rule.

```yaml
disclosure_addendum: |
  The authors acknowledge the following compliance limitations:
  - PRISMA-trAIce M4 (Input Data) not fully reported; rationale: <user text>
  - RAISE principle: transparency — partial; rationale: <user text>
```

### 6.3 Boundary behaviour across skills

When compliance_agent is invoked outside the pipeline (e.g., `academic-paper full` standalone):

- Does not access pipeline state machine
- Writes lightweight local state to `./compliance_reports/<timestamp>.yaml`
- Does not trigger pipeline checkpoint dashboard
- If skill has no finalize stage, hooks before `academic-paper disclosure` mode entry

### 6.4 Resume-time error cases

| Case | Behaviour |
|---|---|
| Loaded passport predates Schema 12 | Auto-migrate: append empty `compliance_history=[]`, warn user |
| compliance_history shows unresolved Stage 2.5 block | Do not auto-retry; display dashboard, let user choose next step |
| Upstream PRISMA-trAIce updated during pause | E6 path; do not interrupt resume |

### 6.5 What does NOT change

- Existing Stage 2.5/4.5 Integrity Check FAIL Loop
- Existing `pipeline_state_machine.md` exception handling
- v3.2 Failure Mode Checklist runs in parallel with compliance, scopes non-overlapping: failure-mode checks research validity; compliance checks reporting transparency

## 7. Testing Strategy

### 7.1 Layer 1 — Unit tests

| Test file | Coverage |
|---|---|
| `scripts/test_compliance_agent.py` | Input contract, mode dispatch, tier-based decision |
| `scripts/test_compliance_report_schema.py` | Schema 12 validator + positive/negative fixtures |
| `scripts/check_compliance_report.py` | CI validator (patterned after `check_repro_lock.py`, `check_benchmark_report.py`) |

**Key unit cases:**
- `mode=systematic_review, stage=2.5` → output contains M1–M10 results only, no T1/R1 etc.
- `mode=primary_research` → `prisma_trAIce` field is null, `raise.mode="principles_only"`, `block_decision ≤ warn`
- Mode mismatch → agent refuses (E2)
- All 17 items pass with empty evidence → CA-3 self-check triggers
- `user_override` with non-empty scope → `disclosure_addendum` must be non-empty
- Tier misclassification (HR item marked Mandatory) → Schema validation fails

### 7.2 Layer 2 — Integration tests

`scripts/test_pipeline_with_compliance.py`:

| Scenario | Verification |
|---|---|
| SR pipeline at Stage 2.5, Mandatory M4 FAIL | Orchestrator shows block; user picks "backfill" → pipeline returns to Stage 2 |
| Same, user overrides 3 times | 3rd round demands ≥ 100 char rationale; post-pass `compliance_report.user_override.rationale` non-empty |
| Primary research path, Stage 2.5 skipped | Compliance re-hooked at pre-finalize; `block_decision` always ≤ warn |
| Resume: passport has `compliance_history[0]=v1_pass` | Stage 2.5 not re-run; advance to Stage 3 |
| Append-only: FAIL → retry pass | `compliance_history` length +2, not overwritten |

### 7.3 Layer 3 — Fixture-based compliance tests

`examples/compliance/` (fictional manuscripts only, no real data):

| Fixture | Expected result |
|---|---|
| `fixture_sr_full_compliant.md` | Full SR report with all AI usage disclosed → all pass |
| `fixture_sr_missing_M4.md` | Missing Input Data description → block at 2.5, Mandatory FAIL |
| `fixture_primary_raise_weak.md` | Primary research, weak human-oversight description → warn (principles_only), no block |

Serves double duty: regression tests + user reference templates.

### 7.4 CI wire-up

Extend `.github/workflows/spec-consistency.yml`:

```yaml
- name: Validate compliance schema
  run: python scripts/check_compliance_report.py examples/compliance/*.yaml

- name: Test compliance agent
  run: python -m scripts.test_compliance_agent

- name: Test pipeline-compliance integration
  run: python -m scripts.test_pipeline_with_compliance

- name: Check PRISMA-trAIce snapshot freshness
  run: python scripts/check_prisma_trAIce_freshness.py
  # Non-blocking; emits warning annotation (E6 behaviour)
```

### 7.5 Anti-regression coverage

| Existing feature | v3.4 verification |
|---|---|
| v3.2 Failure Mode Checklist | Stage 2.5/4.5 runs failure-mode + compliance simultaneously, separate reports, no cross-contamination |
| v3.3 Score Trajectory | After compliance block → retry pass, trajectory correctly records delta |
| Material Passport Schema 9 (repro_lock) | Adding Schema 12 does not break Schema 9; passport migration smoke test |
| `check_spec_consistency.py` | Add SKILL.md version ↔ compliance_agent.md version consistency check |

### 7.6 Calibration (release gate)

compliance_agent is itself subject to calibration — reuses v3.2 `academic-paper-reviewer calibration` framework:

- 5 gold-standard fixture manuscripts (known-ground-truth compliance status)
- 5× ensembling compliance_agent runs
- **Release gate policy: transparent reporting, no hard threshold.** compliance_agent is `task_type: open-ended`. Publishing a hard FNR/FPR threshold would contradict the v3.3.2 truth-in-advertising annotation. Instead, v3.4.0 release notes include the calibration result transparently; users decide their own trust level. This is the self-consistent application of RAISE's transparency principle to ARS itself.

### 7.7 v3.5.0 reading-check tests (independent)

`scripts/test_reading_check.py`:
- `reading-check` sub-mode invoke → generates 3–5 questions
- Question types cover argument / method / limitation (not fact recall)
- Soft-nudge variant: socratic_mentor triggers ≥ 1 probe between rounds 7–15
- Opt-out flag: user says "goal-oriented" / "skip probe" → entire session probes off

## 8. Release Plan

### 8.1 v3.4.0 — Compliance Agent

- Scope: A + B merged into single compliance_agent, plus PERFORMANCE.md long-running session section
- Version bump: suite 3.3.6 → **3.4.0** (minor: new agent + mode behaviour)
- PR branch: `feat/v3.4.0-compliance-agent`
- Release artifacts:
  - New files listed in §3.1
  - SKILL.md diffs
  - CHANGELOG.md entry
  - Calibration result in release notes

### 8.2 v3.5.0 — Reading-check

- Scope: C' (Socratic soft-nudge + standalone `reading-check` sub-mode)
- Version bump: **3.5.0** (minor, because it adds deep-research mode 8 + new agent; patch level reserved for bugfix releases)
- PR branch: `feat/v3.5.0-reading-check`
- Depends on: v3.4.0 merged (clean separation, not code dependency)

### 8.3 PR ordering

1. Open and merge `feat/v3.4.0-compliance-agent`
2. After v3.4.0 stable, open `feat/v3.5.0-reading-check`
3. Each PR carries its own CHANGELOG entry and calibration note

## 9. Open questions / deferred

- Sixth-reviewer cross-model peer review remains in `planned` state (not v3.4 scope). v3.5.0 is a good moment to revisit.
- Codex CLI port (roadmap from private planning notes, 2026-04-19) stays out of scope for v3.4.
- Downstream private-project SCR Loop upgrade to ARS v3.0+ stays separately tracked in private project memory.

## 10. References

- Holst D, et al. *Transparent Reporting of AI in Systematic Literature Reviews: Development of the PRISMA-trAIce Checklist.* JMIR AI. 2025. doi:10.2196/80247
- GitHub canonical: `cqh4046/PRISMA-trAIce` (snapshot: 2025-12-10)
- Thomas J, Flemyng E, Noel-Storr A, et al. *Responsible Use of AI in Evidence Synthesis (RAISE).* NIHR ESG Best Practice Working Group, EPPI-Centre, UCL. 17 July 2025. `https://eppi.ioe.ac.uk/CMS/Portals/0/RAISE%20ESG%20BPWG.pdf`
- Position Statement on AI Use in Evidence Synthesis (Cochrane, Campbell, JBI, CEE), Campbell Systematic Reviews. 2025. doi:10.1002/cl2.70074
- Lu et al. *Towards end-to-end automation of AI research.* Nature 651:914–919, 2026 (cited for calibration methodology)
- Existing ARS infrastructure: `pipeline_state_machine.md`, `handoff_schemas.md`, `ai_research_failure_modes.md`, `score_trajectory_protocol.md`, `calibration_mode_protocol.md`, `anti_leakage_protocol.md`
