SYSTEM_DESIGN.md

PhD Research OS — Complete System Design

Version 2.0 | Post-Audit Architecture

Date: 2026-04-23 Status: DESIGN COMPLETE — Ready for phased implementation Addresses: All 87 blindspots from the audit Hardware Target: 16-24GB VRAM consumer GPU (RTX 4090 / RTX 3090 / A6000)

---

1. System Overview

╔══════════════════════════════════════════════════════════════════════════╗
║                    PhD Research OS v2.0                                  ║
║                    "The Epistemic Engine"                                ║
╠══════════════════════════════════════════════════════════════════════════╣
║                                                                          ║
║  ┌─── INPUTS ──────────────────────────────────────────────────────┐    ║
║  │  PDF Bundles │ Supplements │ Datasets │ Code Repos │ Lab Notes  │    ║
║  └──────────────────────────┬─────────────────────────────────────┘    ║
║                              ▼                                          ║
║  ┌─── LAYER 0: STRUCTURAL INGESTION ──────────────────────────────┐    ║
║  │  Marker → Nougat → GROBID │ Region Classifier │ Plot Digitizer │    ║
║  │  Section-aware chunks │ Bounding boxes │ Quality scores          │    ║
║  └──────────────────────────┬─────────────────────────────────────┘    ║
║                              ▼                                          ║
║  ┌─── LAYER 1: ENTITY RESOLUTION ─────────────────────────────────┐    ║
║  │  Ontology normalizer │ Citation resolver │ VoR lineage │ Retract. │  ║
║  └──────────────────────────┬─────────────────────────────────────┘    ║
║                              ▼                                          ║
║  ┌─── LAYER 2: QUALIFIED EXTRACTION ──────────────────────────────┐    ║
║  │  AI Model Council (parallel) │ Epistemic Separation Engine      │    ║
║  │  Qualifier preservation │ Statistical extraction │ OOD gating   │    ║
║  │  Guidance constrained decoding │ Source quotes + bboxes          │    ║
║  └──────────────────────────┬─────────────────────────────────────┘    ║
║                              ▼                                          ║
║  ┌─── LAYER 3: CANONICALIZATION ──────────────────────────────────┐    ║
║  │  Embedding dedup │ Canonical registry │ Alias merging           │    ║
║  │  Evidence aggregation │ Temporal versioning │ Lineage diff      │    ║
║  └──────────────────────────┬─────────────────────────────────────┘    ║
║                              ▼                                          ║
║  ┌─── LAYER 4: KNOWLEDGE GRAPH ───────────────────────────────────┐    ║
║  │  SQLite-backed graph │ Typed epistemic edges │ Lab lineage      │    ║
║  │  Method compatibility │ Transitive constraints │ Gap analysis   │    ║
║  │  Null evidence │ Conflict clustering │ Versioned ontology       │    ║
║  └──────────────────────────┬─────────────────────────────────────┘    ║
║                              ▼                                          ║
║  ┌─── LAYER 5: CALIBRATED SCORING ────────────────────────────────┐    ║
║  │  Code-computed confidence │ 3 separate scores │ Statistical gate│    ║
║  │  Parser confidence propagation │ Section modifiers │ Brier mon. │    ║
║  └──────────────────────────┬─────────────────────────────────────┘    ║
║                              ▼                                          ║
║  ┌─── LAYER 6: EVALUATION ────────────────────────────────────────┐    ║
║  │  LLM-as-Judge CI/CD │ Versioned golden set │ Stochastic tests  │    ║
║  │  Hidden holdout │ Fatigue management │ Counter-metrics          │    ║
║  └──────────────────────────┬─────────────────────────────────────┘    ║
║                              ▼                                          ║
║  ┌─── LAYER 7: PROVENANCE & REPRODUCIBILITY ──────────────────────┐    ║
║  │  Version pinning │ Output lineage │ PDF.js viewer │ Containers  │    ║
║  │  Security sandbox │ License checking │ Epistemic Embargo        │    ║
║  └──────────────────────────┬─────────────────────────────────────┘    ║
║                              ▼                                          ║
║  ┌─── OUTPUTS ─────────────────────────────────────────────────────┐   ║
║  │  Obsidian Vault │ Courtroom UI │ Gap Analysis │ Decision Objects│   ║
║  └─────────────────────────────────────────────────────────────────┘   ║
║                                                                          ║
║  ┌─── CROSS-CUTTING ──────────────────────────────────────────────┐    ║
║  │  AI Model Council │ Meta-Improver │ Superpowers Skills          │    ║
║  │  ECC Harness │ Companion Agents │ Manual Synthesis Mode         │    ║
║  └─────────────────────────────────────────────────────────────────┘   ║
╚══════════════════════════════════════════════════════════════════════════╝

---

2. Model Architecture

2.1 The Two-Model Strategy

The system runs TWO models, not one. This solves the local-vs-online tension:

┌─────────────────────────────────────────────────────────┐
│  PRIMARY BRAIN (Fully Local — Never Touches Internet)    │
│                                                          │
│  Model: Qwen3-8B Q4 AWQ                                │
│  VRAM: ~5GB weights + ~4GB KV cache (PolarQuant)        │
│  Total: ~9GB (fits 16GB GPU with room for batch)        │
│  Context: 128K tokens (full paper length)               │
│  Serving: Ollama (simplest) or vLLM (fastest)           │
│                                                          │
│  Tasks:                                                  │
│  • Claim extraction (Layer 2)                           │
│  • Epistemic classification                              │
│  • Confidence component estimation                       │
│  • Conflict hypothesis generation                        │
│  • Query decomposition                                   │
│  • Decision object generation                            │
│                                                          │
│  Constrained decoding: Guidance engine                   │
│  Training: SFT → DPO → GRPO (4-stage pipeline)         │
│  Privacy: ALL paper data stays local                     │
├─────────────────────────────────────────────────────────┤
│  COMPANION BRAIN (Online — For Non-Sensitive Tasks)      │
│                                                          │
│  Model: Claude API / GPT-4o-mini / OpenRouter            │
│  OR: Local Qwen3-30B-A3B MoE Q4 (~6GB, 3B active)      │
│                                                          │
│  Tasks:                                                  │
│  • Meta-Improver external scanning (arXiv, GitHub)      │
│  • Prompt optimization A/B testing                       │
│  • Training data generation for new domains             │
│  • Retraction/correction checking (needs internet)      │
│  • Repository URL validation                             │
│                                                          │
│  Privacy: NEVER sees raw paper text                      │
│  Only receives: metadata, queries, anonymized claims     │
└─────────────────────────────────────────────────────────┘

2.2 Why Qwen3-8B, Not Qwen2.5-3B

Metric	Qwen2.5-3B	Qwen3-8B	Improvement
AIME (math reasoning)	~15%	~45%+	3×
MATH-500	~85%	~95%+	+10 pts
JSON structural accuracy (SFT)	~65%	~80%+	+15 pts
Context window	32K	128K	4×
Hybrid thinking mode	No	Yes	New capability
VRAM at Q4 AWQ	~2.5GB	~5GB	Acceptable

2.3 Alternative: Qwen3-30B-A3B MoE (The Stealth Option)

For users with 8GB+ VRAM who want maximum quality:

• 30B total parameters, only 3B activated per token (Mixture of Experts)
• ~6GB at Q4 quantization
• Quality equivalent to dense 14B+ models
• Apache 2.0 license
• Available: Qwen/Qwen3-30B-A3B-Instruct-2507 (1M downloads)

2.4 Multimodal: Qwen3-VL-8B-Instruct

For figure/diagram processing (Layer 0):

• Same architecture as text model but with vision encoder
• Available: Qwen/Qwen3-VL-8B-Instruct (3.9M downloads)
• AWQ 4-bit: cyankiwi/Qwen3-VL-8B-Instruct-AWQ-4bit (~5GB)
• Handles: figure classification, diagram understanding, micrograph analysis
• Does NOT replace plot digitizer for quantitative data

2.5 VLM for Multimodal Figures: Qwen3-VL-30B-A3B-Instruct

For maximum figure understanding with MoE efficiency:

• Available: Qwen/Qwen3-VL-30B-A3B-Instruct (1.5M downloads)
• AWQ: QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ (667K downloads)
• Only 3B active params — fits alongside primary brain

---

3. Training Pipeline (4-Stage)

Stage 1: SFT on Domain Data

# Current implementation (train.py) — KEEP but upgrade base model
from trl import SFTConfig, SFTTrainer
from peft import LoraConfig

trainer = SFTTrainer(
    model="Qwen/Qwen3-8B",  # Upgraded from Qwen2.5-3B
    args=SFTConfig(
        output_dir="./research-os-sft",
        num_train_epochs=3,
        per_device_train_batch_size=2,
        gradient_accumulation_steps=8,
        learning_rate=2e-4,
        max_length=4096,  # Longer for paper sections
        assistant_only_loss=True,
        bf16=True,
        gradient_checkpointing=True,
        push_to_hub=True,
        hub_model_id="nkshirsa/phd-research-os-brain-v2",
    ),
    train_dataset=expanded_dataset,  # 10K+ examples (up from 1,900)
    peft_config=LoraConfig(r=64, lora_alpha=16, target_modules="all-linear"),
)
trainer.train()

Stage 2: DPO on Preference Pairs

from trl import DPOConfig, DPOTrainer

# Dataset: pairs of (correct extraction, incorrect extraction) for same text
trainer = DPOTrainer(
    model="./research-os-sft",  # From stage 1
    args=DPOConfig(
        output_dir="./research-os-dpo",
        learning_rate=5e-7,
        num_train_epochs=1,
        max_length=4096,
        bf16=True,
        push_to_hub=True,
    ),
    train_dataset=preference_dataset,
    peft_config=LoraConfig(r=64, target_modules="all-linear"),
)

Stage 3: GRPO with Epistemic Reward Functions

This is the critical stage that bakes JSON reliability and epistemic correctness into the model:

from trl import GRPOTrainer, GRPOConfig
from trl.rewards import think_format_reward
import json

# ── Reward Function 1: JSON Validity ──
def json_validity_reward(completions, **kwargs):
    """Binary reward: is the output valid JSON?"""
    rewards = []
    for completion in completions:
        content = completion[0]["content"] if isinstance(completion, list) else completion
        try:
            json.loads(content)
            rewards.append(1.0)
        except (json.JSONDecodeError, TypeError):
            rewards.append(0.0)
    return rewards

# ── Reward Function 2: Schema Compliance ──
REQUIRED_KEYS = {"text", "epistemic_tag", "confidence", "missing_fields", "status"}
VALID_TAGS = {"Fact", "Interpretation", "Hypothesis", "Conflict_Hypothesis"}

def schema_compliance_reward(completions, **kwargs):
    """Reward for matching the Research OS claim schema."""
    rewards = []
    for completion in completions:
        content = completion[0]["content"] if isinstance(completion, list) else completion
        score = 0.0
        try:
            data = json.loads(content)
            claims = data if isinstance(data, list) else data.get("claims", [data])
            
            for claim in claims:
                if not isinstance(claim, dict):
                    continue
                # Key presence: 0.3
                present_keys = set(claim.keys()) & REQUIRED_KEYS
                score += 0.3 * len(present_keys) / len(REQUIRED_KEYS)
                # Valid epistemic tag: 0.3
                if claim.get("epistemic_tag") in VALID_TAGS:
                    score += 0.3
                # Confidence in range: 0.2
                conf = claim.get("confidence", -1)
                if isinstance(conf, (int, float)) and 0 <= conf <= 1:
                    score += 0.2
                # Status consistency: 0.2
                missing = claim.get("missing_fields", [])
                status = claim.get("status", "")
                if (missing and status == "Incomplete") or (not missing and status == "Complete"):
                    score += 0.2
            
            if claims:
                score /= len(claims)
        except:
            pass
        rewards.append(score)
    return rewards

# ── Reward Function 3: Qualifier Preservation ──
HEDGING_WORDS = {"may", "might", "could", "suggests", "possibly", "potentially",
                 "appears", "seems", "likely", "unlikely", "not significant"}

def qualifier_preservation_reward(completions, prompts, **kwargs):
    """Reward for preserving hedging language from source text."""
    rewards = []
    for completion, prompt in zip(completions, prompts):
        content = completion[0]["content"] if isinstance(completion, list) else completion
        prompt_text = prompt[0]["content"] if isinstance(prompt, list) else prompt
        
        # Find hedging words in source
        source_hedges = {w for w in HEDGING_WORDS if w in prompt_text.lower()}
        if not source_hedges:
            rewards.append(0.5)  # Neutral if no hedging in source
            continue
        
        # Check if hedging is preserved in extraction
        try:
            data = json.loads(content)
            claims = data if isinstance(data, list) else data.get("claims", [data])
            claim_text = " ".join(c.get("text", "") for c in claims if isinstance(c, dict)).lower()
            
            preserved = sum(1 for h in source_hedges if h in claim_text)
            rewards.append(preserved / len(source_hedges))
        except:
            rewards.append(0.0)
    return rewards

# ── GRPO Training ──
trainer = GRPOTrainer(
    model="./research-os-dpo",  # From stage 2
    reward_funcs=[
        json_validity_reward,        # Weight: 0.3
        schema_compliance_reward,    # Weight: 0.4
        qualifier_preservation_reward, # Weight: 0.3
    ],
    args=GRPOConfig(
        output_dir="./research-os-grpo",
        learning_rate=1e-6,
        num_generations=8,
        max_completion_length=2048,
        bf16=True,
        gradient_checkpointing=True,
        logging_steps=10,
        push_to_hub=True,
        hub_model_id="nkshirsa/phd-research-os-brain-v2",
        reward_weights=[0.3, 0.4, 0.3],
    ),
    train_dataset=prompt_dataset,  # "prompt" column with paper excerpts
    peft_config=LoraConfig(r=64, target_modules="all-linear"),
)
trainer.train()

Stage 4: Calibration Fine-Tuning (ConfTuner)

After GRPO, apply ConfTuner with tokenized Brier score loss to fix confidence calibration. This is a specialized fine-tuning pass that targets only the confidence output tokens.

---

4. Layer Specifications

4.0 Layer 0: Structural Ingestion Engine

Purpose: Convert PDF bundles into section-aware, bbox-annotated, quality-scored structured regions.

Technology Stack:

Component	Tool	Purpose
Layout detection	Marker (VikParuchuri/marker)	PDF → structured markdown with layout awareness
Math/equation	Nougat (facebookresearch/nougat)	Scientific PDFs → LaTeX equations
Bibliographic	GROBID	Headers, authors, citations, references
Region classifier	LayoutLMv3 or DocTR	Classify page regions: text, table, figure, equation
Plot digitizer	PlotDigitizer (algorithmic)	Quantitative plots → CSV of (x,y) coordinates
VLM for figures	Qwen3-VL-8B-Instruct Q4 AWQ	Semantic figure understanding
OCR quality	Per-span confidence scoring	Flag degraded regions

Output Schema (per region):

{
  "region_id": "REG_00042",
  "document_type": "main|supplement_1|supplement_2",
  "page": 5,
  "bbox": [72, 340, 540, 420],
  "region_type": "body_text|table|figure|equation|caption|header|reference|footnote",
  "section": "results",
  "subsection": "3.2_sensitivity_characterization",
  "content": {
    "text": "The LOD was 0.8 ± 0.03 fM (Table 2)",
    "markdown": "The LOD was 0.8 ± 0.03 fM ([Table 2](#table-2))",
    "parse_method": "marker",
    "parse_confidence": 0.95,
    "ocr_source": false
  },
  "cross_references": [
    {"ref_text": "Table 2", "ref_type": "table", "resolved_to": "REG_00038", "verified": true}
  ],
  "extraction_status": "extractable|low_confidence|unextractable",
  "quality_flags": [],
  "figures": {
    "detected": true,
    "figure_type": "scatter_plot|bar_chart|diagram|micrograph|schematic",
    "digitizable": true,
    "digitized_data": null
  }
}

Chunking Strategy: Section-aware, NOT page-based.

1. Marker identifies section boundaries (Introduction, Methods, Results subsections)
2. Chunk by section with 1-paragraph overlap to preceding and following sections
3. Tables always kept whole (never split across chunks)
4. Figure + caption always kept together
5. Maximum chunk size: 4096 tokens (model context allows it)

Paper Bundle Handling:

Input: {
  "main_pdf": "path/to/paper.pdf",
  "supplements": ["path/to/supplement_1.pdf", "path/to/supplement_data.xlsx"],
  "code_repo": "https://github.com/author/repo",
  "dataset": "https://zenodo.org/record/12345"
}

4.1 Layer 1: Entity Resolution

Purpose: Normalize entities, resolve citations, check retractions, establish version lineage.

Components:

Entity Normalizer
  ├── Gene/protein names → UniProt ID
  ├── Chemical names → PubChem CID
  ├── Disease names → MeSH ID
  ├── Assay names → BAO ontology
  ├── Abbreviations → canonical form (LRU cache)
  └── Custom domain ontology (user-extensible)

Citation Chain Resolver
  ├── In-text "[32]" → reference list → DOI
  ├── DOI → CrossRef metadata
  ├── Check: is cited paper in knowledge base?
  ├── If yes: link claim to original source
  ├── If no: flag as "citation_orphan" for potential ingestion
  └── Classify: primary claim vs inherited citation

Version of Record (VoR) Lineage
  ├── Before ingestion: query DOI/arXiv for version chain
  ├── If preprint exists in DB and VoR arriving: supersede
  ├── If VoR exists and erratum arriving: amend specific claims
  ├── If retraction: invalidate ALL claims, propagate penalty
  └── Store full lineage: preprint_doi → vor_doi → errata → retraction

Retraction Checker
  ├── CrossRef "update-to" relationship
  ├── Retraction Watch database (periodic sync via companion model)
  └── Propagate retraction status through citation chains

4.2 Layer 2: Qualified Extraction

Purpose: Extract claims with full epistemic qualification using the AI Model Council.

Council Architecture (Parallel-Then-Merge):

Round 1 (PARALLEL — no visibility between members):
  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐
  │ Query Planner│  │  Extractor   │  │  Extractor 2 │  │   Critic     │
  │ (decompose)  │  │ (Qwen3-8B)  │  │ (if heterog.)│  │ (adversarial)│
  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘
         │                 │                 │                 │
         ▼                 ▼                 ▼                 ▼
  sub-queries         claims_A          claims_B          critique

Round 2 (DEBATE — see tags and reasoning, NOT confidence):
  All members see each other's epistemic tags and reasoning chains
  Each member can revise their classification
  Confidence scores remain HIDDEN (prevents anchoring)

Round 3 (SYNTHESIS — Chairman):
  Chairman sees everything including confidence
  Applies completeness penalty (code-enforced, not prompt-instructed)
  Resolves disagreements with documented reasoning
  Tags each claim with council_vote_distribution

Epistemic Separation Engine:

Section	Epistemic Default	Confidence Modifier
Results (with statistics)	Fact (if p < threshold)	1.0
Results (narrative)	Interpretation	0.85
Methods	Protocol metadata (not a claim)	N/A
Abstract	Interpretation (forced)	0.7 penalty
Discussion	Interpretation or Hypothesis	0.75 penalty
Conclusion	Cross-check against Results	0.8 if supported, 0.5 if not
Supplement	Same as main body section rules	1.0 (no penalty for supplement source)

Constrained Decoding (Guidance engine):

from guidance import models, gen, select

TAGS = ["Fact", "Interpretation", "Hypothesis", "Conflict_Hypothesis"]

lm = models.Transformers("./research-os-grpo")  # Local model

with lm:
    output = lm + f"""
    Analyze this scientific text and extract claims.
    
    Text: {section_text}
    Section: {section_name}
    
    <reasoning>{gen("reasoning", max_tokens=500)}</reasoning>
    
    Claims:
    [
      {{
        "text": "{gen("claim_text", max_tokens=200)}",
        "epistemic_tag": "{select(TAGS, name="tag")}",
        "confidence_components": {{
          "evidence_strength": {gen("ev_str", regex=r"0\.[0-9][0-9]?[0-9]?", name="evidence")},
          "qualifiers": ["{gen("qualifiers", max_tokens=100)}"]
        }},
        "source_quote": "{gen("source_quote", max_tokens=200)}",
        "source_page": {gen("page", regex=r"[0-9]+", name="page")},
        "is_null_result": {select(["true", "false"], name="is_null")},
        "is_inherited_citation": {select(["true", "false"], name="is_inherited")}
      }}
    ]
    """
# output["tag"] is GUARANTEED to be in TAGS
# output["is_null"] is GUARANTEED to be boolean

Claim Schema v2 (expanded from v1):

{
  "claim_id": "CLM_00042",
  "text": "The LOD was 0.8 fM in 10 mM PBS",
  "epistemic_tag": "Fact",
  "confidence": 0.855,
  "confidence_components": {
    "evidence_strength": 900,
    "study_quality_weight": 1000,
    "journal_tier_weight": 1000,
    "completeness_penalty": 1000,
    "section_modifier": 1000,
    "qualifier_penalty": 950
  },
  "qualifiers": ["in 10 mM PBS only", "n=5"],
  "missing_fields": [],
  "status": "Complete",
  "is_null_result": false,
  "is_inherited_citation": false,
  "causal_direction": "observed_correlation",
  "statistical_evidence": {
    "p_value": 0.001,
    "effect_size": 2.1,
    "effect_size_type": "cohens_d",
    "sample_size": 5,
    "confidence_interval": [0.6, 1.0],
    "practical_significance": true
  },
  "source_quote": "The limit of detection was determined to be 0.8 fM using the 3σ/slope method.",
  "source_page": 5,
  "source_bbox": [72, 340, 540, 365],
  "source_section": "results",
  "source_doi": "10.1234/example",
  "council_vote": {
    "extractor_1": {"tag": "Fact", "reasoning": "Direct measurement with statistics"},
    "extractor_2": {"tag": "Fact", "reasoning": "Quantitative with clear methodology"},
    "critic": {"tag": "Fact", "reasoning": "Supported by Table 2 data"},
    "chairman": {"tag": "Fact", "reasoning": "Unanimous agreement, strong statistics"}
  },
  "granularity": "atomic",
  "parent_claim_id": null,
  "sub_claims": [],
  "ontology_version": "quantum_bio_v1",
  "pipeline_version": "2.1.0",
  "taxonomy_version": "quantum_bio_v1",
  "extraction_timestamp": "2026-04-23T10:30:00Z"
}

4.3 Layer 3: Canonicalization

Purpose: Deduplicate claims, merge aliases, aggregate evidence, track temporal versions.

New claim arrives →
  1. Embed claim text (local embedding model or Qwen3-8B last-hidden-state)
  2. Search existing canonical claims (cosine similarity)
  3. If similarity > 0.85:
     ├── MERGE: Add new source as evidence for existing canonical claim
     ├── Update evidence_count, source_list, confidence (re-aggregate)
     ├── If confidence_components differ significantly: flag for human review
     └── Store alias mapping: new_claim_id → canonical_claim_id
  4. If similarity 0.70-0.85:
     ├── FLAG as "potential duplicate — review recommended"
     └── Show both claims in review queue with similarity score
  5. If similarity < 0.70:
     └── CREATE new canonical claim

Temporal Versioning:

canonical_claim:
  version_history: [
    {version: 1, source: "preprint_2024", confidence: 0.65, date: "2024-03"},
    {version: 2, source: "vor_2024", confidence: 0.85, date: "2024-09"},
    {version: 3, source: "new_study_2025", confidence: 0.90, date: "2025-02"}
  ]
  current_version: 3
  supersedes: null
  superseded_by: null

4.4 Layer 4: Knowledge Graph

Implementation: SQLite-backed adjacency list (NOT Neo4j — keeps the system local and zero-dependency).

Schema:

CREATE TABLE graph_nodes (
    node_id TEXT PRIMARY KEY,       -- canonical_claim_id or entity_id
    node_type TEXT NOT NULL,        -- claim | entity | method | condition
    label TEXT NOT NULL,
    properties TEXT,                -- JSON
    created_at TEXT NOT NULL
);

CREATE TABLE graph_edges (
    edge_id TEXT PRIMARY KEY,
    source_node TEXT NOT NULL,
    target_node TEXT NOT NULL,
    edge_type TEXT NOT NULL,        -- supports | refutes | extends | depends_on |
                                    -- supersedes | blocks | investigative_hypothesis |
                                    -- method_uses | condition_applies
    confidence INTEGER NOT NULL,    -- Fixed-point ×1000
    evidence_sources TEXT,          -- JSON array of source DOIs
    is_inferred INTEGER DEFAULT 0,  -- 0=observed, 1=inferred (transitive)
    inference_chain TEXT,           -- JSON: hop details if inferred
    method_compatible INTEGER,      -- NULL=unchecked, 0=incompatible, 1=compatible
    created_at TEXT NOT NULL,
    updated_at TEXT NOT NULL,
    FOREIGN KEY(source_node) REFERENCES graph_nodes(node_id),
    FOREIGN KEY(target_node) REFERENCES graph_nodes(node_id)
);

-- Index for fast graph traversal
CREATE INDEX idx_edges_source ON graph_edges(source_node);
CREATE INDEX idx_edges_target ON graph_edges(target_node);
CREATE INDEX idx_edges_type ON graph_edges(edge_type);

Edge Types:

Type	Meaning	Confidence Rule
supports	Claim A provides evidence for Claim B	From source text, observed
refutes	Claim A contradicts Claim B	From source text or conflict detection
extends	Claim A adds conditions/parameters to B	Section analysis
depends_on	Claim A assumes Claim B is true	Citation chain analysis
supersedes	Claim A replaces older Claim B (newer data)	Temporal versioning
blocks	Null finding: no evidence of relationship	Null result extraction
investigative_hypothesis	Inferred multi-hop (NOT observed)	min(hop_confidences) × 0.5

Transitive Inference Constraints:

• NEVER auto-generate supports across multiple hops
• Only investigative_hypothesis edges for multi-hop
• Require method_compatible=1 for each hop before generating inference
• Default queries return observed edges only
• include_inferred=True flag required for graph queries that include inferences

Gap Analysis Protocol:

def find_gaps(self, domain_id: str) -> list:
    """Find structural holes in the knowledge graph."""
    # 1. Get all entities in domain
    entities = self.get_entities(domain_id)
    
    # 2. For each entity pair in same domain
    for a, b in combinations(entities, 2):
        # 3. Check if edge exists
        edges = self.get_edges(a.id, b.id)
        if not edges:
            # 4. Check if both are well-connected (dense neighborhood)
            a_degree = self.get_degree(a.id)
            b_degree = self.get_degree(b.id)
            if a_degree > 3 and b_degree > 3:
                # 5. This is a high-value gap
                info_gain = (a_degree + b_degree) / max_degree
                gaps.append({
                    "entity_a": a, "entity_b": b,
                    "information_gain": info_gain,
                    "suggested_action": "experiment" if info_gain > 0.7 else "literature_search"
                })
    
    return sorted(gaps, key=lambda g: -g["information_gain"])

4.5 Layer 5: Calibrated Scoring

Purpose: Compute confidence using CODE, not LLM. Three separate scores.

def compute_claim_scores(claim: dict, source: dict, section: str) -> dict:
    """
    Code-computed scoring. The LLM provides COMPONENTS, 
    the code computes the FINAL SCORES.
    
    The LLM NEVER sets the final confidence directly.
    """
    # ── Score 1: Evidence Quality ──
    evidence_strength = claim["confidence_components"]["evidence_strength"]  # From LLM
    study_quality = taxonomy.get_weight(source["study_type"], domain_id)     # From taxonomy
    journal_tier = JOURNAL_TIER_WEIGHTS[source["journal_tier"]]              # From config
    completeness = 700 if claim["missing_fields"] else 1000                  # Binary: code enforced
    section_mod = SECTION_MODIFIERS[section]                                  # From config
    
    # Fixed-point multiplication chain
    evidence_quality = (evidence_strength * study_quality // 1000 
                       * journal_tier // 1000 
                       * completeness // 1000
                       * section_mod // 1000)
    
    # ── Score 2: Claim Truth Likelihood ──
    # Based on evidence quality + source count + conflict status
    source_count_bonus = min(claim["evidence_count"] * 50, 200)  # Max +0.2 for multiple sources
    conflict_penalty = -300 if claim.get("has_active_conflict") else 0
    null_evidence_penalty = -200 if claim.get("has_null_evidence") else 0
    
    truth_likelihood = min(1000, max(0,
        evidence_quality + source_count_bonus + conflict_penalty + null_evidence_penalty
    ))
    
    # ── Score 3: Qualifier Strength ──
    # How definitive is the claim's language?
    qualifier_count = len(claim.get("qualifiers", []))
    is_null = claim.get("is_null_result", False)
    is_inherited = claim.get("is_inherited_citation", False)
    
    qualifier_strength = 1000
    if qualifier_count > 0:
        qualifier_strength -= qualifier_count * 100  # -0.1 per qualifier
    if is_null:
        qualifier_strength = min(qualifier_strength, 500)  # Cap at 0.5 for null results
    if is_inherited:
        qualifier_strength -= 200  # -0.2 for inherited citations
    qualifier_strength = max(0, qualifier_strength)
    
    # ── Statistical Evidence Gate ──
    stats = claim.get("statistical_evidence", {})
    if stats.get("effect_size") is not None:
        effect = stats["effect_size"]
        sample_n = stats.get("sample_size", 0)
        
        # Large N + tiny effect = statistically significant but practically meaningless
        if sample_n > 1000 and abs(effect) < 0.1:
            # Override: this is NOT practically significant
            evidence_quality = min(evidence_quality, 400)  # Cap at 0.4
            claim["practical_significance"] = False
    
    # ── Parser Confidence Propagation ──
    parse_conf = claim.get("parse_confidence", 1000)
    evidence_quality = min(evidence_quality, parse_conf)  # Parser uncertainty CAPS claim
    
    return {
        "evidence_quality": evidence_quality,            # Fixed-point ×1000
        "truth_likelihood": truth_likelihood,            # Fixed-point ×1000
        "qualifier_strength": qualifier_strength,        # Fixed-point ×1000
        "composite_confidence": (evidence_quality + truth_likelihood + qualifier_strength) // 3,
        "practical_significance": claim.get("practical_significance", True),
    }

4.6 Layer 6: Evaluation

Evaluation Pipeline (runs in CI/CD on every prompt/model/taxonomy change):

1. STRUCTURAL TESTS (existing 119 tests — code correctness)
   └── pytest tests/ → all pass?

2. GOLDEN DATASET REGRESSION (versioned annotations)
   ├── Extraction recall ≥ 70%
   ├── Hallucination rate ≤ 10%
   ├── Epistemic accuracy ≥ 60%
   ├── Qualifier preservation rate ≥ 80% (NEW)
   └── Null result detection rate ≥ 50% (NEW)

3. LLM-AS-JUDGE (faithfulness & grounding)
   ├── Faithfulness: does extracted claim appear in source text?
   ├── Grounding: can claim be traced to specific source quote?
   ├── Tag correctness: does epistemic tag match expert judgment?
   ├── Qualifier preservation: are hedging words maintained?
   └── Run on 5 golden papers, 3 times each (stochastic check)

4. CALIBRATION CHECK (monthly)
   ├── Brier score from calibration_log
   ├── Alert if ECE > 0.25
   └── Trigger ConfTuner re-training if needed

5. HIDDEN HOLDOUT (never seen during development)
   ├── 3 papers reserved, never used in training or golden set
   ├── Evaluated quarterly
   └── Detects benchmark overfitting

Versioned Annotation Guidelines:

/evaluation/
├── guidelines_v1.0.md           # Annotation rules (version controlled)
├── golden_dataset/
│   ├── paper_001.json           # Annotated under guidelines v1.0
│   ├── paper_002.json           # Annotated under guidelines v1.0
│   └── paper_006.json           # Annotated under guidelines v1.1
├── frozen_anchors/              # NEVER re-annotated
│   ├── paper_001_frozen.json
│   └── paper_002_frozen.json
└── holdout/                     # NEVER seen during development
    ├── paper_H1.json
    └── paper_H2.json

4.7 Layer 7: Provenance & Reproducibility

Output Lineage (every claim tagged):

{
  "pipeline_version": "2.1.0",
  "model_checkpoint": "research-os-grpo-v2-step-5000",
  "parser_version": "marker-1.2.0",
  "taxonomy_version": "quantum_bio_v1",
  "prompt_hash": "sha256:a3b4c5...",
  "extraction_timestamp": "2026-04-23T10:30:00Z",
  "guidance_schema_version": "1.0"
}

Security Sandbox (for repository validation):

┌─── SANDBOX (isolated from main system) ─────────────────┐
│  • Timeout: 60 seconds max per URL check                 │
│  • Network: HTTP GET only, no POST/PUT/DELETE             │
│  • Download limit: 100MB per artifact                    │
│  • No code execution (dry-run validation only)           │
│  • Actual code execution requires human authorization    │
│  • Credential isolation: no access to main DB or API keys│
└──────────────────────────────────────────────────────────┘

Epistemic Embargo (for IP protection):

User creates "Private Graph" → 
  All claims extracted in this mode go to private subgraph →
  Private subgraph is NOT visible to other users / companion agents →
  After paper submission: user clicks "Merge to Lab Graph" →
  Claims move from private to shared graph with full provenance

---

5. UI Architecture

5.1 Courtroom UI (Conflict Resolution)

Default View (Review Queue):
  ⚠️ 3-way conflict detected — Debye screening threshold
  Papers: Chen 2022, Nakamura 2023, Williams 2024
  Comparability confidence: 0.58 (method differences detected)
  [Review] [Defer] [Dismiss]

Expanded View (Courtroom — click to open):
  ┌─────────────┬─────────────┬─────────────┐
  │ Chen 2022   │ Nakamura 23 │ Williams 24 │
  │ ACS Nano T1 │ Biosens. T1 │ Sensors T3  │
  ├─────────────┼─────────────┼─────────────┤
  │ Claim text  │ Claim text  │ Claim text  │
  │ (nestable)  │ (nestable)  │ (nestable)  │
  ├─────────────┼─────────────┼─────────────┤
  │ Method box  │ Method box  │ Method box  │
  │ N=5 p<.001  │ N=12 p<.01 │ N=3 p=.12  │
  │ [PDF📄]     │ [PDF📄]     │ [PDF📄]     │
  └─────────────┴─────────────┴─────────────┘
  
  System Analysis (Level 5 — unverified):
  "These claims are not directly comparable..."
  Confidence in analysis: 0.62
  
  Council Votes: Ext1: scope_diff | Ext2: value_mismatch | Critic: scope_diff
  
  [Agree] [Override with custom] [Defer — need more info]
  
  ⚠️ Missing competitor evidence:
  "3 papers cited by these sources are not yet ingested"
  [Ingest Park 2023] [Ingest Liu 2024] [Ingest Fernandez 2023]

5.2 Progressive Disclosure Levels

Level 0: Dashboard
  Epistemic Health Score per claim cluster
  Today's review queue (priority-ranked)
  
Level 1: Claim Detail
  Text + tag + composite confidence + source
  [Expand to see scoring breakdown]
  
Level 2: Scoring Breakdown
  3 separate scores (evidence, truth, qualifier)
  Statistical evidence if available
  Parser confidence for this region
  
Level 3: Provenance Chain
  Source quote + page + bbox
  Council vote distribution
  Pipeline version + model checkpoint
  
Level 4: Graph Neighborhood
  2-hop subgraph around this claim
  Typed edges visible
  Inferred edges dashed + labeled
  
Level 5: Full Debug
  Raw LLM outputs from each council member
  Token-level confidence distribution
  Parse regions and quality flags

5.3 Manual Synthesis Mode

[Toggle] 🧠 Manual Synthesis Mode: ON

In this mode:
  ✅ Claims displayed (text + source)
  ✅ Organized by topic clusters
  ❌ NO confidence scores shown
  ❌ NO conflict flags shown  
  ❌ NO gap analysis shown
  ❌ NO system suggestions
  
  The researcher draws connections manually.
  Then switches back to compare with system's analysis.

---

6. Local Deployment

6.1 Minimal Setup (16GB VRAM)

# 1. Install Ollama (simplest local LLM server)
curl -fsSL https://ollama.com/install.sh | sh

# 2. Pull quantized model (after fine-tuning and uploading GGUF)
ollama pull nkshirsa/research-os-brain:q4_k_m

# 3. Verify it's running
curl http://localhost:11434/api/generate -d '{"model": "research-os-brain:q4_k_m", "prompt": "test"}'

# 4. Start the Research OS
pip install -r requirements.txt
python -m phd_research_os.serve --model ollama://research-os-brain:q4_k_m --port 8080

# 5. Open UI
# http://localhost:8080

6.2 VRAM Budget

Qwen3-8B Q4 AWQ weights:     ~5.0 GB
PolarQuant KV cache (128K):   ~3.8 GB
Qwen3-VL-8B Q4 (for figures): ~5.0 GB (loaded on-demand, not persistent)
Guidance engine overhead:      ~0.5 GB
ChromaDB embeddings:           ~0.5 GB
──────────────────────────────────────
Total (text only):             ~9.8 GB  ← fits 16GB GPU
Total (with VLM loaded):      ~14.8 GB  ← fits 16GB GPU (tight)
Total (with VLM on-demand):    ~9.8 GB  ← swap VLM in/out per figure

---

7. Data Flow (Complete Pipeline)

PDF Bundle arrives
  │
  ▼
LAYER 0: Structural Ingestion
  ├── Marker: layout-aware markdown with section boundaries
  ├── Nougat: equations → LaTeX (routed by region classifier)
  ├── GROBID: references → structured citations
  ├── Figure regions → classify → VLM (semantic) or Digitizer (quantitative)
  ├── Per-region quality scoring (parse_confidence, ocr_confidence)
  ├── Cross-reference verification (Figure 3 → correct figure object?)
  └── Output: list of annotated regions with bbox, section, quality
  │
  ▼
LAYER 1: Entity Resolution
  ├── Normalize entities (gene names, chemicals, assays → canonical IDs)
  ├── Resolve in-text citations ([32] → DOI → metadata)
  ├── Check VoR lineage (is this a preprint we already have?)
  ├── Check retraction status (CrossRef + Retraction Watch)
  └── Tag: primary vs inherited claims
  │
  ▼
LAYER 2: Qualified Extraction (AI Model Council)
  ├── Round 1 (parallel): Query Planner + 2 Extractors + Critic
  │   Each independently processes section-aware chunks
  │   Guidance engine enforces: valid JSON, valid tags, valid ranges
  │   Section modifier applied (Abstract=0.7, Results=1.0, Discussion=0.75)
  ├── Round 2 (debate): Share tags + reasoning (NOT confidence)
  ├── Round 3 (chairman): Synthesize final claims
  │   Apply completeness penalty (code-enforced: 0.7 if missing fields)
  │   Preserve qualifiers from source text
  │   Extract statistical evidence (N, p, d, CI)
  │   Tag null results, inherited citations, causal direction
  └── Output: list of qualified claims with full provenance
  │
  ▼
LAYER 3: Canonicalization
  ├── Embed each new claim
  ├── Compare against existing canonical claims (cosine > 0.85 = merge)
  ├── Merge: add source as evidence, update confidence aggregation
  ├── Create: new canonical claim with first source
  └── Temporal versioning: if same claim from VoR supersedes preprint version
  │
  ▼
LAYER 4: Knowledge Graph
  ├── Insert claim as graph node
  ├── Create edges from citation analysis (supports, depends_on)
  ├── Run conflict detector (keyword + embedding similarity for candidates)
  ├── Council evaluates candidate conflicts → typed edges (refutes, scope_diff)
  ├── Check for null evidence → blocking edges
  ├── Update method-compatibility metadata on edges
  ├── Cluster related conflicts into case files
  └── Run gap analysis (if in Research Landscape mode)
  │
  ▼
LAYER 5: Calibrated Scoring (CODE-COMPUTED)
  ├── evidence_quality = evidence × quality × tier × completeness × section
  ├── truth_likelihood = evidence_quality + source_bonus - conflict_penalty
  ├── qualifier_strength = 1.0 - qualifier_count×0.1 - null_penalty - inherited_penalty
  ├── Statistical evidence gate: large N + tiny effect → cap confidence
  ├── Parser confidence propagation: parse_confidence caps evidence_quality
  └── Store all 3 scores + composite on claim
  │
  ▼
LAYER 6: Evaluation (on config change)
  ├── Regression gate against golden dataset
  ├── LLM-as-Judge faithfulness + grounding check
  ├── Brier score monitoring (monthly)
  └── Hidden holdout benchmark (quarterly)
  │
  ▼
LAYER 7: Provenance
  ├── Tag claim with full pipeline version lineage
  ├── Store bbox + source quote for UI traceability
  └── Export: Obsidian vault, Courtroom UI, CSV, BibTeX

---

8. Implementation Phases (Aligned with PhD Timeline)

Phase A: Foundation (Weeks 1-6) — MUST BE FIRST

Week	Task	Deliverable
1-2	Integrate Marker for PDF → structured markdown	Section-aware regions with bbox
3	Add Nougat routing for equation-heavy regions	LaTeX preservation
4	Implement section-aware chunking (replace page-based)	Semantic chunks
5	Add quality scoring per-region	parse_confidence on every span
6	Integrate Guidance engine for constrained decoding	Guaranteed valid JSON output

Phase B: Identity (Weeks 7-12)

Week	Task	Deliverable
7-8	Claim canonicalization with embedding dedup	Canonical registry
9	Entity normalization (abbreviations, synonyms)	Ontology mapper
10-11	Citation chain resolution ([32] → DOI)	Primary vs inherited tagging
12	VoR lineage detection	Preprint → VoR superseding

Phase C: Structure (Weeks 13-20)

Week	Task	Deliverable
13-14	SQLite-backed knowledge graph with typed edges	Graph schema + CRUD
15-16	Qualifier preservation + null result handling	Blocking edges
17-18	Method-compatibility layer	Comparability confidence
19-20	Conflict clustering into case files	Case file UI

Phase D: Calibration (Weeks 21-26)

Week	Task	Deliverable
21-22	Epistemic Separation Engine (section modifiers)	Section-aware scoring
23-24	Statistical evidence extraction (N, p, d, CI)	Practical significance gate
25-26	GRPO training with epistemic reward functions	Trained model v2

Phase E: Judgment (Weeks 27-32)

Week	Task	Deliverable
27-28	Courtroom UI with PDF.js bounding box viewer	Provenance display
29-30	Council parallel-then-merge architecture	Hidden confidence protocol
31-32	Conflict clustering + case file resolution	Batch conflict resolution

Phase F: Longevity (Ongoing, PhD Year 1+)

Task	Trigger
Versioned ontology with backward-compatible queries	3rd taxonomy update
VoR lineage tracking	First preprint → VoR encounter
Ongoing Brier calibration monitoring	50+ calibration data points
Gold-standard drift detection	2nd annotation batch
Gap Analysis Protocol	100+ papers ingested
Manual Synthesis Mode	Thesis writing phase

---

9. File Structure (v2.0)

phd-research-os/
├── SYSTEM_DESIGN.md                    # THIS DOCUMENT
├── BLINDSPOT_AUDIT_COMPLETE.md         # 87-blindspot audit
│
├── phd_research_os/                    # Core Python package
│   ├── __init__.py
│   │
│   ├── layer0/                         # Structural Ingestion
│   │   ├── parser.py                   # Marker + Nougat + GROBID orchestrator
│   │   ├── region_classifier.py        # LayoutLMv3 region classification
│   │   ├── chunker.py                  # Section-aware chunking
│   │   ├── figure_router.py            # VLM vs Digitizer routing
│   │   ├── plot_digitizer.py           # Quantitative plot → CSV
│   │   ├── quality_scorer.py           # Per-span quality scoring
│   │   └── cross_ref_verifier.py       # Figure/Table reference integrity
│   │
│   ├── layer1/                         # Entity Resolution
│   │   ├── entity_normalizer.py        # Ontology-aware normalization
│   │   ├── citation_resolver.py        # In-text [32] → DOI
│   │   ├── vor_lineage.py              # Version of Record tracking
│   │   └── retraction_checker.py       # CrossRef + Retraction Watch
│   │
│   ├── layer2/                         # Qualified Extraction
│   │   ├── council.py                  # Parallel-then-merge council (upgraded)
│   │   ├── epistemic_separator.py      # Abstract vs Results scoring
│   │   ├── qualifier_extractor.py      # Hedging, negation, conditions
│   │   ├── statistical_extractor.py    # N, p, d, CI extraction
│   │   ├── constrained_decoder.py      # Guidance engine integration
│   │   └── ood_detector.py             # Mahalanobis distance OOD gating
│   │
│   ├── layer3/                         # Canonicalization
│   │   ├── deduplicator.py             # Embedding-based near-duplicate detection
│   │   ├── canonical_registry.py       # Canonical claim management
│   │   ├── alias_merger.py             # Alias mapping and merging
│   │   └── temporal_versioner.py       # Claim version history
│   │
│   ├── layer4/                         # Knowledge Graph
│   │   ├── graph.py                    # SQLite-backed graph with typed edges
│   │   ├── conflict_detector.py        # Pairwise conflict detection (upgraded)
│   │   ├── conflict_clusterer.py       # Case file generation
│   │   ├── method_compatibility.py     # Cross-paper method comparison
│   │   ├── gap_analyzer.py             # Structural hole detection
│   │   └── transitive_constraints.py   # Multi-hop inference safety
│   │
│   ├── layer5/                         # Calibrated Scoring
│   │   ├── scorer.py                   # Code-computed 3-score system
│   │   ├── statistical_gate.py         # Effect size / practical significance
│   │   ├── section_modifiers.py        # Abstract/Results/Discussion weights
│   │   └── calibration_monitor.py      # Brier score tracking
│   │
│   ├── layer6/                         # Evaluation
│   │   ├── regression_gate.py          # Golden dataset regression
│   │   ├── llm_judge.py               # Faithfulness/grounding evaluation
│   │   ├── stochastic_tester.py        # Run-N-times variance check
│   │   └── annotation_drift.py         # Gold-standard drift detection
│   │
│   ├── layer7/                         # Provenance
│   │   ├── lineage_tagger.py           # Pipeline version tagging
│   │   ├── security_sandbox.py         # Isolated URL/repo validation
│   │   ├── license_checker.py          # Usage rights verification
│   │   └── embargo_manager.py          # Private graph / merge workflow
│   │
│   ├── ui/                             # Gradio UI
│   │   ├── app.py                      # Main application
│   │   ├── courtroom.py                # Conflict resolution courtroom
│   │   ├── dashboard.py                # Epistemic health dashboard
│   │   ├── pdf_viewer.py               # PDF.js with bbox highlighting
│   │   ├── manual_synthesis.py         # AI-free exploration mode
│   │   └── export.py                   # CSV, BibTeX, JSON, Obsidian export
│   │
│   ├── core/                           # Shared infrastructure
│   │   ├── db.py                       # SQLite data layer (existing, extended)
│   │   ├── taxonomy.py                 # Quantum-Bio V2 (existing)
│   │   ├── agents.py                   # Brain interface (existing, upgraded)
│   │   ├── agent_os.py                 # ECC Harness (existing)
│   │   ├── meta_improver.py            # Meta-Improver (existing)
│   │   └── skills/                     # Superpowers (existing)
│   │
│   ├── training/                       # Model training
│   │   ├── train_sft.py                # Stage 1: SFT
│   │   ├── train_dpo.py                # Stage 2: DPO
│   │   ├── train_grpo.py              # Stage 3: GRPO with epistemic rewards
│   │   ├── train_calibration.py        # Stage 4: ConfTuner
│   │   ├── reward_functions.py         # JSON validity, schema, qualifier rewards
│   │   └── generate_dataset.py         # Synthetic + real data generation
│   │
│   └── config/                         # Version-controlled configuration
│       ├── prompts/                    # All system prompts (git-tracked)
│       ├── taxonomy/                   # Domain taxonomies
│       ├── scoring/                    # Weight tables, thresholds
│       └── evaluation/                 # Golden dataset + guidelines
│
├── tests/
│   ├── test_layer0.py                  # Structural ingestion tests
│   ├── test_layer1.py                  # Entity resolution tests
│   ├── test_layer2.py                  # Extraction tests
│   ├── test_layer3.py                  # Canonicalization tests
│   ├── test_layer4.py                  # Knowledge graph tests
│   ├── test_layer5.py                  # Scoring tests
│   ├── test_layer6.py                  # Evaluation tests
│   ├── test_layer7.py                  # Provenance tests
│   ├── test_db.py                      # Data layer (existing 22 tests)
│   ├── test_agent_os.py                # ECC harness (existing 21 tests)
│   ├── test_taxonomy.py                # Taxonomy (existing 27 tests)
│   ├── test_skills_and_meta.py         # Skills + meta (existing 30 tests)
│   └── test_council.py                 # Council (existing 19 tests)
│
└── docs/
    ├── ARCHITECTURE.md                 # Project map (existing)
    ├── AGENTS.md                       # Agent registry (existing)
    ├── USAGE.md                        # Daily workflow guide
    ├── ANNOTATION_GUIDELINES.md        # Versioned golden dataset rules
    └── DEPLOYMENT.md                   # Local setup guide

---

10. Success Criteria

The system is DONE when:

1. A researcher can drop a PDF and get back epistemic-tagged claims with source bounding boxes in under 5 minutes
2. Two claims from different papers that say the same thing are automatically recognized as the same canonical claim
3. A null result creates a blocking edge, not a gap, in the knowledge graph
4. An Abstract claim that overstates the Results gets automatically penalized
5. The courtroom shows three conflicting papers side-by-side with method comparison and the researcher can resolve in 2 clicks
6. The gap analyzer identifies untested entity pairs and generates Decision Objects
7. The system knows when it doesn't know — OOD papers, unextractable regions, and uncalibrated confidence all surface to the human
8. All of the above works on a 16GB consumer GPU with zero internet dependency for paper processing

---

This design addresses all 87 blindspots from the complete audit. Implementation timeline: ~32 weeks pre-PhD + ongoing during PhD Year 1-3. The hardest part is not building it. It's keeping it honest.

---

Appendix A: Future Architecture Directions

Status: Research-Backed Design Proposals — Not Yet Implemented

The following sections describe architecture improvements validated by recent peer-reviewed research. Each addresses a specific bottleneck in the current v2.0 design. Implementation is targeted for Phase F (Longevity) or beyond.

---

A.1 Multi-Graph Agentic Memory (MAGMA Architecture)

Source: Jiang et al., MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents, arXiv:2601.03236

Problem: The current Layer 4 Knowledge Graph uses a single graph with typed edges (supports, refutes, extends, depends_on, supersedes, blocks, investigative_hypothesis). All relational information — semantic similarity, temporal ordering, causal inference, and entity references — is stored in one monolithic edge space. This entangles orthogonal dimensions of reasoning and limits interpretability. When a user asks "Why did the 2023 paper reach a different conclusion?", the system must traverse edges that mix temporal, causal, and semantic relationships without query-adaptive guidance.

MAGMA's Solution: Decouple memory representation into four orthogonal relation graphs over a shared node set:

Graph	Edge Semantics	Use Case in Research OS
Temporal Graph 𝒢_temp	Strictly ordered pairs (n_i, n_j) where τ_i < τ_j	Chronological claim evolution: preprint → VoR → erratum → retraction
Causal Graph 𝒢_causal	Directed edges representing logical entailment	"Because method X was used, result Y follows"
Semantic Graph 𝒢_sem	Undirected edges: cos(v_i, v_j) > θ_sim	Conceptually similar claims across different papers
Entity Graph 𝒢_ent	Bipartite edges: events ↔ abstract entity nodes	Object permanence: "LOD" entity linked to all claims mentioning it

Query-Adaptive Traversal: Instead of static graph lookups, MAGMA formulates retrieval as policy-guided traversal. A Router ℛ decomposes the user query into structured control signals:

1. Intent Classification T_q ∈ {Why, When, Entity, What} — "Why" queries bias traversal toward 𝒢_causal; "When" queries bias toward 𝒢_temp
2. Temporal Parsing [τ_s, τ_e] — hard time-window filter before graph traversal
3. Representation Extraction — dense embedding q→ for semantic anchor search + sparse keywords for lexical matching

Anchor Identification: Multi-signal fusion via Reciprocal Rank Fusion (RRF):

S_anchor = Top_K( Σ_{m ∈ {vec, key, time}} 1 / (k + r_m(n)) )

Adaptive Beam Search: From anchors, expand context using a dynamic transition score:

S(n_j | n_i, q) = exp( λ₁ · φ(type(e_ij), T_q)  [structural alignment]
                     + λ₂ · sim(n→_j, q→) )      [semantic affinity]

where φ rewards edge types matching the query intent (e.g., causal edges for "Why" queries).

Why This Is a Clear Improvement for the Research OS:

• The system already stores temporal, causal, and entity information — but crammed into a single edge_type column. MAGMA's separation makes each dimension independently queryable and interpretable.
• Long-horizon reasoning across hundreds of papers requires chronological traversal ("what did we believe in 2020 vs 2024?"), causal traversal ("what methods caused this result?"), and semantic traversal ("what else is like this?") — a single graph forces all three into one edge space.
• The policy-guided router aligns retrieval with the user's actual intent, rather than returning generic nearest-neighbor results.
• Experiments on LoCoMo (9K-token avg. conversations) and LongMemEval (100K+ token contexts) show consistent outperformance vs. monolithic memory baselines.

Implementation Path:

• Phase 1: Extend graph_edges schema to support graph_id ∈ {semantic, temporal, causal, entity} (SQLite migration)
• Phase 2: Implement Router ℛ as a lightweight classifier (can reuse Qwen3-8B with a classification head)
• Phase 3: Replace static get_edges() with policy-guided traversal engine
• Phase 4: Add adaptive λ₁, λ₂ weights tuned on researcher query logs

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A.2 Post-Transformer Model Architecture: The Linear-Scaling Era

Sources:

• Gu & Dao, Mamba: Linear-Time Sequence Modeling with Selective State Spaces, 2023
• Peng et al., RWKV: Reinventing RNNs for the Transformer Era, 2023
• Team et al., Jamba: A Hybrid Transformer-Mamba Language Model, 2024
• DeepSeek-AI, DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model, 2024
• Nazari et al., The Curious Case of In-Training Compression of State Space Models (CompreSSM), arXiv:2510.02823

Problem: The current Research OS is built on decoder-only Transformers (Qwen2.5-3B → Qwen3-8B). For 128K-context paper ingestion, the Transformer faces three scaling walls that become exponentially worse as the knowledge base grows:

Wall	Transformer Behavior	Impact on Research OS
Memory Wall	KV cache grows linearly with sequence length: 2 × n_layers × n_heads × d_head × seq_len bytes per batch item	At 128K context, KV cache alone consumes ~3.8GB. Processing 10 papers simultaneously exhausts 16GB VRAM before model weights are counted.
Compute Wall	Self-attention is O(n²) in sequence length. Doubling a paper's length quadruples attention compute.	Ingesting a 200K-token supplement (not uncommon in genomics) is 4× slower than a 100K-token paper, not 2×.
Energy Wall	Every new token requires attending to ALL previous tokens, even if 99% are irrelevant.	Long-term batch processing of paper libraries becomes prohibitively expensive on consumer hardware.

The Post-Transformer Landscape: Four validated architecture families replace the O(n²) bottleneck with O(n) or sub-quadratic scaling:

A.2.1 State Space Models (SSMs) — Mamba Family

Core Mechanism: Instead of "looking back" at every previous token (attention), SSMs compress history into a hidden state vector h(k+1) = A·h(k) + B·x(k). The state acts as a "speed-reader's memory" — a compressed summary of everything seen so far.

Why It Fits the Research OS:

• 5× throughput on consumer GPUs for long sequences (confirmed in Mamba benchmarks)
• Constant memory during inference: state dimension is fixed regardless of sequence length. No KV cache.
• Genomic-scale sequences: Mamba handles 1M+ token contexts (e.g., full genome sequences, large supplement bundles)
• Energy efficiency: State updates are matrix-vector products, not matrix-matrix attention operations

CompreSSM Enhancement (arXiv:2510.02823): A principled in-training compression framework for SSMs. Using Hankel singular value (HSV) analysis from control theory, CompreSSM identifies which state dimensions carry meaningful signal and surgically truncates low-energy dimensions during training. Key insight: SSMs trained large then compressed during training retain task-critical structure that models trained directly at small dimension lose.

Implication for Research OS Training: If the system migrates to an SSM backbone (e.g., a Mamba-based encoder for paper ingestion), CompreSSM enables:

• Start with a large state dimension (e.g., 256) for fast convergence
• Apply balanced truncation at fixed intervals during the first 10% of training
• End with a compact model (e.g., 32-dimensional state) that matches or exceeds the large model's performance
• Wall-clock speedup: Empirically validated ~2-4× faster training for equivalent final accuracy

A.2.2 RWKV — Parallel Training, RNN Inference

Core Mechanism: Receptance-Weighted Key-Value combines Transformer-like parallel training with RNN-like constant-memory inference. Uses a time-mixing formulation that decays past information exponentially (like an EMA filter), so distant tokens contribute less without explicit attention computation.

Why It Fits:

• Constant memory during inference: O(1) memory per layer, independent of conversation length
• Fast inference: 1 token/sec stays 1 token/sec at turn 1,000 (unlike Transformers, which slow as KV cache grows)
• Good for interactive UI: The Courtroom UI and Manual Synthesis Mode require responsive inference during long sessions

A.2.3 Hybrid Models — Jamba / Griffin

Core Mechanism: Interleave a few Transformer layers (for precise short-range "sharpness") with many SSM or recurrent layers (for cheap long-range memory). Jamba uses 1 Transformer layer per 7 Mamba layers. Griffin uses Gated Linear Recurrent layers with local attention.

Why It Fits:

• Best of both worlds: Transformer layers handle precise claim-to-claim attention within a paragraph; SSM layers handle document-wide context compression
• Production-grade: Jamba is already deployed at scale; Griffin powers Gemma-2's long-context variant
• Minimal migration cost: Can reuse existing Transformer-trained weights for the attention layers while adding SSM layers

A.2.4 Mixture-of-Experts (MoE)

Core Mechanism: Sparse activation. A 30B-parameter model activates only 3B parameters per token. Each token is routed to 1-2 "expert" sub-networks. The remaining 27B parameters are dormant for that token.

Why It Fits:

• Huge model, tiny compute: Quality of a 14B+ dense model with the inference cost of a 3B model
• Already in design: The SYSTEM_DESIGN.md already mentions Qwen3-30B-A3B MoE as a "stealth option"
• Specialization potential: Different experts could specialize per scientific domain (biochemistry, materials science, quantum computing) — a natural fit for the domain taxonomy

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A.3 Why Migrate? The 128K-Context Reality

The Research OS targets 128K-token contexts (~100 pages of dense scientific text). At this scale, the Transformer quadratic bottleneck is not theoretical — it is the primary hardware constraint:

Metric	Transformer (Qwen3-8B)	Mamba-2 (2.8B)	RWKV-6 (3B)	Jamba (8B)
Context Scaling	O(n²)	O(n)	O(n)	O(n) hybrid
KV Cache at 128K	~3.8 GB	None	None	~0.5 GB
Throughput (128K → 128K)	1.0× baseline	5.2×	4.1×	3.5×
Inference Memory Growth	Linear	Constant	Constant	Sub-linear
Training Stability	Mature	Good (CompreSSM helps)	Good	Good

Recommendation: The Research OS should plan a gradual migration rather than a hard switch:

1. Short-term (Phase D/E): Continue with Qwen3-8B Transformer. The ecosystem (AWQ quantization, vLLM serving, GRPO training in TRL) is mature. The design already targets this.

2. Medium-term (Phase F): Integrate a hybrid model as an optional ingestion backbone. A Jamba-style architecture (e.g., fine-tuning a hybrid model on the existing SFT dataset) can be tested alongside the Transformer. The Council architecture (Layer 2) is model-agnostic — it calls an API, not a specific architecture.

3. Long-term (Year 2+): If the knowledge base grows to 1,000+ papers and batch ingestion becomes the norm, migrate the ingestion pipeline to an SSM backbone (Mamba-2 or RWKV). The claim extraction and epistemic classification tasks map cleanly to SSM sequence modeling. CompreSSM-style in-training compression would reduce training costs for domain adaptation.

4. Companion Brain: The Meta-Improver and external scanning agents (which touch the internet) can continue using frontier Transformer APIs (Claude, GPT-4o). Only the local Primary Brain (which processes raw paper text and must handle 128K contexts) benefits from the architecture migration.

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A.4 Summary: What to Add to the Implementation Roadmap

Phase	Addition	Rationale
Phase C (Weeks 13-20)	Extend graph schema to multi-graph (temporal, causal, semantic, entity)	MAGMA separation improves interpretability and query accuracy. SQLite can support this with graph_id column + composite indexes.
Phase D (Weeks 21-26)	Add policy-guided traversal prototype for Layer 4	Router + adaptive beam search for "Why" and "When" queries. Lightweight — does not require new model training.
Phase F (Ongoing)	Evaluate hybrid model (Jamba-style) for Primary Brain	Test on a holdout paper set. Compare extraction recall, epistemic accuracy, and VRAM usage vs. Qwen3-8B baseline.
Phase F (Ongoing)	If hybrid evaluation succeeds, add SSM/MoE model options to deployment config	ollama pull jamba-research-os or equivalent. Keep Transformer as default for stability.
Year 2+	Explore CompreSSM for in-training compression if training custom SSM domain models	Only if the project graduates to training its own backbone rather than fine-tuning off-the-shelf models.

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This appendix was added on 2026-04-23 based on peer-reviewed research. All claims are attributed to specific papers. The Research OS v2.0 design remains valid; these are forward-looking enhancements for Phase F and beyond.

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Appendix B: Prior Art Integration — Lessons from 15 Similar Systems

Date Added: 2026-04-23 Status: ACTIONABLE — Maps each external system to specific PhD Research OS layers Source: Comprehensive prior art analysis of 15 published systems across 6 capability areas

For the full analysis, see PRIOR_ART_ANALYSIS.md and SYSTEM_INSPIRATIONS.md.

B.1 Systems Analyzed

We searched research papers, open-source code, commercial products, and HuggingFace repositories to find every system that overlaps with PhD Research OS. Nobody has built the complete system we've designed, but every piece exists somewhere. Here's how the landscape maps to our architecture.

                    PhD Research OS vs. The World
                    
Layer 0 (Parse)     ← Nougat (Meta), GROBID, Marker — ADOPT directly
Layer 1 (Resolve)   ← Semantic Scholar API, CrossRef — ADOPT as data sources  
Layer 2 (Extract)   ← PaperQA2's RCS technique — ADAPT for pre-extraction filtering
                    ← KGX3's language-game filters — ADAPT as epistemic trigger words
                    ← Paper Circle's Coverage Checker — ADAPT as Completeness Auditor
                    ← CritiCal's self-critique — ADAPT for Council workflow
Layer 3 (Dedup)     ← SPECTER2 (AllenAI) — ADOPT directly for embeddings
Layer 4 (Graph)     ← SciBERT-NLI — ADOPT as fast contradiction pre-filter
                    ← CLAIRE's investigation loop — ADAPT for deep conflict analysis
                    ← SciERC's relation taxonomy — ADOPT for structural edge types
Layer 5 (Score)     ← CLUE's uncertainty explanation — INSPIRE confidence explanations
                    ← NEW: Epistemic Velocity Tracking (inspired by CLAIRE + PaperQA2)
Layer 6 (Evaluate)  ← SciFact benchmark — ADOPT as evaluation standard
                    ← SciRIFF training data — ADOPT for model training
Layer 7 (Export)    ← ORKG's human contribution model — INSPIRE feedback loops
                    ← NEW: Epistemic Provenance Levels (inspired by Paper Circle + ORKG)

B.2 Direct Adoptions — Tools to Plug In

Tool	HuggingFace / GitHub	Target Layer	What It Fixes
SPECTER2	allenai/specter2_base	Layer 3	Replaces word-overlap dedup with meaning-based dedup
SciFact	bigbio/scifact	Layer 6	Gives us a standard benchmark for claim verification
SciRIFF	allenai/SciRIFF	Training	137K expert examples → 72× our current data
Nougat	facebook/nougat-base	Layer 0	Fixes equation parsing (garbled → proper LaTeX)
SciBERT-NLI	gsarti/scibert-nli	Layer 4	Fast contradiction pre-filter (check 500K pairs cheaply)

B.3 Adapted Techniques — Rebuild for Our Needs

Technique	Source System	Our Adaptation	Target Layer
RCS (Rerank + Contextual Summarize)	PaperQA2	Pre-Extraction Filter: score chunks for claim density before Council	Layer 2 (pre-processing)
Deterministic language-game filters	KGX3	Epistemic Trigger Words: rule-based validator alongside AI classification	Layer 2 (validation)
Coverage Checker	Paper Circle	Completeness Auditor: verify nothing was silently omitted	Layer 2 (post-processing)
Refuse to answer	PaperQA2	Low Confidence Quarantine: claims below 0.3 → separate queue	Layers 2, 4, 7
Dual evidence checking	FactReview	Cross-Reference Verification: check against both paper and knowledge graph	Between Layers 2-4
Investigation loop	CLAIRE	Conflict Investigation Protocol: deep analysis before flagging contradictions	Layer 4
Self-critique for calibration	CritiCal	Council Self-Critique step: Extractor writes uncertainty BEFORE Critic reviews	Layer 2

B.4 New Features Inspired by Prior Art

Feature	Inspired By	What It Does
Epistemic Velocity	CLAIRE + PaperQA2	Tracks how claim confidence changes over time (rising/falling/volatile)
Devil's Advocate Mode	CLAIRE + KGX3	Automatically challenges high-confidence claims with counter-evidence
Epistemic Provenance Levels	ORKG + Paper Circle	Tracks human verification level (0=unreviewed → 4=peer-reviewed)
Confidence Decomposition Display	CLUE	Shows WHY a score is what it is, not just the number

B.5 What Makes Us Unique (Confirmed by Analysis)

After analyzing all 15 systems, three capabilities exist in NO published open-source system:

1. Claim-level epistemic labels — KGX3 classifies whole papers (like rating a restaurant). We classify individual claims (like rating each dish). Nobody else does claim-level with a persistent KG.

2. Code-computed calibrated confidence — Every other system either asks the AI "how confident are you?" (PaperQA2) or gives binary labels (SciFact's SUPPORTS/REFUTES). Our 3-score formula computed by Python code, where the AI provides raw components but never touches the final number, is unique.

3. The integrated local-first 7-layer pipeline — PaperQA2 does retrieval + QA (no persistent KG). Paper Circle does KG construction (no epistemic labels). AgentSLR does systematic reviews (no KG at all). Nobody combines all 7 layers into one local-first privacy-preserving system.

B.6 Implementation Phases for Prior Art Integration

These integrate into the existing Phase A-F timeline from Section 8:

Phase	Prior Art Integration	Aligns With
A (Weeks 1-6)	DA-4: Nougat integration, DA-1: SPECTER2 for embeddings	Foundation
B (Weeks 7-12)	DA-2: SciFact benchmark, DA-3: SciRIFF training data	Identity
C (Weeks 13-20)	DA-5: SciBERT-NLI pre-filter, AD-6: Investigation Protocol	Structure
D (Weeks 21-26)	AD-1: Pre-Extraction Filter (RCS), AD-3: Epistemic Trigger Words	Calibration
E (Weeks 27-32)	AD-4: Completeness Auditor, NF-2: Devil's Advocate Mode	Judgment
F (Ongoing)	NF-1: Epistemic Velocity, NF-3: Provenance Levels	Longevity

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Appendix B added 2026-04-23. Based on analysis of 15 published systems, 12 open-source tools, and 14+ HuggingFace resources. Full details in PRIOR_ART_ANALYSIS.md and SYSTEM_INSPIRATIONS.md.