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CIRE
Continuous Improvement Research Engine
v5.3 · Institutional Research System · EM Foundation
Freeze: Pauses a project without deleting it. Use it to stop automated and manual cycles until you are ready to resume.
Research Climate STABLE
Status
◉Research Status
⊞Status Dashboard
Synthesis
◉What We Believe Now
◑Change Log
Ecology
⬡Project Ecosystem
⑁Research Species0
◈Fitness Functions
⊛Research Weather
◇Research Dreams0
Discovery
▤Daily Queue0
◈Hypothesis States0
⑂Research Tree
◧Assumption Map
⚔Adversarial Agent0
Meta-Research
⟲Strategy Performance
⍡Research DNA
◈Discovery Prediction
⬡Ecosystem Health
Intelligence
∞Self-Reflection0
⊕Verification Network0
⚗Experiment Design0
▦Discovery Compression
→Project Successor0
◆Novelty Detection0
◇Black Swan Tracker0
Memory
☩Graveyard0
✗Negative Knowledge0
Governance
⊘Wrong Goal Detector0
?Unknown Unknowns0
⑂Goal Decomposition0
▣Legacy Mode
👁Human Review Board0
⚖Research Constitution
▣Continuity Receipts
Control
📚Sources
⬒Cost Control
$Donations & Transactions
⊞All Projects
🗑Recently Deleted0
Support
?User Guide Portal
Daily $$0/$0
Queries0/0
No Project
Balance: $0.00
Add Funds: Opens donation checkout to increase your available research balance. Use it before running additional cycles or higher-cost compute.
Daily Digest: One consolidated email per day covering every active project — last cycle time, whether any Novel Finding or Black Swan fired that day, status, and your balance. Sends even on quiet days so silence is never ambiguous. Off until you turn it on. Separate from the existing event-triggered alerts.
Logout: Ends your authenticated session in this browser. Use it before switching accounts or leaving a shared device.
IDLE
Prosecute: Runs adversarial critique against supported hypotheses. Use it to stress-test whether current conclusions actually hold up.
GLM Toggle: Enables an independent GLM verification pass for each project cycle. Use it when you want a second model perspective on results.
Auto-Cycles: Sets how many scheduled cycles can run per project per UTC day. Set to 0 to disable unattended runs for this project.
GLM disabled
Dream: Generates speculative low-probability hypotheses using the reserved dream budget. Use it to explore unlikely but potentially high-upside directions.
Verify: Runs the Independent Verification Network across multiple perspectives. Use it when a claim needs stronger corroboration.
Run Queue: Executes the next queued cycle for the selected project and applies real balance deductions. Use it to advance research in the current priority order. If the queue appears to run out later in the day, that is usually an intentional daily pacing/cost cap, not a system failure.
Survive: Exports the full machine-readable project state for continuity and disaster recovery. Use it to preserve everything needed to restore the work later.
Legacy: Generates a human-readable research handoff package for long-term transfer. Use it when another person or future team needs to continue the work.
New Project: Opens project creation with mission anchor, limits, and governance settings. Use it to start a separate research program with its own controls.
◉ Research Status? Help
Everything about where this project stands right now, pulled live from the database — progress, grounding, recent activity, flagged Novel Finding/Black Swan events, GLM verification, quantum activity, and cost. Use Generate Status Report for a written narrative summary.
◉
No project selected
Select a project to see its research status.
◈ Grounding Breakdown
📚 Retrieval Source Mix
⚑ Flagged Events — Novel Findings & Black Swans
◑ Recent Activity
⊕ GLM Independent Verification
⚛ Quantum Activity
$ Cost & Balance
✎ Generated Status Report
Not generated yet. This calls the model once and deducts a small real cost from your balance.
⊞ Status Dashboard? Help
One view across every active project — status, progress, confidence, and anything needing attention (pending quantum approvals, pending novelty review, recent Black Swans, low balance). Loaded as a single batched rollup, not one request per project. Click a project to open its full Research Status view.
✎ Generated Portfolio Summary
Not generated yet. This calls the model once across all active projects and deducts a small real cost from your balance.
◉
No project selected
Create a project to initialize the research ecology system.
Mission Anchor
—
Progress
0%
Confidence
0%
Fitness
—
avg score
Species
0
lineages
Dreams
0
wild paths
Graveyard
0
Successors
0
Reflections
0
Ecosystem
0
connections
Breakthrough
—
probability
Mem Health
—
externalized
Spent
$0
0 runs
◉ Current Best Model
Conf: —
Run Synthesize to generate the current model.
Major Unknowns
◎ Confidence Tracking
Started
—
Current
—
Change
+0%
⊛ Research Climate
◎
STABLE
—
◑ What Changed?
Run first cycle.
⑂ Research Tree
No hypotheses yet
◑ Change Log — The Story of This Research
? Help
A timestamped log of every research cycle. Each entry shows what was investigated, what changed, the before/after confidence levels, and the reason for the change. If you're picking up a project after a break, start here — not at the hypothesis list. The Change Log is the narrative thread of the entire research.
◑
No changes yet
⟲ Strategy Performance — The Engine Studies Itself
? Help
Tracks which research strategies actually work. After enough runs, computes win rates for each query type (expand, refute, cross, quantify, experiment, adversarial, revive). The engine then reweights the daily queue to favor high-performing strategies — it literally learns how to research better over time. Run after 10+ cycles for meaningful data. Once weights are set, the queue becomes noticeably smarter. Cross-domain searches typically outperform deep literature review in early-stage projects.
Which research strategies actually work? After enough runs: Cross-domain searches produce more discoveries than deeper literature review. So the engine reallocates effort. This is researching how to research.
Strategy Win Rates
Outcomes that advanced confidence or found novelty
Analyze strategies to compute win rates.
Queue Reweighting
Win rates will be used to reweight the daily queue. High-performing strategies get more budget allocation.
Strategy Insights
Run analysis to generate strategic recommendations.
⍡ Research DNA — Domain Strategy Profiles
? Help
Domain-level strategy profiles built from actual run data across all projects. Physics projects might show Contradiction Analysis wins; Legal projects might show Cross-Domain Analogy wins. Once built, these profiles inform the queue builder — future projects in the same domain start with informed strategy defaults rather than naive rotation. Requires 5+ total runs across at least one domain.
Physics Projects: best strategy = Contradiction Analysis. Legal Projects: best strategy = Cross-Domain Analogy. The system begins selecting methods based on historical performance by domain.
⍡
No DNA profiles built
Requires at least 5 runs across at least one domain. DNA profiles are computed from actual run outcomes.
◈ Discovery Prediction
? Help
Computes breakthrough probability from five pre-discovery signals: rising contradiction rate, confidence dropping before a spike, novel findings in recent runs, source diversity, and hypothesis branching expansion. A score ≥60% means conditions are favorable — increase attention and budget. The score does not predict what will be found, only that the research environment resembles pre-breakthrough conditions. Updated live on every dashboard refresh. Also shown in the top metrics row.
Before major discoveries: contradictions increase, confidence drops, new source categories appear, hypothesis branching expands. The engine can now say: Project entering high-breakthrough probability state.
◈ Breakthrough Probability
—
Run computation to assess current state.
Signal Indicators
Signals computed from: contradiction rate, confidence trajectory, source diversity, hypothesis branching rate, stagnation signals.
Prediction History
No prediction history yet.
⬡ Research Ecosystem Health
? Help
Evaluates all projects together as a research ecosystem, not just individually. Prevents the failure mode of optimizing one project while others stagnate. Scores thirteen metrics: hypothesis creation rate, novel findings, cross-project links, contradictions found, negative knowledge accumulated, species lineages, dream paths, total runs, average progress — and applies penalties for frozen and stagnant projects. Shows a per-project status table for the full picture at a glance.
Projects should not just be measured individually. Is the ecosystem healthy? Prevents optimizing one project while starving others.
⬡
No health assessment
Requires at least two projects. Assesses the collective health of all research activity.
⊘ Wrong Goal Detector
? Help
History is full of researchers who optimized perfectly toward the wrong objective. The Faster Horse Risk score (0–100) measures how likely you are to build a faster horse when what you need is an automobile. The detector analyzes supported hypotheses, contradictions, dead ends, and novel findings to ask whether the stated end state is actually what you want — or whether it's a proxy for a deeper objective. It surfaces reframe options. A FHR score above 50% is a strong signal to pause before spending more budget. Run after research starts converging.
History is full of people who optimized perfectly toward the wrong objective. "Build faster horses." CIRE currently assumes the stated end state is correct. This module asks: Is it? This may be the most valuable question in the system.
⊘
No goal interrogations run
Run after the first research cycle to evaluate whether the stated end state is the actual goal.
? Unknown Unknown Detection
? Help
Most research focuses on known knowns and known unknowns. The real breakthroughs come from unknown unknowns — entire question categories that are not on the radar. This module asks: What important question are we not asking? It analyzes the existing hypothesis corpus and source coverage to find absent question categories. Each unknown comes with its field of origin and a practical first step. Run periodically — not constantly. Blindspots shift as research evolves.
Most research focuses on known knowns and known unknowns. The real breakthroughs come from unknown unknowns. This module asks: What important question are we not asking? One of the hardest things a research institution can do.
?
No unknown unknowns detected
Run periodically to surface questions the research has not yet considered.
⑂ Goal Decomposition
? Help
"Solve AGI" is not researchable. Goal Decomposition breaks large end states into a Research Dependency Tree — which components must be solved first, which are blockers, and which are ready to become standalone CIRE projects. This is different from the Successor Generator (which triggers on stagnation and projects forward). Decomposition works downward at project creation or whenever the goal feels too large. Each leaf component has a Create Sub-Project button that pre-fills the new project form.
"Solve AGI" is not researchable. The engine decomposes large end states into sub-goals, then sub-goals into components — creating Research Dependency Trees instead of impossible objectives.
⑂
No decomposition run
Decompose the current end state into a dependency tree of researchable sub-goals.
▣ Legacy Mode — Transferable Research Institution
? Help
The Legacy Package is for people. The Survival Export is for machines. Legacy Mode generates a human-readable HTML document containing the entire project's accumulated understanding in prose form: executive summary, research history, confirmed findings, disproved claims, negative knowledge summary, direct advice to future researchers, and open questions. Someone 50 years from now could read it without any software. The Institutional Memory Health score shows how well-documented the project is for handoff. Generate at project milestones or before transitions.
When a project reaches maturity: CIRE creates a research constitution, complete history, discoveries, negative knowledge, current understanding, and future questions — packaged as a transferable body of knowledge. Someone 50 years later could inherit it and continue the work.
What Legacy Mode Produces
▣ Research Constitution
▣ Complete History
▣ All Discoveries
▣ Negative Knowledge Archive
▣ Current Belief Model
▣ Open Questions for Successors
▣
No legacy package generated
Generate when the project reaches maturity or before major transitions.
⬡ Project Ecosystem? Help
Detects connections between projects after you click Detect Connections. Most breakthroughs happen at intersections of projects that seem unrelated. Edge types: Foundation, Application, Parallel, Contradiction, Succession. Most valuable with 3+ active projects. Runs an AI cross-project analysis — the connections are computed from actual supported findings.
The engine builds these connections automatically. You shouldn't have to tell it.
⬡
No ecosystem detected
Run "Detect Connections" with two or more projects to map the research ecosystem.
⑁ Research Species — Hypothesis Evolution? Help
Tracks the evolutionary history of hypotheses. When you create a child hypothesis (via the Parent field), a species lineage is created. Each generation is tracked with its outcome. Essential for patents: shows the exact chain of thinking that led to any novel finding. Mutations are also created automatically when research cycles suggest a variation.
Every hypothesis evolves. Original → Variation A → Variation B → Improvement Found. Tracks mutation history of ideas.
⑁
No species lineages yet
Species are created automatically when a hypothesis is modified, adapted, or gives rise to a variation.
◈ Research Fitness Functions? Help
Fitness ≠ Confidence. Confidence = evidence strength. Fitness = attack-resistance. A hypothesis can be well-evidenced (high confidence) but poorly argued (low fitness). High Fitness (≥80) = withstands criticism, strong candidate for elevation. Low Fitness (≤30) = needs revision or burial. Updated after every adversarial prosecution run.
Fitness ≠ Confidence. Fitness measures attack-resistance. What survives repeated criticism?
◈
No fitness scores yet
Fitness is calculated after each adversarial attack. The score measures resistance to criticism, not evidence strength.
⊛ Research Weather? Help
Five research climate states: Stable (normal), Emerging (increase frequency), Volatile (run prosecution), Breakthrough Conditions (maximum attention), Hibernating (exhausted — consider successor). Computed from entropy signals, contradiction rate, and confidence trajectory. Shown live in sidebar. The engine can auto-detect climate shifts from cycle results.
Stable · Emerging · Volatile · Breakthrough Conditions · Hibernating. Based on entropy, new contradictions, discovery rate, and stagnation signals.
⊛
No climate assessment
Click Assess Climate to evaluate current research conditions.
◇ Research Dreams — 1% Budget? Help
1% of daily budget reserved for wild, speculative research paths. The Dream Generator proposes three hypotheses the engine would never queue normally — deliberately unlikely, deliberately cross-domain. Most will fail. Dream hypotheses enter the regular queue marked ◇ and are researched alongside normal hypotheses. Run occasionally — not constantly.
1% of budget allocated to highly unlikely but theoretically possible paths. Most will fail. One might not. Historically, this is where breakthroughs emerge.
Dream Budget Allocation
Dream Budget (1%)
$0.00
Dreams Generated
0
Wild Paths Active
0
◇
No dreams generated
Click Generate Dream to allocate 1% budget to a wild, speculative research path.
▤ Daily Research Queue? Help
The engine's task list for the day. Automatically built from your hypotheses, ordered by state priority and weighted by fitness scores and strategy win rates. This queue is intentionally bounded by your daily limits (query cap, dollar budget, and auto-cycle pacing cap) so research cost cannot run away while unattended. If it empties for the day, that is expected pacing behavior. After Meta-Research analysis, the queue uses historical win rates to pick the most effective query type for each hypothesis. Rebuild to refresh after adding hypotheses. Run all at once or one at a time.
Available Budget
Dollar
$0
Queries
0
Dream Budget
$0
Queue Strategy v5
Priority order + fitness weighting. High-fitness hypotheses queued for adversarial. Low-fitness queued for evidence. Dreams allocated from 1% budget reserve. Successors auto-proposed on stagnation.
▤
Queue empty
◈ Hypothesis States? Help
Every hypothesis moves through a state machine: NEW → RESEARCHING → UNDER_REVIEW → SUPPORTED/CONTRADICTED → IMPROVEMENT_FOUND/REJECTED → ARCHIVED/REVIVED. States cannot be skipped. The table shows Confidence (evidence strength), Fitness (attack-resistance), and Generation (mutation depth). High Confidence + Low Fitness = needs prosecution. High Fitness = withstands criticism.
HypothesisStateQueryConfidenceFitnessGenerationRunsTransition
No hypotheses.
⑂ Research Tree? Help
Visual map of all hypotheses shown as a parent-child tree. Child hypotheses (created using the Parent field) are indented under their parents. Dream paths are marked ◇, mutated hypotheses show generation number. The tree reveals where the research has diversified, where it has deepened, and where dead ends occurred. Use this to understand the structure of your inquiry at a glance.
Tree
State Legend
◧ Assumption Map? Help
Tracks foundational assumptions underlying your research. Rejected assumptions become Revival Candidates rather than being deleted. Rejected assumptions often contain seeds of future breakthroughs — the Revive button adds them back to the hypothesis queue for re-examination. Three seeded assumptions are created automatically with each new project.
Active
Rejected
0
Revived
0
⚔ Adversarial Research Agent? Help
The Research Prosecutor. Its only job is to destroy every conclusion. For each supported hypothesis it asks: What evidence is missing? What assumption failed? What alternative explains the same data? What specific finding would falsify this? A hypothesis that survives prosecution is genuinely strong. Fitness scores are updated after every prosecution run. Run after every 5–10 research cycles.
⚔
No adversarial reports
∞ Self-Reflection Engine? Help
Monthly analysis of the engine's own systematic mistakes. Not: what projects are failing? But: what kinds of mistakes does the engine keep making? Categories: source quality bias, novelty overvaluation, contradiction avoidance, assumption testing gaps, evidence weighting errors, exploration depth. Produces a Health Score (0–100) and specific fixes. Run monthly — patterns only emerge over many cycles.
What am I systematically wrong about? Not: What projects are failing? But: What kinds of mistakes does the engine repeatedly make?
∞
No reflections yet
Run monthly. Analyzes patterns across all runs to identify systematic errors in the research process itself.
⊕ Independent Verification Network? Help
Independent Verification Network. Five parallel AI perspectives evaluate the same claim independently: Skeptic, Advocate, Methodologist, Domain Expert, Statistician. You can add human and expert inputs. The result is a Corroboration Score (0–100%) and consensus verdict. Strong consensus (≥70%) = robust finding. Use before treating any conclusion as established.
Instead of one AI saying X: 5 AI models + 3 human slots + 2 expert slots → Corroboration Score. How much independent agreement exists?
Verification Architecture
AI MODELS (5 parallel) — Claude Sonnet runs the same claim through 5 independent system prompts representing: Skeptic, Advocate, Methodologist, Domain Expert, Statistician.
HUMAN REVIEWERS (3 slots) — Structured input fields for human corroborators. Fill when available.
SUBJECT EXPERTS (2 slots) — Domain specialist corroboration. Fill when available.
Select Claim to Verify
Or enter a custom claim:
⊕
No verifications run
Select a claim and run verification to get a multi-perspective corroboration score.
⚗ Autonomous Experiment Design? Help
Proposes falsifiable experiments ranked by priority score (uncertainty reduction × cost efficiency × feasibility). The highest-scoring experiment is automatically injected at the top of the daily queue. Each proposal includes cost estimate, time estimate, experiment type, and the falsification criterion — the specific result that would definitively kill the hypothesis.
Not experiment execution. Design. The engine asks: What is the cheapest experiment that would most reduce uncertainty? That becomes a first-class objective — and the answer jumps the queue.
Design Objective
Every experiment proposal is scored on: Uncertainty Reduction (how much confidence could this add?) × Cost Efficiency ($ per uncertainty point removed) × Feasibility (can this be done with existing resources?). Highest-scoring experiment is auto-inserted at the top of the daily queue.
⚗
No experiments designed
The engine will propose falsifiable, costed experiments ranked by uncertainty-reduction value.
▦ Discovery Compression? Help
Compresses all receipts and history into three levels: 1-page (essence), 10-page (overview), and 100-page (full narrative). Nobody can read 10,000 receipts. Run at milestones or whenever you need to communicate project status. The 1-page summary is what you'd send to a stakeholder who needs the bottom line.
100,000 receipts → 1-page summary → 10-page summary → 100-page summary → full history. Nobody can read 100K receipts.
▦
No compression run yet
→ Project Successor Generation? Help
When a project stalls: instead of 'Project Failed,' the engine asks 'What project should exist because of this project?' Anti-gravity fails → ion-flow optimization discovered → new project auto-created. Each proposal shows relationship type, what it inherits, and a proposed end state. The Create button pre-fills the new project form.
When a project stalls: instead of "Project Failed," the engine asks "What project should exist because of this project?"
→
No successors generated
Triggered automatically on stagnation, or manually when a project is ready to spawn a new direction.
◆ Novelty Detection? Help
Every novel finding is scored on five value dimensions: Scientific, Educational, Social, Civilizational, and Commercial — plus traditional patent dimensions. Also shows Research Capital: how much future work this discovery unlocks. High Research Capital findings should rank high even with low commercial value, especially for EM Foundation work.
◆
No novelty reports
◇ Black Swan Tracker? Help
Flags findings where actual results diverged significantly from expected (Surprise Score ≥65). These high-divergence events are often where the most important discoveries begin. Nobel-level discoveries frequently start as anomalies. The tracker stores expected result, actual result, and surprise score for each event. Detected automatically from cycle results.
◇
No surprise events
☩ Research Graveyard? Help
Permanent storage for definitively failed hypotheses — why it failed, what evidence killed it, and what would revive it. Different from Archive: graveyarded items have documented explanations. Hypotheses auto-bury when rejected or archived. The revival condition column is critical: a failed hypothesis today can become a breakthrough tomorrow if the right new evidence appears.
☩
Graveyard empty
✗ Negative Knowledge Graph? Help
Structured archive of what didn't work, why, and when retesting would be valid. The + Record + Map option adds relationship fields: Killed By, Blocks, Related To. After years, this becomes one of the most valuable parts of the system — future researchers avoid repeating already-failed paths. The 'Blocks' field shows what future directions a failure forecloses.
Store what failed, why it failed, what killed it, and what it blocks. After years: 1 million failed paths — one of the most valuable datasets on Earth. Future researchers avoid repeating them.
✗
No negative knowledge
👁 Human Review Board? Help
No claim is actionable before human review. Every novel finding enters this queue. Decision options: Discard, Archive, Interesting, Experiment, Patent Review. This is the final gate before a finding is treated as established. Review items show all five value dimensions to inform the decision.
👁
Queue clear
⚖ Research Constitution? Help
Fourteen standing rules injected into every AI research cycle and adversarial run. The engine cannot violate the constitution. Key rules: never delete failures, budget limits are absolute, the Research Prosecutor must attack every supported hypothesis before it is treated as settled. Project-specific amendments are also injected into every cycle.
Standing Rules — Referenced in Every Run
Project Amendments
None.
▣ Continuity Receipts? Help
Every cycle generates a cryptographically hashed receipt: what was queried, what sources were reviewed, what was found, how confidence changed. Six months later you can ask 'why did the engine believe this?' and trace it to the exact cycle. The SHA hash means tampering would be detectable. This is the complete audit trail.
▣
No receipts yet
📚 Source Library? Help
Sources are assigned trust scores by type: Peer-reviewed = 10, Government = 9, Patents/Textbooks = 8, Preprints = 5, Forums = 2. Higher trust sources produce stronger evidence quality scores in research cycles. Add sources before running cycles to improve findings. Relevance (High/Medium/Low) is separate from trust. CIRE never requests third-party login credentials. For external content, paste text or upload a local PDF/TXT file only.
Add Source: Adds a manually entered source record with type, relevance, and summary notes. Use it when you want explicit provenance captured in the library.
Import Content: Imports pasted text or local file content for parser-assisted extraction. Use it when the source is too large or awkward to enter manually.
TitleTypeTrustRelevanceAdded
No sources.
⬒ Cost Control? Help
Shows daily spending vs. budget limits. Budget limits are enforced absolutely — the engine will stop before exceeding them. Dream Budget (1% of daily) is shown separately. Model split reflects parsed cycle deductions for the selected project from transaction history. If budget is exhausted, cycles resume tomorrow when the daily limit resets.
Daily Limit
$0
Spent Today
$0
Query Limit
0
Dream Budget
$0
Claude Spend
$0.000000
GLM Spend
$0.000000
Combined (Parsed)
$0.000000
Split Cycles
0
$ Donations & Transactions? Help
Add Funds: Opens donation checkout and refreshes your spendable balance after completion. Use it when project funds are running low.
Refresh: Pulls the latest balance and transaction history from the backend. Use it after funding or recent spend activity.
Funds are handled as cost-recovery donations that replenish your research balance. This is not a subscription or plan purchase. Successful Stripe donations create a transaction record and increase available balance.
Current Balance
$0.00
Updated: —
Transaction History
DateTypeAmountDescriptionReference
No transactions yet.
⊞ All Projects? Help
Overview of all projects. Shows status, progress, confidence, run count, and research climate. Click any project card to switch to it. Note: all data is in-memory and resets on page refresh — download the Survival Export (▣ Survive) before closing the browser if you want to preserve work.
New Project: Starts a separate project with its own queue, budgets, and lifecycle state. Use it to keep a new line of research isolated from existing work.
🗑 Recently Deleted? Help
Soft-deleted and pending hard-delete projects live here. Restore sends a project back to All Projects. Hard Delete marks a project for permanent purge after 15 days. Cancel Deletion removes the hard-delete schedule but keeps the project deleted.
Preview Purge Candidates: Lists projects that are currently eligible for permanent purge. Use it to review what is at risk before taking action.
Purge Dry Run: Simulates purge execution and reports what would be removed without deleting data. Use it to validate purge behavior safely.
? User Guide Portal
Guide Sections
Getting Started Research Cycles Project Lifecycle Balance and Donations GLM Verification Quantum Stack Governance and Review Data Sources
This guide reflects what is currently built and verified in production. Use it for end-to-end workflow context, then use the inline ? widgets for control-specific reminders.

Getting Started

Welcome to CIRE, the Continuous Improvement Research Engine.

Creating your first project:
1) Click + New Project in the top bar (or + Create First Project if you have none yet).
2) Fill in the project fields. See Research Cycles for what each field means.
3) Select the project from the sidebar dropdown to make it active.

Orientation: The sidebar groups are Research Climate (synthesized findings), Discovery (active investigation), Memory (disproven paths), Governance (self-check and review), and Control (sources, cost, transactions, and project management).

Your account balance appears at the top of the sidebar. New accounts start at $0.

Every clickable control has a ? widget for quick contextual explanation.

Research Cycles

A research cycle is one round of investigation on a single hypothesis: CIRE searches real evidence, reasons over it, and updates belief state.

Project setup fields:
Name, Domain, End state, Why, Constraints, Excluded, and Budget.

Running cycles: Click Run Queue (or use auto-cycles when enabled). Each cycle formulates a query, searches real academic sources, reasons over retrieved evidence, then updates confidence and records findings.

Daily pacing and cost safety: Auto-cycles are capped per project per day by the auto-cycle setting (default 5/day), and all runs still respect daily query and dollar limits. This is intentional cost/pacing control. If your queue appears to "run out" for the day, that usually means the daily cap was reached, not that anything is broken.

Domain determines source mix automatically (for example arXiv, Semantic Scholar, OpenAlex, PubMed Central, CORE, Unpaywall, bioRxiv, medRxiv).

Grounding labels:
Grounded = evidence-backed, Partial = incomplete support, Ungrounded = no supporting evidence found.

Confidence reflects current support strength. Fitness reflects longitudinal performance used for prioritization. Both can move up or down as new evidence arrives.

Use Add Source or Import Content to include lawful user-provided material when needed.

Project Lifecycle

Active: Runs normally and can execute cycles (balance permitting).
Closed (Frozen): Stops new cycles and cost accrual while preserving all state; reversible via Reopen.
Soft Deleted: Hidden from main list and moved to Recently Deleted; recoverable via Restore.
Pending Hard Delete: Starts a 15-day countdown. Recoverable via Cancel Deletion during countdown. After expiration, deletion is permanent and irreversible.

You can export a project as raw JSON or PDF report regardless of lifecycle state.

Balance and Donations

CIRE uses real compute resources. Funding is a prepaid balance model through cost-recovery donations to EM Foundation (nonprofit), not subscription purchase semantics.

New accounts start at $0. Browsing is free; running new cycles requires balance.
Click + Add Funds to donate via Stripe and credit balance immediately.

Completed cycles deduct actual observed cost. GLM verification and quantum execution are itemized separately in transaction history.

If balance runs low, projects pause automatically, you receive email notification, and execution resumes after top-up.

GLM Verification

GLM verification is an optional second independent AI pass at project level.

When enabled, GLM performs its own retrieval and reasoning for the same hypothesis, producing an independent assessment rather than a rubber-stamp review.

Use it for higher-stakes or contested topics where independent agreement matters. It is off by default because it generally increases cycle cost by running a second model path.

In Verification Network and Adversarial Agent outputs:
Agree = aligned conclusions, Partial = partly aligned, Disagree = meaningful split with rationale and evidence differences surfaced.

Quantum Stack

Quantum is used only for genuinely quantum-suited questions and never triggers automatically without explicit user approval.

Flow:
1) Conservative recognition during cycle reasoning.
2) Structured approval request: what was found, what was hypothesized, why classical methods are insufficient, and what quantum would rule in/out.
3) User decision to approve or decline (decline has no penalty).
4) If approved and funded, submission to IBM Quantum or AWS Braket.
5) Real result retrieval and clear quantum-informed labeling in findings.

Current execution is labeled clearly where applicable (for example Amazon SV1 simulator), with transparent itemized settlement in transactions.

Governance and Review

Governance features keep research auditable and self-correcting:

Review Queue / Human Review Board: escalated items requiring direct user judgment.
Adversarial Agent: actively challenges findings to surface weaknesses and disagreement.
Wrong Goal Detector: flags drift from declared End State.
Research Constitution: integrity rules for evidence grounding and honest labeling.
Continuity Receipts: preserved reasoning trail so resumes do not lose context or repeat dead ends.

Data Sources Reference

This page shows both the real retrieval sources CIRE is configured to use and the live evidence counts currently stored in production. Search and filter the integrated sources below, then compare that reference against actual historical usage.
Live evidence inventory is pulled from the real production sources table. Zero means the provider is configured but has not yet produced any stored retrieval rows.
Status:
Guide content published (real operational copy)
Access:
Always available from sidebar Support section
Next step:
Keep this content aligned with product behavior as features evolve

Help Coverage Inventory (Mechanism Phase)

Checklist status: 1) Per-row table actions (hypothesis/source/transaction): Deferred as N/A where rows are read-only and contain no action buttons in current UI build; automatic button binding will cover any row actions if introduced. 2) Modal form field-level widgets: Covered for modal field groups (.fg). 3) Review-board decision buttons: Covered (modal decision controls bind automatically). 4) Long-tail section action controls: Covered via route + mutation rebind.
Pending rollout: none in mechanism scope. Remaining work is guide content authoring and editorial tuning.
◆ New Research Project
30%
Off by default. When enabled, each cycle includes a separate GLM verification pass.
Resource Bounds — never exceeded (1% auto-reserved for Dreams)
0 disables scheduled auto-runs for this project.
$ Add Cost-Recovery Funds
Enter a donation amount to support infrastructure and operating costs. You will be redirected to Stripe Checkout in test/live mode based on your deployed keys.
Framing: cost recovery donation, not subscription sales.
◈ Add Hypothesis
◧ Add Assumption
📚 Add Source
📥 Import External Content
Use only content you already lawfully accessed yourself. CIRE does not ask for or accept third-party login credentials.
✗ Record Negative Finding
☩ Bury in Graveyard
✗ Record Negative Finding + Graph Relationships
⚖ Add Amendment
▶ Research Cycle
Dispatching…
👁 Review Item
☠ Confirm Hard Delete Schedule
Irreversible Schedule Warning
This project will be permanently and irreversibly deleted in 15 days. You can cancel anytime before then from Recently Deleted.
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