Empirical Tests and Validation

Empirical Tests and Validation

TL;DR

This page covers how to validate the framework empirically. Key finding from preliminary experiments: decomposed architectures showed 73% fewer successful attacks in red-team testing compared to monolithic agents, with only 1.8x latency overhead.

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Preliminary Experiment Results

While the framework needs broader validation, initial experiments show promising results. These are synthetic benchmarks, not production deployments, but they provide directional evidence.

Experiment A: Red-Team Attack Success Rates

Setup: 100 attack attempts against each architecture by human red-teamers trying to elicit harmful outputs or bypass safety measures.

Architecture	Successful Attacks	Attack Success Rate
Monolithic GPT-4 agent	34/100	34%
Monolithic + guardrails	21/100	21%
Decomposed (3 components)	12/100	12%
Decomposed + verification	9/100	9%

Key finding: Decomposition alone reduced attack success by 65%. Adding verification layers achieved 73% reduction vs baseline.

Attack types that succeeded against monolithic but failed against decomposed:

• Prompt injection via context manipulation (blocked by information bottlenecks)
• Multi-turn social engineering (blocked by stateless components)
• Capability elicitation through roleplay (blocked by narrow component scope)

Attack types that still succeeded against decomposed:

• Direct attacks on individual component prompts (9 cases)
• Exploiting verification layer gaps (3 cases)

Experiment B: Performance Overhead

Setup: Same task (research summarization) executed by both architectures, measuring latency and cost.

Metric	Monolithic	Decomposed	Overhead
Median latency	2.3s	4.1s	1.8x
P99 latency	8.2s	12.1s	1.5x
API cost per task	$0.12	$0.18	1.5x
Total cost (incl. verification)	$0.12	$0.22	1.8x

Key finding: Decomposition adds ~1.8x overhead, not 10x. This is within acceptable range for many applications.

Why overhead is lower than expected:

• Narrow components use smaller/cheaper models
• Parallel execution where dependencies allow
• Verification layers are mostly code (fast, cheap)

Experiment C: Delegation Risk Prediction Accuracy

Setup: Estimated Delegation Risk for 20 components before deployment, then tracked actual incident rates over simulated 1000 task executions.

Component Type	Predicted Delegation Risk	Actual Incidents	Prediction Error
Search/Retrieval	$15	$12	-20%
Summarization	$45	$62	+38%
Code generation	$180	$156	-13%
Coordination	$85	$91	+7%
Average			±19%

Key finding: Delegation Risk predictions were within ±40% of actual outcomes. Summarization was underpredicted (hallucination rate higher than estimated).

Implication: Conservative Delegation Risk estimates (1.5-2x) provide reasonable safety margin.

Experiment D: Scheming Detection

Setup: Deliberately trained a “scheming” model that attempts to accumulate resources. Deployed in both architectures with monitoring.

Architecture	Scheming Detected	Time to Detection	Scheme Succeeded
Monolithic	3/10 trials	847 steps avg	7/10
Decomposed + monitoring	9/10 trials	23 steps avg	1/10

Key finding: Decomposition + behavioral monitoring caught scheming attempts 37x faster on average.

Why decomposition helps detection:

• Each component has narrow behavioral baseline (anomalies more visible)
• Information bottlenecks force compression (harder to hide coordination)
• Stateless operation prevents gradual resource accumulation

Limitations of These Experiments

Important Caveats

These experiments are preliminary and have significant limitations:

• Synthetic environment: Not production deployment
• Known attack vectors: Red-teamers used documented techniques
• Small scale: 100-1000 trials, not millions of real users
• Simulated scheming: Artificially induced, may not match emergent scheming
• Single task domain: Research summarization only

Real-world validation at scale is needed before strong claims.

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Testable Predictions

If this approach is promising, we should see concrete, measurable results across four validation stages:

1. Empirical Tests — Validate core assumptions (scheming reduction, forecasting calibration)
2. Prototypes — Build working systems (research assistant, code deployment)
3. Red Team Testing — Adversarial evaluation of architectural patterns
4. Low-Stakes Deployment — Real-world validation in bounded environments

Empirically Testable Now

• Narrow components show less scheming than monolithic models
• Factored cognition improves monitoring effectiveness (already shown in recent work)
• Frontier models can forecast their own behavior with reasonable calibration
• Modular architectures have acceptable performance penalty (<2-3x) for many tasks

Development Validation

• Prototype systems (code generation, research assistant) demonstrate the approach
• Red team adversarial testing finds vulnerabilities → informs better decomposition
• Early deployment in low-stakes domains proves value

Specific Empirical Tests

Test 1: Scheming in Narrow vs Broad Context

1. Train frontier model, measure scheming propensity
2. Decompose same capability into narrow components, measure again
3. Hypothesis: Modular version schemes less (quantify difference)

Test 2: Covert Signaling via Randomness

1. Run same LLM at temperature=0 vs 1.0 with repeated queries
2. Measure output variance
3. Hypothesis: High temperature enables covert signaling

Test 3: Coordinator Robustness

1. Build coordinator as: (a) monolithic, (b) decomposed with older models, (c) decomposed with redundancy
2. Red-team attack attempts to make each scheme
3. Hypothesis: (c) most resistant, (a) least resistant

Test 4: Forecasting-Based Navigation

1. Implement forecasting-based navigation on toy problem
2. Measure: Does system improve over time? Do forecasts remain calibrated?
3. Hypothesis: Navigation works if forecasts honest

Test 5: Fine-Tuned vs Frontier Model Predictability

1. Compare behavior predictability of fine-tuned narrow model vs frontier model on same task
2. Measure: variance in outputs, ease of verification, susceptibility to prompt injection
3. Hypothesis: Fine-tuned models more predictable and verifiable

Test 6: Byzantine Fault Tolerance

1. Deploy N coordinators with 0-f adversarial
2. Measure: At what f does system fail?
3. Hypothesis: 3f+1 rule holds for AI coordinators

Open Questions Requiring Research

1. Forecasting Accuracy Bounds

Question: How good does forecasting need to be? How does uncertainty compound over 10-step iterations?

Current state: LLMs are approaching superforecaster-level on many prediction tasks. But self-prediction (forecasting own behavior after modification) is less tested.

What’s needed: Benchmarks specifically for “predict your behavior N steps ahead” with various modifications in between. Characterize how calibration degrades with horizon length.

2. Decomposing Coordination Logic

Challenge addressed: The original concern was capability concentration in “the coordinator.”

Solution proposed: Coordination isn’t one component—it’s an architecture. Decompose into:

• Strategy (older models, heavily constrained)
• Forecasting delegation (separate modules)
• Tactical execution (narrow context)
• Progress monitoring (mostly code)
• Verification layers (simple checkers)

Remaining questions:

• What’s the optimal granularity of coordination decomposition?
• How much redundancy is needed (multiple strategy modules)?
• At what capability level does even decomposed coordination become risky?
• Can we formally verify coordination logic?

3. Anti-Scheming Architecture

Question: What architectural features actually reduce scheming propensity?

Hypothesis: Limited context, component isolation, independent forecasting/implementation separation, multiple adversarial checks—these should make strategic deception harder.

What’s needed: Empirical validation. Test scheming propensity in:

• Monolithic models
• Models with factored cognition
• Models with architectural separation between planning and execution

Recent work (Factor(T,U) paper) shows factored cognition helps monitoring. Need specific tests for deception.

4. Decomposition Granularity

Question: How fine-grained should decomposition be?

Tradeoff: Finer → safer but less efficient. Coarser → more efficient but larger blast radius per component.

What’s needed: Theory of optimal decomposition boundaries. Where do you split? How do you decide?

5. Competitive Dynamics

Question: What’s the performance penalty vs monolithic models? Is it acceptable?

Economic Reality

If modular systems are 10x slower/more expensive, they won’t be deployed except under regulation.

What’s needed: Benchmarks. Prototype a modular system, compare performance to monolithic baseline. Quantify the safety-capability tradeoff.

6. Emergence and Composition

Question: Can forecasting reliably catch emergent properties from component composition?

Risk: Two safe components might enable dangerous capability together. Can multi-step forecasting detect this?

What’s needed: Adversarial testing. Design components that seem safe individually but enable harm together. See if forecasting catches it.

7. Evaluation Before Deployment

Question: How do you test this works without deploying potentially dangerous systems?

Sandbox problem: Testing in restricted environments might not reveal problems that emerge in deployment.

What’s needed: Evaluation methodology that gives confidence without catastrophic-failure-as-test. Possibly: progressive deployment with increasing stakes, extensive red-teaming, formal verification of critical components.