AGISystem2's approach to trustworthy AI opens numerous research directions. This document outlines both immediate opportunities (implementable with current architecture) and longer-term research questions (requiring theoretical advances).
The Opportunity
AGISystem2's deterministic HDC operations enable formal verification of reasoning chains—something impossible with probabilistic neural networks.
Current State
Every proof trace in AGISystem2 is a sequence of explicit operations (BIND, BUNDLE, SIMILARITY) applied to hypervectors. These operations have well-defined algebraic properties.
Research Questions
- Proof certification: Can we generate machine-checkable certificates for AGISystem2 proofs?
- Soundness guarantees: Under what conditions can we guarantee a proof trace is correct?
- Completeness bounds: When a query returns "no proof found," can we characterize what's missing?
- Compositional verification: If module A and module B are verified, is A+B automatically verified?
Potential Impact
Verified AI reasoning for safety-critical applications: medical diagnosis support, legal reasoning, financial compliance.
2. Privacy-Preserving Reasoning
The Opportunity
HDC's holographic representations distribute information across all dimensions. This creates interesting possibilities for privacy-preserving computation.
Current Understanding
When facts are encoded as hypervectors:
- Individual atoms (concepts) are difficult to extract without the encoding keys
- Structural relationships leak through similarity patterns
- Not cryptographically secure, but offers practical obfuscation
Research Questions
- Information leakage quantification: Exactly how much can an adversary learn from bundled knowledge?
- Selective disclosure: Can we answer queries about data without revealing the underlying facts?
- Federated reasoning: Can multiple parties reason over combined knowledge without sharing raw data?
- Differential privacy integration: Can HDC noise tolerance enable DP-style guarantees?
Potential Impact
Privacy-preserving AI for healthcare, finance, and other sensitive domains. See Privacy-Preserving HDC for detailed analysis.
3. LLM+HDC Hybrid Architectures
The Opportunity
LLMs excel at language understanding and generation. HDC excels at structured reasoning and verification. Combining them could yield AI that is both fluent and rigorous.
Current Approaches
- LLM as translator: Natural language → DSL commands → AGISystem2
- AGISystem2 as verifier: LLM generates plans, AGISystem2 validates them
- Reasoning augmentation: LLM produces answer, AGISystem2 provides proof trace
Research Questions
- Optimal division of labor: Which reasoning tasks should LLM vs HDC handle?
- Knowledge synchronization: How to keep LLM and HDC knowledge consistent?
- Confidence calibration: Can HDC verify LLM outputs and quantify uncertainty?
- Explanation generation: LLM verbalizes HDC proof traces naturally
- Error correction: HDC detects LLM hallucination, triggers regeneration
Potential Impact
Trustworthy AI assistants that combine natural conversation with verified reasoning.
4. Continuous Compliance Monitoring
The Opportunity
Research goal: move from periodic compliance audits to real-time verification. Actions checked before execution and decisions traceable via proof traces.
Current Capabilities
- Regulations encoded as formal theories
- Actions checked against all applicable rules
- Proof trace generation (audit logging/export is external)
Research Questions
- Regulation evolution: How to handle changing regulations (new GDPR interpretations)?
- Cross-jurisdiction reasoning: Data flowing between EU, US, China with different rules
- Temporal compliance: Rules with time constraints (consent expiration, retention periods)
- Uncertainty handling: What if data classification is uncertain?
- Remediation planning: Automatically generate compliant alternatives
Potential Impact
Compliance-by-design for regulated industries. Audit costs reduced by order of magnitude.
5. Systematic Bias Detection
The Opportunity
AGISystem2's explicit knowledge representation enables pattern analysis for bias detection that goes beyond surface-level statistics.
Current Capabilities
From DS29 (Creative Writing Consistency & Bias):
- Define bias patterns as formal rules
- Analyze content against patterns
- Generate detailed reports with specific instances
Research Questions
- Bias pattern discovery: Can we automatically identify potential bias patterns from data?
- Causal vs correlational: Is detected correlation actually problematic bias?
- Intersectional analysis: How do multiple attributes interact?
- Mitigation suggestions: Automatically propose less biased alternatives
- Bias in reasoning: Detect bias in inference chains, not just outputs
Potential Impact
Proactive bias detection in AI-generated content, hiring systems, loan approvals.
6. Scientific Theory Validation
The Opportunity
Encode scientific theories formally. Automatically check new claims against established knowledge.
Current Capabilities
From DS31 (Scientific Theory Encoding & Validation):
- Laws and theorems as formal structures
- Consistency checking against established theory
- Cross-theory connection discovery
- Hypothesis exploration ("what if?")
Research Questions
- Theory revision: How to update theories when new evidence arrives?
- Abductive reasoning: Generate explanatory hypotheses for observations
- Analogical transfer: Apply reasoning patterns across domains
- Literature integration: Automatically encode claims from papers
- Contradiction resolution: When theories conflict, how to reason about it?
Potential Impact
AI-assisted scientific review, cross-disciplinary discovery, hypothesis generation.
7. Multi-Level Explanations
The Opportunity
AGISystem2's proof traces are precise but technical. Different stakeholders need different explanation levels.
Explanation Levels
| Audience |
Needs |
Example |
| Developer |
Full proof trace |
Step-by-step rule applications |
| Domain expert |
Key reasoning steps |
"Used inheritance from Dog to Animal" |
| End user |
Natural language summary |
"Spot is an animal because he's a dog" |
| Auditor |
Compliance justification |
"Decision based on rules X, Y, Z" |
Research Questions
- Automatic summarization: Compress proof traces to key steps
- Contrastive explanations: "Why X instead of Y?"
- Counterfactual explanations: "What would change the outcome?"
- Personalized detail: Adapt explanation depth to user expertise
Potential Impact
AI systems that can explain themselves appropriately to any stakeholder.
8. Calibrated Uncertainty
The Opportunity
AGISystem2 can distinguish "unknown" from "false"—a crucial capability most AI systems lack.
Current Understanding
- Open-world assumption: absence of proof is not proof of absence
- Similarity scores provide confidence estimates
- Bundle capacity limits provide degradation bounds
Research Questions
- Confidence calibration: When system says "80% confident," is it right 80% of the time?
- Uncertainty propagation: How does uncertainty in premises affect conclusions?
- What's missing: When query fails, what knowledge would enable it?
- Decision under uncertainty: How to act when knowledge is incomplete?
Potential Impact
AI systems that know what they don't know and communicate uncertainty appropriately.
Contributing to Research
AGISystem2 is designed for research exploration:
- Open source: Core runtime implemented; research patterns are not shipped as runnable theory sets
- Modular architecture: HDC strategies, reasoning engines swappable
- Evaluation suite: Standardized benchmarks for comparison
- Documentation: Theoretical foundations documented in detail
See the API documentation for implementation details.
Related Documentation