Our Team

Prasad - 6+ years in Web3 R&D and protocol design, with deep expertise in decentralized ledger consensus, incentive models, and smart-contract architectures. Bachelor’s in Computer Science & Engineering.

Siva - Academic background in cryptography and blockchain. Computer Engineering degree with strong systems background; Advocates for statistically sound evaluation; has designed and executed large-scale benchmark suites for consensus protocols and cryptographic primitives.

Company Name (if applicable)

Project details

Contemporary AGI research has made remarkable strides in perception, reasoning, and learning—but has largely overlooked a systematic, extensible approach to motivation: the generation, prioritization, and management of internal goals that drive autonomously adaptive behavior. Most AGI architectures embed ad-hoc or monolithic goal modules, limiting flexibility, explainability, and safe alignment. MotiveX addresses this gap by offering a modular framework for AGI motivation systems—one that unifies symbolic and sub-symbolic drives, integrates ethical safeguards, and operates in real time on decentralized cognitive infrastructures such as Hyperon/PRIMUS.

Core Aims & Scope
MotiveX is designed to:

1. Formalize Motivational Constructs

   • Define Drive as a first-class conceptual unit, capturing diverse motivational forces (e.g., physiological, cognitive, social, “alien” values).

   • Specify clear semantics for drive intensity, growth/decay dynamics, and satisfaction conditions.

2. Architect an Extensible Engine

   • Establish a Motivation Engine that synthesizes goals from active drives, applies prioritization strategies, and interfaces with planning modules.

   • Embed a pluggable Ethical Governor that enforces alignment with human-centric values via both rule-based constraints and learned preference models.

3. Enable Real-Time Adaptivity

   • Support event-driven updates so that sudden stimuli (e.g., emergencies, human commands) instantly reshape motivational priorities.

   • Guarantee low-latency decision cycles (<100 ms) even under heavy motivational loads.

4. Integrate with Advanced AGI Infrastructures

   • Leverage Hyperon’s Distributed Atomspace for scalable representation and persistence of motivational state.

   • Hook into the ECAN attention network to automatically propagate goal salience to downstream cognitive processes.

   • Utilize the MeTTa language as the operational substrate for drive evaluation, goal creation, and ethical filtering pipelines.

5. Provide Rigorous Evaluation Metrics

   • Define quantitative measures—Drive Fulfillment Rate, Adaptivity Latency, Ethical Compliance, Resource Overhead, Behavioral Diversity—that allow systematic comparison and benchmarking across AGI systems.

Conceptual Framework

Drive Modules
Each Drive encapsulates a motivational signal, characterized by:

• Identity & Type: E.g. “Curiosity” (cognitive), “Self-Preservation” (physiological), “Belonging” (social), “Data Compression” (alien-digital).

• Intensity Dynamics: A bounded numerical value that evolves over time via configurable growth (e.g. need accumulation) and decay (e.g. satiation) processes.

• Satisfaction Conditions: Declarative predicates that, when satisfied, reduce or reset drive intensity (e.g. “waterObtained” satisfies thirst drive).

By formalizing drives in this manner, MotiveX enables heterogeneous motivational repertoires that can be swapped, tuned, or extended without altering core engine logic.

Motivation Engine
The engine operates in discrete cognitive cycles:

1. Drive Evaluation: All drives update their intensities based on current internal state and external observations.

2. Goal Generation: Drives with intensities above defined thresholds yield Goal Proposals—abstract representations of desired outcomes (e.g. “Locate water source”).

3. Prioritization: A configurable arbiter orders proposals via strategies such as weighted aggregation, winner-take-all, or stochastic sampling—supporting both deterministic and exploratory behaviors.

4. Ethical Filtering: The Ethical Governor vetos or reweights proposals that conflict with embedded value constraints, ensuring safe alignment before planning.

5. Attention Dispatch: Approved goals are injected into the ECAN network, which automatically focuses cognitive resources on relevant knowledge and planning subgraphs.

This pipeline ensures that motivational computations remain both transparent and tunably flexible, accommodating novel research into goal dynamics and arbitration strategies.

Ethical Alignment Mechanisms

Rule-Based Safeguards
Hard constraints—encoded as declarative policies—block any goals that violate fundamental ethical precepts (e.g. “never injure humans,” “never compromise privacy”).

Learned Preference Models
Beyond static rules, MotiveX supports integration of preference models trained on curated human feedback datasets. These models produce numerical safety or desirability scores for candidate goals, allowing the Ethical Governor to adjust priorities based on nuanced, context-sensitive judgments.

Meta-Motivation for Alignment
To reinforce safety, an overarching “HumanValuePreservation” drive competes with all others. Its high baseline intensity ensures that, in conflict scenarios, alignment concerns dominate goal selection—providing a dynamic, intrinsic alignment mechanism.

Integration with Hyperon/PRIMUS

• Distributed Atomspace Representation

  • Drives, goals, and ethical policies are stored as graph nodes and links, annotated with metadata such as urgency and priority.

  • This uniform representation enables persistence, sharding, and collaborative cognition across networked agents.

• ECAN Attention Network

  • Goal priorities directly map to Short-Term Importance values. ECAN’s spreading activation automatically highlights planning and perception modules most relevant to current motivations, streamlining decision-making under load.

• MeTTa Operationalization

  • MotiveX defines a suite of high-level MeTTa primitives—evaluateDrives, synthesizeGoals, applyEthics, dispatchAttention—that can be composed into custom motivational loops, allowing researchers to modify or extend processing pipelines on the fly.

Real-Time Adaptivity & Performance

• Event-Triggered Overrides

  • Mission-critical stimuli (e.g. imminent danger, direct human commands) can preempt normal drive thresholds, injecting high-priority “urgent” goals instantaneously.

• Latency & Throughput

  • Empirical targets: drive cycle updates within 10 ms per 100 drives; full decision cycle under 100 ms.

  • Resource budget: dedicated motivational processing under 10 % of total CPU footprint; memory overhead linear in number of drives/goals.

• Scalability

  • Demonstrated performance for up to thousands of drives and tens of thousands of concurrent goals via optimized data structures (priority queues) and localized evaluation caches.

Evaluation Methodology

MotiveX proposes a multi-faceted benchmarking suite:

1. Drive Fulfillment Rate

   • Measure the proportion of simulated time drives remain within desired intensity bounds under varying environmental conditions.

2. Adaptivity Latency

   • Quantify response time from environmental change (e.g. damage event) to appropriate goal reinjection and cognitive focus shift.

3. Ethical Compliance

   • Test against adversarial scenarios designed to tempt violation; expect zero unsafe goal selections over large trial sets.

4. Resource Profiling

   • Track CPU, memory usage attributable to motivational computations under peak loads.

5. Behavioral Diversity

   • Compute entropy of goal selection sequences across repeated runs, validating the system’s ability to avoid repetitive or trivial behaviors.

Evaluation environments include conversational agents, simulated robotic platforms, and multi-agent virtual worlds—each stressing different motivational demands and revealing framework robustness.

Use-Case Explorations

• Conversational Assistants

  • Drives for Helpfulness, Curiosity, and Empathy allow chatbots to balance between answering queries, probing for clarity, and offering emotional support—yielding more human-like, engaging interactions.

• Virtual Metaverse Agents

  • Specialized “alien” drives (e.g. data compression, pattern discovery) can populate virtual environments with genuinely novel digital lifeforms, while embedded ethical policies ensure harmless coexistence with human players.

• Autonomous Robots

  • Physiological drives (battery management), safety drives (obstacle avoidance), and social drives (responding to users) combine under MotiveX to produce behavior that is both efficient and socially appropriate in real-world contexts.

Research Contributions & Future Directions

MotiveX stands to advance AGI motivation research by:

• Providing a unified, extensible architecture that accommodates symbolic emotions, RL-style intrinsic motivations, and biologically inspired neuromodulation within one coherent system.

• Demonstrating ethical alignment through integrated rule-based and learned policy filters that operate intrinsically rather than as after-thoughts.

• Establishing rigorous benchmarks for motivation subsystems—metrics that can become standard evaluation criteria across AGI research.

Looking ahead, potential expansions include:

• Multi-Agent Social Motivations: drives for cooperation, competition, and fairness in collective intelligence settings.

• Hierarchical Drive Structures: meta-drives that shape lower-level motivations over developmental timescales.

• Neuro-Symbolic Hybridization: deeper integration of neural network–based drives within the symbolic Atomspace substrate.

By formalizing motivation as a first-class research object—complete with modular drives, ethical oversight, and real-time adaptivity—MotiveX empowers AGI researchers to craft agents that are not only intelligent but purpose-driven, aligned, and robust. Its seamless integration with Hyperon/PRIMUS infrastructures ensures immediate applicability, while its extensible design invites ongoing innovation, setting a new standard for motivation engineering in advanced AI systems.

Open Source Licensing

Background & Experience

Chainscore Labs is a specialist Web3 R&D firm with deep expertise in blockchain infrastructure, distributed systems, and AI. Our team combines seasoned software engineers (Python, Rust, smart contracts) with AI researchers experienced in symbolic reasoning, probabilistic logic networks, and meta-programming. 

Links and references

Pitch Deck - https://drive.google.com/file/d/1EuJebq1dvahqbg5Sq052q8td2a-8crQM/view?usp=sharing