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Explore research: Carbon-Aware Inference Under Deployment Constraints: Extending the CCI Framework
Carbon-Aware Inference Under Deployment Constraints: Extending the CCI Framework

Carbon-Aware Inference Under Deployment Constraints: Extending the CCI Framework

Faculty-advised research — team forming

→ Core contributors included as co-authors

AI Energy Research

Extension of the published CCI energy benchmarking framework to deployment environments with infrastructure constraints. Energy cost characterization and carbon-aware model selection studied under constrained deployment conditions.

EnergyCarbon-Aware ComputeLLMDeployment ConstraintsSustainabilityAI Efficiency

Apply by April 30, 2026

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Explore research: AI-Assisted Rubric-Aligned Assessment for Resource-Constrained Educational Environments
AI-Assisted Rubric-Aligned Assessment for Resource-Constrained Educational Environments

AI-Assisted Rubric-Aligned Assessment for Resource-Constrained Educational Environments

Faculty-advised research — team forming

→ Core contributors included as co-authors

Academic AI

Automated assessment support for resource-constrained educational environments with high enrollment-to-instructor ratios. Feedback consistency and throughput under structural volume constraints are the organizing problem. Research conducted in coordination with active institutional deployments.

RAGNLPLLMPrompt EngineeringSelf-ConsistencyAutomated Grading

Apply by April 30, 2026

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Explore research: Multi-Agent Evidence Retrieval with Citation Provenance for Resource-Limited Clinical Settings
Multi-Agent Evidence Retrieval with Citation Provenance for Resource-Limited Clinical Settings

Multi-Agent Evidence Retrieval with Citation Provenance for Resource-Limited Clinical Settings

Faculty-advised research — team forming

→ Core contributors included as co-authors

Evidence Retrieval AI

Evidence retrieval for resource-limited clinical settings where standard access infrastructure is unavailable. The gap between published literature and frontline care decisions is the core constraint. Research conducted with faculty advisors at Boston University.

Multi-AgentEvidence RetrievalClinical SystemsCitation IntegrityResource-Limited Settings

Apply by April 30, 2026

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Explore research: Cost-Constrained Multi-Agent Orchestration for Business Intelligence in Emerging Markets
Cost-Constrained Multi-Agent Orchestration for Business Intelligence in Emerging Markets

Cost-Constrained Multi-Agent Orchestration for Business Intelligence in Emerging Markets

Faculty-advised research — team forming

→ Core contributors included as co-authors

Business Intelligence AI

Cost-constrained intelligence for operators in emerging markets where incumbent analytics tooling is priced out of reach. The analytical access gap is the organizing constraint. Research conducted with active deployment evaluation across geographic regions.

Multi-AgentAWSMCPCost OptimizationAgent OrchestrationBusiness Intelligence

Apply by April 30, 2026

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Explore research: AI-Mediated Coordination Under Intermediary-Dependency Constraints: Multi-Agent Market Access Across Two Field Deployment Verticals
AI-Mediated Coordination Under Intermediary-Dependency Constraints: Multi-Agent Market Access Across Two Field Deployment Verticals

AI-Mediated Coordination Under Intermediary-Dependency Constraints: Multi-Agent Market Access Across Two Field Deployment Verticals

Faculty-advised research — team forming

→ Core contributors included as co-authors

Livelihoods & Market Access Research

Multi-agent coordination under structural intermediary-dependency constraints, deployed across 2 active field verticals. Behavioral design-grounded evaluation framework measures income pathway shifts through agent-mediated market access interventions. 2 institutional partnerships; economic baseline documentation across vertically-isolated deployment environments.

Market AccessLivelihoodsICT4DMulti-AgentField DeploymentBehavioral Design

Apply by April 30, 2026

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Explore research: Cross-Language Agent Framework Performance Benchmarking: Google ADK Go vs Python Agent Ecosystems
Cross-Language Agent Framework Performance Benchmarking: Google ADK Go vs Python Agent Ecosystems

Cross-Language Agent Framework Performance Benchmarking: Google ADK Go vs Python Agent Ecosystems

Faculty-advised research — team forming

→ Core contributors included as co-authors

Agent Framework Research

First systematic performance benchmark across multi-language agent development frameworks, covering latency, throughput, memory, and framework overhead under concurrent and sustained load conditions across Go and Python ecosystems.

Agent FrameworksBenchmarkingGoPythonLangGraphPerformance

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Explore research: Multi-Agent Specialist Debate for Abstract Visual Concept Learning Across Bongard Problem Taxonomies
Multi-Agent Specialist Debate for Abstract Visual Concept Learning Across Bongard Problem Taxonomies

Multi-Agent Specialist Debate for Abstract Visual Concept Learning Across Bongard Problem Taxonomies

Faculty-advised research — team forming

→ Core contributors included as co-authors

Visual AI Research

Multi-agent specialist architecture for abstract visual concept induction. Specialist agents coordinate through structured debate to surface visual rules that no single model reliably extracts.

Multi-AgentVisual AIConcept LearningSpecialist AgentsAbstract Reasoning

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Explore research: Multi-Specialist Retrieval for Water Science: MCP-Enabled Evidence Access Across Hydrology Domains
Multi-Specialist Retrieval for Water Science: MCP-Enabled Evidence Access Across Hydrology Domains

Multi-Specialist Retrieval for Water Science: MCP-Enabled Evidence Access Across Hydrology Domains

Faculty-advised research — team forming

→ Core contributors included as co-authors

Environmental AI

Multi-specialist retrieval architecture adapted for water and hydrology domains. Extends the ClinicalSearch evidence access framework to water science literature with Model Context Protocol integration.

RAGMulti-AgentWater ScienceHydrologyMCPEvidence Retrieval

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Research collaboration? contact@anacodic.ai →

Have a research direction to explore?

We review proposals with our faculty advisors. If there is genuine research potential and faculty interest, it joins the open collaboration queue. Not every proposal is accepted — what we look for is a clear problem, a realistic scope, and a reason it matters.

Research at Anacodic is faculty-advised. Accepted directions are shaped together with Faculty & Advisors before any team is formed.

Submit a Research Direction →