Research & Sources

Demonstrated efficient serving of thousands of LoRA adapters simultaneously with dynamic loading/unloading, validating the scalability of LoRA-based architectures.

Addresses challenges in multi-LoRA composition with caching strategies for improved performance.

Demonstrates that LoRAs trained independently can be merged/composed for multi-skill tasks.

Comprehensive survey showing LoRA as one of the best-performed parameter-efficient fine-tuning paradigms.

Multiple LoRA-adapter fusion fine-tuning outperforms dataset fusion approaches.

Domain-specific fine-tuning significantly improves efficiency and coverage in software testing processes.

Domain adaptation through fine-tuning requires careful strategies to introduce new knowledge while retaining base capabilities.

Comprehensive survey covering code generation, code review, bug detection, test generation, and program repair.

Comprehensive framework characterizing collaboration mechanisms by actors, types, structures, strategies, and coordination.

Dynamic, adaptive orchestration mechanisms enable sophisticated multi-agent collaboration.

Literature review showing growing adoption of multi-agent LLM systems specifically for software development workflows.

Multi-agent frameworks with role-based agents (like product manager, architect, engineer) outperform single-agent approaches.

Smart pruning techniques maintain model quality while significantly reducing computational requirements.

Comprehensive survey showing model compression (pruning, quantization, distillation) as essential for efficient LLM deployment.

Structured pruning methods achieve 30% GPU reduction while maintaining performance.

Comprehensive state-of-the-art review establishing HITL as essential for reliable AI systems.

Study of how programmers interact with LLMs reveals they benefit most from conversational, iterative approaches with human guidance.

User feedback-driven correction significantly improves code generation quality and trust.

76% of developers fall into the "red zone" with low confidence in AI-generated code, highlighting critical need for trust mechanisms.

Knowledge graphs assist in knowledge enhancement and resolve LLM hallucination problems through structured reasoning.

LLM-generated knowledge graphs provide substantial improvements in Q&A performance for complex information analysis.

Continued pre-training of small models offers promising path for domain adaptation with limited computational resources, showing gains in knowledge-intensive tasks (+8.1% MMLU) and contextual understanding (+7.6% HellaSwag).

Curriculum learning that gradually shifts training toward domain-specific concepts cuts compute by an order of magnitude.

Model compression (distillation, quantization, pruning) proven effective for reducing redundancy and enabling faster inference.

Small language models (millions to billions of parameters) offer improved efficiency, accessibility, customizability, and faster inference for domain-specific settings.

10X faster inference than autoregressive LLMs through parallel token generation using diffusion process.

8.5-10.5% higher accuracy, ~2x speedup vs text-based multi-agent communication. LLMs communicate via direct KV-Cache exchanges rather than generating/parsing text.