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CSE(AI&ML) PIONEER’S SUMMIT 2026

Pimpri Chinchwad Education Trust’s

Pimpri Chinchwad College of Engineering

Department of CSE (AI&ML)

Research Paper Title:

TRACK:

Student Name:

Student Name:

Student Name:

Student Name:

Guide Name:

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Introduction & Problem Statement

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Instructions: Clearly define the core challenge your AI solution addresses.

  • Domain Context: Introduce the target industry, workflow, or theoretical field where this problem exists.
  • The Core Bottleneck: Describe the specific limitation of current methods (e.g., high latency, hallucination rates, compute cost, or manual effort).
  • Business / Research Impact: Explain why solving this problem is urgent and the potential value unlocked.

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Objectives

  • Primary Goal
  • Define the main objective of your project (e.g., build an autonomous agent system, reduce model inference time, or achieve state-of-the-art accuracy).
  • Key Performance Targets
  • List target metrics you aimed to hit (e.g., <100ms response time, 95%+ precision/recall, 50% memory reduction).
  • Scope & Constraints
  • Specify operational guardrails, compute limits, privacy standards, or deployment environments considered.

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Dataset & Preprocessing

  • Dataset Composition & Sources
    • Instructions: Detail the data powering your model.
    • Source Open-source, proprietary, or synthetic data origins
    • Volume Total dataset size, token count, or record numbers
    • Splits Detail Train / Validation / Test split ratios
  • Preprocessing & Cleansing
    • Instructions: Explain data preparation techniques.
    • Cleansing Noise removal, deduplication, PII scrubbing
    • Feature Eng. Custom tokenization, embeddings, or chunking
    • Augmentation Synthetic data generation or balancing

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Proposed Solution & Innovation

  • Highlight Your Novel Contribution
  • Instructions: Explain your unique architectural or methodological breakthrough in 2-3 concise paragraphs.
  • Focus on what makes your approach different from standard baselines (e.g., custom attention mechanism, novel RAG workflow, multi-agent collaboration, or specialized loss function).

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System Architecture

Ingestion & Model Serving Layer

Instructions: Outline the input processing and model serving pipeline.

  • Input Layer: Vector DB, API gateway, or streaming pipeline.
  • Inference Engine: vLLM, TensorRT, ONNX, or custom serving stack.

Logic & Integration Layer

Instructions: Map the core decision-making and safety mechanisms.

  • Orchestration: Multi-agent state machine or chain-of-thought routing.
  • Guardrails: Output validators, safety filters, or human-in-the-loop rules.

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Methodology

  • Base Model & Fine-Tuning: Specify base weights used (e.g., Llama 3, Qwen, ViT) and adaptation methods (LoRA, QLoRA, Full Fine-Tuning).
  • Alignment & Optimization: Detail alignment protocols (RLHF, DPO, RLAIF) and optimization hyperparameters (learning rate, optimizer, scheduler).
  • Tool & API Integration: Explain function calling protocols, structured JSON schemas, or external tool execution loops.
  • Validation & Benchmarking: Describe evaluation frameworks (e.g., MMLU, Ragas, domain benchmarks) used to measure progress.

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Impact & Benefits

  • summarize the quantitative proof of your solution's value. Highlight key performance metrics (such as latency reduction, accuracy gains, and compute cost savings), baseline model comparisons, and real-world business or research ROI.

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Future Scope

  • Model Enhancements
  • Outline upcoming technical milestones such as larger scale training, lower-bit quantization, or multimodal expansion.
  • System Scaling
  • Describe planned integrations, cross-platform APIs, edge deployments, or multi-agent collaboration frameworks.
  • Research Vision
  • Detail long-term research directions, self-improving active learning loops, or future benchmark ambitions.

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References

  • Add on all the research papers referred for your research

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