A Coding Implementation for an Agentic AI Framework that Performs Literature Evaluation, Speculation Technology, Experimental Planning, Simulation, and Scientific Reporting

import sys, subprocess


def install_deps():
   pkgs = ["transformers", "scikit-learn", "numpy"]
   subprocess.check_call([sys.executable, "-m", "pip", "install", "-q"] + pkgs)


attempt:
   from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
   from sklearn.feature_extraction.textual content import TfidfVectorizer
   from sklearn.metrics.pairwise import cosine_similarity
   import numpy as np
besides ImportError:
   install_deps()
   from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
   from sklearn.feature_extraction.textual content import TfidfVectorizer
   from sklearn.metrics.pairwise import cosine_similarity
   import numpy as np


from dataclasses import dataclass
from typing import Listing, Dict, Any


np.random.seed(42)


LITERATURE = [
   {"id": "P1","title": "Self-Supervised Protein Language Models for Structure Prediction","field": "computational biology",
    "abstract": "We explore transformer-based protein language models trained on millions of sequences. The models learn residue-level embeddings that improve secondary structure prediction and stability estimation."},
   {"id": "P2","title": "CRISPR Off-Target Detection Using Deep Learning","field": "genome editing",
    "abstract": "We propose a convolutional neural network architecture for predicting CRISPR-Cas9 off-target effects directly from genomic sequences, achieving state-of-the-art accuracy on GUIDE-seq datasets."},
   {"id": "P3","title": "Foundation Models for Scientific Equation Discovery","field": "scientific ML",
    "abstract": "Large language models are combined with symbolic regression to recover governing equations from noisy experimental observations in physics and fluid dynamics."},
   {"id": "P4","title": "Active Learning for Materials Property Optimization","field": "materials science",
    "abstract": "We integrate Bayesian optimization with graph neural networks to actively select candidate materials that maximize target properties while reducing experimental cost."},
   {"id": "P5","title": "Graph-Based Retrieval for Cross-Domain Literature Review","field": "NLP for science",
    "abstract": "We construct a heterogeneous citation and concept graph over multi-domain scientific papers and show that graph-aware retrieval improves cross-domain literature exploration."},
]


corpus_texts = [p["abstract"] + " " + p["title"] for p in LITERATURE]
vectorizer = TfidfVectorizer(stop_words="english")
corpus_matrix = vectorizer.fit_transform(corpus_texts)


MODEL_NAME = "google/flan-t5-small"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
mannequin = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)


def generate_text(immediate: str, max_new_tokens: int = 256) -> str:
   inputs = tokenizer(immediate, return_tensors="pt", truncation=True)
   outputs = mannequin.generate(**inputs, max_new_tokens=max_new_tokens, num_beams=4, early_stopping=True)
   return tokenizer.decode(outputs[0], skip_special_tokens=True)

@dataclass
class PaperHit:
   paper: Dict[str, Any]
   rating: float


class LiteratureAgent:
   def __init__(self, vectorizer, corpus_matrix, papers: Listing[Dict[str, Any]]):
       self.vectorizer = vectorizer
       self.corpus_matrix = corpus_matrix
       self.papers = papers


   def search(self, question: str, ok: int = 3) -> Listing[PaperHit]:
       q_vec = self.vectorizer.remodel([query])
       sims = cosine_similarity(q_vec, self.corpus_matrix)[0]
       idxs = np.argsort(-sims)[:k]
       hits = [PaperHit(self.papers[i], float(sims[i])) for i in idxs]
       return hits

@dataclass
class ExperimentPlan:
   system: str
   speculation: str
   variables: Dict[str, Any]
   protocol: Listing[str]


@dataclass
class ExperimentResult:
   plan: ExperimentPlan
   metrics: Dict[str, float]


class ExperimentAgent:
   def design_experiment(self, query: str, speculation: str, hits: Listing[PaperHit]) -> ExperimentPlan:
       top_field = hits[0].paper["field"] if hits else "computational science"
       protocol = [
           f"Construct dataset combining ideas from: {', '.join(h.paper['id'] for h in hits)}.",
           "Break up information into practice/validation/take a look at.",
           "Evaluate baseline mannequin vs. augmented mannequin implementing the speculation.",
           "Consider utilizing applicable metrics and carry out ablation evaluation.",
       ]
       variables = {
           "baseline_model": "sequence CNN",
           "augmented_model": "protein language mannequin + CNN",
           "n_train_samples": 5000,
           "n_validation_samples": 1000,
           "metric": "AUROC",
       }
       system = f"{top_field} system associated to: {query}"
       return ExperimentPlan(system=system, speculation=speculation, variables=variables, protocol=protocol)


   def run_experiment(self, plan: ExperimentPlan) -> ExperimentResult:
       base = 0.78 + 0.02 * np.random.randn()
       acquire = abs(0.05 + 0.01 * np.random.randn())
       metrics = {
           "baseline_AUROC": spherical(base, 3),
           "augmented_AUROC": spherical(base + acquire, 3),
           "estimated_gain": spherical(acquire, 3),
       }
       return ExperimentResult(plan=plan, metrics=metrics)

class ReportAgent:
   def write_report(self, query: str, hits: Listing[PaperHit], plan: ExperimentPlan, consequence: ExperimentResult) -> str:
       related_work = "n".be a part of(f"- {h.paper['title']} ({h.paper['field']})" for h in hits)
       protocol_str = "n".be a part of(f"- {step}" for step in plan.protocol)
       immediate = f"""
You might be an AI analysis assistant writing a concise research-style report.


Analysis query:
{query}


Speculation:
{plan.speculation}


Related prior work:
{related_work}


Deliberate experiment:
System: {plan.system}
Variables: {plan.variables}
Protocol:
{protocol_str}


Simulated outcomes:
{consequence.metrics}


Write a transparent report with the next sections:
1. Background
2. Proposed Method
3. Experimental Setup
4. Outcomes and Dialogue
5. Limitations and Future Work
"""
       return generate_text(immediate.strip(), max_new_tokens=320)

class ScientificAgent:
   def __init__(self):
       self.lit_agent = LiteratureAgent(vectorizer, corpus_matrix, LITERATURE)
       self.exp_agent = ExperimentAgent()
       self.report_agent = ReportAgent()


   def propose_hypothesis(self, query: str, hits: Listing[PaperHit]) -> str:
       context = " ".be a part of(h.paper["abstract"] for h in hits)
       immediate = f"""
You might be an AI scientist. Given a analysis query and associated abstracts,
suggest a single, testable speculation in 2-3 sentences.


Analysis query:
{query}


Associated abstracts:
{context}
"""
       return generate_text(immediate.strip(), max_new_tokens=96)


   def run_pipeline(self, query: str) -> str:
       hits = self.lit_agent.search(query, ok=3)
       speculation = self.propose_hypothesis(query, hits)
       plan = self.exp_agent.design_experiment(query, speculation, hits)
       consequence = self.exp_agent.run_experiment(plan)
       report = self.report_agent.write_report(query, hits, plan, consequence)
       return report


if __name__ == "__main__":
   research_question = (
       "How can protein language mannequin embeddings enhance CRISPR off-target "
       "prediction in comparison with sequence-only CNN baselines?"
   )
   agent = ScientificAgent()
   final_report = agent.run_pipeline(research_question)
   print(final_report)