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"""Lightweight paper database and lookup helpers for research citations."""
from typing import List, Dict
# Minimal curated set of influential papers to cite in the demo. Add more as needed.
RESEARCH_PAPERS: Dict[str, Dict] = {
"attention": {
"id": "attention",
"title": "Attention Is All You Need",
"authors": "Vaswani et al.",
"year": 2017,
"venue": "NeurIPS",
"url": "https://arxiv.org/abs/1706.03762",
"summary": "Introduced the Transformer architecture using self-attention; foundational for modern LLMs."
},
"rag": {
"id": "rag",
"title": "Retrieval-Augmented Generation for Knowledge-Intensive NLP",
"authors": "Lewis et al.",
"year": 2020,
"venue": "NeurIPS",
"url": "https://arxiv.org/abs/2005.11401",
"summary": "Combines retrieval with generation to ground answers in external documents."
},
"tot": {
"id": "tot",
"title": "Tree of Thoughts: Deliberate Problem Solving with Large Language Models",
"authors": "Yao et al.",
"year": 2023,
"venue": "ArXiv/NeurIPS",
"url": "https://arxiv.org/abs/2305.10601",
"summary": "Explores branching reasoning strategies to improve complex problem solving."
},
"maml": {
"id": "maml",
"title": "Model-Agnostic Meta-Learning for Fast Adaptation",
"authors": "Finn et al.",
"year": 2017,
"venue": "ICML",
"url": "https://arxiv.org/abs/1703.03400",
"summary": "Meta-learning method for quick adaptation to new tasks with few examples."
},
"uncertainty": {
"id": "uncertainty",
"title": "Uncertainty in Deep Learning",
"authors": "Gal",
"year": 2016,
"venue": "PhD Thesis / Review",
"url": "https://arxiv.org/abs/1708.07250",
"summary": "Surveys Bayesian and practical approaches to uncertainty estimation in neural networks."
},
"constitutional": {
"id": "constitutional",
"title": "Constitutional AI: Harmlessness from AI Feedback",
"authors": "Bai et al.",
"year": 2022,
"venue": "ArXiv",
"url": "https://arxiv.org/abs/2212.08073",
"summary": "Technique for using a set of principles to align model behavior with safety goals."
},
"dpo": {
"id": "dpo",
"title": "Direct Preference Optimization",
"authors": "Rafailov et al.",
"year": 2023,
"venue": "ArXiv",
"url": "https://arxiv.org/abs/2305.19343",
"summary": "Preference optimization method that avoids separate reward model training."
},
"gnn": {
"id": "gnn",
"title": "A Comprehensive Survey on Graph Neural Networks",
"authors": "Zhou et al.",
"year": 2020,
"venue": "IEEE Transactions",
"url": "https://arxiv.org/abs/1901.00596",
"summary": "Survey of graph neural network models and applications, relevant for knowledge graphs."
},
"clip": {
"id": "clip",
"title": "Learning Transferable Visual Models From Natural Language Supervision (CLIP)",
"authors": "Radford et al.",
"year": 2021,
"venue": "ICML",
"url": "https://arxiv.org/abs/2103.00020",
"summary": "Demonstrates strong zero-shot transfer for image-text tasks using contrastive learning."
},
"dpr": {
"id": "dpr",
"title": "Dense Passage Retrieval for Open-Domain Question Answering",
"authors": "Karpukhin et al.",
"year": 2020,
"venue": "EMNLP",
"url": "https://arxiv.org/abs/2004.04906",
"summary": "Dense retrieval technique that improves recall for RAG pipelines."
}
}
def get_relevant_papers(prompt: str = "", mode: str = "standard", top_k: int = 3) -> List[Dict]:
"""Return a small list of relevant papers for a prompt or mode.
Heuristic matching: checks for keywords in the prompt and preferred mode
to return a concise, curated set of citations suitable for display in the UI.
"""
prompt_l = (prompt or "").lower()
keys = set()
# Mode-based preferences
if mode == "socratic":
keys.add("tot")
if mode == "technical":
keys.add("attention")
keys.add("dpr")
if mode == "code" or "implement" in prompt_l:
keys.add("dpr")
keys.add("rag")
if mode == "analogy":
keys.add("attention")
if mode == "eli5":
keys.add("constitutional")
# Keyword heuristics
if any(k in prompt_l for k in ["attention", "transformer", "self-attention"]):
keys.add("attention")
if any(k in prompt_l for k in ["retrieval", "rag", "search", "docs", "sources"]):
keys.add("rag")
keys.add("dpr")
if any(k in prompt_l for k in ["reasoning", "tree", "thought", "chain of thought"]):
keys.add("tot")
if any(k in prompt_l for k in ["meta", "maml", "adapt", "few-shot"]):
keys.add("maml")
if any(k in prompt_l for k in ["uncertainty", "confidence", "calibration"]):
keys.add("uncertainty")
if any(k in prompt_l for k in ["safety", "harm", "alignment", "constitutional"]):
keys.add("constitutional")
if any(k in prompt_l for k in ["graph", "knowledge graph", "gnn"]):
keys.add("gnn")
if any(k in prompt_l for k in ["image", "vision", "clip"]):
keys.add("clip")
# If no keys were found, return general high-impact papers
if not keys:
keys.update(["attention", "rag", "dpr"])
# Build list preserving some priority: attention -> rag -> dpr -> others
priority = ["attention", "rag", "dpr", "tot", "maml", "uncertainty", "constitutional", "dpo", "gnn", "clip"]
selected = [RESEARCH_PAPERS[k] for k in priority if k in keys and k in RESEARCH_PAPERS]
return selected[:top_k]
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