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Paper Lantern distills 2M+ Computer Science research papers into the right method for your problem — its tradeoffs, benchmarks, and how to implement it — delivered directly to your coding agent.

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❯ Implement chunking for my RAG pipeline
⚡ research paper-lantern
Detected context: RAG pipeline · technical documents · multi-source corpus · accuracy-critical
Analyzed 6 papers from Jan–Feb 2026. 4 directly relevant:
● Cross-Document Topic-Aligned (CDTA) Chunking
arxiv:2601.05265 · Jan 2026
Instead of chunking each document independently, CDTA identifies topics across your entire corpus, maps segments to each topic, and synthesizes them into unified chunks. This eliminates the "knowledge fragmentation" problem where answers are scattered across multiple docs.
Why it matters here: Your multi-source corpus means answers often span documents. CDTA reaches 0.93 faithfulness vs 0.78 for semantic chunking (+12% over current best practice). At k=3, it maintains 0.91 while semantic methods drop to 0.68.
● G²-Reader: Dual Evolving Graphs
arxiv:2601.22055 · Jan 2026
Replaces flat chunking with a Content Graph that preserves document structure (headings, tables, figures) and a Planning Graph that tracks which evidence has been found. Prevents the common failure where iterative retrieval loops on partial evidence.
Why it matters here: If your docs have tables or diagrams, flat chunking breaks cross-modal alignment. G²-Reader with an open-source model outperforms standalone GPT-5 (66.2% vs 53.1%).
● Information Gain Pruning (IGP)
arxiv:2601.17532 · Jan 2026
Shows that NDCG (retrieval relevance) correlates weakly with QA quality and can become negatively correlated when injecting multiple passages. IGP scores each passage by how much it actually helps the generator, then prunes weak or harmful ones before they reach the LLM.
Why it matters here: Applies post-chunking as a reranking step. Delivers +12–20% F1 while reducing input tokens by 76–79% — directly cutting your LLM costs.
● Diversity-Focused RAG (DF-RAG)
arxiv:2601.17212 · Jan 2026
Standard cosine similarity retrieval maximizes relevance but returns redundant chunks. DF-RAG extends Maximal Marginal Relevance to dynamically optimize diversity per query at test time — no fine-tuning required.
Why it matters here: For open-ended or multi-facet queries, diversity-focused retrieval improves F1 by 4–10% over vanilla cosine similarity. Drop-in replacement.
⟐ Synthesis across papers
These papers address different stages of your pipeline and can be combined:
1. Chunking stage — Use CDTA if your queries need info from multiple documents (most RAG use cases). Use G²-Reader's structure-preserving approach if your docs are multimodal.
2. Retrieval stage — Add DF-RAG's diversity scoring to avoid redundant chunks, especially for open-ended queries.
3. Post-retrieval — Apply IGP to prune low-utility passages before they reach the LLM. This alone cuts token costs ~76% while improving quality.
Recommendation: Start with CDTA chunking + IGP pruning. This gives the highest impact for the lowest implementation effort — better answers at a fraction of the token cost.

Your coding agent searches blogs and Stack Overflow — a fraction of what's known. Paper Lantern finds methods published this month that can cut your development time from weeks to days — with benchmarks to prove it.

2 million+ papers. Techniques your agent can draw from:

RAG & Retrieval
Chunking strategies700+
Query expansion & routing500+
Reranking methods400+
Hybrid & multi-stage search300+
LLM Serving & Inference
Quantization & pruning1,100+
Batching & scheduling500+
KV cache optimization300+
Speculative decoding200+
Agent Design
Planning & reasoning400+
Memory & context management300+
Tool use & function calling200+
Multi-agent coordination200+
Fine-tuning & Training
LoRA / QLoRA / adapters900+
Distributed training700+
RLHF & preference tuning500+
Data efficiency & curriculum400+
Prompting & Evaluation
Few-shot & in-context learning800+
LLM-as-judge & evaluation500+
Output control & formatting400+
Chain-of-thought & decomposition300+
Search, Ranking & Recs
Recommendation systems2,100+
Knowledge graphs1,800+
Vector indexes & ANN500+
Learned sparse retrieval400+

… plus systems design, networking, databases, security, NLP, computer vision, and more

What changes when research informs your code

Decisions engineers face every week — with research-backed answers

"MY LLM APP GIVES WRONG ANSWERS"
Your agent adds more context. With Paper Lantern, it learns that retrieval relevance barely correlates with answer quality — and that pruning weak passages actually improves results.
+20%answer quality
"MY API IS TOO SLOW"
Your agent optimizes your code. With Paper Lantern, it discovers a decoding-level technique that can multiply your LLM throughput with zero quality loss — no infrastructure changes needed.
2–3×faster response times
"MY AGENT KEEPS LOOPING"
Your agent debugs the loop. With Paper Lantern, it sees that the architecture itself is the problem — planning-first approaches complete more tasks in fewer steps than reactive loops.
–40%tool calls, higher completion
"FINE-TUNING IS EATING MY GPU BUDGET"
Your agent suggests full fine-tuning. With Paper Lantern, it finds that at your data size, an adapter method matches the same quality — papers pinpoint the exact crossover.
4×less compute, same quality
"MY CURSOR / CLAUDE BILL IS TOO HIGH"
Your agent stuffs context to be safe. With Paper Lantern, it learns that fewer, better-selected passages actually improve answers — research-backed pruning cuts input tokens by 76% while boosting quality.
–76%token costs, better answers
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