I’m curious about the mechanics of the MCP integration. When Paper Lantern sends results back to the coding agent, how much context is actually included? Are you returning full paper sections, or more compressed summaries?
I’m especially thinking about token budget constraints. If the agent is already operating with a large codebase in its context window, does Paper Lantern’s output compete for that same space?
we don't send raw paper sections to the paper or simply compressed summaries. we send simple explanations of the method used (for the human user), implementation instructions for the coding agent, and benchmarks to compare methods.
we've struck a balance between giving enough details and using too many tokens to make sure that every token we use provides value.
compared to the modern 200K / 1M context windows, Paper Lantern's output wont use a significant portion of it
I don't mean to be a jerk, I think this looks cool... but unless I'm missing something this is just a sign up page for something that may or may not exist someday?
From the Show HN rules: "Off topic: blog posts, sign-up pages, newsletters, lists, and other reading material. Those can't be tried out, so can't be Show HNs. Make a regular submission instead."
The best engineering knowledge is locked in research papers. Paper Lantern unlocks it for your code.
Paper Lantern is an MCP server that distills 2M+ CS research papers into the right method for your problem — its tradeoffs, benchmarks, and how to implement it — delivered directly to your coding agent. Works with Claude Code, Cursor, Copilot, any MCP client.
Your coding agent can search for papers, but it's searching the open web — not a purpose-built research index. And even when it finds papers, the hard questions remain: which methods actually matter for your problem? What are the tradeoffs at your scale? What was tried and failed? What should you actually implement? That reasoning lives in papers and it never reaches your code.
EXAMPLE
ask your agent to implement chunking for a RAG pipeline. Paper Lantern detects your context — multi-source corpus, accuracy-critical, technical documents — then searches across 2M+ papers and finds 4 from January 2026 that directly apply. It explains each technique in plain language, shows why it matters for your specific setup, synthesizes how they address different pipeline stages, and recommends what to start with and why — with implementation details your coding agent can act on immediately.
One of those papers: a cross-document topic-aligned chunking approach hitting 0.93 faithfulness vs 0.78 for semantic chunking (arxiv:2601.05265). Another: a pruning method that cuts input tokens 76% while improving answer quality (arxiv:2601.17532).
The index covers agent design, RAG and retrieval, LLM inference, fine-tuning, evaluation, search and ranking — hundreds of techniques across applied CS.
BACKGROUND
I spent 7 years leading various LLM and RAG teams at AWS Bedrock (IIT-Bombay, Stanford). Paper Lantern started as a research discovery platform - this is the same engine with additional reasoning, now plugged into coding workflows via MCP.
Looking for engineers who use coding agents daily. Happy to answer questions about the search, the synthesis, or the MCP integration.
You mention the index covers "applied CS" — where does it draw the line? If I'm implementing a time series forecasting model, does it cover the stats/econometrics literature too, or only CS-venue papers? A lot of the best techniques for engineering problems live in adjacent fields. Same question for computational biology, NLP-for-legal, etc.
for now, we are covering all of Computer Science - most techniques that are code-able likely show up there - but yes, if it is impactful we are open to expanding to other nearby domains too :)
How do you evaluate the synthesis quality? Like if I ask about chunking strategies and it recommends method A over method B for my use case — how do you know that recommendation is actually good? Have you run any benchmarks on recommendation accuracy, or is this more of a "trust the retrieval + LLM reasoning" setup?
we've run a few tests for this. The first was quite innovative - we created a mapping of method-to-target-problem by using the papers themselves because each paper says which problems it's methods target. Then we checked whether our recommendation followed that mapping or not.
The best test was of course to actually run coding agents without and with our mcp server, we saw performance improvements on the metrics that the user requested for on 100+ such real scenarios, so this was convincing enough then.
Amazing work. Really nice that you included actual numbers (0.93 vs 0.78) in your semantic chunking example. When the synthesis recommends a method, does it pull benchmark comparisons across papers so I can see how methods perform on the same metrics? That's the thing that takes me forever to compile manually.
Yes - that's a pain, especially because different papers often use different test sets, test settings or different metrics. What we do is find benchmarks across papers; aggregate them, highlight what is comparable and what is not
actually, we also just give a short set of bullet points to summarize all of it for you - so accepting the output is easy for you
Interesting. A few questions about the search layer: are you using dense retrieval, sparse, or hybrid? At 2M papers, how do you handle the drift between how engineers describe problems ("my RAG pipeline hallucinates on long docs") vs how papers describe solutions ("cross-document coherence in retrieval-augmented generation")? That query-document vocabulary gap is the hard part of academic search.
we've been working on search for over a year now - it's a complex hybrid system now. so it does use primitives like word-based search and embeddings etc. but it's power comes from a unqiue combination of all these and more techniques together.
yes, the gap between engineer descriptions and paper description is real - we had to work on that. we use a combinations of LLMs, vectors and a few more techniques to create a good mapping between the two. the vocab gap didn't harm us too much because we aren't only using word overlaps etc.
With the landscape of AI/ ML space changing so rapidly, the ability for coding agents having access to SOTA research in their hand makes me feel that I suddenly would have the ability to solve some of the toughest engineering problems and expedite my journey in contributing in solving problems in new areas which I have always aspired to solve.
this is exactly what we are aiming for - that you can apply your ideas and imagination, without having to be an expert in everything. most applications need expertise in multiple areas to work but you might have some great ideas in a specific part of it.
Paper Lantern enables you to get the best version of all other aspects and then you can iterate on your ideas for the aspect you care about - to boost a much more compelx application
Thank you, I think that's the best part where you can contribute in areas where you might not have direct expertise but you may still care about.
Would this be also be able to provide research ideas for multi-modal space and what is your recommended approach for the best way of experimenting with the research recommendations based on your experience for an early understanding of whether a particular solution works for your use-case or not? Thanks!
for every single paper, we describe the method and when it is relevant; we give our final recommendation on whether a certain technique would work for your use-case; ultimately we make it easy for you to understand the options, make recommendations but you choose which one to finally go ahead with.
the recommendations are generally good - so if you dont feel opinionated about a certain area - go ahead with it :)
Appreciate MCP server availability, will help integrate with Claude. I literally have a Notion database where I tag papers by technique and link them to our codebase decisions. It's a nightmare to maintain. Does Paper Lantern keep any kind of project memory - like "for this repo, we previously decided on approach Y because of paper Z" - so the reasoning compounds over time?
yes - it instructs the coding agent to maintain a markdown file for the outputs from paper lantern, which the coding agent can refer to later. it also asks the coding agent to record whether the human user accepted the suggestion or not.
that way it helps you keep record of things - but also, you can come later and ask : which of our decisions now have updates in research ? can we adopt them ?
I’m curious about the mechanics of the MCP integration. When Paper Lantern sends results back to the coding agent, how much context is actually included? Are you returning full paper sections, or more compressed summaries? I’m especially thinking about token budget constraints. If the agent is already operating with a large codebase in its context window, does Paper Lantern’s output compete for that same space?
we don't send raw paper sections to the paper or simply compressed summaries. we send simple explanations of the method used (for the human user), implementation instructions for the coding agent, and benchmarks to compare methods.
we've struck a balance between giving enough details and using too many tokens to make sure that every token we use provides value.
compared to the modern 200K / 1M context windows, Paper Lantern's output wont use a significant portion of it
I don't mean to be a jerk, I think this looks cool... but unless I'm missing something this is just a sign up page for something that may or may not exist someday?
From the Show HN rules: "Off topic: blog posts, sign-up pages, newsletters, lists, and other reading material. Those can't be tried out, so can't be Show HNs. Make a regular submission instead."
The best engineering knowledge is locked in research papers. Paper Lantern unlocks it for your code.
Paper Lantern is an MCP server that distills 2M+ CS research papers into the right method for your problem — its tradeoffs, benchmarks, and how to implement it — delivered directly to your coding agent. Works with Claude Code, Cursor, Copilot, any MCP client.
Your coding agent can search for papers, but it's searching the open web — not a purpose-built research index. And even when it finds papers, the hard questions remain: which methods actually matter for your problem? What are the tradeoffs at your scale? What was tried and failed? What should you actually implement? That reasoning lives in papers and it never reaches your code.
EXAMPLE ask your agent to implement chunking for a RAG pipeline. Paper Lantern detects your context — multi-source corpus, accuracy-critical, technical documents — then searches across 2M+ papers and finds 4 from January 2026 that directly apply. It explains each technique in plain language, shows why it matters for your specific setup, synthesizes how they address different pipeline stages, and recommends what to start with and why — with implementation details your coding agent can act on immediately.
One of those papers: a cross-document topic-aligned chunking approach hitting 0.93 faithfulness vs 0.78 for semantic chunking (arxiv:2601.05265). Another: a pruning method that cuts input tokens 76% while improving answer quality (arxiv:2601.17532).
The index covers agent design, RAG and retrieval, LLM inference, fine-tuning, evaluation, search and ranking — hundreds of techniques across applied CS.
BACKGROUND I spent 7 years leading various LLM and RAG teams at AWS Bedrock (IIT-Bombay, Stanford). Paper Lantern started as a research discovery platform - this is the same engine with additional reasoning, now plugged into coding workflows via MCP.
Looking for engineers who use coding agents daily. Happy to answer questions about the search, the synthesis, or the MCP integration.
code.paperlantern.ai
You mention the index covers "applied CS" — where does it draw the line? If I'm implementing a time series forecasting model, does it cover the stats/econometrics literature too, or only CS-venue papers? A lot of the best techniques for engineering problems live in adjacent fields. Same question for computational biology, NLP-for-legal, etc.
for now, we are covering all of Computer Science - most techniques that are code-able likely show up there - but yes, if it is impactful we are open to expanding to other nearby domains too :)
Yes - would be interested in other domains. I'm particularly interested in computational biology. Let me know when that becomes available.
you should checkout paperlantern.ai , our main website - it already has biomedcine search over 45M+ papers :)
if you want MCP access to it, we can definitely do it - let us know
How do you evaluate the synthesis quality? Like if I ask about chunking strategies and it recommends method A over method B for my use case — how do you know that recommendation is actually good? Have you run any benchmarks on recommendation accuracy, or is this more of a "trust the retrieval + LLM reasoning" setup?
we've run a few tests for this. The first was quite innovative - we created a mapping of method-to-target-problem by using the papers themselves because each paper says which problems it's methods target. Then we checked whether our recommendation followed that mapping or not.
The best test was of course to actually run coding agents without and with our mcp server, we saw performance improvements on the metrics that the user requested for on 100+ such real scenarios, so this was convincing enough then.
Amazing work. Really nice that you included actual numbers (0.93 vs 0.78) in your semantic chunking example. When the synthesis recommends a method, does it pull benchmark comparisons across papers so I can see how methods perform on the same metrics? That's the thing that takes me forever to compile manually.
Yes - that's a pain, especially because different papers often use different test sets, test settings or different metrics. What we do is find benchmarks across papers; aggregate them, highlight what is comparable and what is not
actually, we also just give a short set of bullet points to summarize all of it for you - so accepting the output is easy for you
Interesting. A few questions about the search layer: are you using dense retrieval, sparse, or hybrid? At 2M papers, how do you handle the drift between how engineers describe problems ("my RAG pipeline hallucinates on long docs") vs how papers describe solutions ("cross-document coherence in retrieval-augmented generation")? That query-document vocabulary gap is the hard part of academic search.
we've been working on search for over a year now - it's a complex hybrid system now. so it does use primitives like word-based search and embeddings etc. but it's power comes from a unqiue combination of all these and more techniques together.
yes, the gap between engineer descriptions and paper description is real - we had to work on that. we use a combinations of LLMs, vectors and a few more techniques to create a good mapping between the two. the vocab gap didn't harm us too much because we aren't only using word overlaps etc.
With the landscape of AI/ ML space changing so rapidly, the ability for coding agents having access to SOTA research in their hand makes me feel that I suddenly would have the ability to solve some of the toughest engineering problems and expedite my journey in contributing in solving problems in new areas which I have always aspired to solve.
yes :)
this is exactly what we are aiming for - that you can apply your ideas and imagination, without having to be an expert in everything. most applications need expertise in multiple areas to work but you might have some great ideas in a specific part of it.
Paper Lantern enables you to get the best version of all other aspects and then you can iterate on your ideas for the aspect you care about - to boost a much more compelx application
Thank you, I think that's the best part where you can contribute in areas where you might not have direct expertise but you may still care about.
Would this be also be able to provide research ideas for multi-modal space and what is your recommended approach for the best way of experimenting with the research recommendations based on your experience for an early understanding of whether a particular solution works for your use-case or not? Thanks!
yes works for multi-modal
for every single paper, we describe the method and when it is relevant; we give our final recommendation on whether a certain technique would work for your use-case; ultimately we make it easy for you to understand the options, make recommendations but you choose which one to finally go ahead with.
the recommendations are generally good - so if you dont feel opinionated about a certain area - go ahead with it :)
Appreciate MCP server availability, will help integrate with Claude. I literally have a Notion database where I tag papers by technique and link them to our codebase decisions. It's a nightmare to maintain. Does Paper Lantern keep any kind of project memory - like "for this repo, we previously decided on approach Y because of paper Z" - so the reasoning compounds over time?
yes - it instructs the coding agent to maintain a markdown file for the outputs from paper lantern, which the coding agent can refer to later. it also asks the coding agent to record whether the human user accepted the suggestion or not.
that way it helps you keep record of things - but also, you can come later and ask : which of our decisions now have updates in research ? can we adopt them ?