Ziyue Yang and coauthors claim RoofLang's implementation-independent DSL lets an optimizer agent discover inference architectures with evaluated throughput and interactivity gains of 6.23–50.1% for DeepSeek V4 Pro on NVIDIA B300, potentially expanding optimization beyond existing software-stack limits.

state: seedheat: mediumuncertainty: mediumconvergesscott: lowinference-economics llm-serving inference-optimization ai-systems-designZiyue YangYuting JiangLei QuPeng Cheng

What is this?

The case describes RoofLang as a domain-specific language for AI-driven design of LLM inference systems, attributed to Ziyue Yang and coauthors, with a general workload representation and a verifiable space of changes independent of implementation. It claims optimizer-agent designs yield 6.23–50.1% throughput and interactivity gains for DeepSeek V4 Pro on NVIDIA B300, but none of the supplied web snippets mentions RoofLang or verifies its authorship, evaluation method, baselines, or results. The search instead returns NVIDIA software-stack optimizations and a separate ZFLOW AI announcement of simulation-guided SGLang configuration tuning; these do not substantiate RoofLang's claimed advance beyond existing stack limits.

Why it matters to Scott

RoofLang’s claimed implementation-independent, verifiable design space directionally converges with Scott’s Spec-Driven Development position: represent design and constraints upstream of implementation so agents can explore alternatives. On the supplied evidence this remains an unverified example, not an established extension or actionable serving improvement for his projects; the radar tracks adjacent inference-design optimizers in Profile and Backpressure, but no supplied page tracks RoofLang itself.

ip:concept.spec-driven-developmentradar:profile-cost-aware-inference-optimizerradar:backpressure-llm-serving-simulator

queries asked of Scott's wikis

agent optimization loops verifiable search spaces
domain-specific languages implementation-independent system design
inference economics throughput latency tradeoffs
simulation-guided optimization hardware validation
serving runtime constraints versus architecture search

Measured heat

now 0 pts/hpeak 0 pts/hcomments 0/hpeers p14momentum: steady2 platformsage 746h

points/hour across evidence · reading as of 2026-10-12 02:59:37.977291+11:00 · deterministic, not a model opinion

How the heat travelled

09-10 14:00	⭐ origin echo-reconstructed	The authors present RoofLang as a DSL providing a general workload representation, verifiable mutation space, and implementation-independent Ziyue Yang, Yuting Jiang, Lei Qu, Peng Cheng on paper (echo) · attributed from hn.story.49704018	—
09-14 21:06	first on hacker news · published · +103.1h	RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems matt_d	—
09-14 21:06	amplified on hacker news 👑	hn.story.49704018 matt_d	peak 7 · 0 comments · 99% of case engagement
09-14 21:21	our radar first saw it · +103.3h	discovery anchor: hn.story.49704018	—

pace: p28 vs 519 stories at the 720h mark (now 746h old) — ahead of aafp-commons-signed-agent-notebook (2.0x), behind agentgate-signed-agent-receipts (0.7x)

Evidence (2) — ⭐ canonical anchor

source	object	author	score	comments
🟧 hn	RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems Retrieved article excerpt Open article · Retrieved 2026-09-14T21:22:04.438212+00:00 # Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2609.12551 (cs) [Submitted on 11 Sep 2026] # Title:RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems Authors:[Ziyue Yang](https://arxiv.org/search/cs?searchtype=author&query=Yang,+Z), [Yuting Jiang](https://arxiv.org/search/cs?searchtype=author&query=Jiang,+Y), [Lei Qu](https://arxiv.org/search/cs?searchtype=author&query=Qu,+L), [Peng Cheng](https://arxiv.org/search/cs?searchtype=author&query=Cheng,+P) View a PDF of the paper titled RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems, by Ziyue Yang and 3 other authors [View PDF](https://arxiv.org/pdf/2609.12551) [HTML (experimental)](https://arxiv.org/html/2609.12551v1) > Abstract:AI is beginning to make substantive contributions to LLM inference optimization. Existing AI optimizations are predominantly profiling-based. Profiling-bound feedback confines the search to the capabilities and performance of an existing software stack, preventing a fundamentally better architecture of LLM inference systems from being identified. To enable the AI-driven LLM inference system architecting loop, we argue that a general workload representation, a verifiable mutation space, and an implementation-independent evaluator are required. We present the RoofLang domain-specific language (DSL) that provides these features. In our evaluation, RoofLang reveals that DeepSeek V4-series models could achieve 3.5-39.5$\times$ higher peak decode throughput than other representative models. This gap is disproportionate to their total parameter counts and arises largely from compact KV-cache designs that support larger batches and reduce memory traffic. A persistent optimizer agent further discovered several new architectures that improved both throughput and interactivity of DeepSeek V4 Pro on NVIDIA B300 by 6.23-50.1%. \| \| \| \| --- \| --- \| \| Subjects: \| Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI) \| \| Cite as: \| [arXiv:2609.12551](https://arxiv.org/abs/2609.12551) [cs.DC] \| \| \| (or [arXiv:2609.12551v1](https://arxiv.org/abs/2609.12551v1) [cs.DC] for this version) \| \| \| <https://doi.org/10.48550/arXiv.2609.12551> Focus to learn more arXiv-issued DOI via DataCite (pending registration) \| ## Submission history From: Ziyue Yang [[view email](https://arxiv.org/show-email/b2713765/2609.12551)] [v1] Fri, 11 Sep 2026 07:57:28 UTC (249 KB) Full-text links: ## Access Paper: View a PDF of the paper titled RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems, by Ziyue Yang and 3 other authors - [View PDF](https://arxiv.org/pdf/2609.12551) - [HTML (experimental)](https://arxiv.org/html/2609.12551v1) - [TeX Source](https://arxiv.org/src/2609.12551) [view license](http://arxiv.org/licenses/nonexclusive-distrib/1.0/ "Rights to this article") ### Current browse context: cs.DC [< prev](https://arxiv.org/prevnext?id=2609.12551&function=prev&context=cs.DC "previous in cs.DC (accesskey p)") \| [next >](https://arxiv.org/prevnext?id=2609.12551&function=next&context=cs.DC "next in cs.DC (accesskey n)") [new](https://arxiv.org/list/cs.DC/new) \| [recent](https://arxiv.org/list/cs.DC/recent) \| [2026-09](https://arxiv.org/list/cs.DC/2026-09) Change to browse by: [cs](https://arxiv.org/abs/2609.12551?context=cs) [cs.AI](https://arxiv.org/abs/2609.12551?context=cs.AI) ### References & Citations - [NASA ADS](https://ui.adsabs.harvard.edu/abs/arXiv:2609.12551) - [Google Scholar](https://scholar.google.com/scholar_lookup?arxiv_id=2609.12551) - [Semantic Scholar](https://api.semanticscholar.org/arXiv:2609.12551) export BibTeX citation Loading... ## BibTeX formatted citation × loading... Data provided by: ### Bookmark [BibSonomy](http://www.bibsonomy.org/BibtexHandler?requTask=upload&url=https://arxiv.org/abs/2609.12551&description=RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems "Bookmark on BibSonomy") [Reddit](https://reddit.com/submit?url=https://arxiv.org/abs/2609.12551&title=RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems "Bookmark on Reddit") Bibliographic Tools # Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ([What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer)) Connected Papers Toggle Connected Papers ([What is Connected Papers?](https://www.connectedpapers.com/about)) Litmaps Toggle Litmaps ([What is Litmaps?](https://www.litmaps.co/)) scite.ai Toggle scite Smart Citations ([What are Smart Citations?](https://www.scite.ai/)) Code, Data, Media # Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ([What is alphaXiv?](https://alphaxiv.org/)) Links to Code Toggle CatalyzeX Code Finder for Papers ([What is CatalyzeX?](https://www.catalyzex.com)) DagsHub Toggle DagsHub ([What is DagsHub?](https://dagshub.com/)) GotitPub Toggle Gotit.pub ([What is GotitPub?](http://gotit.pub/faq)) Huggingface Toggle Hugging Face ([What is Huggingface?](https://huggingface.co/huggingface)) ScienceCast Toggle ScienceCast ([What is ScienceCast?](https://sciencecast.org/welcome)) Demos # Demos Replicate Toggle Replicate ([What is Replicate?](https://replicate.com/docs/arxiv/about)) Spaces Toggle Hugging Face Spaces ([What is Spaces?](https://huggingface.co/docs/hub/spaces)) Spaces Toggle TXYZ.AI ([What is TXYZ.AI?](https://txyz.ai)) Related Papers # Recommenders and Search Tools Link to Influence Flower Influence Flower ([What are Influence Flowers?](https://influencemap.cmlab.dev/)) Core recommender toggle CORE Recommender ([What is CORE?](https://core.ac.uk/services/recommender)) - Author - Venue - Institution - Topic About arXivLabs # arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? [Learn more about arXivLabs](https://info.arxiv.org/labs/index.html). [Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2609.12551) \| Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))	matt_d	7	0
🟧 echo.paper ⭐	The authors present RoofLang as a DSL providing a general workload representation, verifiable mutation space, and implementation-independent	Ziyue Yang, Yuting Jiang, Lei Qu, Peng Cheng	—	—

source

object

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comments

🟧 hn

RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems

Retrieved article excerpt

Open article · Retrieved 2026-09-14T21:22:04.438212+00:00

# Computer Science > Distributed, Parallel, and Cluster Computing

**arXiv:2609.12551** (cs)

[Submitted on 11 Sep 2026]

# Title:RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems

Authors:[Ziyue Yang](https://arxiv.org/search/cs?searchtype=author&query=Yang,+Z), [Yuting Jiang](https://arxiv.org/search/cs?searchtype=author&query=Jiang,+Y), [Lei Qu](https://arxiv.org/search/cs?searchtype=author&query=Qu,+L), [Peng Cheng](https://arxiv.org/search/cs?searchtype=author&query=Cheng,+P)

View a PDF of the paper titled RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems, by Ziyue Yang and 3 other authors

[View PDF](https://arxiv.org/pdf/2609.12551)
[HTML (experimental)](https://arxiv.org/html/2609.12551v1)
> Abstract:AI is beginning to make substantive contributions to LLM inference optimization. Existing AI optimizations are predominantly profiling-based. Profiling-bound feedback confines the search to the capabilities and performance of an existing software stack, preventing a fundamentally better architecture of LLM inference systems from being identified. To enable the AI-driven LLM inference system architecting loop, we argue that a general workload representation, a verifiable mutation space, and an implementation-independent evaluator are required. We present the RoofLang domain-specific language (DSL) that provides these features. In our evaluation, RoofLang reveals that DeepSeek V4-series models could achieve 3.5-39.5$\times$ higher peak decode throughput than other representative models. This gap is disproportionate to their total parameter counts and arises largely from compact KV-cache designs that support larger batches and reduce memory traffic. A persistent optimizer agent further discovered several new architectures that improved both throughput and interactivity of DeepSeek V4 Pro on NVIDIA B300 by 6.23-50.1%.

|  |  |
| --- | --- |
| Subjects: | Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI) |
| Cite as: | [arXiv:2609.12551](https://arxiv.org/abs/2609.12551) [cs.DC] |
|  | (or  [arXiv:2609.12551v1](https://arxiv.org/abs/2609.12551v1) [cs.DC] for this version) |
|  | <https://doi.org/10.48550/arXiv.2609.12551> Focus to learn more  arXiv-issued DOI via DataCite (pending registration) |

## Submission history

From: Ziyue Yang [[view email](https://arxiv.org/show-email/b2713765/2609.12551)]   
 **[v1]**
Fri, 11 Sep 2026 07:57:28 UTC (249 KB)

Full-text links:

## Access Paper:

View a PDF of the paper titled RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems, by Ziyue Yang and 3 other authors

- [View PDF](https://arxiv.org/pdf/2609.12551)
- [HTML (experimental)](https://arxiv.org/html/2609.12551v1)
- [TeX Source](https://arxiv.org/src/2609.12551)

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matt_d

🟧 echo.paper ⭐

The authors present RoofLang as a DSL providing a general workload representation, verifiable mutation space, and implementation-independent

Ziyue Yang, Yuting Jiang, Lei Qu, Peng Cheng

—

Interpretation history

2026-09-14T21:28:41Z
grounded: converges/low — RoofLang’s claimed implementation-independent, verifiable design space directionally converges with Scott’s Spec-Driven Development position: represent design a
2026-09-14T21:22:26Z
case created — A distinct first-party research artifact offers a concrete architecture-search mechanism and quantitative claims, without yet establishing realized serving gains.

Decision trace

10-04 08:32review_dormantscheduled targets exhausted or 28 quiet days
10-04 08:32drop_targetsquiet through full ladder or over cap 8
09-15 07:28groundRoofLang’s claimed implementation-independent, verifiable design space directionally converges with Scott’s Spec-Driven Development position: represent design and constraints upstream of implementatio
09-15 07:22createA distinct first-party research artifact offers a concrete architecture-search mechanism and quantitative claims, without yet establishing realized serving gains.