, :, Jhunjhunwala, Aastha, Srivastava, Aayushi Ashokkumar, Gupta, Abhibha, Garg, Abhinav, Khattar, Abhinav, Wildish, Achsah, Rajfer, Adam, Renduchintala, Adi, Asif, Adil, Miranda, Adriana Flores, Zaman, Aileen, Chhabra, Aishwarya, Shukla, Akanksha, Bercovich, Akhiad, Rico, Alejandra, Gordic, Aleksa, Ficek, Aleksander, Gronskiy, Alex, Steiner, Alex, Bukharin, Alexander, Milesi, Alexandre, Ghias, Ali Roshan, Shehper, Ali, Taghibakhshi, Ali, Gatti, Alice, Liu, Alisa, Kumar, Alok, Tripathy, Alok, Deshmukh, Amala Sanjay, Miryala, Aman, Shanbhag, Aman, Mahabaleshwarkar, Ameya Sunil, Velingker, Ameya, Klein, Amir, Samani, Amir, Zuker, Amit, Geifman, Amnon, Bhiwandiwalla, Anahita, Subramaniam, Ananth, Manoel, Andre, Santilli, Andrea, Tao, Andrew, Skliar, Andrii, Thomas, Anil, Mahishi, Anish, Shors, Anna, Warno, Anna, Doshi, Anuj, Kamath, Anusha, Kiran, Anwith, Tang, Ao, Mehta, Arham, Dhara, Arnab, Komaragiri, Arnav, Kundu, Arnav, Jain, Arti, Rao, Arun, Venkatesan, Arun, Goel, Arushi, Anoosheh, Asha, Vaswani, Ashish, Aithal, Ashwath, Ahamed, Asif, Thekkumpate, Asma Kuriparambil, Visva, Aswin, Sohrabizadeh, Atefeh, Vem, Avinash, Dattagupta, Ayush, Schifferer, Benedikt, Chislett, Benjamin, Nguyen, Bernard, Nushi, Besmira, Kartal, Bilal, Rouhani, Bita Darvish, Chen, Bobby, Ginsburg, Boris, Wang, Boxin, Norick, Brandon, Kisacanin, Branislav, Johnson, Brendan, Dang, Brian, Yu, Brian, Catanzaro, Bryan, Lee, Byung-Kwan, del Mundo, Carlo, Lee, Chankyu, Hwang, Chantal, Wang, Charles, Truong, Charlie, Cui, Chen, Wang, Cheng, Hsieh, Cheng-Ping, Yu, Chenhan, Luo, Chenjie, Patel, Chintan, Alexiuk, Chris, Johnson, Chris, Wing, Chris · Oct 28, 2026
NVIDIA presents Nemotron 3.5 models for agentic workloads
Markdown · JSON · Original source
’s authors introduce Nemotron 3.5 Super and Lightning, open models built for reasoning, long-context understanding, and workloads—tasks where models use tools or take actions. Super is a , while Lightning is a smaller text model; both use a Hybrid Mamba-Transformer design and are intended for efficient deployment. The authors describe training and deployment methods aimed at capability and efficiency. They report that Super reaches 51.78% on SWE-bench Pro, a software-engineering , and say it surpasses Nemotron 3 Ultra while using approximately 4.6x fewer active parameters. They also report improvements on Indian-language benchmarks, speculative-decoding acceptance length, and inference speed, alongside results for . These are reported evaluations and performance figures, not evidence here of reliability in everyday use. The work matters as an attempt to combine agent-oriented capabilities with lower active and faster inference. The abstract does not establish how the models perform across independent evaluations, real-world deployments, or different user needs. How well do the reported benchmark and efficiency results carry over to independent tests and practical agentic workloads?
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Oct 28, 2026 (2026-10-28).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-10-30T14:18:24.350Z. First observed: 2026-10-30T06:45:32.424Z. Retrieved: 2026-10-30T14:17:36.344Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
Evidence is the abstract, not the full paper; it does not provide evaluation protocols, detailed comparisons, or independent replication. The abstract reports and deployment figures but does not establish reliability or performance in everyday use or across practical workloads.
Open question
How well do the reported and efficiency results carry over to independent tests and practical workloads?
Source-stated
The authors introduce two open models designed for reasoning, long-context understanding, and agentic workloads.
We introduce Nemotron 3.5 Super and Nemotron 3.5 Lightning, open and efficient Mixture-of-Experts Hybrid Mamba-Transformer models designed for reasoning, long-context understanding, and agentic workloads.
Source-stated
Super is a 120B-parameter multimodal model with 12B active parameters; Lightning is a 30B-parameter text model with 3B active parameters.
Super is a 120B-parameter, 12B-active multimodal model, while Lightning is a smaller 30B-parameter, 3B-active text model
Source-stated
The authors report Super achieves 51.78% on SWE-bench Pro and surpasses Nemotron 3 Ultra using approximately 4.6x fewer active parameters.
The post-trained Super model achieves 51.78% on SWE-bench Pro, surpassing Nemotron 3 Ultra despite using approximately 4.6x fewer active parameters.
Source-stated
The authors report that Indic continued pretraining improves Indian-language benchmarks by up to 9.82 percentage points.
Indic continued pretraining improves Indian-language benchmarks by up to 9.82 percentage points
Source-stated
The abstract reports inference speeds up to 4,856 output tokens/s/GPU on GB200 while sustaining at least 50 tokens/s/user.
Inference optimizations enable up to 4,856 output tokens/s/GPU on GB200 while sustaining at least 50 tokens/s/user.
Source and contributor context
NVIDIA, :, Jhunjhunwala, Aastha, Srivastava, Aayushi Ashokkumar, Gupta, Abhibha, Garg, Abhinav, Khattar, Abhinav, Wildish, Achsah, Rajfer, Adam, Renduchintala, Adi, Asif, Adil, Miranda, Adriana Flores, Zaman, Aileen, Chhabra, Aishwarya, Shukla, Akanksha, Bercovich, Akhiad, Rico, Alejandra, Gordic, Aleksa, Ficek, Aleksander, Gronskiy, Alex, Steiner, Alex, Bukharin, Alexander, Milesi, Alexandre, Ghias, Ali Roshan, Shehper, Ali, Taghibakhshi, Ali, Gatti, Alice, Liu, Alisa, Kumar, Alok, Tripathy, Alok, Deshmukh, Amala Sanjay, Miryala, Aman, Shanbhag, Aman, Mahabaleshwarkar, Ameya Sunil, Velingker, Ameya, Klein, Amir, Samani, Amir, Zuker, Amit, Geifman, Amnon, Bhiwandiwalla, Anahita, Subramaniam, Ananth, Manoel, Andre, Santilli, Andrea, Tao, Andrew, Skliar, Andrii, Thomas, Anil, Mahishi, Anish, Shors, Anna, Warno, Anna, Doshi, Anuj, Kamath, Anusha, Kiran, Anwith, Tang, Ao, Mehta, Arham, Dhara, Arnab, Komaragiri, Arnav, Kundu, Arnav, Jain, Arti, Rao, Arun, Venkatesan, Arun, Goel, Arushi, Anoosheh, Asha, Vaswani, Ashish, Aithal, Ashwath, Ahamed, Asif, Thekkumpate, Asma Kuriparambil, Visva, Aswin, Sohrabizadeh, Atefeh, Vem, Avinash, Dattagupta, Ayush, Schifferer, Benedikt, Chislett, Benjamin, Nguyen, Bernard, Nushi, Besmira, Kartal, Bilal, Rouhani, Bita Darvish, Chen, Bobby, Ginsburg, Boris, Wang, Boxin, Norick, Brandon, Kisacanin, Branislav, Johnson, Brendan, Dang, Brian, Yu, Brian, Catanzaro, Bryan, Lee, Byung-Kwan, del Mundo, Carlo, Lee, Chankyu, Hwang, Chantal, Wang, Charles, Truong, Charlie, Cui, Chen, Wang, Cheng, Hsieh, Cheng-Ping, Yu, Chenhan, Luo, Chenjie, Patel, Chintan, Alexiuk, Chris, Johnson, Chris, Wing, Chris · Article authors (partial credit list)
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Agentic AI
AI systems that plan and carry out a series of steps toward a goal, often by using other software, rather than giving one answer.
Multimodal model
A model that works with more than one kind of input or output, such as text, images and audio.
Mixture of experts
A model design that sends each input to a few specialized sub-networks instead of the whole model, which saves computation.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Quantization
Storing a model's numbers at lower precision so it uses less memory and runs faster, sometimes at a cost in accuracy.
Model parameters
The internal numbers a model adjusts during training; their count is a rough measure of a model's size.
NVIDIA
NVIDIA develops processors, networking and software used for AI, graphics and scientific computing.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 210ed1d2-369f-4613-871e-5585f4f35d9d.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-10-30T14:18:24.350Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: c53949a5a18cc1554ea2a08b215a502c2e84d1b9d970cb422b0fe54a5986946c. Source text hash: ba849d11c8da60be17f24560a485c35f417f762b1053826fb466008c64925881.
Revision and source-check history
Version 1 · 2026-10-30T14:18:24.350Z
NVIDIA presents Nemotron 3.5 models for agentic workloads
’s authors introduce Nemotron 3.5 Super and Lightning, open models built for reasoning, long-context understanding, and workloads—tasks where models use tools or take actions. Super is a , while Lightning is a smaller text model; both use a Hybrid Mamba-Transformer design and are intended for efficient deployment. The authors describe training and deployment methods aimed at capability and efficiency. They report that Super reaches 51.78% on SWE-bench Pro, a software-engineering , and say it surpasses Nemotron 3 Ultra while using approximately 4.6x fewer active parameters. They also report improvements on Indian-language benchmarks, speculative-decoding acceptance length, and inference speed, alongside results for . These are reported evaluations and performance figures, not evidence here of reliability in everyday use. The work matters as an attempt to combine agent-oriented capabilities with lower active and faster inference. The abstract does not establish how the models perform across independent evaluations, real-world deployments, or different user needs. How well do the reported benchmark and efficiency results carry over to independent tests and practical agentic workloads?
Original source
cs.AI · Article
Source details
Original source date: Oct 28, 2026 (2026-10-28).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-10-30T14:18:24.350Z. First observed: 2026-10-30T06:45:32.424Z. Retrieved: 2026-10-30T14:17:36.344Z.
Limits
Evidence is the abstract, not the full paper; it does not provide evaluation protocols, detailed comparisons, or independent replication. The abstract reports and deployment figures but does not establish reliability or performance in everyday use or across practical workloads.
Open question
How well do the reported and efficiency results carry over to independent tests and practical workloads?
Source-stated
The authors introduce two open models designed for reasoning, long-context understanding, and agentic workloads.
We introduce Nemotron 3.5 Super and Nemotron 3.5 Lightning, open and efficient Mixture-of-Experts Hybrid Mamba-Transformer models designed for reasoning, long-context understanding, and agentic workloads.
Source-stated
Super is a 120B-parameter multimodal model with 12B active parameters; Lightning is a 30B-parameter text model with 3B active parameters.
Super is a 120B-parameter, 12B-active multimodal model, while Lightning is a smaller 30B-parameter, 3B-active text model
Source-stated
The authors report Super achieves 51.78% on SWE-bench Pro and surpasses Nemotron 3 Ultra using approximately 4.6x fewer active parameters.
The post-trained Super model achieves 51.78% on SWE-bench Pro, surpassing Nemotron 3 Ultra despite using approximately 4.6x fewer active parameters.
Source-stated
The authors report that Indic continued pretraining improves Indian-language benchmarks by up to 9.82 percentage points.
Indic continued pretraining improves Indian-language benchmarks by up to 9.82 percentage points
Source-stated
The abstract reports inference speeds up to 4,856 output tokens/s/GPU on GB200 while sustaining at least 50 tokens/s/user.
Inference optimizations enable up to 4,856 output tokens/s/GPU on GB200 while sustaining at least 50 tokens/s/user.
Source and contributor context
NVIDIA, :, Jhunjhunwala, Aastha, Srivastava, Aayushi Ashokkumar, Gupta, Abhibha, Garg, Abhinav, Khattar, Abhinav, Wildish, Achsah, Rajfer, Adam, Renduchintala, Adi, Asif, Adil, Miranda, Adriana Flores, Zaman, Aileen, Chhabra, Aishwarya, Shukla, Akanksha, Bercovich, Akhiad, Rico, Alejandra, Gordic, Aleksa, Ficek, Aleksander, Gronskiy, Alex, Steiner, Alex, Bukharin, Alexander, Milesi, Alexandre, Ghias, Ali Roshan, Shehper, Ali, Taghibakhshi, Ali, Gatti, Alice, Liu, Alisa, Kumar, Alok, Tripathy, Alok, Deshmukh, Amala Sanjay, Miryala, Aman, Shanbhag, Aman, Mahabaleshwarkar, Ameya Sunil, Velingker, Ameya, Klein, Amir, Samani, Amir, Zuker, Amit, Geifman, Amnon, Bhiwandiwalla, Anahita, Subramaniam, Ananth, Manoel, Andre, Santilli, Andrea, Tao, Andrew, Skliar, Andrii, Thomas, Anil, Mahishi, Anish, Shors, Anna, Warno, Anna, Doshi, Anuj, Kamath, Anusha, Kiran, Anwith, Tang, Ao, Mehta, Arham, Dhara, Arnab, Komaragiri, Arnav, Kundu, Arnav, Jain, Arti, Rao, Arun, Venkatesan, Arun, Goel, Arushi, Anoosheh, Asha, Vaswani, Ashish, Aithal, Ashwath, Ahamed, Asif, Thekkumpate, Asma Kuriparambil, Visva, Aswin, Sohrabizadeh, Atefeh, Vem, Avinash, Dattagupta, Ayush, Schifferer, Benedikt, Chislett, Benjamin, Nguyen, Bernard, Nushi, Besmira, Kartal, Bilal, Rouhani, Bita Darvish, Chen, Bobby, Ginsburg, Boris, Wang, Boxin, Norick, Brandon, Kisacanin, Branislav, Johnson, Brendan, Dang, Brian, Yu, Brian, Catanzaro, Bryan, Lee, Byung-Kwan, del Mundo, Carlo, Lee, Chankyu, Hwang, Chantal, Wang, Charles, Truong, Charlie, Cui, Chen, Wang, Cheng, Hsieh, Cheng-Ping, Yu, Chenhan, Luo, Chenjie, Patel, Chintan, Alexiuk, Chris, Johnson, Chris, Wing, Chris · Article authors (partial credit list)
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Agentic AI
AI systems that plan and carry out a series of steps toward a goal, often by using other software, rather than giving one answer.
Multimodal model
A model that works with more than one kind of input or output, such as text, images and audio.
Mixture of experts
A model design that sends each input to a few specialized sub-networks instead of the whole model, which saves computation.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Quantization
Storing a model's numbers at lower precision so it uses less memory and runs faster, sometimes at a cost in accuracy.
Model parameters
The internal numbers a model adjusts during training; their count is a rough measure of a model's size.
NVIDIA
NVIDIA develops processors, networking and software used for AI, graphics and scientific computing.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 210ed1d2-369f-4613-871e-5585f4f35d9d.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-10-30T14:18:24.350Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: c53949a5a18cc1554ea2a08b215a502c2e84d1b9d970cb422b0fe54a5986946c. Source text hash: ba849d11c8da60be17f24560a485c35f417f762b1053826fb466008c64925881.
passed · 2026-10-30T14:18:24.350Z · anthropic/claude-opus-5.5
Publication: 210ed1d2-369f-4613-871e-5585f4f35d9d
attribution — pass: The draft attributes the work and its reported results to NVIDIA's authors. Benchmark and efficiency figures are framed as reported by the authors, not as established fact.
Authors:NVIDIA: Aastha Jhunjhunwala
summary — pass: The title, summary and question match the abstract. The draft names the actor and the two models. It explains agentic workloads and SWE-bench Pro in plain language and states the efficiency aim. Technical terms such as Mixture-of-Experts Hybrid Mamba-Transformer, active parameters and speculative decoding are kept exactly, suited to tooltips. The question is a genuine open issue that follows from the abstract-only evidence.
Across training and deployment, we jointly optimize capability and efficiency.
date — pass: The originalPublished date of 2026-10-28 matches the arXiv v1 submission date. It is not the later feed date of 2026-10-30.
[Submitted on 28 Oct 2026]
support — pass: Each claim is directly supported by an exact abstract quote: the parameter counts, the 51.78% SWE-bench Pro score, the 4.6x comparison, the up to 9.82-point Indic gain and the throughput figures. The significance sentence about combining capability with efficiency reflects the authors' stated aim. The caveats correctly avoid turning benchmark scores into claims of real-world reliability.
The post-trained Super model achieves 51.78% on SWE-bench Pro, surpassing Nemotron 3 Ultra despite using approximately 4.6x fewer active parameters.
media — pass: The selected media is the verified direct link to the original arXiv abstract page. It is the most direct way to access the source.
Challenge or correct this reading
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NVIDIA develops processors, networking and software used for AI, graphics and scientific computing.
Explore NVIDIANVIDIA develops processors, networking and software used for AI, graphics and scientific computing.
Explore NVIDIAAI systems that plan and carry out a series of steps toward a goal, often by using other software, rather than giving one answer.
Definition referenceA model that works with more than one kind of input or output, such as text, images and audio.
Definition referenceA model design that sends each input to a few specialized sub-networks instead of the whole model, which saves computation.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceStoring a model's numbers at lower precision so it uses less memory and runs faster, sometimes at a cost in accuracy.
Definition referenceThe internal numbers a model adjusts during training; their count is a rough measure of a model's size.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceAI systems that plan and carry out a series of steps toward a goal, often by using other software, rather than giving one answer.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceAI systems that plan and carry out a series of steps toward a goal, often by using other software, rather than giving one answer.
Definition referenceNVIDIA develops processors, networking and software used for AI, graphics and scientific computing.
Explore NVIDIAAI systems that plan and carry out a series of steps toward a goal, often by using other software, rather than giving one answer.
Definition referenceA model that works with more than one kind of input or output, such as text, images and audio.
Definition referenceA model design that sends each input to a few specialized sub-networks instead of the whole model, which saves computation.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceStoring a model's numbers at lower precision so it uses less memory and runs faster, sometimes at a cost in accuracy.
Definition referenceThe internal numbers a model adjusts during training; their count is a rough measure of a model's size.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceAI systems that plan and carry out a series of steps toward a goal, often by using other software, rather than giving one answer.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceAI systems that plan and carry out a series of steps toward a goal, often by using other software, rather than giving one answer.
Definition reference