We introduce Intern-S2-Mobius, a 35B foundation model built on the Mobius-v0 architecture realized by Xtuner and LMDeploy. Instead of binding knowledge storage and reasoning computation layer by layer as in conventional Transformer models, Mobius organizes knowledge into a globally shared Memory and lets multiple Reasoners iteratively query and refine hidden states against this shared repository.
This knowledge-reasoning separation gives Intern-S2-Mobius two native capabilities: Backward Residual Connection, where reasoning stages can access knowledge beyond their local layer hierarchy, and Dynamic Latent Reasoning, where deliberation, refinement, and multi-token prediction are internalized into high-density continuous states. Continual-pretrained from Qwen3.5-35B and further post-trained with SFT and RL, Intern-S2-Mobius preserves strong downstream capability while achieving substantially higher end-to-end inference efficiency, with nearly 4x speedup reported in the technical report.
Features
Knowledge-reasoning decoupled architecture. Intern-S2-Mobius separates knowledge vectors from reasoning operators by replacing layer-bound FFN knowledge storage with a globally shared Memory. This gives each Reasoner access to a broader knowledge space and improves knowledge compression compared with a standard Transformer layout.
Backward Residual Connection. Through shared Memory, shallow and deep reasoning stages can access knowledge across the model rather than relying only on forward layer-wise information flow. This enables more flexible cross-layer knowledge composition and helps the model synthesize useful information in fewer reasoning steps.
Dynamic Latent Reasoning. Mobius refines continuous hidden states through recurrent latent iteration before decoding. This internalizes part of the deliberation process, reduces reliance on long visible chain-of-thought, and dynamically allocates computation to different tokens.
Higher inference efficiency with concise reasoning. On reasoning benchmarks, Intern-S2-Mobius reaches comparable or stronger scores than the Qwen3.5-35B baseline while producing markedly shorter reasoning traces and higher request throughput, leading to nearly 4x end-to-end inference speedup in the reported evaluation.
Strong general and scientific performance. Intern-S2-Mobius improves the reported average score over Qwen3.5-35B on general reasoning benchmarks, and shows large gains on scientific tasks such as Biology-Instructions, Mol-Instructions, and MolecularIQ.
Mobius inference efficiency
Fig1: Inference efficiency on reasoning benchmarks. Intern-S2-Mobius improves request throughput over the Transformer baseline while maintaining strong reasoning performance, with gains largely coming from shorter, more compact reasoning traces.
chain of thought
Fig2: The average output length of Mobius continual pre-trained from Qwen3.5.
Performance
We evaluate the Intern-S2-Mobius on various benchmarks, including general datasets and scientific datasets. We report the performance comparison with Qwen3.5-35B below. We use the OpenCompass to evaluate all models. For text benchmarks, Intern-S2-Mobius is evaluated with a maximum inference length of 64K tokens on MMLU Pro, SimpleQA, and HLE, and 128K tokens on the remaining text benchmarks.
performance
Fig3: Performance comparison across general and scientific benchmarks. The higher score in each row is highlighted in bold.
case study
Fig4: Step-aligned comparison between Intern-S2-Mobius-35B and Qwen3.5-35B
on a linear-algebra multiple-choice question. Both models select the correct
answer (Option C). Token counts are computed using the Qwen3.5-35B tokenizer. Mobius completes the same reasoning steps with fewer tokens, which mainly benefits from the model's elimination of repeated derivation and checks.
Quick Start
The Intern-S2-Mobius release is a 35B model stored in bfloat16 weight format. This guide provides deployment examples for the following configurations:
MTP speculative decoding (Recommended)
Basic serving without MTP
NOTE: The commands below are reference configurations. Inference frameworks are under active development, so use the latest framework documentation and your local validation results when tuning production deployments.
Intern-S2-Mobius can be deployed using any of the following LLM inference frameworks:
LMDeploy
Transformer
vLLM
Sampling Parameters
We recommend using the following hyperparameters to ensure better results
python
1top_p =12top_k =503min_p =0.04temperature =0.8
LMDeploy
Use the latest LMDeploy with Intern-S2-Mobius support. The examples below use single-GPU serving.
