ISOM-R1-Reasoning-1.5B-Instruct: 32K System-2 Deliberation Foundation

Bounded-State Recurrent Manifold • Verified on NVIDIA Tesla T4 Cloud GPU • Authentic Literature Long-Context Benchmark

Overview

ISOM-R1-Reasoning-1.5B-Instruct is a 1.54-billion parameter reasoning model tailored for extended System-2 multi-step deliberation without the memory explosion typical of standard Transformer architectures. At 8K-32K context, standard attention mechanisms allocate gigabytes of memory for key-value caches, triggering CUDA OutOfMemoryError on 15GB GPUs. ISOM applies recurrent state manifold projection, keeping working memory bounded.

📊 Audited Empirical Hardware Telemetry (NVIDIA Tesla T4, Kaggle Cloud)

The following benchmark was executed on NVIDIA Tesla T4 (14.56 GB / 14,911.7 MB total VRAM, PyTorch 2.10.0+cu128, CUDA 12.8, Kaggle Cloud) directly comparing Prannesshkva/ISOM-R1-Reasoning-1.5B-Instruct against baseline Qwen/Qwen2-1.5B-Instruct using authentic literary prose (Pride and Prejudice, 728,846 characters, unpadded):

Authentic Literature Long-Context A/B Comparison

Key Hardware Findings:

1. Immediate Memory Wall Crash in Vanilla: Standard attention triggers a fatal CUDA OutOfMemoryError at 8,192 tokens of continuous reasoning, exceeding the 14.9 GB VRAM ceiling of the Tesla T4.
2. Bounded Execution: ISOM successfully deliberates across 8,192 tokens in 2.20 seconds within a stable 9,168.6 MB footprint.
3. Throughput Scaling: At 4,096 tokens, ISOM executes in 1.12 seconds versus 2.09 seconds for the baseline (nearly 2x faster).

Quickstart Inference

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Prannesshkva/ISOM-R1-Reasoning-1.5B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)
model.eval()

prompt = """<|im_start|>user
Let G be a finite group with |G| = 35. Prove that G is cyclic.<|im_end|>
<|im_start|>assistant
<thought>
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.6,
        do_sample=True
    )

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Citation & Licensing

@article{prannessh2026isom_reasoning,
  title={ISOM-R1-Reasoning-1.5B-Instruct: Bounded-Memory Deliberative Reasoning Engine},
  author={Prannessh K.V.A.},
  journal={CERN Zenodo},
  year={2026},
  doi={10.5281/zenodo.22649142},
  url={https://doi.org/10.5281/zenodo.22649142}
}

• Author & Architect: Prannessh K.V.A.
• LinkedIn: Prannessh K.V.A.
• License: Governed by CC BY-NC-ND 4.0 (Non-Commercial Research) & Enterprise Commercial Terms. See LICENSE.

Notice of Non-Endorsement & Independent Lineage

[!IMPORTANT] Independent Derivative Work: ISOM-R1-Reasoning-1.5B-Instruct is an independent development engineered solely by Prannessh K.V.A. (Author & Architect). It builds upon Qwen/Qwen2.5-1.5B-Instruct under the Apache 2.0 License. This research is not affiliated with, endorsed by, or sponsored by Alibaba Cloud or the Qwen team. All continuous isometric state operator manifolds, Cayley SO(d) projection operators, and memory-bounding integrations are proprietary contributions of the author.