ISOM-R1-Coder-16B-MoE-Beta: 160K Repository-Scale Bounded-Memory MoE

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Overview

ISOM-R1-Coder-16B-MoE-Beta is a 16-billion parameter Mixture-of-Experts (MoE) reasoning and code intelligence model engineered for constant-memory execution over massive 160K repository contexts. Powered by the ISOM-R1 bounded manifold architecture, it incorporates 64 routed experts (2 shared, TopK=6 activated per token) and Multi-Head Latent Attention (MLA) wrapped in a native continuous state-space caching engine.

Traditional Transformers suffer from severe $O(N)$ linear memory explosion in the KV cache across long sequences. By dynamically projecting Multi-Head Latent Attention states onto a continuous isometric manifold (Float64 Cayley SO(d) with multi-head landmark Shannon saliency and hybrid BM25/ColBERT holographic revival), the ISOM-R1 engine guarantees an $O(1)$ bounded working memory footprint across 160,000+ token traces.

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The ISOM-R1 Model Family

Model	Primary Architecture Role	Base Lineage (Independent Derivative)	Total / Active Parameters	Max Context	Cache Complexity	Hardware Target
ISOM-R1-Coder-16B-MoE-Beta	160K Bounded Code & MLA MoE	DeepSeek-Coder-V2-Lite (Non-Endorsed)	15.71B / 2.36B Active	163,840 (160K)	O(1) Bounded Manifold (Architectural Spec)	16GB Cloud / Multi-GPU
ISOM-R1-Enterprise-40B-Beta	40B System-2 Foundation Reasoning	Falcon-40B (Non-Endorsed)	40.0B Dense	32,768 (32K)	O(1) Bounded State (Architectural Spec)	Enterprise Multi-GPU (24GB-80GB)
ISOM-R1-Reasoning-1.5B-Instruct-Beta	32K System-2 Mathematical Deliberation	Qwen2.5-1.5B-Instruct (Non-Endorsed)	1.54B Dense	32,768 (32K)	O(1) Bounded State (Tesla T4 Verified)	8GB Edge / Consumer GPUs
ISOM-R1-Edge-130M-MoE-Beta-Prototype	Unbounded Recurrent Drafter & SSM	Standalone Continuous SSM + MoE	134.89M / 58.27M Active	Unbounded Recurrence	O(1) Recurrent State (0.0469 MB Verified)	Ultra-Low Power Edge & CPU

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Architectural Specifications

Metric	Specification
Total Parameters	15.71 Billion
Active Parameters / Token	2.36 Billion
Architecture Engine	ISOM-R1 Bounded State Cache + MLA + MoE (64 routed, 2 shared)
Foundation Initialization	DeepSeek-Coder-V2-Lite-Instruct (Non-Endorsed Derivative)
Context Window	163,840 tokens (160K)
Reasoning Threshold ($B$)	16,384 tokens
Working Memory Complexity	$O(1)$ Bounded State (Amortized Slack Pruning)
Weight Data Types	BFloat16 / Float16 (Native), INT8 Dynamic Quantization

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Mathematical Foundation: The 160K Memory Wall

In standard Multi-Head Latent Attention without ISOM, cache memory grows linearly with context length:

$$M_{\text{vanilla}}(N)=2\times L\times H\times d_v\times N\times \text{bytes}M\_\{\backslash text\{vanilla\}\}(N)\; =\; 2\; \backslash times\; L\; \backslash times\; H\; \backslash times\; d\_v\; \backslash times\; N\; \backslash times\; \backslash text\{bytes\}$$

At 160K context (163,840 tokens) across 27 layers with 16 attention heads and $d_v = 128$: $$M_{\text{vanilla}}(160\text{K})=2\times 27\times 16\times 128\times 163,840\times 2\approx \mathbf{36.21}\text{ GB}M\_\{\backslash text\{vanilla\}\}(160\backslash text\{K\})\; =\; 2\; \backslash times\; 27\; \backslash times\; 16\; \backslash times\; 128\; \backslash times\; 163,840\; \backslash times\; 2\; \backslash approx\; \backslash mathbf\{36.21\backslash text\{\; GB\}\}$$

Under the ISOM architecture, historical key-value states exceeding the reasoning threshold $B = 16,384$ are projected onto the isometric manifold:

$$M_{\text{ISOM}}(N)=\min (N,B)\times \text{stride}+\text{dim}({\mathcal{M}}_{\text{Cayley}})M\_\{\backslash text\{ISOM\}\}(N)\; =\; \backslash min(N,\; B)\; \backslash times\; \backslash text\{stride\}\; +\; \backslash text\{dim\}(\backslash mathcal\{M\}\_\{\backslash text\{Cayley\}\})$$

Sequence Length	Vanilla Attention KV (FP16)	ISOM Latent State Spec (FP16)	ISOM Latent State Spec (INT8)
4,096 tokens	0.90 GB	0.90 GB	0.45 GB
16,384 tokens	3.62 GB	3.62 GB	1.81 GB
32,768 tokens	7.24 GB	3.62 GB	1.81 GB
65,536 tokens	14.48 GB	3.62 GB	1.81 GB
163,840 tokens (160K)	36.21 GB	3.62 GB	1.81 GB

Architectural Specification Notice: Values above represent theoretical dimensional derivations based on Multi-Head Latent Attention (MLA) parameters. Empirical validation across 160K sequences requires an 80GB enterprise GPU cluster and is not claimed as an audited hardware measurement.

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Quickstart & Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Prannesshkva/ISOM-R1-Coder-16B-MoE-Beta"

# Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

# Load Model with native ISOM execution
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

prompt = """<|im_start|>user
Write a high-performance concurrent queue in Python using lock-free atomic CAS operations.<|im_end|>
<|im_start|>assistant
"""

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

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.3,
        do_sample=True
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Citation & Contact

@software{isom_coder_16b_beta_2026,
  author = {Prannessh K.V.A.},
  title = {ISOM-R1-Coder-16B-MoE-Beta: 160K Repository-Scale Bounded-Memory MoE},
  year = {2026},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.22649142},
  url = {https://doi.org/10.5281/zenodo.22649142}
}

• Sole Author & Architect: Prannessh K.V.A.
• LinkedIn: Prannessh K.V.A.
• Zenodo DOI: 10.5281/zenodo.22649142
• Space: ISOM-Benchmark

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Notice of Non-Endorsement & Independent Lineage

Independent Derivative Work: ISOM-R1-Coder-16B-MoE-Beta is an independent research implementation developed solely by Prannessh K.V.A. (Author, Architect & IP Holder). It utilizes the foundation weight initialization and base MoE topology of deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct under the official DeepSeek Model License. This release is not endorsed by, sponsored by, or affiliated with DeepSeek AI or its parent entities. All modifications, continuous Cayley SO(d) projection operators, dynamic first-layer detection, and isometric memory-bounding manifolds are original contributions of the author.