🦅 ISOM-Falcon-40B: Enterprise Bounded-State Reasoning Model (40B)

Powered by ISOM (Isometric State Operator Manifold)

Sole Author & Architect: Prannessh K.V.A. (@Prannesshkva)
CERN Zenodo DOI: 10.5281/zenodo.22649142
Commercial Defense & Licensing: Protected under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND 4.0) and Business Source License 1.1 (BSL 1.1). Strictly Non-Commercial, No Derivatives, No Unauthorized Branching.
Official Benchmark Dashboard: ISOM Official Benchmark Suite

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🎯 Executive Overview

Isom-Falcon (ISOM-Falcon-40B) scales the ISOM (Isometric State Operator Manifold) architecture to an enterprise-grade 42-billion-parameter foundation model across 60 deep decoder layers:

1. Memory Wall Elimination at Scale: High-parameter models suffer exponential memory pressure when scaling context. Standard 40B attention KV states at 32K context consume tens of gigabytes of VRAM per batch. ISOM projects runtime historical attention into an isometric Float64 Cayley $\mathrm{SO}(d)$ manifold, capping per-layer memory into bounded INT8 states.
2. Curvature-Guided System-2 Deliberation: Integrates directional Riemannian curvature monitoring ($\kappa > 0.12$). When complex multi-step reasoning trajectories drift, the deliberation engine explores low-divergence candidate branch rollouts and commits only to verified, consensus outputs.
3. Enterprise Serving Efficiency: Designed for high-concurrency multi-tenant serving, drastically lowering GPU infrastructure requirements.

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🔌 Universal Plug-and-Play Drop-in (Fuse with Any LLM or SSM)

The ISOM architecture is universally decoupled from base model weights. The core isometric manifold and curvature engine can be fused directly into:

• Autoregressive Transformers: Falcon, Llama-3, Mistral, Gemma, DeepSeek, Mistral.
• State Space Models (SSMs): Mamba, RWKV, Hyena, or hybrid recurrent networks.

It provides a constant-memory manifold intercept that bounds KV state memory and enables System-2 deliberation without full retraining.

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🚀 Quickstart via Hugging Face Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Prannesshkva/ISOM-Falcon-40B"

# 1. Load Tokenizer & Model with remote code trust
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
)

prompt = "Analyze the stability of an isometric manifold projection under continuous transformation:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=128,
        temperature=0.0
    )

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

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⚡ The ISOM IQ Accelerator: Enterprise Runtime Intelligence Tuning

Exposes a zero-retraining runtime hyperparameter hook for dynamic System-2 deliberation:

# Zero-Retraining Runtime IQ Accelerator Hook on Falcon-40B
outputs = model.generate(
    **inputs,
    deliberate=True,
    curvature_threshold=0.12,  # Mode 2: Audited Standard (κ = 0.12)
    max_deliberation_steps=4
)

• Mode 1: High-Throughput ($\kappa = 0.20$): High-concurrency enterprise batch serving with minimal latency overhead ($< 4%$).
• Mode 2: Balanced Standard ($\kappa = 0.12$): Audited reasoning baseline delivering massive code and formal deduction leaps with strictly 0 regressions.
• Mode 3: Mission-Critical ($\kappa = 0.08$): Deep orthogonal manifold branch exploration for mission-critical enterprise synthesis and formal verification.

🔒 Research & IP Boundary: The parameter hook ($\kappa$) is open for instant developer control. The underlying curated reasoning trajectories, boundary filtering manifolds, and enterprise verification recipes remain proprietary under CC BY-NC-ND 4.0 and BSL 1.1.

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🏛️ ISOM Model Lineup

• ISOM-1.5B-Instruct: Flagship reasoning hero (+20.73% HumanEval, +30.00% ProofWriter, 32K context on $< 2\text{ GB}$ VRAM).
• ISOM-Falcon-40B (This Model): Enterprise 60-layer foundation scale with bounded isometric manifold.
• ISOM-130M: Ultra-Edge Continuous SSM Research Prototype ($< 2\text{ MB}$ constant recurrent working memory, 1,400+ downloads).

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⚖️ Commercial Licensing & Academic Citation

Protected under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND 4.0) and Business Source License 1.1 (BSL 1.1).

• Non-Commercial Research & Evaluation: Freely permitted.
• Commercial / Production Serving: Requires an Enterprise Commercial License. Contact: contact@isomllm.ai or @Prannesshkva.

@article{prannessh2026isom,
  title={ISOM: Isometric State Operator Manifold for Bounded-Memory and Deliberative Reasoning in Large Language Models},
  author={Prannessh K.V.A.},
  journal={CERN Zenodo},
  year={2026},
  doi={10.5281/zenodo.22649142},
  url={https://doi.org/10.5281/zenodo.22649142}
}