Project summary

Quantized inference pipelines spanning the 4-16-bit spectrum (INT4, NF4, FP8, INT8, FP16/BF16) frequently experience localized entropy degradation under concurrent or adversarial workloads. Standard early-exit triggers rely exclusively on heuristic Top-1 confidence thresholds, making them fundamentally blind to internal logic drift and structural uncertainty within the remaining token distribution. This causes catastrophic hallucination cascades ("Dead Compute"), where the system commits to an erroneous token sequence while maintaining a false high-confidence metric.

We propose a non-invasive, runtime validation framework (Monitoring Layer) that actively segregates the safety mechanism into two distinct, decoupled operational layers: Detection and Remediation. Layer 1 blocks the corrupted early-exit path based on a hardware-aware Shannon Entropy calculation, and Layer 2 intercepts the execution loop to perform an "Inference Rewind"—flushing the corrupted KV-cache step and forcing a full-precision dense core recalculation to guarantee sequence integrity.

What are this project's goals? How will you achieve them?

Goals:

1. Deliver a fully validated, non-invasive hardware-aware runtime validation framework for 4-16-bit LLMs.

2. Publish a comprehensive technical report (Whitepaper) outlining the mathematical framework and empirical results.

3. Demonstrate production-ready benchmarks showcasing a double-digit reduction in "Dead Compute" operational costs for enterprise inference pipelines.

How we will achieve them:

* Month 1-2: Develop and integrate the Detection Module (Layer 1) using low-level NVML bindings to calculate post-softmax token probability Shannon Entropy dynamically calibrated against Tensor Core thermal coefficients (KT).

For mathematical validation of Layer 1, we utilize the Shannon Entropy formula to identify anomalies in token probability distributions:

H(p) = -Sum(pi * log(pi + ε))

* Month 3: Implement the Remediation Module (Layer 2) to manage the local KV-cache flush and deterministic state machine for the Inference Rewind execution loop.

For precise threshold calibration based on hardware state, we apply the following correlation model:

KT = 1.0 + max(0, T - 30) * 0.01

CL = f(Profile) + (Nf * KT)

(Where KT is the dynamic thermal coefficient, CL is the calculated execution threshold, and T represents the chip temperature in Celsius).

* Month 4: Run extensive stress-tests on dedicated bare-metal nodes to optimize the asymmetric cost-benefit profile, ensuring runtime overhead remains under 0.2-0.5%.

How will this funding be used?

The requested $20,000 budget is optimized and split 50/50 over the 4-month lifecycle:

1. Personnel Compensation / Research Grant ($10,000):

* Covers full-time engineering labor, custom CUDA hook optimization, integration of low-level NVML bindings, and the authorship of the final technical whitepaper.

2. Bare-Metal Compute Infrastructure ($10,000):

* 100% disbursed directly into decentralized bare-metal GPU infrastructure providers with native cryptographic settlement rails (such as Latitude.sh, LeaderGPU).

* This allocation guarantees dedicated, non-virtualized root access to H100/A100 instances to monitor raw hardware registers without hypervisor jitter.

Who is on your team? What's your track record on similar projects?

I am an independent technical auditor, systems engineer, and strategic risk consultant specializing in large language model inference efficiency, hardware-level optimization, and infrastructure safety auditing. I have a proven track record of analyzing edge-case performance degradation, DVFS/thermal behaviors under production workloads, and developing robust monitoring protocols for deployment safety. Operating solo ensures maximum execution speed and direct allocation of all resources into pure engineering and dedicated bare-metal testing.

What are the most likely causes and outcomes if this project fails?

Causes of Failure:

1. Potential localized hardware register reading restrictions across specific, non-standard cloud bare-metal hypervisors (mitigated by strictly selecting dedicated bare-metal nodes with raw NVML access).

2. Minor baseline entropy profile deviations across edge-case, un-tuned model architectures (mitigated by embedding adaptive domain-specific thresholding profiles).

Outcomes if it fails:

Even if the framework encounters unexpected edge-case constraints on specific architectures, the empirical data gathered during bare-metal thermal and precision stress-testing will be fully structured and published. This will provide the AI Safety community with a valuable open-access dataset regarding 4-16-bit logit drift dynamics under extreme compute loads, defining clear boundaries for future runtime validation research.

How much money have you raised in the last 12 months, and from where?

$0. This project is entirely self-initiated and independent. No prior funding, institutional grants, or venture capital have been raised for this research in the last 12 months, ensuring complete autonomy and alignment with open public goods.