GeckOpt Framework for LLM & HEP Efficiency

• GeckOpt is a dual-domain framework that optimizes system efficiency by applying intent-based tool selection in LLM systems and GPU offloading in optical photon simulations.
• The LLM instance uses GPT-4-Turbo for precise intent detection to filter tool registries, reducing token usage by up to 24.6% while maintaining high task accuracy.
• The GPU simulation instance accelerates photon propagation in HEP detectors, achieving up to 10× speedups with sub-millimeter accuracy compared to traditional CPU methods.

GeckOpt is the name of two distinct technical frameworks that independently target system efficiency in large‐scale computational settings: (1) a GPT-driven intent-based tool selection layer for LLM copilot systems, and (2) a hybrid CPU–GPU optical photon simulation workflow for high-energy physics (HEP) detectors, specifically the LHCb RICH 1 subsystem. Both instances share a focus on runtime resource optimization and transparent drop-in compatibility with established research pipelines, though each is developed within a separate domain and architectural context (Fore et al., 2024, Li et al., 2023).

1. Intent-Based Tool Selection for LLM Systems

GeckOpt, as described in "GeckOpt: LLM System Efficiency via Intent-Based Tool Selection" (Fore et al., 2024), introduces an intent-gating layer integrated with LLM planners for dynamic narrowing of system toolsets. The primary motivation is the observation that LLM-based copilots frequently waste a significant portion of the token budget serializing and reasoning over large, generic tool registries, even though many user prompts require only a small subset of capabilities.

Architecture and System Workflow

GeckOpt modifies the standard LLM-planner → tool-router pipeline by interposing a lightweight, GPT-4-Turbo–based intent detection phase. The typical pipeline proceeds as follows:

• Prompt Intake: The user submits a natural language prompt, $p$, to the copilot.
• Intent Detection: A GPT-4-Turbo classifier consumes $p$ and outputs a discrete intent label $I^\ast$ (e.g., “Load→Filter→Plot”, “Information Seeking”, “UI/Web Navigation”).
• Tool Registry Lookup: Each intent $I$ maps to a specific subset of the API library $\mathcal{L}$ through an offline-constructed tool registry, yielding $$T_{\text{filtered}} = \text{Registry}[I^\ast] \subset \mathcal{L}$$.
• Planner Execution: The narrowed toolset $T_{\text{filtered}}$ is included in the system prompt, and the planner LLM (usually with chain-of-thought or ReAct scaffolding) issues function calls.
• API Routing and Response Aggregation: An API router executes the requested function calls and returns results until the task flow completes.

Pseudocode for the GeckOpt processing step:

def geckopt_step(prompt): I_star = IntentLLM(prompt) T = Registry[I_star] response = PlannerLLM(prompt, tools=T) return response

If the selected toolset cannot satisfy an intermediate planner request, GeckOpt instructs the model to revert to the full tool registry.

Intent Inference Mechanism

The core insight is that a powerful LLM can serve as its own intent classifier. For an intent set $\{I_1, ..., I_n\}$, the intent is chosen by maximizing the probability: $I^* = \arg\max_k P(I_k \mid p) ,$ where $P(I_k \mid p)$ is estimated by an LLM completion over all available intents, optionally thresholded by a confidence parameter $\tau$. Empirical evaluation in geospatial applications reports over 90% classification accuracy with a single LLM call and an average overhead of ~100 tokens per task (Fore et al., 2024).

Tool Selection and Token Efficiency

Because each tool is serialized into the LLM prompt, the per-request context size scales approximately linearly with the number of candidate tools. If $C_{\text{tool}}$ is the mean token count per tool, the reduction in prompt size is

$$\Delta_{\text{ctx}} = C_{\text{tool}} \cdot (T_{\text{orig}} - T_{\text{filtered}}),$$

with $T_{\text{orig}} = |\mathcal{L}|$, and $T_{\text{filtered}} = |\text{Registry}[I^*]|$. In practice, with $T_{\text{orig}} \approx 40$ and $T_{\text{filtered}}$ averaging $8$–$12$, token usage was reduced by up to 24.6%, yielding significant cloud cost savings without material loss in task performance metrics.

2. GPU Offloading in Optical Photon Simulation

The independent GeckOpt framework described in "GPU-based optical photon simulation for the LHCb RICH 1 Detector" (Li et al., 2023) is a hybrid CPU–GPU approach for accelerating optical photon propagation in Geant4-based HEP simulations using the Opticks library and the NVIDIA OptiX ray-tracing API.

Hybrid Workflow and Execution Model

The GeckOpt workflow is partitioned into three stages:

• Initialization: Geant4’s detector geometry is parsed, and all optical-relevant properties are encoded into Opticks-specific GPU-compatible buffers. Domain geometry is translated, acceleration structures (BVH) built via OptiX, and cached if available.
• Event Handling: During each simulation event, Geant4 handles charged particle transport and records “genstep” structures for each Cherenkov/scintillation photon generation opportunity, instead of immediately propagating optical photons.
• GPU Offload: Accumulated gensteps are transferred to the GPU; the OptiX ray-generation program instantiates and propagates all required photons, applying Geant4-equivalent optical processes (boundary reflection, refraction, absorption, scattering) in CUDA. Once a photon reaches a sensor or is absorbed, hit data is recorded and finally transferred back to the CPU for integration into the Geant4 event.

Key features include explicit translation of all material, surface, and geometry information; all optical photon physics processes (Fresnel, Rayleigh, absorption) are inherited without additional approximation.

3. Implementation Details and Resource Management

LLM Tool Selection

• Intent Classifier: One GPT-4-Turbo completion per prompt, with intent inventory and aggressive prompt engineering for high-accuracy intent mapping.
• Tool Registry: Offline, curated mapping of intents to API subsets; manual in the geospatial domain but extensible to new task domains with further validation.
• Token Efficiency: Sharp decrease in serialized context size leads directly to lower cloud costs and improved throughput in multi-user, high parallelism settings (Fore et al., 2024).

GPU Photon Simulation

• RT Pipeline: OptiX pipeline comprised of RayGeneration, Miss, AnyHit, and ClosestHit shaders; all physics kernels implemented in CUDA.
• Memory: Geometry, materials, gensteps, and hit buffers reside in global GPU memory; hit buffers sized to accommodate multi-million photon events.
• Thread Scheduling: Launch dimensions matched to total photon count across events, with optimized block sizes for divergence/hardware utilization.
• Batch Processing: Performance scales linearly with photon count; offload efficiency reaches ≥95% for launches of order $10^5$ photons/event, resulting in up to $10\times$ speedup versus CPU-only Geant4 propagation (Li et al., 2023).

4. Validation, Metrics, and Performance

LLM System Evaluation

Experimental deployment on the GeoLLM-Engine platform (100+ GPT-4-Turbo nodes) using the GeoLLM-Engine-5k and -10k benchmarks yielded:

• Up to 24.6% reduction in system token count per task (e.g., from $32.5\times10^3$ to $25.1\times10^3$ tokens in ReAct few-shot condition).
• <1% relative change in standard task success metrics (accuracy, F1, Rouge-L).
• 10–15% fewer API calls per task.
• Overall latency reduction of 8–12%.
• Extrapolated cloud cost reduction of ~20% at scale (Fore et al., 2024).

Optical Photon Simulation Validation

Statistical and physics validation was performed with both simplified and full RICH 1 detector geometries:

• Mean per-muon MaPMT hit counts were consistent: Geant4 $\langle N \rangle = 60.2 \pm 7.1$, GeckOpt $\langle N \rangle = 60.0 \pm 7.0$.
• Spatial hit distributions (Cherenkov rings) overlapped to ${\mathcal O}(10^{-3})$ mm accuracy.
• Stacking $10^3$ events per launch enabled overall speedup of $5\times$, and pure photon propagation time for $>10^5$ photons was $10\times$ faster on GPU than on CPU (Li et al., 2023).
• Kernel-launch and BVH build fixed overheads were amortized as photon count increased.

5. Limitations and Forward Directions

LLM Intent Gating

• Domain specificity: Offline mappings require manual curation and may not transfer across heterogeneous benchmarks (e.g., ToolLLM, WebArena) without revalidation.
• Misclassification: Approximately 5% mis-gates necessitate fallback to the full toolset, incurring additional latency.
• Multi-intent prompts pose classification challenges.
• Proposed evolutions include semi-supervised clustering, dynamic tool set learning, and support for edge/on-device execution where token budgets interact directly with latency/energy constraints (Fore et al., 2024).

GPU Simulation

• Geometry conversion issues arise with complex CSG trees in LHCb’s RICH 1; incomplete or unbalanced boolean trees may require manual adjustment or geometry reduction.
• Physics parity with Geant4 depends on porting any user-implemented or “fast-kill” processes to corresponding CUDA kernels in Opticks.
• Migration to OptiX 7 offers explicit memory/module management and shader linking in exchange for increased code complexity.
• Ongoing integration with large-scale Gauss-based production systems and stability of the new OptiX 7 interface are active areas (Li et al., 2023).

6. Practical Implications and Contributions

GeckOpt, in both domains, demonstrates quantitatively significant reductions in computational and financial costs at scale without compromising core research or application objectives.

• In LLM systems, the framework presents a minimal-intrusion method for practitioners to lower token budgets, cloud expenses, and improve system throughput by delegating minimal extra work to an intent classifier. This paradigm shift—from all-tools-on-deck to intent-filtered toolset—retains multi-tool access and consistent user-facing results (Fore et al., 2024).
• In HEP photon simulations, GeckOpt provides a transparent, high-fidelity GPU offload workflow that reproduces Geant4 hit statistics to within errors while achieving $5$–$10\times$ acceleration, representing a potentially transformative resource saving for future high-occupancy experimental runs. Portability and modularity enable the extension of this approach to further detector components and simulation tasks (Li et al., 2023).

Domain	Key Optimization Mechanism	Quantitative Impact
LLM tool selection	Intent-based runtime gating	24.6% token/cost reduction, <1% Δmetrics
HEP photon simulation	GPU photon transport offload	$5$–$10\times$ CPU cost reduction, sub-mm accuracy

Both instantiations of GeckOpt embody data-driven, architecture-aware methodology for system-side efficiency enhancements and maintain technical and experimental transparency through exhaustive statistical validation and open performance reporting (Fore et al., 2024, Li et al., 2023).