A team from the University of Michigan has introduced a gradient-refined adaptive activation steering framework called G-ACT to guide large language models toward generating scientific code in specific programming languages. G-ACT grew out of experiments that probed causal links in model activations, clustering per-prompt differences into steering directions. Lightweight probes train and adapt at each network layer during generation, selecting vectors that shift behavior toward the desired language. This design offers concept-level control, remains interpretable and scales to full-size models, delivering reproducible output in agentic systems that must choose consistent languages for scientific computing tasks.
Large language models have matured into natural language processors capable of managing agentic workflows. Scientific software depends heavily on C++, CUDA and other low-level languages that appear infrequently in standard pretraining corpora. Generated code often suffers from syntax or semantic errors, leading to build failures or unstable runtime behavior. Existing agents rely strongly on user-specified control primitives and carefully structured prompts—elements that models can misread, resulting in unpredictable execution flows.
Prior steering techniques tackle this problem at several levels. Supervised fine-tuning, weight modulation and reinforcement learning with human feedback give direct influence over model behavior but impose heavy computational costs and can weaken overall robustness. Activation patching adopts a corrupted-input baseline for fine-grained control, yet it requires exhaustive sweeps across millions of activations and finds most use on multiple-choice benchmarks rather than in real-world deployments.
G-ACT emerged after profiling five instruction-tuned models on scientific coding prompts. It aggregates activation response differences per prompt into distinct steering vectors. During code generation, small per-layer probes are trained online to detect when and how to apply each vector. These probes remain lightweight, ensuring the overhead stays practical and the steering remains transparent.
The evaluation suite covered Llama-3.2-3B-Instruct, Llama-3.3-70B-Instruct, Qwen2.5-Coder-32B-Instruct, Qwen2.5-14B-Instruct-1M and QwQ-32B. Each model tackled 84 benchmark questions, repeating each prompt 25 times at a sampling temperature of 1.0 to gather stable statistics. Baseline results revealed strong reproducible biases: Llama-3.2-3B-Instruct defaulted to Java in 76.2 percent of cases, while Llama-3.3-70B-Instruct favored Python at 73.8 percent. Within the Qwen family, the 32B coder edition leaned toward Python (59.5 percent) and the 14B variant preferred Julia (66.7 percent). These figures tie model scale, architectural decisions and fine-tuning data to consistent language preferences.
A static method analysis tested causal control by selectively activating individual MLP neurons in Llama-3.2-3B-Instruct. Targeting C++ generation triggered nearly 100 percent C++ outputs, effectively suppressing Python, Java and Julia. Further code generation tests revealed two behavioral regimes: tasks with high-level operations still produced between 40 and 80 percent Python outputs, whereas performance-critical routines shifted 60 to 90 percent toward C++.
When G-ACT steered generation, probe classification accuracy in early layers of LLaMA-3.2-3B rose from 0 percent to 61.5 percent. Generation slowed by roughly 1.3 to 1.4 times, but selective layer steering and caching made the runtime overhead manageable. By embedding persistent transformation matrices, G-ACT supports interventions that go beyond language choice, applying concept-level shifts to model representations. The framework delivers repeatable behavior across users and sets a new standard for reliable steering of scientific code generation in agentic systems.
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