ConVA: Controlled Value Vector Activation

• Controlled Value Vector Activation (ConVA) is a methodology that manipulates latent neural activations to precisely align outputs with desired values.
• It employs contrastive datasets and linear probes to isolate and steer value vectors in deep learning and reinforcement learning systems.
• ConVA enhances model transparency and consistent behavior, enabling reliable value alignment even under adversarial prompts.

Controlled Value Vector Activation (ConVA) refers to a class of methodologies that enable the explicit manipulation and alignment of neural network activations—particularly within LLMs and deep reinforcement learning systems—so that internal representations and model outputs reflect desired concepts, values, or behavioral attributes in a precise, interpretable, and controllable manner. By identifying and steering salient value vectors in hidden layers, ConVA methods provide mechanisms for reliably aligning model behaviors with human values, improving system transparency, and facilitating precise content or value control even in the presence of conflicting or adversarial prompts.

1. Conceptual Foundation

Controlled Value Vector Activation builds on the representation of high-level concepts or values as specific directions (i.e., vectors) within the latent activation space of neural models. The premise is that a model’s response to particular values or concepts can be isolated via careful construction of contrastive datasets and linear probes, resulting in a weight vector that reliably signifies the presence of the target value. This vector, when manipulated, allows for the systematic control or amplification/suppression of the associated value in subsequent model activations and outputs (Zhang et al., 10 Jan 2025, Jin et al., 15 Jul 2025).

In practice, ConVA frameworks operationalize this by:

• Constructing context-controlled datasets where only the target value or concept systematically differs between samples.
• Training simple linear classifiers to distinguish value-positive from value-negative activations.
• Using the classifier’s weight vector, normalized, as the canonical direction for the value—the "value vector".
• Introducing mechanisms to steer activations along or against these directions, modulated by input relevance and user-defined control strength.

2. Methodological Advances

Context-Controlled Value Vector Identification

The precision of value direction identification is enhanced by context-controlled data generation. In this approach, contrastive sample pairs are constructed such that positive and negative examples differ only in their orientation to the target value, thereby minimizing confounding influences from topic, syntax, or background context (Jin et al., 15 Jul 2025). The classifier

$$P_V(\mathbf{e}) = \sigma(\mathbf{w}^\top \mathbf{e} + b)$$

is trained to separate these groups, and the value vector is obtained as

$\mathbf{v} = \frac{\mathbf{w}}{\|\mathbf{w}\|}$

where $\mathbf{e}$ is the activation vector, $\sigma$ is the sigmoid function, $\mathbf{w}$ is the weight, and $b$ is the bias.

Gated Value Vector Activation

To achieve minimal intervention and avoid unintended side effects on model fluency or unrelated content, a gating mechanism is introduced (Jin et al., 15 Jul 2025). The method perturbs the activation only when the input is determined to be value-relevant. For an embedding $\mathbf{e}$, the steered embedding is defined as

$$\hat{\mathbf{e}} = \mathbf{e} + \epsilon \cdot \mathbf{v}$$

with the minimal $\epsilon$ solving

$$\min |\epsilon| \quad \text{subject to} \quad \mathbb{I}[g(x) > g_0] \cdot [P_V(\mathbf{e} + \epsilon \mathbf{v}) - P_0] \geq 0$$

where $g(x)$ is a gating function, $g_0$ is the gate threshold, $P_0$ is a value encoding target, and $\mathbb{I}$ is an indicator. The closed-form solution for $\epsilon$ is

$$\epsilon = \mathbb{I} \cdot \frac{\sigma^{-1}(P_0) - \mathbf{w}^\top \mathbf{e} - b}{\mathbf{w}^\top \mathbf{v}}$$

This architecture enables value control to be activated only when appropriate, ensuring model functionality and fluency is maintained.

Granular and Multi-Concept Control

ConVA frameworks such as GCAV further implement per-sample adaptive steering strengths and joint multi-concept optimization. When controlling multiple concepts $v_i$ and $u_j$ concurrently, the system solves

$\min \sum_i |\epsilon_i| + \sum_j |\delta_j|$

under equality and inequality constraints on the probabilities $P_i$ and $P_j$ after steering activations. These formulations enable precise, input-specific, and simultaneous steering of several attributes (Zhang et al., 10 Jan 2025).

3. Theoretical Guarantees and Bias Alleviation

In deep reinforcement learning, ConVA is applied through generalized-activated weighting operators. These operators replace traditional max-based value estimation with a flexible, non-decreasing activation weighting: $$\text{GA}_g(Q(s, \pi(s)); \psi) = \frac{\int_{a \in A} g(Q(s, a); \psi) Q(s, a)\, da}{\int_{a' \in A} g(Q(s, a'); \psi)\, da'}$$ where $g(\cdot; \psi)$ is a parameterized activation. By tuning $g$, one can control the trade-off between overestimation (max) and underestimation (clipped min) biases (Lyu et al., 2021).

The theoretical distance between the classical max operator and the generalized-activated operator is bounded, permitting controlled bias correction. Fine-tuning these activation functions (e.g., choosing polynomial, tanh, or exponential forms) enables soft or sharp weighting, and empirical results show improved convergence and accuracy in continuous control tasks.

4. Empirical Performance and Practical Impact

Controlled Value Vector Activation has been validated on a wide range of generative and reinforcement learning benchmarks:

• On LLMs, ConVA achieves top control success rates—outperforming baselines such as in-context alignment and contrastive activation addition—across 10 basic values (as defined by Schwartz) without impacting model fluency. Fluency rates remain above 97%, and user studies confirm consistent alignment with target values across adversarial or value-opposite prompts (Jin et al., 15 Jul 2025).
• In content moderation and sentiment control, GCAV demonstrates superior reduction in toxicity and reliable sentiment/style steering, with flexible layer selection and per-input magnitude optimization (Zhang et al., 10 Jan 2025).
• In reinforcement learning, GD3 (a ConVA-type framework) surpasses DDPG, TD3, and SAC in terms of sample efficiency and bias alleviation, with particularly strong results when using task-specific activation functions for value-weighting (Lyu et al., 2021).

These performance gains are realized with minimal extra computational cost, as the required linear probe training and context-controlled data generation are lightweight relative to full model fine-tuning.

5. Applications and Deployment Considerations

Controlled Value Vector Activation finds application in several practical domains:

Application Domain	Practical Use Case	Deployment Notes
LLM Value Alignment	Imposing ethical/social values in chatbots	Source code and datasets for context-controlled vector identification are public (Jin et al., 15 Jul 2025)
Content Moderation	Real-time toxicity and bias reduction	Granular, layer- and magnitude-adaptive control (Zhang et al., 10 Jan 2025)
Multi-Attribute Generation	Simultaneous control of style, topic, sentiment	Supports joint multi-concept optimization (Zhang et al., 10 Jan 2025)
Reinforcement Learning	Alleviating value estimation bias in actors	Flexible, task-adaptive weighting (Lyu et al., 2021)

Deployment does not require re-training of the base model. Instead, only contrastive data generation, shallow classifier training, and efficient vector arithmetic during inference are necessary. Gating mechanisms and threshold calibration are recommended to avoid over-manipulation or unintended side effects on general model capability (Jin et al., 15 Jul 2025).

6. Interpretability, Transparency, and Limitations

ConVA methodologies enhance model interpretability by exposing explicit, human-understandable directions corresponding to concepts and values within latent spaces. By using context-controlled sampling and linear probes, the process for identifying these directions is transparent and reproducible. However, several limitations and considerations apply:

• The quality and neutrality of the context-controlled dataset is critical; poor design could compromise separation of value from confounders (Jin et al., 15 Jul 2025).
• The precision of control is subject to the linearity assumption of concept encodings at the selected layers.
• Application of ConVA does not fundamentally change the underlying generative model and therefore cannot correct for all forms of model bias or incapacity.
• On reinforcement learning tasks, over-biasing the value weighting operator can wash out signal and degrade performance if not tuned carefully (Lyu et al., 2021).

A plausible implication is that further work on robust, automated context generation and non-linear probing could strengthen control fidelity and resilience.

7. Source Code, Evaluation, and Research Directions

Open-source implementations of ConVA are available for academic and applied usage (Jin et al., 15 Jul 2025). Evaluation protocols include the control success rate (CSR), fluency rate (FR), and indirect assessment of generalization performance (e.g., on MMLU). These protocols confirm robust value alignment without loss of fluency or general capacity across a variety of LLM platforms.

Prospective research may target:

• Extension to multi-modal and multi-lingual models.
• Automated or scalable context design for novel value sets.
• Adaptive non-linear value vector identification.
• Systematic analysis of potential adversarial prompt robustness.

Controlled Value Vector Activation stands as a rigorous, computationally efficient, and interpretable technique for both internal and output-level neural model control. It is applicable to any deep network with meaningful internal representations, and provides a foundation for future work on value-sensitive, transparent, and controllable AI systems.