Inference-Aware LLM Interaction Design

• Inference-aware design in LLM interactions is a framework that unifies probabilistic reasoning, active learning, and decision theory to optimize user communication and system performance.
• It employs algorithms for adaptive question selection, minimizing uncertainty and improving evaluation metrics like RMSE, MAE, and F1 scores.
• System-level optimizations integrate dynamic batching and hardware enhancements to boost throughput, reduce latency, and increase energy efficiency.

Inference-aware design in LLM interactions encompasses algorithms, interface principles, and systems engineering strategies that seek to optimize the process by which LLMs infer, elicit, and communicate information aligned with user intent, computational resources, and operational constraints. This approach unifies probabilistic reasoning, active learning, decision theory, and system co-design to drive efficient, adaptive, and comprehensible interactions between users and LLMs.

1. Probabilistic and Decision-Theoretic LLM Inference

Inference-aware LLM design frequently adopts probabilistic frameworks to represent uncertainty and optimize for informativeness during interactions. Notably, in the context of active user preference elicitation, an LLM is equipped with an inference-time probabilistic reasoning mechanism that models the joint distribution over latent user choices ($x$), candidate queries ($q$), and possible user answers ($a$) (Piriyakulkij et al., 2023):

$p(x, q, a) = p(a | x, q)\, p(x)\, p(q)$

Within this structure, $p(a|x,q)$ is evaluated by prompting the LLM to score binary consistency, while $p(x)$ and $p(q)$ are typically uniform over their respective spaces. Upon receiving user feedback, the posterior updates as:

$p(x | q, a) \propto p(a | x, q)$

Critically, question selection is governed by decision-theoretic objectives: either minimizing expected posterior entropy (information-theoretic uncertainty) or maximizing expected model change (measured by the KL divergence between post- and pre-answer beliefs). These objectives are provably equivalent given the model’s assumptions, leading to the selection of questions that optimize the expected information gain.

In regression and scoring tasks, inference-aware decoding replaces the standard selection of the mode of $p(y|x)$ with minimum Bayes risk (MBR) estimation (Lukasik et al., 7 Mar 2024). For instance, squared error minimization leads to mean prediction, whereas absolute error aligns with the median. Empirically, metric-aware predictions—such as using the sample mean or median from response samples—outperform traditional greedy decoding across evaluation metrics like RMSE, MAE, and F1.

2. Informative Question Generation and Interaction Optimization

Optimizing the sequence of questions or prompts in LLM interaction is central to efficient, inference-aware design. The entropy minimization algorithm in the active preference inference setting leverages the LLM’s generative capacity to propose diverse, candidate queries, selecting those with maximal expected entropy reduction (Piriyakulkij et al., 2023):

$$\arg\min_{q \sim r(q|c)}\, \mathbb{E}_{p(a|c,q)}\left[\, H(p(x|c,q,a))\,\right]$$

This actively guides the conversation toward rapid resolution of the user’s latent preference, typically achieving the same accuracy as strong baselines with fewer interaction rounds. In practice, information gain per question peaks in early rounds, consistent with theoretical predictions.

In the field of human–LLM cooperative systems, participatory design methods reinterpret Gricean Maxims—quantity, quality, relation, manner—for LLM settings (Kim et al., 2 Mar 2025). Such reinterpretations drive actionable design strategies such as hierarchical information presentation (cognitive load management), reasoning transparency in outputs (trust calibration), and contextually adaptive clarification mechanisms, all mapped to different interaction stages.

3. System-Level Optimization for Throughput, Latency, and Resource Utilization

Inference-aware LLM interaction extends beyond algorithmic question optimization to resource-aware scheduling, batching, and system orchestration.

Dynamic batching methodologies monitor memory usage in real time and adjust GPU batch sizes at each scheduling interval to maximize throughput subject to memory and latency constraints (Pang et al., 7 Mar 2025). The batch size $b_t$ is adaptively bounded based on the probability of memory over-commitment using statistical estimates:

$P(M(b_t) > M_{max}) \leq \epsilon_M$

A latency feedback mechanism further refines $b_t$ to satisfy SLAs, guaranteeing that per-request latency thresholds are met. This dynamic approach yields throughput gains of $8$–$28$\%, increased GPU utilization, and improved query capacity versus static batches.

In multi-tenant, cloud-based inference, resource and constraint-aware schedulers such as ExeGPT (Oh et al., 15 Mar 2024) and TAPAS (Stojkovic et al., 5 Jan 2025) employ optimization frameworks that balance encoding/decoding batch sizes, tensor parallelism, and latency targets, exploiting monotonic relationships among control variables. TAPAS, in particular, incorporates regression models for thermal and power behavior, managing resource provisioning to prevent hotspot formation and reduce throttling events by $>$95\%.

4. Hardware and Storage Optimizations for Edge and Mobile LLM Inference

Inference-aware design at the hardware and storage layers improves efficiency for LLM interactions in resource-constrained environments.

Correlation-aware storage management schemes, such as Ripple (Wang et al., 25 Oct 2024), reorder neuron placement in flash memory on smartphones, grouping co-activated neurons to minimize IOPS bottlenecks. By leveraging neuron co-activation probabilities ($P(i)$, $P(ij)$), Ripple reduces I/O latency by up to $5.93\times$ and increases effective bandwidth, with policies for continuity-centric caching and access collapsing reflecting system-aware inference optimization.

On-chip memory controller enhancements for LLMs, as detailed in (Xie et al., 24 Mar 2025), reorganize weights and KV caches into bit-plane disaggregated forms. This enables more effective application of compression algorithms (LZ4, ZSTD), yielding $25.2$\% reduction in model weights and $46.9$\% in KV cache memory footprint, while dynamic quantization scales memory bandwidth and energy usage with runtime required precision.

Ternary quantization and sparsity-exploiting inference accelerators such as TENET (Huang et al., 17 Sep 2025) further reduce computation and memory movement for real-time edge LLM. TENET’s Sparse Ternary LUT (STL) core, dynamic N:M activation sparsity, and LUT-based ternary weight decompression module collectively achieve $21.1\times$ energy efficiency and $2.7\times$ latency speedup (ASIC vs. GPU).

5. Interaction Quality, Personalization, and Human-AI Communication

Cooperative, user-centered LLM interaction is an emerging theme in inference-aware design. Grounding in communication theory, specifically via the Gricean Maxims, LLM systems are guided not only to optimize for utility metrics but to adapt their communication strategies in real time (Kim et al., 2 Mar 2025). Actionable design considerations include:

• Task decomposition before output to clarify intent and enable modification.
• Hierarchical and expandable response formatting to match cognitive load.
• Transparent reasoning traces or justifications to facilitate user trust and output verification.
• Adaptive clarification questions when ambiguity or information gaps are detected.

These considerations collectively improve the capacity of LLMs to accurately infer user goals, elucidate underlying reasoning, and support multi-turn, evolving interactions—not merely as pattern matchers but as inferentially robust interlocutors.

6. Broader Implications, Generalization, and Future Research

Inference-aware LLM interaction design exhibits wide applicability, from web-based shopping assistants optimized for informative questioning (Piriyakulkij et al., 2023), to dynamic memory and resource allocation in multi-tenant and edge scenarios (Oh et al., 15 Mar 2024, Huang et al., 17 Sep 2025), and cooperative dialog grounded in communication theory (Kim et al., 2 Mar 2025).

A plausible implication is the emergence of inference-aware, hybrid designs that leverage computational resources to minimize user intervention, context shifting, or physical resource consumption. As LLM inference costs decrease and deployment diversifies, research directions include extending mechanisms beyond yes/no questioning (open-ended answer modeling), developing more expressive probabilistic user preference models, and further integrating communication-theoretic reasoning and system-level feedback into all layers of the interaction stack.

Overall, inference-aware design represents a convergence of probabilistic modeling, decision theory, system engineering, and human-computer interaction principles—a core direction for the evolution of efficient, adaptive, and user-aligned LLM-based systems.