Interactive Scaling: Dynamic Feedback in Systems

• Interactive scaling is the systematic enhancement of a system's ability to handle increasing interactivity, dynamic inputs, and user-driven feedback.
• It leverages mathematical models and architectural innovations in ML infrastructure, stream analytics, and visualization to optimize real-time responsiveness.
• Empirical studies report significant improvements such as an 8–10 point accuracy boost and throughput speedups, highlighting its practical impact across various domains.

Interactive scaling refers to the systematic expansion of agent, model, or system capability to operate efficiently and responsively under increased interactivity—more dynamic inputs, deeper feedback loops, or larger user/task populations—such that usability, accuracy, or throughput persists or improves with increased interactive demand. This concept spans theoretical frameworks in multi-agent communication (0801.0756), large-scale machine learning infrastructure (Reuther et al., 2018, Kontaxakis et al., 2020), tool-augmented reasoning agents (Team et al., 14 Nov 2025), online communities (Zeng et al., 2017), and user-driven media generation (Li et al., 8 Aug 2024, Wu et al., 24 May 2024, Kruchten et al., 2022). Interactive scaling contrasts with traditional batch scaling by requiring not just capacity (e.g., more cores, longer context windows) but explicit architectural adaptation for high-frequency, user- or environment-mediated feedback and control.

1. Principled Foundations: Mathematical Models and Scaling Laws

Interactive scaling is often formalized using multi-dimensional power laws that extend conventional scaling axes (model size, input/context length) to interactions. For large language-model agents, the performance $P$ is hypothesized to follow:

$P(D, C, I) \approx \alpha D^{a} C^{b} I^{c}$

where $D$ is model size, $C$ is context length, and $I$ is interaction depth (e.g., number of agent-tool feedback cycles) (Team et al., 14 Nov 2025). Empirical findings show that, holding $D$ and $C$ fixed, increasing $I$ yields clear, predictable performance gains, such as an 8–10 point accuracy increase on BrowseComp when raising tool-call depth from ~100 to ~500.

In distributed source coding and information theory, interactive protocols realize abrupt scaling regime transitions. For example, computing the minimum of $M$ Bernoulli sources in a star topology via non-interactive coding yields a sum-rate $R_1(M) = \Theta(M)$, while using $2(M-1)$ rounds of interaction collapses rate to $R_{\text{int}}(M) = O(1)$—an order-of-magnitude improvement (0801.0756). This is achieved by quantizing partial state variables and successively updating only the relevant minimum, rather than reconstructing all source bits.

Online attention networks exhibit emergent scaling: in "interest space," the collective Lévy-flight browsing of $N$ users induces community activity $A(N) \sim N^\alpha$, diversity $D(N) \sim N^\beta$, edge count $E(N) \sim N^\gamma$, and densification $E \sim D^\theta$ (Zeng et al., 2017). Here, $\alpha$, $\beta$, and $\theta$ are determined by microscopic interaction parameters, enabling inference of individual behavior distributions from macroscopic scaling exponents.

2. Architectural Strategies for Interactive Scaling

Realizing interactive scaling in practical systems requires architectural and algorithmic innovations:

Large-Scale ML Infrastructure: Interactive supercomputing platforms (e.g., MIT’s SuperCloud) utilize tunable launchers, prepositioned software stacks on local disks, and hierarchical process forking to enable "burst" launches of tens of thousands of parallel ML jobs in seconds, ensuring rapid user feedback cycles and mitigating scheduler or file-system bottlenecks (Reuther et al., 2018).

Stream Analytics: The Synopses Data Engine (SDE) implements "synopsis-as-a-service" via a modular pipeline on Apache Flink, supporting thousands of concurrent data summaries (sketches, quantiles, coresets) per thousands of streams. SDE achieves horizontal scaling (per-worker throughput), vertical scaling (per-stream concurrency), and federated scaling (across data centers) with fine-grained management, caching, and cost-based plan rewriting for workflow optimization (Kontaxakis et al., 2020).

Multimodal World Models: iVideoGPT combines compressive tokenization, autoregressive transformers, and massive pre-training, allowing step-level interactivity (actions/observations/rewards as input tokens) over millions of human/robotic trajectories. Token and sequence compression ensures sequence lengths remain tractable, enabling transformers to scale to hundreds of millions of parameters and millions of sequences, while preserving actionable interactive exploration and planning (Wu et al., 24 May 2024).

Interactive Visualization: VegaFusion partitions Vega dataflow DAGs into server- and client-executable subgraphs. Supported transforms are executed on a multi-threaded, cached Rust backend, allowing <100 ms "brushing" frame rates across 1 M+ row datasets and enabling multi-user, multi-session concurrent interactivity. Partitioning runs in linear time and can, in principle, be extended to cost-aware dynamic splitting (Kruchten et al., 2022).

3. Interaction, Feedback, and Learning Dynamics

Interactive scaling fundamentally involves agent-environment or user-system feedback loops. In tool-augmented reasoning agents (MiroThinker), deeper loops of “think → act (tool call) → observe” drive performance improvement by using external feedback to correct and refine reasoning chains (Team et al., 14 Nov 2025). Training involves sequential stages: supervised trajectory imitation, preference optimization, and reinforcement learning atop live tool-augmented rollouts, with RL specifically targeting higher interaction depths (up to 600 tool calls per episode). This results in superior research benchmark accuracy and responsiveness relative to static, non-interactive scaling.

In interactive geometry editing (NeRF manipulation), interaction is mapped to user-driven deformations: mouse-drags parameterize continuous scaling, which is smoothly propagated via a trilinear blend between user-defined cages. No network retraining is required; instead, sample points are pre-warped before querying a frozen MLP model, enabling real-time editing rates (Li et al., 2023).

Recent interactive video generative models (Puppet-Master) achieve scaling not just in resolution or complexity, but in user-specified motion trajectories (“drags”). Conditioning is handled via adaptive normalization (FiLM) and drag tokens injected into cross-attention layers, and a novel all-to-first spatial attention structurally links every frame back to a clean reference for visual fidelity. Architectures maintain responsiveness for up to five simultaneous drags and moderate frame counts (Li et al., 8 Aug 2024).

4. Empirical Scaling, Benchmarks, and Bottlenecks

Interactive scaling is validated by empirical scaling curves, rate measurements, and benchmark comparisons:

System	Scaling Dimension	Quantitative Metric
MiroThinker (Team et al., 14 Nov 2025)	Tool-call depth ($I$)	+8–10 pt accuracy, up to 81.9% GAIA
SuperCloud (Reuther et al., 2018)	Process launches ($n$)	8,000 TensorFlow/s, 6,550 Octave/s
SDE (Kontaxakis et al., 2020)	Streams/workers/federation	11.5× throughput (5,000 streams), 10× comm savings
VegaFusion (Kruchten et al., 2022)	Data volume, user counts	10–20× speedup (1M rows), 60 fps brushing
Puppet-Master (Li et al., 8 Aug 2024)	User drags, object parts	FVD, SSIM, and LPIPS gains; robust zero-shot performance

Performance is generally sub-linear or logarithmic in the interactive dimension, consistent with theoretical scaling predictions. Bottlenecks arise from core scheduler overhead (SuperCloud), shared file system metadata contention, slow task instantiation, communication latency (VegaFusion remote), and inefficient feedback policies (over-calling tools in MiroThinker), all of which are addressed by architectural modifications, caching, or cost-based optimizations.

5. Applications Across Domains

Interactive scaling enables:

• Research agents: Deep tool-augmented reasoning, sustained multi-turn workflows, efficient error correction, and data-driven multi-agent exploration (Team et al., 14 Nov 2025).
• Extreme-scale machine learning: Real-time hyperparameter sweeps, ensemble model training, and iterative data analysis on tens of thousands of cores (Reuther et al., 2018).
• Streaming analytics: Millisecond-latency interactive queries across thousands of real-time data streams in financial or IoT domains, federated across data centers (Kontaxakis et al., 2020).
• User-controllable media generation: Real-time part-level video synthesis with user-driven constraints, direct interactive editing of virtual scenes, and adaptable motion priors for compositional inputs (Li et al., 8 Aug 2024).
• Collaborative online systems: Modeling attention flows and engagement in large user communities, enabling prediction, recommendation, and public opinion management via inferred behavioral scales (Zeng et al., 2017).
• Command and control settings: Scalable interactive ML for joint human-AI planning, trust calibration, and real-time adaptation under dynamic operational conditions (Madison et al., 9 Feb 2024).
• Machine comprehension and QA: Information-seeking agents that interactively reveal relevant text fragments, enabling efficient web-scale question answering (Yuan et al., 2019).

6. Limitations, Trade-Offs, and Open Problems

Interactive scaling introduces new design axes with attendant limitations and challenges. Tool-use efficiency and overlong reasoning trajectories can reduce practical gains in agent performance (Team et al., 14 Nov 2025). For interactive supercomputing, local disk prepositioning mitigates—but does not eliminate—file system bottlenecks. In visualizations, offloading computation server-side improves throughput for large datasets but adds network latency, requiring UI adaptation. In world models, compressive tokenization yields faster training but may obscure fine-grained details needed for precision control (Wu et al., 24 May 2024). Multi-human, multi-agent learning in command and control remains limited by the integration of diverse feedback channels and by incomplete theoretical convergence guarantees (Madison et al., 9 Feb 2024).

A plausible implication is that, as interactive scaling becomes central to practical system design, further research is required on cost-based partitioning, dynamic feedback allocation, adaptive trust calibration, and federated scaling of interaction loops across heterogeneous settings.

7. Future Directions and Research Roadmap

Interactive scaling is increasingly formalized as a principal design axis on par with model size or context length. Open challenges include:

• Establishing open benchmarks for interaction depth and scalability (Madison et al., 9 Feb 2024).
• Developing cost-aware planners and optimizers that trade off local versus remote interactivity, user intent, and hardware constraints (Kruchten et al., 2022, Kontaxakis et al., 2020).
• Advancing RL and multi-agent protocols to exploit hierarchical, crowdsourced, or compositional interactions (Team et al., 14 Nov 2025, Madison et al., 9 Feb 2024).
• Extending interactive architectures for greater generalization capabilities in zero-shot, multi-part, or federated settings (Li et al., 8 Aug 2024, Wu et al., 24 May 2024).
• Formalizing the interplay between interactivity, engagement, and emergent scaling laws in complex user systems (Zeng et al., 2017).

Continued work will require cross-disciplinary efforts integrating hardware, software systems, distributed algorithms, human factors, and domain expertise to fully realize and optimize interactive scaling at extreme scale and fidelity.