VectraFlow Stream Processing System

• VectraFlow is an LLM-augmented stream processing system that integrates continuous prompts and semantic operators for real-time analytics over unstructured data streams.
• The system implements an innovative architecture featuring LLM-driven operators, a dynamic optimizer, and operator fusion to enhance throughput and accuracy.
• Empirical evaluations on industrial-scale pipelines demonstrate VectraFlow's efficacy in handling dynamic workloads using adaptive MOBO-based runtime optimizations.

VectraFlow is an LLM-augmented stream processing system explicitly designed to maintain persistent, semantics-aware computation over evolving unstructured data streams. Implementing Continuous Prompts (CPs), VectraFlow enables stateful, long-running analytics using LLMs, which are traditionally stateless and operate in one-shot fashion. The system comprises a suite of semantic operators, optimization techniques exploiting LLM properties, and a dynamic, learning-based optimizer for balancing throughput and accuracy. VectraFlow has been evaluated on industrial-scale pipelines for real-time event and information monitoring.

1. System Architecture

At its core, VectraFlow extends classical data-flow engines to support continuous, stateful processing with LLM-driven operators and an adaptive runtime optimizer. The system architecture consists of the following layers:

• Input Layer: Accepts unbounded streams $S = \{(t_i, x_i)\}$, where each tuple contains timestamp $t_i$ and payload $x_i$—potentially comprising structured fields and unstructured attributes (text, images, documents).
• Semantic Operator Library: Provides continuous, stateful operators (σₛ, πₛ, γₛ, τₛ, ⨝ₛ, ωₛ, μₛ, ρₛ) for semantics-aware filtering, mapping, aggregation, top-k ranking, joining, windowing, grouping, and Retrieval-Augmented Generation (RAG).
  • Each operator $o_j$ maintains streaming state $s_j(t)$, incrementally updated per tuple.
• Execution Engine: Realizes query plans as directed acyclic graphs of semantic operators. It delivers exactly-once, order-preserving tuple propagation—either singly or in mini-batches—and supports operator fusion for jointly executed LLM calls.
• Inference & Retrieval Layer: Orchestrates LLM invocations (via vLLM server) for CP operators; also supports embedding-based variants for lightweight execution/fallback.
• Telemetry & Statistics: Continuously monitors per-operator throughput (tuples/s) and accuracy (via shadow probes), supplying data for model fitting in the dynamic planner.
• Dynamic Optimizer: Generates and adapts execution plans, including batching schemes, fusion layouts, and implementation variants (LLM, embedding). It fits throughput and accuracy models, predicts pipeline-level performance, constructs Pareto frontiers, and triggers online plan switching within resource constraints.

2. Continuous Prompts and Semantic Operators

Continuous Prompts (CPs) represent the principal extension in VectraFlow, allowing operators to maintain long-lived LLM sessions and evolving prompts corresponding to streaming state (window boundaries, retrieval contexts). This mechanism adapts batch-style RAG to streaming computation through a library of continuous semantic operators:

Operator	Symbol	Functionality
Semantic Filter	$\sigma_s$	LLM predicate → Boolean
Semantic Map	$\pi_s$	LLM: unstructured input → structured record
Semantic Aggregate	$\gamma_s$	LLM summary/trend over semantic window $W$
Semantic Top-$k$	$t_i$0	Maintains top $t_i$1 tuples by LLM scoring
Semantic Join	$t_i$2	Correlate streams via semantic similarity
Semantic Window	$t_i$3	Dynamically detects semantic change points (windowing)
Semantic Group-By	$t_i$4	Incrementally clusters tuples by meaning
Continuous RAG	$t_i$5	Continual retrieval of prompt-relevant context

Each operator implements a state-update scheme. For instance, semantic aggregate $t_i$6 follows:

• $t_i$7
• $t_i$8
• $t_i$9 summary

Streaming semantics: $x_i$0

3. Execution Optimizations: Tuple Batching and Operator Fusion

Proper exploitation of LLM properties necessitates two execution optimizations: tuple batching and operator fusion, both of which trade accuracy for speed and are governed by empirical cost/accuracy models.

3.1 Tuple Batching

Batching aggregates $x_i$1 input tuples into a single LLM prompt, reducing startup and token overhead. Construction involves shared prefix (system, instruction, schema), enumeration, and requesting a matching output list.

• Throughput model (Affine): $x_i$2, $x_i$3
• Accuracy model (Exponential decay): $x_i$4

Parameters $x_i$5 are empirically fit per operator using microbenchmarks.

3.2 Operator Fusion

Fusion consolidates a sequence $x_i$6 of adjacent operators into a single LLM call, sharing boilerplate and latency.

• Fused schema: $x_i$7
• Metric: Speedup $x_i$8, $x_i$9(non-fused) $o_j$0 $o_j$1(fused)

Experimental evidence indicates high effectiveness for light transformations (e.g., map→filter), but fragility for ranking and aggregation (semantics-sensitive ops). Fusion benefit is further modulated by filter selectivity $o_j$2 (lower $o_j$3 reduces utility due to processing dropped tuples).

4. Dynamic Optimization Framework

VectraFlow employs a runtime planner to adapt pipeline configuration under changing workload dynamics and resource limits.

4.1 Plan Generation and Pruning

• Enumerates operator batch sizes $o_j$4, fusion blocks, and implementation types (LLM/embedding).
• Pruning rules: prohibit fusion across window boundaries; enforce non-decreasing block sizes ($o_j$5), and batch size limit $o_j$6 (window size).

4.2 Per-Operator Cost Modeling

• Throughput surrogate: $o_j$7
• Accuracy surrogate: $o_j$8

4.3 End-to-End Prediction

• Pipeline-parallel/bottleneck mode: $o_j$9
• Sequential mode: $s_j(t)$0
• Accuracy (independence assumption): $s_j(t)$1

4.4 Plan Selection

• Constructs Pareto frontier over $s_j(t)$2.
• User-specified target throughput/accuracy selects best feasible plan.

4.5 Multi-Objective Bayesian Optimization (MOBO)

Efficient frontier learning within probing budget $s_j(t)$3 is achieved via cost-aware MOBO:

• Objective: Maximize $s_j(t)$4, subject to $s_j(t)$5
• Surrogate models: Per-operator GPs, initialized by observed cost/accuracy.
• Acquisition: Cost-aware Expected Hypervolume Improvement (EHVI).
• Procedure: Warm-up probes to fit priors, iterative selection of $s_j(t)$6 maximizing acquisition, update GPs/frontier, terminate upon budget exhaustion.

5. Empirical Evaluation on Real-World Pipelines

VectraFlow has demonstrated robust performance on multiple unstructured streaming tasks, with detailed metrics and optimization behaviors.

Case 5.1: Stock News Monitoring (FNSPID Dataset)

Pipeline: cts_filter → sem_map → sem_groupby → sem_window → sem_topk → sem_agg

• MOBO sampling achieves ≈100% true Pareto frontier by $s_j(t)$7 probes (vs. ≈60% using heuristics).
• Among Pareto-efficient plans, batching is present in ≈90%, fusion in ≈30%.
• Under simulated Poisson arrival ramp (1,200 tuples, increasing $s_j(t)$8): baseline throughput saturates early; heuristic planner drops accuracy under overload; MOBO planner dynamically tracks $s_j(t)$9, trading accuracy to sustain throughput as needed.

Case 5.2: Misinformation Event Monitoring (MiDe22 Dataset)

Pipeline: sem_filter → sem_groupby → sem_window → sem_topk

• MOBO leads to higher recall/precision of Pareto plans than heuristics (converging at $\sigma_s$0).
• In Pareto-efficient plans (excluding static baseline): batching in ≈94%, operator variant selection (embedding/LLM) in 100%, fusion rarely (~6%).
• Optimization sequence along frontier: (1) sem_groupby(embedding) + batching, (2) add sem_window(pairwise), (3) switch to sem_window(clustering), (4) finally add fusion for maximal throughput.

6. Significance and Operational Implications

VectraFlow, by integrating Continuous Prompts with LLM-specific batching/fusion strategies and an adaptive MOBO-based planner, enables persistent, semantics-aware queries at scale over highly dynamic and unstructured streams. The system is empirically shown to sustain robust throughput and scalable accuracy under evolving workloads, adaptively balancing efficiency and inference fidelity (Chen et al., 3 Dec 2025). A plausible implication is that such dynamic LLM-augmented streaming frameworks will become essential for long-running analytics in domains where the semantic richness and non-stationarity of data streams cannot be addressed by stateless, batch-oriented LLM tools.