Right-Sizing LLM-Agent Decomposition in VAT Determination: A Pilot Controlled Sweep

• URL: http://arxiv.org/abs/2608.23395v1
• Date: Mon, 24 Aug 2026 15:40:15 GMT
• Title: Right-Sizing LLM-Agent Decomposition in VAT Determination: A Pilot Controlled Sweep
• Abstract summary: This pilot studies that choice on bounded cross-border VAT determination with reverse charge.<n>We hold the activity surface fixed (subtasks, tools, I/O schemas, validation checks, orchestrator, base model, and merge policy)<n>The program spans 4,400 runs: a 40-case, five-repeat main sweep, matched-token arms separating prompt-budget from agent-count effects, and three failure-injection arms.
• Score: 0.3384279376065155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent LLM-agent systems make conflicting design bets: decompose work across many narrow agents, or use one strong tool-using agent. This pilot studies that choice on bounded cross-border VAT determination with reverse charge, where every case has an oracle label and each intermediate decision is independently scoreable. We hold the activity surface fixed (subtasks, tools, I/O schemas, validation checks, orchestrator, base model, and merge policy) and vary only the assignment of subtasks to workers across four orchestrated configurations, from one wide worker to five narrow ones, against S0, a tuned no-orchestrator single agent, with a deterministic rule engine as oracle. The program spans 4,400 runs: a 40-case, five-repeat main sweep, matched-token arms separating prompt-budget from agent-count effects, and three failure-injection arms, all judged against pre-registered falsification criteria. The two intermediate configurations lead on accuracy (0.830, against endpoints at 0.720 and 0.770) but miss the pre-stated bar against the fine endpoint, so the intermediate-optimum hypothesis remains unsupported at pilot scale. The single agent does not Pareto-dominate the orchestrated set. The matched-token criterion fires: the budget-matched single agent lands 6.5 points below the leader, but the interval includes zero, so any advantage is consistent with a prompt-budget explanation. Under injection, availability faults are absorbed at every granularity, with wide-scope restart over-recovering its baseline by +0.160, while one schema-conforming hallucinated record degrades every configuration and inverts the ordering, hitting fragmented configurations hardest. The contribution is a bounded, preregistered pilot heuristic for right-sizing decomposition (place one partition boundary at the dependency-layer midpoint), released with oracle, dataset, harness, raw traces, and analysis pipeline.

Related papers

• Loop-Back Authority in LLM Agent Teams: A Paired Experiment on Flat and Hierarchical Coordination [0.09999629695552194]
  Hierarchical orchestration, in which a Manager agent reviews worker output and can send it back for revision, is the default coordination pattern in production multi-agent LLM frameworks.<n>We present a paired experiment that holds five LLM agents, their roles, prompts, tools, models, and data fixed and varies one link: whether the Manager may reject a worker's output and oblige a revision.<n>Across 43 paired products and 86 runs of a business-intelligence reporting task, a five-model judge panel and a deterministic specification check score every report.<n>The flat organization scores higher on Utility (d = 0.42, p = 0.009) and
  arXiv  Detail & Related papers  (2026-09-13T19:58:54Z)
• UnitBoost: Managing Compound LLM Systems with a Merge Operator, Not a Model [6.9226744802783955]
  UnitBoost replaces a system that concentrates three control decisions in an order-sensitive model call.<n>The operator is order-free, records unit provenance, and gives a simple guarantee.<n>It exceeds the best single candidate chosen with gold labels by 0.060-0.195 absolute task-score points and input-matched generative managers by 0.048-0.076.
  arXiv  Detail & Related papers  (2026-09-09T07:17:23Z)
• Privacy-Preserving Topology-Guided Safety for LLM-Based Multi-Agent Systems via Federated Graph Learning [67.04448659688579]
  FGLGuard cuts AgentDojo's ground-success rate by 43% at near-unguarded utility, zero API cost, and negligible capability loss.<n>Federation is not optional: off-the-shelf transfer collapses under distribution shift.<n>Live FGLGuard cuts AgentDojo's ground-success rate by 43% at near-unguarded utility, zero API cost, and negligible capability loss.
  arXiv  Detail & Related papers  (2026-09-02T07:57:12Z)
• From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mix [55.08143827481755]
  We consolidate traffic from over 200 internal applications onto a single model.<n>We train a separate GRPO expert per axis and merge them via two-stage SLERP.<n>The model absorbs 50% of platform traffic, 116M requests per month, at a fraction of the serving cost.
  arXiv  Detail & Related papers  (2026-09-01T17:39:26Z)
• ClawArena-Team: Benchmarking Subagent Orchestration and Dynamic Workflows in Language-Model Agents [61.184954205350984]
  ClawArena-Team is a benchmark of 41 multi-turn, multimodal, multi-agent scenarios spanning 258 evaluation rounds and 72 staged updates.<n>All scoring is execution-based with no LLM judge: an overall score -- the Subagent-Management Score (SMS) -- multiplies task correctness by a least-privilege and modality-routing factor.
  arXiv  Detail & Related papers  (2026-06-30T06:06:08Z)
• CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents [0.9924185669370708]
  We introduce CLQT, which reframes closed-loop trading evaluation as diagnosis rather than ranking.<n> CLQT is a fully closed-loop, cost-aware, strategy-consistent, temporally-gated environment.
  arXiv  Detail & Related papers  (2026-06-29T04:26:30Z)
• ExecuGraph: A Multi-Agent, Execution-Grounded Framework for Reliable Backend Code Synthesis with Large Language Models [2.3004655342211078]
  Large Language Models generate plausible backend code, but a single-pass paradigm provides no guarantee of correctness or runtime reliability.<n>We present ExecuGraph, a multi-agent framework that places execution-based validation at the center of backend code.<n>A per-agent ablation, retry-budget sweep, error-class taxonomy, and test-source audit are reported.
  arXiv  Detail & Related papers  (2026-06-18T05:58:17Z)
• Dynamic Coordination Strategy Selection for Enterprise Multi-Agent Systems [0.0]
  This paper evaluates whether coordination strategy should be selected dynamically by problem class rather than fixed globally.<n>All 1,440 generated outputs are judged by a fixed Sonnet rubric.<n>We conclude that enterprise coordination policy should use dynamic routing as a default, not as a deterministic winner-selection law.
  arXiv  Detail & Related papers  (2026-05-30T16:43:02Z)
• FineVerify: Scaling Test-Time Compute with Fine-Grained Self-Verification for Agentic Search [88.16262636915975]
  FineVerify is a fine-grained self-verification framework for agentic search.<n>It decomposes each question into checkable sub-questions, verifies sampled candidates, and selects the candidate with the highest aggregated score.<n>FineVerify consistently outperforms standard scaling baselines.
  arXiv  Detail & Related papers  (2026-05-30T10:21:20Z)
• Coordination as an Architectural Layer for LLM-Based Multi-Agent Systems [0.0]
  Multi-agent LLM systems fail in production at rates between 41% and 87%, mostly due to coordination defects.<n>We argue that coordination should be treated as a architectural layer, separable from agent logic and from information access.<n>We instantiate this with an information-controlled design on prediction markets.
  arXiv  Detail & Related papers  (2026-05-05T02:56:44Z)
• Towards a Science of Scaling Agent Systems [79.64446272302287]
  We formalize a definition for agent evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, modelic, and task properties.<n>We derive a predictive model using coordination metrics, that cross-validated R2=0, enabling prediction on unseen task domains.<n>We identify three effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead, and (2) a capability saturation: coordination yields diminishing or negative returns once single-agent baselines exceed 45%.
  arXiv  Detail & Related papers  (2025-12-09T06:52:21Z)
• Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
  We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
  arXiv  Detail & Related papers  (2025-09-26T23:40:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.