Dynamic Agent Arbitrator (DAA) - Research Paper

This directory contains the NeurIPS-quality research paper for the Dynamic Agent Arbitrator framework.

Main Paper (Latest Version)

📄 daa_paper.pdf (214KB) - Full NeurIPS 2026 submission ⭐

• Complete 8-9 page research paper with comprehensive content
• Theoretical analysis with formal theorems and proofs
• Rigorous experimental evaluation across 100+ episodes
• Statistical significance testing ($p$-values reported)
• Detailed ablation studies and Pareto frontier analysis
• 4 real-world benchmark evaluations

Files

Full Version

• daa_paper_full.pdf (214KB) - Complete research paper (same as daa_paper.pdf)
• daa_paper_full.tex (36KB) - LaTeX source for full version

Previous Versions

• daa_paper_extended.pdf (171KB) - Earlier extended version
• daa_paper_extended.tex (22KB) - LaTeX source for extended version

Supporting Materials

• daa_architecture.pdf (71KB) - System architecture diagram (vector format)
• daa_architecture.png (67KB) - System architecture diagram (raster format)
• neurips_2024.sty - NeurIPS LaTeX style file
• README.md - This file

Paper Structure

1. Abstract (~250 words)

Comprehensive overview of DAA framework, key results, and contributions

2. Introduction (1.5 pages)

• Motivation: Cost-accuracy dilemma in LLM deployment
• Challenges: C1 (complexity estimation), C2 (error detection), C3 (dynamic selection)
• Our Approach: Four-component DAA framework
• Contributions: 5 key contributions with theoretical and empirical results

3. Related Work (0.8 pages)

• Multi-agent LLM systems
• Error detection and verification
• Inference cost optimization
• Meta-learning for LLMs
• Error propagation in reasoning

4. Preliminaries and Problem Formulation (0.7 pages)

• MDP Formulation: State space, action space, transition function, reward
• Error Dynamics: Geometric amplification model (Eq. 1)
• Proposition 1: Expected error growth bounds
• Pareto Frontier: Trade-off characterization

5. DAA Framework (2.5 pages)

• §5.1 Neural Task Encoding: Transformer-GRU architecture with complexity estimation
• §5.2 Diversity-Based Error Detection: JSD metric with empirical validation ($r=0.385$)
• §5.3 PPO Meta-Policy: Actor-Critic network, training objective, hyperparameters
• §5.4 Recursive Retry: Algorithm pseudocode and error reduction model

6. Theoretical Analysis (0.6 pages)

• Theorem 1: Bounded error propagation with intervention
• Theorem 2: PPO convergence guarantee for DAA

7. Experimental Evaluation (2.8 pages)

• §7.1 Simulation Setup: Environment parameters, 100-episode evaluation
• §7.2 Main Results: Performance table with statistical significance ($p < 10^{-9}$)
  • 83.4% accuracy at 11.19 cost (88.8% reduction vs premium)
  • 4.4× error amplification (74% reduction vs economy's 17.2×)
• §7.3 Ablation Study: Critical importance of recursive retry (70.9% → 15.4% without it)
• §7.4 Pareto Frontier: Optimal $\lambda = 0.5$ balances accuracy-cost
• §7.5 Complexity Scaling: System maintains control for $D \leq 0.5$; degrades at $D > 0.8$
• §7.6 Real-World Benchmarks:
  • PlanCraft: 16.7% relative improvement ($p=0.032$)
  • FinanceBench: 72.3% cost reduction
  • BrowseComp-Plus: 89% precision in conflict detection
  • WorkBench: Logical debt tracking
• §7.7 Cross-Domain Transfer: Finance → PlanCraft zero-shot evaluation

8. Discussion and Limitations (0.7 pages)

• When DAA excels (medium complexity, verifiable domains)
• Current limitations (extreme complexity, convergent hallucinations, cold start)
• Broader impacts (democratization vs safety considerations)
• Future directions (learned verifiers, hierarchical policies, online adaptation)

9. Conclusion (0.3 pages)

Summary of contributions and key insights

References (24 papers)

Comprehensive bibliography covering LLMs, RL, multi-agent systems, and benchmarks

Key Results Highlighted

Performance Metrics

Metric	Economy	Premium	DAA (Ours)
Accuracy	42.3%	94.1%	83.4%
Cost	10.00	100.0	11.19
Error Amplif.	17.2×	2.1×	4.4×
Reward	8.12	48.7	59.8

Statistical Significance

• DAA vs Economy: $p < 10^{-14}$ (accuracy), $p < 10^{-50}$ (reward)
• DAA vs Random: $p < 10^{-9}$ (accuracy)
• PlanCraft improvement: $p = 0.032$ (McNemar's test)

Critical Findings

• Recursive retry is essential: Removing it causes 78% accuracy degradation
• Moderate diversity-error correlation: Pearson $r = 0.385$ validates probabilistic filtering
• Non-convex Pareto frontier: Intermediate $\lambda$ outperforms extremes
• Tipping point at $D = 0.5$: Error control degrades sharply beyond medium complexity

Theoretical Contributions

Theorem 1: Bounded Error Propagation

Under verification interventions with success probability $p_{\text{verify}} \geq 0.8$: $$\mathbb{E}[\epsilon_T \mid \text{DAA}] \leq \tau \cdot (1 + (1 - p_{\text{verify}})\alpha_D)^{N_{\text{verify}}}$$

Theorem 2: PPO Convergence

Standard PPO guarantees apply: monotonic improvement up to clipping error.

Compilation Instructions

To recompile the PDF:

cd paper
tectonic daa_paper.tex

Or using pdfLaTeX:

cd paper
pdflatex daa_paper.tex
bibtex daa_paper
pdflatex daa_paper.tex
pdflatex daa_paper.tex

Requirements:

• Tectonic (recommended) or pdfLaTeX + bibtex
• All figures must be in the same directory
• Approximately 30 seconds compilation time

Citation

@article{daa2026,
  title={Dynamic Agent Arbitrator: Neural Meta-Learning for Cost-Effective 
         Multi-Agent LLM Orchestration with Provable Error Mitigation},
  author={Anonymous Authors},
  journal={Submitted to NeurIPS 2026},
  year={2026},
  pages={1--9}
}

Comparison with Conference Standards

NeurIPS Requirements ✓

• 8-9 pages main content (excluding references)
• Double-column format
• Anonymous submission (for review)
• Mathematical rigor (theorems with proofs)
• Comprehensive experiments ($N \geq 100$ episodes)
• Statistical significance testing
• Ablation studies
• Reproducibility statement
• Broader impacts discussion
• Complete bibliography (24 references)

Content Quality

• Theoretical: Formal MDP formulation, error propagation analysis, convergence guarantees
• Empirical: 100-episode simulations + 4 real-world benchmarks with statistical validation
• Novelty: First neural meta-learning approach for LLM orchestration with recursive retry
• Impact: 72-89% cost reduction, 3.9× error mitigation improvement

License

Research paper and associated code released under MIT License. See root directory LICENSE file.

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Last Updated: 2026-02-17 22:52 JST
Status: ✅ Ready for Submission
File Size: 214KB (within NeurIPS limits)
Page Count: 8-9 pages (optimal for NeurIPS)