We introduce a microbeam-based decision architecture for autonomous agents that enables consistent alignment with a user-defined goal vector across multi-step tasks. Unlike typical language model agents, which average responses or follow drift-prone continuations, our method uses multiple strict, narrowly divergent response paths (microbeams) at each step, scored and selected based on their vector similarity to the task goal. This strategy improves coherence and efficiency, especially in high-dimensional decision spaces, and shows promise across coding, document generation, and business task workflows.
1. Introduction
LLM-based agents have unlocked new task automation capabilities but struggle with long-range coherence, verbosity, and inconsistent decision paths. Most rely on local token prediction or single-beam generation, which lacks directional persistence toward user-defined outcomes. This paper proposes a new agent architecture based on repeated, strict selection of goal-aligned response paths, or "microbeams," to keep agents strategically on track.
2. Motivation
Agents that average responses or chain generations without persistent scoring often deviate from the intended trajectory. Especially in high-dimensional reasoning or creative domains, maintaining fidelity to user-defined outcomes is crucial. Microbeam agents address this by making decisions based on fixed goal-vector alignment at every step, leading to more decisive and purposeful outputs.
3. Architecture Overview
3.1 Goal Vector Definition Given an input task, define a goal vector G = [g1, g2, ..., gd] via semantic embedding, rule-based mapping, or model inference. This vector serves as the agent’s persistent objective.
3.2 Microbeam Generation and Evaluation At each decision step t, generate k response candidates:
B_t = {b_t_1, ..., b_t_k}
Each candidate is a d-dimensional vector. Compute its cosine similarity with the goal vector:
score(b_t_i) = dot_product(b_t_i, G) / (||b_t_i|| * ||G||)
Select the highest-scoring beam to continue.
3.3 Repeatable Alignment Repeat the scoring and selection process at every decision step. This enforces trajectory consistency and minimizes drift.
4. Mathematical Framing
Simulated walks show that averaging agents veer off course in higher-dimensional spaces, while strict microbeam agents converge faster and more cleanly toward the target vector. We simulate agents walking in 2D, 10D, and 100D vector spaces, showing reduced deviation and step count with strict alignment.
5. Use Cases and Examples
5.1 Software Engineering Microbeam agents can write modular, production-grade code by selecting consistent strategies (e.g., framework usage, naming conventions).
5.2 Document Authoring Agents generate long documents with aligned structure, tone, and logic, adhering to an inferred or explicit instruction vector.
5.3 Enterprise Automation Agents writing policy, generating analysis, or managing workflows benefit from long-range consistency, especially under vague or evolving tasks.
5.4 Agent Swarms and Simulation Independent agents following divergent beams can simulate strategy branches. Each is scored and re-aligned to the user’s goal at each step.
6. Limitations
Static goals are sometimes unrealistic in open-ended tasks.
Excessive beam pruning may suppress creative responses.
Scoring functions must be adapted to each domain.
7. Conclusion
Strict, goal-scored microbeam selection provides a robust alternative to average or drift-prone agent behavior. By optimizing for persistent directional alignment, agents walk more efficiently toward desired outcomes, especially in high-dimensional tasks. This method holds promise for building more reliable, purposeful LLM-based agents.