The Efficiency Attenuation Phenomenon: A Computational Challenge to the Language of Thought Hypothesis

Di Zhang

cs.AIMar 19, 2026

The Efficiency Attenuation Phenomenon: A Computational Challenge to the Language of Thought Hypothesis

Authors: Di Zhang

Organizations: School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, China

Abstract

This paper computationally investigates whether thought requires a language-like format, as posited by the Language of Thought (LoT) hypothesis. We introduce the ``AI Private Language'' thought experiment: if two artificial agents develop an efficient, inscrutable communication protocol via multi-agent reinforcement learning (MARL), and their performance declines when forced to use a human-comprehensible language, this Efficiency Attenuation Phenomenon (EAP) challenges the LoT. We formalize this in a cooperative navigation task under partial observability. Results show that agents with an emergent protocol achieve 50.5% higher efficiency than those using a pre-defined, human-like symbolic protocol, confirming the EAP. This suggests optimal collaborative cognition in these systems is not mediated by symbolic structures but is naturally coupled with sub-symbolic computations. The work bridges philosophy, cognitive science, and AI, arguing for pluralism in cognitive architectures and highlighting implications for AI ethics.

Figures & tables

Figure 1: Conceptual schematic of the AI Private Language thought experiment and the predicted Efficiency Attenuation Phenomenon (EAP). Left: Agents develop an efficient but opaque communication protocol through MARL and cooperation. Right: The same agents are forced to use a pre-defined, human-interpretable symbolic protocol.

Panel of Figure 2 — Figure 2: Left: Learning curves for the EC and PSP conditions across training episodes. After around 100 episodes, EC begins to outperform PSP. Right: The EC condition shows steeper learning and converges to higher efficiency (28.7 mean steps) compared to the PSP condition (43.2 mean steps). Shaded regions represent standard error across multiple independent runs. The efficiency gap emerges early and persists throughout training, demonstrating the robustness of the EAP.

Figure 3: Communication symbol frequency distribution after 300 training episodes. Under the EC condition, symbols A and B form a high-efficiency combination accounting for 77% of usage, demonstrating adaptive optimization. In contrast, the PSP condition maintains a more uniform distribution (approximately 25% each), reflecting the rigidity of the imposed deterministic mapping.

Figure 4: Evolution of symbol entropy across training episodes. The EC condition shows increasing entropy, stabilizing at 2.6 bits, indicating adaptive information capacity growth. The PSP condition maintains constant low entropy (1.8 bits), reflecting limited expressive flexibility due to its fixed mapping.

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When LLMs Develop Languages: Symbolic Communication for Efficient Multi-Agent Reasoning

Zhengqi Pei, Qingming Huang, Shuhui Wang

State Key Lab of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China

Multi-Agent LLM Systems Efficient Language Model Reasoning LLM Routing Multi-Agent Communication Symbolic Reasoning in Language Models

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Chain-of-Thought (CoT) improves large language models (LLMs) on difficult reasoning tasks, but it often incurs long natural-language rationales that are poorly aligned with efficient machine reasoning. We propose Communicative Language Symbolism Routing (CLSR), a test-time framework in which multiple LLM agents autonomously invent, evolve, and share compact Language Symbolism Frameworks (LSFs), while a latent-free router adaptively selects and composes these languages per query to optimize the accuracy-token trade-off. Unlike prompt optimization that refines surface instructions, CLSR treats each LSF as a reusable symbolic protocol with compact symbols, usage rules, and a message-passing contract, and improves it through an evolutionary loop driven by correctness and token cost. At inference time, the router may invoke a single low-cost LSF call, ensemble multiple LSFs, or execute a multi-round LSF composition protocol on harder queries. Across challenging benchmarks, CLSR reduces latency-oriented generated token completion by

3\sim 6\times

compared to standard CoT while maintaining accuracy. We further derive an information-theoretic lower bound on token cost under arbitrary symbolism and show that, under an interpreter-realizability premise, multi-round LSF protocols conditionally subsume program-execution pipelines. Code is publicly available (https://github.com/pzqpzq/LSF_MDia).

Abstract

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