Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning
arXiv:2605.23315v1 Announce Type: cross Abstract: Large language models trained under diverse objectives and architectures have been shown to develop increasingly similar internal representations, an observation formalized as the Platonic Representation Hypothesis. Whether this representational convergence extends to the reasoning processes that operate over shared representations remains untested. We evaluate representational similarity across 16 language models from 8 families (1.5B to 72B parameters) on 800 reasoning problems spanning mathematics, science, commonsense, and truthfulness, str
The proliferation of diverse large language models and increasing academic scrutiny into their internal mechanisms makes this research timely.
Understanding the discrepancy between representational convergence and reasoning divergence is critical for developing more robust, reliable, and interpretable AI systems.
This research reveals a fundamental limitation of current AI development, indicating that diverse model architectures might arrive at similar data encoding but not necessarily at similar cognitive processes, challenging assumptions about 'understanding'.
- · AI safety researchers
- · Interpretability researchers
- · Developers of specialized reasoning models
- · Developers solely focused on scaling model parameters
- · Applications requiring high-fidelity, generalized reasoning
- · Investors expecting rapid AGI breakthroughs based on current paradigms
Further research will be spurred into disentangling representation from reasoning in LLMs.
This could lead to a bifurcation in AI development, with distinct tracks for representation learning and reasoning architecture.
Future AI systems may involve modular designs where specialized reasoning modules are explicitly trained to operate on common representational layers.
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Read at arXiv cs.AI