Semantic channel theory achieves deductive compression where minimum block length under closure fidelity depends on the irredundant semantic core size rather than full knowledge-base size.
Graph neural networks meet neural- symbolic computing: A survey and perspective
3 Pith papers cite this work. Polarity classification is still indexing.
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GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
PTMC is a proposed Monte Carlo estimator that generates market-outcome distributions by simulating continuous double-auction interactions among persona-conditioned neural-policy bots whose heterogeneity is drawn from a learned distribution.
citing papers explorer
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Semantic Channel Theory: Deductive Compression and Structural Fidelity for Multi-Agent Communication
Semantic channel theory achieves deductive compression where minimum block length under closure fidelity depends on the irredundant semantic core size rather than full knowledge-base size.
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Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
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Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book
PTMC is a proposed Monte Carlo estimator that generates market-outcome distributions by simulating continuous double-auction interactions among persona-conditioned neural-policy bots whose heterogeneity is drawn from a learned distribution.