The paper justifies the composite coherence metric in event-based narrative extraction via an information-geometric decomposition on the product manifold and an axiomatic uniqueness proof for the geometric mean.
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5 Pith papers cite this work. Polarity classification is still indexing.
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Proves finite-shot mean-squared-error laws for virtual distillation and symmetry verification that define certified operating windows and a selection trichotomy for their comparison.
Machine translation preserves embedding similarity structure for ten languages but distorts it for four in the Manifesto Corpus, via a new non-inferiority testing framework.
PSIRNet produces diagnostic-quality free-breathing PSIR LGE cardiac MRI from a single interleaved IR/PD acquisition over two heartbeats using a physics-guided deep learning network trained on over 800,000 slices.
KG-CFR decouples planning from execution via knowledge-grounded counterfactual reasoning, preventing critical degradation in over 95% of perturbed runs and raising argument quality from 0.694 to 0.822 in a 1v1v1 simulation.
citing papers explorer
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An Information-Geometric Justification for Composite Coherence in Event-Based Narrative Extraction
The paper justifies the composite coherence metric in event-based narrative extraction via an information-geometric decomposition on the product manifold and an axiomatic uniqueness proof for the geometric mean.
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Certified Finite-Shot Operating Windows for Virtual Distillation and Symmetry Verification
Proves finite-shot mean-squared-error laws for virtual distillation and symmetry verification that define certified operating windows and a selection trichotomy for their comparison.
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Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpus
Machine translation preserves embedding similarity structure for ten languages but distorts it for four in the Manifesto Corpus, via a new non-inferiority testing framework.
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Decoupling Thought from Speech: Knowledge-Grounded Counterfactual Reasoning for Resilient Multi-Agent Argumentation
KG-CFR decouples planning from execution via knowledge-grounded counterfactual reasoning, preventing critical degradation in over 95% of perturbed runs and raising argument quality from 0.694 to 0.822 in a 1v1v1 simulation.