paper_id	claim	claim_status	evidence_status	source_depth	source_quote	page_or_section	taxonomy_fit	audit_status
2304.08691v1	We present LTC-SE, an improved version of the Liquid Time-Constant (LTC) neural network algorithm originally proposed by Hasani et al. in 2021.	supported	has evidence row	full-text	Results from our experiments indicate that the proposed LTC -SE model outperforms the or iginal LTC algorithm and other state -of-the-art neural network models for embedded systems in terms of accuracy, computational efficiency, and memory usage.	Results and Discussion	in-scope: LLM extractor confirmed direction match	pass; full-text verified; report=audit_report.md
2106.13898v2	We find an approximate closed-form solution for the interaction of neurons and synapses and build a strong artificial neural network model out of it.	supported	has evidence row	full-text	We observe that CfCs perform competitively to other baselines while performing 160 times faster training time compared to ODE-RNNs and 220 times compared to continuous latent models.	Main Text	in-scope: LLM extractor confirmed direction match	pass; full-text verified; report=audit_report.md
2307.04772v1	This paper proposes a novel framework for addressing the barriers to clinical twin-modeling created by computational costs and modeling complexities.	supported	has evidence row	full-text	This paper proposes a novel framework for addres sing the barriers to clinical twin - modeling creat ed by computational costs and modeling complexities.	IV. LIQUID NEURAL NETWORKS	in-scope: LLM extractor confirmed direction match	pass; full-text verified; report=audit_report.md
2006.04439v4	LTCs exhibit stable and bounded behavior.	supported	has evidence row	full-text	The networks were initialized by weights N (0;2 w=k), and biasesN (0;2 b).	Section 6	in-scope: LLM extractor confirmed direction match	pass; full-text verified; report=audit_report.md
1811.00321v1	We show that any finite trajectory of an n-dimensional continuous dynamical system can be approximated by the internal state of the hidden units and n output units of an LTC network.	supported	has evidence row	full-text	We show that any finite trajectory of an n- dimensional continuous dynamical system can be approxi- mated by the internal state of the hidden units and nout- put units of an LTC network.	Section 3	in-scope: LLM extractor confirmed direction match	pass; full-text verified; report=audit_report.md