{"total":30,"items":[{"citing_arxiv_id":"2606.30270","ref_index":9,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Cyclic Attractor Detection in Boolean Network Dynamics under Local Logical Constraints","primary_cat":"cs.CC","submitted_at":"2026-06-29T13:17:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"For every fixed k ≥ 2 the cyclic attractor detection problem is NP-complete precisely when the local Boolean function class contains majority-like self-dual rules or mixed conjunctive-disjunctive monotone families, and polynomial-time solvable in all other Post classes.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.25342","ref_index":23,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Lifelong In-Context Learning with Transformers Requires Parametric Forms of Attention","primary_cat":"cs.LG","submitted_at":"2026-06-24T03:14:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Argues that parametric attention forms are necessary for lifelong in-context learning in transformers to maintain constant memory footprint over arbitrary sequence lengths.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.24953","ref_index":109,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"How Complexity Contributes to Learning Opacity in Machine Learning","primary_cat":"cs.LG","submitted_at":"2026-06-23T08:17:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Neural network learning opacity stems from three dynamical complexity properties in training, rendering some sources of opacity irreducible.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.18339","ref_index":18,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Ground state preparation of random all-to-all Hamiltonians using ADAPT-VQE","primary_cat":"quant-ph","submitted_at":"2026-06-16T18:00:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"TETRIS-ADAPT-VQE achieves fidelities above 99.3% for SYK (N=20) and 99.9998% for SK (L=18) but requires large resources for SYK models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.12968","ref_index":86,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Quantum-Driven Neuromorphic Computing for Million-Qubit-Scale Workloads","primary_cat":"quant-ph","submitted_at":"2026-06-11T06:55:19+00:00","verdict":"REJECT","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Apollo is a room-temperature 10000-node CMOS neuromorphic chip whose p-qubit network emulates transverse-field quantum annealing via Suzuki-Trotter and reportedly achieves lower energies than cryogenic QA on 3D spin-glass benchmarks across 300 realizations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.12059","ref_index":21,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Attention by Synchronization in Coupled Oscillator Networks","primary_cat":"cs.LG","submitted_at":"2026-06-10T13:28:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Kuramoto synchronization dynamics implement a provably unique and globally attractive attention mechanism that replaces softmax for physical substrates and shows competitive empirical performance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.06424","ref_index":50,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Intrinsic Computational Functionalism: From Observer-Relative Maps to Observer-Independent Structures","primary_cat":"q-bio.NC","submitted_at":"2026-06-04T17:28:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Intrinsic computational functionalism uses system-intrinsic instantiation (C1) and causal-dynamical organisation under intervention (C2) to identify observer-independent computational structures for consciousness via a three-tier decomposition of identification work.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.01841","ref_index":31,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"The Neuromorphic Supremacy","primary_cat":"q-bio.NC","submitted_at":"2026-06-01T07:52:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Hybrid neuromorphic-ANN models outperform standard deep learning on few-shot benchmarks and under occlusion/impulse noise via astrocytic modulation and spiking dynamics.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.27476","ref_index":8,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Balancing Fidelity and Diversity in Diffusion Models via Symmetric Attention Decomposition: Hopfield Perspective","primary_cat":"cs.LG","submitted_at":"2026-05-26T11:56:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Decomposes pre-softmax attention QK^T into symmetric and skew-symmetric components to derive Hopfield stability measures that correlate with fidelity-diversity in diffusion generation and introduces a circulation-based modulation knob.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.24611","ref_index":7,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Beyond Fixed Points: Superpolynomial Capacity of Asymmetric Hopfield Networks","primary_cat":"cs.LG","submitted_at":"2026-05-23T14:46:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"Construction shows n-neuron asymmetric Hopfield networks support exp(Ω(n/(log n)^2)) limit-cycle attractors of length exp(Ω(√n/log n)) each, robust to 1/2-o(1) noise.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.23603","ref_index":10,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Preisach Attention: A Hysteretic Model of Sequential Memory","primary_cat":"cs.LG","submitted_at":"2026-05-22T13:12:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"PAL uses the classical Preisach hysteresis operator with learned thresholds and an extrema stack to model sequences, proving O(1)-depth Turing completeness via two-stack PDA simulation and incomparability with standard transformers on rate-independent vs. random-access functions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14998","ref_index":56,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Learning Developmental Scaffoldings to Guide Self-Organisation","primary_cat":"cs.AI","submitted_at":"2026-05-14T16:01:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Joint training of NCA rules and SIREN pre-patterns improves robustness, encoding capacity, and symmetry breaking compared to purely self-organizing models by offloading information to initial conditions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12394","ref_index":8,"ref_count":2,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Detecting overfitting in Neural Networks during long-horizon grokking using Random Matrix Theory","primary_cat":"cs.LG","submitted_at":"2026-05-12T16:57:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Random Matrix Theory detects overfitting via growing Correlation Traps in weight spectra during the anti-grokking phase of neural network training.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"call these outliers Correlation Traps, and track them through extended training, connecting them to overfitting in the anti-grokking phase. We note that Correlation Traps were first proposed in [14]. Self-averaging and overfitting.Our overfitting criterion connects to statistical-mechanics accounts of glassy learning, where poor generalization reflects sample-specific structure rather than a single stable rule [ 8, 1, 7, 23, 4]. The MP law gives a self-averaging baseline for randomized layer spectra, and Correlation Traps violate that baseline. Such traps can support a non-self-averaging generalization error such as through localization, where a small coordinate set retains O(1) variance under subsampling, or through condensation, where a dominant spectral mode carries macroscopic"},{"citing_arxiv_id":"2605.08645","ref_index":16,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Energy-based models for diagnostic reconstruction and analysis in a laboratory plasma device","primary_cat":"physics.plasm-ph","submitted_at":"2026-05-09T03:29:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A single energy-based model trained on LAPD plasma data enables diagnostic reconstruction, inverse inference of probe position, conditional trend sampling, and unconditional mode reproduction for potential anomaly detection.","context_count":1,"top_context_role":"background","top_context_polarity":"support","context_text":"One notable application in the physical sciences has been in the high-energy physics community: EBMs were used for modeling event patterns in the Large Hadron Collider (LHC) for anomaly detection and to augment a classifier [4] with success. 1.3 Introduction to energy-based models (EBMs) Energy-based models interpret a probability distribution through the lens of the Boltzmann distribution [16, 1, 20]. In the EBM formulation, the unnormalized probability density is parameterized by an energy function, that is ˜p(x) = exp(−Eθ(x)), where θ are the parameters of this energy function. In this work and the works cited, this energy function is parameterized by a neural network. EBMs have been historically difficult to train, but recent work has demonstrated high-quality sampling"},{"citing_arxiv_id":"2605.05978","ref_index":14,"ref_count":2,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Efficient event-driven retrieval in high-capacity kernel Hopfield networks","primary_cat":"cs.NE","submitted_at":"2026-05-07T10:21:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Asynchronous sequential updates in KLR Hopfield networks produce statistically indistinguishable trajectories from synchronous dynamics, achieve empirical capacities near P/N=30, and converge with event counts close to initial Hamming distance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.00366","ref_index":1,"ref_count":4,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks","primary_cat":"cs.NE","submitted_at":"2026-05-01T03:04:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"KLR Hopfield networks reach P/N storage of ~16 for random patterns and ~20 for structured data, with limits set by dynamical instability against noise rather than geometric separability per Cover's theorem.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Key Words:kernel Hopfield network, associative memory, storage capacity, attractor geometry, signal- to-noise ratio, exemplar-based memory 1. Introduction Associative memory, the ability to retrieve complete data patterns from partial or noisy cues, is a funda- mental mechanism in both biological and artificial neural systems. The Hopfield network [1] provides a canonical model for this process, where memories are stored as stable fixed points (attractors) of an energy landscape. While the classical model is theoretically elegant, it suffers from a severe storage capacity limit for random patterns (𝑃≈0.14𝑁) [2], thereby limiting its practical applicability. Recent research has explored two primary avenues"},{"citing_arxiv_id":"2604.26082","ref_index":16,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"How is gene-regulatory evolution affected by cell-to-cell variability?","primary_cat":"q-bio.PE","submitted_at":"2026-04-28T19:46:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Cell-to-cell variability selects for aligned, motif-enriched gene regulatory networks that are robust to developmental noise and mutations.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"express optimal phenotypes even in highly variable environments. (C) Successful GRNs become highly aligned with the optimal phenotype, meaning that very few regulatory connections lead to deviations from achieving the optimal phenotype. Alignment can be seen as the GRNs developing highly robust solutions that mimic the Hopfield learning rule for storing memories [16] in an energy landscape. (D) Alignment is found to promote specific types of network motifs in the GRN, in particular coherent FFLs and positive FBLs. phenotype, ranging from intracellular processes to environmental sensitivity. Through this change in perspective, we ask whether evolutionary processes are reliant not only on genetic variation but also on the interplay between phenotypic"},{"citing_arxiv_id":"2604.16553","ref_index":15,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Emergent Information Formation in Prebiotic Protocell Clusters: A Computational Mechanics Framework of $\\epsilon$-Machines and Attractor Memory","primary_cat":"cond-mat.soft","submitted_at":"2026-04-17T07:50:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Casimir-stabilized protocell clusters form ε-machines whose attractor states and transitions create emergent prebiotic information through physical memory rather than molecular polymers.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.10016","ref_index":25,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Predicting Associations between Solar Flares and Coronal Mass Ejections Using SDO/HMI Magnetograms and a Hybrid Neural Network","primary_cat":"astro-ph.SR","submitted_at":"2026-04-11T04:01:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Hybrid neural network predicts eruptive versus confined solar flares from SDO/HMI magnetogram sequences, reports good performance, and links results to magnetic flux cancellation in polarity inversion lines.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Inceoglu et al. 2018). More recently, deep learning (Y. LeCun et al. 2015), a subfield of machine learning, has emerged as a powerful tool to predict solar eruptions. A suite of deep learning methods has been developed, ranging from recurrent neural networks, including long short-term memory and gated recurrent units, to convolutional neural networks (J. J. Hopfield 1982; S. Hochreiter & J. Schmidhuber 1997; Y. LeCun et al. 2015), to predict eruptive events (H. Liu et al. 2019; X. Li et al. 2020; H. Liu et al. 2020; P. Sun et al. 2022; Y. Abduallah et al. 2023; D. Xu et al. 2025). It is now widely believed that flares and CMEs are manifestations of solar coronal relaxation in terms of excess magnetic energy or magnetic helicity (B."},{"citing_arxiv_id":"2604.09833","ref_index":45,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Beyond Silicon: Materials, Mechanisms, and Methods for Physical Neural Computing","primary_cat":"cs.NE","submitted_at":"2026-04-10T19:04:31+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"background","top_context_polarity":"support","context_text":"bulk solutions. This architecture has demonstrated high- accuracy classification of image patterns by physically preventing unwanted feedback loops [44]. To process time-varying signals or solve optimization problems, architectures have incorporated feedback loops and autocatalysis. Building on the foundational associa- tive memory models proposed by Hopfield [45], DNA- based Hopfield networks utilize the energy landscape of the reaction system to store state information. Recent work demonstrated a discrete Hopfield network capable of solving combinatorial optimization tasks (such as Su- doku puzzles) by relaxing into a stable energy minimum representing the solution [46]. Data entry is typically achieved by introducing specific"},{"citing_arxiv_id":"2604.07401","ref_index":2,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Geometric Entropy and Retrieval Phase Transitions in Continuous Thermal Dense Associative Memory","primary_cat":"cond-mat.dis-nn","submitted_at":"2026-04-08T09:21:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Geometric entropy on the N-sphere sets retrieval phase boundaries in continuous thermal dense associative memories, achieving maximum capacity α=0.5 at zero temperature with kernel-dependent critical lines separating retrieval from failure.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.06667","ref_index":9,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Computing In Spintronic Memory: A Thermal Perspective","primary_cat":"cs.ET","submitted_at":"2026-04-08T04:35:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Spintronic CiM shows uniform temperature that increases linearly with participating memory cells and decreases linearly with array size.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.04743","ref_index":8,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Hallucination Basins: A Dynamic Framework for Understanding and Controlling LLM Hallucinations","primary_cat":"cs.CL","submitted_at":"2026-04-06T15:08:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LLM hallucinations arise from task-dependent basins in latent space, with separability varying by task and geometry-aware steering reducing their probability.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.06875","ref_index":2,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Stochastic Attention via Langevin Dynamics on the Modern Hopfield Energy","primary_cat":"cs.LG","submitted_at":"2026-03-06T20:50:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Langevin sampling on the modern Hopfield energy produces training-free stochastic attention that transitions from exact retrieval to generation as temperature rises, with an entropy inflection condition marking the shift.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.02622","ref_index":9,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Implicit Bias in Deep Linear Discriminant Analysis","primary_cat":"cs.LG","submitted_at":"2026-03-03T05:49:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Gradient flow on deep diagonal linear LDA networks with balanced initialization converts additive updates to multiplicative updates, automatically conserving the (2/L) quasi-norm.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2601.01253","ref_index":30,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Stochastic Thermodynamics of Associative Memory","primary_cat":"cond-mat.stat-mech","submitted_at":"2026-01-03T18:25:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"DenseAMs show tradeoffs between entropy production, retrieval accuracy, and speed at intermediate loads, with a new failure mode in higher-order networks at finite temperature.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.26745","ref_index":73,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Deep sequence models tend to memorize geometrically; it is unclear why","primary_cat":"cs.LG","submitted_at":"2025-10-30T17:40:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Deep sequence models develop geometric memory in embeddings that encodes novel global relationships, transforming l-fold composition tasks into 1-step navigation via a natural spectral bias connected to Node2Vec.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"ing larger language models with less training data and smaller model sizes.arXiv preprint arXiv:2305.02301, 2023. [72] Edward S. Hu, Kwangjun Ahn, Qinghua Liu, Haoran Xu, Manan Tomar, Ada Langford, Dinesh Jayaraman, Alex Lamb, and John Langford. The belief state transformer. InThe Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net, 2025. [73] Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry P. Heck. Learning deep structured semantic models for web search using clickthrough data. In Qi He, Arun Iyengar, Wolfgang Nejdl, Jian Pei, and Rajeev Rastogi, editors,22nd ACM International Conference on Information and Knowledge Management, CIKM'13, San Francisco, CA, USA, October 27 - November 1, 2013, pages 2333-2338."},{"citing_arxiv_id":"2510.11825","ref_index":33,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Memories of amplitude and direction coexist and compete in non-Brownian suspensions","primary_cat":"cond-mat.soft","submitted_at":"2025-10-13T18:25:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Memories of amplitude and direction in non-Brownian suspensions coexist and compete, with a specific amplitude suppressing directional memory and restoring symmetry.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2412.01459","ref_index":40,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI","primary_cat":"cs.CY","submitted_at":"2024-12-02T12:51:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Experts rate AI scenarios as more likely, less risky, more beneficial, and more valuable than the public, applying different weightings to risk versus benefit.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2312.12264","ref_index":13,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Exploring Non-Steady-State Charge Transport Dynamics in Information Processing: Insights from Reservoir Computing","primary_cat":"physics.chem-ph","submitted_at":"2023-12-19T15:47:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Non-steady-state chemical charge transport dynamics integrated into reservoir computing enable waveform recognition, voice identification, and chaos prediction, with performance governed by frequency alignment that functions as a chemically-tuned band-pass filter.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}