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Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Molecular Timing Channels under Pulsatile Drift: A Correc...
[Submitted on 17 Feb 2026 (v1), last revised 6 Sep 2026 (this ve · 2026-02-17 · via cs.IT updates on arXiv.org

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Abstract:In a one-dimensional molecular timing channel with a perfectly absorbing receiver, constant positive drift yields an inverse-Gaussian (IG) first-hitting-time distribution, whereas pulsatile drift makes the arrival statistics depend on the molecular release phase. We propose a phase-normalized corrected inverse-Gaussian (C-IG) approximation for pulsatile drift with positive mean. The model combines an exponential term determined by cumulative drift with a Gaussian positive-part prefactor. A phase-dependent normalization factor ensures unit probability mass. For a known periodic environment, these factors can be tabulated offline, enabling subsequent pointwise density evaluation without solving a time-recursive integral equation. The model also recovers the classical IG law exactly under constant positive drift. Comparisons with a numerical Volterra solution and independent particle simulations assess both density and cumulative-distribution accuracy. In a representative pulsatile case, C-IG better captures the oscillatory density structure than constant-drift IG models, while an oracle IG fit achieves a smaller cumulative-distribution error. An amplitude-frequency sweep over release phases at a fixed Peclet number identifies the operating regimes in which C-IG meets prescribed accuracy criteria. Most tested settings below the flow-reversal threshold meet these criteria, whereas transient flow reversal substantially reduces accuracy and can lead to discrepancies in density peak counts. These results support C-IG as a computationally convenient model for phase-dependent arrival statistics within its numerically assessed range of validity.

Submission history

From: Yen-Chi Lee [view email]
[v1] Tue, 17 Feb 2026 03:47:40 UTC (168 KB)
[v2] Fri, 27 Mar 2026 04:19:54 UTC (168 KB)
[v3] Sun, 28 Jun 2026 06:24:01 UTC (85 KB)
[v4] Sun, 6 Sep 2026 09:36:37 UTC (184 KB)