This paper tackles digital-domain self-interference cancellation in in-band full-duplex radios, where conventional adaptive filters degrade when the desired signal is much stronger than the residual interference.
The authors reformulate cancellation as an operator-learning problem: a Transformer neural operator learns the temporal evolution of the self-interference channel from historical observations and reconstructs the current interference from the known transmit signal.
Trained in a desired-signal-free setting and applied during contaminated reception, the method reduces reliance on corrupted instantaneous updates and reports strong high-SIR stability.

Feel-Good Thompson Sampling (FGTS) addresses slow exploration in vanilla TS by adding an optimism term to the posterior. This paper proves the first high-probability regret bounds for both cumulative and current-step FGTS under general realizable function approximation, showing that in linear contextual bandits the regret scales as $O(d\sqrt{T}\log(2T)+\sqrt{T}\log(1/\delta))$ with probability at least $1-\delta$. The core technique is applying Markov's inequality to exponential quantities derived from the posterior normalizer, avoiding explicit confidence-set constructions.

Quantum key distribution deployments provision security margins assuming stationary channels, yet physical devices drift between calibrations. This paper frames adaptive eavesdropping under trusted channel noise and device drift as a constrained Markov decision process, where an attacker selects compact learnt quantum circuits each round while the noise follows an Ornstein–Uhlenbeck process. By bounding the adaptive value between the best fixed circuit and a dynamic-programming upper bound, the authors let operators measure how conservative their margins are and tune recalibration intervals, addressing a key economic barrier to QKD adoption.

The paper addresses adaptive broadcast of data-intensive sensory streams (e.g., camera/LiDAR) to heterogeneous edge devices with diverse channel conditions and computational budgets. It proposes Nonlinear Transform Rateless Source-Channel Coding (NTRSCC), integrating learned nonlinear transforms with physical-layer Luby Transform (LT) codes to enable receivers to adaptively adjust the number of received symbols and belief propagation iterations. This achieves an explicit, controllable tradeoff between distortion, transmission rate, and decoding complexity—addressing key limitations of fixed-rate DeepJSCC schemes that either underserve capable devices or require costly retransmissions.

Diffusion language models (DLMs) enable parallel token generation, but their efficiency depends critically on the decoding strategy that determines which tokens to unmask and when. This paper investigates confidence-based decoding—specifically an entropy sum strategy that adaptively batches tokens until cumulative prediction uncertainty exceeds a threshold—and proves it achieves $\varepsilon$-accurate sampling in KL divergence with expected iteration complexity $\widetilde{O}(H(X_0)/\varepsilon)$. When the data distribution has low entropy ($H(X_0) \ll L$), this yields sublinear complexity in sequence length, providing the first theoretical foundation for why confidence-based methods accelerate sampling without sacrificing fidelity.

This paper tackles the lack of shared formalism for comparing hierarchical memory systems in language agents. It proposes a unifying theory based on three operators: extraction (α) that maps raw data to atomic units, coarsening (C = (π, ρ)) that partitions and summarizes units, and traversal (τ) that selects content under a token budget. The core insight is the self-sufficiency spectrum of representatives ρ, which constrains viable retrieval strategies—an observation the authors call the coarsening-traversal (C–T) coupling.

The paper addresses multi-UAV coordination under intermittent communications by proposing a Spatio-Temporal Attention enhanced MADRL (STA-MADRL) framework. It combines delay-penalized rewards to incentivize information exchange with a prediction module that recovers missing state data using temporal and spatial attention mechanisms. The authors claim 75% throughput improvements over communication-limited baselines while achieving near-ideal performance without requiring real-time global state sharing.