Molecular communication simulators are computationally expensive, so machine learning models are often trained to estimate performance metrics like round-trip time, but protocol evaluation proceeds sequentially from simple to complex settings and causes catastrophic forgetting when models are fine-tuned. This paper proposes Affine Memory Replay (AMR), a continual learning method that stores past simulation samples and synthesizes additional replay samples through affine interpolation and extrapolation to regularize a small neural network. AMR aims to retain knowledge from earlier experimental settings while adapting to new ones, offering a practical way to amortize simulation costs in molecular communication research.

This paper tackles offline parameter identification for a non-ideal DC-DC buck converter using Physics-Informed Neural Networks (PINNs). The core idea is a staged training schedule that progressively unfreezes physical parameters—inductance, capacitance, and parasitic resistances—to improve convergence and interpretability. The work matters because accurate component-level estimates enable condition monitoring and predictive maintenance without intrusive frequency-response tests.

Training-free inference-time scaling aims to improve LLM reasoning without updating model weights or relying on external reward models. Existing reward-free methods sharpen the full sequence distribution, which can suppress diverse reasoning paths that support the same correct answer, and suffer from path collapse in Sequential Monte Carlo samplers. This paper proposes Decoupled Marginal Sharpening (DMS), a two-stage framework that separately controls reasoning-path concentration and answer-marginal consensus via independent exponents, paired with a novel Fully Adapted Annealed SMC sampler. The authors report the highest mean pass@1 across three base models and four benchmarks, including mathematics, science, and code tasks.

This critical review reframes interactive segmentation, active learning, and machine teaching through a shared information-theoretic lens. It separates target information gain from predictive distortion reduction and human effort, exposing why predicting a teacher's clicks does not by itself improve reusable learning. By formalizing corrective interactions as nested memory records, the paper shows that current-mask refinement and reusable-model training are distinct objectives requiring different evaluation assumptions.

CktHGVAE tackles analog/mixed-signal CMOS circuit automation by learning a joint latent manifold of design parameters and transistor-level operating states. The core idea is a symmetric heterogeneous graph VAE that treats SPICE-derived state variables and design variables equally as inputs and outputs, enabling closed-loop design verification without a separate surrogate model. This is significant because prior graph learning approaches for analog circuits largely rely on macromodel abstractions or topology generation, leaving fine-grained operating-state modeling underexplored.

This paper asks how shallow transformers learn on unbounded-length inputs and whether layer-wise training can be decoupled. It proves that a one-step attention-then-output schedule fails to learn the occurrence language, while a three-phase output-attention-output schedule succeeds in both population and finite-sample settings. The finding is significant because it formally explains why attention weights often lag behind the output layer during training, challenging the feedforward intuition that feature learning must come first.

Speculative decoding drafters trained on short sequences collapse on long-context inputs because their position encodings extrapolate poorly. FOLD fixes this without retraining by keeping exact positions in a local window and compressing all distant context into a shared-position far memory. A concentration factor $\alpha$ in a decoupled softmax prevents attention dilution as the memory grows. Across six target–drafter pairs and contexts up to 256K tokens, the method consistently raises speedup, in the best case more than doubling it.

LLM workflows rely on fast binary decisions like moderation and reranking, but autoregressive generation is too slow for tight latency budgets. This paper studies System One models—lightweight scorers that emit bounded decision scores—and proposes Learn Then Test (LTT) to certify thresholds that maximize the fraction of inputs receiving the utility-maximizing action while controlling the False Omission Rate (FOR) with a finite-sample guarantee. The authors show across six datasets that naive thresholding at the target risk level exceeds the budget in roughly half of settings, whereas LTT violates it in only 7.8% of cases, making a strong case that a probability-like interface is insufficient for safe deployment under asymmetric risk.

Low-rank adapters can waste their parameter budget because not every learned direction improves downstream performance. ReGRA attacks this by splitting adaptation into two stages: first train a standard LoRA adapter, then estimate task gradients, project out the components already captured by the first stage, and use the residual directions to initialize a second adapter with an orthogonality regularizer. By merging both updates into the backbone, it keeps inference cost identical while increasing average accuracy within the same total rank budget.

Power grid steady-state analysis demands fast, generalizable surrogates for power flow, contingency screening, and voltage stability margin estimation. This paper investigates whether a pretrained tabular foundation model (TabPFN) can serve as a unified neural solver through in-context learning, without task-specific fine-tuning. The authors evaluate the approach on 118- and 2000-bus systems, arguing that it offers a flexible alternative to training separate graph or MLP baselines for each task and topology.

The paper studies proper hypothesis selection: given $n$ candidate distributions and sample access to an unknown target $P$, the goal is to return one candidate with total variation distance at most $3\min_i d_{TV}(P,H_i)+\varepsilon$. The core idea is to maintain weights over the candidates, define a weighted median of their densities as an unnormalized intermediate measure, and perform multiplicative weight updates using balanced tests derived from this median. The result achieves the optimal factor-3 approximation and sample complexity $O(\varepsilon^{-2}\log(n/\delta))$ while improving the running time to $O(n\varepsilon^{-2}\log(n/\delta))$, answering an open question of Aamand et al. (COLT 2026) by reducing the failure-probability dependence from $1/\delta$ to $\log(1/\delta)$.

This paper studies online convex optimization against comparators that switch $k$ times among only $m$ distinct points, generalizing the classic experts tracking problem to continuous domains. While discretizing the domain and applying the Bousquet–Warmuth long-term-memory algorithm yields information-theoretic regret $\widetilde O(md+k)$, this is computationally inefficient. The authors prove that, under the hypergraphic planted-clique conjecture, no polynomial-time algorithm can achieve $\widetilde O(k^a d^b)$ regret whenever the exponents violate $\theta a + b < \theta + 1/2$, establishing a fundamental computational barrier for exp-concave losses even when $m=2$.

Gaussian diffusion models poorly cover extreme regions for heavy-tailed data because their light-tailed corruption kernels yield exponentially sparse tail samples. This paper introduces Annealed Lévy Diffusion (ALD), which specifies time-dependent $\alpha(t)$-stable perturbation marginals via a Gaussian scale-mixture representation, enabling a two-phase schedule that transitions from heavy-tailed exploration to Gaussian refinement. The construction retains conditional-Gaussian score matching with only one added scalar input, making it compatible with standard diffusion training while targeting distributions with polynomial tails.

C-Slerp addresses long-horizon, sparse-reward goal-conditioned reinforcement learning without prior data, rewards, or demonstrations by leveraging hyperspherical contrastive representations for adaptive waypoint planning. Instead of training a separate high-level policy, it infers intermediate subgoals in closed form via spherical linear interpolation on a learned unit hypersphere and dynamically adjusts the number of waypoints to match each task's difficulty. Experiments on robotic manipulation and navigation show the method solves tasks like eight-cube tower stacking from scratch where strong baselines entirely fail, suggesting that adaptive task decomposition—not merely added hierarchy—is key to scaling goal-reaching RL.

Contextual partial monitoring is a sequential learning problem where the learner observes only partial feedback instead of incurred losses. The paper proposes E2D.PMSide, a computationally efficient instantiation of the Estimation-to-Decisions (E2D) meta-algorithm that reduces contextual partial monitoring to online regression. Its core contribution is replacing E2D's intractable per-round minimax exploration rule with tractable surrogates—epsilon-greedy for globally observable games and inverse gap weighting for locally observable ones—thereby achieving optimal $O(T^{2/3})$ and $O(\sqrt{T})$ regret rates. The work matters because it provides the first oracle-efficient algorithms for this setting with general function approximation and demonstrates practical benefits on LLM routing and label-efficient prediction.

One-step generative models typically demand complex distillation or fragile end-to-end distributional training. This paper introduces Priming Models, a two-stage framework that first pretrains with a simple reconstruction objective—surprisingly, even fixed-noise denoising suffices—and then refines the network with feature-space distributional losses. The core claim is that this reconstruction stage fully removes the training difficulty of distributional optimization, enabling state-of-the-art quality at roughly one-tenth the compute of prior one-step methods.

Neural operators learn mappings between function spaces, yet training deep architectures remains difficult. This paper proposes viewing each operator layer as the minimizer of a Bregman-regularized functional optimization problem in Banach spaces. This unifies classical neural operators under a proximal framework and yields a new Bregman variant that adds an inverse activation term, showing improved depth scaling and accuracy on standard PDE benchmarks.

COUNT tackles the GPU-memory bottleneck of scaling language-model embeddings by replacing learned n-gram tables with precomputed corpus counts stored on cheap host RAM. It constructs input embeddings via a weighted sum of the model's own unembedding vectors, requiring no extra learned parameters and enabling training-free domain adaptation at inference. On 60M--1B-parameter backbones, the authors demonstrate consistent perplexity and downstream gains without increasing GPU memory pressure.

Current evaluations of Knowledge Extraction Enhancement (KEE) techniques collapse recovery and disruption into a single accuracy score, concealing critical trade-offs. MAPLE introduces a unified diagnostic framework built on a pretraining-verified benchmark and three complementary metrics—surface-level $\text{Gain}/\text{Loss}$, probability-level $\text{PRS}$, and beyond-sampling $\text{SGR}/\text{DGR}$—to dissect intervention effects at multiple levels. Across six techniques and three model families, MAPLE shows that prompting recovers deep failures but disrupts many successes, while decoding and steering often reshuffle token probabilities without consistent benefit. The work offers a principled, actionable standard for comparing KEE methods beyond headline accuracy.

Feldman (2021) gave an influential lower bound linking the cost of not fitting training labels to excess population risk via frequency-dependent coefficients $\tau_{n,\ell}$. This paper demonstrates that Feldman's central conditioning identity fails because the counts of other points convey information about the sampling distribution, and it provides an exact three-point counterexample together with an exponentially large multiplicative gap. The authors recover a valid bound within the same model by replacing the count-only coefficient with the exact full-sample posterior mean, which depends on both multiplicity and the complete frequency profile.