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Newest papers, page 194
Interpretability of AI weather models via intermediate decoding
AI weather prediction models are often regarded as black boxes.Looped GNNs Beyond Fixed Depth: Graph Size Generalization and Parameter-Efficient Adaptation
Generalizing from small graphs to larger ones can require computation beyond the depth of a neural model used during training.Graph-Regularized Synthetic Control for Stable Counterfactual Estimation under Donor Redundancy
Synthetic control methods estimate counterfactual outcomes by weighting untreated donor units to match a treated unit before treatment.Do LMs Know What Variables Do? Interpreting Code Representations via Variable Roles
Variable roles describe recurring behaviors such as accumulation, traversal, and retaining the best candidate encountered.Prefix-Tuning for Arbitrary Output Sequences on Pretrained Transformers
Prefix-tuning is a parameter-efficient fine-tuning method for large language models (LLMs) that steers model outputs by prepending task-specific prefix representations to the input sequence.On the Selectivity of Generative Models in Structure-Based Drug Design
Designing small-molecule drugs that selectively bind to a target protein while avoiding unintended interactions with other proteins is essential for reducing side effects that contribute…Data-Driven Resource Aggregation for Scalable Digital Twin and Simulation Model Generation
As industrial digitization accelerates, the use of digital twins has become widespread.Recording-Aware Evaluation of Multimodal OT Intrusion Detection
Machine-learning-based intrusion detection systems (IDSs) for operational technology (OT) are commonly evaluated on benchmark datasets in which normal and attack data are collected in separate…Reducing double-dipping in single-cell topic models with three-way count splitting
Topic models learn gene programs from single-cell RNA sequencing (scRNA-seq) data, and the same counts are then used to test which genes and donors are associated with those programs.The Weight of Teamwork: Does Communication Structure Help LLM Scientists Search for Scientific Laws?
Team performance is often reported as one delivered score, even though that score is produced in two stages: a system generates candidate answers and a selection rule returns one of them.Inside the Loop: A Mechanistic Study of Weight-Tied Transformers on Depth-Bound Algorithmic Tasks
Solving algorithmic tasks with many sequential steps typically requires scaling up transformer depth or chain-of-thought length, incurring unfavorable parameter and inference-compute costs…Continual Test-Time Learning with Evolution Strategies
Large language models increasingly operate in open-ended environments, where they are expected to continually adapt to evolving tasks and distributions, often without access to external supervision.Sequence-to-Sequence Neural Surrogate Modeling of Transient Responses in High-Speed Links with Variable Design Parameters
This paper presents sequence-to-sequence (seq2seq) neural surrogates for high-speed transient response modeling and signal integrity analysis in fixed and variable design parameter spaces.lepidoptera: A Domain-Agnostic Engine for Causal Simulation with Mixed Variable Types, Vine Copula Dependence, and Individual-Level Counterfactual Propagation
lepidoptera is an open-source R and Python package for simulation from structural causal models with heterogeneous variable types.Budgeted Bayesian Bandits for Future Utilization
The multi-armed bandit problem models the trade-off between exploration and exploitation.Margin Certificates for Voting Classifiers under Mixing Dependence
Margin bounds explain why weighted votes generalize after training error vanishes, but dependent observations require concentration arguments that keep the dependence model and failure budget…Bootstrap Stochastic Interpolant Tree for Inference-time Search Under Sparse Reward
Tree-based samplers offer a compelling alternative for scalable inference-time search, bypassing the per-step reward and gradient dependencies that introduce systematic bias into SMC-style frameworks.On Reparameterizing the Score Function
Policy gradients for autoregressive sequence models differentiate each token's log-probability while holding the conditioning history fixed.Candidate-dependent residual routing trades top-of-list accuracy for recall in sequential recommendation
Candidate-dependent routing lets a sequential recommender weight a user's past behaviors differently for each item it scores.Certified neural approximations for parabolic obstacle problems
Residual-trained neural surrogates carry no error guarantee, a limitation for free-boundary problems such as phase change and contact.