February 2026

arXiv:2602.12470v1 Announce Type: new Abstract: RNA design, the task of finding a sequence that folds into a target secondary structure, has broad biological and biomedical impact but remains computationally challenging due to the exponentially large sequence space and exponentially many competing folds. Traditional approaches treat it as an optimization problem, relying on per-instance heuristics or constraint-based search. We instead reframe RNA design as conditional sequence generation and introduce a reusable neural approximator, instantiated as an autoregressive language model […]

Ver mais

arXiv:2602.12469v1 Announce Type: new Abstract: Deep ensemble methods often improve predictive performance, yet they suffer from three practical limitations: redundancy among base models that inflates computational cost and degrades conditioning, unstable weighting under multicollinearity, and overfitting in meta-learning pipelines. We propose a regularized meta-learning framework that addresses these challenges through a four-stage pipeline combining redundancy-aware projection, statistical meta-feature augmentation, and cross-validated regularized meta-models (Ridge, Lasso, and ElasticNet). Our multi-metric de-duplication strategy removes near-collinear predictors using correlation and MSE […]

Ver mais

arXiv:2602.12468v1 Announce Type: new Abstract: Diffusion language models offer a promising alternative to autoregressive models due to their global, non-causal generation process, but their continuous latent dynamics make discrete constraints — e.g., the output should be a JSON file that matches a given schema — difficult to impose. We introduce a training-free guidance method for steering continuous diffusion language models to satisfy formal syntactic constraints expressed using regular expressions. Our approach constructs an analytic score estimating the probability […]

Ver mais

arXiv:2602.12461v1 Announce Type: new Abstract: Recent studies have shown that CLIP model’s adversarial robustness in zero-shot classification tasks can be enhanced by adversarially fine-tuning its image encoder with adversarial examples (AEs), which are generated by minimizing the cosine similarity between images and a hand-crafted template (e.g., ”A photo of a {label}”). However, it has been shown that the cosine similarity between a single image and a single hand-crafted template is insufficient to measure the similarity for image-text pairs. […]

Ver mais

arXiv:2602.12458v1 Announce Type: new Abstract: A central challenge in multi-agent reinforcement learning is enabling agents to adapt to previously unseen teammates in a zero-shot fashion. Prior work in zero-shot coordination often follows a two-stage process, first generating a diverse training pool of partner agents, and then training a best-response agent to collaborate effectively with the entire training pool. While many previous works have achieved strong performance by devising better ways to diversify the partner agent pool, there has […]

Ver mais

arXiv:2602.12452v1 Announce Type: new Abstract: This paper demonstrates the implementation and validation of a microwave testbed for directionally modulated transmission. Directional modulation enables multiple communication and/or radar signals to be transmitted in multiple directions simultaneously using a single phased array aperture, helping to relieve spectral congestion. A two-element transmitter array is driven by a Xilinx ZCU208 Radio Frequency System on a Chip (RFSoC). Our testbed provides a foundation for developing a fully reconfigurable array transmitter for multi-user multiple-input […]

Ver mais

arXiv:2602.12450v1 Announce Type: new Abstract: Psychotherapy is a primary treatment for many mental health conditions, yet the interplay among therapist behaviors, client responses, and the therapeutic relationship remains difficult to untangle. This work advances a computational approach for modeling these moment-to-moment processes. We first developed automated methods using large language models (LLMs) to assess therapist behaviors (e.g., empathy, exploration), relational qualities (e.g., rapport), and client outcomes (e.g., disclosure, self-directed and outward-directed negative emotions). These measures showed strong alignment […]

Ver mais

arXiv:2602.12448v1 Announce Type: new Abstract: Military and disaster relief operations increasingly rely on unmanned vehicles (UxVs). It is important to develop a network control system (NCS) that can continuously coordinate and optimize the movement of UxVs based on mission objectives. However, prior research on NCS aims to always maintain a connected network topology, which limits the utility of the resulting systems. In this paper, we present an approach to systematically increase the topology flexibility for an NCS by […]

Ver mais

arXiv:2602.12446v1 Announce Type: new Abstract: Temporal graphs are graphs where the presence or properties of their vertices and edges change over time. When time is discrete, a temporal graph can be defined as a sequence of static graphs over a discrete time span, called lifetime, or as a single graph where each edge is associated with a specific set of time instants where the edge is alive. For static graphs, Courcelle’s Theorem asserts that any graph problem expressible […]

Ver mais

arXiv:2602.12445v1 Announce Type: new Abstract: Language models (LMs) are known to be prone to response biases, which present as option preference biases in fixed-response questions. It is therefore imperative to develop low-cost and effective response bias correction methods to improve LM performance and enable more accurate evaluations of model abilities. Here, we propose a simple response bias correction strategy ($texttt{RBCorr}$) and test it on 12 open-weight language models using yes-no, entailment, and multiple choice questions. We show that […]

Ver mais