RTD-Guard: A Black-Box Textual Adversarial Detection Framework via Replacement Token Detection
arXiv:2603.12582v1 Announce Type: new Abstract: Textual adversarial attacks pose a serious security threat to Natural Language Processing (NLP) systems by introducing imperceptible perturbations that mislead deep learning models. While adversarial example detection offers a lightweight alternative to robust training, existing...
Using a Human-AI Teaming Approach to Create and Curate Scientific Datasets with the SCILIRE System
arXiv:2603.12638v1 Announce Type: new Abstract: The rapid growth of scientific literature has made manual extraction of structured knowledge increasingly impractical. To address this challenge, we introduce SCILIRE, a system for creating datasets from scientific literature. SCILIRE has been designed around...
98$\times$ Faster LLM Routing Without a Dedicated GPU: Flash Attention, Prompt Compression, and Near-Streaming for the vLLM Semantic Router
arXiv:2603.12646v1 Announce Type: new Abstract: System-level routers that intercept LLM requests for safety classification, domain routing, and PII detection must be both fast and operationally lightweight: they should add minimal latency to every request, yet not require a dedicated GPU...
Continual Learning in Large Language Models: Methods, Challenges, and Opportunities
arXiv:2603.12658v1 Announce Type: new Abstract: Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge and sequential tasks while mitigating catastrophic forgetting-a critical limitation of the static pre-training paradigm...
MetaKE: Meta-learning Aligned Knowledge Editing via Bi-level Optimization
arXiv:2603.12677v1 Announce Type: new Abstract: Knowledge editing (KE) aims to precisely rectify specific knowledge in Large Language Models (LLMs) without disrupting general capabilities. State-of-the-art methods suffer from an open-loop control mismatch. We identify a critical "Semantic-Execution Disconnect": the semantic target...
Experimental evidence of progressive ChatGPT models self-convergence
arXiv:2603.12683v1 Announce Type: new Abstract: Large Language Models (LLMs) that undergo recursive training on synthetically generated data are susceptible to model collapse, a phenomenon marked by the generation of meaningless output. Existing research has examined this issue from either theoretical...
EvolveCoder: Evolving Test Cases via Adversarial Verification for Code Reinforcement Learning
arXiv:2603.12698v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) is a promising approach for improving code generation in large language models, but its effectiveness is limited by weak and static verification signals in existing coding RL datasets. In...
A Method for Learning Large-Scale Computational Construction Grammars from Semantically Annotated Corpora
arXiv:2603.12754v1 Announce Type: new Abstract: We present a method for learning large-scale, broad-coverage construction grammars from corpora of language use. Starting from utterances annotated with constituency structure and semantic frames, the method facilitates the learning of human-interpretable computational construction grammars...
Rethinking Multiple-Choice Questions for RLVR: Unlocking Potential via Distractor Design
arXiv:2603.12826v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) significantly enhances the reasoning capabilities of Large Language Models. When applied to RLVR, Multiple-Choice Questions (MCQs) offer a scalable source of verifiable data but risk inducing reward hacking, where...
Learning from Child-Directed Speech in Two-Language Scenarios: A French-English Case Study
arXiv:2603.12906v1 Announce Type: new Abstract: Research on developmentally plausible language models has largely focused on English, leaving open questions about multilingual settings. We present a systematic study of compact language models by extending BabyBERTa to English-French scenarios under strictly size-matched...
Long-form RewardBench: Evaluating Reward Models for Long-form Generation
arXiv:2603.12963v1 Announce Type: new Abstract: The widespread adoption of reinforcement learning-based alignment highlights the growing importance of reward models. Various benchmarks have been built to evaluate reward models in various domains and scenarios. However, a significant gap remains in assessing...
Interpretable Semantic Gradients in SSD: A PCA Sweep Approach and a Case Study on AI Discourse
arXiv:2603.13038v1 Announce Type: new Abstract: Supervised Semantic Differential (SSD) is a mixed quantitative-interpretive method that models how text meaning varies with continuous individual-difference variables by estimating a semantic gradient in an embedding space and interpreting its poles through clustering and...
Mending the Holes: Mitigating Reward Hacking in Reinforcement Learning for Multilingual Translation
arXiv:2603.13045v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capability in machine translation on high-resource language pairs, yet their performance on low-resource translation still lags behind. Existing post-training methods rely heavily on high-quality parallel data, which are...
DIALECTIC: A Multi-Agent System for Startup Evaluation
arXiv:2603.12274v1 Announce Type: cross Abstract: Venture capital (VC) investors face a large number of investment opportunities but only invest in few of these, with even fewer ending up successful. Early-stage screening of opportunities is often limited by investor bandwidth, demanding...
Multi-Step Semantic Reasoning in Generative Retrieval
arXiv:2603.12368v1 Announce Type: cross Abstract: Generative retrieval (GR) models encode a corpus within model parameters and generate relevant document identifiers directly for a given query. While this paradigm shows promise in retrieval tasks, existing GR models struggle with complex queries...
NeuroLoRA: Context-Aware Neuromodulation for Parameter-Efficient Multi-Task Adaptation
arXiv:2603.12378v1 Announce Type: cross Abstract: Parameter-Efficient Fine-Tuning (PEFT) techniques, particularly Low-Rank Adaptation (LoRA), have become essential for adapting Large Language Models (LLMs) to downstream tasks. While the recent FlyLoRA framework successfully leverages bio-inspired sparse random projections to mitigate parameter interference,...
Reinforcement Learning for Diffusion LLMs with Entropy-Guided Step Selection and Stepwise Advantages
arXiv:2603.12554v1 Announce Type: cross Abstract: Reinforcement learning (RL) has been effective for post-training autoregressive (AR) language models, but extending these methods to diffusion language models (DLMs) is challenging due to intractable sequence-level likelihoods. Existing approaches therefore rely on surrogate likelihoods...
Speech-Worthy Alignment for Japanese SpeechLLMs via Direct Preference Optimization
arXiv:2603.12565v1 Announce Type: cross Abstract: SpeechLLMs typically combine ASR-trained encoders with text-based LLM backbones, leading them to inherit written-style output patterns unsuitable for text-to-speech synthesis. This mismatch is particularly pronounced in Japanese, where spoken and written registers differ substantially in...
No More DeLuLu: Physics-Inspired Kernel Networks for Geometrically-Grounded Neural Computation
arXiv:2603.12276v1 Announce Type: new Abstract: We introduce the yat-product, a kernel operator combining quadratic alignment with inverse-square proximity. We prove it is a Mercer kernel, analytic, Lipschitz on bounded domains, and self-regularizing, admitting a unique RKHS embedding. Neural Matter Networks...
Multi-objective Genetic Programming with Multi-view Multi-level Feature for Enhanced Protein Secondary Structure Prediction
arXiv:2603.12293v1 Announce Type: new Abstract: Predicting protein secondary structure is essential for understanding protein function and advancing drug discovery. However, the intricate sequence-structure relationship poses significant challenges for accurate modeling. To address these, we propose MOGP-MMF, a multi-objective genetic programming...
Generalist Large Language Models for Molecular Property Prediction: Distilling Knowledge from Specialist Models
arXiv:2603.12344v1 Announce Type: new Abstract: Molecular Property Prediction (MPP) is a central task in drug discovery. While Large Language Models (LLMs) show promise as generalist models for MPP, their current performance remains below the threshold for practical adoption. We propose...
Spatial PDE-aware Selective State-space with Nested Memory for Mobile Traffic Grid Forecasting
arXiv:2603.12353v1 Announce Type: new Abstract: Traffic forecasting in cellular networks is a challenging spatiotemporal prediction problem due to strong temporal dependencies, spatial heterogeneity across cells, and the need for scalability to large network deployments. Traditional cell-specific models incur prohibitive training...
SpectralGuard: Detecting Memory Collapse Attacks in State Space Models
arXiv:2603.12414v1 Announce Type: new Abstract: State Space Models (SSMs) such as Mamba achieve linear-time sequence processing through input-dependent recurrence, but this mechanism introduces a critical safety vulnerability. We show that the spectral radius rho(A-bar) of the discretized transition operator governs...
Overcoming the Modality Gap in Context-Aided Forecasting
arXiv:2603.12451v1 Announce Type: new Abstract: Context-aided forecasting (CAF) holds promise for integrating domain knowledge and forward-looking information, enabling AI systems to surpass traditional statistical methods. However, recent empirical studies reveal a puzzling gap: multimodal models often fail to outperform their...
Bases of Steerable Kernels for Equivariant CNNs: From 2D Rotations to the Lorentz Group
arXiv:2603.12459v1 Announce Type: new Abstract: We present an alternative way of solving the steerable kernel constraint that appears in the design of steerable equivariant convolutional neural networks. We find explicit real and complex bases which are ready to use, for...
Modal Logical Neural Networks for Financial AI
arXiv:2603.12487v1 Announce Type: new Abstract: The financial industry faces a critical dichotomy in AI adoption: deep learning often delivers strong empirical performance, while symbolic logic offers interpretability and rule adherence expected in regulated settings. We use Modal Logical Neural Networks...
Adaptive Conditional Forest Sampling for Spectral Risk Optimisation under Decision-Dependent Uncertainty
arXiv:2603.12507v1 Announce Type: new Abstract: Minimising a spectral risk objective, defined as a convex combination of expected cost and Conditional Value-at-Risk (CVaR), is challenging when the uncertainty distribution is decision-dependent, making both surrogate modelling and simulation-based ranking sensitive to tail...
Byzantine-Robust Optimization under $(L_0, L_1)$-Smoothness
arXiv:2603.12512v1 Announce Type: new Abstract: We consider distributed optimization under Byzantine attacks in the presence of $(L_0,L_1)$-smoothness, a generalization of standard $L$-smoothness that captures functions with state-dependent gradient Lipschitz constants. We propose Byz-NSGDM, a normalized stochastic gradient descent method with...
Learning Pore-scale Multiphase Flow from 4D Velocimetry
arXiv:2603.12516v1 Announce Type: new Abstract: Multiphase flow in porous media underpins subsurface energy and environmental technologies, including geological CO$_2$ storage and underground hydrogen storage, yet pore-scale dynamics in realistic three-dimensional materials remain difficult to characterize and predict. Here we introduce...
Curriculum Sampling: A Two-Phase Curriculum for Efficient Training of Flow Matching
arXiv:2603.12517v1 Announce Type: new Abstract: Timestep sampling $p(t)$ is a central design choice in Flow Matching models, yet common practice increasingly favors static middle-biased distributions (e.g., Logit-Normal). We show that this choice induces a speed--quality trade-off: middle-biased sampling accelerates early...