QQ: A Toolkit for Language Identifiers and Metadata
arXiv:2603.00620v1 Announce Type: new Abstract: The growing number of languages considered in multilingual NLP, including new datasets and tasks, poses challenges regarding properly and accurately reporting which languages are used and how. For example, datasets often use different language identifiers;...
BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languages
arXiv:2603.00634v1 Announce Type: new Abstract: Multilingual falsehoods threaten information integrity worldwide, yet detection benchmarks remain confined to English or a few high-resource languages, leaving low-resource linguistic communities without robust defense tools. We introduce BLUFF, a comprehensive benchmark for detecting false...
RAVEL: Reasoning Agents for Validating and Evaluating LLM Text Synthesis
arXiv:2603.00686v1 Announce Type: new Abstract: Large Language Models have evolved from single-round generators into long-horizon agents, capable of complex text synthesis scenarios. However, current evaluation frameworks lack the ability to assess the actual synthesis operations, such as outlining, drafting, and...
DRIV-EX: Counterfactual Explanations for Driving LLMs
arXiv:2603.00696v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as reasoning engines in autonomous driving, yet their decision-making remains opaque. We propose to study their decision process through counterfactual explanations, which identify the minimal semantic changes to...
LaSTR: Language-Driven Time-Series Segment Retrieval
arXiv:2603.00725v1 Announce Type: new Abstract: Effectively searching time-series data is essential for system analysis, but existing methods often require expert-designed similarity criteria or rely on global, series-level descriptions. We study language-driven segment retrieval: given a natural language query, the goal...
Constitutional Black-Box Monitoring for Scheming in LLM Agents
arXiv:2603.00829v1 Announce Type: new Abstract: Safe deployment of Large Language Model (LLM) agents in autonomous settings requires reliable oversight mechanisms. A central challenge is detecting scheming, where agents covertly pursue misaligned goals. One approach to mitigating such risks is LLM-based...
KVSlimmer: Theoretical Insights and Practical Optimizations for Asymmetric KV Merging
arXiv:2603.00907v1 Announce Type: new Abstract: The growing computational and memory demands of the Key-Value (KV) cache significantly limit the ability of Large Language Models (LLMs). While KV merging has emerged as a promising solution, existing methods that rely on empirical...
Towards Orthographically-Informed Evaluation of Speech Recognition Systems for Indian Languages
arXiv:2603.00941v1 Announce Type: new Abstract: Evaluating ASR systems for Indian languages is challenging due to spelling variations, suffix splitting flexibility, and non-standard spellings in code-mixed words. Traditional Word Error Rate (WER) often presents a bleaker picture of system performance than...
How RL Unlocks the Aha Moment in Geometric Interleaved Reasoning
arXiv:2603.01070v1 Announce Type: new Abstract: Solving complex geometric problems inherently requires interleaved reasoning: a tight alternation between constructing diagrams and performing logical deductions. Although recent Multimodal Large Language Models (MLLMs) have demonstrated strong capabilities in visual generation and plotting, we...
Transit Network Design with Two-Level Demand Uncertainties: A Machine Learning and Contextual Stochastic Optimization Framework
arXiv:2603.00010v1 Announce Type: new Abstract: Transit Network Design is a well-studied problem in the field of transportation, typically addressed by solving optimization models under fixed demand assumptions. Considering the limitations of these assumptions, this paper proposes a new framework, namely...
StaTS: Spectral Trajectory Schedule Learning for Adaptive Time Series Forecasting with Frequency Guided Denoiser
arXiv:2603.00037v1 Announce Type: new Abstract: Diffusion models have been used for probabilistic time series forecasting and show strong potential. However, fixed noise schedules often produce intermediate states that are hard to invert and a terminal state that deviates from the...
CARE: Confounder-Aware Aggregation for Reliable LLM Evaluation
arXiv:2603.00039v1 Announce Type: new Abstract: LLM-as-a-judge ensembles are the standard paradigm for scalable evaluation, but their aggregation mechanisms suffer from a fundamental flaw: they implicitly assume that judges provide independent estimates of true quality. However, in practice, LLM judges exhibit...
Attn-QAT: 4-Bit Attention With Quantization-Aware Training
arXiv:2603.00040v1 Announce Type: new Abstract: Achieving reliable 4-bit attention is a prerequisite for end-to-end FP4 computation on emerging FP4-capable GPUs, yet attention remains the main obstacle due to FP4's tiny dynamic range and attention's heavy-tailed activations. This paper presents the...
Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies
arXiv:2603.00041v1 Announce Type: new Abstract: Causal machine learning (ML) recovers graphical structures that inform us about potential cause-and-effect relationships. Most progress has focused on cross-sectional data with no explicit time order, whereas recovering causal structures from time series data remains...
Maximizing the Spectral Energy Gain in Sub-1-Bit LLMs via Latent Geometry Alignment
arXiv:2603.00042v1 Announce Type: new Abstract: We identify the Spectral Energy Gain in extreme model compression, where low-rank binary approximations outperform tiny-rank floating-point baselines for heavy-tailed spectra. However, prior attempts fail to realize this potential, trailing state-of-the-art 1-bit methods. We attribute...
Breaking the Factorization Barrier in Diffusion Language Models
arXiv:2603.00045v1 Announce Type: new Abstract: Diffusion language models theoretically allow for efficient parallel generation but are practically hindered by the "factorization barrier": the assumption that simultaneously predicted tokens are independent. This limitation forces a trade-off: models must either sacrifice speed...
REMIND: Rethinking Medical High-Modality Learning under Missingness--A Long-Tailed Distribution Perspective
arXiv:2603.00046v1 Announce Type: new Abstract: Medical multi-modal learning is critical for integrating information from a large set of diverse modalities. However, when leveraging a high number of modalities in real clinical applications, it is often impractical to obtain full-modality observations...
Mag-Mamba: Modeling Coupled spatiotemporal Asymmetry for POI Recommendation
arXiv:2603.00053v1 Announce Type: new Abstract: Next Point-of-Interest (POI) recommendation is a critical task in location-based services, yet it faces the fundamental challenge of coupled spatiotemporal asymmetry inherent in urban mobility. Specifically, transition intents between locations exhibit high asymmetry and are...
Expert Divergence Learning for MoE-based Language Models
arXiv:2603.00054v1 Announce Type: new Abstract: The Mixture-of-Experts (MoE) architecture is a powerful technique for scaling language models, yet it often suffers from expert homogenization, where experts learn redundant functionalities, thereby limiting MoE's full potential. To address this, we introduce Expert...
Certainty-Validity: A Diagnostic Framework for Discrete Commitment Systems
arXiv:2603.00070v1 Announce Type: new Abstract: Standard evaluation metrics for machine learning -- accuracy, precision, recall, and AUROC -- assume that all errors are equivalent: a confident incorrect prediction is penalized identically to an uncertain one. For discrete commitment systems (architectures...
SEval-NAS: A Search-Agnostic Evaluation for Neural Architecture Search
arXiv:2603.00099v1 Announce Type: new Abstract: Neural architecture search (NAS) automates the discovery of neural networks that meet specified criteria, yet its evaluation procedures are often hardcoded, limiting the ability to introduce new metrics. This issue is especially pronounced in hardware-aware...
Wideband Power Amplifier Behavioral Modeling Using an Amplitude Conditioned LSTM
arXiv:2603.00101v1 Announce Type: new Abstract: Wideband power amplifiers exhibit complex nonlinear and memory effects that challenge traditional behavioral modeling approaches. This paper proposes a novel amplitude conditioned long short-term memory (AC-LSTM) network that introduces explicit amplitude-dependent gating to enhance the...
LIDS: LLM Summary Inference Under the Layered Lens
arXiv:2603.00105v1 Announce Type: new Abstract: Large language models (LLMs) have gained significant attention by many researchers and practitioners in natural language processing (NLP) since the introduction of ChatGPT in 2022. One notable feature of ChatGPT is its ability to generate...
MAML-KT: Addressing Cold Start Problem in Knowledge Tracing for New Students via Few-Shot Model-Agnostic Meta Learning
arXiv:2603.00137v1 Announce Type: new Abstract: Knowledge tracing (KT) models are commonly evaluated by training on early interactions from all students and testing on later responses. While effective for measuring average predictive performance, this evaluation design obscures a cold start scenario...
NNiT: Width-Agnostic Neural Network Generation with Structurally Aligned Weight Spaces
arXiv:2603.00180v1 Announce Type: new Abstract: Generative modeling of neural network parameters is often tied to architectures because standard parameter representations rely on known weight-matrix dimensions. Generation is further complicated by permutation symmetries that allow networks to model similar input-output functions...
A medical coding language model trained on clinical narratives from a population-wide cohort of 1.8 million patients
arXiv:2603.00221v1 Announce Type: new Abstract: Medical coding translates clinical documentation into standardized codes for billing, research, and public health, but manual coding is time-consuming and error-prone. Existing automation efforts rely on small datasets that poorly represent real-world patient heterogeneity. We...
CoPeP: Benchmarking Continual Pretraining for Protein Language Models
arXiv:2603.00253v1 Announce Type: new Abstract: Protein language models (pLMs) have recently gained significant attention for their ability to uncover relationships between sequence, structure, and function from evolutionary statistics, thereby accelerating therapeutic drug discovery. These models learn from large protein databases...
Polynomial Surrogate Training for Differentiable Ternary Logic Gate Networks
arXiv:2603.00302v1 Announce Type: new Abstract: Differentiable logic gate networks (DLGNs) learn compact, interpretable Boolean circuits via gradient-based training, but all existing variants are restricted to the 16 two-input binary gates. Extending DLGNs to Ternary Kleene $K_3$ logic and training DTLGNs...
Vectorized Adaptive Histograms for Sparse Oblique Forests
arXiv:2603.00326v1 Announce Type: new Abstract: Classification using sparse oblique random forests provides guarantees on uncertainty and confidence while controlling for specific error types. However, they use more data and more compute than other tree ensembles because they create deep trees...
StethoLM: Audio Language Model for Cardiopulmonary Analysis Across Clinical Tasks
arXiv:2603.00355v1 Announce Type: new Abstract: Listening to heart and lung sounds - auscultation - is one of the first and most fundamental steps in a clinical examination. Despite being fast and non-invasive, it demands years of experience to interpret subtle...