CINDI: Conditional Imputation and Noisy Data Integrity with Flows in Power Grid Data
arXiv:2603.11745v1 Announce Type: new Abstract: Real-world multivariate time series, particularly in critical infrastructure such as electrical power grids, are often corrupted by noise and anomalies that degrade the performance of downstream tasks. Standard data cleaning approaches often rely on disjoint...
Leveraging Large Language Models and Survival Analysis for Early Prediction of Chemotherapy Outcomes
arXiv:2603.11594v1 Announce Type: new Abstract: Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve patient management and informed decision-making. Predictive models for chemotherapy outcomes using...
Try, Check and Retry: A Divide-and-Conquer Framework for Boosting Long-context Tool-Calling Performance of LLMs
arXiv:2603.11495v1 Announce Type: new Abstract: Tool-calling empowers Large Language Models (LLMs) to interact with external environments. However, current methods often struggle to handle massive and noisy candidate tools in long-context tool-calling tasks, limiting their real-world application. To this end, we...
Can Small Language Models Use What They Retrieve? An Empirical Study of Retrieval Utilization Across Model Scale
arXiv:2603.11513v1 Announce Type: new Abstract: Retrieval augmented generation RAG is widely deployed to improve factual accuracy in language models yet it remains unclear whether smaller models of size 7B parameters or less can effectively utilize retrieved information. To investigate this...
One Supervisor, Many Modalities: Adaptive Tool Orchestration for Autonomous Queries
arXiv:2603.11545v1 Announce Type: new Abstract: We present an agentic AI framework for autonomous multimodal query processing that coordinates specialized tools across text, image, audio, video, and document modalities. A central Supervisor dynamically decomposes user queries, delegates subtasks to modality-appropriate tools...
Where Matters More Than What: Decoding-aligned KV Cache Compression via Position-aware Pseudo Queries
arXiv:2603.11564v1 Announce Type: new Abstract: The Key-Value (KV) cache is crucial for efficient Large Language Models (LLMs) inference, but excessively long contexts drastically increase KV cache memory footprint. Existing KV cache compression methods typically rely on input-side attention patterns within...
Streaming Translation and Transcription Through Speech-to-Text Causal Alignment
arXiv:2603.11578v1 Announce Type: new Abstract: Simultaneous machine translation (SiMT) has traditionally relied on offline machine translation models coupled with human-engineered heuristics or learned policies. We propose Hikari, a policy-free, fully end-to-end model that performs simultaneous speech-to-text translation and streaming transcription...
QChunker: Learning Question-Aware Text Chunking for Domain RAG via Multi-Agent Debate
arXiv:2603.11650v1 Announce Type: new Abstract: The effectiveness upper bound of retrieval-augmented generation (RAG) is fundamentally constrained by the semantic integrity and information granularity of text chunks in its knowledge base. To address these challenges, this paper proposes QChunker, which restructures...
Multi-Task Reinforcement Learning for Enhanced Multimodal LLM-as-a-Judge
arXiv:2603.11665v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have been widely adopted as MLLM-as-a-Judges due to their strong alignment with human judgment across various visual tasks. However, most existing judge models are optimized for single-task scenarios and struggle...
Semi-Synthetic Parallel Data for Translation Quality Estimation: A Case Study of Dataset Building for an Under-Resourced Language Pair
arXiv:2603.11743v1 Announce Type: new Abstract: Quality estimation (QE) plays a crucial role in machine translation (MT) workflows, as it serves to evaluate generated outputs that have no reference translations and to determine whether human post-editing or full retranslation is necessary....
Compression Favors Consistency, Not Truth: When and Why Language Models Prefer Correct Information
arXiv:2603.11749v1 Announce Type: new Abstract: Why do language models sometimes prefer correct statements even when trained on mixed-quality data? We introduce the Compression--Consistency Principle: next-token prediction favors hypotheses that allow shorter and more internally consistent descriptions of the training data....
Legal-DC: Benchmarking Retrieval-Augmented Generation for Legal Documents
arXiv:2603.11772v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a promising technology for legal document consultation, yet its application in Chinese legal scenarios faces two key limitations: existing benchmarks lack specialized support for joint retriever-generator evaluation, and mainstream...
Large Language Models for Biomedical Article Classification
arXiv:2603.11780v1 Announce Type: new Abstract: This work presents a systematic and in-depth investigation of the utility of large language models as text classifiers for biomedical article classification. The study uses several small and mid-size open source models, as well as...
DatedGPT: Preventing Lookahead Bias in Large Language Models with Time-Aware Pretraining
arXiv:2603.11838v1 Announce Type: new Abstract: In financial backtesting, large language models pretrained on internet-scale data risk introducing lookahead bias that undermines their forecasting validity, as they may have already seen the true outcome during training. To address this, we present...
Bielik-Minitron-7B: Compressing Large Language Models via Structured Pruning and Knowledge Distillation for the Polish Language
arXiv:2603.11881v1 Announce Type: new Abstract: This report details the creation of Bielik-Minitron-7B, a compressed 7.35B parameter version of the Bielik-11B-v3.0 model, specifically optimized for European languages. By leveraging a two-stage compression methodology inspired by the NVIDIA Minitron approach, we combined...
PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents
arXiv:2603.11955v1 Announce Type: new Abstract: Digital footprints (records of individuals' interactions with digital systems) are essential for studying behavior, developing personalized applications, and training machine learning models. However, research in this area is often hindered by the scarcity of diverse...
CHiL(L)Grader: Calibrated Human-in-the-Loop Short-Answer Grading
arXiv:2603.11957v1 Announce Type: new Abstract: Scaling educational assessment with large language models requires not just accuracy, but the ability to recognize when predictions are trustworthy. Instruction-tuned models tend to be overconfident, and their reliability deteriorates as curricula evolve, making fully...
BTZSC: A Benchmark for Zero-Shot Text Classification Across Cross-Encoders, Embedding Models, Rerankers and LLMs
arXiv:2603.11991v1 Announce Type: new Abstract: Zero-shot text classification (ZSC) offers the promise of eliminating costly task-specific annotation by matching texts directly to human-readable label descriptions. While early approaches have predominantly relied on cross-encoder models fine-tuned for natural language inference (NLI),...
IndexCache: Accelerating Sparse Attention via Cross-Layer Index Reuse
arXiv:2603.12201v1 Announce Type: new Abstract: Long-context agentic workflows have emerged as a defining use case for large language models, making attention efficiency critical for both inference speed and serving cost. Sparse attention addresses this challenge effectively, and DeepSeek Sparse Attention...
CLASP: Defending Hybrid Large Language Models Against Hidden State Poisoning Attacks
arXiv:2603.12206v1 Announce Type: new Abstract: State space models (SSMs) like Mamba have gained significant traction as efficient alternatives to Transformers, achieving linear complexity while maintaining competitive performance. However, Hidden State Poisoning Attacks (HiSPAs), a recently discovered vulnerability that corrupts SSM...
Sparking Scientific Creativity via LLM-Driven Interdisciplinary Inspiration
arXiv:2603.12226v1 Announce Type: new Abstract: Despite interdisciplinary research leading to larger and longer-term impact, most work remains confined to single-domain academic silos. Recent AI-based approaches to scientific discovery show promise for interdisciplinary research, but many prioritize rapidly designing experiments and...
Comparison of Outlier Detection Algorithms on String Data
arXiv:2603.11049v1 Announce Type: new Abstract: Outlier detection is a well-researched and crucial problem in machine learning. However, there is little research on string data outlier detection, as most literature focuses on outlier detection of numerical data. A robust string data...
Structure-Aware Epistemic Uncertainty Quantification for Neural Operator PDE Surrogates
arXiv:2603.11052v1 Announce Type: new Abstract: Neural operators (NOs) provide fast, resolution-invariant surrogates for mapping input fields to PDE solution fields, but their predictions can exhibit significant epistemic uncertainty due to finite data, imperfect optimization, and distribution shift. For practical deployment...
Interventional Time Series Priors for Causal Foundation Models
arXiv:2603.11090v1 Announce Type: new Abstract: Prior-data fitted networks (PFNs) have emerged as powerful foundation models for tabular causal inference, yet their extension to time series remains limited by the absence of synthetic data generators that provide interventional targets. Existing time...
Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information
arXiv:2603.11094v1 Announce Type: new Abstract: Streaming sources of data are becoming more common as the ability to collect data in real-time grows. A major concern in dealing with data streams is concept drift, a change in the distribution of data...
Graph Tokenization for Bridging Graphs and Transformers
arXiv:2603.11099v1 Announce Type: new Abstract: The success of large pretrained Transformers is closely tied to tokenizers, which convert raw input into discrete symbols. Extending these models to graph-structured data remains a significant challenge. In this work, we introduce a graph...
Learning Tree-Based Models with Gradient Descent
arXiv:2603.11117v1 Announce Type: new Abstract: Tree-based models are widely recognized for their interpretability and have proven effective in various application domains, particularly in high-stakes domains. However, learning decision trees (DTs) poses a significant challenge due to their combinatorial complexity and...
A Learning-Based Superposition Operator for Non-Renewal Arrival Processes in Queueing Networks
arXiv:2603.11118v1 Announce Type: new Abstract: The superposition of arrival processes is a fundamental yet analytically intractable operation in queueing networks when inputs are general non-renewal streams. Classical methods either reduce merged flows to renewal surrogates, rely on computationally prohibitive Markovian...
Group Resonance Network: Learnable Prototypes and Multi-Subject Resonance for EEG Emotion Recognition
arXiv:2603.11119v1 Announce Type: new Abstract: Electroencephalography(EEG)-basedemotionrecognitionre- mains challenging in cross-subject settings due to severe inter-subject variability. Existing methods mainly learn subject-invariant features, but often under-exploit stimulus-locked group regularities shared across sub- jects. To address this issue, we propose the Group...
Beyond Barren Plateaus: A Scalable Quantum Convolutional Architecture for High-Fidelity Image Classification
arXiv:2603.11131v1 Announce Type: new Abstract: While Quantum Convolutional Neural Networks (QCNNs) offer a theoretical paradigm for quantum machine learning, their practical implementation is severely bottlenecked by barren plateaus -- the exponential vanishing of gradients -- and poor empirical accuracy compared...