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...
A Retrieval-Augmented Language Assistant for Unmanned Aircraft Safety Assessment and Regulatory Compliance
arXiv:2603.09999v1 Announce Type: cross Abstract: This paper presents the design and validation of a retrieval-based assistant that supports safety assessment, certification activities, and regulatory compliance for unmanned aircraft systems. The work is motivated by the growing complexity of drone operations...
Dissecting Chronos: Sparse Autoencoders Reveal Causal Feature Hierarchies in Time Series Foundation Models
arXiv:2603.10071v1 Announce Type: new Abstract: Time series foundation models (TSFMs) are increasingly deployed in high-stakes domains, yet their internal representations remain opaque. We present the first application of sparse autoencoders (SAEs) to a TSFM, training TopK SAEs on activations of...
ES-dLLM: Efficient Inference for Diffusion Large Language Models by Early-Skipping
arXiv:2603.10088v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) are emerging as a promising alternative to autoregressive models (ARMs) due to their ability to capture bidirectional context and the potential for parallel generation. Despite the advantages, dLLM inference remains...
Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation
arXiv:2603.10093v1 Announce Type: new Abstract: Recent 3D molecular generation methods primarily use asynchronous auto-regressive or synchronous diffusion models. While auto-regressive models build molecules sequentially, they're limited by a short horizon and a discrepancy between training and inference. Conversely, synchronous diffusion...
Rethinking Adam for Time Series Forecasting: A Simple Heuristic to Improve Optimization under Distribution Shifts
arXiv:2603.10095v1 Announce Type: new Abstract: Time-series forecasting often faces challenges from non-stationarity, particularly distributional drift, where the data distribution evolves over time. This dynamic behavior can undermine the effectiveness of adaptive optimizers, such as Adam, which are typically designed for...
Discovery of a Hematopoietic Manifold in scGPT Yields a Method for Extracting Performant Algorithms from Biological Foundation Model Internals
arXiv:2603.10261v1 Announce Type: new Abstract: We report the discovery and extraction of a compact hematopoietic algorithm from the single-cell foundation model scGPT, to our knowledge the first biologically useful, competitive algorithm extracted from a foundation model via mechanistic interpretability. We...
Taming Score-Based Denoisers in ADMM: A Convergent Plug-and-Play Framework
arXiv:2603.10281v1 Announce Type: new Abstract: While score-based generative models have emerged as powerful priors for solving inverse problems, directly integrating them into optimization algorithms such as ADMM remains nontrivial. Two central challenges arise: i) the mismatch between the noisy data...
Federated Active Learning Under Extreme Non-IID and Global Class Imbalance
arXiv:2603.10341v1 Announce Type: new Abstract: Federated active learning (FAL) seeks to reduce annotation cost under privacy constraints, yet its effectiveness degrades in realistic settings with severe global class imbalance and highly heterogeneous clients. We conduct a systematic study of query-model...
Sun Valley Orchards, LLCv. United States Department of Labor
In SEC v. Jarkesy, the Supreme Court failed to fully clarify the “unquestionably muddy” relationship between Article III and the Seventh Amendment. Yet it...The post<em>Sun Valley Orchards, LLC<br>v. United States Department of Labor</em>appeared first onHarvard Law Review.
Abundant Intelligence and Deficient Demand: A Macro-Financial Stress Test of Rapid AI Adoption
arXiv:2603.09209v1 Announce Type: new Abstract: We formalize a macro-financial stress test for rapid AI adoption. Rather than a productivity bust or existential risk, we identify a distribution-and-contract mismatch: AI-generated abundance coexists with demand deficiency because economic institutions are anchored to...
LDP: An Identity-Aware Protocol for Multi-Agent LLM Systems
arXiv:2603.08852v1 Announce Type: new Abstract: As multi-agent AI systems grow in complexity, the protocols connecting them constrain their capabilities. Current protocols such as A2A and MCP do not expose model-level properties as first-class primitives, ignoring properties fundamental to effective delegation:...
Build, Borrow, or Just Fine-Tune? A Political Scientist's Guide to Choosing NLP Models
arXiv:2603.09595v1 Announce Type: new Abstract: Political scientists increasingly face a consequential choice when adopting natural language processing tools: build a domain-specific model from scratch, borrow and adapt an existing one, or simply fine-tune a general-purpose model on task data? Each...
Surgical Repair of Collapsed Attention Heads in ALiBi Transformers
arXiv:2603.09616v1 Announce Type: new Abstract: We identify a systematic attention collapse pathology in the BLOOM family of transformer language models, where ALiBi positional encoding causes 31-44% of attention heads to attend almost entirely to the beginning-of-sequence token. The collapse follows...
The Radio-Frequency Transformer for Signal Separation
arXiv:2603.09201v1 Announce Type: new Abstract: We study a problem of signal separation: estimating a signal of interest (SOI) contaminated by an unknown non-Gaussian background/interference. Given the training data consisting of examples of SOI and interference, we show how to build...
A Dynamic Self-Evolving Extraction System
arXiv:2603.06915v1 Announce Type: new Abstract: The extraction of structured information from raw text is a fundamental component of many NLP applications, including document retrieval, ranking, and relevance estimation. High-quality extractions often require domain-specific accuracy, up-to-date understanding of specialized taxonomies, and...
Scale Dependent Data Duplication
arXiv:2603.06603v1 Announce Type: new Abstract: Data duplication during pretraining can degrade generalization and lead to memorization, motivating aggressive deduplication pipelines. However, at web scale, it is unclear what constitutes a ``duplicate'': beyond surface-form matches, semantically equivalent documents (e.g. translations) may...
Know When You're Wrong: Aligning Confidence with Correctness for LLM Error Detection
arXiv:2603.06604v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed in critical decision-making systems, the lack of reliable methods to measure their uncertainty presents a fundamental trustworthiness risk. We introduce a normalized confidence score based on output...
Evo: Autoregressive-Diffusion Large Language Models with Evolving Balance
arXiv:2603.06617v1 Announce Type: new Abstract: We introduce \textbf{Evo}, a duality latent trajectory model that bridges autoregressive (AR) and diffusion-based language generation within a continuous evolutionary generative framework. Rather than treating AR decoding and diffusion generation as separate paradigms, Evo reconceptualizes...
Advances in GRPO for Generation Models: A Survey
arXiv:2603.06623v1 Announce Type: new Abstract: Large-scale flow matching models have achieved strong performance across generative tasks such as text-to-image, video, 3D, and speech synthesis. However, aligning their outputs with human preferences and task-specific objectives remains challenging. Flow-GRPO extends Group Relative...
Trust Aware Federated Learning for Secure Bone Healing Stage Interpretation in e-Health
arXiv:2603.06646v1 Announce Type: new Abstract: This paper presents a trust aware federated learning (FL) framework for interpreting bone healing stages using spectral features derived from frequency response data. The primary objective is to address the challenge posed by either unreliable...
Governor DeSantis Directs Florida State Agencies to Partner with Future of Life Institute to Shield Families from AI Harm
The collaboration will produce a Crisis Counselor Training Curriculum and a statewide AI Harms Reporting Form targeting dangerous AI companion applications
Omni-C: Compressing Heterogeneous Modalities into a Single Dense Encoder
arXiv:2603.05528v1 Announce Type: cross Abstract: Recent multimodal systems often rely on separate expert modality encoders which cause linearly scaling complexity and computational overhead with added modalities. While unified Omni-models address this via Mixture-of-Expert (MoE) architectures with specialized experts and routing,...
Let's Talk, Not Type: An Oral-First Multi-Agent Architecture for Guaran\'i
arXiv:2603.05743v1 Announce Type: new Abstract: Although artificial intelligence (AI) and Human-Computer Interaction (HCI) systems are often presented as universal solutions, their design remains predominantly text-first, underserving primarily oral languages and indigenous communities. This position paper uses Guaran\'i, an official and...
ROSE: Reordered SparseGPT for More Accurate One-Shot Large Language Models Pruning
arXiv:2603.05878v1 Announce Type: new Abstract: Pruning is widely recognized as an effective method for reducing the parameters of large language models (LLMs), potentially leading to more efficient deployment and inference. One classic and prominent path of LLM one-shot pruning is...
Confidence Before Answering: A Paradigm Shift for Efficient LLM Uncertainty Estimation
arXiv:2603.05881v1 Announce Type: new Abstract: Reliable deployment of large language models (LLMs) requires accurate uncertainty estimation. Existing methods are predominantly answer-first, producing confidence only after generating an answer, which measure the correctness of a specific response and limits practical usability....
Who We Are, Where We Are: Mental Health at the Intersection of Person, Situation, and Large Language Models
arXiv:2603.05953v1 Announce Type: new Abstract: Mental health is not a fixed trait but a dynamic process shaped by the interplay between individual dispositions and situational contexts. Building on interactionist and constructionist psychological theories, we develop interpretable models to predict well-being...
CRIMSON: A Clinically-Grounded LLM-Based Metric for Generative Radiology Report Evaluation
arXiv:2603.06183v1 Announce Type: new Abstract: We introduce CRIMSON, a clinically grounded evaluation framework for chest X-ray report generation that assesses reports based on diagnostic correctness, contextual relevance, and patient safety. Unlike prior metrics, CRIMSON incorporates full clinical context, including patient...
Aligning the True Semantics: Constrained Decoupling and Distribution Sampling for Cross-Modal Alignment
arXiv:2603.05566v1 Announce Type: new Abstract: Cross-modal alignment is a crucial task in multimodal learning aimed at achieving semantic consistency between vision and language. This requires that image-text pairs exhibit similar semantics. Traditional algorithms pursue embedding consistency to achieve semantic consistency,...
Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy
arXiv:2603.05719v1 Announce Type: new Abstract: Training machine learning models for radioisotope identification using gamma spectroscopy remains an elusive challenge for many practical applications, largely stemming from the difficulty of acquiring and labeling large, diverse experimental datasets. Simulations can mitigate this...