More Rounds, More Noise: Why Multi-Turn Review Fails to Improve Cross-Context Verification
arXiv:2603.16244v1 Announce Type: new Abstract: Cross-Context Review (CCR) improves LLM verification by separating production and review into independent sessions. A natural extension is multi-turn review: letting the reviewer ask follow-up questions, receive author responses, and review again. We call this...
AdaMem: Adaptive User-Centric Memory for Long-Horizon Dialogue Agents
arXiv:2603.16496v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external memory to support long-horizon interaction, personalized assistance, and multi-step reasoning. However, existing memory systems still face three core challenges: they often rely too heavily on semantic...
DanceHA: A Multi-Agent Framework for Document-Level Aspect-Based Sentiment Analysis
arXiv:2603.16546v1 Announce Type: new Abstract: Aspect-Based Sentiment Intensity Analysis (ABSIA) has garnered increasing attention, though research largely focuses on domain-specific, sentence-level settings. In contrast, document-level ABSIA--particularly in addressing complex tasks like extracting Aspect-Category-Opinion-Sentiment-Intensity (ACOSI) tuples--remains underexplored. In this work, we...
Tokenization Tradeoffs in Structured EHR Foundation Models
arXiv:2603.15644v1 Announce Type: new Abstract: Foundation models for structured electronic health records (EHRs) are pretrained on longitudinal sequences of timestamped clinical events to learn adaptable patient representations. Tokenization -- how these timelines are converted into discrete model inputs -- determines...
Alternating Reinforcement Learning with Contextual Rubric Rewards
arXiv:2603.15646v1 Announce Type: new Abstract: Reinforcement Learning with Rubric Rewards (RLRR) is a framework that extends conventional reinforcement learning from human feedback (RLHF) and verifiable rewards (RLVR) by replacing scalar preference signals with structured, multi-dimensional, contextual rubric-based evaluations. However, existing...
Steering Frozen LLMs: Adaptive Social Alignment via Online Prompt Routing
arXiv:2603.15647v1 Announce Type: new Abstract: Large language models (LLMs) are typically governed by post-training alignment (e.g., RLHF or DPO), which yields a largely static policy during deployment and inference. However, real-world safety is a full-lifecycle problem: static defenses degrade against...
How to Achieve Prototypical Birth and Death for OOD Detection?
arXiv:2603.15650v1 Announce Type: new Abstract: Out-of-Distribution (OOD) detection is crucial for the secure deployment of machine learning models, and prototype-based learning methods are among the mainstream strategies for achieving OOD detection. Existing prototype-based learning methods generally rely on a fixed...
Beyond Reward Suppression: Reshaping Steganographic Communication Protocols in MARL via Dynamic Representational Circuit Breaking
arXiv:2603.15655v1 Announce Type: new Abstract: In decentralized Multi-Agent Reinforcement Learning (MARL), steganographic collusion -- where agents develop private protocols to evade monitoring -- presents a critical AI safety threat. Existing defenses, limited to behavioral or reward layers, fail to detect...
Evidential Domain Adaptation for Remaining Useful Life Prediction with Incomplete Degradation
arXiv:2603.15687v1 Announce Type: new Abstract: Accurate Remaining Useful Life (RUL) prediction without labeled target domain data is a critical challenge, and domain adaptation (DA) has been widely adopted to address it by transferring knowledge from a labeled source domain to...
Tackling Over-smoothing on Hypergraphs: A Ricci Flow-guided Neural Diffusion Approach
arXiv:2603.15696v1 Announce Type: new Abstract: Hypergraph neural networks (HGNNs) have demonstrated strong capabilities in modeling complex higher-order relationships. However, existing HGNNs often suffer from over-smoothing as the number of layers increases and lack effective control over message passing among nodes....
OMNIFLOW: A Physics-Grounded Multimodal Agent for Generalized Scientific Reasoning
arXiv:2603.15797v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated exceptional logical reasoning capabilities but frequently struggle with the continuous spatiotemporal dynamics governed by Partial Differential Equations (PDEs), often resulting in non-physical hallucinations. Existing approaches typically resort to costly,...
Generative Inverse Design with Abstention via Diagonal Flow Matching
arXiv:2603.15925v1 Announce Type: new Abstract: Inverse design aims to find design parameters $x$ achieving target performance $y^*$. Generative approaches learn bidirectional mappings between designs and labels, enabling diverse solution sampling. However, standard conditional flow matching (CFM), when adapted to inverse...
Evaluating Causal Discovery Algorithms for Path-Specific Fairness and Utility in Healthcare
arXiv:2603.15926v1 Announce Type: new Abstract: Causal discovery in health data faces evaluation challenges when ground truth is unknown. We address this by collaborating with experts to construct proxy ground-truth graphs, establishing benchmarks for synthetic Alzheimer's disease and heart failure clinical...
Deriving Hyperparameter Scaling Laws via Modern Optimization Theory
arXiv:2603.15958v1 Announce Type: new Abstract: Hyperparameter transfer has become an important component of modern large-scale training recipes. Existing methods, such as muP, primarily focus on transfer between model sizes, with transfer across batch sizes and training horizons often relying on...
W2T: LoRA Weights Already Know What They Can Do
arXiv:2603.15990v1 Announce Type: new Abstract: Each LoRA checkpoint compactly stores task-specific updates in low-rank weight matrices, offering an efficient way to adapt large language models to new tasks and domains. In principle, these weights already encode what the adapter does...
Residual Stream Duality in Modern Transformer Architectures
arXiv:2603.16039v1 Announce Type: new Abstract: Recent work has made clear that the residual pathway is not mere optimization plumbing; it is part of the model's representational machinery. We agree, but argue that the cleanest way to organize this design space...
Adaptive regularization parameter selection for high-dimensional inverse problems: A Bayesian approach with Tucker low-rank constraints
arXiv:2603.16066v1 Announce Type: new Abstract: This paper introduces a novel variational Bayesian method that integrates Tucker decomposition for efficient high-dimensional inverse problem solving. The method reduces computational complexity by transforming variational inference from a high-dimensional space to a lower-dimensional core...
The Supreme Court of Canada
Welcome to SCOTUSblog’s recurring series in which we interview experts on different supreme courts around the world and how they compare to our own. For our debut column, we covered […]The postThe Supreme Court of Canadaappeared first onSCOTUSblog.
The biggest names on the briefs
Empirical SCOTUS is a recurring series by Adam Feldman that looks at Supreme Court data, primarily in the form of opinions and oral arguments, to provide insights into the justices’ decision making and […]The postThe biggest names on the briefsappeared...
Artificial intelligence-driven improvement of hospital logistics management resilience: a practical exploration based on H Hospital
arXiv:2603.13816v1 Announce Type: new Abstract: Hospital logistics management faces growing pressure from internal operations and external emergencies, with artificial intelligence (AI) holding untapped potential to boost its resilience. This study explores AI's role in enhancing logistics resilience via a mixed-methods...
Benchmarking Large Language Models on Reference Extraction and Parsing in the Social Sciences and Humanities
arXiv:2603.13651v1 Announce Type: new Abstract: Bibliographic reference extraction and parsing are foundational for citation indexing, linking, and downstream scholarly knowledge-graph construction. However, most established evaluations focus on clean, English, end-of-document bibliographies, and therefore underrepresent the Social Sciences and Humanities (SSH),...
Projection-Free Evolution Strategies for Continuous Prompt Search
arXiv:2603.13786v1 Announce Type: new Abstract: Continuous prompt search offers a computationally efficient alternative to conventional parameter tuning in natural language processing tasks. Nevertheless, its practical effectiveness can be significantly hindered by the black-box nature and the inherent high-dimensionality of the...
DyACE: Dynamic Algorithm Co-evolution for Online Automated Heuristic Design with Large Language Model
arXiv:2603.13344v1 Announce Type: new Abstract: The prevailing paradigm in Automated Heuristic Design (AHD) typically relies on the assumption that a single, fixed algorithm can effectively navigate the shifting dynamics of a combinatorial search. This static approach often proves inadequate for...
The AI Fiction Paradox
arXiv:2603.13545v1 Announce Type: new Abstract: AI development has a fiction dependency problem: models are built on massive corpora of modern fiction and desperately need more of it, yet they struggle to generate it. I term this the AI-Fiction Paradox and...
When Alpha Breaks: Two-Level Uncertainty for Safe Deployment of Cross-Sectional Stock Rankers
arXiv:2603.13252v1 Announce Type: new Abstract: Cross-sectional ranking models are often deployed as if point predictions were sufficient: the model outputs scores and the portfolio follows the induced ordering. Under non-stationarity, rankers can fail during regime shifts. In the AI Stock...
GroupGuard: A Framework for Modeling and Defending Collusive Attacks in Multi-Agent Systems
arXiv:2603.13940v1 Announce Type: new Abstract: While large language model-based agents demonstrate great potential in collaborative tasks, their interactivity also introduces security vulnerabilities. In this paper, we propose and model group collusive attacks, a highly destructive threat in which multiple agents...
The ARC of Progress towards AGI: A Living Survey of Abstraction and Reasoning
arXiv:2603.13372v1 Announce Type: new Abstract: The Abstraction and Reasoning Corpus (ARC-AGI) has become a key benchmark for fluid intelligence in AI. This survey presents the first cross-generation analysis of 82 approaches across three benchmark versions and the ARC Prize 2024-2025...
GRPO and Reflection Reward for Mathematical Reasoning in Large Language Models
arXiv:2603.14041v1 Announce Type: new Abstract: The enhancement of reasoning capabilities in large language models (LLMs) has garnered significant attention, with supervised fine-tuning (SFT) and reinforcement learning emerging as dominant paradigms. While recent studies recognize the importance of reflection in reasoning...
State Algebra for Probabilistic Logic
arXiv:2603.13574v1 Announce Type: new Abstract: This paper presents a Probabilistic State Algebra as an extension of deterministic propositional logic, providing a computational framework for constructing Markov Random Fields (MRFs) through pure linear algebra. By mapping logical states to real-valued coordinates...
Automating Document Intelligence in Statutory City Planning
arXiv:2603.13245v1 Announce Type: new Abstract: UK planning authorities face a legislative conflict between the Planning Act, which mandates public access to application documents, and the Data Protection Act, which requires protection of personal information. This situation creates a manually intensive...