SQL-ASTRA: Alleviating Sparse Feedback in Agentic SQL via Column-Set Matching and Trajectory Aggregation
arXiv:2603.16161v1 Announce Type: new Abstract: Agentic Reinforcement Learning (RL) shows promise for complex tasks, but Text-to-SQL remains mostly restricted to single-turn paradigms. A primary bottleneck is the credit assignment problem. In traditional paradigms, rewards are determined solely by the final-turn...
Recursive Language Models Meet Uncertainty: The Surprising Effectiveness of Self-Reflective Program Search for Long Context
arXiv:2603.15653v1 Announce Type: new Abstract: Long-context handling remains a core challenge for language models: even with extended context windows, models often fail to reliably extract, reason over, and use the information across long contexts. Recent works like Recursive Language Models...
Agent-based imitation dynamics can yield efficiently compressed population-level vocabularies
arXiv:2603.15903v1 Announce Type: new Abstract: Natural languages have been argued to evolve under pressure to efficiently compress meanings into words by optimizing the Information Bottleneck (IB) complexity-accuracy tradeoff. However, the underlying social dynamics that could drive the optimization of a...
NLP Occupational Emergence Analysis: How Occupations Form and Evolve in Real Time -- A Zero-Assumption Method Demonstrated on AI in the US Technology Workforce, 2022-2026
arXiv:2603.15998v1 Announce Type: new Abstract: Occupations form and evolve faster than classification systems can track. We propose that a genuine occupation is a self-reinforcing structure (a bipartite co-attractor) in which a shared professional vocabulary makes practitioners cohesive as a group,...
IRAM-Omega-Q: A Computational Architecture for Uncertainty Regulation in Artificial Agents
arXiv:2603.16020v1 Announce Type: new Abstract: Artificial agents can achieve strong task performance while remaining opaque with respect to internal regulation, uncertainty management, and stability under stochastic perturbation. We present IRAM-Omega-Q, a computational architecture that models internal regulation as closed-loop control...
From Workflow Automation to Capability Closure: A Formal Framework for Safe and Revenue-Aware Customer Service AI
arXiv:2603.15978v1 Announce Type: new Abstract: Customer service automation is undergoing a structural transformation. The dominant paradigm is shifting from scripted chatbots and single-agent responders toward networks of specialised AI agents that compose capabilities dynamically across billing, service provision, payments, and...
Robust Language Identification for Romansh Varieties
arXiv:2603.15969v1 Announce Type: new Abstract: The Romansh language has several regional varieties, called idioms, which sometimes have limited mutual intelligibility. Despite this linguistic diversity, there has been a lack of documented efforts to build a language identification (LID) system that...
Did You Check the Right Pocket? Cost-Sensitive Store Routing for Memory-Augmented Agents
arXiv:2603.15658v1 Announce Type: new Abstract: Memory-augmented agents maintain multiple specialized stores, yet most systems retrieve from all stores for every query, increasing cost and introducing irrelevant context. We formulate memory retrieval as a store-routing problem and evaluate it using coverage,...
Compiled Memory: Not More Information, but More Precise Instructions for Language Agents
arXiv:2603.15666v1 Announce Type: new Abstract: Existing memory systems for language agents address memory management: how to retrieve and page more information within a context budget. We address a complementary problem -- memory utility: what experience is worth keeping, and how...
Survey of Various Fuzzy and Uncertain Decision-Making Methods
arXiv:2603.15709v1 Announce Type: new Abstract: Decision-making in real applications is often affected by vagueness, incomplete information, heterogeneous data, and conflicting expert opinions. This survey reviews uncertainty-aware multi-criteria decision-making (MCDM) and organizes the field into a concise, task-oriented taxonomy. We summarize...
Optimizing Hospital Capacity During Pandemics: A Dual-Component Framework for Strategic Patient Relocation
arXiv:2603.15960v1 Announce Type: new Abstract: The COVID-19 pandemic has placed immense strain on hospital systems worldwide, leading to critical capacity challenges. This research proposes a two-part framework to optimize hospital capacity through patient relocation strategies. The first component involves developing...
Safety is Non-Compositional: A Formal Framework for Capability-Based AI Systems
arXiv:2603.15973v1 Announce Type: new Abstract: This paper contains the first formal proof that safety is non-compositional in the presence of conjunctive capability dependencies: two agents each individually inca- pable of reaching any forbidden capability can, when combined, collectively reach a...
Neural-Symbolic Logic Query Answering in Non-Euclidean Space
arXiv:2603.15633v1 Announce Type: new Abstract: Answering complex first-order logic (FOL) queries on knowledge graphs is essential for reasoning. Symbolic methods offer interpretability but struggle with incomplete graphs, while neural approaches generalize better but lack transparency. Neural-symbolic models aim to integrate...
Theoretical Foundations of Latent Posterior Factors: Formal Guarantees for Multi-Evidence Reasoning
arXiv:2603.15674v1 Announce Type: new Abstract: We present a complete theoretical characterization of Latent Posterior Factors (LPF), a principled framework for aggregating multiple heterogeneous evidence items in probabilistic prediction tasks. Multi-evidence reasoning arises pervasively in high-stakes domains including healthcare diagnosis, financial...
SEAHateCheck: Functional Tests for Detecting Hate Speech in Low-Resource Languages of Southeast Asia
arXiv:2603.16070v1 Announce Type: new Abstract: Hate speech detection relies heavily on linguistic resources, which are primarily available in high-resource languages such as English and Chinese, creating barriers for researchers and platforms developing tools for low-resource languages in Southeast Asia, where...
ClaimFlow: Tracing the Evolution of Scientific Claims in NLP
arXiv:2603.16073v1 Announce Type: new Abstract: Scientific papers do more than report results $-$ they advance $\textit{claims}$ that later work supports, extends, or sometimes refutes. Yet existing methods for citation and claim analysis capture only fragments of this dialogue. In this...
CounterRefine: Answer-Conditioned Counterevidence Retrieval for Inference-Time Knowledge Repair in Factual Question Answering
arXiv:2603.16091v1 Announce Type: new Abstract: In factual question answering, many errors are not failures of access but failures of commitment: the system retrieves relevant evidence, yet still settles on the wrong answer. We present CounterRefine, a lightweight inference-time repair layer...
Polyglot-Lion: Efficient Multilingual ASR for Singapore via Balanced Fine-Tuning of Qwen3-ASR
arXiv:2603.16184v1 Announce Type: new Abstract: We present Polyglot-Lion, a family of compact multilingual automatic speech recognition (ASR) models tailored for the linguistic landscape of Singapore, covering English, Mandarin, Tamil, and Malay. Our models are obtained by fine-tuning Qwen3-ASR-0.6B and Qwen3-ASR-1.7B...
SpecSteer: Synergizing Local Context and Global Reasoning for Efficient Personalized Generation
arXiv:2603.16219v1 Announce Type: new Abstract: Realizing personalized intelligence faces a core dilemma: sending user history to centralized large language models raises privacy concerns, while on-device small language models lack the reasoning capacity required for high-quality generation. Our pilot study shows...
PyPhonPlan: Simulating phonetic planning with dynamic neural fields and task dynamics
arXiv:2603.16299v1 Announce Type: new Abstract: We introduce PyPhonPlan, a Python toolkit for implementing dynamical models of phonetic planning using coupled dynamic neural fields and task dynamic simulations. The toolkit provides modular components for defining planning, perception and memory fields, as...
On the Emotion Understanding of Synthesized Speech
arXiv:2603.16483v1 Announce Type: new Abstract: Emotion is a core paralinguistic feature in voice interaction. It is widely believed that emotion understanding models learn fundamental representations that transfer to synthesized speech, making emotion understanding results a plausible reward or evaluation metric...
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...
XLinear: Frequency-Enhanced MLP with CrossFilter for Robust Long-Range Forecasting
arXiv:2603.15645v1 Announce Type: new Abstract: Time series forecasters are widely used across various domains. Among them, MLP (multi-layer perceptron)-based forecasters have been proven to be more robust to noise compared to Transformer-based forecasters. However, MLP struggles to capture complex features,...
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...
A federated learning framework with knowledge graph and temporal transformer for early sepsis prediction in multi-center ICUs
arXiv:2603.15651v1 Announce Type: new Abstract: The early prediction of sepsis in intensive care unit (ICU) patients is crucial for improving survival rates. However, the development of accurate predictive models is hampered by data fragmentation across healthcare institutions and the complex,...
Spectral Edge Dynamics of Training Trajectories: Signal--Noise Geometry Across Scales
arXiv:2603.15678v1 Announce Type: new Abstract: Despite hundreds of millions of parameters, transformer training trajectories evolve within only a few coherent directions. We introduce \emph{Spectral Edge Dynamics} (SED) to measure this structure: rolling-window SVD of parameter updates reveals a sharp boundary...
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...
Transition Flow Matching
arXiv:2603.15689v1 Announce Type: new Abstract: Mainstream flow matching methods typically focus on learning the local velocity field, which inherently requires multiple integration steps during generation. In contrast, Mean Velocity Flow models establish a relationship between the local velocity field and...
Mastering the Minority: An Uncertainty-guided Multi-Expert Framework for Challenging-tailed Sequence Learning
arXiv:2603.15708v1 Announce Type: new Abstract: Imbalanced data distribution remains a critical challenge in sequential learning, leading models to easily recognize frequent categories while failing to detect minority classes adequately. The Mixture-of-Experts model offers a scalable solution, yet its application is...