AI ‘actor’ Tilly Norwood put out the worst song I’ve ever heard
This song is an AI actor's rallying cry to other AI actors, urging them to keep going despite the naysayers who doubt their humanity. Literally no one can relate to this.
An Empirical Study and Theoretical Explanation on Task-Level Model-Merging Collapse
arXiv:2603.09463v1 Announce Type: new Abstract: Model merging unifies independently fine-tuned LLMs from the same base, enabling reuse and integration of parallel development efforts without retraining. However, in practice we observe that merging does not always succeed: certain combinations of task-specialist...
The Confidence Gate Theorem: When Should Ranked Decision Systems Abstain?
arXiv:2603.09947v1 Announce Type: new Abstract: Ranked decision systems -- recommenders, ad auctions, clinical triage queues -- must decide when to intervene in ranked outputs and when to abstain. We study when confidence-based abstention monotonically improves decision quality, and when it...
AutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents
arXiv:2603.09716v1 Announce Type: new Abstract: Autonomous agent frameworks still struggle to reconcile long-term experiential learning with real-time, context-sensitive decision-making. In practice, this gap appears as static cognition, rigid workflow dependence, and inefficient context usage, which jointly limit adaptability in open-ended...
Automated Thematic Analysis for Clinical Qualitative Data: Iterative Codebook Refinement with Full Provenance
arXiv:2603.08989v1 Announce Type: new Abstract: Thematic analysis (TA) is widely used in health research to extract patterns from patient interviews, yet manual TA faces challenges in scalability and reproducibility. LLM-based automation can help, but existing approaches produce codebooks with limited...
The FABRIC Strategy for Verifying Neural Feedback Systems
arXiv:2603.08964v1 Announce Type: new Abstract: Forward reachability analysis is a dominant approach for verifying reach-avoid specifications in neural feedback systems, i.e., dynamical systems controlled by neural networks, and a number of directions have been proposed and studied. In contrast, far...
MultiGraSCCo: A Multilingual Anonymization Benchmark with Annotations of Personal Identifiers
arXiv:2603.08879v1 Announce Type: new Abstract: Accessing sensitive patient data for machine learning is challenging due to privacy concerns. Datasets with annotations of personally identifiable information are crucial for developing and testing anonymization systems to enable safe data sharing that complies...
Enhancing Debunking Effectiveness through LLM-based Personality Adaptation
arXiv:2603.09533v1 Announce Type: new Abstract: This study proposes a novel methodology for generating personalized fake news debunking messages by prompting Large Language Models (LLMs) with persona-based inputs aligned to the Big Five personality traits: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness....
Curveball Steering: The Right Direction To Steer Isn't Always Linear
arXiv:2603.09313v1 Announce Type: new Abstract: Activation steering is a widely used approach for controlling large language model (LLM) behavior by intervening on internal representations. Existing methods largely rely on the Linear Representation Hypothesis, assuming behavioral attributes can be manipulated using...
LooComp: Leverage Leave-One-Out Strategy to Encoder-only Transformer for Efficient Query-aware Context Compression
arXiv:2603.09222v1 Announce Type: new Abstract: Efficient context compression is crucial for improving the accuracy and scalability of question answering. For the efficiency of Retrieval Augmented Generation, context should be delivered fast, compact, and precise to ensure clue sufficiency and budget-friendly...
AI Act Evaluation Benchmark: An Open, Transparent, and Reproducible Evaluation Dataset for NLP and RAG Systems
arXiv:2603.09435v1 Announce Type: new Abstract: The rapid rollout of AI in heterogeneous public and societal sectors has subsequently escalated the need for compliance with regulatory standards and frameworks. The EU AI Act has emerged as a landmark in the regulatory...
EPIC-EuroParl-UdS: Information-Theoretic Perspectives on Translation and Interpreting
arXiv:2603.09785v1 Announce Type: new Abstract: This paper introduces an updated and combined version of the bidirectional English-German EPIC-UdS (spoken) and EuroParl-UdS (written) corpora containing original European Parliament speeches as well as their translations and interpretations. The new version corrects metadata...
N-gram-like Language Models Predict Reading Time Best
arXiv:2603.09872v1 Announce Type: new Abstract: Recent work has found that contemporary language models such as transformers can become so good at next-word prediction that the probabilities they calculate become worse for predicting reading time. In this paper, we propose that...
Model Merging in the Era of Large Language Models: Methods, Applications, and Future Directions
arXiv:2603.09938v1 Announce Type: new Abstract: Model merging has emerged as a transformative paradigm for combining the capabilities of multiple neural networks into a single unified model without additional training. With the rapid proliferation of fine-tuned large language models~(LLMs), merging techniques...
BiCLIP: Domain Canonicalization via Structured Geometric Transformation
arXiv:2603.08942v1 Announce Type: cross Abstract: Recent advances in vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities, yet adapting these models to specialized domains remains a significant challenge. Building on recent theoretical insights suggesting that independently trained VLMs are related by...
Generalized Reduction to the Isotropy for Flexible Equivariant Neural Fields
arXiv:2603.08758v1 Announce Type: new Abstract: Many geometric learning problems require invariants on heterogeneous product spaces, i.e., products of distinct spaces carrying different group actions, where standard techniques do not directly apply. We show that, when a group $G$ acts transitively...
Are Expressive Encoders Necessary for Discrete Graph Generation?
arXiv:2603.08825v1 Announce Type: new Abstract: Discrete graph generation has emerged as a powerful paradigm for modeling graph data, often relying on highly expressive neural backbones such as transformers or higher-order architectures. We revisit this design choice by introducing GenGNN, a...
Uncovering a Winning Lottery Ticket with Continuously Relaxed Bernoulli Gates
arXiv:2603.08914v1 Announce Type: new Abstract: Over-parameterized neural networks incur prohibitive memory and computational costs for resource-constrained deployment. The Strong Lottery Ticket (SLT) hypothesis suggests that randomly initialized networks contain sparse subnetworks achieving competitive accuracy without weight training. Existing SLT methods,...
MAcPNN: Mutual Assisted Learning on Data Streams with Temporal Dependence
arXiv:2603.08972v1 Announce Type: new Abstract: Internet of Things (IoT) Analytics often involves applying machine learning (ML) models on data streams. In such scenarios, traditional ML paradigms face obstacles related to continuous learning while dealing with concept drifts, temporal dependence, and...
MAPLE: Elevating Medical Reasoning from Statistical Consensus to Process-Led Alignment
arXiv:2603.08987v1 Announce Type: new Abstract: Recent advances in medical large language models have explored Test-Time Reinforcement Learning (TTRL) to enhance reasoning. However, standard TTRL often relies on majority voting (MV) as a heuristic supervision signal, which can be unreliable in...
An accurate flatness measure to estimate the generalization performance of CNN models
arXiv:2603.09016v1 Announce Type: new Abstract: Flatness measures based on the spectrum or the trace of the Hessian of the loss are widely used as proxies for the generalization ability of deep networks. However, most existing definitions are either tailored to...
Strategically Robust Multi-Agent Reinforcement Learning with Linear Function Approximation
arXiv:2603.09208v1 Announce Type: new Abstract: Provably efficient and robust equilibrium computation in general-sum Markov games remains a core challenge in multi-agent reinforcement learning. Nash equilibrium is computationally intractable in general and brittle due to equilibrium multiplicity and sensitivity to approximation...
Beyond Test-Time Training: Learning to Reason via Hardware-Efficient Optimal Control
arXiv:2603.09221v1 Announce Type: new Abstract: Associative memory has long underpinned the design of sequential models. Beyond recall, humans reason by projecting future states and selecting goal-directed actions, a capability that modern language models increasingly require but do not natively encode....
DendroNN: Dendrocentric Neural Networks for Energy-Efficient Classification of Event-Based Data
arXiv:2603.09274v1 Announce Type: new Abstract: Spatiotemporal information is at the core of diverse sensory processing and computational tasks. Feed-forward spiking neural networks can be used to solve these tasks while offering potential benefits in terms of energy efficiency by computing...
Democratising Clinical AI through Dataset Condensation for Classical Clinical Models
arXiv:2603.09356v1 Announce Type: new Abstract: Dataset condensation (DC) learns a compact synthetic dataset that enables models to match the performance of full-data training, prioritising utility over distributional fidelity. While typically explored for computational efficiency, DC also holds promise for healthcare...
Reforming the Mechanism: Editing Reasoning Patterns in LLMs with Circuit Reshaping
arXiv:2603.06923v1 Announce Type: new Abstract: Large language models (LLMs) often exhibit flawed reasoning ability that undermines reliability. Existing approaches to improving reasoning typically treat it as a general and monolithic skill, applying broad training which is inefficient and unable to...
Hierarchical Latent Structures in Data Generation Process Unify Mechanistic Phenomena across Scale
arXiv:2603.06592v1 Announce Type: new Abstract: Contemporary studies have uncovered many puzzling phenomena in the neural information processing of Transformer-based language models. Building a robust, unified understanding of these phenomena requires disassembling a model within the scope of its training. While...
RILEC: Detection and Generation of L1 Russian Interference Errors in English Learner Texts
arXiv:2603.07366v1 Announce Type: new Abstract: Many errors in student essays can be explained by influence from the native language (L1). L1 interference refers to errors influenced by a speaker's first language, such as using stadion instead of stadium, reflecting lexical...
A Joint Neural Baseline for Concept, Assertion, and Relation Extraction from Clinical Text
arXiv:2603.07487v1 Announce Type: new Abstract: Clinical information extraction (e.g., 2010 i2b2/VA challenge) usually presents tasks of concept recognition, assertion classification, and relation extraction. Jointly modeling the multi-stage tasks in the clinical domain is an underexplored topic. The existing independent task...
Bolbosh: Script-Aware Flow Matching for Kashmiri Text-to-Speech
arXiv:2603.07513v1 Announce Type: new Abstract: Kashmiri is spoken by around 7 million people but remains critically underserved in speech technology, despite its official status and rich linguistic heritage. The lack of robust Text-to-Speech (TTS) systems limits digital accessibility and inclusive...