arXiv:2605.14980v1 Announce Type: cross Abstract: Analyzing microscopy images to extract biological object properties (e.g., their morphological organization, temporal dynamics, and population density) is fundamental to various biomedical research. Yet conducting this manually is costly and time-consuming. Though deep learning-based approaches have been explored to automate this process, the substantial diversity of microscopy analysis settings in […]
COREKG: Coreset-Guided Personalized Summarization of Knowledge Graphs
arXiv:2605.14900v1 Announce Type: new Abstract: Knowledge Graphs (KGs) are extensively used across different domains and in several applications. Often, these KGs are very large in size. Such KGs become unwieldy for tasks such as question answering and visualization. Summarization of KGs offers a viable alternative in such cases. Furthermore, personalized KG summarization is crucial in […]
M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling
arXiv:2603.14360v2 Announce Type: replace-cross Abstract: Transformers are highly parallel but are limited to computations in the TC$^0$ complexity class, excluding tasks such as entity tracking and code execution that provably require greater expressive power. Motivated by this limitation, we revisit non-linear Recurrent Neural Networks (RNNs) for language modeling and introduce Matrix-to-Matrix RNN (M$^2$RNN): an architecture […]
Break-the-Beat! Controllable MIDI-to-Drum Audio Synthesis
arXiv:2605.14555v1 Announce Type: cross Abstract: Current methods for creating drum loop audio in digital music production, such as using one-shot samples or resampling, often demand non-trivial efforts of creators. While recent generative models achieve high fidelity and adhere to text, they lack the specific control needed for such a task. Existing symbolic-to-audio research often focuses […]
AgentForesight: Online Auditing for Early Failure Prediction in Multi-Agent Systems
arXiv:2605.08715v2 Announce Type: replace-cross Abstract: LLM-based multi-agent systems are increasingly deployed on long-horizon tasks, but a single decisive error is often accepted by downstream agents and cascades into trajectory-level failure. Existing work frames this as emphpost-hoc failure attribution, diagnosing the responsible agent and step after the trajectory has ended. However, this paradigm forfeits any opportunity […]
Resolving Action Bottleneck: Agentic Reinforcement Learning Informed by Token-Level Energy
arXiv:2605.14558v1 Announce Type: cross Abstract: Agentic reinforcement learning trains large language models using multi-turn trajectories that interleave long reasoning traces with short environment-facing actions. Common policy-gradient methods, such as PPO and GRPO, treat each token in a trajectory equally, leading to uniform credit assignment. In this paper, we critically demonstrate that such uniform credit assignment […]
From Organization to Viability: A Multi-Level Analysis of Gait Dynamics Under Occlusal Constraint
arXiv:2605.13893v1 Announce Type: new Abstract: Clinical interpretation often assumes that observable performance provides sufficient information about the organization of an adaptive system. However, similar observable performance may correspond to distinct latent organizations. This study extends a previous multi-level framework by introducing a fourth analytical level centered on longitudinal viability. Using an exploratory single-case design in […]
ML-Embed: Inclusive and Efficient Embeddings for a Multilingual World
arXiv:2605.15081v1 Announce Type: cross Abstract: The development of high-quality text embeddings is increasingly drifting toward an exclusionary future, defined by three critical barriers: prohibitive computational costs, a narrow linguistic focus that neglects most of the world’s languages, and a lack of transparency from closed-source or open-weight models that stifles research. To dismantle these barriers, we […]
A general classification of the replication dynamics with a unique fixed point in the interior of simplex $S_N$
arXiv:2605.13883v1 Announce Type: new Abstract: The replication dynamics (differential equation system) is the foundation of evolutionary game theory. When n=2, there are four possible types of replication dynamics. When n=3, there are 49 possible types of replication dynamics. However, when n>3, the classification of replication dynamics has not been solved. In this article, the sufficient […]
Consciousness as Uncommon Self-Knowledge: A Synergistic Information Framework
arXiv:2605.13884v1 Announce Type: new Abstract: We propose uncommon self-knowledge (USK) as a candidate criterion for consciousness: synergistic information a system carries about itself that exists only in the joint of its subsystems and is destroyed by decomposition. Drawing on Gottwald’s partition-lattice grounding of Partial Information Decomposition (PID), where redundancy corresponds to Aumann’s common knowledge and […]
Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons
arXiv:2603.02115v2 Announce Type: replace-cross Abstract: General-purpose robot reward models are typically trained to predict absolute task progress from expert demonstrations, providing only local, frame-level supervision. While effective for expert demonstrations, this paradigm scales poorly to large-scale robotics datasets where failed and suboptimal trajectories are abundant and assigning dense progress labels is ambiguous. We introduce Robometer, […]
Positive Alignment: Artificial Intelligence for Human Flourishing
arXiv:2605.10310v2 Announce Type: replace Abstract: Existing alignment research is dominated by concerns about safety and preventing harm: safeguards, controllability, and compliance. This paradigm of alignment parallels early psychology’s focus on mental illness: necessary but incomplete. What we call Positive Alignment is the development of AI systems that (i) actively support human and ecological flourishing in […]