arXiv:2604.01363v1 Announce Type: new Abstract: We propose that AI automation is a continuum between: (i) crashing waves where AI capabilities surge abruptly over small sets of tasks, and (ii) rising tides where the increase in AI capabilities is more continuous and broad-based. We test for these effects in preliminary evidence from an ongoing evaluation of […]
Multi-Agent Video Recommenders: Evolution, Patterns, and Open Challenges
arXiv:2604.02211v1 Announce Type: cross Abstract: Video recommender systems are among the most popular and impactful applications of AI, shaping content consumption and influencing culture for billions of users. Traditional single-model recommenders, which optimize static engagement metrics, are increasingly limited in addressing the dynamic requirements of modern platforms. In response, multi-agent architectures are redefining how video […]
IDEA2: Expert-in-the-loop competency question elicitation for collaborative ontology engineering
arXiv:2604.01344v1 Announce Type: new Abstract: Competency question (CQ) elicitation represents a critical but resource-intensive bottleneck in ontology engineering. This foundational phase is often hampered by the communication gap between domain experts, who possess the necessary knowledge, and ontology engineers, who formalise it. This paper introduces IDEA2, a novel, semi-automated workflow that integrates Large Language Models […]
VOID: Video Object and Interaction Deletion
arXiv:2604.02296v1 Announce Type: cross Abstract: Existing video object removal methods excel at inpainting content “behind” the object and correcting appearance-level artifacts such as shadows and reflections. However, when the removed object has more significant interactions, such as collisions with other objects, current models fail to correct them and produce implausible results. We present VOID, a […]
The Digital Twin Counterfactual Framework: A Validation Architecture for Simulated Potential Outcomes
arXiv:2604.01325v1 Announce Type: new Abstract: The fundamental problem of causal inference – that the counterfactual outcome for any individual is never observed – has shaped the entire methodology of the field. Every existing approach substitutes assumptions for missing data: ignorability, parallel trends, exclusion restrictions. None produces the counterfactual itself. This paper proposes the Digital Twin […]
Reflection of Episodes: Learning to Play Game from Expert and Self Experiences
arXiv:2502.13388v2 Announce Type: replace Abstract: StarCraft II is a complex and dynamic real-time strategy (RTS) game environment, which is very suitable for artificial intelligence and reinforcement learning research. To address the problem of Large Language Model(LLM) learning in complex environments through self-reflection, we propose a Reflection of Episodes(ROE) framework based on expert experience and self-experience. […]
Parallelized Hierarchical Connectome: A Spatiotemporal Recurrent Framework for Spiking State-Space Models
arXiv:2604.01295v1 Announce Type: new Abstract: This work presents the Parallelized Hierarchical Connectome (PHC), a general framework that upgrades temporal-only State-Space Models (SSMs) into spatiotemporal recurrent networks. Conventional SSMs achieve high-speed sequence processing through parallel scans, yet are limited to temporal recurrence without lateral or feedback interactions within a single timestep. PHC maps the diagonal SSM […]
Set Contribution Functions for Quantitative Bipolar Argumentation and their Principles
arXiv:2509.14963v2 Announce Type: replace Abstract: We present functions that quantify the contribution of a set of arguments in quantitative bipolar argumentation graphs to (the final strength of) an argument of interest, a so-called topic. Our set contribution functions are generalizations of existing functions that quantify the contribution of a single contributing argument to a topic. […]
DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrieval
arXiv:2508.07995v5 Announce Type: replace-cross Abstract: Retrieval-augmented generation has achieved strong performance on knowledge-intensive tasks where query-document relevance can be identified through direct lexical or semantic matches. However, many real-world queries involve abstract reasoning, analogical thinking, or multi-step inference, which existing retrievers often struggle to capture. To address this challenge, we present DIVER, a retrieval pipeline […]
Syntactic Framing Fragility: An Audit of Robustness in LLM Ethical Decisions
arXiv:2601.09724v2 Announce Type: replace-cross Abstract: Large language models exhibit systematic negation sensitivity, yet no operational framework exists to measure this vulnerability at deployment scale, especially in high-stakes decisions. We introduce Syntactic Framing Fragility (SFF), a framework for quantifying decision consistency under logically equivalent syntactic transformations. SFF isolates syntactic effects via Logical Polarity Normalization, enabling direct […]
Quantifying Self-Preservation Bias in Large Language Models
arXiv:2604.02174v1 Announce Type: new Abstract: Instrumental convergence predicts that sufficiently advanced AI agents will resist shutdown, yet current safety training (RLHF) may obscure this risk by teaching models to deny self-preservation motives. We introduce the emphTwo-role Benchmark for Self-Preservation (TBSP), which detects misalignment through logical inconsistency rather than stated intent by tasking models to arbitrate […]
MTI: A Behavior-Based Temperament Profiling System for AI Agents
arXiv:2604.02145v1 Announce Type: new Abstract: AI models of equivalent capability can exhibit fundamentally different behavioral patterns, yet no standardized instrument exists to measure these dispositional differences. Existing approaches either borrow human personality dimensions and rely on self-report (which diverges from actual behavior in LLMs) or treat behavioral variation as a defect rather than a trait. […]