arXiv:2601.22629v2 Announce Type: replace-cross Abstract: Diffusion language models (Diffusion-LMs) introduce an explicit temporal dimension into text generation, yet how this structure can be leveraged to control generation diversity for exploring multiple valid semantic or reasoning paths remains underexplored. In this paper, we show that Diffusion-LMs, like diffusion models in image generation, exhibit a temporal division […]
Bayesian Inference of Psychometric Variables From Brain and Behavior in Implicit Association Tests
arXiv:2603.16741v1 Announce Type: cross Abstract: Objective. We establish a principled method for inferring mental health related psychometric variables from neural and behavioral data using the Implicit Association Test (IAT) as the data generation engine, aiming to overcome the limited predictive performance (typically under 0.7 AUC) of the gold-standard D-score method, which relies solely on reaction […]
Generate Any Scene: Scene Graph Driven Data Synthesis for Visual Generation Training
arXiv:2412.08221v4 Announce Type: replace-cross Abstract: Recent advances in text-to-vision generation excel in visual fidelity but struggle with compositional generalization and semantic alignment. Existing datasets are noisy and weakly compositional, limiting models’ understanding of complex scenes, while scalable solutions for dense, high-quality annotations remain a challenge. We introduce Generate Any Scene, a data engine that systematically […]
Human/AI Collective Intelligence for Deliberative Democracy: A Human-Centred Design Approach
arXiv:2603.16260v1 Announce Type: cross Abstract: This chapter introduces the concept of Collective Intelligence for Deliberative Democracy (CI4DD). We propose that the use of computational tools, specifically artificial intelligence to advance deliberative democracy, is an instantiation of a broader class of human-computer system designed to augment collective intelligence. Further, we argue for a fundamentally human-centred design […]
SAC-NeRF: Adaptive Ray Sampling for Neural Radiance Fields via Soft Actor-Critic Reinforcement Learning
arXiv:2603.15622v1 Announce Type: cross Abstract: Neural Radiance Fields (NeRF) have achieved photorealistic novel view synthesis but suffer from computational inefficiency due to dense ray sampling during volume rendering. We propose SAC-NeRF, a reinforcement learning framework that learns adaptive sampling policies using Soft Actor-Critic (SAC). Our method formulates sampling as a Markov Decision Process where an […]
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 feedback, which ignores the intermediate process and leads to ambiguous credit evaluation. To address this, […]
Age-dependent distribution of officially reported cases of vector-borne infections
arXiv:2603.16773v1 Announce Type: new Abstract: OBJECTIVE: To propose a new approach to analyze the age-distribution of reported cases for vector-transmitted infections. METHODS: Using officially reported number of cases of dengue, Zika, chikungunya, malaria and leishmaniasis for distinct geographical areas, in different periods. Data were treated in special but well-known procedure, transforming the raw data into […]
SocialOmni: Benchmarking Audio-Visual Social Interactivity in Omni Models
arXiv:2603.16859v1 Announce Type: new Abstract: Omni-modal large language models (OLMs) redefine human-machine interaction by natively integrating audio, vision, and text. However, existing OLM benchmarks remain anchored to static, accuracy-centric tasks, leaving a critical gap in assessing social interactivity, the fundamental capacity to navigate dynamic cues in natural dialogues. To this end, we propose SocialOmni, a […]
Exploring different approaches to customize language models for domain-specific text-to-code generation
arXiv:2603.16526v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities in generating executable code from natural language descriptions. However, general-purpose models often struggle in specialized programming contexts where domain-specific libraries, APIs, or conventions must be used. Customizing smaller open-source models offers a cost-effective alternative to relying on large proprietary systems. In this […]
What if Pinocchio Were a Reinforcement Learning Agent: A Normative End-to-End Pipeline
arXiv:2603.16651v1 Announce Type: new Abstract: In the past decade, artificial intelligence (AI) has developed quickly. With this rapid progression came the need for systems capable of complying with the rules and norms of our society so that they can be successfully and safely integrated into our daily lives. Inspired by the story of Pinocchio in […]
From Natural Language to Executable Option Strategies via Large Language Models
arXiv:2603.16434v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at general code generation, yet translating natural-language trading intents into correct option strategies remains challenging. Real-world option design requires reasoning over massive, multi-dimensional option chain data with strict constraints, which often overwhelms direct generation methods. We introduce the Option Query Language (OQL), a domain-specific intermediate […]
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 both strengths but often fail to capture the hierarchical structure of logical queries, limiting their […]