AI Soccer Analyst is a mixed‑initiative system that guides analysts through six revisable stages—Data Understanding, Problem Definition, Structured Planning, Execution, Evidence‑Grounded Reporting, and Interaction and Refinement—to produce soccer data analyses. A formative study with five analysts shaped design goals around automation, verifiability, human control, and accessibility, while a task‑based evaluation with 16 participants showed that 33 of 48 tasks met completion criteria and participants reported high quality, reliability, and verifiability of outputs. Interaction logs revealed that domain knowledge emerged during clarification, planning, and refinement, demonstrating that stage‑aware human‑AI collaboration can produce inspectable, revisable, and verifiable analyses while keeping domain experts in control.
arXiv:2607. 16597v1 Announce Type: new Abstract: The rapid digitalisation of elite sport has created new opportunities for integrating artificial intelligence (AI), performance analytics, and decision-support systems into athlete development and competition management.
The paper presents a systematic mapping of recent chess research involving humans, engines, neural and reinforcement‑learning systems, large language models (LLMs), and hybrid approaches. It identifies 84 core study families and classifies them by agent type, strategic‑reasoning stages, and evaluation dimensions, highlighting a strong focus on situation assessment, evaluation, and action selection while noting gaps in planning, explanation, metacognition, and human–AI collaboration. The study also distinguishes hybrid systems by integration timing and cautions that improved human performance in evaluations does not automatically imply human–AI synergy.
Football score forecasting combines a strong statistical core with a difficult contextual edge. Dynamic Poisson-family models estimate team strength, expected goals, and coherent score probabilities, but do not directly understand roles, tactical matchups, motivation, or how a first goal changes behaviour.
arXiv:2608. 05030v1 Announce Type: new Abstract: Football score forecasting combines a strong statistical core with a difficult contextual edge.
arXiv:2609.21841v1 Announce Type: new Abstract: Frontier language models now produce professional deliverables that expert graders judge to match human work on a substantial share of economically val...
arXiv:2607. 14673v1 Announce Type: new Abstract: Evaluations (Evals) are a deployment bottleneck for real-world AI applications: public benchmarks rarely match a team's users, context, or policies, and human review is often tedious to scale.
The paper introduces Atria Dawn Preview, a foundation agentic language model aimed at scientific research and engineering workflows. Trained through a Verifiable Experience Pipeline, it performs competitively across 16 real‑world benchmarks, achieving the highest scores on five. The authors also present a detailed case study of human–AI collaboration, showing that while AI proposes methods and revisions, humans retain final decision‑making and guide the research direction.
The paper investigates whether open‑source Vision‑Language Models (VLMs) can perform zero‑shot action quality assessment (AQA) on Olympic diving videos. Using the AQA‑7 benchmark, the authors propose a regression framework that combines VLM‑generated semantic reasoning, phase‑level sub‑scores, TF‑IDF vectorization, dimensionality reduction, and ensemble learning to predict final competition scores. While individual VLMs achieve moderate Spearman correlations (<0.32), the ensemble approach boosts performance to 0.67, demonstrating that VLM‑derived textual reasoning features are more informative than raw numerical sub‑scores for AQA. whyItMatters":"The study shows that VLMs can serve as explainable, semi‑automated tools for evaluating sports performance, potentially aiding expert judging in complex, subjective Olympic events."
The paper introduces CoSLR, a Human‑AI collaborative system for systematic literature reviews that incorporates mandatory human checkpoints within a three‑phase pipeline using large language models and Retrieval‑Augmented Generation. In a survey of 63 participants, 42.9 % rated the system’s usability highly, yet 34.9 % indicated they would trust AI‑generated summaries without further human verification after brief interaction. The study highlights that effective human oversight in AI‑assisted literature reviews depends on users’ willingness to engage with the checkpoints, underscoring a calibration issue that interface design must directly address.
The study investigates how different AI support formats influence human decision-making across two tasks: abstract visual reasoning with RAVEN matrices and deductive logical reasoning with LSAT problems. Findings reveal that in visual reasoning, predictions alone and predicted probabilities best support accuracy and error recovery, while in logical reasoning, LLM explanations outperform other supports. The results suggest that effective human–AI collaboration requires task‑specific support strategies rather than a one‑size‑fits‑all approach.
The paper explores how Artificial Intelligence can enhance cooperative engineering workflows, focusing on the European Rover Challenge where student teams design complex rover systems under tight deadlines. A 40‑question survey of 14 teams revealed common bottlenecks such as poor documentation, unclear requirements, fragmented communication, informal task monitoring, and significant integration rework. Based on these findings, the authors propose requirements for AI‑augmented workflows and outline an assistant system architecture that integrates user interfaces, credential management, service selection, specialized AI services, and external engineering tools to support task clarification, requirement compliance, communication summarization, integration risk detection, and continuous knowledge capture.
arXiv:2605. 02050v2 Announce Type: replace-cross Abstract: This work establishes a framework for standardizing AI evaluation RCTs (sometimes called human uplift studies).