OpenRaiser/Intern-Atlas huggingface dataset:
Developing a Multi-Agent System to Generate Next Generation Science Assessments with Evidence-Centered Design
Venue: arXiv cs.CY 2026
Year: 2026

Contemporary science education reforms such as the Next Generation Science Standards (NGSS) demand assessments to understand students' ability to use science knowledge to solve problems and design solutions. To elicit such higher-order ability, educators need performance-based assessments, which are challenging to develop. One solution that has been broadly adopted is Evidence-Centered Design (ECD), which emphasizes interconnected models of the learner, evidence, and tasks. Although ECD provides a framework to safeguard assessment validity, its implementation requires diverse expertise (e.g., content and assessment), which is both costly and labor-intensive. To address this challenge, this study proposed integrating the ECD framework into Multi-Agent Systems (MAS) to generate NGSS-aligned assessment items automatically. This integrated MAS system ensembles multiple large language models with varying expertise, enabling the automation of complex, multi-stage item generation workflows traditionally performed by human experts. We examined the quality of AI-generated NGSS-aligned items and compared them with human-developed items across multiple dimensions of assessment design. Results showed that AI-generated items have overall comparable quality to human-developed items in terms of alignment with NGSS three-dimensional standards and cognitive demands. Divergent patterns also emerged: AI-generated items demonstrated a distinct strength in inclusivity, while also exhibiting limitations in clarity, conciseness, and multimodal design. AI- and human-developed items both showed weaknesses in evidence collectability and student interest alignment. These findings suggest that integrating ECD into MAS can support scalable and standards-aligned assessment design, while human expertise remains essential.
Assessing the Reliability of Persona-Conditioned LLMs as Synthetic Survey Respondents
Venue: arXiv cs.CY 2026
Year: 2026

Using persona-conditioned LLMs as synthetic survey respondents has become a common practice in computational social science and agent-based simulations. Yet, it remains unclear whether multi-attribute persona prompting improves LLM reliability or instead introduces distortions. Here we contribute to this assessment by leveraging a large dataset of U.S. microdata from the World Values Survey. Concretely, we evaluate two open-weight chat models and a random-guesser baseline across more than 70K respondent-item instances. We find that persona prompting does not yield a clear aggregate improvement in survey alignment and, in many cases, significantly degrades performance. Persona effects are highly heterogeneous as most items exhibit minimal change, while a small subset of questions and underrepresented subgroups experience disproportionate distortions. Our findings highlight a key adverse impact of current persona-based simulation practices: demographic conditioning can redistribute error in ways that undermine subgroup fidelity and risk misleading downstream analyses.
The Landscape of AI in Science Education: What is Changing and How to Respond
Venue: arXiv cs.CY 2026
Year: 2026

This introductory chapter explores the transformative role of artificial intelligence (AI) in reshaping the landscape of science education. Positioned at the intersection of tradition and innovation, AI is altering educational goals, procedures, learning materials, assessment practices, and desired outcomes. We highlight how AI-supported tools, such as intelligent tutoring systems, adaptive learning platforms, automated feedback, and generative content creation--enhance personalization, efficiency, and equity while fostering competencies essential for an AI-driven society, including critical thinking, creativity, and interdisciplinary collaboration. At the same time, this chapter examines the ethical, social, and pedagogical challenges that arise, particularly issues of fairness, transparency, accountability, privacy, and human oversight. To address these tensions, we argue that a Responsible and Ethical Principles (REP) framework is needed to offer guidance for aligning AI integration with values of fairness, scientific integrity, and democratic participation. Through this lens, we synthesize the changes brought to each of the five transformative aspects and the approaches introduced to meet the changes according to the REP framework. We argue that AI should be viewed not as a replacement for human teachers and learners but as a partner that supports inquiry, enriches assessment, and expands access to authentic scientific practices. Aside from what is changing, we conclude by exploring the roles that remain uniquely human, engaging as moral and relational anchors in classrooms, bringing interpretive and ethical judgement, fostering creativity, imagination, and curiosity, and co-constructing meaning through dialogue and community, and assert that these qualities must remain central if AI is to advance equity, integrity, and human flourishing in science education.
The Chancellor Trap: Administrative Mediation and the Hollowing of Sovereignty in the Algorithmic Age
Venue: arXiv cs.CY 2026
Year: 2026

The contemporary governance discourse on Artificial Intelligence often emphasizes catastrophic loss-of-control scenarios. This article suggests that such framing may obscure a more immediate failure mode: chancellorization, or the gradual hollowing out of sovereignty through administrative mediation. In high-throughput, digitally legible organizations, AI-mediated decision support can reduce the probability that failures become publicly legible and politically contestable, even when underlying operational risk does not decline. Drawing on the institutional history of Imperial China, the article formalizes this dynamic as a principal-agent problem characterized by a verification gap, in which formal authority (auctoritas) remains downstream while effective governing capacity (potestas) migrates to intermediary layers that control information routing, drafting defaults, and evaluative signals. Empirical support is provided through a multi-method design combining historical process tracing with a cross-national panel plausibility probe (2016-2024). Using incident-based measures of publicly recorded AI failures and administrative digitization indicators, the analysis finds that higher state capacity and digitalization are systematically associated with lower public visibility of AI failures, holding AI ecosystem expansion constant. The results are consistent with a paradox of competence: governance systems may become more effective at absorbing and resolving failures internally while simultaneously raising the threshold at which those failures become politically visible. Preserving meaningful human sovereignty therefore depends on institutional designs that deliberately reintroduce auditable friction.
PIPE-RDF: An LLM-Assisted Pipeline for Enterprise RDF Benchmarking
Venue: arXiv cs.DB 2026
Year: 2026

Enterprises rely on RDF knowledge graphs and SPARQL to expose operational data through natural language interfaces, yet public KGQA benchmarks do not reflect proprietary schemas, prefixes, or query distributions. We present PIPE-RDF, a three-phase pipeline that constructs schema-specific NL-SPARQL benchmarks using reverse querying, category-balanced template generation, retrieval-augmented prompting, deduplication, and execution-based validation with repair. We instantiate PIPE-RDF on a fixed-schema company-location slice (5,000 companies) derived from public RDF data and generate a balanced benchmark of 450 question-SPARQL pairs across nine categories. The pipeline achieves 100% parse and execution validity after repair, with pre-repair validity rates of 96.5%-100% across phases. We report entity diversity metrics, template coverage analysis, and cost breakdowns to support deployment planning. We release structured artifacts (CSV/JSONL, logs, figures) and operational metrics to support model evaluation and system planning in real-world settings. Code is available at https://github.com/suraj-ranganath/PIPE-RDF.
A Computer Vision Framework for Multi-Class Detection and Tracking in Soccer Broadcast Footage
Venue: arXiv cs.CV 2026
Year: 2026

Clubs with access to expensive multi-camera setups or GPS tracking systems gain a competitive advantage through detailed data, whereas lower-budget teams are often unable to collect similar information. This paper examines whether such data can instead be extracted directly from standard broadcast footage using a single-camera computer vision pipeline. This project develops an end-to-end system that combines a YOLO object detector with the ByteTrack tracking algorithm to identify and track players, referees, goalkeepers, and the ball throughout a match. Experimental results show that the pipeline achieves high performance in detecting and tracking players and officials, with strong precision, recall, and mAP50 scores, while ball detection remains the primary challenge. Despite this limitation, our findings demonstrate that AI can extract meaningful player-level spatial information from a single broadcast camera. By reducing reliance on specialized hardware, the proposed approach enables colleges, academies, and amateur clubs to adopt scalable, data-driven analysis methods previously accessible only to professional teams, highlighting the potential for affordable computer vision-based soccer analytics.
Depth from Defocus via Direct Optimization
Venue: arXiv cs.CV 2026
Year: 2026

Though there exists a reasonable forward model for blur based on optical physics, recovering depth from a collection of defocused images remains a computationally challenging optimization problem. In this paper, we show that with contemporary optimization methods and reasonable computing resources, a global optimization approach to depth from defocus is feasible. Our approach rests on alternating minimization. When holding the depth map fixed, the forward model is linear with respect to the all-in-focus image. When holding the all-in-focus image fixed, the depth at each pixel can be computed independently, enabling embarrassingly parallel computation. We show that alternating between convex optimization and parallel grid search can effectively solve the depth-from-defocus problem at higher resolutions than current deep learning methods. We demonstrate our approach on benchmark datasets with synthetic and real defocus blur and show promising results compared to prior approaches. Our code is available at github.com/hollyjackson/dfd.
Wide Open Gazes: Quantifying Visual Exploratory Behavior in Soccer with Pose Enhanced Positional Data
Venue: arXiv cs.LG 2026
Year: 2026

Traditional approaches to measuring visual exploratory behavior in soccer rely on counting visual exploratory actions (VEAs) based on rapid head movements exceeding 125°/s, but this method suffer from player position bias (i.e., a focus on central midfielders), annotation challenges, binary measurement constraints (i.e., a player is scanning, or not), lack the power to predict relevant short-term in-game future success, and are incompatible with fundamental soccer analytics models such as pitch control. This research introduces a novel formulaic continuous stochastic vision layer to quantify players' visual perception from pose-enhanced spatiotemporal tracking. Our probabilistic field-of-view and occlusion models incorporate head and shoulder rotation angles to create speed-dependent vision maps for individual players in a two-dimensional top-down plane. We combine these vision maps with pitch control and pitch value surfaces to analyze the awaiting phase (when a player is awaiting the ball to arrive after a pass for a teammate) and their subsequent on-ball phase. We demonstrate that aggregated visual metrics - such as the percentage of defended area observed while awaiting a pass - are predictive of controlled pitch value gained at the end of dribbling actions using 32 games of synchronized pose-enhanced tracking data and on-ball event data from the 2024 Copa America. This methodology works regardless of player position, eliminates manual annotation requirements, and provides continuous measurements that seamlessly integrate into existing soccer analytics frameworks. To further support the integration with existing soccer analytics frameworks we open-source the tools required to make these calculations.
JAEGER: Joint 3D Audio-Visual Grounding and Reasoning in Simulated Physical Environments
Venue: arXiv cs.CV 2026
Year: 2026

Current audio-visual large language models (AV-LLMs) are predominantly restricted to 2D perception, relying on RGB video and monaural audio. This design choice introduces a fundamental dimensionality mismatch that precludes reliable source localization and spatial reasoning in complex 3D environments. We address this limitation by presenting JAEGER, a framework that extends AV-LLMs to 3D space, to enable joint spatial grounding and reasoning through the integration of RGB-D observations and multi-channel first-order ambisonics. A core contribution of our work is the neural intensity vector (Neural IV), a learned spatial audio representation that encodes robust directional cues to enhance direction-of-arrival estimation, even in adverse acoustic scenarios with overlapping sources. To facilitate large-scale training and systematic evaluation, we propose SpatialSceneQA, a benchmark of 61k instruction-tuning samples curated from simulated physical environments. Extensive experiments demonstrate that our approach consistently surpasses 2D-centric baselines across diverse spatial perception and reasoning tasks, underscoring the necessity of explicit 3D modelling for advancing AI in physical environments. Our source code, pre-trained model checkpoints and datasets will be released upon acceptance.
VLANeXt: Recipes for Building Strong VLA Models
Venue: arXiv cs.CV 2026
Year: 2026

Following the rise of large foundation models, Vision-Language-Action models (VLAs) emerged, leveraging strong visual and language understanding for general-purpose policy learning. Yet, the current VLA landscape remains fragmented and exploratory. Although many groups have proposed their own VLA models, inconsistencies in training protocols and evaluation settings make it difficult to identify which design choices truly matter. To bring structure to this evolving space, we reexamine the VLA design space under a unified framework and evaluation setup. Starting from a simple VLA baseline similar to RT-2 and OpenVLA, we systematically dissect design choices along three dimensions: foundational components, perception essentials, and action modelling perspectives. From this study, we distill 12 key findings that together form a practical recipe for building strong VLA models. The outcome of this exploration is a simple yet effective model, VLANeXt. VLANeXt outperforms prior state-of-the-art methods on the LIBERO and LIBERO-plus benchmarks and demonstrates strong generalization in real-world experiments. We will release a unified, easy-to-use codebase that serves as a common platform for the community to reproduce our findings, explore the design space, and build new VLA variants on top of a shared foundation.
LAPIS: Lightweight API Specification for Intelligent Systems
Venue: arXiv cs.SE 2026
Year: 2026

Large Language Models (LLMs) increasingly serve as consumers of API specifications, whether for code generation, autonomous agent interaction, or API-assisted reasoning. The de facto standard for API description, OpenAPI, was designed for documentation tools and code generators, resulting in substantial token overhead when used as LLM context. We present LAPIS (Lightweight API Specification for Intelligent Systems), a domain-specific format optimized for LLM consumption that preserves the semantic information necessary for API reasoning while minimizing token usage. Through empirical evaluation against five real-world production API specifications including GitHub (1,080 endpoints), Twilio (197 endpoints), DigitalOcean (545 endpoints), Petstore, and HTTPBin we demonstrate an average token reduction of 85.5% compared to OpenAPI YAML and 88.6% compared to OpenAPI JSON, measured with the cl100k_base tokenizer. LAPIS introduces domain-specific structural innovations, including centralized error definitions, webhook trigger conditions, structured rate limit descriptions, and operation flow declarations information that OpenAPI either duplicates redundantly or cannot represent at all. The format is fully convertible from OpenAPI 3.x via an automated converter, requires no special parser for LLM consumption, and is released as an open specification under CC BY 4.0.
Tower of Babel in Cross-Cultural Communication: A Case Study of #Give Me a Chinese Name# Dialogues During the "TikTok Refugees'' Event
Venue: arXiv cs.HC 2026
Year: 2026

The sudden influx of "TikTok refugees'' into the Chinese platform RedNote in early 2025 created an unprecedented, large-scale online cross-cultural communication event between the West and East. Although prior HCI research has studied user behavior in social media, most work remains confined to monolingual or single-cultural contexts, leaving cross-linguistic and cultural dynamics underexplored. To address this gap, we focused on a particularly challenging cross-cultural encoding-decoding task that remains stubbornly beyond the reach of machine translation, i.e., foreign newcomers asking Chinese users for Chinese names, and examined how people collectively constructed a digital "Babel Tower'' through various information encoding strategies. We collected and analyzed over 70,000 comments from RedNote with a creative human-in-the-loop approach using large language models, deriving a systematic framework summarizing cross-cultural information encoding strategies, how they are combined and layered to complicate decoding, and how they relate to engagement metrics such as the number of likes.
Debug2Fix: Supercharging Coding Agents with Interactive Debugging Capabilities
Venue: arXiv cs.SE 2026
Year: 2026

While significant progress has been made in automating various aspects of software development through coding agents, there is still significant room for improvement in their bug fixing capabilities. Debugging and investigation of runtime behavior remains largely a manual, developer-driven process. Popular coding agents typically rely on either static analysis of the code or iterative test-fix cycles, which is akin to trial and error debugging. We posit that there is a wealth of rich runtime information that developers routinely access while debugging code, which agents are currently deprived of due to design limitations. Despite how prevalent debuggers are in modern IDEs and command-line tools, they have surprisingly not made their way into coding agents. In this work, we introduce Debug2Fix, a novel framework that incorporates interactive debugging as a core component of a software engineering agent via a subagent architecture. We incorporate debuggers for Java and Python into our agent framework and evaluate against GitBug-Java and SWE-Bench-Live and achieve >20% improvement in performance compared to the baseline for certain models. Furthermore, using our framework, we're able to make weaker models like GPT-5 and Claude Haiku 4.5 match or exceed the performances of stronger models like Claude Sonnet 4.5, showing that better tool design is often just as important as switching to a more expensive model. Finally, we conduct systematic ablations demonstrating the importance of both the subagent architecture and debugger integration.
Ensemble Prediction of Task Affinity for Efficient Multi-Task Learning
Venue: arXiv cs.LG 2026
Year: 2026

A fundamental problem in multi-task learning (MTL) is identifying groups of tasks that should be learned together. Since training MTL models for all possible combinations of tasks is prohibitively expensive for large task sets, a crucial component of efficient and effective task grouping is predicting whether a group of tasks would benefit from learning together, measured as per-task performance gain over single-task learning. In this paper, we propose ETAP (Ensemble Task Affinity Predictor), a scalable framework that integrates principled and data-driven estimators to predict MTL performance gains. First, we consider the gradient-based updates of shared parameters in an MTL model to measure the affinity between a pair of tasks as the similarity between the parameter updates based on these tasks. This linear estimator, which we call affinity score, naturally extends to estimating affinity within a group of tasks. Second, to refine these estimates, we train predictors that apply non-linear transformations and correct residual errors, capturing complex and non-linear task relationships. We train these predictors on a limited number of task groups for which we obtain ground-truth gain values via multi-task learning for each group. We demonstrate on benchmark datasets that ETAP improves MTL gain prediction and enables more effective task grouping, outperforming state-of-the-art baselines across diverse application domains.
MapTab: Can MLLMs Master Constrained Route Planning?
Venue: arXiv cs.LG 2026
Year: 2026

Systematic evaluation of Multimodal Large Language Models (MLLMs) is crucial for advancing Artificial General Intelligence (AGI). However, existing benchmarks remain insufficient for rigorously assessing their constrained reasoning capabilities. To bridge this gap, we introduce MapTab, a multimodal benchmark specifically designed to evaluate constrained reasoning in MLLMs via route planning tasks. MapTab requires MLLMs to perceive and ground visual cues from map images alongside route attributes (e.g., Time, Price) from structured tabular data. The benchmark encompasses two scenarios: Metromap, covering metro networks in 160 cities across 52 countries, and Travelmap, depicting 168 representative tourist attractions from 19 countries. In total, MapTab comprises 328 images, 196,800 route planning queries, and 3,936 QA queries, all incorporating 4 key constraints: Time, Price, Comfort, and Reliability. Extensive evaluations across 15 representative MLLMs reveal that current models face substantial challenges in constrained multimodal reasoning. Notably, under conditions of limited visual perception, multimodal collaboration often underperforms compared to unimodal approaches. We believe MapTab provides a challenging and realistic testbed to advance the systematic evaluation of MLLMs.