AI News Archive: May 4, 2026 — Part 11

Sourced from 500+ daily AI sources, scored by relevance.

• mdok-style at SemEval-2026 Task 10: Finetuning LLMs for Conspiracy Detection

  SemEval-2026 Task 10 is focused on conspiracy detection. Specifically, the goal is to detect whether a Reddit comment expresses a conspiracy belief. Our submitted mdok-style system utilizes data augmentation and self-training (to cope with a rather small amount of training data) to finetune the Qwen...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02712v1
• ForecastLabs

  Skill-based sports prediction platform with AI coaching.

  🧰 ToolsMay 4, 2026https://www.producthunt.com/products/forecastlabs-ai?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+the500feed+%28ID%3A+283491%29
• SAIL: Structure-Aware Interpretable Learning for Anatomy-Aligned Post-hoc Explanations in OCT

  Optical coherence tomography (OCT), a commonly used retinal imaging modality, plays a central role in retinal disease diagnosis by providing high-resolution visualization of retinal layers. While deep learning (DL) has achieved expert-level accuracy in OCT-based retinal disease detection, its "black...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02707v1
• Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions

  Learning data-efficient object dynamics models for robotic manipulation remains challenging, especially for deformable objects. A popular approach is to model objects as sets of 3D particles and learn their motion using graph neural networks. In practice, this is not enough to maintain physical feas...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02699v1
• mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection

  SemEval-2026 Task 9 is focused on multilingual polarization detection. Specifically, it covers the identification of multilingual, multicultural and multievent polarization along three axes (in subtasks), namely detection, type, and manifestation. Online polarization presents a concern, because it i...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02695v1
• Hybrid Inspection and Task-Based Access Control in Zero-Trust Agentic AI

  Authorizing Large Language Model (LLM)-driven agents to dynamically invoke tools and access protected resources introduces significant security risks, and the risks grow dramatically as agents engage in multi-turn conversations and scale toward distributed collaboration. A compromised or malicious a...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02682v1
• Deciphering Shortcut Learning from an Evolutionary Game Theory Perspective

  Shortcut learning causes deep learning models to rely on non-essential features within the data. However, its formation in deep neural network training still lacks theoretical understanding. In this paper, we provide a formal definition of core and shortcut features and employ evolutionary game theo...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02658v1
• Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution

  As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), is increasingly deployed in high-stakes domains, ensuring their trustworthiness has become a central challenge. However, the core trustworthy AI objectives, such as fairness, robustness, privacy, and...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02640v1
• SCGNN: Semantic Consistency enhanced Graph Neural Network Guided by Granular-ball Computing

  Capturing semantic consistency among nodes is crucial for effective graph representation learning. Existing approaches typically rely on $k$-nearest neighbors ($k$NN) or other node-level full search algorithms (FSA) to mine semantic relationships via exhaustive pairwise similarity computation, which...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02617v1
• Validation of an AI-based end-to-end model for prostate pathology using long-term archived routine samples

  Artificial intelligence (AI) is becoming a clinical tool for prostate pathology, but generalization across variations in sample preparation and preservation over prolonged time periods remains poorly understood. We evaluated GleasonAI, an end-to-end attention-based multiple instance learning model, ...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02614v1
• CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation

  While Large Language Models (LLMs) and Vision-Language Models (VLMs) demonstrate remarkable capabilities in high-level reasoning and semantic understanding, applying them directly to contact-rich manipulation remains a challenge due to their lack of explicit physical grounding and inability to perfo...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02600v1
• IConFace: Identity-Structure Asymmetric Conditioning for Unified Reference-Aware Face Restoration

  Blind face restoration is highly ill-posed under severe degradation, where identity-critical details may be missing from the degraded input. Same-identity references reduce this ambiguity, but mismatched pose, expression, illumination, age, makeup, or local facial states can lead to overuse of refer...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02814v1
• Fine-Grained Graph Generation through Latent Mixture Scheduling

  Structure aware graph generation aims to generate graphs that satisfy given topological properties. It has applications in domains such as drug discovery, social network modeling, and knowledge graph construction. Unlike existing methods that only provide coarse control over graph properties, we int...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02780v1
• TOC-SR: Task-Optimal Compact diffusion for Image Super Resolution

  Diffusion models have recently demonstrated strong performance for image restoration tasks, including super-resolution. However, their large model size and iterative sampling procedures make them computationally expensive for practical deployment. In this work, we present TOC-SR, a framework for bui...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02767v1
• Mitigating Misalignment Contagion by Steering with Implicit Traits

  Language models (LMs) are increasingly used in high-stakes, multi-agent settings, where following instructions and maintaining value alignment are critical. Most alignment research focuses on interactions between a single LM and a single user, failing to address the risk of misaligned behavior sprea...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02751v1
• Triple Spectral Fusion for Sensor-based Human Activity Recognition

  The field of sensor-based human activity recognition (HAR) mainly uses posture, motion and context data of Inertial Measurement Units (IMUs) to identify daily activities. Despite the advancements in learning-based methods, it is challenging to perform information fusion from the temporal perspective...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02743v1
• Foundation Models to Unlock Real-World Evidence from Nationwide Medical Claims

  Evidence derived from large-scale real-world data (RWD) is increasingly informing regulatory evaluation and healthcare decision-making. Administrative claims provide population-scale, longitudinal records of healthcare utilization, expenditure, and detailed coding of diagnoses, procedures, and medic...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02740v1
• AI and Open-data Driven Scalable Solar Power Profiling

  Solar photovoltaic (PV) deployment is expanding rapidly, yet detailed, up-to-date information on the spatial distribution and capacity of rooftop PV remains limited. This paper presents an open, scalable framework for detecting solar panels from open data and generating city-level solar power profil...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02738v1
• Coherent Hierarchical Multi-Label Learning to Defer for Medical Imaging

  Learning to Defer (L2D) enables a model to predict autonomously or defer to an expert, but prior work largely assumes flat label spaces. We study the first L2D setting with hierarchical multi-label decisions, motivated by medical-imaging workflows in which findings are organised by clinical taxonomi...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02734v1
• ProPACT: A Proactive AI-Driven Adaptive Collaborative Tutor for Pair Programming

  Effective pair programming depends on coordination of attention, cognitive effort, and joint regulation over time, yet most adaptive learning systems remain individual-centric and reactive. This paper introduces ProPACT, a proactive AI-driven adaptive collaborative tutor that treats collaboration it...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02703v1
• Fuzzy Fingerprinting Encoder Pre-trained Language Models for Emotion Recognition in Conversations: Human Assessment and Validity Study

  In Emotion Recognition in Conversations (ERC), model decisions should align with nuanced human perception and ideally provide insights on the classification process. Standard encoder pre-trained language models (PLMs) are the state-of-the-art at these tasks but offer little insight into why a certai...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02665v1
• AcademiClaw: When Students Set Challenges for AI Agents

  Benchmarks within the OpenClaw ecosystem have thus far evaluated exclusively assistant-level tasks, leaving the academic-level capabilities of OpenClaw largely unexamined. We introduce AcademiClaw, a bilingual benchmark of 80 complex, long-horizon tasks sourced directly from university students' rea...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02661v1
• ViewSAM: Learning View-aware Cross-modal Semantics for Weakly Supervised Cross-view Referring Multi-Object Tracking

  Cross-view Referring Multi-Object Tracking (CRMOT) aims to track multiple objects specified by natural language across multiple camera views, with globally consistent identities. Despite recent progress, existing methods rely heavily on costly frame-level spatial annotations and cross-view identity ...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02638v1
• Counterfactual Reasoning in Automated Planning

  Automated planning traditionally assumes that all aspects of a planning task (initial state, goals, and available actions) are fully specified in advance, an approach well-suited to domains with fixed rules and deterministic execution. However, real-world planning often requires flexibility, allowin...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02603v1
• Reinforcement Learning for LLM-based Multi-Agent Systems through Orchestration Traces

  As large language model (LLM) agents evolve from isolated tool users into coordinated teams, reinforcement learning (RL) must optimize not only individual actions but also how work is spawned, delegated, communicated, aggregated, and stopped. This paper studies RL for LLM-based multi-agent systems t...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02801v1
• FunFuzz: An LLM-Powered Evolutionary Fuzzing Framework

  Modern fuzzers increasingly use Large Language Models (LLMs) to generate structured inputs, but LLM-driven fuzzing is sensitive to prompt initialization and sampling variance, which can reduce exploration efficiency and lead to redundant inputs. We present FunFuzz, a multi-island evolutionary fuzzin...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02789v1
• ContextualJailbreak: Evolutionary Red-Teaming via Simulated Conversational Priming

  Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety alignment and elicit harmful responses. A growing body of work shows that contextual priming, where earlier turns covertly bias later replies, constitutes a powerful attack surface, with hand-crafted multi-turn sc...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02647v1
• Beating the Style Detector: Three Hours of Agentic Research on the AI-Text Arms Race

  Reproducing an empirical NLP study used to take weeks. Given the released data and a modern agentic-research harness, we redo every experiment of a recent ACL\,2026 study on personal-style post-editing of LLM drafts -- and add three new ones -- with the human investigator acting only as a reviewer-i...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02620v1
• SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures

  We present our shared task on evaluating the adaptability of LLMs and NLP systems across multiple languages and cultures. The task data consist of an extended version of our manually constructed BLEnD benchmark (Myung et al. 2024), covering more than 30 language-culture pairs, predominantly represen...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02601v1
• Benchmarking Retrieval Strategies for Biomedical Retrieval-Augmented Generation: A Controlled Empirical Study

  Retrieval-Augmented Generation (RAG) offers a well-established path to grounding large language model (LLM) outputs in external knowledge, yet the question of which retrieval strategy works best in a high-stakes domain such as biomedicine has not received the controlled, multi-metric treatment it de...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02520v1
• Revisiting Semantic Role Labeling: Efficient Structured Inference with Dependency-Informed Analysis

  Semantic Role Labeling (SRL) provides an explicit representation of predicate-argument structure, capturing linguistically grounded relations such as who did what to whom. While recent NLP progress has been dominated by large language models (LLMs), these systems often rely on implicit semantic repr...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02505v1
• A multilingual hallucination benchmark: MultiWikiQHalluA

  Most hallucination evaluations focus on English, leaving it unclear whether findings transfer to lower-resource languages. We investigate faithfulness hallucinations, defined as model-generated content that is fluent and plausible but diverges from the provided input or is internally inconsistent. L...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02504v1
• Semantic Risk-Aware Heuristic Planning for Robotic Navigation in Dynamic Environments: An LLM-Inspired Approach

  The integration of Large Language Model (LLM) reasoning principles into classical robot path planning represents a rapidly emerging research direction. In this paper, we propose a Semantic Risk-Aware Heuristic (SRAH) planner that encodes LLM-inspired cost functions penalising geometrically cluttered...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02862v1
• Salessims

  AI-driven conversational role-play to practice sales calls

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• A Systematic Benchmark of Machine Transliteration Models for the Tajik-Farsi Language Pair: A Comparative Study from Rule-Based to Transformer Architectures

  This paper presents the first comprehensive comparative analysis of modern machine learning architectures for transliteration between Tajik (Cyrillic script) and Persian (Arabic script). A key contribution is the creation and validation of a unique parallel corpus aggregated from multiple heterogene...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02270v1
• Reliability-Oriented Multilingual Orthopedic Diagnosis: A Domain-Adaptive Modeling and a Conceptual Validation Framework

  Large Language Models (LLMs) are increasingly proposed for clinical decision support including multilingual diagnosis in low-resource settings. However, their reliability, calibration and safety characteristics remain insufficiently understood for structured, high-risk tasks. We present a system-lev...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02266v1
• Zero-Shot Confidence Estimation for Small LLMs: When Supervised Baselines Aren't Worth Training

  How reliably can a small language model estimate its own correctness? The answer determines whether local-to-cloud routing-escalating queries a cheap local model cannot handle-can work without supervised training data. As inference costs dominate large language model (LLM) deployment budgets, routin...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02241v1
• Perturbation Dose Responses in Recursive LLM Loops: Raw Switching, Stochastic Floors, and Persistent Escape under Append, Replace, and Dialog Updates

  Recursive language-model loops often settle into recognizable attractor-like patterns. The practical question is how much injected text is needed to move a settled loop somewhere else, and whether that move lasts. We study this in 30-step recursive loops by separating the model from the context-upda...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02236v1
• Bucketing the Good Apples: A Method for Diagnosing and Improving Causal Abstraction

  We present a method for diagnosing interpretation in neural networks by identifying an input subspace where a proposed interpretation is highly faithful. Our method is particularly useful for causal-abstraction-style interpretability, where a high-level causal hypothesis is evaluated by interchange ...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02234v1
• CLaC at SemEval-2026 Task 6: Response Clarity Detection in Political Discourse

  In this paper, we present our system for SemEval-2026 Task 6 (CLARITY) on response clarity and evasion detection in question-answer pairs from U.S. presidential interviews, comparing fine-tuned encoders with prompt-based LLMs. Our LLM ensemble achieves 80 macro-F1 on the 3-class Task 1 (9th/41) and ...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02170v1
• Sharpness-Aware Pretraining Mitigates Catastrophic Forgetting

  Pretraining optimizers are tuned to produce the strongest possible base model, on the assumption that a stronger starting point yields a stronger model after subsequent changes like post-training and quantization. This overlooks the geometry of the base model which controls how much of the base mode...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02105v1
• FlexSQL: Flexible Exploration and Execution Make Better Text-to-SQL Agents

  Text-to-SQL over large analytical databases requires navigating complex schemas, resolving ambiguous queries, and grounding decisions in actual data. Most current systems follow a fixed pipeline where schema elements are retrieved once upfront and the database is only revisited for post-hoc repair, ...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02815v1
• PubMed-Ophtha: An open resource for training ophthalmology vision-language models on scientific literature

  Vision-language models hold considerable promise for ophthalmology, but their development depends on large-scale, high-quality image-text datasets that remain scarce. We present PubMed-Ophtha, a hierarchical dataset of 102,023 ophthalmological image-caption pairs extracted from 15,842 open-access ar...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02720v1
• Synthetic Users, Real Differences: an Evaluation Framework for User Simulation in Multi-Turn Conversations

  There is growing interest in exploring user simulation as an alternative to gathering and scoring real user-chatbot interactions for AI chatbot evaluation. For this purpose, it is important to ensure the realism of the simulation, i.e., the extent to which simulated dialogues reflect real dialogues ...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02624v1
• Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages

  Transformer-based models achieve state-of-the-art dependency parsing for high-resource languages, yet their advantage over simpler architectures in low-resource settings remains poorly understood. We evaluate four parsers -- the Biaffine LSTM, Stack-Pointer Network, AfroXLMR-large, and RemBERT -- ac...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02608v1
• Shadow-Loom: Causal Reasoning over Graphical World Model of Narratives

  Stories hold a reader's attention because they have causes, secrets, and consequences. Shadow-Loom is an experimental open-source framework that turns a narrative into a versioned graphical world model and lets two engines act on it: a causal physics grounded in Pearl's ladder of causation and a rec...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02475v1
• Accurate Legal Reasoning at Scale: Neuro-Symbolic Offloading and Structural Auditability for Robust Legal Adjudication

  Legal texts often contain computational legal clauses--provisions whose understanding requires complex logic. While frontier Large Reasoning Models (LRMs) can describe such clauses, building production-ready systems is limited by reasoning errors and the high cost of inference. We propose Amortized ...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02472v1
• Leveraging Argument Structure to Predict Content Hatefulness

  Information disorder is a challenging phenomenon that affects society at large. This phenomenon entails the diffusion of misleading, misinforming, and hateful content online. In different contexts, one aspect of the problem may prevail, but overall, this is a broad problem that requires comprehensiv...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02457v1
• Measuring AI Reasoning: A Guide for Researchers

  In this paper, we offer a guide for researchers on evaluating reasoning in language models, building the case that reasoning should be assessed through evidence of adaptive, multi-step search rather than final-answer accuracy alone. Under an evaluation-oriented definition, reasoning requires selecti...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02442v1
• Automatic Reflection Level Classification in Hungarian Student Essays

  Reflective thinking is a key competency in education, but assessing reflective writing remains a time-consuming and subjective task for education experts. While automated reflective analysis has been explored in several languages, Hungarian language was not researched extensively. In this paper, we ...

  📄 ResearchMay 4, 2026http://arxiv.org/abs/2605.02402v1