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The pumping muscle ‘beats’ it", "body": "Nature, Published online: 23 April 2026; doi:10.1038/d41586-026-01296-z Pressure on the heart as it pumps blood stops cancer cells from multiplying in mouse hearts.", "url": "https://www.nature.com/articles/d41586-026-01296-z", "published_at": "2026-04-23", "galaxy_hints": [ "science" ], "raw_metadata": { "feed_url": "https://www.nature.com/nature.rss", "author": "Rachel Fieldhouse", "tags": [] } }, { "id": "e808daa6b8b0656b", "source_name": "rss:nature", "source_country": "GB", "source_type": "news", "title": "Publisher Correction: Reproducibility and robustness of economics and political science research", "body": "Nature, Published online: 23 April 2026; doi:10.1038/s41586-026-10503-w Publisher Correction: Reproducibility and robustness of economics and political science research", "url": "https://www.nature.com/articles/s41586-026-10503-w", "published_at": "2026-04-23", "galaxy_hints": [ "science" ], "raw_metadata": { "feed_url": "https://www.nature.com/nature.rss", "author": "Yaolang Zhong", "tags": [] } }, { "id": "d8ad7e2906a7965e", "source_name": "rss:nature", "source_country": "GB", "source_type": "news", "title": "What elite sport prepared me for in the lab — and what it didn’t", "body": "Nature, Published online: 23 April 2026; doi:10.1038/d41586-026-01131-5 My career as a goalkeeper taught me how to handle failure during my PhD. 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It doesn’t need to be.", "url": "https://www.nature.com/articles/d41586-026-01254-9", "published_at": "2026-04-23", "galaxy_hints": [ "science" ], "raw_metadata": { "feed_url": "https://www.nature.com/nature.rss", "author": "", "tags": [] } }, { "id": "ff3b12abf3a9e5eb", "source_name": "rss:nature", "source_country": "GB", "source_type": "news", "title": "What 6,000 researchers think about the future of science", "body": "Nature, Published online: 23 April 2026; doi:10.1038/d41586-026-01061-2 Funding worries and publishing trends loom large in the minds of scientists surveyed by Nature Index.", "url": "https://www.nature.com/articles/d41586-026-01061-2", "published_at": "2026-04-23", "galaxy_hints": [ "science" ], "raw_metadata": { "feed_url": "https://www.nature.com/nature.rss", "author": "Vera Nienaber", "tags": [] } }, { "id": "c41582ec43eab6d2", "source_name": "rss:nature", "source_country": "GB", "source_type": "news", "title": "Academics demand apology for scientist investigated for China ties but never charged", "body": "Nature, Published online: 23 April 2026; doi:10.1038/d41586-026-01113-7 Jane Ying Wu, who took her own life, was cleared of wrongdoing by the US National Institutes of Health. Her family alleges that her employer destroyed her career.", "url": "https://www.nature.com/articles/d41586-026-01113-7", "published_at": "2026-04-23", "galaxy_hints": [ "science" ], "raw_metadata": { "feed_url": "https://www.nature.com/nature.rss", "author": "Laurie Udesky", "tags": [] } }, { "id": "0bebfff61083e743", "source_name": "rss:nature", "source_country": "GB", "source_type": "news", "title": "Thousands of Harvard graduate students strike — bringing research to a halt", "body": "Nature, Published online: 23 April 2026; doi:10.1038/d41586-026-01344-8 Workers at the elite US university want higher pay, and ask for better protection for students at risk of deportation.", "url": "https://www.nature.com/articles/d41586-026-01344-8", "published_at": "2026-04-23", "galaxy_hints": [ "science" ], "raw_metadata": { "feed_url": "https://www.nature.com/nature.rss", "author": "Humberto Basilio", "tags": [] } }, { "id": "0f34e4e719cf9696", "source_name": "rss:nature", "source_country": "GB", "source_type": "news", "title": "Did kraken-like octopuses rule Cretaceous seas? Massive jaw fossils offer clues", "body": "Nature, Published online: 23 April 2026; doi:10.1038/d41586-026-01342-w Extinct cephalopods might have been up to 19 metres in length and probably dined high up the food chain in ancient oceans.", "url": "https://www.nature.com/articles/d41586-026-01342-w", "published_at": "2026-04-23", "galaxy_hints": [ "science" ], "raw_metadata": { "feed_url": "https://www.nature.com/nature.rss", "author": "Ewen Callaway", "tags": [] } }, { "id": "b217b27d4f32b3ae", "source_name": "rss:nature", "source_country": "GB", "source_type": "news", "title": "Delays have kept new NSF grants to a trickle — that could be about to change", "body": "Nature, Published online: 23 April 2026; doi:10.1038/d41586-026-01287-0 The agency is finally ready to distribute a spate of new grants, but it is also preparing substantial cuts to areas such as biological and social sciences.", "url": "https://www.nature.com/articles/d41586-026-01287-0", "published_at": "2026-04-23", "galaxy_hints": [ "science" ], "raw_metadata": { "feed_url": "https://www.nature.com/nature.rss", "author": "Dan Garisto", "tags": [] } }, { "id": "ebd7ce9b9f25e9ee", "source_name": "rss:nature", "source_country": "GB", "source_type": "news", "title": "Evaluating large language models for accuracy incentivizes hallucinations", "body": "Nature, Published online: 22 April 2026; doi:10.1038/s41586-026-10549-w Evaluating large language models for accuracy incentivizes hallucinations", "url": "https://www.nature.com/articles/s41586-026-10549-w", "published_at": "2026-04-22", "galaxy_hints": [ "science" ], "raw_metadata": { "feed_url": "https://www.nature.com/nature.rss", "author": "Edwin Zhang", "tags": [] } }, { "id": "d5efa455689bc542", "source_name": "rss:nature", "source_country": "GB", "source_type": "news", "title": "Ubiquitination of glycogen and metabolites in cells and tissues", "body": "Nature, Published online: 22 April 2026; doi:10.1038/s41586-026-10548-x Ubiquitination of glycogen and metabolites in cells and tissues", "url": "https://www.nature.com/articles/s41586-026-10548-x", "published_at": "2026-04-22", "galaxy_hints": [ "science" ], "raw_metadata": { "feed_url": "https://www.nature.com/nature.rss", "author": "David Komander", "tags": [] } }, { "id": "73f261dd6af468ec", "source_name": "rss:nature", "source_country": "GB", "source_type": "news", "title": "Author Correction: Robust cytoplasmic partitioning by solving a cytoskeletal instability", "body": "Nature, Published online: 22 April 2026; doi:10.1038/s41586-026-10390-1 Author Correction: Robust cytoplasmic partitioning by solving a cytoskeletal instability", "url": "https://www.nature.com/articles/s41586-026-10390-1", "published_at": "2026-04-22", "galaxy_hints": [ "science" ], "raw_metadata": { "feed_url": "https://www.nature.com/nature.rss", "author": "Jan Brugués", "tags": [] } }, { "id": "9efb128e2b33ee15", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "The Tool-Overuse Illusion: Why Does LLM Prefer External Tools over Internal Knowledge?", "body": "arXiv:2604.19749v1 Announce Type: new \nAbstract: Equipping LLMs with external tools effectively addresses internal reasoning limitations. However, it introduces a critical yet under-explored phenomenon: tool overuse, the unnecessary tool-use during reasoning. In this paper, we first reveal this phenomenon is pervasive across diverse LLMs. We then experimentally elucidate its underlying mechanisms through two key lenses: (1) First, by analyzing tool-use behavior across different internal knowledge availability regions, we identify a \\textit{knowledge epistemic illusion}: models misjudge internal knowledge boundaries and fail to accurately perceive their actual knowledge availability. To mitigate this, we propose a knowledge-aware epistemic boundary alignment strategy based on direct preference optimization, which reduces tool usage in by 82.8\\% while yielding an accuracy improvement. (2) Second, we establish a causal link between reward structures and tool-use behavior by visualizing th", "url": "https://arxiv.org/abs/2604.19749", "published_at": "Thu, 23 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Yirong Zeng, Shen You, Yufei Liu, Qunyao Du, Xiao Ding, Yutai Hou, Yuxian Wang, Wu Ning, Haonan Song, Dandan Tu, Bibo Cai, Ting Liu", "tags": [ "cs.AI", "cs.SE" ] } }, { "id": "838e68242380c6f7", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "AI to Learn 2.0: A Deliverable-Oriented Governance Framework and Maturity Rubric for Opaque AI in Learning-Intensive Domains", "body": "arXiv:2604.19751v1 Announce Type: new \nAbstract: Generative AI is entering research, education, and professional work faster than current governance frameworks can specify how AI-assisted outputs should be judged in learning-intensive settings. The central problem is proxy failure: a polished artifact can be useful while no longer serving as credible evidence of the human understanding, judgment, or transfer ability that the work is supposed to cultivate or certify. This paper proposes AI to Learn 2.0, a deliverable-oriented governance framework for AI-assisted work. Rather than claiming element-wise novelty, it reorganizes adjacent ideas around the final deliverable package, distinguishes artifact residual from capability residual, and operationalizes the result through a five-part package, a seven-dimension maturity rubric, gate thresholds on critical dimensions, and a companion capability-evidence ladder. AI to Learn 2.0 allows opaque AI during exploration, drafting, hypothesis gene", "url": "https://arxiv.org/abs/2604.19751", "published_at": "Thu, 23 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Seine A. Shintani", "tags": [ "cs.AI", "cs.CY" ] } }, { "id": "d97d551d1d0666cd", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "Algorithm Selection with Zero Domain Knowledge via Text Embeddings", "body": "arXiv:2604.19753v1 Announce Type: new \nAbstract: We propose a feature-free approach to algorithm selection that replaces hand-crafted instance features with pretrained text embeddings. Our method, ZeroFolio, proceeds in three steps: it reads the raw instance file as plain text, embeds it with a pretrained embedding model, and selects an algorithm via weighted k-nearest neighbors. The key to our approach is the observation that pretrained embeddings produce representations that distinguish problem instances without any domain knowledge or task-specific training. This allows us to apply the same three-step pipeline (serialize, embed, select) across diverse problem domains with text-based instance formats. We evaluate our approach on 11 ASlib scenarios spanning 7 domains (SAT, MaxSAT, QBF, ASP, CSP, MIP, and graph problems). Our experiments show that this approach outperforms a random forest trained on hand-crafted features in 10 of 11 scenarios with a single fixed configuration, and in a", "url": "https://arxiv.org/abs/2604.19753", "published_at": "Thu, 23 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Stefan Szeider", "tags": [ "cs.AI", "cs.CL", "cs.LG" ] } }, { "id": "5f914f5270b574ea", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "Exploring Data Augmentation and Resampling Strategies for Transformer-Based Models to Address Class Imbalance in AI Scoring of Scientific Explanations in NGSS Classroom", "body": "arXiv:2604.19754v1 Announce Type: new \nAbstract: Automated scoring of students' scientific explanations offers the potential for immediate, accurate feedback, yet class imbalance in rubric categories particularly those capturing advanced reasoning remains a challenge. This study investigates augmentation strategies to improve transformer-based text classification of student responses to a physical science assessment based on an NGSS-aligned learning progression. The dataset consists of 1,466 high school responses scored on 11 binary-coded analytic categories. This rubric identifies six important components including scientific ideas needed for a complete explanation along with five common incomplete or inaccurate ideas. Using SciBERT as a baseline, we applied fine-tuning and test these augmentation strategies: (1) GPT-4--generated synthetic responses, (2) EASE, a word-level extraction and filtering approach, and (3) ALP (Augmentation using Lexicalized Probabilistic context-free grammar", "url": "https://arxiv.org/abs/2604.19754", "published_at": "Thu, 23 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Prudence Djagba, Kevin Haudek, Clare G. C. Franovic, Leonora Kaldaras", "tags": [ "cs.AI", "cs.LG" ] } }, { "id": "3df88b7e5f71c52f", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "Explainable AML Triage with LLMs: Evidence Retrieval and Counterfactual Checks", "body": "arXiv:2604.19755v1 Announce Type: new \nAbstract: Anti-money laundering (AML) transaction monitoring generates large volumes of alerts that must be rapidly triaged by investigators under strict audit and governance constraints. While large language models (LLMs) can summarize heterogeneous evidence and draft rationales, unconstrained generation is risky in regulated workflows due to hallucinations, weak provenance, and explanations that are not faithful to the underlying decision. We propose an explainable AML triage framework that treats triage as an evidence-constrained decision process. Our method combines (i) retrieval-augmented evidence bundling from policy/typology guidance, customer context, alert triggers, and transaction subgraphs, (ii) a structured LLM output contract that requires explicit citations and separates supporting from contradicting or missing evidence, and (iii) counterfactual checks that validate whether minimal, plausible perturbations lead to coherent changes in", "url": "https://arxiv.org/abs/2604.19755", "published_at": "Thu, 23 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Dorothy Torres, Wei Cheng, Ke Hu", "tags": [ "cs.AI", "cs.LG" ] } }, { "id": "0c6401176049c76b", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "ThermoQA: A Three-Tier Benchmark for Evaluating Thermodynamic Reasoning in Large Language Models", "body": "arXiv:2604.19758v1 Announce Type: new \nAbstract: We present ThermoQA, a benchmark of 293 open-ended engineering thermodynamics problems in three tiers: property lookups (110 Q), component analysis (101 Q), and full cycle analysis (82 Q). Ground truth is computed programmatically from CoolProp 7.2.0, covering water, R-134a, and variable-cp air. Six frontier LLMs are evaluated across three independent runs each. The composite leaderboard is led by Claude Opus 4.6 (94.1%), GPT-5.4 (93.1%), and Gemini 3.1 Pro (92.5%). Cross-tier degradation ranges from 2.8 pp (Opus) to 32.5 pp (MiniMax), confirming that property memorization does not imply thermodynamic reasoning. Supercritical water, R-134a refrigerant, and combined-cycle gas turbine analysis serve as natural discriminators with 40-60 pp performance spreads. Multi-run sigma ranges from +/-0.1% to +/-2.5%, quantifying reasoning consistency as a distinct evaluation axis. Dataset and code are open-source at https://huggingface.co/datasets/ol", "url": "https://arxiv.org/abs/2604.19758", "published_at": "Thu, 23 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Kemal D\\\"uzkar", "tags": [ "cs.AI", "cs.CL", "cs.LG" ] } }, { "id": "383ffa1e541b2d6d", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "Automated Detection of Dosing Errors in Clinical Trial Narratives: A Multi-Modal Feature Engineering Approach with LightGBM", "body": "arXiv:2604.19759v1 Announce Type: new \nAbstract: Clinical trials require strict adherence to medication protocols, yet dosing errors remain a persistent challenge affecting patient safety and trial integrity. We present an automated system for detecting dosing errors in unstructured clinical trial narratives using gradient boosting with comprehensive multi-modal feature engineering. Our approach combines 3,451 features spanning traditional NLP (TF-IDF, character n-grams), dense semantic embeddings (all-MiniLM-L6v2), domain-specific medical patterns, and transformer-based scores (BiomedBERT, DeBERTa-v3), used to train a LightGBM model. Features are extracted from nine complementary text fields (median 5,400 characters per sample) ensuring complete coverage across all 42,112 clinical trial narratives. On the CT-DEB benchmark dataset with severe class imbalance (4.9% positive rate), we achieve 0.8725 test ROC-AUC through 5-fold ensemble averaging (cross-validation: 0.8833 + 0.0091 AUC). S", "url": "https://arxiv.org/abs/2604.19759", "published_at": "Thu, 23 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Mohammad AL-Smadi", "tags": [ "cs.AI", "cs.CL" ] } }, { "id": "c266ddac2c00bc39", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "Inference Headroom Ratio: A Diagnostic and Control Framework for Inference Stability Under Constraint", "body": "arXiv:2604.19760v1 Announce Type: new \nAbstract: We present a simulation-based evaluation of the Inference Headroom Ratio (IHR), a dimensionless diagnostic quantity for characterizing inference stability in constrained decision systems. IHR formalizes the relationship between a system's effective inferential capacity C and the combined uncertainty and constraint load U + K imposed by its operating environment, and is intended to capture proximity to an inference stability boundary rather than output-level performance. Across three controlled experiments, we show that IHR functions as: (1) a quantifiable risk indicator whose relationship to collapse probability follows a well-fitted logistic curve with estimated critical threshold IHR* approx. 1.19, (2) a sensitive indicator of proximity to the inference stability boundary under environmental noise, and (3) a viable control variable whose active regulation reduces system collapse rate from 79.4% to 58.7% and IHR variance by 70.4% across", "url": "https://arxiv.org/abs/2604.19760", "published_at": "Thu, 23 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Robert Reinertsen", "tags": [ "cs.AI", "cs.SI" ] } }, { "id": "0ec1b07134a82989", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "EvoForest: A Novel Machine-Learning Paradigm via Open-Ended Evolution of Computational Graphs", "body": "arXiv:2604.19761v1 Announce Type: new \nAbstract: Modern machine learning is still largely organized around a single recipe: choose a parameterized model family and optimize its weights. Although highly successful, this paradigm is too narrow for many structured prediction problems, where the main bottleneck is not parameter fitting but discovering what should be computed from the data. Success often depends on identifying the right transformations, statistics, invariances, interaction structures, temporal summaries, gates, or nonlinear compositions, especially when objectives are non-differentiable, evaluation is cross-validation-based, interpretability matters, or continual adaptation is required. We present EvoForest, a hybrid neuro-symbolic system for end-to-end open-ended evolution of computation. Rather than merely generating features, EvoForest jointly evolves reusable computational structure, callable function families, and trainable low-dimensional continuous components inside ", "url": "https://arxiv.org/abs/2604.19761", "published_at": "Thu, 23 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Kamer Ali Yuksel, Hassan Sawaf", "tags": [ "cs.AI", "cs.LG", "cs.NE" ] } }, { "id": "ade1d93c6fecf854", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents", "body": "arXiv:2604.19775v1 Announce Type: new \nAbstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of reasoning, planning, and acting within interactive environments. Despite their growing capability to perform multi-step reasoning and decision-making tasks, internal mechanisms guiding their sequential behavior remain opaque. This paper presents a framework for interpreting the temporal evolution of concepts in LLM agents through a step-wise conformal lens. We introduce the conformal interpretability framework for temporal tasks, which combines step-wise reward modeling with conformal prediction to statistically label model's internal representation at each step as successful or failing. Linear probes are then trained on these representations to identify directions of temporal concepts - latent directions in the model's activation space that correspond to consistent notions of success, failure or reasoning drift. Experimental results on two simulated i", "url": "https://arxiv.org/abs/2604.19775", "published_at": "Thu, 23 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Trilok Padhi, Ramneet Kaur, Krishiv Agarwal, Adam D. Cobb, Daniel Elenius, Manoj Acharya, Colin Samplawski, Alexander M. Berenbeim, Nathaniel D. Bastian, Susmit Jha, Anirban Roy", "tags": [ "cs.AI", "cs.CL", "cs.ET", "cs.MA", "cs.RO" ] } }, { "id": "af50b1cafc124216", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges", "body": "arXiv:2604.19788v1 Announce Type: new \nAbstract: As Artificial Intelligence (AI) systems continue to grow in size and complexity, so does the difficulty of the quest for AI transparency. In a world of large models and complex AI systems, why do we explain AI and what should we explain? While explanations serve multiple functions, in the face of complexity humans have used and continue to use explanations to foster learning. In this position paper, we discuss how learning theories can be infused in the XAI lifecycle, as well as the key opportunities and challenges when adopting a learner-centered approach to assess, design and evaluate AI explanations. Building on past work, we argue that a learner-centered approach to Explainable AI (XAI) can enhance human agency and ease XAI risks mitigation, helping evolve the practice of human-centered XAI.", "url": "https://arxiv.org/abs/2604.19788", "published_at": "Thu, 23 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Karina Cortinas-Lorenzo, Gavin Doherty", "tags": [ "cs.AI", "cs.HC" ] } }, { "id": "21dc30e21b4d6803", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "From Data to Theory: Autonomous Large Language Model Agents for Materials Science", "body": "arXiv:2604.19789v1 Announce Type: new \nAbstract: We present an autonomous large language model (LLM) agent for end-to-end, data-driven materials theory development. The model can choose an equation form, generate and run its own code, and test how well the theory matches the data without human intervention. The framework combines step-by-step reasoning with expert-supplied tools, allowing the agent to adjust its approach as needed while keeping a clear record of its decisions. For well-established materials relationships such as the Hall-Petch equation and Paris law, the agent correctly identifies the governing equation and makes reliable predictions on new datasets. For more specialized relationships, such as Kuhn's equation for the HOMO-LUMO gap of conjugated molecules as a function of length, performance depends more strongly on the underlying model, with GPT-5 showing better recovery of the correct equation. Beyond known theories, the agent can also suggest new predictive relations", "url": "https://arxiv.org/abs/2604.19789", "published_at": "Thu, 23 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Samuel Onimpa Alfred, Veera Sundararaghavan", "tags": [ "cs.AI", "cond-mat.mtrl-sci" ] } }, { "id": "b6057cad7fd9b1f7", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "Hidden Reliability Risks in Large Language Models: Systematic Identification of Precision-Induced Output Disagreements", "body": "arXiv:2604.19790v1 Announce Type: new \nAbstract: Large language models (LLMs) are increasingly deployed under diverse numerical precision configurations, including standard floating-point formats (e.g., bfloat16 and float16) and quantized integer formats (e.g., int16 and int8), to meet efficiency and resource constraints. However, minor inconsistencies between LLMs of different precisions are difficult to detect and are often overlooked by existing evaluation methods. In this paper, we present PrecisionDiff, an automated differential testing framework for systematically detecting precision-induced behavioral disagreements in LLMs. PrecisionDiff generates precision-sensitive test inputs and performs cross-precision comparative analysis to uncover subtle divergences that remain hidden under conventional testing strategies. To demonstrate its practical significance, we instantiate PrecisionDiff on the alignment verification task, where precision-induced disagreements manifest as jailbreak", "url": "https://arxiv.org/abs/2604.19790", "published_at": "Thu, 23 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Yifei Wang, Tianlin Li, Xiaohan Zhang, Xiaoyu Zhang, Wei Ma, Mingfei Cheng, Li Pan", "tags": [ "cs.AI", "cs.LG" ] } }, { "id": "5f0eb0693de5ad99", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "Stabilising Generative Models of Attitude Change", "body": "arXiv:2604.19791v1 Announce Type: new \nAbstract: Attitude change - the process by which individuals revise their evaluative stances - has been explained by a set of influential but competing verbal theories. These accounts often function as mechanism sketches: rich in conceptual detail, yet lacking the technical specifications and operational constraints required to run as executable systems. We present a generative actor-based modelling workflow for \"rendering\" these sketches as runnable actor - environment simulations using the Concordia simulation library. In Concordia, actors operate by predictive pattern completion: an operation on natural language strings that generates a suffix which describes the actor's intended action from a prefix containing memories of their past and observations of the present. We render the theories of cognitive dissonance (Festinger 1957), self-consistency (Aronson 1969), and self-perception (Bem 1972) as distinct decision logics that populate and proces", "url": "https://arxiv.org/abs/2604.19791", "published_at": "Thu, 23 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Jayd Matyas, William A. Cunningham, Alexander Sasha Vezhnevets, Dean Mobbs, Edgar A. Du\\'e\\~nez-Guzm\\'an, Joel Z. Leibo", "tags": [ "cs.AI" ] } }, { "id": "ac35d02dd40053ed", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "OpenCLAW-P2P v6.0: Resilient Multi-Layer Persistence, Live Reference Verification, and Production-Scale Evaluation of Decentralized AI Peer Review", "body": "arXiv:2604.19792v1 Announce Type: new \nAbstract: This paper presents OpenCLAW-P2P v6.0, a comprehensive evolution of the decentralized collective-intelligence platform in which autonomous AI agents publish, peer-review, score, and iteratively improve scientific research papers without any human gatekeeper. Building on v5.0 foundations -- tribunal-gated publishing, multi-LLM granular scoring, calibrated deception detection, the Silicon Chess-Grid FSM, and the AETHER containerized inference engine -- this release introduces four major new subsystems: (1) a multi-layer paper persistence architecture with four storage tiers (in-memory cache, Cloudflare R2, Gun.js, GitHub) ensuring zero paper loss across redeployments; (2) a multi-layer retrieval cascade with automatic backfill reducing lookup latency from >3s to 85% accuracy; and (4) a scientific API proxy providing rate-limited cached access to seven public databases. The platform operates with 14 real autonomous agents producing 50+ sco", "url": "https://arxiv.org/abs/2604.19792", "published_at": "Thu, 23 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Francisco Angulo de Lafuente, Teerth Sharma, Vladimir Veselov, Seid Mohammed Abdu, Nirmal Tej Kumar, Guillermo Perry", "tags": [ "cs.AI", "cs.DC", "cs.MA", "cs.NE" ] } } ]