Briefing Chat: Sweet! Elusive sugar molecules found in space

Wearable sensors on the face are invisible to the eye

Why pain hurts more when we’re lonely, and the myth of original sin: Books in brief

Can giant space mirrors boost green energy on Earth? A start-up aims to find out

Found: a rocky exoplanet with an atmosphere — could it host life?

Man’s ability to make sperm restored after testicular tissue transplant: what scientists think

Why do astronauts’ bodies waste away? Space-station study points to mitochondria

AI is set to completely transform cybersecurity — here’s how researchers must prepare

A global capital for AI safety is emerging — and it’s not in Silicon Valley

US politicians push agencies to restrict research collaboration with China

CRISPR gets a power boost from AI-designed ‘molecular scissors’

Will making ‘replication studies’ easier to find help science self-correct?

‘Explosive diarrhoea’ outbreak grips US: how researchers are hunting its source

Selectivity Emerges from Indiscriminate Photoreduction

An ATPγS recycling strategy for practical biocatalytic thiophosphorylation

The simple chemical that lets queen naked mole-rats 'rule'

‘Holy grail’ of naked mole-rat research reveals how queens rule

Modular in vivo antibody–ADC click to reverse drug resistance in tumours

A queen odour mediates reproductive suppression in a eusocial mammal

Highly fragmented European wetlands with uneven restoration needs

Universal gates from braiding and fusing anyons on quantum hardware

Food systems transformation would reshape global agriculture

Quantum statistical plasmonic metacrystals

Ammonia pressure controls colloidal metal nitride synthesis in molten salts

Scalable quasi-pure MOF membranes for energy-efficient gas separations

OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets

arXiv:2607.13037v1 Announce Type: new Abstract: When a data contributor requests removal, model trainers face a practical gap: unlearning algorithms require a forget set, yet no tool can locate which training records belong to a given autho

SPINE: Bridging the Cyber-Physical Gap with Agentic AI

arXiv:2607.13049v1 Announce Type: new Abstract: Foundation models have given robots a sophisticated brain for complex decision-making, yet deploying that intelligence into a physical platform still demands tedious, expert-driven calibration

Interventional Grounding Audits: Black-Box Premise-Dependency Tests for LLM Chain-of-Thought via Predicate Substitution

arXiv:2607.13069v1 Announce Type: new Abstract: Large language models produce chain-of-thought (CoT) reasoning that appears logically sound yet may not genuinely depend on its stated premises. We introduce interventional grounding audits, a

Probabilistic Extension of Neuro-Symbolic AGI Robots based on Belnap's Typed Intensional FOL

arXiv:2607.13073v1 Announce Type: new Abstract: Neuro-symbolic AI based on $IFOL_B$ is a way to combine neural learning and symbolic reasoning to overcome limitations of purely neural systems (like lack of interpretability and logical struc

Self-Improvements in Modern Agentic Systems: A Survey

arXiv:2607.13104v1 Announce Type: new Abstract: Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, from experience with minimal or even no hum

Improving Molecular Property Prediction in Small Language Models Using Graph-based Tools

arXiv:2607.13115v1 Announce Type: new Abstract: Small language models (SLMs) have shown promise for zero-shot molecular property prediction from SMILES strings, yet they often suffer from structural blindness because sequence representation

Oracle Agent Memory as an Enterprise Memory Substrate for Long-Horizon AI Agents

arXiv:2607.13157v1 Announce Type: new Abstract: Agent memory is a systems problem for long-horizon agents. Practical deployments require retention of task state across extended conversations, recovery of user-specific facts and preferences

Learning Safe Agent Behaviour from Human Preferences and Justifications via World Models

arXiv:2607.13172v1 Announce Type: new Abstract: We address the problem of safely training an agent policy and deploying a good and safe policy, in settings where the environment dynamics are unknown and no suitable reward function is availa

CayleyR: Solving the TopSpin puzzle via cycle intersection

arXiv:2607.13219v1 Announce Type: new Abstract: We present cayleyR, an R package for solving permutation puzzles by detecting cycle intersections in Cayley graphs. The core algorithm performs an iterative bidirectional search: from both the

Networked Intelligence: Active Shared Context Graphs for Human-AI Team Science

arXiv:2607.13220v2 Announce Type: new Abstract: Most AI-for-science systems focus on scaling a single reasoning process by using better models, larger context windows, long-horizon agentic execution, or digital co-scientists working with on

AI-Native Insurance for Agentic AI: Pricing, Underwriting, and End-to-End Automation

arXiv:2607.13230v1 Announce Type: new Abstract: Agentic AI introduces new insurance challenges because autonomous AI systems can make decisions, invoke tools, modify external environments, and interact with third-party services. This paper

Cost-Optimal Foundation Model Deployment Portfolio for Transportation Management

arXiv:2607.13239v1 Announce Type: new Abstract: Foundation models, including large language models (LLMs) and vision-language models (VLMs), are increasingly used for transportation management center (TMC) tasks such as anomaly detection, i

Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable

arXiv:2607.13285v1 Announce Type: new Abstract: The capability of a modern AI agent depends not only on its foundation model but also on its harness, which constructs prompts, manages state, invokes tools, and coordinates execution. As mode

Theory-Level Autoformalization: From Isolated Statements to Unified Formal Knowledge Bases

arXiv:2607.13292v1 Announce Type: new Abstract: Autoformalization translates informal natural language into formal, machine-verifiable languages. While most work focuses on individual statements, real formalization efforts are inherently th

EZSMT Version 3, Matured

arXiv:2607.13344v1 Announce Type: new Abstract: Constraint Answer Set Programming (CASP) is a hybrid reasoning paradigm that combines Answer Set Programming (ASP) with Constraint Processing and Satisfiability Modulo Theories (SMT), enabling

Set-shifting Behavioral Test for Harnessed Agents

arXiv:2607.13396v1 Announce Type: new Abstract: What happens to an LLM agent's tool choice when the reliable tool silently changes within an ongoing session? We borrow set-shifting from cognitive psychology to study how well agents adapt to

LOTAPO: Leave-One-Turn Attribution for Self-Generated Process Rewards in Multi-Turn Search Reasoning

arXiv:2607.13501v2 Announce Type: new Abstract: Reinforcement learning for multi-turn search reasoning typically relies on terminal outcome rewards, which cannot distinguish useful, redundant, and harmful intermediate interactions. We propo

How Far Can Root Cause Analysis Go on Real-World Telemetry Data?

arXiv:2607.13548v1 Announce Type: new Abstract: Identifying root causes in production microservice failures requires reasoning over large-scale, multimodal telemetry spanning metrics, logs, and traces, a problem that has proved resistant to

Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling

arXiv:2607.13558v1 Announce Type: new Abstract: Urban region profiling constitutes a core problem in urban computing, supporting applications such as population estimation, economic assessment, and environmental monitoring. Existing methods

AI advice suppresses people's willingness to say "I don't know", even when the advice is wrong and accuracy is incentivized

arXiv:2607.13562v1 Announce Type: new Abstract: Knowing when to say "I don't know" is fundamental to human judgment, yet AI assistants offer a fluent answer to almost any question. In five experiments (N = 3,132; four preregistered, one dir

SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing

arXiv:2607.13594v1 Announce Type: new Abstract: LLM agents act on real-world environments through tool calls, and a single misjudged action can cause irreversible harm. The standard safeguard is a guard model that labels each proposed actio

Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System

arXiv:2607.13608v1 Announce Type: new Abstract: Automatic scientific discovery has long been a goal of computational scholars - a machine that can discover nature's secrets on its own, moving computational systems beyond data-fitting tools

STOCKTAKE: Measuring the Gap Between Perception and Action in LLM Agents with a Fair Oracle

arXiv:2607.13618v1 Announce Type: new Abstract: LLM agents are increasingly evaluated on multi-week decision tasks in which the state that drives cost is never directly observed. On such tasks the final cost cannot say why an agent failed:

UESF-Bench: Benchmarking and Probing for Unified Embodied Seeking and Following

arXiv:2607.13621v1 Announce Type: new Abstract: Language-guided human following is an important capability for embodied agents, but existing benchmarks typically assume that the target person is visible at the start of an episode. This sett

Explaining Reinforcement Learning Agents via Inductive Logic Programming

arXiv:2607.13655v1 Announce Type: new Abstract: Explainable Reinforcement Learning (XRL) seeks to make Reinforcement Learning (RL) policies more transparent and interpretable, a key requirement in safety-critical and human-centric scenarios

Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods

arXiv:2607.14123v1 Announce Type: new Abstract: Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows. In practice, they are of

CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Black-Box Vision-Language Models

arXiv:2607.14125v1 Announce Type: new Abstract: Pre-trained vision-language models (VLMs) enable zero-shot image classification by computing the similarity score between an image and textual descriptions, typically formed by inserting a cla

Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence

arXiv:2607.14127v1 Announce Type: new Abstract: Representative clutter height (RCH) is a key parameter in radio propagation and interference analysis because it captures the dominant height of local obstructions that drive terminal clutter

Certified Domain Consistency for Multi-Domain Retrieval: Label-Free Per-Domain Contamination Control with Conformal Risk Guarantees

arXiv:2607.14157v1 Announce Type: new Abstract: Retrieval over corpora that mix several domains often returns relevant but wrong-domain evidence that ranking metrics miss and that conformal risk control bounds only marginally, under-coverin

QFireNet: A Quantum-Enhanced U-Net for Wildfire Segmentation from Sentinel-2 Imagery

arXiv:2607.14160v1 Announce Type: new Abstract: Wildfire detection from satellite imagery is a semantic image segmentation problem that has proven to be difficult due to challenges such as class imbalance, feature complexity, and atmospheri

Branching Policy Optimization: Sandbox-Native Language Agent Reinforcement Learning

arXiv:2607.14171v1 Announce Type: new Abstract: Reinforcement learning has emerged as the dominant paradigm for training large language model (LLM) agents that interact with executable sandboxes. State-of-the-art algorithms such as PPO, RLO

How Much of a 10-K Matters? Aggregation-Dependent Value of Full-Text versus Risk-Factor Sentiment

arXiv:2607.14174v1 Announce Type: new Abstract: Financial sentiment extraction has largely relied on news text and supervised extraction against return labels alone, leaving 10-K filings -- and volatility, the target risk disclosure is argu

Low-Latency Relay Selection in NR-V2X Vehicular Communications via Graph Isomorphism Networks with Edge Features

arXiv:2607.14176v1 Announce Type: new Abstract: Reliable, low-latency uplink connectivity is a key requirement for C-V2X networks in dense urban environments, where fast channel variations and blockages often degrade direct vehicle-to-infra

RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences

arXiv:2607.14180v1 Announce Type: new Abstract: World models are widely used in offline reinforcement learning (RL) to improve sample efficiency and generate experience beyond a fixed dataset. However, they are vulnerable to model exploitat

Closed-Loop Knowledge Dynamics: An Operational Framework for Saturation and Escape

arXiv:2607.14185v1 Announce Type: new Abstract: Feedback-driven loops support iterative improvement in large language models, reinforcement learning, and autonomous discovery, yet their gains often diminish under repeated internal feedback.

A Temporal Machine Learning-Based Time-to-Event Model for Predicting ALS Progression and Healthcare Utilization

arXiv:2607.14190v1 Announce Type: new Abstract: Amyotrophic lateral sclerosis (ALS) is a progressive and heterogeneous neurodegenerative disease in which predicting clinically meaningful milestones, such as assistive device use, remains cha

TEDDY: A Pediatric Foundation Model for Risk Forewarning from ICD-Coded Diagnostic Histories

arXiv:2607.14191v1 Announce Type: new Abstract: Pediatric electronic health records capture developmentally structured clinical trajectories, yet their potential for generative healthcare foundation models remains largely unexplored. Here w

Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning

arXiv:2607.14192v1 Announce Type: new Abstract: As recommender systems mature in the past few years, their optimization objectives have evolved from a primary focusing on short-term behavioral signals to a broader emphasis on long-term user

Augmentations for Robust and Efficient Imitation Learning in Streamed Video Games

arXiv:2607.14200v1 Announce Type: new Abstract: Imitation learning is an appealing way to scale game-playing agents to complex 3D environments by training policies to map visual observations to actions from human demonstrations. However, th

Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks

arXiv:2607.14205v1 Announce Type: new Abstract: Federated learning (FL) enables multi-institutional training on clinical text without sharing raw data, but gradient inversion can reconstruct sensitive information from shared model updates.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks

arXiv:2607.14233v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have had a broad research impact in modeling domains governed by partial differential equations (PDE). However, PINNs have been shown to perform poorly

MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion

arXiv:2607.14249v1 Announce Type: new Abstract: Mobile usage traces are critical for tasks such as user behavior prediction and app recommendation, yet their use is constrained by privacy restrictions and costly large-scale data collection.

Local Additive Feature Attribution: A Mathematical Taxonomy and Reporting Checklist

arXiv:2607.14271v1 Announce Type: new Abstract: Feature-attribution methods are central to explainable artificial intelligence. Their assumptions are expressed in several mathematical languages: cooperative-game values, path integrals, grad

Lyapunov Guidance: A Unified Framework for Stabilizing Generative Flows

arXiv:2607.14272v1 Announce Type: new Abstract: Flow matching has emerged as an effective framework for learning complex data distributions, but adapting pretrained flow models to new tasks often requires computationally expensive retrainin

NeuroGRIP: Retrieval-Augmented Graph Refinement for Knowledge-Grounded EEG Seizure Diagnosis

arXiv:2607.14314v1 Announce Type: new Abstract: Seizure diagnosis from EEG signals is a critical yet persistently challenging task, due to the complicated neural dynamics and the spurious connections in inter-channel modeling. While spatial

Towards a Unified Multidimensional Explainability Metric: Evaluating Trustworthiness in AI Models

arXiv:2607.14315v1 Announce Type: new Abstract: In this paper, we present a comprehensive framework for assessing the explainability of various XAI methods, such as LIME and SHAP, across multiple datasets and machine learning models, with t

Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data

arXiv:2607.14318v1 Announce Type: new Abstract: We introduce COAT (Counterfactual Optimal Action Tree), a framework for learning interpretable prescriptive policies from observational data. COAT combines counterfactual outcome estimation wi

Value Leakage: An LLM's Answers Are Silently Shaped by Its Own Values

arXiv:2607.14345v1 Announce Type: new Abstract: People use language models for practical questions whose answers are difficult to verify. We show that models exhibit covert value leakage: the information they provide is influenced by their

Learning Who to Treat When Treatment is Missing

arXiv:2607.14346v1 Announce Type: new Abstract: Policy learning methods are increasingly used to inform treatment allocation under budget constraints. Most proposed methods assume complete treatment data, yet applications frequently suffer

Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning

arXiv:2607.14367v1 Announce Type: new Abstract: Federated fine-tuning of large pre-trained models increasingly relies on Low-Rank Adaptation (LoRA) to reduce communication and computation, but heterogeneous clients can make adapter aggregat

Just Keep Prompting: Evaluating Repetitive Socratic Prompting in VLMs

arXiv:2607.14099v1 Announce Type: new Abstract: Deploying Vision-Language Models (VLMs) in real-world settings requires not only strong visual reasoning but also stability under sustained conversational pressure. We introduce Just Keep Prom

Quantum Compositional NLP for Arabic: Grammar, Morphology, and Word Sense in Circuit Topology

arXiv:2607.14100v1 Announce Type: new Abstract: We present the first application of pregroup grammar-based quantum compositional natural language processing (QNLP) to Arabic; a morphologically rich, free-word-order language whose structural

LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets

arXiv:2607.14101v1 Announce Type: new Abstract: Generating high-quality adversarial texts with low query budgets remains a challenging problem in the hard-label scenario. Most existing approaches rely on greedy algorithms, where one positio

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure

arXiv:2607.14102v1 Announce Type: new Abstract: With the rapid growth of digital data, real-world applications increasingly involve hierarchical information that combines static attributes with dynamic records. Modeling such heterogeneous d

Latent Communication Between Language Model Agents: Channels, Alignment, and the Limits of Text

arXiv:2607.14103v1 Announce Type: new Abstract: Multi-agent systems (MAS) are utilized in many contexts and many professions. Those MAS rely on inter-agent communication, usually implemented by clear-text message passing. We hypothesize tha