Position: Every Ground Truth is a Human Construction, not an Objective Truth
arXiv:2607.09668v1 Announce Type: new
Abstract: Ground truth datasets play a fundamental role as reference values in the training and evaluation of machine learning models. This position paper argues that ground truths are not neutral objective measurements that are naturally given, but instead tha...
Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey
arXiv:2607.09666v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) have emerged as a powerful paradigm in Knowledge Graphs (KGs) due to their intrinsic ability to model graph-structured data. However, there remains a lack of a systematic review about GNN-based methodologies across the ent...
Alan Turing's biggest AI assumption may have been wrong
A new book claims AI has been built on a flawed assumption dating back to Alan Turing's famous 1950 paper. Peter J. Denning argues that the most important parts of human intelligence, including common sense, intuition, culture, and practical know-how, cannot be encoded into computers. He believes th...
The AI Sovereignty Paradox: Should Countries Buy, Build, or Lease to Maintain Strategic Control of Their AI?
As nations invest billions to reduce reliance on foreign AI providers, a new Stanford HAI report surveys commercial sovereignty solutions and assesses the extent to which they meaningfully reduce dependencies on U.S. tech giants.
Proactive Agent Research Environment: Simulating Active Users to Evaluate Proactive Assistants
Proactive agents that anticipate user needs and autonomously execute tasks hold great promise as digital assistants, yet the lack of realistic user simulation frameworks hinders their development. Existing approaches model apps as flat tool-calling APIs, failing to capture the stateful and sequentia...
Scientists discovered the brain doesn't make decisions the way we thought
A new study suggests the brain begins making decisions much earlier than scientists previously thought. Researchers found that even primary sensory regions are influenced by higher brain areas through rapid feedback loops, rather than simply passing information forward. This more dynamic view of bra...
A Formalization of the Mean-Field Derivation of the Vlasov Equation: AI-Assisted Lean Formalization as a Strategy Game
arXiv:2607.08986v1 Announce Type: new
Abstract: We formalize a research result in the Lean 4 proof assistant by having a mathematician direct an AI system, and frame the activity as a formalization game. The objective is to turn a LaTeX document into Lean. The game is won when the development compi...
Long-Horizon-Terminal-Bench: Testing the Limits of Agents on Long-Horizon Terminal Tasks with Dense Reward-Based Grading
arXiv:2607.08964v1 Announce Type: new
Abstract: AI agents have become capable of autonomously completing short, well-specified tasks. However, existing terminal benchmarks largely focus on simple problems that finish within minutes and are evaluated only by their final outcome. This setup overlooks...
GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning
arXiv:2607.08894v1 Announce Type: new
Abstract: Large Language Model (LLM) agents have shown promise in multi-step planning tasks, but existing approaches like LATS (Language Agent Tree Search) and ReAct rely heavily on LLM inference during planning, leading to high computational costs and stochast...
CogniConsole: Externalizing Inference-Time Control as a Formal Abstraction for Reliable LLM Interactions
arXiv:2607.08774v1 Announce Type: new
Abstract: Reliability in large language model (LLM) systems is typically framed as a function of model capability. We challenge this by demonstrating that reliability is significantly influenced by \emph{inference-time control} -- the computational layer govern...
Interval Certifications for Multilayered Perceptrons via Lattice Traversal
arXiv:2607.08773v1 Announce Type: new
Abstract: In this work we present a rigorous theoretical framework to a foundational problem of AI safety, namely adversarial robustness. In particular, we show that the adversarial robustness problem can be reduced to a lattice traversal problem. Each element ...
Sticky Routing: Training MoE Models for Memory-Efficient Inference
arXiv:2607.08780v1 Announce Type: new
Abstract: Mixture-of-Experts (MoE) models activate only a sparse subset of experts per token, yet consecutive tokens frequently activate different experts -- causing constant weight swapping between slow storage and fast memory on edge devices. Existing remedie...
A Unified Approach to Interpreting Knowledge Distillation for Large Language Models via Interactions
arXiv:2607.08776v1 Announce Type: new
Abstract: Despite the success of knowledge distillation (KD) in Large Language Models (LLMs), the underlying mechanism behind its efficacy remains unclear. In this paper, we propose a unified approach to explore the common mechanism of various KD methods using ...
Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning
arXiv:2607.07761v1 Announce Type: new
Abstract: Large language models (LLMs) have emerged as important tools in healthcare, showing growing potential for clinical reasoning and patient care. This survey examines recent progress in medical LLMs, focusing on reasoning applications and requirements. W...
arXiv:2607.07721v1 Announce Type: new
Abstract: Retrieval-Augmented Generation (RAG) and agentic frameworks have advanced enterprise AI considerably, yet agents remain fundamentally reactive: they wait for a human query before acting. This paper argues that genuine enterprise productivity gains req...
AI-integrated models for assessing agricultural resilience
arXiv:2607.07759v1 Announce Type: new
Abstract: Agricultural supply chains are vulnerable to disruptions through linked biophysical and economic systems. We develop an AI-powered tool that integrates economic models (GTAP) with biophysical models (APSIM) to analyze supply chain shocks, enabling pol...
Adversarial Social Epistemology for Assemblies of Humans and Large Language Models
arXiv:2607.07760v1 Announce Type: new
Abstract: We outline an adversarial social epistemology (ASE) for densely interactive communicative landscapes in which public assertions are scaffolded by chains of testimony, inference, institutional certification, and tacit trust. In such landscapes, agents ...
Alignment Plausibility: A New Standard for Assuring AI in Healthcare
arXiv:2607.07766v1 Announce Type: new
Abstract: Large language models (LLMs) have become significant providers of mental health support, yet they remain products of an attention economy whose operational and commercial targets favour sustained engagement over the friction that effective psychologic...
Towards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attribution
arXiv:2607.07716v1 Announce Type: new
Abstract: Temporal graphs are ubiquitous in real-world applications and Temporal Graph Networks (TGNs) have achieved superior predictive accuracy. Understanding which historical events drive model predictions can enhance trustworthiness of TGNs. Existing explan...
ReCoLoRA: Spectrum-Aware Recursive Consolidation for Continual LLM Fine-Tuning
arXiv:2607.07719v1 Announce Type: new
Abstract: Parameter-efficient fine-tuning adapts a large language model to one task cheaply, but across a task sequence LoRA-style methods keep stacking low-rank updates on the same frozen weight, so each new task tends to overwrite the previous ones. We presen...
Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS--ANS Dynamic
arXiv:2607.07720v1 Announce Type: new
Abstract: Sleep physiology arises from the coordinated dynamics of the central nervous system (CNS) and autonomic nervous system (ANS), as reflected by multimodal polysomnography signals including EEG, EOG, EMG, ECG, and respiration. However, existing sleep fou...