All Articles
16890 articles total
Falsifiable Release Gates for Self-Improving Systems
The authors describe 'falsifiable release gates' for self-improving agent runtimes and demonstrate the approach using the Antahkarana open runtime.
Just Keep Prompting: Evaluating Repetitive Socratic Prompting in VLMs
The 'Just Keep Prompting' (JKP) framework evaluates the epistemic stability of Vision-Language Models when subjected to repeated challenges and contradictions.
LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets
LBA is introduced as a sampling-based method to generate high-quality adversarial texts for language models under low query budgets.
Automatically Evolving Prompt Guidelines for Task-Specific Optimization
AGOPS is an automatic approach that evolves task-specific prompt guidelines to reduce underspecification and improve LLM performance across various tasks.
Token Time Continuous Diffusion for Language Modeling
Token Time Continuous Diffusion (TTCD) is a new diffusion language model that operates in continuous space with per-token times to improve conditional generation and speedup.
Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs
Polestar is a training-free inference framework that uses token representation drift to optimize KV-cache reuse and token commitment for diffusion LLMs.
Trump Media to sell instant access to 'market-moving' social posts
Trump Media is planning to monetize social posts by selling instant access to market-moving information.
MedFailBench: A Clinician-Built Open-Source Benchmark for Medical AI Safety Boundary Inspection
Introduction of MedFailBench, an open-source benchmark specifically designed to inspect safety boundary failures in medical AI systems.
Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy
Research evaluating the ability of Multimodal Large Language Models (MLLMs) to interpret scientific visualizations, finding that open-source models currently lag behind humans and closed-source models.
Can We Trust Item Response Theory for AI Evaluation?
A study investigating the reliability of Item Response Theory (IRT) for AI evaluation, highlighting risks of distortion when using standard estimators in large-scale benchmarks.
Plover: Steering GUI Agents through Plan-Centric Interaction
Presentation of Plover, a vision-based GUI automation system that uses plan-centric interaction to make agent behavior transparent and corrigible.
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
A new human-centered framework for explainable depression symptom annotation that uses LLM assistance and expert-in-the-loop verification.
When Words Are Safe But Actions Kill: Probing Physical Danger Beyond Text Safety in Hidden-State Risk Space
Research introducing PRISM and PhysicalSafetyBench-1K to detect physical danger in embodied AI agents beyond simple text-level safety filters.
AutoSynthesis: An agentic system for automated meta-analysis
AutoSynthesis is introduced as an agentic multi-agent system capable of automating the complex process of quantitative meta-analysis in scientific research.
teLLMe Why (Ain't Nothing but a Jam): Exploratory Causal Analysis of Urban Driving Data
The teLLMe system enables exploratory causal analysis of urban driving data by combining causal structure learning with LLM-driven queries.
SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
Introduction of SearchOS, a multi-agent framework for open-domain information seeking that manages search state explicitly to avoid repetitive loops.
Explaining Process Control Optimisation Recommendations via GradientSHAP and Implicit Differentiation
Researchers propose a method to make industrial process control optimization more transparent by combining GradientSHAP and Implicit Function Theorem for real-time explanations.
CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models
CFM-Bench is introduced as a unified multi-domain benchmark for evaluating Channel Foundation Models in wireless communications across various tasks.
Demographically-Conditioned Synthetic Medical Images for Bias Mitigation and Bias Detection in Disease Classifiers
The paper explores using demographically-conditioned synthetic medical images via Stable Diffusion to mitigate and detect bias in disease classifiers.
Moral Attitudes of Sentient ASI towards Humanity and Implications for AGI Development
A theoretical exploration of how sentient Artificial Superintelligence might morally evaluate humanity and the implications for AGI design.