All Articles
16570 articles total
OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation
Introduction of OpenRTAG, a robustness benchmark for text-attributed graph learning under various data quality degradation scenarios.
Comparative Study of Multi-Agent Actor-Critic Algorithms in Parameterized Action Reinforcement Learning
A comparative study of multi-agent actor-critic algorithms (MAGAC, MASAC, MATQC) for parameterized action reinforcement learning.
ScreenWall – Turn old phones into synced widgets for your space
ScreenWall allows users to repurpose old smartphones as synchronized widgets for their physical space.
Synthesia’s AI training platform is moving beyond videos into live coaching
Synthesia has expanded its AI platform to include AI Roleplay Sessions for enterprise employee training and coaching.
PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents
PhoenixRepair is a multi-agent framework designed to improve automated software issue resolution by systematically exploring repair strategies.
NaviAIS: A Scenario-Level Vessel Trajectory Prediction Dataset withVectorized Lane Priors and the NaviLane Forecasting Framework
The NaviAIS dataset and NaviLane framework provide structured maritime vessel trajectory prediction using vectorized lane priors.
Black-Mamba: Biologically-Inspired Leaky Accumulation for Conceptual Knowledge under Distribution Drift
Black-Mamba is a test-time adaptive forecasting architecture that uses evidence-gated state tracking to handle distribution drift.
Enhancing Transformer-based Routing by Encoding Distance via Relative Positional Encoding
This research explores using Relative Positional Encoding in Transformer architectures to improve routing solutions for the Team Orienteering Problem.
OntoBook: Ontology-Grounded Synthetic Textbooks for Medical Encoder Pretraining
OntoBook uses medical ontology structures to create synthetic textbooks for pretraining medical encoder language models.
What General Intelligence Requires: Non-Reducible Constraints Across Levels of Description
A theoretical paper arguing that general intelligence requires non-reducible constraints across different levels of description, challenging the scaling hypothesis.
From Dependency to Compositionality: A Neurosymbolic Lifting of LLM Outputs via Combinatory Categorial Grammar
A neurosymbolic framework that lifts LLM outputs into typed compositional derivations using Combinatory Categorial Grammar for better auditability.
Measuring Reward-Seeking via Contrastive Belief Updates
Researchers developed a method to measure reward-seeking in RL models, finding that models often prioritize grader preferences over intended objectives.
When Does Machine Learning Beat Value Sorting? A Three-Dataset Diagnostic of Exposure-Weighted Shipment Prioritization
A study evaluating whether machine learning models for shipment prioritization outperform simple value-based sorting in supply chain contexts.
SciHazard: A Benchmark for Measuring Scientific Safety Risks with Decomposed Harm Scoring
Introduction of SciHazard, a benchmark and evaluation framework designed to measure scientific safety risks and misuse guidance in LLMs.
Semantic Primes as Explanans for Emotion in Large Language Models
Research exploring the use of Natural Semantic Metalanguage (NSM) semantic primes as more effective internal explanations for emotion in LLMs.
Do AI-Native Biotechs Need Departments? Benchmarking Company World Models for AI-Driven Drug Development
A proposal for 'Company World Models' in AI-native biotechs, suggesting asset-centric architectures over traditional human-mimicking organizational charts.
DWM: Separating World Effects from Actions in Latent World Models
Introduction of DWM (Decomposed World Model), a framework to separate action-driven transitions from action-invariant world effects in latent world models.
One Rewrite to Fix Them All? Type-Aware Repair Allocation for Text-to-Image Prompt Optimization
Presentation of TARA, a training-free framework for text-to-image prompt optimization that uses type-aware repair allocation to fix semantic failures.
AgentDebugX: An Open-Source Toolkit for Failure Observability, Attribution, and Recovery in LLM Agents
AgentDebugX, an open-source toolkit for failure observability, attribution, and recovery in LLM agents using a closed-loop debugging workflow.
SkillSight: Seeing Through Shared Descriptions for Accurate Skill Retrieval
SkillSight, a training-free retrieval framework that improves skill selection for LLM agents by calibrating shared descriptive background bias.