All Articles
16640 articles total
PEARL: Auditable Repair for Scientific Reasoning Graph Extraction
PEARL is a training-free framework that converts noisy LLM-generated scientific reasoning graphs into auditable, semantically valid graphs.
The Autonomous Agency Scale: A Behavioral Framework for Measuring Self-Directed Behavior in AI Systems
The Autonomous Agency Scale (AAS) is introduced as a behavioral framework to measure self-directed behavior in AI systems across seven dimensions of agency.
Towards Agentic Agent-based Models: Feasibility, Performance, and Statistical Model Checking
This study explores the feasibility and impact of integrating LLM-driven decisions into Agent-based Models (ABMs) using the Mesa Python library.
OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs
OntoExtend is a RAG-based framework designed to scale ontology extension by using LLMs to propose grounded extensions based on requirements.
SAGE: Subgoal-Conditioned Action Generation for Latent World Model Planning
SAGE introduces a prior-conditioned planner that uses latent subgoals to improve long-horizon planning in world models.
Do Maps Still Matter for Machines: Revisiting the Role of Choropleth Maps in Foundation Model Spatial Understanding
The ChoroplethMap-Bench benchmark demonstrates that combining maps with symbolic data significantly improves foundation models' spatial reasoning.
PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning
PAMD introduces a Pairwise Adaptive Mahalanobis Distance to improve latent state similarity measurements in visual reinforcement learning.
Rethinking Heterogeneous LLM Merging: A Weighted Model Averaging Perspective
This research explores a simple, training-free approach to merging heterogeneous LLMs using dimensional adaptation and weighted averaging.
AdaHome: An Adaptive Smart Home Assistant using Local Small Language Models
AdaHome is an adaptive smart home assistant that uses local small language models and an intent-aware planning framework to reduce latency and enhance privacy.
Postmortem of a British Startup: Tract
A postmortem analysis of the British startup Tract, discussing the reasons behind its failure.
Gritt exits stealth with $34 million for robots to build solar plants—then, everything else
Robotics startup Gritt emerges from stealth with $34 million in funding to automate construction site tasks, specifically for solar plants.
Who’s afraid of the big, bad GPU?
An exploration of the environmental and social costs of the AI GPU boom, including energy consumption, water usage, and e-waste.
WuYu-EnvLE-Bench: A Benchmark for Evaluating Large Language Models in Environmental Law Enforcement
Introduction of WuYu-EnvLE-Bench, a benchmark for evaluating LLMs in environmental law enforcement cases.
Dynamic Defense Profiling Enables Cognitive Jailbreak of Text-to-Image Models
MIND, a cognitive jailbreak framework for text-to-image models that models latent defense mechanisms to generate adversarial prompts.
Financial Audit Assistance using Misinformation Detection and Explanation
A system using unsupervised techniques for misinformation detection and explanation in financial audit assistance.
PGN: Design and Implementation of a Vision-Language Navigation System Based on Pangu Multimodal Foundation Model
PGN (Pangu Navigator), an offline vision-language navigation system based on the OpenPangu-7B multimodal foundation model.
A Hardware-oriented Approach for Efficient Bayesian Inference Computation and Deployment
A hardware-oriented approach to accelerate discrete Bayesian inference on embedded GPUs via memory layout restructuring and tensor clustering.
Exploratory and Assimilating Reflection: Reflective Recall Cycle for Long-term Memory
The EAR framework introduces Exploratory and Assimilating Reflection to improve long-term memory retrieval for LLM-based autonomous agents.
ST-Veto: Spatio-Temporal Token Veto for Diffusion MLLMs via Taylor Prediction and Visual Grounding
ST-Veto, a training-free method to improve diffusion multimodal LLM reasoning by vetoing temporally unstable and weakly grounded tokens.
Mechanistic Attention Guidance for Agent Memory Refinement
Researchers propose AGMR, a framework that uses retrieval-head attention signals to guide targeted memory updates for AI agents, improving efficiency and performance over text-only methods.