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2,343 notes. Notes 901 to 950, page 19 of 47.
Beyond Sycophancy: Structured Resistance and Compliance in LLM Moral Reasoning
Researchers Wang and Koch propose a framework to improve large language models' ability to make independent moral judgments instead of just following others. They identify three dimensions that structure how models…
Bridging the Gap Between Plausibility and Admissibility: Constraint-Aware Flow Maps for Dynamic Graph Systems
A new model is proposed to improve the accuracy of generative trajectory modeling in dynamic graph-structured systems by applying hard filtering, soft weighting, or projection-based repair using post-sampling symbolic…
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
A new approach called Master-Agent Proto-plan System (MAPS) is proposed to improve the coordination of autonomous vehicles at unsignalized intersections. MAPS uses a hierarchical deep reinforcement learning…
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
The paper introduces a new deep reinforcement learning architecture called Master-Agent Proto-plan System (MAPS) for coordinating autonomous vehicles at unsignalized intersections. The MAPS system uses a centralized…
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
Researchers proposed a new system called Master-Agent Proto-plan System (MAPS) for coordinating autonomous vehicles at unsignalized intersections using deep reinforcement learning. MAPS outperformed state-of-the-art…
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
Researchers present MAPS, a deep reinforcement learning architecture for coordinating autonomous vehicles at unsignalized intersections. The system uses a central Master agent that generates a global coordination…
Detecting LLM-Generated Tokens in Human--LLM Coauthored Text
The paper presents a new method to detect which parts of a text are generated by language models (LLMs) in collaborative human-AI writing, operating at the token level and improving upon existing token-level detection…
Detecting LLM-Generated Tokens in Human--LLM Coauthored Text
Researchers present a new method for detecting parts of text generated by language models (LLMs) in human-AI coauthored documents. The approach works at the token level, using existing scores and a smoothing technique…
Detecting LLM-Generated Tokens in Human--LLM Coauthored Text
A new method for detecting parts of text generated by a language model (LLM) within human-written text is introduced. This method operates at the token level, reducing variability in adjacent tokens while using an…
Detecting LLM-Generated Tokens in Human--LLM Coauthored Text
The paper presents a new method for identifying parts of a text generated by Language Model (LLM) at the token level, improving on existing document-level methods. The method is based on smoothing adjacent token scores…
Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
The paper introduces Euclid-MCP, an open-source MCP server designed to provide deterministic logical reasoning using SWI-Prolog. It presents a new engine-agnostic intermediate representation for Horn-clause logic…
Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
The paper introduces Euclid-MCP, an open-source MCP server that provides deterministic logical reasoning via SWI-Prolog. It aims to improve the reliability of Large Language Models (LLMs) in multi-step logical…
Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
The paper introduces Euclid-MCP, an open-source MCP server that provides deterministic logical reasoning via SWI-Prolog. It addresses the issue of unreliable multi-step logical reasoning in Large Language Models (LLMs)…
Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
The paper introduces Euclid-MCP, an open-source MCP server that provides deterministic logical reasoning via SWI-Prolog. It aims to bridge the gap between Large Language Models (LLMs) and multi-step logical reasoning…
Expert Behavior Prior Reinforcement Learning
A new method called Expert Behavior Prior Reinforcement Learning (EBP) is proposed to improve the sample efficiency in online reinforcement learning. This method generates expert policy priors directly from the online…
Expert Behavior Prior Reinforcement Learning
The paper proposes a new method called Expert Behavior Prior (EBP) for behavior prior reinforcement learning (BPRL). EBP aims to improve the effectiveness of policy priors by generating expert policy priors directly…
Explainable Belief Harmonization under Dynamic Epistemic Partitions
The paper presents a new framework for handling dynamic changes in epistemic partitions (groups of agents with shared knowledge) during execution, where agents might gain or lose observational capacity. The framework…
Explainable Belief Harmonization under Dynamic Epistemic Partitions
This paper presents a formal framework for handling dynamic changes in epistemic partitions (groups of agents with shared information) during the execution of multi-agent systems. The framework uses a hybrid approach…
From Resource Flow to Executable Tests: Petri-Net-Guided LLM Test Generation for Concurrent Stateful Rust APIs
The paper proposes a methodology using Petri nets to generate tests for concurrent stateful Rust APIs, aiming to address issues with large language models (LLMs) in generating executable tests that may violate API…
From Resource Flow to Executable Tests: Petri-Net-Guided LLM Test Generation for Concurrent Stateful Rust APIs
The authors present a methodology using Petri nets to generate tests for concurrent stateful Rust APIs, aiming to bridge the gap between formal scenario design and low-cost test concretization.
From Resource Flow to Executable Tests: Petri-Net-Guided LLM Test Generation for Concurrent Stateful Rust APIs
The authors propose a methodology for generating executable tests for concurrent stateful Rust APIs using Petri nets. This approach aims to bridge the gap between formal scenario design and low-cost test…
From Resource Flow to Executable Tests: Petri-Net-Guided LLM Test Generation for Concurrent Stateful Rust APIs
The paper presents a methodology using Petri nets for test generation in concurrent stateful Rust APIs, aiming to bridge the gap between formal scenario design and low-cost test concretization. It allows large language…
From Static Bibliometrics to Dynamic Knowledge Graphs: An LLM-Powered Framework for Modernizing Science, Technology, and Innovation (STI) Analytics
The paper proposes a new framework that combines traditional bibliometric indicators, dynamic knowledge graphs, and large language models to modernize science, technology, and innovation (STI) analytics. The goal is to…
From Static Bibliometrics to Dynamic Knowledge Graphs: An LLM-Powered Framework for Modernizing Science, Technology, and Innovation (STI) Analytics
The text discusses a new framework for modernizing Science, Technology, and Innovation (STI) analytics. The proposed framework aims to improve upon traditional bibliometric indicators by using dynamic knowledge graphs…
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
The authors present GRADRAG, a framework for improving Retrieval-Augmented Generation (RAG) systems by coordinating updates across multiple agents in the pipeline. The system uses feedback from an Evaluator to update…
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
The paper introduces GRADRAG, a framework for improving Retrieval-Augmented Generation (RAG) systems by coordinating improvements across different components of the pipeline. It uses feedback from an Evaluator to…
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
The paper introduces GRADRAG, a framework that improves Retrieval-Augmented Generation (RAG) systems by coordinating improvements across the pipeline. Instead of optimizing components in isolation, GRADRAG models the…
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
The authors introduce GRADRAG, a framework for improving Retrieval-Augmented Generation (RAG) systems by coordinating improvements across multiple agents in the pipeline, rather than optimizing components in isolation…
GraphVid: Interactive Graph-Controllable Video Generation
The paper introduces GraphVid, a graph-conditioned image-to-video generation model that allows for interactive control through structured interaction graphs. The authors claim that GraphVid delivers strong…
GraphVid: Interactive Graph-Controllable Video Generation
The researchers introduce GraphVid, a model that generates interactive videos using structured interaction graphs, allowing for flexible and precise control. The model shows strong performance compared to Motion-I2V in…
GS-Agent: Creating 4D Physical Worlds With Generative Simulation
Researchers have developed GS-Agent, a system that uses natural language descriptions to automatically generate realistic, dynamic, and controllable 4D physical worlds. This is achieved through an end-to-end…
GS-Agent: Creating 4D Physical Worlds With Generative Simulation
Researchers have developed GS-Agent, a system that generates realistic, dynamic, and controllable 4D physical worlds from natural language descriptions using an end-to-end multi-agent framework. The system emulates…
ICAE-Bench: Evaluating Coding Agents as Interactive Project Builders
The paper introduces ICAE-Bench, a benchmark for evaluating coding agents in interactive project building settings. Instead of being limited to completing code under fully specified instructions, these agents are…
ICAE-Bench: Evaluating Coding Agents as Interactive Project Builders
The paper introduces a new benchmark called ICAE-Bench for evaluating coding agents in interactive project building settings. This benchmark aims to test the ability of agents to transform incomplete product intent…
Improved lower bounds for the Shannon capacity of odd cycles
Researchers have improved lower bounds for the Shannon capacity of odd cycles, which quantifies the maximum rate at which information can be transmitted with zero error over a noisy channel. They achieved this by…
M$^3$-Gen: Interpretable Multimodal Generation of Gene Expression Profiles Using Clinical and Imaging Data
M$^3$-Gen is a new framework that generates synthetic gene expression profiles using both clinical metadata and histopathology images. The method leverages a Generative Adversarial Network to create biologically…
MIRROR: Learning from the Other View for Multi-Modal Reasoning
The paper presents MIRROR, a method to improve multi-modal reasoning in AI models by learning from different views (text, image, combined text and image) of the same problems. The study reveals that vision-language…
MIRROR: Learning from the Other View for Multi-Modal Reasoning
Researchers have created a new approach called Modality-Informed Reciprocal Reasoning Optimization (MIRROR) to improve the reasoning abilities of both large language models and vision-language models. The approach uses…
MSBraM: A Multi-scale Self-supervised Brain Foundation Model for Hierarchical EEG Dynamics Learning
The authors propose a new model named MSBraM to analyze electroencephalogram (EEG) signals more effectively. Existing methods struggle with the multi-scale temporal structure of EEG data, which includes both local…
Multimodal Pretraining for Generalizable EEG Representation Learning
Researchers have developed a multimodal EEG foundation model that can be used for various EEG-related tasks without being limited to specific datasets or tasks. The model uses a raw signal encoder, a time-frequency…
OpenForgeRL: Train Harness-native Agents in Any Environment
OpenForgeRL is an open-source framework designed to train AI agents end-to-end in various environments using complex harnesses like Claude Code, Codex, and OpenClaw. The framework enables large-scale training by…
OpenForgeRL: Train Harness-native Agents in Any Environment
OpenForgeRL is an open-source framework designed to train AI agents using complex inference harnesses like Claude Code, Codex, and OpenClaw, in various environments. The framework allows end-to-end training of these…
OpenForgeRL: Train Harness-native Agents in Any Environment
OpenForgeRL is an open-source framework for training AI agents end-to-end in various environments using complex harnesses like Claude Code, Codex, and OpenClaw. The framework enables scaling by using a lightweight…
OpenForgeRL: Train Harness-native Agents in Any Environment
OpenForgeRL is an open-source framework for training AI agents using various complex inference harnesses in diverse environments. It allows for end-to-end training with open infrastructure and has been validated across…
OpenForgeRL: Train Harness-native Agents in Any Environment
OpenForgeRL is an open-source framework for training AI agents using various inference harnesses like Claude Code, Codex, OpenClaw, etc., in diverse environments. The framework allows end-to-end training with standard…
OpenForgeRL: Train Harness-native Agents in Any Environment
The paper introduces OpenForgeRL, an open-source framework for training AI agents using harnesses like Claude Code, Codex, and OpenClaw in various environments. It allows end-to-end training of these complex harnesses…
pAI-Econ-claude: A Gated Human-in-the-Loop Multi-Agent Architecture for AI-Assisted Economic Theory Development
The paper introduces pAI-Econ-claude, a multi-agent architecture that helps in AI-assisted economic theory development. The system uses a gated, human-in-the-loop approach to coordinate agents and ensure the final…
pAI-Econ-claude: A Gated Human-in-the-Loop Multi-Agent Architecture for AI-Assisted Economic Theory Development
A new multi-agent architecture, named pAI-Econ-claude, was proposed for AI-assisted economic theory development. This architecture uses a gated human-in-the-loop system to ensure reliability in tasks where no machine…
PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning
The authors propose a new training paradigm called PATS for reinforcement learning agents that learn long-term tasks. PATS converts rollout groups from the latest policy into evidence cards and adjusts the context used…
PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning
The paper 'PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning' proposes a new training paradigm called Pats, which aims to improve the performance of long-horizon reinforcement learning agents…
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