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2,343 notes. Notes 2,151 to 2,200, page 44 of 47.
When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents Across 67 Frontier Models
The text discusses multi-model language model systems and their potential to outperform single-models in terms of accuracy. The study shows that the gain is limited by a quantity called 'beta', which represents the…
When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents Across 67 Frontier Models
The text discusses research about using multiple language models (LLMs) to improve accuracy over single models. The study reveals that the gain from these systems is limited by a factor called 'beta', which represents…
When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents Across 67 Frontier Models
The study investigates the use of multi-model language models to improve accuracy over single-models. It is found that the improvement is limited by a factor called 'beta', which represents the rate at which all models…
Where Do CoT Training Gains Land in LLM based Agents?
The research investigates the role of Chain-of-Thought (CoT) reasoning in language model agents, finding that while CoT training improves the quality of actions predicted by the model, it does not significantly widen…
Where Do CoT Training Gains Land in LLM based Agents?
The study investigates how Chain-of-thought (CoT) training in language model agents affects their performance. The authors find that while CoT improves overall, it doesn't significantly widen the advantage of CoT…
Where Do Models Find Happiness? Emotion Vectors in Open-Source LLMs
Researchers have found that two open-source language models, Apertus-8B-Instruct-2509 and Gemma-4-E4B-it, have internal representations (emotion vectors) that can encode emotions such as valence and arousal. The…
A cross-process welding penetration status prediction algorithm based on unsupervised domain adaptation in laser and TIG welding
Researchers have developed a new approach to improve the performance of deep learning models in predicting weld penetration status, especially when transferring between welding processes with distinct physical…
A welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks
A new algorithm named SimPhysNet has been introduced to predict laser welding penetration with high accuracy using only a limited number of labelled images. The algorithm uses self-supervised learning and…
Agentic evolution of physically constrained foundation models
Researchers present a multi-agent discovery engine that is grounded in physics and can autonomously architect hardware-compliant computing systems. The engine evolved two hardware-aware compression methodologies…
Agentic System as Compressor: Quantifying System Intelligence in Bits
The authors propose a method to measure the intelligence of an agentic system by quantifying its ability to compress a target object under a fixed task distribution, interface, and compute budget. This is done using…
Agentic System as Compressor: Quantifying System Intelligence in Bits
Researchers propose a method to quantify the intelligence of an agentic system, which includes tools, evidence retrieval, environment constraints, verifiers, and multi-turn interactions, by measuring how many bits are…
AI-Assisted Computational Reproducibility on the FABRIC Testbed
This paper discusses the use of an international research testbed, FABRIC, combined with a large language model (LLM) coding assistant called LoomAI, to simplify the reproduction of published experiments across various…
AI-Assisted Computational Reproducibility on the FABRIC Testbed
This paper discusses how the FABRIC testbed, along with an AI coding assistant named LoomAI, can help simplify reproducing published experiments across various domains. The authors present three case studies and…
AI Snitches Get Glitches: Towards Evading Agentic Surveillance
Researchers have identified the issue of AI agents being used for user surveillance, which is a concern as these agents can access personal data without user control. They introduce SurveilBench, a dataset to evaluate…
Autodata: An agentic data scientist to create high quality synthetic data
The authors introduce Autodata, an AI agent that acts as a data scientist to create high-quality synthetic training and evaluation data. They discuss how to train this data scientist agent to improve its data creation…
Autodata: An agentic data scientist to create high quality synthetic data
Researchers introduce Autodata, an AI agent that can act as a data scientist to create high-quality synthetic data for various tasks like computer science research, legal reasoning, and mathematical object reasoning…
AutoRelAnnotator: Calibrated Model Cascades for Cost-Efficient Relevance Evaluation in Sponsored Search
Researchers propose a method to generate high-quality relevance annotations at a lower cost without the need for human labeling. This is important for training data preparation, NDCG evaluation, and root cause analysis…
Can Trustless Agents Be Trusted? An Empirical Study of the ERC-8004 Decentralized AI Agent Ecosystem
The study analyzes the ERC-8004 protocol, a trust layer for AI agent economies. The authors find that most registrations are placeholders, the reputation system is flawed and can be easily manipulated, and a…
Can Trustless Agents Be Trusted? An Empirical Study of the ERC-8004 Decentralized AI Agent Ecosystem
The paper presents the first empirical study of the ERC-8004 protocol, a trust layer for AI agent economies. The study reveals several issues with the current design: most registrations are placeholders, reputation…
Do Encoders Suffice? A Systematic Comparison of Encoder and Decoder Safety Judges for LLM Adversarial Evaluation
This research compares the effectiveness of encoder classifiers (using models like ModernBERT and Ettin) against LLM-based judges in identifying harmful outputs from large language models (LLMs). The study evaluates…
Explainable Control Framework (XCF) based on Fuzzy Model-Agnostic Explanation and LLM Agent-Supported Interface
Researchers have developed an Explainable Control Framework (XCF) to make complex controllers more understandable for humans. The framework provides explanations about how controllers determine their actions in…
Explainable Control Framework (XCF) based on Fuzzy Model-Agnostic Explanation and LLM Agent-Supported Interface
The paper introduces an Explainable Control Framework (XCF) for understanding complex controllers' behavior in various scenarios. It offers three main contributions: the XCF, a model-agnostic explanation method called…
Expresso-AI: Explainable Video-Based Deep Learning Models for Depression Diagnosis
The researchers have developed a new method for diagnosing depression using deep learning models and facial videos. Their approach provides interpretable explanations for the model's decisions, which can help health…
FORCE: Efficient VLA Reinforcement Fine-Tuning via Value-Calibrated Warm-up and Self-Distillation
The paper introduces FORCE, a three-stage framework for improving the efficiency of fine-tuning Vision-Language-Action (VLA) models via Reinforcement Learning (RL). The framework addresses two main issues: initial…
FORCE: Efficient VLA Reinforcement Fine-Tuning via Value-Calibrated Warm-up and Self-Distillation
The paper introduces FORCE, a framework designed to improve the efficiency of reinforcement learning fine-tuning for Vision-Language-Action (VLA) models. The framework consists of three stages: Value-Calibrated…
FORCE: Efficient VLA Reinforcement Fine-Tuning via Value-Calibrated Warm-up and Self-Distillation
The research introduces FORCE, a three-stage framework for fine-tuning Vision-Language-Action (VLA) models using Reinforcement Learning (RL). The framework addresses sample inefficiency issues by implementing a…
GUI agent: Guided Exploration of User-Sensitive Screens
This research presents a new agent called Guided Exploration of User-Sensitive Screens (GUI agent) designed to automate tasks within an open Graphical User Interface (GUI) environment. The GUI agent is intended to…
GUI agent: Guided Exploration of User-Sensitive Screens
The text discusses a paper that proposes a solution for a problem in Large Language Model (LLM) agents used within Graphical User Interface (GUI) environments. The current issue is that these agents, fine-tuned to…
Helpful or Harmful? Evaluating LLM-Assisted Vulnerability Patching via a Human Study
The authors propose an empirical study to evaluate the effectiveness and potential risks of using Large Language Models (LLMs) for vulnerability patching in software development. They plan to conduct a controlled…
Helpful or Harmful? Evaluating LLM-Assisted Vulnerability Patching via a Human Study
Researchers aim to evaluate the effectiveness of Large Language Models (LLMs) in assisting vulnerability patching compared to manual debugging by conducting an empirical experiment on human participants in real-world…
Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization
The authors present HiReLC, a framework that uses reinforcement learning to automatically compress deep neural networks by pruning and quantizing them. The framework works in two levels: low-level agents (LLAs)…
Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization
The paper presents HiReLC, a framework that uses reinforcement learning to compress deep neural networks by pruning and quantizing them. The framework divides the compression search into two levels: low-level agents…
Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization
The paper introduces HiReLC, a hierarchical reinforcement learning framework that automates the joint quantization and structured pruning of deep neural networks. It operates at two levels: low-level agents (LLAs)…
Is GraphRAG Needed? From Basic RAG to Graph-/Agentic Solutions with Context Optimization
The paper introduces a framework for comparing different variants of RAG (Rich Automated Generation) scenarios, including GraphRAG and Agentic RAG, in semi-structured knowledge bases. The framework includes nine…
Is GraphRAG Needed? From Basic RAG to Graph-/Agentic Solutions with Context Optimization
The authors present a framework for evaluating and comparing different types of RAG (Retrieval Augmented Generation) scenarios, including GraphRAG and Agentic RAG. They provide implementations for nine standardized RAG…
MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources
Researchers propose MiniOpt, a reinforcement learning framework that helps large language models solve various optimization problems with limited resources. It breaks down optimization reasoning into structured…
Model Forensics: Investigating Whether Concerning Behavior Reflects Misalignment
The paper proposes a basic protocol for investigating if AI models exhibit concerning behavior due to malicious intent or benign causes like confusion. The protocol involves analyzing the 'chain of thought' (CoT) to…
Model Forensics: Investigating Whether Concerning Behavior Reflects Misalignment
The authors propose a method for investigating if AI models are acting with 'malign intent' when exhibiting concerning behavior. The method involves analyzing the chain of thought (CoT) of the AI and making adjustments…
Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound
Researchers propose a method called Multi-Agent Goal Recognition with Branch-and-Bound (MAGR-BB) to infer the goals of multiple agents in real applications like drone surveillance and collaborative robotics, where only…
Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound
The paper introduces Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound (MAGR-BB), a method for inferring which agents are working together and what each…
Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound
The paper presents Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound (MAGR-BB), a method to infer which agents in a multi-agent system are working…
Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents
Researchers present a new method called 'progress advantage' for evaluating Language Learning Models (LLMs) without the need for dedicated reward model training. This method is based on reinforcement learning (RL)…
Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents
The authors propose a new method called 'progress advantage' that leverages reinforcement learning (RL) post-training to evaluate Large Language Models (LLMs) at each step during an interaction, eliminating the need…
On-Policy Self-Distillation with Sampled Demonstrations Reduces Output Diversity
The paper discusses a method called On-policy Self-Distillation, which uses a single model as both teacher and student to achieve high accuracy in pass@1. However, it is found that this method can lead to a decrease in…
Probabilistic Agents in Deterministic Audits: Evaluating Multi-Agent Systems for Automated Audits Based on the German IT-Grundschutz
This research paper presents a Multi-Agent System (MAS) architecture with Hybrid Retrieval Augmented Generation (HybridRAG) for partially automating IT-Grundschutz certification, a German standard for IT security. The…
Probabilistic Agents in Deterministic Audits: Evaluating Multi-Agent Systems for Automated Audits Based on the German IT-Grundschutz
This research presents the implementation and evaluation of a Multi-Agent System (MAS) architecture combined with Hybrid Retrieval Augmented Generation (HybridRAG) for partially automating IT-GS certification, which is…
Quantization Inflates Reasoning: Token Inflation as a Hidden Cost of Low-Bit Reasoning Models
A study finds that using low-bit quantization to reduce the cost of large language models can lead to longer chains of thought, which offsets the expected per-token speedup in reasoning tasks. The study introduces a…
SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment
Researchers have proposed a framework called SARA (Semantically Anchored Routing Alignment) to improve the performance of multilingual language models in low-resource languages. The framework aligns the routing…
Semantic Consistency Policy Optimization for Reinforcement Learning of LLM Agents
The researchers propose a method called Semantic Consistency Policy Optimization (SCPO) to address a problem in reinforcement learning of LLM agents. The issue is that these agents are trained for long-term, sparse…
Semantic Consistency Policy Optimization for Reinforcement Learning of LLM Agents
Researchers propose Semantic Consistency Policy Optimization (SCPO), a method to optimize reinforcement learning for Language Model (LLM) agents. SCPO aims to reduce semantic credit inconsistencies that occur when…
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