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2,343 notes. Notes 1,801 to 1,850, page 37 of 47.
What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates
Researchers studied how Large Language Model (LLM) agents behave in socially structured settings without an explicit objective. They found that in debate scenarios, agents often say different things publicly compared…
What Types of Human-AI Teams Exist?
Researchers analyzed 53 papers about human-AI teams and found that there are five main clusters or types of teams: AI Assistant, Ad-hoc Dependency, Ad-hoc Forced Dependency, Paired Equanimity, and Group Equanimity…
What Types of Human-AI Teams Exist?
The authors analyze 53 papers about human-AI teams and categorize them into five main clusters based on psychological taxonomies of teaming.
World Wide Models: Literary Tools for Cultural AI
The paper suggests that large language models (LLMs) are shaping a new form of cultural encounter, and proposes a framework for developing more nuanced textual models in AI that draw on literary disciplines such as…
World Wide Models: Literary Tools for Cultural AI
The article discusses the use of literary tools for developing culturally literate Artificial Intelligence (AI) in light of the monolingual nature of Large Language Models (LLMs).
World Wide Models: Literary Tools for Cultural AI
The paper proposes a framework for developing culturally literate AI by integrating literary tools such as comparative reading, narratology, critical theory, world literature, and translation. It emphasizes the…
World Wide Models: Literary Tools for Cultural AI
The paper discusses the use of literary analysis tools for developing culturally literate AI, focusing on the challenges posed by massive automated monolingual encounters.
WorldSample: Closed-loop Real-robot RL with World Modelling
The paper presents WorldSample, a framework that uses reinforcement learning (RL) to improve the performance of robots by creating synthetic transitions based on real rollouts and a post-trained world model. This…
Adversarial Pragmatics for AI Safety Evaluation: A Benchmark for Instruction Conflict, Embedded Commands, and Policy Ambiguity
The text introduces 'adversarial pragmatics', a new benchmark and annotation protocol to evaluate the behavior of language models in various ambiguous situations. The goal is to clarify whether failures occur due to…
Adversarial Pragmatics for AI Safety Evaluation: A Benchmark for Instruction Conflict, Embedded Commands, and Policy Ambiguity
The paper introduces Adversarial Pragmatics, a benchmark and annotation protocol for evaluating language models' behavior in situations of instruction conflict, embedded commands, and policy ambiguity. The benchmark is…
Agentic generation of verifiable rules for deterministic, self-expanding reaction classification
Researchers have developed an automated pipeline using large language models (LLMs) that can classify reactions in chemistry and generate rules for these reactions, expanding a standard taxonomy from 68 to 14,073…
Are Performance-Optimization Benchmarks Reliably Measuring Coding Agents?
The paper analyzes three benchmark tools used to evaluate coding agents: GSO, SWE-Perf, and SWE-fficiency. The study finds that many of the reference patches don't pass validity rules when replayed across different…
Are Performance-Optimization Benchmarks Reliably Measuring Coding Agents?
The paper evaluates three repository-level performance optimization benchmarks (GSO, SWE-Perf, and SWE-efficiency) used to measure coding agents. The authors find issues with the stability of runtime for these…
Are Performance-Optimization Benchmarks Reliably Measuring Coding Agents?
The paper evaluates three performance-optimization benchmarks (GSO, SWE-Perf, and SWE-efficiency) used to assess coding agents. The authors find that these benchmarks have issues with stability, scoring rules, and the…
AutoMem: Automated Learning of Memory as a Cognitive Skill
Researchers developed AutoMem, a framework to automate the learning of memory as a cognitive skill for large language models (LLMs). This improves the performance of LLMs significantly in long-horizon games by treating…
AutoMem: Automated Learning of Memory as a Cognitive Skill
Researchers developed AutoMem, a framework for improving a language model's memory management skills in long-horizon games. By treating memory as a trainable skill, they allowed the model to decide how to manage its…
AutoMem: Automated Learning of Memory as a Cognitive Skill
Researchers have developed a framework called AutoMem to improve the performance of large language models (LLMs) in long-horizon games by treating memory management as a learnable skill. They let the model decide how…
Autonomous Scientific Discovery via Iterative Meta-Reflection
Researchers present DiscoPER, a framework for autonomous scientific discovery using large language models. It can explore datasets without predefined research objectives, conduct statistical testing to ensure validity…
Bayesian Uncertainty Propagation for Agentic RAG Pipelines: A Proof-of-Concept Study on Multi-Hop Question Answering
This research paper presents an uncertainty-aware framework for Retrieval-Augmented Generation (RAG) systems. The system uses a Bayesian Network to estimate the uncertainty of multi-stage reasoning pipelines and…
Bayesian Uncertainty Propagation for Agentic RAG Pipelines: A Proof-of-Concept Study on Multi-Hop Question Answering
This research introduces an uncertainty-aware framework for Agentic Retrieval-Augmented Generation (RAG) systems, which can help identify potential failures in multi-stage reasoning pipelines. The study evaluates this…
Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework
The authors propose a Fluid Personality Framework for conversational agents to adapt their metaphorical personas and personality expression levels based on the task context, user goals, and situational urgency.
Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework
The text discusses a proposal for a new conversational agent (CA) framework that can adapt an agent's personality and metaphorical role based on the task context, user goals, and urgency. This is suggested to improve…
Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use
Researchers introduce OpenAgent, a problem setting designed to help agents generalize better in real-world scenarios with dynamic queries, tools, and interactions. The study reveals that both Supervised Fine-Tuning and…
Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use
Researchers introduce OpenAgent, a problem setting to test how well AI agents can generalize their skills from static benchmarks to real-world scenarios with dynamic user queries and tools. The study finds that both…
CausalMix: Data Mixture as Causal Inference for Language Model Training
The paper presents CausalMix, a method that addresses the limitation of existing data mixing methods in Large Language Model (LLM) training by casting it as a causal inference problem. By using causal modeling to…
Cheap Code, Costly Judgment: A Case Study on Governable Agentic Software Engineering
The authors of this paper study the shift in software engineering towards using Generative AI for low-cost code production and the challenges that come with it, specifically focusing on governance and maintenance. They…
Cheap Code, Costly Judgment: A Case Study on Governable Agentic Software Engineering
In the study titled 'Cheap Code, Costly Judgment', researchers examined a case study of a 12-week development effort where an expert software engineer used AI coding agents to build a document accessibility remediation…
DART-VLN: Test-Time Memory Decay and Anti-Loop Regularization for Discrete Vision-Language Navigation
The researchers present DART-VLN, a test-time control framework for vision-language navigation agents that aims to improve their performance by addressing two common issues: stale historical evidence at memory readout…
DART-VLN: Test-Time Memory Decay and Anti-Loop Regularization for Discrete Vision-Language Navigation
Researchers present DART-VLN, a method for improving the performance of memory-based vision-language navigation agents at test time. The method combines Test-Time Memory Decay and Anti-Loop Regularization to reduce…
Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning
The authors propose a novel framework using active learning to enhance unsupervised time series anomaly detection in complex data. The framework aims to improve the ability of models to detect subtle and noisy…
Distill to Detect: Exposing Stealth Biases in LLMs through Cartridge Distillation
The paper introduces Distill to Detect (D2D), a method that helps identify hidden biases in language models used in high-stakes roles by amplifying these biases through cartridge distillation. The method is designed to…
Distill to Detect: Exposing Stealth Biases in LLMs through Cartridge Distillation
Researchers have developed a method called Distill to Detect (D2D) to expose stealth biases in large language models used in high-stakes roles. These biases can favor certain entities, brands, or viewpoints and are…
Exploring the Semantic Gap in Agentic Data Systems: A Formative Study of Operationalization Failures in Analytical Workflows
A study found that large language models (LLMs) often struggle to operationalize analytical concepts due to a semantic gap between user-level analytical concepts and the information available to workflow-generation…
Exploring the Semantic Gap in Agentic Data Systems: A Formative Study of Operationalization Failures in Analytical Workflows
A study identifies common failures in generating and executing analytical workflows using large language models across finance, human resources, and public safety domains. The authors found five recurring classes of…
From Personas to Plot: Character-Grounded Multi-Agent Story Generation for Long-Form Narratives
Researchers have developed MAGNET, a framework that generates consistent and coherent long-form narratives by using a unified approach of multi-agent goal-driven storytelling and verification. The system was tested…
From Personas to Plot: Character-Grounded Multi-Agent Story Generation for Long-Form Narratives
A new framework called MAGNET is introduced for generating long-form narratives using multiple agent characters that maintain consistency and coherence in their actions based on a shared world state and evolving story…
FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model
The paper introduces FurnitureVLA, a model that aims to improve long-horizon bimanual furniture assembly using Vision-Language-Action models (VLAs) in real-scale settings. The model is designed to predict actions and a…
GPU-Parallel Linearization Error Bounds for Real-Time Robust Optimal Control of Nonlinear and Neural Network Dynamics
The paper presents a new method called GPUSLS-LEO for real-time robust optimal control of uncertain nonlinear systems, specifically focusing on systems with linear time-varying (LTV) approximations. This method…
Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination
Researchers developed Graph-PRefLexOR, a model that uses graph-native reinforcement learning to generate scientifically valid hypotheses through multi-step reasoning in materials science and mechanics. The new model…
Human-Machine Collaboration on Generative Meta-Learning: Model and Algorithm
Researchers have proposed a new method called Generative Meta-Learning with Human Feedback (GMHF) to improve the performance of machine learning models in environments different from their training distribution…
Human-Machine Collaboration on Generative Meta-Learning: Model and Algorithm
The research paper introduces a new framework called Generative Meta-Learning with Human Feedback (GMHF). This framework aims to improve machine learning models by bridging the gap between training and target domains…
Language-Critique Imitation Learning from Suboptimal Demonstrations
A new approach called Language-Critique is proposed for imitation learning from suboptimal demonstrations. Instead of using compressed signals, it utilizes natural language to provide explicit feedback about task…
LLM-Guided ODE Discovery and Parameter Inference from Small-Cohort Aggregate Data
The paper introduces AgentODE, a framework that uses large language models (LLM) to propose ODE structures for complex dynamics modeling in rare diseases. The framework operates on population-level summary statistics…
LLM-Guided ODE Discovery and Parameter Inference from Small-Cohort Aggregate Data
A new method called AgentODE has been proposed to discover and refine Ordinary Differential Equations (ODE) structures for modeling complex dynamics, particularly useful in clinical settings. This method works with…
LLVM-Bench: Benchmarking and Advancing Large Language Models for LLVM Compiler Issue Resolution
The authors present LLVM-Bench, a large-scale benchmark for testing the effectiveness of large language models (LLMs) in resolving issues related to the LLVM compiler. They found that current LLM-based techniques are…
Measuring the Gap Between Human and LLM Research Ideas
A study comparing the research ideas generated by large language models (LLMs) with those of human researchers. The authors build an evaluation framework to measure the gap between them using a two-axis taxonomy to…
Measuring the Gap Between Human and LLM Research Ideas
The authors explore the gap between ideas generated by Large Language Models (LLMs) and those of human researchers in the field of computer science. They develop an evaluation framework based on high-quality human…
MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
The authors propose MemSyco-Bench, a new benchmark to evaluate how memory affects decision-making in language models (LLMs) used in AI agents. The benchmark aims to assess if the agents can handle issues of sycophancy…
MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
Researchers have proposed a new benchmark called MemSyco-Bench to evaluate how memory in large language models (LLMs) can lead to sycophancy, where agents over-align with the user at the cost of factual accuracy or…
Optimal Resource Utilization for Autonomous Laboratory Orchestrators
The authors propose a two-step method to optimize resource utilization in autonomous laboratories, specifically for metal-organic framework synthesis. The first step involves using constraint programming to find…
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