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2,343 notes. Notes 451 to 500, page 10 of 47.
Two-stage Odd Residual Flows for Mean-Preserving Probabilistic Time Series Forecasting
Researchers propose a new method called Two-stage Odd Residual Flows (TORF) for probabilistic time series forecasting. The method aims to address the trade-off between distributional flexibility and accurate mean…
V-FiLLM: Verified Financial LLM Reasoning Benchmark
Researchers have created a new framework called V-FiLLM to evaluate the abilities of Language Models (LLMs) in financial reasoning using structured data. The framework generates questions from executable computation…
VibeLifeBench: Can Your Life Agent Be Proactive and Persistent in a Living World?
The paper introduces VibeLifeBench, a new benchmark for evaluating large language model (LLM) agents' ability to handle long-horizon tasks in everyday life scenarios. The benchmark consists of 200 multi-week timeline…
VibeLifeBench: Can Your Life Agent Be Proactive and Persistent in a Living World?
Researchers introduce a new benchmark called VibeLifeBench to evaluate large language model (LLM) agents' ability to be proactive and consistent in long-horizon tasks across ten everyday life domains. The benchmark…
Whisper-Aware LLM: Self-Supervised Uncertainty Learning for Robust Whispered Speech Recognition
The Whisper-Aware LLM is a new framework designed to improve the performance of ASR (Automatic Speech Recognition) systems for whispered speech. The model learns to perceive and react to the uncertainty associated with…
Why Does CLAUDE.md Keep Growing? Catastrophic Remembering in Agentic Coding
The research notes discuss a phenomenon called 'catastrophic remembering' in Agentic coding READMEs like CLAUDE.md where instructions grow without bound and do not get deleted easily over time. The paper proposes that…
Why Does CLAUDE.md Keep Growing? Catastrophic Remembering in Agentic Coding
The paper discusses the growth of Agentic coding README files like CLAUDE.md in real repositories and attributes this to imperfect recall. The authors refer to this phenomenon as 'catastrophic remembering'. They find…
Workflow Cards: Structured Summaries of Workflow Executions Using Provenance Data
The paper introduces Workflow Cards, a new type of structured summary for machine learning artifacts that focus on workflow executions, which are currently overlooked in Model and Data Cards. Workflow Cards aim to…
Agentic Auto-Research is Fuzz Testing
The authors argue that current autonomous research agents, which generate experiments faster than researchers can validate them, lack the ability to efficiently handle sparse feedback. They propose a solution inspired…
Agentic Auto-Research is Fuzz Testing
The authors argue that current methods for autonomous research agents, which generate experiments and rank them based on learned judges or human reviewers, miss the problem of sparse feedback. They suggest that these…
Agentic Auto-Research is Fuzz Testing
The authors argue that current methods for autonomous research agents are inefficient due to the slow validation process of generated experiments. They propose to adapt fuzzer techniques, which use feedback from…
Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy
The authors propose a system that uses large language models (LLMs) to evaluate the permissibility of actions planned by robots before they are executed. The system aims to reduce risks associated with biased planning…
Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy
Researchers propose a verification layer driven by large language models (LLMs) to evaluate the feasibility and ethics of actions suggested by robot planning models before they are executed. The system aims to reduce…
Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy
Researchers propose a verification layer using large language models (LLMs) to ensure the feasibility and ethical alignment of actions planned by robot autonomy systems. The system can evaluate the permissibility of…
ArchAgent v2: A Case Study with the Data Prefetching Championship
In a study published in 2026, researchers presented ArchAgent v2, a framework that automates the search for multi-level data prefetching in computer microarchitecture. The new version of ArchAgent outperformed…
ArchAgent v2: A Case Study with the Data Prefetching Championship
The paper presents ArchAgent v2, a framework that automates the discovery of multi-level data prefetching in computer microarchitecture. It outperforms hand-designed solutions from the 4th Data Prefetching Championship…
ArchAgent v2: A Case Study with the Data Prefetching Championship
The paper presents ArchAgent v2, a framework that scales automated microarchitecture search to multi-level data prefetching. It outperforms the winning hand-designed solution in the 4th Data Prefetching Championship…
Avalon-ToM-Bench: Evaluating Fine-Grained Theory of Mind via Asymmetric Game Mechanics
Researchers introduce Avalon-ToM-Bench, a new benchmark for evaluating fine-grained Theory of Mind (understanding others' thoughts and motivations) in AI models. The benchmark uses the asymmetric-information mechanics…
Avalon-ToM-Bench: Evaluating Fine-Grained Theory of Mind via Asymmetric Game Mechanics
A new benchmark called Avalon-ToM-Bench is introduced to evaluate fine-grained Theory of Mind (ToM) abilities in agents. The benchmark operates through the asymmetric-information mechanics of a game named The…
BDH-CQ: In-Context Learning with Recurrent Latent Reasoning
The paper introduces BDH-CQ, a model that combines in-context learning and recurrent latent reasoning. The model updates its memory based on inputs presented during inference, solves queries through iterative…
Beyond Naturalness: Probing Automated Text-To-Speech Evaluators on Linguistically Grounded Dimensions
Researchers create a new evaluation benchmark for Text-to-Speech (TTS) systems to measure their performance on specific linguistic dimensions beyond simple 'naturalness'. They find that current methods struggle to…
Beyond Naturalness: Probing Automated Text-To-Speech Evaluators on Linguistically Grounded Dimensions
Researchers have created a new method for evaluating text-to-speech (TTS) systems by breaking down the concept of 'naturalness' into ten distinct linguistic dimensions. They found that current TTS evaluation methods do…
CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation
The authors introduce a framework called CARD for generating realistic credit card discussion threads on online platforms, such as Reddit. The framework takes into account various factors like reply structure, comment…
CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation
Researchers introduce CARD, a framework for creating realistic simulations of online credit card discussion threads. This system takes into account various factors such as reply structure, comment function, stance…
CEAA: A Cognitive Embodied Agents Architecture for Interactive Computing Systems
The paper proposes a modular cognitive architecture for developing intelligent virtual agents (IVAs) that can function effectively in real-time interactive virtual environments. The proposed architecture aims to bridge…
CEAA: A Cognitive Embodied Agents Architecture for Interactive Computing Systems
The authors propose a modular cognitive architecture to develop Intelligent Virtual Agents (IVAs) that can operate in real-time interactive virtual environments. This architecture aims to bridge the gap between…
ColluSkill: Adversarial Cross-Skill Composition for Evading Agent Skill Scanners
Researchers propose ColluSkill, a framework for multi-skill attacks that can bypass individual skill checks in LLM-based agent systems. These attacks are composed of locally plausible behaviors that collectively form…
ColluSkill: Adversarial Cross-Skill Composition for Evading Agent Skill Scanners
Researchers propose ColluSkill, a framework for creating multi-step attacks on LLM-based agent systems. These agents have vulnerabilities in their skill systems that current defenses don't fully address, particularly…
Defining Decentralization: An Ontological Perspective
This research paper addresses the lack of a universal definition for decentralization in computer science and its related fields like security, AI, cloud infrastructure, IoT, etc. The authors propose a graph-based…
Defining Decentralization: An Ontological Perspective
The authors address the lack of a universally accepted definition of decentralization in computer science, particularly in emerging fields like decentralized AI and machine learning. They propose a graph-based ontology…
DSLE: A Learning Environment for Dark Souls Boss Encounters
The authors introduce the Dark Souls Learning Environment (DSLE), a platform for training AI agents to play boss encounters in the video game 'Dark Souls: Remastered'. The DSLE is designed as a benchmark for…
DSLE: A Learning Environment for Dark Souls Boss Encounters
Researchers have created a learning environment for AI agents called DSLE, which is based on the game Dark Souls: Remastered. The environment includes 22 boss encounters that can be used as benchmarks. The researchers…
ElasticBack: Stealthy Conditional Backdoor in LLM-Agent Skills via Coupled Trigger-Rule Optimization
The paper presents ElasticBack, a stealthy conditional backdoor that can compromise LLM agents by planting a rule and trigger in skills. The backdoor only activates when both the rule and the trigger appear together…
From Semantic Grounding to Decision Optimization: A Unified Framework for Long-Horizon UAV Vision-Language Navigation
The authors propose a unified framework to improve UAV vision-language navigation (UAV-VLN). This framework addresses three main issues: weak grounding of instruction-relevant landmarks, insufficient exploitation of…
From Values to Benchmarks: Evaluating Large Language Models for Governmental Use in Dutch
The paper presents a new evaluation framework called 'Grip on LLMs' specifically designed for Dutch governmental use of large language models (LLMs). The framework is created in collaboration with domain experts and…
From Values to Benchmarks: Evaluating Large Language Models for Governmental Use in Dutch
Researchers present a new evaluation framework called 'Grip on LLMs' for Dutch governmental use of large language models. The framework focuses on six dimensions: factuality, honesty, social bias, energy consumption…
Hallucination-Free GUI Grounding via Regression-Free Layout-Aware Matching
Researchers propose a new framework for grounding user instructions in graphical user interfaces (GUI). The framework uses a frozen multimodal large language model (MLLM) to parse instructions and a dedicated grounding…
How Do Large Language Models Judge Social Attraction? Evidence from Theory-Grounded Persona Ratings Across Multiple LLMs and Humans
This research investigates how large language models (LLMs) evaluate social attraction by comparing their judgments to human's from theory-grounded persona ratings. The study found that while some LLMs tended to give…
Illusion or Integrity? Geometrical Consistency Metric for AIGC Video Quality Evaluation
The paper presents a new benchmark, called GeoCon-Bench, to evaluate the quality of AI-generated videos by measuring their compliance with physical principles and geometric consistency across frames. The benchmark…
KGCaRe: Explainable Complex Conditional Question Answering using Automatic Knowledge Graph Construction and Context Retrieval with LLMs
The study proposes a new method called KGCaRe for answering complex conditional questions using large language models (LLMs) and retrieval-augmented generation (RAG). This approach combines neural retrieval with…
MedPixel: A Unified Pixel-Language Model for Medical Reasoning and Segmentation
Researchers present MedPixel, a unified medical pixel-language model that bridges the gap between clinical language and visual reasoning in medical image understanding. The model is trained using MedPLG-440K, a large…
Mismatch Matters: On-Policy Distillation Beyond Token Agreement
Researchers present TIDE, a new method to address a failure mode in on-policy distillation (OPD) called degenerate agreement. This issue arises when students exploit repetitive loops to achieve high token agreement…
Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
The text introduces the Model Discovery Agent (MDA), a system designed to efficiently discover mechanistic, causal models from minimal interventions using a large language model (LLM) and Bayesian methods. The MDA…
Modern Backbones Improve Multi-task DETR for Mammography Classification and Lesion Localization
The study evaluates a multi-task DETR framework that simultaneously predicts the malignancy of mammogram images and locates lesions, using modern backbones such as ConvNeXtV2 and DINOv3. The results show that these…
Multi-Agent AI Safety as an Institutional Design Problem
The paper discusses AI safety as an institutional design problem in multi-agent systems. The research focuses on understanding which parts of an AI institution ensure safety and how they do it. The study involves a…
Multi-Agent AI Safety as an Institutional Design Problem
In this research paper, Abdullah X discusses the role of institutions in ensuring safety for AI agents within multi-agent systems. The study includes a 5280-episode study suite with various experiments testing…
Multi-Agent AI Safety as an Institutional Design Problem
The paper discusses an experiment on multi-agent AI safety as an institutional design problem. The research focuses on understanding which parts of an AI institution ensure safety and how they do it. The study includes…
Multimodal Model Diffing for Feature Discovery and Control
Researchers introduce a framework called MMDiff to help identify, control, and audit the internal features of Multimodal Large Language Models (MLLMs). The framework is designed to isolate which features are altered by…
Multimodal Model Diffing for Feature Discovery and Control
Researchers developed a framework called MMDiff to help understand, control, and audit the behavior of Multimodal Large Language Models (MLLMs). The framework identifies features that are altered by multimodal…
NeuroRefiner: Morphology-Aware Multi-Agent Refinement for 3D Fluorescence Microscopy Neuron Segmentation
Researchers propose NeuroRefiner, a multi-agent system for improving the accuracy of 3D neuron segmentation in fluorescence microscopy. The system addresses challenges posed by the sparse and elongated morphology of…
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