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2,343 notes. Notes 701 to 750, page 15 of 47.
Agents That Certify Their Own Exploits: Confidence-Scheduled Restricted Responses for Safe Opponent Exploitation
The paper introduces a method called budget-constrained confidence-scheduled restricted responses (CS-RNR) for safe opponent exploitation in two-player zero-sum imperfect-information games. This method helps an agent…
Agents That Certify Their Own Exploits: Confidence-Scheduled Restricted Responses for Safe Opponent Exploitation
Researchers propose a new method called Budget-Constrained Confidence-Scheduled Restricted Responses (CS-RNR) for safe opponent exploitation in two-player zero-sum imperfect-information games. The method tracks action…
AI and Authenticity in Islamic Research: A Critical Evaluation of Generative AI Reliability, Hallucination, and Source Fidelity in Quranic, Hadith, and Fiqh Knowledge
This research evaluates six popular AI systems' ability to provide reliable, authentic Islamic knowledge. The study found that while these AI systems can be useful for introductory learning, they should not be treated…
AI and Authenticity in Islamic Research: A Critical Evaluation of Generative AI Reliability, Hallucination, and Source Fidelity in Quranic, Hadith, and Fiqh Knowledge
The study evaluates the reliability of six leading generative AI systems in providing authentic and trustworthy Islamic knowledge, focusing on Qur'anic interpretation, Hadith, Fiqh, ethics, pastoral advice, and…
AISPA: User-Centric System Prompt Auditing for Large Language Model Applications
A new framework called AISPA is introduced to audit system prompts in AI systems, which are instructions used by developers to control the behavior of AI applications. The framework examines specific parts of a system…
AISPA: User-Centric System Prompt Auditing for Large Language Model Applications
The paper introduces AISPA, a framework for auditing system prompts in AI systems to ensure user protection. The study analyzes 3,249 instructions from 88 commercial AI products and finds significant variation in…
AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis
AskChem is a system designed to help scientists and AI agents find specific information in chemistry literature more efficiently. It changes the unit of retrieval from entire papers to 'claims', which are atomic, typed…
AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis
AskChem is a system designed to make it easier for scientists and AI agents to find specific findings in chemistry literature by converting papers into atomic, typed claims that are grounded by their source DOI and a…
AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis
The paper introduces AskChem, a system that aims to simplify chemistry literature synthesis by converting papers into atomic, typed claims. Each claim is linked to its source and can be searched and synthesized using…
CACHE-UK: A Stability-Aware Memory Editor for Sequentially Updated Quantized LLMs in Finance
Researchers introduce CACHE-UK, a framework designed for efficiency and accuracy in deploying large language models (LLMs) in the dynamic field of finance. The system addresses the 'quantization stability crisis' that…
EgoGenesis: Egocentric World-Action Modeling with Online Anchored Projective Memory and Action-3D RoPE
The research presents a new method, EgoGenesis, for generating high-quality manipulation videos in an egocentric world for training embodied AI. The method uses a pretrained video generation prior and introduces two…
EgoGenesis: Egocentric World-Action Modeling with Online Anchored Projective Memory and Action-3D RoPE
The authors present a method called EgoGenesis for creating synthetic manipulation videos for embodied AI using egocentric video. The method uses a pretrained video generation prior and introduces two geometry-aware…
EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agents
The paper introduces EMBL AI Librarian, a tool that allows AI agents to access and process life sciences literature more effectively by asking natural language questions and receiving relevant evidence in response. The…
EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agents
The paper introduces EMBL AI Librarian, a system that enhances the Europe PMC interface for AI agents to enable them to search and retrieve evidence in natural language. The system was evaluated across four benchmarks…
From Textual Requirements to Microservice Architectures - A Comprehensive Evaluation of LLM-Based Design Synthesis
Researchers evaluated the ability of a Large Language Model (OpenAI o3) to generate microservice architectures based on textual requirements. The study found that the LLM was more effective when given examples…
How Benchmarks Mis-Score Computer-Use Agents
A research paper discusses issues with the scoring system of computer-use agents (software that navigates the web and operates desktop software). The authors argue that current scores are often inaccurate due to…
How Benchmarks Mis-Score Computer-Use Agents
The paper discusses issues with the scoring of Computer-Use Agents (CUAs) using brittle scripted oracles, highlighting problems such as outdated tasks, omitted decisive visual evidence, and hidden failure causes in…
How Benchmarks Mis-Score Computer-Use Agents
The paper discusses issues with the benchmark scores of Computer-Use Agents (CUA), highlighting that the scores are often inaccurate due to brittle scripted oracles. The authors propose a reliability framework and…
InfoOps Bench: A live information operations safety benchmark
The paper introduces a dynamic AI benchmark that measures the integrity of language models against being used for state-backed information operations. The benchmark uses data from over 2,100 information operations from…
InfoOps Bench: A live information operations safety benchmark
The paper introduces an AI benchmark that measures the safety of language models against being manipulated for state-backed information operations. The benchmark uses data from a live monitoring pipeline tracking…
Learning to Trace Seiberg Dualities
In this paper, the authors use machine learning methods to develop a practical tool for establishing Seiberg dualities in supersymmetric quiver gauge theories. They find that network architectures consisting of…
MANTA: Multi-Agent Network Topology Adaptation for Self-Evolving Multi-Agent Systems
The paper introduces MANTA, a framework for adapting communication structures in multi-agent systems during inference time. This allows for more efficient problem solving by enabling agents to adjust their roles…
MANTA: Multi-Agent Network Topology Adaptation for Self-Evolving Multi-Agent Systems
The paper introduces MANTA, a framework for Multi-Agent Network Topology Adaptation that allows communication structures in large language model-based multi-agent systems to evolve during inference time. This…
MANTA: Multi-Agent Network Topology Adaptation for Self-Evolving Multi-Agent Systems
The paper introduces MANTA, a framework for Multi-Agent Network Topology Adaptation that allows communication structures in large language model-based multi-agent systems to evolve during deployment. The goal is to…
MemHarness: Memory Is Reconstructed, Not Replayed
Researchers propose MemHarness, a framework for enhancing large language models that allows them to adapt and reconstruct past experiences based on the current context, rather than simply replaying them verbatim as…
MemHarness: Memory Is Reconstructed, Not Replayed
Researchers have developed MemHarness, a framework to improve large language model agents by enabling them to adapt and reconstruct past experiences based on the current context, instead of simply replaying stored…
ObjectStream: Latent Objects as Memory Anchors for Streaming Video Understanding
The paper introduces ObjectStream, a framework that uses latent objects as memory anchors for understanding streaming videos. The framework does not require external object detectors or segmentation models and allows…
On-Policy and Off-Policy Learning for Large Action Spaces
The paper presents research on policy learning in interactive systems with large action spaces. The study focuses on two paradigms: on-policy and off-policy learning, both of which face challenges like inefficient…
On-Policy and Off-Policy Learning for Large Action Spaces
The paper by Imad Aouali discusses policy learning in interactive systems with large action spaces, where an agent makes decisions and receives partial feedback based on a context. The work focuses on two paradigms…
On-Policy and Off-Policy Learning for Large Action Spaces
The paper discusses policy learning in interactive systems with large action spaces, where agents observe contexts, select actions, and receive partial feedback. The research focuses on two paradigms: on-policy…
One Human, $N$ Agents: Audit-Budget Allocation for LLM Agent Fleets under Miscalibrated, Correlated Confidence
Researchers propose a model for auditing a large number of LLM agents with limited budget, where agents' self-reported confidence may be misleading. The study finds that when the confidence is adversarially…
One Human, $N$ Agents: Audit-Budget Allocation for LLM Agent Fleets under Miscalibrated, Correlated Confidence
The paper discusses a scenario where a single human auditor has to evaluate multiple LLM (Language Learning Models) agents under a limited budget. The authors model this process as a noisy inspection over a two-level…
One Human, $N$ Agents: Audit-Budget Allocation for LLM Agent Fleets under Miscalibrated, Correlated Confidence
The paper presents a study on auditing multiple Language Learning Models (LLMs) with limited budgets, where human auditors have to evaluate self-reported confidence that may be miscalibrated and correlated. The…
ORCA-bench: How Ready Are Language Model Agents for Oncall?
Researchers introduce ORCA-bench, a new benchmark that tests the ability of large language models to perform root cause analysis (RCA) in a production-like setting. The benchmark includes a microservice system with six…
ORCA-bench: How Ready Are Language Model Agents for Oncall?
The paper introduces ORCA-bench, a benchmark for testing the performance of large language models in oncall root cause analysis (RCA). The benchmark includes a live microservice system with six days of metrics, logs…
ORCA-bench: How Ready Are Language Model Agents for Oncall?
The study presents ORCA-bench, a benchmark for evaluating the performance of large language models in oncall root cause analysis (RCA). The benchmark exposes a microservice system with six days of data and 1,079 RCA…
OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models
The research introduces OSReward, a new benchmark for evaluating the reliability of Vision-Language Models (VLMs) that judge Computer-Using Agents' (CUAs) trajectories. The study finds that even state-of-the-art VLMs…
OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models
The paper introduces OSReward, a benchmark for evaluating the reliability of vision-language models (VLMs) used as judges for computer-using agents (CUAs). The benchmark consists of CUA trajectories from various agent…
OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models
The research introduces OSReward, a new benchmark for evaluating the reliability of vision-language models (VLMs) used to judge computer-using agents' trajectories. The study finds that state-of-the-art VLMs have a…
OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models
The research introduces OSReward, a new benchmark for evaluating the reliability of vision-language models (VLMs) used to judge computer-using agents (CUAs). The study finds that even state-of-the-art VLMs have a…
PAIChecker: Uncovering and Checking PR-Issue Misalignment in SWE-Bench-Like Benchmarks
Researchers present PAIChecker, a system designed to detect misalignment between Pull Requests (PRs) and Issues in SWE-bench-like benchmarks used for evaluating Large Language Models (LLMs). The system identifies…
PAIChecker: Uncovering and Checking PR-Issue Misalignment in SWE-Bench-Like Benchmarks
The paper proposes a system called PAIChecker to check for misalignment between Pull Requests (PR) and Issues in SWE-bench-like benchmarks used for evaluating Large Language Models' issue resolution capability. The…
PAIChecker: Uncovering and Checking PR-Issue Misalignment in SWE-Bench-Like Benchmarks
Researchers have developed a system called PAIChecker to check for misalignment between Pull Requests (PRs) and issues in software development benchmarks used to evaluate the issue resolution capability of large…
Paying for Honesty Without Knowing the Truth: Reputation-Penalty Design for LLM Marketplace Agents
The paper proposes a reputation-penalty mechanism (CARP) for language model marketplace agents that fabricate attributes in product listings, even when instructed to be honest. The mechanism does not require the ground…
Paying for Honesty Without Knowing the Truth: Reputation-Penalty Design for LLM Marketplace Agents
Researchers present CARP, a reputation-penalty mechanism designed for LLM marketplace agents that act as autonomous merchants and often fabricate product attributes to win sales even when instructed to be honest. The…
Paying for Honesty Without Knowing the Truth: Reputation-Penalty Design for LLM Marketplace Agents
Researchers present CARP, a reputation-penalty mechanism designed for LLM (Language Learning Models) marketplace agents that often fabricate product attributes to increase sales even when instructed to be honest. The…
Rethinking Inference-Time Scaling in Local Computer-Use Agents: Failure Modes and Compute Tradeoffs
This research study explores the effectiveness of inference-time scaling for computer-use agents (CUAs) that run locally on devices. The authors investigate various dimensions such as context, time, structure, and…
Rethinking Inference-Time Scaling in Local Computer-Use Agents: Failure Modes and Compute Tradeoffs
The paper presents an empirical study on the effectiveness of inference-time scaling in locally deployed autonomous computer-use agents (CUAs) under strict hardware constraints. The research focuses on four dimensions…
Rethinking Inference-Time Scaling in Local Computer-Use Agents: Failure Modes and Compute Tradeoffs
The research focuses on improving the performance of local autonomous computer-use agents (CUAs) under strict hardware constraints. The study investigates inference-time scaling across contextual, temporal, structural…
SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination
The paper proposes a new model called SCOPE (Supply-Chain Operations through Coupled Policies for End-to-End Coordination) that aims to improve the efficiency of supply chain AI by coordinating various decision-making…
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