Daily Arxiv

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OmniEVA: Embodied Versatile Planner via Task-Adaptive 3D-Grounded and Embodiment-aware Reasoning

Open-sci-ref-0.01: open and reproducible reference baselines for language model and dataset comparison

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining

Musculoskeletal simulation of limb movement biomechanics in Drosophila melanogaster

Between a Rock and a Hard Place: Exploiting Ethical Reasoning to Jailbreak LLMs

Cut Costs, Not Accuracy: LLM-Powered Data Processing with Guarantees

The Architecture of AI Transformation: Four Strategic Patterns and an Emerging Frontier

IS${}^3$ : Generic Impulsive--Stationary Sound Separation in Acoustic Scenes using Deep Filtering

Beyond Ensembles: Simulating All-Atom Protein Dynamics in a Learned Latent Space

HiddenObject: Modality-Agnostic Fusion for Multimodal Hidden Object Detection

Input-Time Scaling

Counterfactual Probabilistic Diffusion with Expert Models

Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments

Bridging the Gap: A Framework for Real-World Video Deepfake Detection via Social Network Compression Emulation

Imposing AI: Deceptive design patterns against sustainability

LMAR: Language Model Augmented Retriever for Domain-specific Knowledge Indexing

Data-Driven Discovery of Mobility Periodicity for Understanding Urban Systems

Privacy Risks of LLM-Empowered Recommender Systems: An Inversion Attack Perspective

Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges

Atherosclerosis through Hierarchical Explainable Neural Network Analysis

Survivability of Backdoor Attacks on Unconstrained Face Recognition Systems

Geological Everything Model 3D: A Promptable Foundation Model for Unified and Zero-Shot Subsurface Understanding

HiLight: A Hierarchical Reinforcement Learning Framework with Global Adversarial Guidance for Large-Scale Traffic Signal Control

AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning

OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval

The Precautionary Principle and the Innovation Principle: Incompatible Guides for AI Innovation Governance?

A Framework for Testing and Adapting REST APIs as LLM Tools

D\'ej\`a Vu: Multilingual LLM Evaluation through the Lens of Machine Translation Evaluation

JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse

Prompt Programming: A Platform for Dialogue-based Computational Problem Solving with Generative AI Models

Auxiliary Discrminator Sequence Generative Adversarial Networks (ADSeqGAN) for Few Sample Molecule Generation

Neural Force Field: Few-shot Learning of Generalized Physical Reasoning

Towards Developing Socially Compliant Automated Vehicles: Advances, Expert Insights, and A Conceptual Framework

A Novel Approach to Balance Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes and its Implementation in BEACON

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection

Tokens, the oft-overlooked appetizer: Large language models, the distributional hypothesis, and meaning

Polish-English medical knowledge transfer: A new benchmark and results

InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction

AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion models

A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research

A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts

The Overcooked Generalization Challenge: Evaluating Cooperation with Novel Partners in Unknown Environments Using Unsupervised Environment Design

Modeling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need

Slaves to the Law of Large Numbers: An Asymptotic Equipartition Property for Perplexity in Generative Language Models

Interpretable Data-driven Anomaly Detection in Industrial Processes with ExIFFI

Analyzing the Impact of Adversarial Examples on Explainable Machine Learning

TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning Potential

Sufficient Invariant Learning for Distribution Shift

TORSO: Template-Oriented Reasoning Towards General Tasks

ForTIFAI: Fending Off Recursive Training Induced Failure for AI Models

Oyster-I: Beyond Refusal -- Constructive Safety Alignment for Responsible Language Models

Building Self-Evolving Agents via Experience-Driven Lifelong Learning: A Framework and Benchmark

QuantX: A Framework for Hardware-Aware Quantization of Generative AI Workloads

Learning to Plan with Personalized Preferences

Multi-Turn Human-LLM Interaction Through the Lens of a Two-Way Intelligence Protocol

Spatio-Temporal Graphical Counterfactuals: An Overview

Standards in the Preparation of Biomedical Research Metadata: A Bridge2AI Perspective

Is In-Context Learning Learning?

Multimodal SAM-adapter for Semantic Segmentation

Diversified recommendations of cultural activities with personalized determinantal point processes

Improving Audio Event Recognition with Consistency Regularization

Data distribution impacts the performance and generalisability of contrastive learning-based foundation models of electrocardiograms

Towards Understanding Visual Grounding in Visual Language Models

GLAM: Geometry-Guided Local Alignment for Multi-View VLP in Mammography

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation

Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Data

We Need a New Ethics for a World of AI Agents

SignClip: Leveraging Mouthing Cues for Sign Language Translation by Multimodal Contrastive Fusion

Openness in AI and downstream governance: A global value chain approach

SI-FACT: Mitigating Knowledge Conflict via Self-Improving Faithfulness-Aware Contrastive Tuning

Benchmark of stylistic variation in LLM-generated texts

BenchECG and xECG: a benchmark and baseline for ECG foundation models

Efficient Learning-Based Control of a Legged Robot in Lunar Gravity

Population-Aligned Persona Generation for LLM-based Social Simulation

Realism Control One-step Diffusion for Real-World Image Super-Resolution

Generating Energy-Efficient Code via Large-Language Models -- Where are we now?

Established Psychometric vs. Ecologically Valid Questionnaires: Rethinking Psychological Assessments in Large Language Models

Predictive Spike Timing Enables Distributed Shortest Path Computation in Spiking Neural Networks

TwinTac: A Wide-Range, Highly Sensitive Tactile Sensor with Real-to-Sim Digital Twin Sensor Model

Multimodal Mathematical Reasoning Embedded in Aerial Vehicle Imagery: Benchmarking, Analysis, and Exploration

Reinforcement learning for spin torque oscillator tasks

Exploring Expert Specialization through Unsupervised Training in Sparse Mixture of Experts

Intrinsic Dimension Estimating Autoencoder (IDEA) Using CancelOut Layer and a Projected Loss

Unsupervised Hallucination Detection by Inspecting Reasoning Processes

Drone-Based Multispectral Imaging and Deep Learning for Timely Detection of Branched Broomrape in Tomato Farms

Securing LLM-Generated Embedded Firmware through AI Agent-Driven Validation and Patching

Large Language Models Meet Legal Artificial Intelligence: A Survey

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes

Zero-Shot Referring Expression Comprehension via Visual-Language True/False Verification

Adaptive Token Merging for Efficient Transformer Semantic Communication at the Edge

SmartCoder-R1: Towards Secure and Explainable Smart Contract Generation with Security-Aware Group Relative Policy Optimization

WALL: A Web Application for Automated Quality Assurance using Large Language Models

An Autoencoder and Vision Transformer-based Interpretability Analysis of the Differences in Automated Staging of Second and Third Molars

Tackling One Health Risks: How Large Language Models are leveraged for Risk Negotiation and Consensus-building

Self-Augmented Robot Trajectory: Efficient Imitation Learning via Safe Self-augmentation with Demonstrator-annotated Precision

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining

From Hugging Face to GitHub: Tracing License Drift in the Open-Source AI Ecosystem

Emulating Public Opinion: A Proof-of-Concept of AI-Generated Synthetic Survey Responses for the Chilean Case

Vibe Check: Understanding the Effects of LLM-Based Conversational Agents' Personality and Alignment on User Perceptions in Goal-Oriented Tasks

A Framework for Testing and Adapting REST APIs as LLM Tools

Created by

Haebom

Author

Jayachandu Bandlamudi, Ritwik Chaudhuri, Neelamadhav Gantayat, Sambit Ghosh, Kushal Mukherjee, Prerna Agarwal, Renuka Sindhgatta, Sameep Mehta

Outline

This paper presents a testing framework for autonomous agents based on large-scale language models (LLMs) to overcome the challenges of complex input schemas and detailed responses associated with enterprise APIs, enabling them to perform complex tasks. This framework systematically evaluates enterprise APIs wrapped in Python tools. It generates test cases that recognize data, translate these into natural language commands, and evaluates whether the agent correctly invokes the tool, processes input, and processes responses. We generate over 2,400 test cases across various domains and develop a classification system for common errors, including input misinterpretation, output failures, and schema mismatches, to support debugging and tool improvement. Ultimately, this framework provides a systematic approach for leveraging enterprise APIs as reliable tools for agent-based applications.

Takeaways, Limitations

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Takeaways:

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Providing a systematic framework for evaluating the reliability of enterprise APIs for LLM-based agents.

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Efficient API evaluation through data recognition test case generation and natural language command translation.

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Generate extensive test cases across various domains and develop a general fault classification scheme.

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Support for debugging API errors and improving tools.

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Improving reliability for agent-based application development.

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Limitations:

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The current framework is limited to Python tools, which may limit its applicability to other language-based tools.

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The accuracy of the evaluation results may vary depending on the scope and depth of the test cases.

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Dependency on specific enterprise systems and APIs may make generalization difficult.

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Additional validation of the comprehensiveness and accuracy of the error classification scheme may be required.

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