FARM: Field-Aware Resolution Model for Intelligent Trigger-Action Automation

• URL: http://arxiv.org/abs/2601.15687v1
• Date: Thu, 22 Jan 2026 06:12:18 GMT
• Title: FARM: Field-Aware Resolution Model for Intelligent Trigger-Action Automation
• Abstract summary: We study the function-level configuration problem: generating complete applets with correct ingredient-to-field bindings.<n>We propose FARM (Field-Aware Resolution Model), a two-stage architecture for automated applet generation with full configuration.<n> FARM achieves 81% joint accuracy on Gold (62% Noisy, 70% One-shot) at the function level, where both trigger and action functions must match the ground truth.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trigger-Action Programming (TAP) platforms such as IFTTT and Zapier enable Web of Things (WoT) automation by composing event-driven rules across heterogeneous services. A TAP applet links a trigger to an action and must bind trigger outputs (ingredients) to action inputs (fields) to be executable. Prior work largely treats TAP as service-level prediction from natural language, which often yields non-executable applets that still require manual configuration. We study the function-level configuration problem: generating complete applets with correct ingredient-to-field bindings. We propose FARM (Field-Aware Resolution Model), a two-stage architecture for automated applet generation with full configuration. Stage 1 trains contrastive dual encoders with selective layer freezing over schema-enriched representations, retrieving candidates from 1,724 trigger functions and 1,287 action functions (2.2M possible trigger-action pairs). Stage 2 performs selection and configuration using an LLM-based multi-agent pipeline. It includes intent analysis, trigger selection, action selection via cross-schema scoring, and configuration verification. Agents coordinate through shared state and agreement-based selection. FARM achieves 81% joint accuracy on Gold (62% Noisy, 70% One-shot) at the function level, where both trigger and action functions must match the ground truth. For comparison with service-level baselines, we map functions to their parent services and evaluate at the service level. FARM reaches 81% joint accuracy and improves over TARGE by 23 percentage points. FARM also generates ingredient-to-field bindings, producing executable automation configurations.

Related papers

• EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL [54.09410318521061]
  We introduce EnvFactory, a fully automated framework for Agentic Reinforcement Learning (Agentic RL) training.<n>EnvFactory autonomously explores and stateful, executable tool environments from authentic resources.<n>It synthesizes natural multi-turn trajectories through topology-aware sampling and calibrated refinement.<n>It achieves superior training efficiency and downstream performance, improving Qwen3-series models by up to +15% on BFCLv3, +8.6% on MCP-Atlas, and +6% on conversational benchmarks including $2$-Bench and VitaBench.
  arXiv  Detail & Related papers  (2026-05-18T17:37:40Z)
• Training LLMs for Multi-Step Tool Orchestration with Constrained Data Synthesis and Graduated Rewards [76.49428173793386]
  LLMs must invoke multiple dependent APIs in the correct order while propagating intermediate outputs.<n>Training models to handle such faces two obstacles: existing environments focus on simple per-turn function calls with simulated data, and binary rewards.<n>We present a framework addressing both challenges. First, we construct a reinforcement learning environment backed by a large-scale cache of real API responses, enabling a data synthesis pipeline that samples valid multi-step orchestration traces.<n>Second, we propose a graduated reward design that decomposes correctness into atomic validity and orchestration.
  arXiv  Detail & Related papers  (2026-03-25T18:31:39Z)
• SKILLS: Structured Knowledge Injection for LLM-Driven Telecommunications Operations [0.0]
  General-purpose language model (LLM) agents execute telecom operations through real API interfaces, or do they require structured domain guidance?<n>We introduce SKILLS, a benchmark framework comprising 37 telecom operations scenarios spanning 8 TM Forum Open API domains.<n>Results across 5 open-weight model conditions and 185 scenario-runs show consistent skill lift across all models.
  arXiv  Detail & Related papers  (2026-03-16T14:48:53Z)
• AgentSkiller: Scaling Generalist Agent Intelligence through Semantically Integrated Cross-Domain Data Synthesis [30.512393568258105]
  Large Language Model agents demonstrate potential in solving real-world problems via tools, yet generalist intelligence is bottlenecked by scarce high-quality, long-horizon data.<n>We propose AgentSkiller, a fully automated framework synthesizing multi-turn interaction data across realistic, semantically linked domains.
  arXiv  Detail & Related papers  (2026-02-10T03:21:42Z)
• Parameter Aware Mamba Model for Multi-task Dense Prediction [69.94454603308196]
  We introduce a novel decoder-based framework, Aware Mamba Model (PAMM), specifically designed for dense prediction in multi-task learning setting.<n>It features dual state space parameter experts that integrate and set task-specific parameter priors, capturing the intrinsic properties of each task.<n>We employ the Multi-Directional Hilbert Scanning method to construct multi-angle feature sequences, thereby enhancing the sequence model's perceptual capabilities for 2D data.
  arXiv  Detail & Related papers  (2025-11-18T13:48:00Z)
• CorrSteer: Generation-Time LLM Steering via Correlated Sparse Autoencoder Features [1.5874067490843806]
  We propose CorrSteer, which selects features by correlating sample correctness with SAE activations from generated tokens at inference time.<n>Our work establishes correlation-based selection as an effective and scalable approach for automated SAE steering across language model applications.
  arXiv  Detail & Related papers  (2025-08-18T00:01:42Z)
• RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory [57.449129198822476]
  RCR is a role-aware context routing framework for multi-agent large language model (LLM) systems.<n>It dynamically selects semantically relevant memory subsets for each agent based on its role and task stage.<n>A lightweight scoring policy guides memory selection, and agent outputs are integrated into a shared memory store.
  arXiv  Detail & Related papers  (2025-08-06T21:59:34Z)
• Evaluating LLMs on Sequential API Call Through Automated Test Generation [10.621357661774244]
  StateGen is an automated framework designed to generate diverse coding tasks involving sequential API interactions.<n>We construct StateEval, a benchmark encompassing 120 verified test cases spanning across three representative scenarios.<n> Experimental results confirm that StateGen can effectively generate challenging and realistic API-oriented tasks.
  arXiv  Detail & Related papers  (2025-07-13T03:52:51Z)
• GTA1: GUI Test-time Scaling Agent [97.58177633084915]
  Graphical user interface (GUI) agents autonomously complete tasks across platforms (eg, Linux) by sequentially decomposing user instructions into action proposals.<n>This paper investigates the aforementioned challenges with our textbfGUI textbfTest-time Scaling textbfAgent, namely GTA1.
  arXiv  Detail & Related papers  (2025-07-08T08:52:18Z)
• SOPBench: Evaluating Language Agents at Following Standard Operating Procedures and Constraints [59.645885492637845]
  SOPBench is an evaluation pipeline that transforms each service-specific SOP code program into a directed graph of executable functions.<n>Our approach transforms each service-specific SOP code program into a directed graph of executable functions and requires agents to call these functions based on natural language SOP descriptions.<n>We evaluate 18 leading models, and results show the task is challenging even for top-tier models.
  arXiv  Detail & Related papers  (2025-03-11T17:53:02Z)
• Pilot: Building the Federated Multimodal Instruction Tuning Framework [79.56362403673354]
  Our framework integrates two stages of "adapter on adapter" into the connector of the vision encoder and the LLM.<n>In stage 1, we extract task-specific features and client-specific features from visual information.<n>In stage 2, we build the cross-task Mixture-of-Adapters(CT-MoA) module to perform cross-task interaction.
  arXiv  Detail & Related papers  (2025-01-23T07:49:24Z)
• ToolACE: Winning the Points of LLM Function Calling [139.07157814653638]
  ToolACE is an automatic agentic pipeline designed to generate accurate, complex, and diverse tool-learning data.<n>We demonstrate that models trained on our synthesized data, even with only 8B parameters, achieve state-of-the-art performance on the Berkeley Function-Calling Leaderboard.
  arXiv  Detail & Related papers  (2024-09-02T03:19:56Z)
• Multiscale Vision Transformers meet Bipartite Matching for efficient single-stage Action Localization [27.472705540825316]
  Action localization is a challenging problem that combines detection and recognition tasks, which are often addressed separately. We show that a single MViTv2-S architecture trained with bipartite matching to perform both tasks surpasses the same MViTv2-S when trained with RoI align on pre-computed bounding boxes.
  arXiv  Detail & Related papers  (2023-12-29T17:08:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.