arXiv AI
Jul 29
Desktop-Delta Bench: Do Computer-Use Models Understand Desktop GUI Transitions?
arXiv:2607. 26041v1 Announce Type: new Abstract: Computer-use agents (CUAs) increasingly act through desktop GUIs to complete long-horizon tasks.
Structured State Reconciliation for Human-AI Task Handover
Related stories
arXiv AI
Sep 21
DENSE: Distilling Agent Trajectories into Evidence-Grounded Shortcut Trees for Self-Refinement
arXiv:2609.21423v1 Announce Type: new Abstract: Online agent deployments produce abundant execution traces, while task-specific verification and expert annotation are costly to scale. We study how to...
arXiv Computation and Language
Oct 28
Benchmarking Task Takeover Across Agent Handoffs
The paper introduces HSC-Bench, a benchmark with 1,039 tasks designed to evaluate how well receiving AI models can take over tasks after handoffs. It measures two key aspects: the ability to recover relevant repository evidence and the ability to continue from established conversational, tool, and file-work states. Experiments with six models show that structured handoff information consistently improves evidence recovery and, for most models, continuation performance, while the best performers differ between the two tracks.
arXiv AI
Aug 18
Intent-Driven Situation Tracking for User-Centric Multi-Turn Agents
arXiv:2608. 15755v1 Announce Type: new Abstract: User-centric multi-turn agents must act on an evolving task situation shaped by changing user intents, accumulated tool-grounded facts, missing information, and execution constraints.
arXiv AI
Oct 31
Goal-Aware Dynamic Multi-Agent Collaboration for Long-Horizon Tasks
The paper introduces Goal-Aware Dynamic Multi-Agent Collaboration (GAD), a framework that assigns distinct duties to separate agents with individualized contexts to tackle long-horizon tasks. It employs a growing execution graph to structure subtasks and their dependencies, ensuring each agent retains the original instruction, task goal, and acceptance conditions. GAD dynamically adapts collaboration and information sharing, using verification and recovery checks to monitor subtask results and overall task completion, providing feedback for repair when checks fail.
arXiv AI
Jun 16
FactCheck: Feasibility-aware Long-term Action Anticipation with Multi-agent Collaboration
arXiv:2606. 14778v1 Announce Type: cross Abstract: Long-term action anticipation (LTA) aims to predict an ordered sequence of future verb-noun actions from a partially observed video.
Hugging Face Trending Papers
Oct 29
Goal-Aware Dynamic Multi-Agent Collaboration for Long-Horizon Tasks
The paper investigates how large language model agents handle long‑horizon tasks and finds that verification is crucial for success but often occurs too late. It shows that sharing a common context dilutes duty‑relevant information and that simply separating duties among agents is insufficient, as agents can lose sight of the overall goal. To address these issues, the authors propose Goal‑Aware Dynamic Multi‑Agent Collaboration (GAD), which uses an execution graph to organize subtasks, keeps each agent’s context focused on its specific goal, and dynamically adjusts task dependencies and information sharing based on execution evidence, with verification and recovery checks providing feedback for repair.
arXiv Computation and Language
Oct 21
OptionWorld: A Benchmark for Diagnosing Failures of LLMs as Long-Term Personal Assistants
OptionWorld is a new benchmark that tracks how large language models handle long‑term personal assistant tasks, focusing on information gathering, decision making, actions, and outcomes over simulated daily life scenarios. The study finds that in 35.5% of failed cases the key facts are still present in the model’s last input, and identifies three main failure modes: missing or uncombined information, unused action opportunities, and unnecessary restrictions imposed by the agent. Follow‑up experiments show that providing missing facts, adding action reminders, and correcting erroneous restrictions can improve task success, highlighting gaps in state maintenance, deadline adherence, and assumption handling.
arXiv Machine Learning
Jul 31
LEDGERMIND: Provenance-Constrained Multimodal Agentic Reasoning with a Structured Evidence Ledger
arXiv:2607. 28374v1 Announce Type: new Abstract: Multimodal agents for visual question answering increasingly operate as multi-step trajectories that interleave perception, retrieval, and reasoning, yet evaluation still largely reduces to final-answer accuracy.
Hugging Face Trending Papers
Jul 30
LEDGERMIND: Provenance-Constrained Multimodal Agentic Reasoning with a Structured Evidence Ledger
Multimodal agents for visual question answering increasingly operate as multi-step trajectories that interleave perception, retrieval, and reasoning, yet evaluation still largely reduces to final-answer accuracy. This aggregate signal cannot tell whether a correct answer was reached through grounded evidence, language priors, or accidental error cancellation.
arXiv AI
Jun 15
TwinBI: An Agentic Digital Twin for Efficient Augmented Interactions with Business Intelligence Dashboards
arXiv:2606. 13731v1 Announce Type: new Abstract: Business intelligence (BI) increasingly combines dashboard interaction with LLM-based assistance, but these two modes often fall out of sync during multi-step analysis.
arXiv AI
Sep 21
Outcome-Conditioned End-Effector Geometry Across Vision-Language-Action Policies
The study examines how different vision‑language‑action (VLA) policies execute a manipulation task by comparing the geometry of their end‑effectors across 15,000 closed‑loop LIBERO rollouts. By pairing 3,600 configuration‑matched policy executions, the authors find that when both policies succeed, their end‑effector trajectories are much closer (median DTW distance 0.0120 m) than when only one succeeds (0.0380 m), a pattern consistent across all tasks, policy pairs, and nine representations. Even successful executions remain as far from same‑task demonstrations as the demonstrations are from each other, indicating that task‑associated geometry, rather than training data overlap, drives these differences.