arXiv AI
Generate-then-Rank: Learning Symbolic Goal Interpretation for Embodied Agents
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arXiv AI
Oct 21
How Far Are LLMs from Symbolic Planners? An NLP-Based Perspective
arXiv:2508.01300v2 Announce Type: replace Abstract: AI planning provides agents the ability to act autonomously towards achieving a goal in a given environment. Recent advances in Large Language Mode...
arXiv AI
Sep 28
Agentick: A Unified Benchmark for General Sequential Decision-Making Agents
Agentick is a unified benchmark for sequential decision‑making agents that evaluates RL, LLM, VLM, hybrid, and human agents on 37 procedurally generated tasks across six capability categories, four difficulty levels, and five observation modalities via a single Gymnasium‑compatible interface. It includes a Coding API, oracle reference policies, pre‑built SFT datasets, a composable agent harness, and a live leaderboard. An evaluation of 27 configurations and over 90,000 episodes shows no single approach dominates, with GPT‑5 mini leading overall, PPO excelling in planning and multi‑agent tasks, and the reasoning harness boosting LLM performance by 3‑10×, while ASCII observations outperform natural language.
arXiv AI
Jun 2
Token Predictors Are Not Planners: Building Physically Grounded Causal Reasoners
arXiv:2606. 01810v1 Announce Type: new Abstract: Current benchmarks for embodied vision-language planning often favor linguistic next-token prediction over physically grounded next-state reasoning.
arXiv AI
Oct 28
Does The Way You Plan Matter? An Empirical Study of Planning Representations for LLM Web Agents
The paper investigates how different natural language plan representations affect the performance of large language model (LLM) web agents. Using the PlanAhead framework, the authors automatically classify WebArena tasks into three difficulty levels and evaluate four plan types—sequential subgoals, narrative, pseudocode, and checklist—on hard tasks across multiple multimodal LLM agents. They introduce a decomposition of Success Rate into Achievement Rate and Solved-Task Consistency to better understand stochastic variability, finding that both plan formulation and the LLM used to generate the plan significantly influence agent robustness and success.
arXiv Machine Learning
Aug 27
Infer Human's Intentions Before Following Natural Language Instructions
The paper introduces FISER, a framework that explicitly infers human goals and intentions before planning actions for AI agents to follow natural language instructions in collaborative embodied tasks. It employs Transformer-based models and is evaluated on the HandMeThat benchmark, outperforming end-to-end approaches and strong baselines such as Chain of Thought prompting. FISER achieves state‑of‑the‑art performance on this embodied social reasoning task.
arXiv AI
Oct 1
X-Planner: Event-Structured Task Planning for Embodied Intelligence
arXiv:2609.25187v2 Announce Type: replace Abstract: Task planning bridges high-level instructions and executable behavior in long-horizon manipulation, yet modern Vision-Language-Action (VLA) systems...