Action Models

Source papers 19

The Arxiv papers behind this sub-topic. Links may take you off-site to arxiv.org.

• Agent Workflow Memory
  Despite the potential of language model-based agents to solve real-world tasks such as web navigation, current methods still struggle with long-horizon tasks with complex action trajectories. In contr…
• CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization
  Language agents have shown some ability to interact with an external environment, e.g., a virtual world such as ScienceWorld, to perform complex tasks, e.g., growing a plant, without the startup costs…
• Causal Reflection with Language Models
  While Large Language Models (LLMs) exhibit impressive fluency and factual recall, they struggle with robust causal reasoning often relying on spurious correlations and brittle patterns. Similarly, tra…
• Code as Agent Harness
  Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository-level software engineering. In emerging agent…
• Decision Transformer: Reinforcement Learning via Sequence Modeling
  We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. This allows us to draw upon the simplicity and scalability of the Transformer architecture, and asso…
• FlowMind: Automatic Workflow Generation with LLMs
  The rapidly evolving field of Robotic Process Automation (RPA) has made significant strides in automating repetitive processes, yet its effectiveness diminishes in scenarios requiring spontaneous or u…
• Improving Generalization in Task-oriented Dialogues with Workflows and Action Plans
   Task-oriented dialogue is difficult in part because it involves understanding user inten…
• Large Action Models: From Inception to Implementation
  This evolution requires the transition from traditional Large Language Models (LLMs), which excel at generating textual responses, to Large Action Models (LAMs), designed for action generation and exe…
• Learning Human-Object Interaction as Groups
  Human-Object Interaction Detection (HOI-DET) aims to localize human-object pairs and identify their interactive relationships. To aggregate contextual cues, existing methods typically propagate inform…
• Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction
  The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms—from static imitation to incentive-driven decision mak…
• Planning in Strawberry Fields: Evaluating and Improving the Planning and Scheduling Capabilities of LRM o1
  OpenAI claims that their recent o1 (Strawberry) model has been specifically constructed and trained to escape the normal limitations of autoregressive LLMs–making it a new kind of model: a Large Reaso…
• React - Synergizing Reasoning And Acting In Language Models
  “While large language models (LLMs) have demonstrated impressive performance across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-though…
• Reasoning-Driven Synthetic Data Generation and Evaluation
  Although many AI applications of interest require specialized multi-modal models, relevant data to train such models is inherently scarce or inaccessible. Filling these gaps with human annotators is p…
• SkillOpt: Executive Strategy for Self-Evolving Agent Skills
  Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision—none of which behaves like a deep-learning optimizer for the skill, and none of which relia…
• The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
  The emergence of agentic reinforcement learning (Agentic RL) marks a paradigm shift from conventional reinforcement learning applied to large language models (LLM RL), reframing LLMs from passive sequ…
• Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction
  Abstract: The current paradigm of test-time scaling relies on generating long reasoning traces (“thinking” more) before producing a response. In agent problems that require interaction, this can be do…
• ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis
  Supervised fine-tuning (SFT) is a common method to enhance the tool calling capabilities of Large Language Models (LLMs), with the training data often being synthesized. The current data synthesis pro…
• Tree Search for Language Model Agents
  Autonomous agents powered by language models (LMs) have demonstrated promise in their ability to perform decision-making tasks such as web automation. However, a key limitation remains: LMs, primarily…
• Working with AI: Measuring the Occupational Implications of Generative AI
  In this work, we take a step toward that goal by analyzing the work activities people do with AI, how successfully and broadly those activities are done, and combine that with data on what occupations…