Large Action Models

• Large Action Models are advanced AI systems defined as parameterized policies mapping high-level tasks and observations to executable action sequences.
• LAMs integrate perception, reasoning, and execution through modular pipelines that combine symbolic planning, neural methods, and human-in-the-loop verification.
• Empirical evaluations demonstrate LAMs excel in action anticipation and safe robotic control, achieving high accuracy and improved task success rates.

Large Action Models (LAMs) are advanced AI systems characterized by their ability to generate, plan, and execute sequences of structured actions—ranging from API calls to physical motions—grounded in perception and high-level task context. Unlike conventional LLMs, which operate solely in text generation modality, LAMs extend those reasoning capabilities into dynamic, context-aware action-taking in interactive environments, enabling progress toward embodied, agentic intelligence and robust autonomous agents. LAMs underpin breakthroughs in long-term action anticipation, intelligent robotics, function-calling agents, and multi-modal understanding across varied application domains.

1. Formal Definitions and Scope

A Large Action Model is formally described as a parameterized policy mapping high-level tasks and environment states to executable actions, often modeled as a conditional sequence model. For instance, a LAM in robotics is defined as $\mathcal{A} : (o_{1:t},u) \mapsto a_{1:n}$, where $o_{1:t}$ encodes perceptual streams, $u$ represents a natural-language command, and $a_{1:n}$ is a high-level action trajectory. Probabilistically, $$p_{\text{LAM}}(a_{1:n}|o_{1:t},u) = \prod_{k=1}^n p(a_k|a_{1:k-1},o_{1:t},u)$$ (Sangchai et al., 12 Dec 2025).

LAMs differ from LLMs by:

• Output modality: LLMs produce text, while LAMs emit structured action calls or plans directly (JSON, ASTs, API invocations, sequences of grounded steps).
• Grounding: LAMs interpret environment observations (e.g., UI trees, images, sensor data) and produce executable outputs; LLMs typically require external tool-calling “glue.”
• Planning and execution: LAMs combine symbolic planning, imitation, self-exploration, and reward optimization to ensure action executability (Wang et al., 13 Dec 2024).

2. Representative Architectures and Model Composition

Robotic and Agentic LAM Pipelines

LAM architectures are modular, often factorized into pipeline stages:

• Perception: Multi-modal signal embedding (vision, audio, speech). Foundation models such as CLIP, ViT, SAM, BLIP2 supply dense representations (Sangchai et al., 12 Dec 2025, Liang et al., 13 Mar 2024).
• Reasoning/core: Either LLM-based cognitive agent for neural-direct reasoning or neuro-symbolic planners generating PDDL-compliant action plans.
• Execution/interface: Medium-level orchestration (e.g., via LangChain), low-level control (MoveIt2 for robotics, pywinauto for GUI automation).
• Symbolic wrappers & verification: Plans are parsed/deterministically checked—syntax (balanced parentheses, argument types) and semantics (predicate-object matches, precondition satisfaction).
• Human-in-the-loop verification: Operators validate and edit plans before real-world execution, dramatically mitigating hallucinations and unsafe actions (Sangchai et al., 12 Dec 2025).

Dataflow and API Interfacing

Function-calling LAMs (e.g., xLAM series (Zhang et al., 5 Sep 2024)) rely on unified task formats capturing all relevant metadata, tool descriptions, signatures, and stepwise traces. Robust data pipelines clean, augment, and synthesize agent trajectories, ensuring generalizability for on-device execution and large-scale agent tasks.

Multi-modal Fusion

LAMs in video action anticipation couple frozen visual encoders (SlowFast, ViT-G/14, SwinV2) and action recognizers with LoRA-adapted LLMs or LVLMs (e.g., Llama2-7B, LLaVA-13B), using serialized action tokens and context-prompts for bidirectional sequence modeling (Sato et al., 1 Aug 2025, Peng et al., 6 Sep 2025, Mittal et al., 30 May 2024, Wang et al., 1 Jan 2025).

3. Learning Objectives, Training Protocols, and Prompt Design

Bidirectional and Consistency Objectives

Recent LAMs employ joint forward–backward regularization for sequence modeling. BiAnt (Sato et al., 1 Aug 2025) fine-tunes an LLM on both past→future and future→past sequence loss:

$$L_{\text{total}} = \alpha L_{\text{fwd}} + \beta L_{\text{bwd}}$$

where $L_{\text{fwd}}$ and $L_{\text{bwd}}$ are cross-entropy over autoregressive predictions, and $\alpha = \beta = 1.0$. Backward supervision enforces consistency, reducing cascading errors and yielding improved edit distance on video anticipation tasks.

Semantic Tokenization and Natural-Language Prompts

Video-to-semantic-token modules (VSTs) transform dense frame features into discrete, temporally consistent tokens, which, together with concise instructions (“What is happening?”), can be attended over by LVLMs to perform classification and reasoning (Peng et al., 6 Sep 2025).

Counterfactual and Plausibility Losses

Advanced LAMs (PlausiVL (Mittal et al., 30 May 2024)) incorporate:

• Counterfactual plausible-sequence learning: Contrastive loss penalizing model similarity to implausible action sequences, defined using logical and temporal constraints.
• Long-horizon repetition penalization: Increasing penalties for repeated actions over extended horizons, enhancing diversity and temporal coherence in generated sequences.

Reinforcement and Imitation Protocols

Agentic LAMs are often trained over multi-phase protocols:

• SFT (task-plan pairs)
• Imitation on expert traces
• Self-boosting via model rollouts (e.g., retries on GPT-4o failures)
• Offline RL (e.g., PPO on trajectory reward signals) (Wang et al., 13 Dec 2024)

4. Empirical Evaluation and Quantitative Benchmarks

Action Anticipation and Recognition

Models such as BiAnt (Sato et al., 1 Aug 2025) demonstrate edit-distance improvements on the Ego4D v2 benchmark: BiAnt (ED=0.8655) outperforms both forward-only LLM baselines (AntGPT, Palm). Ablation studies confirm further gains when control tokens (<fwd>/<bwd>) are prepended.

LVLM-VAR (Peng et al., 6 Sep 2025) achieves top-1 action recognition accuracy on NTU RGB+D (94.1%) and NTU RGB+D 120 (90.0%) with state-of-the-art semantic interpretability via natural-language explanations.

PlausiVL (Mittal et al., 30 May 2024) delivers state-of-the-art edit-distance and BLEU gains by enforcing plausibility and diversity in action sequence generation.

ActionLLM (Wang et al., 1 Jan 2025) outperforms prior visual, multimodal, and LLM-based baselines on mean-of-classes accuracy for long-term action anticipation.

Agent Ability and Tool Use

xLAM (Zhang et al., 5 Sep 2024) leads the Berkeley Function-Calling Leaderboard with 87.31% overall accuracy, outperforming proprietary models (GPT-4, Claude-3).

Agentic LAM deployments (Windows OS, AppAgent) yield 81.2% task success rate offline, 71.0% online, with significant gains in completion time (30.4s vs. GPT-4o’s 86.4s) (Wang et al., 13 Dec 2024).

Robotic Planning and Safety

Neuro-symbolic robotic LAMs reach up to 100% task success using tool-based plans, 91% for PDDL-generated plans (subject to symbolic verification). Human-in-the-loop corrections prevent 100% of hallucination-induced unsafe tool uses (Sangchai et al., 12 Dec 2025).

Industrial Recognition

LSFM-based LAMs (LRIHAR (Liang et al., 13 Mar 2024)) deliver 96.84% accuracy, recall over 81% on unseen lines, and real-time inference (<10 ms/image) with massive annotation cost and training time reductions.

5. Interpretability, Verification, and Safety Mechanisms

LAM frameworks embed formal verification at multiple levels:

• Syntax and type checking: Filtering unacceptable outputs, especially in symbolic planning (e.g., PDDL parsing and argument matching) (Sangchai et al., 12 Dec 2025).
• Semantic validation: Ensuring object–predicate and action–precondition congruence prior to execution.
• Human approval workflows: Operators review plans through GUIs, enabling real-time correction of logic errors, preventing action hallucinations, and preserving audit trails.

Neuro-symbolic LAMs provide explainability by outputting intermediate artifacts (PDDL, tool lists) and enabling deterministic replay of plans (Sangchai et al., 12 Dec 2025). LVLM-based sequence models supply natural-language explanations that support human–model agreement (up to 95.2%) (Peng et al., 6 Sep 2025).

6. Limitations, Open Challenges, and Future Directions

• Context-window stress: Scaling to longer horizons remains nontrivial; memory-augmented attention and retrieval-based prompts are proposed (Sato et al., 1 Aug 2025).
• Distribution alignment: Mutual KL or contrastive losses between forward–backward sequence distributions may improve bidirectional consistency.
• Joint perception–action learning: Historically, visual modules are frozen; end-to-end fine-tuning or smaller, domain-specific LLMs could enhance holistic performance (Sato et al., 1 Aug 2025).
• Multi-modal integration: Future LAMs aim to incorporate richer cues (audio, object state, depth), possibly via adapter layers.
• Environment specificity: Ports to new GUIs or robotic platforms require costly new data collection and retraining (Wang et al., 13 Dec 2024).
• Safety, ethical, and regulatory issues: Formal methods for rollback, sandboxing, and bias mitigation are essential as LAMs are deployed in critical infrastructure (Wang et al., 13 Dec 2024, Sangchai et al., 12 Dec 2025).
• Scalability and democratization: Community initiatives supply open LAM benchmarks, code, and datasets, facilitating transparency and robust evaluation (Zhang et al., 5 Sep 2024).

7. Conceptual Extensions and Integration Paradigms

The integration of Large Reasoning Models (LRMs) and LAMs is advocated for robust service composition: LRMs supply intent interpretation and planning (“why”), while LAMs address grounded execution (“how”) including error recovery in dynamic environments (Georgievski et al., 24 Jul 2025). Coordinated inference architectures connect user requests, semantic planning, and service interfacing across layers, establishing closed-loop, fully automated systems.

Symbolic action languages (e.g., BC⁺) are increasingly coupled with LLMs—forming hybrid LLM+AL frameworks for complex reasoning and systematic search, with iterative semantic parsing, program synthesis, and solver-backed feedback loops (Ishay et al., 1 Jan 2025).

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In summary, Large Action Models synthesize perception, reasoning, and execution in scalable, interpretable, and verifiable pipelines—enabling structured agentic interaction across vision, language, planning, and physical control. Progress in LAM research reflects pivotal advances toward autonomous systems that not only understand but robustly act upon their environments, with ongoing innovation at the boundaries of multi-modal fusion, symbolic–neural integration, and safe deployment in diverse, real-world domains.