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BirdsongChat: A Hybrid Multi-Agent Framework for Multimodal Embodied Behavior Simulation
Callie C. Liao, Duoduo Liao, Ellie L. Zhang
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 93%
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Summary
The paper introduces BirdsongChat, a hybrid multi-agent framework for multimodal embodied behavior simulation. It utilizes a Unified Parameter Representation (UPR) to bridge LLM-based semantic reasoning with physically-based simulation agents. This approach generates synchronized 3D motion, spatialized soundscapes, and environmental behaviors from text and image inputs, demonstrating high cross-modal coherence and affective consistency in avian behavior simulation.
Entities (13)
Relation Signals (14)
BirdsongChat → uses → Unified Parameter Representation
confidence 98% · We present a hybrid multi-agent framework... through a Unified Parameter Representation (UPR).
Unified Parameter Representation → bridges → semantic reasoning
confidence 95% · bridges semantic reasoning and physical execution through a Unified Parameter Representation (UPR)
Unified Parameter Representation → bridges → physical execution
confidence 95% · bridges semantic reasoning and physical execution through a Unified Parameter Representation (UPR)
Orchestrator Agent → coordinates → reasoning agents
confidence 95% · The Orchestrator Agent coordinates interactions among reasoning agents
Orchestrator Agent → coordinates → simulation agents
confidence 95% · The Orchestrator Agent coordinates interactions among ... physically-based simulation agents
Interpretation Agent → extracts → semantic attributes
confidence 95% · The Interpretation Agent analyzes multimodal inputs and extracts semantic attributes
Kinematic Agent → generates → 3D trajectories
confidence 95% · The Kinematic Agent generates physically grounded 3D trajectories
Acoustic Agent → generates → vocalizations
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Abstract
Abstract:Multimodal embodied systems require translating human intentions into interpretable and coordinated behaviors across heterogeneous modalities. However, existing multimodal agents often rely on implicit representations, limiting controllability and cross-modal consistency. We present a hybrid multi-agent framework for interactive multimodal behavior simulation that bridges semantic reasoning and physical execution through a Unified Parameter Representation (UPR). LLM-based reasoning agents transform multimodal inputs into UPR, which encodes behavioral states and interpretable control parameters for simulation agents generating synchronized 3D motion, spatialized soundscapes, and environmental behaviors. We develop BirdsongChat as a prototype implementation of the proposed framework, using interactive avian behavior simulation as a testbed that tightly couples motion, vocalization, and environmental context. BirdsongChat is evaluated on text- and image-guided scenarios involving species, behaviors, affective states, environments, and multi-bird interactions. The system achieves normalized scores of 94.4\% for cross-modal coherence, 100% for affective consistency, and 92.6% for generation consistency. These results demonstrate that an explicit intermediate representation effectively bridges semantic reasoning and physical execution, improving controllability and multimodal synchronization. The proposed framework thus offers a generalizable design principle for embodied AI systems requiring interpretable semantic-to-physical coordination across modalities, with potential applications in bio-inspired ecoacoustics, swarm robotics, virtual environments, and creative multimedia.
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- Source: https://arxiv.org/abs/2609.20887v1
- Canonical: https://arxiv.org/abs/2609.20887v1
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BirdsongChat: A Hybrid Multi-Agent Framework for Multimodal Embodied Behavior Simulation Callie C. Liao∗ Affiliation: Dept. of Computer Science Stanford University Stanford, CA, USA ccliao@cs.stanford.edu Duoduo Liao∗ Affiliation: School of Computing George Mason University Fairfax, VA, USA dliao2@gmu.edu Ellie L. Zhang∗ Affiliation: IntelliSky McLean, VA, USA Abstract Multimodal embodied systems require translating human intentions into interpretable and coordinated behaviors across heterogeneous modalities. However, existing multimodal agents often rely on implicit representations, limiting controllability and cross-modal consistency. We present a hybrid multi-agent framework for interactive multimodal behavior simulation that bridges semantic reasoning and physical execution through a Unified Parameter Representation (UPR). LLM-based reasoning agents transform multimodal inputs into UPR, which encodes behavioral states and interpretable control parameters for simulation agents generating synchronized 3D motion, spatialized soundscapes, and environmental behaviors. We develop BirdsongChat† as a prototype implementation of the proposed framework, using interactive avian behavior simulation as a testbed that tightly couples motion, vocalization, and environmental context. BirdsongChat is evaluated on text- and image-guided scenarios involving species, behaviors, affective states, environments, and multi-bird interactions. The system achieves normalized scores of 94.4% for cross-modal coherence, 100% for affective consistency, and 92.6% for generation consistency. These results demonstrate that an explicit intermediate representation effectively bridges semantic reasoning and physical execution, improving controllability and multimodal synchronization. The proposed framework thus offers a generalizable design principle for embodied AI systems requiring interpretable semantic-to-physical coordination across modalities, with potential applications in bio-inspired ecoacoustics, swarm robotics, virtual environments, and creative multimedia. Index Terms: Embodied AI, Multi-Agent Systems, Large Language Models, Semantic-to-Physical Transformation, Unified Parameter Representation, Cross-Modal Consistency, Avian Behavior Simulation, Birdsong Soundscapes. **footnotetext: Equal contribution.†footnotetext: The demo video is available via the provided link. Fig. 1: BirdsongChat: Interactive Multimodal Control and Generation of Synchronized 3D Bird Motion and Soundscape. Foundation models perform semantic interpretation of species, behavior, and context from text and images. The resulting UPR parameters control algorithmic simulators for motion, sound, and environment generation. Fig. 2: Overview of the BirdsongChat framework for physically grounded multimodal generation and control. The architecture bridges high-level semantic reasoning with low-level physical execution through a hierarchical design including four functional layers, maintaining interpretability, physical plausibility, and cross-modal coherence. I Introduction Recent advances in Large Language Models (LLMs) and multimodal foundation models have significantly improved the ability of AI systems to interpret human instructions, reason over complex contexts, and interact with multimodal information. Vision-language models such as Flamingo [1], embodied models such as PaLM-E [2], and unified multimodal models such as VILA-U [3] demonstrate progress toward integrated multimodal understanding and generation. Recent generative models further achieve high-quality synthesis across image, audio, and motion domains [4, 5]. These advances motivate embodied systems that translate multimodal intentions into coordinated physical behaviors. However, a fundamental challenge remains: how can multimodal reasoning be connected to interpretable, physically grounded, and cross-modally consistent behavior generation? Existing embodied agents primarily focus on planning and action execution [2, 6], while multimodal generative models often rely on implicit representations for end-to-end synthesis [4, 5]. Although effective for understanding and generation, these approaches provide limited control over intermediate behavioral states and physical constraints, motivating explicit representations that bridge semantic reasoning and physical execution. In this work, we study how multimodal semantic intentions can be transformed into coordinated physical behaviors in embodied environments using interactive avian behavior simulation as a concrete testbed in which motion, vocalization, and environmental context are tightly coupled. Building on agent-based behavioral models and procedural simulation approaches [7], we present a hybrid multi-agent framework that connects LLM-based reasoning agents and physically-based simulation agents through an interpretable Unified Parameter Representation (UPR). The proposed framework combines reasoning agents for multimodal interpretation, orchestration, and semantic-to-physical transformation with simulation agents that execute UPR parameters to generate coordinated motion, vocalizations, spatialized soundscapes, and environment-aware scenes. The UPR serves as a shared interface between semantic representations and physical controls, preserving consistency across modalities. Unlike latent-based end-to-end generation, this architecture separates reasoning from execution, providing controllable and interpretable multimodal behavior generation. As a prototype implementation, BirdsongChat is evaluated on text- and image-guided scenarios involving species, behaviors, affective states, environments, and multi-bird interactions. Results demonstrate that an explicit intermediate representation effectively bridges semantic reasoning and physical execution, improving controllability and multimodal synchronization. The framework can further accommodate additional modalities, with broad applicability to bio-inspired ecoacoustics, swarm robotics, virtual environments, and interactive multimedia. The main contributions are as follows: • Unified Parameter Representation (UPR): An interpretable intermediate representation that bridges high-level semantic reasoning and low-level physical execution through shared behavioral states and explicit control parameters, maintaining cross-modal consistency and controllability. • UPR-driven hybrid multi-agent framework: A hybrid architecture that translates multimodal semantic reasoning into physical behaviors by encoding behavioral information into UPR to drive coordinated simulation agents, providing synchronized motion, sound, and visual scene generation and a generalizable design principle for embodied AI. • BirdsongChat: An interactive avian simulation that demonstrates the proposed framework by generating synchronized avian motion, vocalizations, spatialized soundscapes, and environment-aware scenes from text and image inputs. I Related Work I-A LLM-based Embodied Agents and Grounding LLMs have increasingly been integrated into embodied systems as high-level reasoning and decision-making components. SayCan [8] combines language reasoning with robot skill selection, while PaLM-E [2], RT-2 [9], and vision-language-action models such as OpenVLA [6] extend multimodal reasoning toward embodied control. Recent foundation-model-based systems further explore reasoning, orchestration, and interaction in complex environments [10, 11]. These approaches demonstrate the potential of foundation models for instruction understanding and physical action coordination. However, they primarily focus on task planning, skill invocation, or action prediction, with limited explicit representations connecting semantic intent to continuous physical behaviors. The proposed framework extends this direction by introducing the UPR that provides an interpretable interface between semantic reasoning and physically executable simulation. I-B Multimodal Foundation Models and Cross-Modal Generation Multimodal foundation models have advanced cross-modal understanding and generation across language, vision, and audio. Flamingo [1] and BLIP-2 [12] demonstrate strong vision-language alignment, while unified models such as VILA-U [3], OmniFlow [13], and MIO [14] explore joint multimodal understanding and generation. Recent generative models also achieve high-quality synthesis across modalities, including text-to-image, audio, and motion generation [4, 15, 16]. However, these models typically rely on implicit representations optimized for end-to-end generation, providing limited control over intermediate behavioral states and physically constrained processes. Our framework addresses this limitation through an explicit semantic-to-physical interface that maps multimodal reasoning outputs to interpretable kinematic, acoustic, and environmental parameters. I-C Physically Grounded Simulation and Multi-Agent Systems Physically-based simulation provides interpretable mechanisms for generating complex behaviors under explicit constraints. Agent-based models such as Boids [7] demonstrate emergent collective behavior through local interaction rules, while stochastic dynamical systems [17, 18] provide foundations for controllable motion variation. Recent work on embodied intelligence and spatial understanding further emphasizes the importance of grounding agents in physical environments [19]. Meanwhile, language-driven agent frameworks such as ReAct [20], Voyager [21], Generative Agents [22], and AgentMaster [23] demonstrate the ability of LLMs to support reasoning, planning, memory, exploration, and multi-agent interaction. However, these lines of work largely emphasize either language-driven decision-making or physically grounded behavior generation, with limited focus on tightly coupling LLM reasoning with interpretable, physically based multi-agent simulation. Our framework bridges these two paradigms by integrating LLM-based reasoning agents with physically based simulation agents through the UPR, enabling synchronized and interpretable multimodal behavior generation. I Methodology I-A System Overview The proposed hybrid multi-agent framework integrates LLM-based reasoning agents with physically-based simulation agents for interactive multimodal behavior simulation. It generates coordinated 3D motion, procedural spatialized soundscapes, and environment-aware scenes from text and image inputs. As shown in Figure 2, They architecture consists of four layers: (1) an Input Layer for multimodal interaction, (2) a Reasoning and Orchestration Layer with LLM-based agents for semantic interpretation and semantic-to-physical transformation, (3) an Execution Layer with specialized agents for physically-based simulation, and (4) an Output Layer for synchronized audiovisual rendering. The key design principle is to separate semantic reasoning from modality-specific simulation. LLM-based reasoning agents transform multimodal inputs into semantic and behavioral representations, which are mapped to interpretable control parameters through the UPR. The UPR serves as an intermediate representation between reasoning and execution, encoding shared behavioral states that coordinate hybrid simulation agents. Motion, acoustic, and rendering agents derive modality-specific parameters from the same UPR state, maintaining temporal alignment and behavioral consistency across generated modalities. Rather than directly synthesizing audiovisual outputs with neural generative models, the system uses UPR-driven algorithmic simulators to produce controllable and physically grounded multimodal behaviors. I-B Unified Parameter Representation (UPR) I-B1 UPR Definition The Unified Parameter Representation (UPR) is an intermediate representation that bridges LLM-based reasoning agents and physically-based simulation agents. It encodes shared semantic, behavioral, kinematic, acoustic, and environmental states required for coordinated multimodal behavior generation: =sem,beh,kin,acous,env.P=\P_sem,P_beh,P_kin,P_acous,P_env\. (1) Here, semP_sem represents semantic attributes inferred from multimodal inputs, behP_beh represents intermediate behavioral states, kinP_kin contains kinematic control parameters, acousP_acous contains acoustic synthesis and spatialization parameters, and envP_env represents environmental conditions. The UPR defines a hierarchical semantic-to-physical transformation: (sem,env)→beh→(kin,acous),(P_sem,P_env) _beh→(P_kin,P_acous), (2) where high-level multimodal descriptions are transformed into interpretable behavioral states and modality-specific control parameters. By maintaining a shared behavioral representation, the UPR preserves consistency between user intent, simulated motion, acoustic events, and environmental rendering. For instance, given the prompt “An alarmed robin calls out from a wind-swayed tree branch,” the UPR maps semantic intent (semP_sem) and environmental conditions (envP_env) into a shared high-alert behavioral state (behP_beh). This intermediate state then jointly drives physical execution of both modalities, producing rapid head-turn trajectories (kinP_kin) alongside high-pitched, spatialized alarm calls (acousP_acous). A unified behavioral state ensures cross-modal consistency across motion, acoustics, and environmental responsiveness. Unlike task-oriented representations that primarily encode action sequences or symbolic plans, UPR encodes semantic attributes, behavioral states, and continuous physical controls across heterogeneous modalities. Rather than merely specifying behaviors, UPR provides a shared representation for coordinating modality-specific simulation processes through consistent behavioral states and physical controls. I-B2 UPR State Initialization and Evolution During generation and interactive refinement, the UPR is maintained as a constrained behavioral state representation updated by reasoning agents according to multimodal inputs and simulation objectives. Given an initial UPR state (t)p(t), multimodal observations and semantic interpretations (t)Z(t), and global objectives G, the Orchestrator Agent computes state updates: ℱ:((t),(t),)→Δ,F:(Z(t),p(t),G)→ , (3) where G specifies constraints including semantic consistency, multi-agent coordination, and interactive requirements. The update consists of two components. Observation-conditioned updates, ΔH=ℋ((t),(t)), _H=H(Z(t),p(t)), (4) capture changes introduced by new multimodal inputs and semantic interpretation. Goal-conditioned updates, ΔΩ=Ω((t),), _ = (p(t),G), (5) maintain consistency among simulated agents, behavioral states, and environmental conditions. The complete update is: Δ=ΔH+ΔΩ, = _H+ _ , (6) followed by projection into a valid state space: (t+Δt)=Πvalid((t)+Δ),p(t+ t)= _P_valid(p(t)+ ), (7) where Πvalid _P_valid enforces semantic, physical, and cross-modal constraints before the updated UPR state is interpreted by physically-based simulation agents. In the current implementation, ℋH and Ω are realized through structured prompting strategies and rule-based constraints rather than learned state dynamics. I-C Input Layer The Input Layer receives multimodal instructions, including text, images, and optional audio descriptions. Inputs may specify object types, behavioral states, affective expressions, environmental conditions, and interaction scenarios. The system first extracts structured semantic attributes, which are then transformed into behavioral representations and physical control parameters through the reasoning and orchestration layers. I-D Reasoning and Orchestration Layer The Reasoning and Orchestration Layer performs multimodal interpretation, behavioral reasoning, agent coordination, and semantic-to-physical transformation through two classes of specialized LLM-based agents: the Orchestrator Agent and Reasoning Agents. I-D1 Orchestrator Agent The Orchestrator Agent coordinates interactions among reasoning agents and physically-based simulation agents, maintaining consistency across semantic states, behavioral representations, and execution parameters by updating the shared UPR throughout initial generation and interactive refinement. I-D2 Reasoning Agents The LLM-based reasoning agents operate on multimodal inputs and maintain the shared UPR representation that connects high-level semantic intent with physically executable simulation parameters. Interpretation Agent. The Interpretation Agent analyzes multimodal inputs and extracts semantic attributes, including type identity, environmental context, behavioral intent, affective state, and interaction information. These attributes form the semantic representation used for downstream transformation. Transformation Agent. The Transformation Agent performs semantic-to-physical mapping by converting semantic and behavioral representations into interpretable kinematic and acoustic parameters stored in the UPR. These parameters are subsequently executed by physically-based simulation agents. I-D3 Semantic-to-Physical Mapping The proposed architecture performs explicit semantic-to-physical transformation through the UPR rather than directly generating motion and sound from multimodal inputs. Semantic attributes are represented as: sem=stype,sbehavior,semotion,sinteraction,P_sem=\s_type,s_behavior,s_emotion,s_interaction\, (8) and environmental attributes are represented as: env=escene,etime,eweather.P_env=\e_scene,e_time,e_weather\. (9) These attributes are transformed into a shared behavioral state within the UPR: beh=F(sem,env),P_beh=F(P_sem,P_env), (10) where behP_beh encodes behavior-level properties, including activity intensity, movement style, vocalization tendency, and interaction patterns. The shared behavioral state is then interpreted by modality-specific simulation agents to derive execution parameters: kin=Gkin(beh,sem,env),P_kin=G_kin(P_beh,P_sem,P_env), (11) acous=Gacous(beh,sem,env).P_acous=G_acous(P_beh,P_sem,P_env). (12) The resulting kinematic and acoustic parameters provide a compact, interpretable control representation for the physically-based simulation agents, governing flight speed, trajectory variation, vocalization rate, frequency characteristics, and spatial sound properties. Through the joint representation of motion and acoustics, the UPR enables the simulation agents to generate coordinated 3D behaviors and spatialized soundscapes while preserving consistency with the original multimodal intent. The low-dimensional parameterization provides direct control over both kinematic and acoustic behaviors, supporting synchronized and expressive simulation. I-E Execution Layer The Execution Layer translates the shared UPR state into coordinated multimodal outputs through specialized physics-based simulation agents. Each agent derives modality-specific control parameters from the UPR and executes them using interpretable computational models with explicit control over kinematic, acoustic, and environmental variables. These agents directly execute the UPR without semantic reasoning or decision-making, producing physically grounded, reproducible, and cross-modally consistent simulations. I-E1 Kinematic Agent The Kinematic Agent generates physically grounded 3D trajectories using kinematic parameters derived from the UPR state. The motion model uses stochastic continuous-time dynamics to produce smooth and controllable flight behaviors with plausible variations [7]. Species characteristics, behavioral states, and environmental conditions are encoded through the kinematic parameters, providing diverse and interpretable motion generation. Position Each flying or moving object is modeled as a stochastic self-propelled particle in a three-dimensional environment. The state of object i at time t is represented by its position i(t)=[xi(t)yi(t)zi(t)],x_i(t)= bmatrixx_i(t)\\ y_i(t)\\ z_i(t) bmatrix, (13) where i(t)x_i(t) denotes the object’s position in world coordinates. Velocity The translational velocity of object i is represented by its horizontal speed, horizontal heading, and vertical speed: ˙i(t)=[vi(t)cosφi(t)vi(t)sinφi(t)vz,i(t)], x_i(t)= bmatrixv_i(t) _i(t)\\ v_i(t) _i(t)\\ v_z,i(t) bmatrix, (14) where vi(t)v_i(t) denotes the horizontal speed, φi(t) _i(t) specifies the horizontal heading (azimuth), and vz,i(t)v_z,i(t) denotes the vertical speed. Thus, the translation motion state is characterized by φi(t) _i(t), vi(t)v_i(t), and vz,i(t)v_z,i(t). This formulation determines the continuous position evolution while allowing stochastic motion in three-dimensional space [7]. The motion dynamics and orientation dynamics evolve according to stochastic Ornstein–Uhlenbeck process [17]. The motion state is controlled by the UPR kinematic representation kinP_kin with parameter definitions and behavioral effects given in Table I. I-E2 Acoustic Agent The acoustic agent generates procedural bird-like vocalizations and multi-object spatialized soundscapes [24] through signal-based synthesis and spatial acoustic modeling [25]. The agent directly controls acoustic, temporal, and spatial properties through interpretable variables, with acousP_acous derived from semantic, behavioral, species, and environmental attributes and coordinated with the UPR to ensure consistency between vocal behavior and simulated motion. 3D Spatialization Each object i emits a sequence of chirps, where ci,k(t)c_i,k(t) denotes the waveform of the k-th chirp emitted by object i at time t. The chirp is parameterized by species-specific acoustic features. Each vocal event is spatialized along the corresponding object trajectory i(t)x_i(t): i,k(t)=A(i(t))ci,k(t),s_i,k(t)=A\! (x_i(t) )c_i,k(t), (15) where A(i(t))A\! (x_i(t) ) denotes the spatial acoustic operator for distance attenuation, directional gain, and stereo localization. Independent trajectories produce independently positioned vocal sources, forming a synchronized multi-agent 3D sound field. I-E3 Rendering Agent The Rendering Agent generates environment-aware visual scenes from envP_env and simulated object trajectories. Environmental attributes, including habitat, lighting, and weather conditions, are synchronized with motion and acoustic generation to maintain consistency across visual, auditory, and behavioral modalities. I-E4 Kinematic and Acoustic Control Parameters Table I summarizes the kinematic and acoustic parameters forming a compact, interpretable representation for coordinated 3D motion and spatialized soundscapes. TABLE I: Kinematic and Acoustic Control Parameters Parameter Description Behavioral Effect Kinematic x→0 x_0 Equilibrium position Nominal CoM location Δx→ x Oscillation amplitudes Path curvature λ Relaxation rates Agility σ Noise amplitudes Stochastic variability v0v_0 Preferred horizontal speed Target forward velocity vz,0v_z,0 Preferred vertical speed Target climb/descent rate ψ0 _0 Equilibrium pitch Nominal body orientation Acoustic f0,f1f_0,f_1 Chirp start/end frequencies Frequency sweep range d Chirp duration Length of vocalization α Trill strength Pitch oscillation ftrillf_trill Trill rate Vibrato speed A()A(p) Spatial gain Stereo localization TABLE I: Category Definitions Dimension Values Description Bird Species owl, nightingale, lark, warbler, sparrow, finch, raven Distinct vocal patterns for species-specific acoustic assessment Environments night, moon, moonlight, daytime, sun, forest, storm, rain, wind, sand, home Guides scene composition and 3D auditory spatialization Emotional States happy, fear, sad Modulates motion (speed, trajectory) and sound (pitch, rhythm, intensity) TABLE I: Score definitions for cross-modal coherence, affective consistency, and generation consistency (0–3 scale). Score Cross-Modal Coherence Affective Consistency Generation Consistency 3 Strong motion, sound, and environment alignment; physically plausible Behavior and sound match the specified affective descriptor Stable outputs across runs with natural variation 2 Minor mismatch but overall coherent Partial affective correspondence Noticeable variation with preserved semantics 1 Major mismatch or implausible behavior Weak or unclear affective correspondence Large variation affecting consistency 0 Failed multimodal generation No affective correspondence Inconsistent outputs across runs I-F Output Layer and Interactive Loop The Output Layer integrates the outputs of the physically-based simulation agents into a synchronized multimodal scene containing 3D motion, spatialized birdsong, and environment-aware rendering. The framework supports iterative interaction through a closed-loop process. Users can modify species, behaviors, affective states, or environmental conditions through additional multimodal instructions. The LLM-based reasoning agents update semantic and behavioral representations, the Orchestrator Agent propagates these changes through the UPR, and then the physically-based simulation agents regenerate the resulting multimodal outputs. This architecture maintains an interpretable connection between LLM-based reasoning agents and physically-based simulation agents through the UPR. By decoupling semantic reasoning from physical execution, it provides explicit control over semantic, behavioral, kinematic, acoustic, and environmental states, forming a general framework for interactive multimodal embodied behavior generation. IV Experiments and Evaluations We develop BirdsongChat as a prototype system to implement and evaluate the proposed hybrid multi-agent framework, which integrates reasoning agents with physics-based simulation agents to generate coordinated multi-bird motion, spatialized birdsong soundscapes, and environment-aware scenes from multimodal inputs. The evaluation examines semantic alignment, cross-modal coherence, interactive refinement, and simulation consistency across text- and image-guided scenarios. IV-A Experimental Setup The BirdsongChat prototype is deployed on Amazon Web Services (AWS). For these experiments, the prototype uses the Amazon Nova 2 multimodal AI foundation model to support reasoning agents for multimodal interpretation, orchestration, and semantic-to-physical transformation; however, the model can be interchanged with any other model. The evaluation covers combinations of bird species, behavioral descriptions, affective states, and environmental conditions represented in the UPR. Table I summarizes the evaluated input categories. TABLE IV: Text prompt alignment effectiveness with per-sample scores (0–3 scale). ID Text Prompt Environment Affect Species Correct Coh. Aff. Gen. 1 The moonlight shines brightly on the damp forest floor. Night, Moon, Forest Fear/Sad Nightingale, Owl Yes 3 3 3 2 The backyard forest sings harmonically with the newly bloomed flowers. Daytime, Sun, Forest Happy Sparrow, Finch, Raven Yes 3 3 3 3 The birds sing happily with another under the gleaming sun. Sun, Wind, Forest Happy Sparrow, Finch, Raven Yes 3 3 3 4 In a gloomy dark night, birds chirp silently in the forbidden forest of fear. Night, Forest Fear Nightingale, Owl Yes 3 3 3 5 The birds shudder as a haunting call echoes from the distance. Night, Moon Fear Owl, Nightingale Yes 3 3 3 6 Everyone in the forest chirps excitedly as the new dawn comes. Sun, Wind, Forest Happy Sparrow, Raven, Finch Yes 3 3 3 7 The forest is ravaged by a storm, the birds displaced from their homes. Night, Forest, Wind Sad Finch Yes 3 3 3 8 The birds freak out and scatter away. Storm, Wind, (Forest) Fear Raven, Sparrow Yes 3 3 2 9 The birds danced on the dunes, feeling elated that the sunlight has finally come. Sun, Wind, (Sand) Happy Lark Yes 3 3 2 10 The birds all mourn in silence at night, the passings still fresh in their minds. Night, Moon, Forest Sad Nightingale, Owl Yes 3 3 3 11 The birds cower from the shadows looming over the forest. Night, Forest Fear/Sad Owl, Nightingale Yes 3 3 3 12 Under the sunlight, the birds fly around animatedly. Sun, Wind, (Forest) Happy Sparrow, Raven, Finch Yes 3 3 3 TABLE V: Image prompt evaluation results with per-sample scores (0–3 scale). ID Image Context Environment Affect Species Coh. Aff. Gen. 1 Sunset Sun, Forest, Storm Happy Raven, Sparrow, Finch 2 3 2 2 Beach with sand and sea Sun, Sand, Daytime Happy Lark 3 3 3 3 Beach with sand, sea, and pine tree Sun, Sand Happy Lark 3 3 3 4 Nighttime moon and sea Night, Moon, Sea Fear Nightingale, Owl 3 3 3 5 Thunderstorm Storm, Wind Fear Raven, Sparrow 3 3 3 6 Foggy golf course Fog, Forest Happy Nightingale, Owl 1 3 2 (a) Image 1 (b) Image 2 (c) Image 3 (d) Image 4 (e) Image 5 (f) Image 6 Fig. 3: Experimental image prompt examples. (a) BirdsongChat generates birdsong with 3D flight trajectories and spectrograms, visualizing bird species, routes, and trail points. A melancholic nighttime prompt produces slower, lower-frequency sounds featuring nocturnal species such as owls and nightingales. Only trail points are shown for a clearer view of bird positions. (b) The display shows the L–R spectrum, with the lower graph highlighting frequency differences and left–right channel bias. Collapsed GUI sliders slightly enlarge the bird objects. Flight trails, trail points, and axes are hidden. (c) This display shows the birdsong activity timeline and stereo waveforms. The lower graph highlights channel differences, while the timeline marks bird calls by species and color. Call patterns vary, with warblers producing shorter, more consistent calls than finches. Only the birds are shown to simulate a natural flight scene. (d) This display shows bird frequencies, pitches, and call timing. Higher pitches suggest positive sentiment, while lower pitches suggest negative sentiment. Here, fast, high-frequency calls reflect a positive mood. Enlarged birds and flight trails highlight each bird’s calculations. Fig. 4: BirdsongChat interface and examples. Users can directly copy prompts to try the AWS web-based prototype system. TABLE VI: Quantitative evaluation results (0–3 scale). Each prompt is executed five times to evaluate semantic alignment and generation consistency. Metric Average Min Max Score (%) Text Prompts (12 prompts, 60 runs) Cross-modal Coherence 3.00 3 3 100% Affective Consistency 3.00 3 3 100% Generation Consistency 2.83 2 3 94.4% Overall 2.94 2 3 98.1% Image Prompts (6 prompts, 30 runs) Cross-modal Coherence 2.50 1 3 83.3% Affective Consistency 3.00 3 3 100% Generation Consistency 2.67 2 3 88.9% Overall 2.72 1 3 90.7% Overall Evaluation (18 prompts, 90 runs) Cross-modal Coherence 2.83 1 3 94.4% Affective Consistency 3.00 3 3 100% Generation Consistency 2.78 2 3 92.6% Overall 2.87 1 3 95.7% IV-B Qualitative Evaluation BirdsongChat is evaluated across representative scenarios to assess whether the interaction between LLM-based reasoning agents and physics-based simulation agents maintains semantic consistency, behavioral plausibility, and cross-modal alignment among generated motion, sound, and environmental context (Figures 4). The evaluation focuses on affective behavior mapping, environmental grounding, multimodal consistency, stochastic variation, multi-bird coordination, and interactive refinement. Affective Behavior Mapping. The LLM-based reasoning agents interpret affective descriptors such as happy, sad, and fear and transform them into behavioral representations within the UPR. Physically-based simulation agents subsequently convert these representations into coordinated motion and acoustic variations. High-activity descriptors result in increased flight speed, trajectory variation, and denser vocalization patterns, whereas low-activity descriptors produce slower motion and sparser acoustic events. These results demonstrate the effectiveness of the UPR as an intermediate semantic-to-physical interface. Environment-Conditioned Behaviors. Multimodal inputs, including text and images, provide environmental context that influences semantic interpretation, behavioral representation, and scene generation. Different habitats and temporal conditions lead to corresponding variations in simulated species, motion patterns, acoustic characteristics, and environmental rendering. These results demonstrate that contextual information is propagated from reasoning agents to physically-based simulation agents through the UPR. Cross-Modal Alignment. Motion and acoustic generation are coordinated through the shared UPR representation. Changes in simulated bird trajectories influence spatial sound properties, including localization, attenuation, and temporal relationships among multiple vocal sources. The resulting audiovisual outputs maintain consistency among simulated motion, acoustic events, and environmental context. Stochastic Variation and Simulation Stability. The physically-based simulation agents incorporate bounded stochastic processes to introduce natural variation while preserving controllability. Stochastic motion dynamics and acoustic modulation generate diverse trajectories and vocalization patterns without significant deviation from the intended semantic conditions. This enables repeatable simulation while maintaining behavioral diversity. Multi-Bird Control and Interactive Refinement. The framework supports simultaneous simulation of multiple birds and species within shared environments. Through the UPR interface, users can modify species, behaviors, affective descriptors, and environmental conditions using conversational commands or image prompts. These updates are propagated from reasoning agents to simulation agents while preserving consistency across motion, sound, and rendering. IV-C Quantitative Evaluation IV-C1 Quantitative Evaluation Protocol Representative scenarios cover combinations of bird species, environmental contexts, and affective descriptors defined in Section IV-A. Each generated simulation is evaluated according to the criteria defined in Table I. The evaluation measures three aspects of system performance: (1) cross-modal coherence, measuring alignment among semantic inputs, generated motion, acoustic outputs, and environmental context; (2) affective consistency, measuring whether simulated behavioral and acoustic characteristics correspond to the affective descriptors specified in the input; and (3) simulation consistency, measuring the stability of generated behaviors across repeated executions under identical prompts. IV-C2 Evaluation Datasets and Procedures 18 prompts are evaluated, including 12 text-based prompts and 6 image-based prompts. Each prompt is executed five times, resulting in 90 generated simulations. The user-provided input consists only of the text or image prompt, while species selection, behavioral interpretation, and simulation parameters are determined by the LLM-based reasoning agents and transformed into UPR parameters. Generated simulations are manually evaluated according to the criteria defined in Table I. Detailed per-prompt evaluation results are provided for text-based inputs in Table IV and image-based inputs in Table V. IV-C3 Quantitative Results Table VI summarizes the quantitative evaluation results. Repeated executions evaluate semantic alignment and simulation consistency under stochastic generation. The cross-modal coherence scores demonstrate the effectiveness of the UPR in maintaining consistency between semantic descriptions, simulated trajectories, vocalization patterns, and environmental context. For text prompts (12 prompts, 60 runs), BirdsongChat achieves perfect cross-modal coherence and affective consistency, with average scores of 3.00 (100%) for both metrics. Simulation consistency reaches 2.83 (94.4%), indicating stable behavior generation while preserving controlled stochastic variations. For image prompts (6 prompts, 30 runs), the system maintains perfect affective consistency with a score of 3.00 (100%). Cross-modal coherence achieves 2.50 (83.3%), with lower scores mainly resulting from ambiguity in visual interpretation, including environmental understanding and species selection. Simulation consistency remains high at 2.67 (88.9%), demonstrating reliable repeated simulation under visually conditioned scenarios. Across all evaluations (18 prompts, 90 runs), BirdsongChat achieves 2.83 (94.4%) for cross-modal coherence, 3.00 (100%) for affective consistency, and 2.78 (92.6%) for simulation consistency. These results demonstrate that the UPR effectively maintains semantic, kinematic, acoustic, and environmental alignment while supporting stable multimodal simulation across repeated executions. IV-C4 Analysis and Discussion The quantitative results demonstrate the feasibility of the proposed hybrid multi-agent framework for connecting multimodal reasoning with physically based simulation. The UPR provides an explicit interface between reasoning and execution, encoding behavioral states and interpretable control parameters for coordinating motion, acoustics, and environmental rendering. As a prototype implementation, BirdsongChat demonstrates this design in interactive avian behavior simulation. Evaluation across text and image inputs shows consistent affective alignment, with 100% affective consistency for both modalities. Text inputs achieve higher cross-modal coherence (100%) and generation consistency (94.4%) than image inputs (83.3% and 88.9%), respectively. The lower image-based coherence suggests that visually complex or ambiguous environmental contexts remain challenging for semantic-to-physical grounding, while the 90.7% overall image consistency indicates that UPR remains effective for heterogeneous inputs. These results suggest that separating semantic interpretation from physical execution through an explicit intermediate representation can support interpretable and controllable behavior generation. Although demonstrated in an avian simulation setting, the proposed design provides a potential basis for embodied systems requiring coordinated translation of multimodal intent into physical behavior. V Conclusion We presented a hybrid multi-agent framework for interactive cross-modal semantic-to-physical coordination in embodied environments. The framework uses a UPR to provide an interpretable interface between multimodal reasoning agents and physically based simulation agents. BirdsongChat, a prototype implementation of this framework, demonstrates its feasibility in interactive avian behavior simulation, demonstrating how shared behavioral and control parameters can coordinate motion, acoustics, and environmental rendering. Across the evaluated text- and image-guided scenarios, the system achieves 94.4% cross-modal coherence, 100% affective consistency, and 92.6% generation consistency. The results suggest that explicit intermediate representations can help preserve semantic intent across heterogeneous modalities while enabling controllable physical execution. More broadly, the proposed framework offers a general design principle for embodied AI systems that require interpretable coordination between multimodal reasoning and physical behavior. VI Limitations and Future Work Although the proposed framework provides an interpretable approach to semantic-to-physical simulation, several challenges remain. First, the reasoning layer depends on foundation models and is therefore susceptible to semantic ambiguity and reasoning errors that may propagate to downstream simulation. Future work will explore uncertainty-aware reasoning, verification mechanisms, and more robust multi-agent coordination. Second, mapping high-level semantic descriptions to physical parameters remains challenging. While UPR provides an explicit interface, more adaptive representations are needed to generalize across species, behaviors, and application domains. Third, the current evaluation is limited in scale. Our experiments provide an initial validation using 18 prompts with repeated executions and manual assessment based on predefined semantic, affective, and cross-modal consistency criteria. Larger evaluations with more diverse scenarios and independent human assessments are needed to examine behavioral plausibility, realism, and user perception. Finally, BirdsongChat currently emphasizes controllable and perceptually plausible simulation rather than comprehensive ecological modeling. Future work should investigate richer ecological interactions, larger multi-agent environments, and real-time execution. VII Broader Impact and Ethics The proposed framework contributes to embodied AI research by exploring an interpretable interface between multimodal semantic reasoning and controllable physical simulation through coordinated reasoning and simulation agents. BirdsongChat, as a prototype implementation, demonstrates this approach in interactive avian behavior simulation. The framework may support future applications in bio-inspired ecoacoustics, swarm robotics, interactive simulation, virtual environments, ecological education, and creative media. 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