OmniAgent: Unified Multi-Modal AI

• OmniAgent is a family of architectures that integrate multi-modal perception, dynamic memory, reasoning, and action into a unified, context-aware framework.
• It employs active perception via native POMDP formulations and LLM-mediated tool orchestration, optimizing computational resources by selectively processing task-relevant evidence.
• Applications span video understanding, audio-visual reasoning, mobile-native systems, and hierarchical multi-agent orchestration, achieving state-of-the-art performance in efficiency and accuracy.

Searching arXiv for papers on OmniAgent and closely related usages of the term. “OmniAgent” is not a single standardized model in the arXiv literature but a recurring designation for agent architectures that unify perception, memory, reasoning, and action across multiple modalities or execution environments. Recent usages span native omni-modal video understanding, audio-guided active perception, Android-based personal agents, memory backbones for personalized long-horizon assistants, and hierarchical multi-agent systems for long-video generation. Across these formulations, the common objective is to replace passive, uniformly exhaustive processing with selective, context-aware interaction loops that allocate computation only where task-relevant evidence is needed (Xing et al., 17 Jun 2026, Tao et al., 29 Dec 2025, Ren et al., 7 May 2026, Wei et al., 25 Oct 2025).

1. Scope, nomenclature, and early lineage

The term appears in several distinct but related senses. In some papers it denotes a single omni-modal agent that directly interleaves perception and reasoning; in others it names an architectural stack of modules; in still others it denotes a graph of specialized collaborating agents.

Usage	Domain	Defining mechanism
OmniAgent	Long video understanding	POMDP-based Observation–Thought–Action cycle
OmniAgent	Audio-video understanding	Audio-guided Think–Act–Observe–Reflect loop
X-OmniClaw “OmniAgent” architecture	Android ecosystem	Omni Perception, Omni Memory, Omni Action
OmniAgent	Long video generation	Hierarchical graph, hypergraph nodes, cyclic feedback

A useful antecedent is OmAgent, which predates the later “OmniAgent” naming pattern and already instantiated two persistent themes: retrieval-backed long-video understanding and agentic task decomposition. OmAgent combines Video2RAG with a Divide-and-Conquer Loop, and on a 2,000-question long-form video benchmark it reports 45.5% overall accuracy, compared with 28.6% for Frames+STT and 27.3% for Video2RAG only; on Event Loc. it reports 19.1% versus 2.4% and 4.8%, and on External Knowl. it reports 57.2% versus 19.5% and 23.4% (Zhang et al., 2024). This earlier system suggests that later OmniAgent variants inherit a preexisting agentic video-understanding line rather than emerging ex nihilo.

2. Native active perception as omni-modal reasoning

The most explicit formalization appears in “Native Active Perception as Reasoning for Omni-Modal Understanding”, which presents OmniAgent as the first native omni-modal agent and casts video understanding as a POMDP over iterative perception, reasoning, and action (Xing et al., 17 Jun 2026). The POMDP is defined by the tuple $(S, A, O, T, R)$, with state

$s_k = (\mathcal{M}_k, \mathcal{E}_k),$

where $\mathcal{M}_k$ is a persistent textual memory and $\mathcal{E}_k$ is a transient multimodal percept. The action space is

$$A_k \in \mathcal{A} = \{\texttt{get\_frames}(s,e,n),\ \texttt{get\_audio}(s,e),\ \texttt{get\_clip}(s,e),\ \texttt{answer}(y)\}.$$

Observations are text summaries distilled from the transient percept, and memory is updated as a growing sequence of triples $(O_j,T_j,A_j)$.

Operationally, the system runs an Observation–Thought–Action cycle. At each turn, it first distills the current percept into a compact text encoding, then performs chain-of-thought reasoning over the accumulated memory to identify information gaps, and finally selects one of the media-access actions or terminates with an answer. Internally, a visual encoder $\phi_v$ maps frames to embeddings, an audio encoder $\phi_a$ maps waveform to embeddings, and a summarization head $\psi$ produces

$$O_k = \psi(\phi_v(\text{frames}), \phi_a(\text{audio}), \mathcal{M}_{k-1}; \theta).$$

Because the memory stores text rather than raw media, the paper states that context size is decoupled from raw video duration.

Training proceeds in two stages. Agentic Supervised Fine-Tuning synthesizes trajectories via a teacher LLM with best-of-$s_k = (\mathcal{M}_k, \mathcal{E}_k),$0 sampling and dual-stage quality control: Outcome Verification retains trajectories with reward above task thresholds, and Rationality Audit uses GPT-4o to score each reasoning step on a 5-point scale, retaining trajectories with coherence $s_k = (\mathcal{M}_k, \mathcal{E}_k),$1. Agentic Reinforcement Learning with TAURA then addresses what the paper calls “advantage homogenization” in standard GRPO by rescaling trajectory-level advantage with turn-level entropy so that uncertain turns receive amplified credit or penalty (Xing et al., 17 Jun 2026).

A central empirical claim is positive test-time scaling. On VideoMME-Long, increasing the maximum allowed turns from $s_k = (\mathcal{M}_k, \mathcal{E}_k),$2 to $s_k = (\mathcal{M}_k, \mathcal{E}_k),$3 raises accuracy from 53.4% to 59.6%, a +6.2% gain, while the agent self-terminates after approximately 11.7 turns on average. Across ten benchmarks, OmniAgent-7B is reported as state of the art among open-source models: VideoMME overall 67.8% versus Qwen2.5-Omni 64.8%, MLVU 71.1% versus 65.2%, and LVBench 50.5% versus Qwen2.5-VL-72B 47.3%. For audio-visual reasoning it reports DailyOmni 64.8% (+4.7%) and OmniVideo 37.1% (+7.8%); for temporal grounding, LongVALE IoU 39.1% (+33.4%) and VUE-TR 36.5% (+33.0%). The paper further states that a 7B active model outperforms a 72B passive model while sampling 73% fewer frames (Xing et al., 17 Jun 2026).

3. Audio-guided active perception and tool orchestration

A distinct use of the name appears in “OmniAgent: Audio-Guided Active Perception Agent for Omnimodal Audio-Video Understanding”, where OmniAgent is a fully audio-guided active perception agent built around a frozen LLM that orchestrates specialized tools rather than a natively trained end-to-end omni-modal policy (Tao et al., 29 Dec 2025). The inference loop is Think–Act–Observe–Reflect: the LLM planner inspects the query and memory, emits a tool call, receives a textual observation, appends it to memory, and either halts with ANSWER or continues.

The paper’s core mechanism is a coarse-to-fine audio-guided perception paradigm. An Event Location tool first uses the audio stream to localize candidate intervals:

$s_k = (\mathcal{M}_k, \mathcal{E}_k),$4

after which a Clip_QA tool inspects the corresponding video segment at higher frame rate:

$s_k = (\mathcal{M}_k, \mathcal{E}_k),$5

The tool inventory includes Global_QA and Clip_QA for video, ASR, Global_Caption, and Audio_QA for audio, and Event_List and Event_Location for audio-event reasoning. The paper is explicit that this is an inference-only framework: the LLM “agent brain” is a frozen OpenAI o3, the tools are pre-trained models, and there is no unified loss or curriculum (Tao et al., 29 Dec 2025).

On three benchmarks the paper reports state-of-the-art results. On Daily-Omni, OmniAgent attains 82.71%, compared with 72.7% for Gemini 2.5-Flash w/ CoT and 72.1% for Qwen3-Omni-30B. On OmniVideoBench, it reports 59.1%, compared with 52.4% for the best closed-source model and 38.4% for the best open-source model. On WorldSense, it reports 61.2%, compared with 50.9% for the best closed-source model and 56.5% for video-SALMONN 2+. Tool-usage analysis indicates that agents begin with Global_Caption (audio) and later call VCA, and the paper states that removing audio guidance drops accuracy by approximately 12 percentage points. A practical limitation is latency: inference is reported at approximately 71 s, though this is still below the 104 s DVD baseline (Tao et al., 29 Dec 2025).

Taken together with the native POMDP formulation above, this line of work shows that “active perception” in OmniAgent research has two distinct meanings: native policy learning over an integrated action space, and LLM-mediated orchestration of external perceptual tools. This suggests an unresolved design split rather than a settled consensus.

4. Mobile-native and memory-centric OmniAgent systems

In “X-OmniClaw Technical Report: A Unified Mobile Agent for Multimodal Understanding and Interaction,” the report explicitly describes an OmniAgent architecture composed of Omni Perception, Omni Memory, and Omni Action, running entirely on the user’s Android device (Ren et al., 7 May 2026). The data flow is specified as: Multimodal trigger → Temporal alignment & buffering → VLM-assisted scene intent parsing → Agent Loop (reason + planning + memory retrieval) → Grounding via hybrid XML/vision/OCR → Android action execution → New screen/UI context. Perception ingests UI dumps, screen-projected visuals, live camera frames, microphone audio and aligns them with a Temporal Alignment Module based on shared timestamps.

The memory subsystem combines runtime working memory with long-term personal memory distilled from local data. The action subsystem uses a hybrid grounding strategy that rescales candidate UI elements by structural XML metadata, visual grounding, and OCR:

$s_k = (\mathcal{M}_k, \mathcal{E}_k),$6

The report also emphasizes Behavior Cloning and Trajectory Replay, in which user navigation traces are summarized into reusable skill descriptors and replayed by deep-link launch, structural tapping fallback, or task-stack restoration.

The evaluation in this report is qualitative rather than benchmark-based. Scenario A reduces user effort from approximately 1 minute of manual search to approximately 5 seconds of automated summary; Scenario B compresses a 5–10 minute manual video-composition workflow into approximately 3 automated steps; Scenario C reduces a 6-step navigation to a single query invocation. The report explicitly notes that it does not include a formal benchmark table or numeric metrics and there is no ablation study or head-to-head comparison with other mobile-agent systems (Ren et al., 7 May 2026).

A complementary memory-centered formulation appears in “O-Mem: Omni Memory System for Personalized, Long Horizon, Self-Evolving Agents,” where O-Mem serves as the memory backbone of an “OmniAgent” (Wang et al., 17 Nov 2025). Its architecture comprises Persona Memory, Working Memory, Episodic Memory, and an Inference Interface. Persona updates are driven by active user profiling, while retrieval combines persona similarity, topic continuity, and clue-based episodic recall. Quantitatively, O-Mem reports 51.67% on LoCoMo, a nearly 3% improvement upon LangMem, and 62.99% on PERSONAMEM, a 3.5% improvement upon A-Mem. On LoCoMo with GPT-4.1, Direct RAG is reported at F1=50.25%, Tokens≈2.6K, latency=4.01 s, whereas O-Mem reports F1=51.67%, Tokens≈1.5K, latency=2.36 s, corresponding to approximately 42% token reduction and 41% latency reduction. The paper further states that a profile alignment score increases monotonically and empirically converges after 100–200 exchanges, while deep-research-task alignment improves from 42.14% to 44.49% (Wang et al., 17 Nov 2025).

5. OmniAgent as hierarchical multi-agent orchestration

A further usage appears in “Hollywood Town: Long-Video Generation via Cross-Modal Multi-Agent Orchestration,” where OmniAgent is a hierarchical, graph-based multi-agent framework for long video generation rather than a single conversational agent (Wei et al., 25 Oct 2025). The system organizes specialized agents into a film-production-inspired pipeline, spanning concept development, scriptwriting, storyboarding, asset generation, editing, rendering, and audio.

Formally, the orchestration graph is a directed cyclic graph

$s_k = (\mathcal{M}_k, \mathcal{E}_k),$7

with each node $s_k = (\mathcal{M}_k, \mathcal{E}_k),$8 containing private memory, communication partners, a reasoning function, and tool inventory. Two mechanisms distinguish this system. First, hypergraph nodes support temporary group discussions when an agent lacks sufficient context. Second, the framework allows reverse edges with a bounded retry budget, replacing a strict DAG with a cyclic graph that supports reflective upstream revision. The paper reports a retry budget of $s_k = (\mathcal{M}_k, \mathcal{E}_k),$9.

The hypergraph mechanism is intended to reduce memory burden. The paper contrasts a baseline per-agent memory of $\mathcal{M}_k$0 with $\mathcal{M}_k$1 under OmniAgent’s context engineering, and states that in practice $\mathcal{M}_k$2 yields 4–8× memory reduction (Wei et al., 25 Oct 2025). This is a markedly different sense of “OmniAgent” from the single-policy video-understanding papers: here the problem is not selective media inspection but scalable coordination among role-specialized agents.

Experimental validation uses three single-sentence prompts targeting approximately 1 minute of generated video and a six-dimension FilmEval rubric: NS/AT/AE/RF/EE/OE, each scored on a 1–5 Likert scale by 12 audience members and 4 experts. Compared with a flat chain, setting2_hier_no_ctx improves NS +0.12 and RF +0.14, while setting3_full improves AE +0.40, EE +0.52, AT +0.10, and OE +0.25. Expert evaluation reports significant gains on Aesthetics/Expression (AE; $\mathcal{M}_k$3, $\mathcal{M}_k$4) in pooled analyses (Wei et al., 25 Oct 2025).

6. Position within the broader omni-modal agent literature

The OmniAgent literature sits within a larger shift toward native omni-modal agents, tool-integrated reasoning, and persistent autonomy. OmniGAIA and OmniAtlas provide a benchmark-centered perspective. OmniGAIA comprises 360 open-form, multi-domain tasks in video + audio and image + audio settings, and the paper states that it is the only benchmark supporting video+audio, image+audio, multi-hop reasoning, external tools, multi-domain coverage, and open-form answers (Li et al., 26 Feb 2026). Under its Pass@1 protocol, Qwen3-Omni reports 13.3%, while OmniAtlas-Qwen3-Omni reports 20.8%; Gemini-3-Pro reports 62.5%. Error analysis attributes many failures to ineffective tool calls (35%–92%) and reasoning errors (16%–80%), while perception errors remain high at approximately 30%–50%. The same paper reports that for strong agents, native ingestion outperforms “vision as a tool” or “audio as a tool” in both accuracy and cost (Li et al., 26 Feb 2026).

At the embodied end of the spectrum, OmniAct argues that persistent autonomy requires a hierarchical asynchronous architecture with explicit separation of planning, memory, and verification (Shi et al., 25 Jun 2026). It integrates a multimodal semantic planner, adaptive hierarchical memory with event-boundary-driven compression, and an asynchronous visual preemption engine. Across 40 real-world long-horizon tasks on two robotic platforms coordinating four IoT devices, the paper reports improved end-to-end success at all complexity levels, near-flat token consumption over under 100k+ accumulated interaction tokens, and substantial gains for mid-scale open-weight models. Its context-growth comparison is especially relevant to OmniAgent research: Raw per-call context rises from 20 k at round 50 to 130 k at round 270, whereas OmniAct rises from 6 k to 9.5 k over the same horizon (Shi et al., 25 Jun 2026).

These neighboring results clarify three recurrent misconceptions. First, omni-modal does not necessarily imply a monolithic end-to-end model; the literature includes both native policies and tool-mediated systems. Second, active perception is not synonymous with external tool use; in some formulations it is intrinsic to the learned policy itself. Third, the label OmniAgent does not identify a single benchmarked interface or canonical architecture. A plausible implication is that the term functions as a family-resemblance label for architectures that jointly optimize modality integration, selective evidence acquisition, persistent memory, and action under long-horizon constraints. Future directions named across these papers include parallelized multi-turn exploration, richer memory architectures, dynamic memory lifecycle, self-evolving execution, secure device–cloud gateways, and broader transfer to other continuous-signal domains and everyday physical autonomy (Xing et al., 17 Jun 2026, Ren et al., 7 May 2026, Shi et al., 25 Jun 2026).