Omni-DeepSearch is a 640-sample benchmark for audio-driven omni-modal search where the best model reaches only 43.44% accuracy, exposing bottlenecks in audio inference, tool use, and cross-modal reasoning.
Canonical reference
Omnigaia: Towards native omni-modal ai agents
Canonical reference. 80% of citing Pith papers cite this work as background.
9
Pith papers citing it
Background
80% of classified citations
abstract
Human intelligence naturally intertwines omni-modal perception -- spanning vision, audio, and language -- with complex reasoning and tool usage to interact with the world. However, current multi-modal LLMs are primarily confined to bi-modal interactions (e.g., vision-language), lacking the unified cognitive capabilities required for general AI assistants. To bridge this gap, we introduce OmniGAIA, a comprehensive benchmark designed to evaluate omni-modal agents on tasks necessitating deep reasoning and multi-turn tool execution across video, audio, and image modalities. Constructed via a novel omni-modal event graph approach, OmniGAIA synthesizes complex, multi-hop queries derived from real-world data that require cross-modal reasoning and external tool integration. Furthermore, we propose OmniAtlas, a native omni-modal foundation agent under tool-integrated reasoning paradigm with active omni-modal perception. Trained on trajectories synthesized via a hindsight-guided tree exploration strategy and OmniDPO for fine-grained error correction, OmniAtlas effectively enhances the tool-use capabilities of existing open-source models. This work marks a step towards next-generation native omni-modal AI assistants for real-world scenarios.
citation-role summary
background 3
baseline 1
dataset 1
citation-polarity summary
years
2026 9verdicts
UNVERDICTED 9representative citing papers
TraceAV-Bench is the first benchmark for multi-hop trajectory reasoning over long audio-visual videos, showing top models reach only 51-68% accuracy with substantial room for improvement.
Chain of Modality dynamically orchestrates multimodal input topologies and bifurcates cognitive execution to overcome static fusion biases in Omni-MLLMs.
LiveServe exposes audio playback and barge-in signals to the scheduler and KV manager, lowering P90 audio TTFP by 1.55x on average and raising completed-request throughput by 1.15x on two Omni-LMs.
Agent-World autonomously synthesizes verifiable real-world tasks and uses continuous self-evolution to train 8B and 14B agents that outperform proprietary models on 23 benchmarks.
A skill-lifecycle agent (create, memory, manage, evaluate, refine) beats Hermes, Codex, and Claude Code on SkillsBench/SkillLearnBench and transfers skills better.
This survey categorizes agentic environments for LLMs by eight attributes and domains, introduces symbolic and neural synthesis paradigms with evaluation, and outlines four agent evolution pathways plus three environment evolution paradigms.
MM-ToolBench introduces 100 closed-loop multimodal tasks across two domains with 27 MCP servers and 324 tools, where agents must execute, inspect artifacts, and revise before final output.
Qwen3.5-Omni scales an omnimodal model to hundreds of billions of parameters with 256k context, introduces ARIA for stable speech synthesis, and reports SOTA performance on 215 audio-visual benchmarks while adding multilingual and audio-visual coding capabilities.
citing papers explorer
-
Omni-DeepSearch: A Benchmark for Audio-Driven Omni-Modal Deep Search
Omni-DeepSearch is a 640-sample benchmark for audio-driven omni-modal search where the best model reaches only 43.44% accuracy, exposing bottlenecks in audio inference, tool use, and cross-modal reasoning.
-
TraceAV-Bench: Benchmarking Multi-Hop Trajectory Reasoning over Long Audio-Visual Videos
TraceAV-Bench is the first benchmark for multi-hop trajectory reasoning over long audio-visual videos, showing top models reach only 51-68% accuracy with substantial room for improvement.
-
Chain of Modality: From Static Fusion to Dynamic Orchestration in Omni-MLLMs
Chain of Modality dynamically orchestrates multimodal input topologies and bifurcates cognitive execution to overcome static fusion biases in Omni-MLLMs.
-
LiveServe: Interaction-Aware Serving for Real-Time Omni-Modal LLMs
LiveServe exposes audio playback and barge-in signals to the scheduler and KV manager, lowering P90 audio TTFP by 1.55x on average and raising completed-request throughput by 1.15x on two Omni-LMs.
-
Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Agent-World autonomously synthesizes verifiable real-world tasks and uses continuous self-evolution to train 8B and 14B agents that outperform proprietary models on 23 benchmarks.
-
MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation
A skill-lifecycle agent (create, memory, manage, evaluate, refine) beats Hermes, Codex, and Claude Code on SkillsBench/SkillLearnBench and transfers skills better.
-
Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application
This survey categorizes agentic environments for LLMs by eight attributes and domains, introduces symbolic and neural synthesis paradigms with evaluation, and outlines four agent evolution pathways plus three environment evolution paradigms.
-
TOBench: A Task-Oriented Omni-Modal Benchmark for Real-World Tool-Using Agents
MM-ToolBench introduces 100 closed-loop multimodal tasks across two domains with 27 MCP servers and 324 tools, where agents must execute, inspect artifacts, and revise before final output.
-
Qwen3.5-Omni Technical Report
Qwen3.5-Omni scales an omnimodal model to hundreds of billions of parameters with 256k context, introduces ARIA for stable speech synthesis, and reports SOTA performance on 215 audio-visual benchmarks while adding multilingual and audio-visual coding capabilities.