RedVox benchmark shows speech model safety and fairness vulnerabilities persist under non-adversarial conditions, worsen in non-English languages, and increase with spoken inputs.
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Baichuan-omni-1.5 technical report
25 Pith papers cite this work. Polarity classification is still indexing.
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AVI-Bench is a cognitively inspired benchmark that evaluates Omni-MLLMs on joint audio-visual tasks and reveals substantial limitations in current models.
VoiceGiraffe is a new benchmark showing that long-range memory persistence remains a key bottleneck for large audio language models on hour-scale audio.
Chain of Modality dynamically orchestrates multimodal input topologies and bifurcates cognitive execution to overcome static fusion biases in Omni-MLLMs.
OmniGUI is the first step-level benchmark supplying interleaved image, audio, and video inputs across 709 expert episodes in 29 smartphone apps to evaluate multimodal GUI agents.
XModBench is a tri-modal benchmark that systematically measures cross-modal consistency, modality disparities, and directional imbalances in omni-language models across five task families and all modality combinations.
DualToken disentangles semantics and appearance via separate codebooks in one tokenizer, reporting 0.25 rFID, 82% ImageNet zero-shot accuracy, and gains over VILA-U on understanding and generation benchmarks.
AVOC is a retrieval-inspired token compression framework that improves long-form audio-video understanding in multimodal LLMs by selecting informative tokens based on classical IR principles.
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.
HPAA uses typographic manipulations to create text that humans flag as harmful at 86%+ rates while LLM moderation systems detect it below 1% with only three queries.
Audio-Interaction unifies offline and online audio tasks into one streaming model via the SoundFlow framework and a new 2.6M-item streaming corpus, enabling real-time instruction following and proactive responses.
OmniDrop is a training-free layer-wise token pruning framework for omni-modal LLMs that uses query guidance and temporal diversity to reduce prefill latency by up to 40% and memory by 14.7% while improving benchmark scores by up to 3.58 points.
Video MLLMs show an audio-visual Clever Hans effect relying on visual-acoustic correlations rather than audio verification; Thud interventions diagnose it and a 10K-sample preference alignment improves intervention performance by 28 points.
Omni-modal LLMs exhibit visual preference that emerges in mid-to-late layers, enabling hallucination detection without task-specific training.
Self-distillation of Qwen3-Omni-Thinking on 545k Cogito-Pipe audio reasoning traces yields the best open-source MMAR CoT scores and top-tier challenge ranking.
DeepEyesV2 uses a two-stage cold-start plus reinforcement learning pipeline to produce an agentic multimodal model that adaptively invokes tools and outperforms direct RL on real-world reasoning benchmarks.
Circle-RoPE achieves cross-modal positional disentanglement in VLMs by mapping 2D image tokens to a cone-like annulus orthogonal to the text axis, with PTD=0 eliminating RoPE geometric bias while preserving intra-image structure via alternating geometry encoding.
CogniRoute adds a cognitive schema and route-aware RL to an omni-modal MoE, reaching 59.38% accuracy on a new 118K-example social video QA benchmark and beating prior baselines by 15-27 points.
DuplexOmni achieves real-time full-duplex multimodal interaction by separating an interaction layer from a pluggable thinking layer, supported by a Writer-Director pipeline for continuous-interaction training data.
SEATS adaptively selects and removes non-text tokens before and inside the LLM layers of omni-modal models, yielding 9.3x FLOPs reduction and 4.8x prefill speedup at 10% token retention while keeping 96.3% performance.
Multimodal LLMs suffer Safety Geometry Collapse from modality-induced drift that reduces refusal separability; ReGap corrects drift at inference time using self-rectification signals to restore safety without retraining.
A hybrid-reward progressive RL curriculum enables high-quality chain-of-thought to emerge in audio language models without prior supervised CoT training, yielding SOTA results on MMAR, MMAU, and MMSU benchmarks.
Valley3 is an omni MLLM for e-commerce that uses a four-stage pre-training pipeline plus post-training for controllable reasoning and agentic search, outperforming baselines on e-commerce benchmarks while staying competitive on general ones.
OmniFysics is an omni-modal network using a dynamic physical data engine and evolutive tuning to improve performance on multimodal benchmarks and physics-oriented tasks.
citing papers explorer
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RedVox: Safety and Fairness Gaps in Speech Models Across Languages
RedVox benchmark shows speech model safety and fairness vulnerabilities persist under non-adversarial conditions, worsen in non-English languages, and increase with spoken inputs.
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AVI-Bench: Toward Human-like Audio-Visual Intelligence of Omni-MLLMs
AVI-Bench is a cognitively inspired benchmark that evaluates Omni-MLLMs on joint audio-visual tasks and reveals substantial limitations in current models.
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VoiceGiraffe: A Benchmark for Extreme Long-Context Audio-Language Understanding
VoiceGiraffe is a new benchmark showing that long-range memory persistence remains a key bottleneck for large audio language models on hour-scale audio.
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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.
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OmniGUI: Benchmarking GUI Agents in Omni-Modal Smartphone Environments
OmniGUI is the first step-level benchmark supplying interleaved image, audio, and video inputs across 709 expert episodes in 29 smartphone apps to evaluate multimodal GUI agents.
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XModBench: Benchmarking Cross-Modal Capabilities and Consistency in Omni-Language Models
XModBench is a tri-modal benchmark that systematically measures cross-modal consistency, modality disparities, and directional imbalances in omni-language models across five task families and all modality combinations.
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DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies
DualToken disentangles semantics and appearance via separate codebooks in one tokenizer, reporting 0.25 rFID, 82% ImageNet zero-shot accuracy, and gains over VILA-U on understanding and generation benchmarks.
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AVOC: Enhancing Hour-Level Audio-Video Understanding in Omni-Modal LLMs via Retrieval-Inspired Token Compression
AVOC is a retrieval-inspired token compression framework that improves long-form audio-video understanding in multimodal LLMs by selecting informative tokens based on classical IR principles.
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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.
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What the Eyes See, the LLMs Miss: Exploiting Human Perception for Adversarial Text Attacks
HPAA uses typographic manipulations to create text that humans flag as harmful at 86%+ rates while LLM moderation systems detect it below 1% with only three queries.
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Audio Interaction Model
Audio-Interaction unifies offline and online audio tasks into one streaming model via the SoundFlow framework and a new 2.6M-item streaming corpus, enabling real-time instruction following and proactive responses.
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OmniDrop: Layer-wise Token Pruning for Omni-modal LLMs via Query-Guidance
OmniDrop is a training-free layer-wise token pruning framework for omni-modal LLMs that uses query guidance and temporal diversity to reduce prefill latency by up to 40% and memory by 14.7% while improving benchmark scores by up to 3.58 points.
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When Vision Speaks for Sound
Video MLLMs show an audio-visual Clever Hans effect relying on visual-acoustic correlations rather than audio verification; Thud interventions diagnose it and a 10K-sample preference alignment improves intervention performance by 28 points.
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Beyond Text-Dominance: Understanding Modality Preference of Omni-modal Large Language Models
Omni-modal LLMs exhibit visual preference that emerges in mid-to-late layers, enabling hallucination detection without task-specific training.
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Audio-Cogito: Towards Deep Audio Reasoning in Large Audio Language Models
Self-distillation of Qwen3-Omni-Thinking on 545k Cogito-Pipe audio reasoning traces yields the best open-source MMAR CoT scores and top-tier challenge ranking.
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DeepEyesV2: Toward Agentic Multimodal Model
DeepEyesV2 uses a two-stage cold-start plus reinforcement learning pipeline to produce an agentic multimodal model that adaptively invokes tools and outperforms direct RL on real-world reasoning benchmarks.
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Circle-RoPE: Cone-like Decoupled Rotary Positional Embedding for Large Vision-Language Models
Circle-RoPE achieves cross-modal positional disentanglement in VLMs by mapping 2D image tokens to a cone-like annulus orthogonal to the text axis, with PTD=0 eliminating RoPE geometric bias while preserving intra-image structure via alternating geometry encoding.
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CogniRoute: Learning to Route Social Evidence in Omni-Modal Models
CogniRoute adds a cognitive schema and route-aware RL to an omni-modal MoE, reaching 59.38% accuracy on a new 118K-example social video QA benchmark and beating prior baselines by 15-27 points.
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DuplexOmni: Real-Time Listening, Seeing, Thinking, and Speaking for Full-Duplex Interaction
DuplexOmni achieves real-time full-duplex multimodal interaction by separating an interaction layer from a pluggable thinking layer, supported by a Writer-Director pipeline for continuous-interaction training data.
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Stage-adaptive Token Selection for Efficient Omni-modal LLMs
SEATS adaptively selects and removes non-text tokens before and inside the LLM layers of omni-modal models, yielding 9.3x FLOPs reduction and 4.8x prefill speedup at 10% token retention while keeping 96.3% performance.
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Safety Geometry Collapse in Multimodal LLMs and Adaptive Drift Correction
Multimodal LLMs suffer Safety Geometry Collapse from modality-induced drift that reduces refusal separability; ReGap corrects drift at inference time using self-rectification signals to restore safety without retraining.
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Audio-DeepThinker: Progressive Reasoning-Aware Reinforcement Learning for High-Quality Chain-of-Thought Emergence in Audio Language Models
A hybrid-reward progressive RL curriculum enables high-quality chain-of-thought to emerge in audio language models without prior supervised CoT training, yielding SOTA results on MMAR, MMAU, and MMSU benchmarks.
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Valley3: Scaling Omni Foundation Models for E-commerce
Valley3 is an omni MLLM for e-commerce that uses a four-stage pre-training pipeline plus post-training for controllable reasoning and agentic search, outperforming baselines on e-commerce benchmarks while staying competitive on general ones.
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OmniFysics: Towards Physical Intelligence Evolution via Omni-Modal Signal Processing and Network Optimization
OmniFysics is an omni-modal network using a dynamic physical data engine and evolutive tuning to improve performance on multimodal benchmarks and physics-oriented tasks.
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A Survey of Audio Reasoning in Multimodal Foundation Models
A survey that provides a unified formulation of audio reasoning and reviews advances across Audio-to-Text, Audio-to-Speech, Audio-Visual, and Agentic paradigms while discussing challenges and future directions.