arXiv:2604. 00513v3 Announce Type: replace-cross Abstract: With the rapid growth of e-commerce, exploring general representations rather than task-specific ones has attracted increasing attention.
TLive-Omni is an omni‑modal understanding model designed for e‑commerce live streaming, integrating image, video, audio, and text inputs into a unified representation. It introduces Per‑vGrid for timestamped token organization, a three‑stage supervised training pipeline, and a Faithful‑RFT reinforcement fine‑tuning stage to enhance answer faithfulness and expression quality. The model is supported by a scenario‑oriented capability taxonomy and a compact data production engine that generates training signals for tasks such as speech recognition, product visual grounding, and omni‑modal QA, achieving strong performance on live‑commerce benchmarks and good generalization to general tasks.
Recent advances have enabled unified omni-modal models in understanding audio, vision, and language. However, existing benchmarks, training data, and learning methods largely treat the modalities inde...
OmniReasoning introduces a new benchmark, OmniReasoningBench, that requires both audio and visual evidence for answering 1,150 multiple-choice and open-ended questions across two tasks. The authors also develop OmniQA, a data engine that automatically generates evidence‑grounded QA pairs with time‑stamped clue chains, producing training datasets OmniReasoning‑SFT‑112K and OmniReasoning‑RL‑19K. Finally, they propose Modality‑Factored Self‑Distillation (MFSD), an on‑policy self‑distillation method that assigns token‑level credit by evaluating responses under modality‑specific clue contexts, enabling the OmniReasoning‑30B‑A3B model to achieve significant performance gains on both the new benchmark and existing video benchmarks.
The paper introduces AdsCVR, a benchmark for e‑commerce cross‑video reasoning with 2,483 videos and 6,110 QA pairs across six reasoning dimensions. It proposes AdSeek, an agentic framework that actively selects visual and audio tools during multi‑turn exploration and uses an offline trajectory rectification mechanism to improve reinforcement learning. AdSeek achieves 74.30% accuracy on AdsCVR, outperforming its backbone by 27.90 percentage points and generalizes to the open‑domain CrossVid benchmark.
arXiv:2610.02181v1 Announce Type: new Abstract: We present OmniSeek, an agentic framework that transforms an Omni Large Language Model (Omni-LLM) into an active, multi-turn reasoning agent with nativ...
arXiv:2602. 22897v3 Announce Type: replace Abstract: Human intelligence naturally intertwines omni-modal perception -- spanning vision, audio, and language -- with complex reasoning and tool usage to interact with the world.
arXiv:2609.18323v1 Announce Type: new Abstract: Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H...
arXiv:2610.02819v1 Announce Type: new Abstract: Improving joint audio-visual reasoning in Omni Large Language Models typically incurs substantial data construction and training costs. Our diagnostics...
arXiv:2606. 15231v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated impressive capabilities in many visual tasks, but they often struggle with factual grounding when confronted with complex, open-world scenarios.
The paper introduces Training-Free Omni (TFO), a plug‑and‑play framework that transforms a frozen vision‑language model (VLM) into a speech‑centric omni model without modifying its architecture or requiring multimodal re‑alignment. TFO leverages Whisper to generate confidence‑filtered, timestamped transcripts and routes them through the VLM’s existing language interface, leaving the visual pathway untouched. Evaluations on 56 benchmarks across 21 languages show that TFO matches or surpasses native omni models on audio‑visual tasks, improves audio‑only performance, and preserves strong visual and reasoning capabilities.
The paper investigates whether state‑of‑the‑art multi‑modal large language models (LLMs) can detect deepfake images. Twelve recent LLMs were benchmarked against traditional detectors on multiple datasets, including real‑world deepfakes, and prompt tuning was used to improve performance. Results show that the best LLMs can match or exceed conventional methods on out‑of‑distribution data, though many LLMs perform poorly; larger model size helps, but newer versions and reasoning abilities do not necessarily improve detection.