Pith. sign in

REVIEW 4 cited by

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2501.05901 v2 pith:4GY6E7E5 submitted 2025-01-10 cs.CV

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Ziheng Wu , Zhenghao Chen , Ruipu Luo , Can Zhang , Yuan Gao , Zhentao He , Xian Wang , Haoran Lin
show 1 more author
Minghui Qiu
This is my paper
classification cs.CV
keywords modelsvalley2e-commercelargemodelmultimodalperformancevideo
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Recently, vision-language models have made remarkable progress, demonstrating outstanding capabilities in various tasks such as image captioning and video understanding. We introduce Valley2, a novel multimodal large language model designed to enhance performance across all domains and extend the boundaries of practical applications in e-commerce and short video scenarios. Notably, Valley2 achieves state-of-the-art (SOTA) performance on e-commerce benchmarks, surpassing open-source models of similar size by a large margin (79.66 vs. 72.76). Additionally, Valley2 ranks second on the OpenCompass leaderboard among models with fewer than 10B parameters, with an impressive average score of 67.4. The code and model weights are open-sourced at https://github.com/bytedance/Valley.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Learning to Deny: Action Denial in Multimodal Large Language Models

    cs.CV 2026-06 unverdicted novelty 7.0

    MLLMs drop from over 85% accuracy on action presence to under 50% on matched action-denial videos, exposing a causal verification gap that causal graph prompts partially close.

  2. R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model?

    cs.LG 2026-02 conditional novelty 6.0

    A collective adversarial data-synthesis pipeline produces 20K synthetic multimodal training examples whose GRPO-trained 7B model beats several listed open-source MLLMs on reasoning benchmarks.

  3. Valley3: Scaling Omni Foundation Models for E-commerce

    cs.AI 2026-05 unverdicted novelty 4.0

    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 comp...

  4. VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding

    cs.CV 2025-01 unverdicted novelty 4.0

    VideoLLaMA3 uses a vision-centric training paradigm and token-reduction design to reach competitive results on image and video benchmarks.