AI

Foresight: planning future perception in streaming VLMs without retraining

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 06/10/2026, 16:18:24 UTC.

Briefings

This article presents FORESIGHT, a training-free streaming vision-language model architecture that dynamically configures future computation based on real-time scene dynamics. By employing two Siamese LLMs with shared weights, FORESIGHT anticipates immediate future events to plan computation and generate responses without interrupting the streaming inference pipeline. The system achieves significant performance gains on OmniPro Online, beating the strongest trained baseline by 9.5% in mean joint F1, while improving the backbone by 6.7% on StreamingBench and 15.4% on OVO-Bench.

Key points

• FORESIGHT uses a dual-stream architecture with two Siamese LLMs sharing weights to anticipate future context and plan computation dynamically.
• The model achieves 23.0 mean joint F1 on OmniPro Online, outperforming the strongest trained baseline by 9.5%.

Context

Streaming vision-language models (VLMs) currently have fixed computational pathways that cannot adapt to evolving scene dynamics. FORESIGHT addresses this by leveraging inherent anticipation capabilities to dynamically configure future computation in a training-free manner, enabling online reconfiguration with negligible overhead.

Limitations

The study relies on a single source article from 2026. Results are based on a frozen Qwen3-VL-8B backbone and may not generalize to other backbones or hardware configurations. Performance metrics are snapshot-based and do not account for long-term stability or offline evaluation scenarios.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 22/10/2026, 05:55:15 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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