# Seen, Said, or Forgotten? A Causal Audit of Visual KV Memory Across Dialog Turns

arXiv: https://arxiv.org/abs/2607.25467 · HTML: https://mlsanity.com/paper/2607.25467/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 25 references that are in the map.
- First posted on arXiv 28 Jul 2026.

## Builds on (most cited first, 21 of 21)

- [Efficient Streaming Language Models with Attention Sinks](https://mlsanity.com/paper/2309.17453/) (2023)
- [SnapKV: LLM Knows What You are Looking for Before Generation](https://mlsanity.com/paper/2404.14469/) (2024)
- [KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache](https://mlsanity.com/paper/2402.02750/) (2024)
- [Visual Dialog](https://mlsanity.com/paper/1611.08669/) (2016)
- [Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference](https://mlsanity.com/paper/2405.05803/) (2024)
- [No Token Left Behind: Reliable KV Cache Compression via Importance-Aware Mixed Precision Quantization](https://mlsanity.com/paper/2402.18096/) (2024)
- [LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference](https://mlsanity.com/paper/2406.18139/) (2024)
- [NACL: A General and Effective KV Cache Eviction Framework for LLMs at Inference Time](https://mlsanity.com/paper/2408.03675/) (2024)
- [VL-Cache: Sparsity and Modality-Aware KV Cache Compression for Vision-Language Model Inference Acceleration](https://mlsanity.com/paper/2410.23317/) (2024)
- [Memory-efficient Streaming VideoLLMs for Real-time Procedural Video Understanding](https://mlsanity.com/paper/2504.13915/) (2025)
- [SparseVILA: Decoupling Visual Sparsity for Efficient VLM Inference](https://mlsanity.com/paper/2510.17777/) (2025)
- [PrefixKV: Adaptive Prefix KV Cache is What Vision Instruction-Following Models Need for Efficient Generation](https://mlsanity.com/paper/2412.03409/) (2024)
- [Make Each Token Count: Towards Improving Long-Context Performance with KV Cache Eviction](https://mlsanity.com/paper/2605.09649/) (2026)
- [FlowKV: Enhancing Multi-Turn Conversational Coherence in LLMs via Isolated Key-Value Cache Management](https://mlsanity.com/paper/2505.15347/) (2025)
- [Predicting Future Utility: Global Combinatorial Optimization for Task-Agnostic KV Cache Eviction](https://mlsanity.com/paper/2602.08585/) (2026)
- [CalibQuant: 1-Bit KV Cache Quantization for Multimodal LLMs](https://mlsanity.com/paper/2502.14882/) (2025)
- [Revisiting Multimodal KV Cache Compression: A Frequency-Domain-Guided Outlier-KV-Aware Approach](https://mlsanity.com/paper/2511.16786/) (2025)
- [SONIC: Segmented Optimized Nexus for Information Compression in Key-Value Caching](https://mlsanity.com/paper/2601.21927/) (2026)
- [RetentiveKV: State-Space Memory for Uncertainty-Aware Multimodal KV Cache Eviction](https://mlsanity.com/paper/2605.04075/) (2026)
- [NestedKV: Nested Memory Routing for Long-Context KV Cache Compression](https://mlsanity.com/paper/2605.26678/) (2026)
- [Last But Not Least: Boundary Attention CalibratiON for Multimodal KV Cache Compression](https://mlsanity.com/paper/2606.14782/) (2026)

## Built on by (most cited first, 1 of 1)

- [PhoenixNest-Video: Evidence-Grounded Multimodal Agent Framework for Automated Video Interview Assessment](https://mlsanity.com/paper/2609.02231/) (2026)

## Abstract (arXiv)

Stateful multimodal assistants encode an image once but may answer questions about it many turns later. Attention-guided visual-KV eviction assumes that evidence irrelevant now will remain dispensable, although future questions are unknown. We ask when a visual fact is actually safe to forget and introduce the Causal Visual Memory Audit (CVMA), a paired single-prefill framework that tests what later answers lose when a visual region, the whole image, or prior assistant text becomes unavailable. On VisDial and ConvBench, current attention can rank future-useful regions worse than random even though a diagnostic marginal-utility control shows substantial selection headroom. Aggregate scores hide this failure when later turns do not need vision; controlled and stock-generated histories reveal a second escape route, in which assistant-text KV replaces image KV for facts already stated but not reliably for unstated facts. In the tested stacks, safe forgetting is supported by low future visual dependence or fact-specific verbalization---not by low current attention.