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awesome-streaming-agents

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A curated list of streaming agents, covering AI systems (LLM/VLM/VLA/video gen.) that are time-sensitive, real-time responsive, proactive, and capable of processing unbounded streaming inputs.

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> A curated list of **streaming agents**, covering AI systems (VLM/VLA/video gen.) that are **time-sensitive**, **real-time responsive**, **proactive** (continuously activated without explicit user prompts), and capable of processing **unbounded streaming inputs**. Papers listed in reverse chronological order.

## Contents

- [A. Streaming Multimodal Model](#a-streaming-multimodal-model)
- [B. Proactive Interaction & Continuous Activation](#b-proactive-interaction--continuous-activation)
- [C. Streaming VLA & Real-Time Embodied Control](#c-streaming-vla--real-time-embodied-control)
- [D. Time-Sensitive Serving & Streaming Backends](#d-time-sensitive-serving--streaming-backends)
- [Benchmarks](#benchmarks)
- [Contributing](#contributing)

---

**Note on AI-Generated / Automated Research:**
With the rise of AI agents in scientific discovery, we notice an increasing number of exploratory preprints fully or heavily generated by automated AI research pipelines. While we respect these automated explorations, their technical depth, soundness, and reproducibility may vary significantly from traditional human-authored peer-reviewed works. To help readers filter information efficiently, such papers will be explicitly tagged.

---

## A. Streaming Multimodal Model

*Models that accept continuous, unbounded video/audio streams with constant-memory KV-cache or low-latency constraints, maintaining long-term understanding. Includes full-duplex audio-visual interaction models.*

- **Wan-Streamer**: *Wan-Streamer v0.1: End-to-end Real-time Interactive Foundation Models*. *arXiv, Jun 2026*.
  - **Authors/Institution**: Lianghua Huang, Zhifan Wu, Wei Wang et al. (Alibaba Wan Team)
  - **Core Contribution**: First native streaming end-to-end real-time full-duplex audio-visual interaction foundation model. Causal streaming of language, audio, and video within a single Transformer (~200ms model latency, ~550ms total). No external VAD/ASR/LLM/TTS/avatar modules.
  - **Links**: [[Paper](https://arxiv.org/abs/2606.25041)] | [[Project](https://wan-streamer.com/)]

- **Stream3D-VLM**: *Stream3D-VLM: Online 3D Spatial Understanding with Incremental Geometry Priors*. *arXiv, Jun 2026*.
  - **Authors/Institution**: Hanxun Yu, Xuan Qu, Lei Ke et al. (Zhejiang University; Tencent Hunyuan)
  - **Core Contribution**: First streaming online 3D VLM — autoregressive streaming control via next-token prediction for response timing, Visual-Spatial Feature Integration (VSFI) for incremental geometry priors, and Geometry-Adaptive Voxel Compression (GAVC) for efficient token compression. 1M+ online 3D QA pairs across 29 tasks.
  - **Links**: [[Paper](https://arxiv.org/abs/2606.06891)] | [[Project](https://stream3d-vlm.github.io/)]

- **DuplexOmni**: *DuplexOmni: Real-Time Listening, Seeing, Thinking, and Speaking for Full-Duplex Interaction*. *arXiv, Jun 2026*.
  - **Authors/Institution**: Muye Huang, Lingling Zhang, Xingyu Yu et al. (Xi'an Jiaotong University, Peking University; Meituan)
  - **Core Contribution**: Decouples real-time multimodal full-duplex interaction into asynchronous parallel dual-layer architecture — interaction layer (end-to-end streaming audio/video → text/speech, low latency) + thinking layer (pluggable reasoning and tool-use, background execution). Full-duplex supports in-speech interruption and continuous perception-expression.
  - **Links**: [[Paper](https://arxiv.org/abs/2606.09186)]

- **JoyAI-VL-Interaction**: *JoyAI-VL-Interaction: Real-Time Vision-Language Interaction Intelligence*. *JD.com Tech Report, Jun 2026*.
  - **Authors/Institution**: Dingyu Yao, Junhao Zhou, Chenxu Yang et al. (JD.com)
  - **Core Contribution**: Real-time streaming VLM with a predictive video codec (AdaCodec) that spends only a few tokens on predictable frames to keep token growth sub-linear, plus a pluggable long-horizon memory for cross-hour context. Proactive (vision-triggered) responses are learned from 4M+ second-aligned clips and refined with reinforcement learning. Supports AI-companion scenarios: live commentary, guidance, real-time translation, monitoring/alerting, and agent delegation.
  - **Links**: [[Project](https://joyai-vl-video-future-academy-jd.github.io/JoyAI-VL-Interaction/)] | [[Code](https://github.com/jd-opensource/JoyAI-VL-Interaction)]

- **StreamReady**: *StreamReady: Learning What to Answer and When in Long Streaming Videos*. *CVPR, 2026*.
  - **Authors/Institution**: Shehreen Azad, Vibhav Vineet, Yogesh Singh Rawat (UCF CRCV, Microsoft Research)
  - **Core Contribution**: First work treating "when to answer" as a first-class metric. Introduces Answer Readiness Score (asymmetric early/late penalties), three-tier visual memory tree (FIFO → K-Means → EMA prototypes), and `<RDY>` token with readiness head. ProReady-QA benchmark (32 videos, 5000 proactive QA with evidence-window annotations).
  - **Links**: [[Paper](https://arxiv.org/abs/2603.08620)] | [[Project](https://sacrcv.github.io/StreamReady-website/)]

- **VST (Video Streaming Thinking)**: *Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously*. *arXiv, Mar 2026*.
  - **Authors/Institution**: Yiran Guan, Liang Yin, Dingkang Liang et al. (HUST, Xiaomi MiLM)
  - **Core Contribution**: Introduces "thinking while watching" paradigm — interleaves proactive pre-query reasoning with video consumption, amortizing LLM reasoning latency over playback. VST-SFT + VST-RL post-training pipeline with video knowledge-graph data synthesis. VST-7B: 79.5% StreamingBench, 15.7× faster than Video-R1.
  - **Links**: [[Paper](https://arxiv.org/abs/2603.12262)] | [[Code](https://github.com/1ranGuan/VST)]

- **OmniStream**: *OmniStream: Mastering Perception, Reconstruction and Action in Continuous Streams*. *arXiv, Mar 2026*.
  - **Authors/Institution**: SJTU, Shanghai Innovation Institute, VGG Oxford
  - **Core Contribution**: Unified causal streaming ViT backbone — extends DINOv3 with causal spatiotemporal attention, 3D-RoPE, and KV-cache inference. Frozen backbone generalizes across semantic, 3D reconstruction, VLM, and VLA tasks. Streaming inference reduces complexity from O(T²) to O(T); 15× faster at T=64.
  - **Links**: [[Paper](https://arxiv.org/abs/2603.12265)]

- **FluxMem**: *FluxMem: Adaptive Hierarchical Memory for Streaming Video Understanding*. *CVPR, 2026*.
  - **Authors/Institution**: Yiweng Xie, Bo He, Junke Wang et al. (Fudan University; University of Maryland)
  - **Core Contribution**: Training-free adaptive hierarchical memory for streaming video: a short/mid/long-term memory bank with dynamic retention/eviction (capacity-driven overflow) and self-adaptive token compression via two lightweight modules — TAS (Temporal Adjacency Selection) and SDC (Spatial Domain Consolidation) — with Otsu-based data-driven thresholds. Keeps long-term context under a constant memory budget.
  - **Links**: [[Paper](https://arxiv.org/abs/2603.02096)]

- **HERMES**: *HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding*. *ACL, 2026*.
  - **Authors/Institution**: Haowei Zhang, Shudong Yang, Jinlan Fu et al. (Fudan University; National University of Singapore)
  - **Core Contribution**: Training-free hierarchical KV-cache organization for streaming video — a short/mid/long-term memory framework spanning multiple granularities. At query time it reads the pre-built hierarchical KV cache directly with no auxiliary computation, enabling efficient streaming without retraining or architectural changes.
  - **Links**: [[Paper](https://arxiv.org/abs/2601.14724)]

- **VideoScaffold**: *VideoScaffold: Elastic-Scale Visual Hierarchies for Streaming Video Understanding in MLLMs*. *arXiv, Dec 2025*.
  - **Authors/Institution**: Naishan Zheng, Jie Huang, Qingpei Guo, Feng Zhao et al.
  - **Core Contribution**: Dynamic representation framework with Elastic-scale Event Segmentation (EES) and Hierarchical Event Consolidation (HEC). Adaptively adjusts event granularity based on video duration, enabling smooth transition from fine-grained frame understanding to abstract event reasoning in streaming. Plug-and-play for image-based MLLMs.
  - **Links**: [[Paper](https://arxiv.org/abs/2512.22226)] | [[Code](https://github.com/zheng980629/VideoScaffold)]

- **StreamingVLM**: *StreamingVLM: Real-Time Understanding for Infinite Video Streams*. *ICLR, 2026*.
  - **Authors/Institution**: Ruyi Xu, Guangxuan Xiao, Yukang Chen et al. (MIT Han Lab)
  - **Core Contribution**: Unified framework aligning training (overlapped-chunk SFT) with streaming inference (compact KV-cache: attention sinks + rolling vision buffer + rolling text buffer + contiguous RoPE). 8 FPS on H100 for 2h+ videos. 66.18% win rate vs GPT-4o mini on Inf-Streams-Eval; also boosts offline VQA (+4.30 LongVideoBench).
  - **Links**: [[Paper](https://arxiv.org/abs/2510.09608)] | [[Code](https://github.com/mit-han-lab/streaming-vlm)] | [[Demo](https://streamingvlm.hanlab.ai)]

- **JanusVLN**: *JanusVLN: Decoupling Semantics and Spatiality with Dual Implicit Memory for Vision-Language Navigation*. *ICLR, 2026*.
  - **Authors/Institution**: Shuang Zeng, Dekang Qi, Xinyuan Chang, Feng Xiong, Shichao Xie, Xiaolong Wu, Shiyi Liang, Mu Xu, Xing Wei, Ning Guo (MIV, Xi'an Jiaotong University)
  - **Core Contribution**: Streaming VLN agent over a continuous video stream with a dual implicit neural memory that decouples spatial-geometric and visual-semantic memory into separate, compact, fixed-size neural representations (inspired by left-brain semantics / right-brain spatiality). Extends the MLLM with a 3D prior from a spatial-geometric encoder for RGB-only 3D reasoning; builds dual implicit memory from historical KV caches, retaining only KVs of tokens in the initial + sliding window for efficient incremental updates. SOTA over 20+ methods on VLN-CE (e.g., R2R-CE).
  - **Links**: [[Paper](https://arxiv.org/abs/2509.22548)] | [[Code](https://github.com/MIV-XJTU/JanusVLN)] | [[Project](https://miv-xjtu.github.io/JanusVLN.github.io/)]

- **InfiniPot-V**: *InfiniPot-V: Memory-Constrained KV Cache Compression for Streaming Video Understanding*. *NeurIPS, 2025*.
  - **Authors/Institution**: Minsoo Kim, Kyuhong Shim, Jungwook Choi, Simyung Chang et al.
  - **Core Contribution**: First training-free, query-agnostic KV-cache compression with hard length-independent memory cap. Temporal-axis Redundancy (TaR) removal + Value-Norm (VaN) ranking. Cuts peak GPU memory by up to 94% across four MLLMs; sustains real-time generation in multi-turn dialogues.
  - **Links**: [[Paper](https://arxiv.org/abs/2506.15745)]

- **StreamBridge**: *StreamBridge: Turning Your Offline Video Large Language Model into a Proactive Streaming Assistant*. *NeurIPS, 2025*.
  - **Authors/Institution**: Haibo Wang, Bo Feng, Zhengfeng Lai et al. (Apple)
  - **Core Contribution**: Transforms offline Video-LLMs into proactive streaming assistants via round-decayed compression memory buffer for long-context multi-turn interaction + lightweight decoupled activation model for continuous proactive responses. Stream-IT dataset. Outperforms GPT-4o and Gemini 1.5 Pro.
  - **Links**: [[Paper](https://arxiv.org/abs/2505.05467)]

- **Flash-VStream**: *Flash-VStream: Efficient Real-Time Understanding for Long Video Streams*. *ICCV, 2025*.
  - **Authors/Institution**: Haoji Zhang, Yiqin Wang, Yansong Tang et al.
  - **Core Contribution**: Flash Memory module with dual-tier design: low-capacity context memory aggregating temporal information + high-capacity augmentation memory retrieving spatial details. SOTA on EgoSchema, MLVU, LVBench, MVBench, Video-MME with significant latency reduction.
  - **Links**: [[Paper](https://arxiv.org/abs/2506.23825)] | [[Code](https://github.com/IVGSZ/Flash-VStream)]

- **Dispider**: *Dispider: Enabling Video LLMs with Active Real-Time Interaction via Disentangled Perception, Decision, and Reaction*. *arXiv, Jan 2025*.
  - **Authors/Institution**: Rui Qian, Shuangrui Ding, Xiaoyi Dong et al. (Show Lab, CUHK)
  - **Core Contribution**: Disentangles streaming video LLM into three async modules — Perception (continuous video monitoring), Decision (optimal interaction moment identification), Reaction (detailed response generation while perception continues). Lightweight proactive streaming module with event-triggered activation.
  - **Links**: [[Paper](https://arxiv.org/abs/2501.03218)] | [[Code](https://github.com/Mark12Ding/Dispider)]

- **VideoLLM-online**: *VideoLLM-online: Online Video Large Language Model for Streaming Video*. *CVPR, 2024*.
  - **Authors/Institution**: Joya Chen, Zhaoyang Lv, Shiwei Wu et al. (Show Lab, NUS; Meta Reality Labs)
  - **Core Contribution**: Pioneering streaming Video LLM with LIVE (Learning-In-Video-Stream) framework: temporally-aligned training objective, offline-to-streaming data synthesis via LLM prompting, and parallelized async inference pipeline. 10+ FPS on A100 for 5-min clips; SOTA on offline benchmarks.
  - **Links**: [[Paper](https://arxiv.org/abs/2406.11816)] | [[Code](https://github.com/showlab/videollm-online)] | [[Project](https://showlab.github.io/videollm-online)]

## B. Proactive Interaction & Continuous Activation

*Agents that monitor streaming environments (e.g., egocentric video) and spontaneously initiate dialogue or actions without explicit user prompts.*

- **EgoPro-Bench**: *EgoPro-Bench: Benchmarking Personalized Proactive Interaction in Egocentric Video Streams*. *arXiv, May 2026*.
  - **Authors/Institution**: Dongchuan Ran, Linyu Ou, Xueheng Li et al.
  - **Core Contribution**: Large-scale benchmark (2400 eval + 12000 train videos, 12 interaction types) for proactive interaction with simulated user profiles. Introduces "short thinking, better interaction" principle — limited token budget before intent recognition for low-latency HMI.
  - **Links**: [[Paper](https://arxiv.org/abs/2605.07299)]

- **Pro²Assist**: *Pro²Assist: Continuous Step-Aware Proactive Assistance with Multimodal Egocentric Perception for Long-Horizon Procedural Tasks*. *arXiv, May 2026*.
  - **Authors/Institution**: Lilin Xu, Bufang Yang, Siyang Jiang et al. (Columbia University)
  - **Core Contribution**: Step-aware proactive assistant tracking fine-grained task progress via multimodal AR glasses data. Continuous reasoning over multi-scale temporal dynamics and task knowledge. 21% procedural understanding improvement, 2.29× proactive timing accuracy; 90% user-rated useful.
  - **Links**: [[Paper](https://arxiv.org/abs/2605.04227)]

- **Proact-VL**: *Proact-VL: A Proactive VideoLLM for Real-Time AI Companions*. *ICML, 2026*.
  - **Authors/Institution**: Weicai Yan, Yuhong Dai, Qi Ran et al. (Zhejiang University, Shenzhen University, Microsoft Research Asia)
  - **Core Contribution**: General framework shaping multimodal LLMs into proactive real-time AI companions. Jointly models continuous perception, autonomous response triggering, and controllable commentary generation under latency constraints. Built on Qwen2-VL/2.5-VL/3-VL backbones with three interaction modes (solo/co-commentary/guidance).
  - **Links**: [[Paper](https://arxiv.org/abs/2603.03447)] | [[Project](https://proact-vl.github.io/)]

- **ProAgent**: *ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems*. *arXiv, Dec 2025*.
  - **Authors/Institution**: (Multiple institutions)
  - **Core Contribution**: Proactive LLM agent with on-demand tiered perception — always-on low-cost sensors (location, motion, audio) trigger high-cost vision capture on demand. Temporal constraint mechanism avoids repeated disturbances via semantic similarity gating.
  - **Links**: [[Paper](https://arxiv.org/abs/2512.06721)]

- **AgileThinker**: *Real-Time Reasoning Agents in Evolving Environments*. *arXiv, Nov 2025*.
  - **Authors/Institution**: Yule Wen, Yixin Ye, Yanzhe Zhang, Diyi Yang, Hao Zhu
  - **Core Contribution**: Formalizes "real-time reasoning" as a new problem class for agents in dynamic environments. Dual-thread architecture: reactive agent (bounded reasoning for fast response) + planning agent (extended reasoning for complex problems). Real-Time Reasoning Gym benchmark.
  - **Links**: [[Paper](https://arxiv.org/abs/2511.04898)]

## C. Streaming VLA & Real-Time Embodied Control

*Models that output continuous action trajectories (VLA) or jointly predict future video and actions with actions as the primary control target (WAM). Emphasizes high-frequency action stream smoothing for robotics, autonomous driving, and game agents.*

- **The Latent Bridge**: *The Latent Bridge: A Continuous Slow–Fast Channel for Real-Time Game Agents*. *arXiv, Jun 2026*. 
  > *(Note: This work appears to be generated by auto-research agents.)*
  - **Authors/Institution**: Bojie Li, Noah Shi (Pine AI, University of Washington) 
  - **Core Contribution**: Couples frozen reasoning VLM (Qwen3-VL-8B-Thinking) with reactive VLM (MiniCPM-o 4.5) via a learned continuous latent bridge that projects slow model residuals into fast model's embedding space — no text round-trip. On 7 Atari games + MetaDrive; Latent Bridge significantly beats Text Bridge (+57% MsPacman, +28% RoadRunner). Code and replay recordings released.
  - **Links**: [[Paper](https://arxiv.org/abs/2606.24470)] | [[Code](https://github.com/19PINE-AI/latent-bridge-games)] | [[Project](https://01.me/research/latent-bridge-games)]

- **CausalDrive**: *CausalDrive: Real-time Causal World Models for Autonomous Driving*. *arXiv, Jun 2026*.
  - **Authors/Institution**: Tianyi Yan, Huan Zheng, Dubing Chen et al.
  - **Core Contribution**: Real-time causal driving WAM at 12 FPS — Context-Forced DMD over causal AR Flow-Matching, strictly avoids future-layout leakage. Enables closed-loop RL post-training via Video2Reward and human-in-the-loop simulation. SocioDrive-Bench for causal driving sociology.
  - **Links**: [[Paper](https://arxiv.org/abs/2606.15341)]

- **AutoMoT**: *AutoMoT: A Unified Vision-Language-Action Model with Asynchronous Mixture-of-Transformers for End-to-End Autonomous Driving*. *ICML, 2026*.
  - **Authors/Institution**: Wenhui Huang, Songyan Zhang, Qihang Huang, Zhidong Wang, Zhiqi Mao, Collister Chua, Zhan Chen, Long Chen, Chen Lv (Xiaomi Auto / Xiaomi EV; NTU AutoMan Lab; Harvard)
  - **Core Contribution**: End-to-end autonomous-driving VLA unifying reasoning and action in a single model via a Mixture-of-Transformers (MoT) architecture with layer-wise joint attention sharing — a slow Understanding Expert (4B Qwen3-VL) feeds high-level scene understanding directly into a fast Action Expert (~1.6B) through shared attention, enabling fast-slow inference via asynchronous execution at different task frequencies. Bench2Drive 87.34 DS / 70.00% SR; nuScenes open-loop SOTA. Checkpoints, code, and NuSync data released.
  - **Links**: [[Paper](https://arxiv.org/abs/2603.14851)] | [[Code](https://github.com/OscarHuangWind/AutoMoT)] | [[Project](https://automot-website.github.io/)]

- **GigaWorld-Policy**: *GigaWorld-Policy: An Efficient Action-Centered World–Action Model*. *arXiv, Mar 2026*.
  - **Authors/Institution**: Angen Ye, Boyuan Wang, Chaojun Ni et al. (GigaAI)
  - **Core Contribution**: Action-centered WAM — causal mask prevents future video tokens from influencing action tokens, so inference skips entire video branch (360ms vs Motus 3231ms, 9× faster). Training uses future video as dense physics supervision but deploys action-only. 7% task success gain, 95% improvement over π0.5 on RoboTwin 2.0.
  - **Links**: [[Paper](https://arxiv.org/abs/2603.17240)] | [[Code](https://github.com/open-gigaai/giga-world-policy)] | [[Project](https://gigaai-research.github.io/GigaWorld-Policy/)]

- **StreamingVLA**: *StreamingVLA: Streaming Vision-Language-Action Model with Action Flow Matching and Adaptive Early Observation*. *arXiv, Mar 2026*.
  - **Authors/Institution**: Yiran Shi, Dongqi Guo, Tianchen Zhao et al. (Tsinghua University, Lenovo Research)
  - **Core Contribution**: First systematic asynchronous streaming VLA — eliminates action chunking, adopts action flow matching to overlap generation with execution, and uses saliency-aware adaptive early observation to overlap execution with observation. 2.4× latency speedup, 6.5× reduction in execution halting; maintains LIBERO success rate (94.9% vs 95.1%).
  - **Links**: [[Paper](https://arxiv.org/abs/2603.28565)]

- **DreamZero**: *World Action Models are Zero-shot Policies*. *arXiv, Feb 2026*.
  - **Authors/Institution**: Seonghyeon Ye, Yunhao Ge, Kaiyuan Zheng et al. (NVIDIA)
  - **Core Contribution**: 14B WAM built on Wan2.1-I2V-14B video diffusion backbone — jointly predicts future video and robot actions, enabling real-time closed-loop control at 7Hz (38× acceleration via system/model optimizations). 2×+ improvement in zero-shot generalization vs SOTA VLAs; cross-embodiment transfer with 10–20 min video demos.
  - **Links**: [[Paper](https://arxiv.org/abs/2602.15922)] | [[Project](https://dreamzero0.github.io/)]

- **DynamicVLA**: *DynamicVLA: A Vision-Language-Action Model for Dynamic Object Manipulation*. *arXiv, Jan 2026*.
  - **Authors/Institution**: Haozhe Xie, Beichen Wen, Jiarui Zheng et al. (S-Lab, Nanyang Technological University)
  - **Core Contribution**: 0.4B compact VLA with Continuous Inference (overlaps reasoning and execution to eliminate inter-chunk waiting) and Latent-aware Action Streaming (discards outdated actions, prioritizes most recent predictions for temporal alignment). DOM benchmark: 200K synthetic + 2K real-world dynamic manipulation episodes.
  - **Links**: [[Paper](https://arxiv.org/abs/2601.22153)] | [[Code](https://github.com/hzxie/DynamicVLA)] | [[Project](https://haozhexie.com/project/dynamic-vla)]

- **VLASH**: *VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference*. *arXiv, Nov 2025*.
  - **Authors/Institution**: Jiaming Tang, Yufei Sun, Yilong Zhao et al. (MIT; NVIDIA; Tsinghua; UC Berkeley; UCSD; Caltech)
  - **Core Contribution**: General async inference framework for VLAs — estimates future execution-time state by rolling robot state forward with previously generated action chunk, bridging prediction-execution gap. Up to 2.03× speedup, 17.4× reaction latency reduction. First to enable VLA playing ping-pong and whack-a-mole. Works with existing VLAs (π0.5) without architectural changes.
  - **Links**: [[Paper](https://arxiv.org/abs/2512.01031)] | [[Code](https://github.com/mit-han-lab/vlash)]

- **FiS-VLA**: *Fast-in-Slow: A Dual-System Foundation Model Unifying Fast Manipulation within Slow Reasoning*. *arXiv, Jun 2025*.
  - **Authors/Institution**: Hao Chen, Jiaming Liu, Chenyang Gu et al. (CUHK, Peking University, AI2Robotics, BAAI)
  - **Core Contribution**: Dual-system VLA built on a Prismatic-VLM / LLaMA2-7B backbone — repurposes the slow reasoning model's last K=2 transformer blocks as a fast execution module (System 1) for streaming manipulation while retaining full System 2 reasoning. Action generation is formulated as diffusion denoising embedded in these shared blocks (no separate policy head). Ablation shows K>2 yields diminishing returns.
  - **Links**: [[Paper](https://arxiv.org/abs/2506.01953)] | [[Code](https://github.com/CHEN-H01/Fast-in-Slow)] | [[Project](https://fast-in-slow.github.io/)]

## D. Time-Sensitive Serving & Streaming Backends

*Systems designed for streaming agents: preemptive scheduling, segmented generation, dynamic prioritization, and speculative decoding — supporting time-sensitive LLM/VLA inference.*

- **Stream2LLM**: *Stream2LLM: Overlap Context Streaming and Prefill for Reduced Time-to-First-Token (TTFT)*. *MLSys, 2026*.
  - **Authors/Institution**: Rajveer Bachkaniwala, Chengqi Luo, Richard So et al. (Divya Mahajan, Kexin Rong — Georgia Tech)
  - **Core Contribution**: Streaming-aware LLM serving for concurrent prefill-decode disaggregated deployments. Adaptive scheduling for append-mode (progressive context accumulation) and update-mode (iterative refinement with cache invalidation). Longest common prefix matching minimizes redundant recomputation. Up to 11× TTFT improvement.
  - **Links**: [[Paper](https://arxiv.org/abs/2604.16395)] | [[Code](https://github.com/rajveerb/stream2llm/tree/mlsys_artifact)]

- **PASCAL**: *PASCAL: A Phase-Aware Scheduling Algorithm for Serving Reasoning-based Large Language Models*. *HPCA, 2026*.
  - **Authors/Institution**: Eunyeong Cho, Jehyeon Bang, Ranggi Hwang, Minsoo Rhu (KAIST)
  - **Core Contribution**: Phase-aware scheduling distinguishing reasoning vs. answering phases. Prioritizes reasoning to reduce TTFT; controlled preemption and token pacing during answering. Dynamic migration at phase boundaries. Up to 72% tail TTFT reduction for DeepSeek-R1 models.
  - **Links**: [[Paper](https://arxiv.org/abs/2602.11530)]

- **TokenFlow**: *TokenFlow: Responsive LLM Text Streaming Serving under Request Burst via Preemptive Scheduling*. *EuroSys, 2026*.
  - **Authors/Institution**: Junyi Chen, Chuheng Du, Renyuan Liu et al.
  - **Core Contribution**: Preemptive request scheduling + proactive KV-cache management for LLM text streaming. Dynamic prioritization based on real-time token buffer occupancy and consumption rate. Background GPU↔CPU KV-cache transfer overlapping I/O with computation. 82.5% effective throughput improvement, 80.2% P99 TTFT reduction.
  - **Links**: [[Paper](https://arxiv.org/abs/2510.02758)]

- **TimelyLLM**: *TimelyLLM: Segmented LLM Serving System for Time-sensitive Robotic Applications*. *arXiv, Dec 2024*.
  - **Authors/Institution**: Neiwen Ling, Guojun Chen, Lin Zhong (Yale University)
  - **Core Contribution**: First time-sensitive LLM serving system for robotic agents. Content-aware segmented generation (detects executable robot skills and pauses/resumes at semantic boundaries). Time Utility Function (TUF)-based scheduling with potential utility density prioritization. 1.97× time utility, 84% waiting time reduction; integrated with vLLM.
  - **Links**: [[Paper](https://arxiv.org/abs/2412.18695)]

- **EAGLE / EAGLE-2**: *EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty* (+ *EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees*). *ICML, 2024 / ACL, 2024*.
  - **Authors/Institution**: Yuhui Li, Fangyun Wei, Chao Zhang, Hongyang Zhang (PKU, Microsoft)
  - **Core Contribution**: Self-speculative decoding at feature level — trains draft head on target LLM's second-to-last hidden state for 2–3× acceleration. EAGLE-2 discovers draft softmax confidence ≈ acceptance rate (strong calibration), enabling adaptive draft length without extra training. Foundational infrastructure for VLA streaming acceleration.
  - **Links**: [[Paper (EAGLE)](https://arxiv.org/abs/2401.15077)] | [[Paper (EAGLE-2)](https://arxiv.org/abs/2406.16858)] | [[Code](https://github.com/SafeAILab/EAGLE)]

- **LayerSkip**: *LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding*. *ACL, 2024*.
  - **Authors/Institution**: Mostafa Elhoushi, Akshat Shrivastava et al. (Meta AI / FAIR)
  - **Core Contribution**: Early exit inference with shared LM head across layers and layer dropout self-distillation. Optimal early exit at ~L/4. Demonstrates self-distillation for efficient inference with ground-truth label supervision. Adaptable to on-policy VLA streaming with distribution-level KL upgrade.
  - **Links**: [[Paper](https://arxiv.org/abs/2404.16710)] | [[Code](https://github.com/facebookresearch/LayerSkip)]

## Benchmarks

Key benchmarks for evaluating streaming agent capabilities:

| Benchmark | Focus | Source |
|:---|:---|:---|
| **Inf-Streams-Eval** | 2h+ video, dense per-second frame-text alignment | [StreamingVLM](https://arxiv.org/abs/2510.09608) (ICLR 2026) |
| **ProReady-QA** | 30–60min video, evidence-window proactive QA (5000 QA, 5 task types) | [StreamReady](https://arxiv.org/abs/2603.08620) (CVPR 2026) |
| **StreamingBench** | Online streaming video QA with continuity / forward splits | [StreamingBench](https://github.com/thunlp-mt/streamingbench) |
| **OVO-Bench** | Online video open-ended QA with Real / Back temporal splits | [OVO-Bench](https://github.com/joeleelyf/ovo-bench) (CVPR 2025) |
| [**VStream-QA**](https://huggingface.co/datasets/IVGSZ/VStream-QA) | Online streaming video question answering | [Flash-VStream](https://arxiv.org/abs/2506.23825) |
| **EgoPro-Bench** | Egocentric proactive interaction (2400 eval + 12000 train, 12 types) | [EgoPro-Bench](https://arxiv.org/abs/2605.07299) (2026) |
| **Stream-IT** | Streaming video understanding with interleaved video-text sequences | [StreamBridge](https://arxiv.org/abs/2505.05467) (2025) |
| **Live Gaming Benchmark** | Real-time game commentary, co-commentary, and guidance | [Proact-VL](https://arxiv.org/abs/2603.03447) (2026) |
| **Real-Time Reasoning Gym** | Dynamic environment agent reasoning under temporal constraints | [AgileThinker](https://arxiv.org/abs/2511.04898) (2025) |
| **DOM (Dynamic Object Manipulation)** | Dynamic object manipulation (200K synthetic + 2K real, multi-embodiment) | [DynamicVLA](https://arxiv.org/abs/2601.22153) (2026) |
| **SocioDrive-Bench** | Causal driving sociology for interactive autonomous driving simulation | [CausalDrive](https://arxiv.org/abs/2606.15341) (2026) |

---

## Contributing

Contributions welcome! Please follow the format and create a pull request:

```markdown
- **ShortName**: *Full Paper Title*. *Venue, Year*.
  - **Authors/Institution**: First Author, Second Author et al. (Institution1, Institution2)
  - **Core Contribution**: 1–2 sentences on how it addresses streaming / time-sensitivity / proactivity / unbounded input, and the underlying model type (LLM / VLM / VLA / WAM).
  - **Links**: [[Paper](url)] | [[Code](url)] | [[Project](url)]
```

**Criteria for inclusion**: The work must explicitly address at least one of: (1) time-sensitive processing, (2) real-time/low-latency response, (3) proactive/continuous activation without explicit prompts, (4) unbounded streaming input processing.

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