newsReddit r/artificialTrust 52 · CommunityPublished 1mo agoLive · 1mo ago
We're building agents that can read millions of documents, but still forget a video they watched yesterday.
One thing has felt odd to me while working with AI agents. We've gotten pretty good at giving them memory for text. They can search documentation, index repositories, retrieve past conversations, and even build long-term memory over time. Videos, though, are still treated as temporary input. The agent watches a recording, answers a few questions, and when the session ends, that understanding is usually gone. Next session, the same vid
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- PossiblePossibly related (embedding) · 60%basicmachines-co/basic-memory →
- PossiblePossibly related (embedding) · 56%sandst1/remind →
- PossiblePossibly related (embedding) · 56%rajkripal/cashew →
- PossiblePossibly related (embedding) · 56%smixs/iva →
- PossiblePossibly related (embedding) · 56%phasespace-labs/palinode →
- PossiblePossibly related (embedding) · 49%Streaming Multi-Agent Autoregressive Diffusion Model with World State Registers →
- PossiblePossibly related (embedding) · 46%fajarhide/omni →
- PossiblePossibly related (embedding) · 53%StreamTTT: Reconciling Real-Time Perception and Long-Term Memory in Streaming VLMs →
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Covers (incoming)
paperStreaming Multi-Agent Autoregressive Diffusion Model with World State Registersrepofajarhide/omnipaperStreamTTT: Reconciling Real-Time Perception and Long-Term Memory in Streaming VLMspaperOn the Fragility of Self-Improving Agents: Variance, Task Order, and UnderspecificationpaperThinking Beyond Videos: Unifying Video Reasoning and Deep Research for Open-World Video AgentsrepoTeleAI-UAGI/telememrepomemvid/memvidpaperRemember When It Matters: Proactive Memory Agent for Long-Horizon AgentspaperDeepStress: Stress-Testing Deep Search AgentspaperSparse Evidence Can Suffice: Agentic Evidence Seeking for Multimodal Video Misinformation DetectionpaperUtility Under Attack: Agent Memory Poisoning and the Limits of Content Screening and Provenance RankingpaperMRMS: A Multi-Resolution Memory Substrate for Long-Lived AI Agentsreponossa-y/activity-framesrepoNovasPlace/CSMreporaiyanyahya/recallpaperLightMem-Ego: Your AI Memory for Everyday Liferepoteolex2020/aura-memoryrepoitsPremkumar/Automated-Video-Generatorrepoarchit15singh/memoripaperHuman-AI Agent Interaction as a Neuroplastic Training EnvironmentpaperSearching Videos as Trees: Self-Correcting Agents for Grounded Long Video QA
Related across the graph
paperOn the Fragility of Self-Improving Agents: Variance, Task Order, and UnderspecificationpaperStreamTTT: Reconciling Real-Time Perception and Long-Term Memory in Streaming VLMsrepobasicmachines-co/basic-memoryrepoteolex2020/aura-memoryreposmixs/ivarepophasespace-labs/palinodepaperDeepStress: Stress-Testing Deep Search AgentspaperMRMS: A Multi-Resolution Memory Substrate for Long-Lived AI AgentsrepoTeleAI-UAGI/telememreporajkripal/cashewrepoitsPremkumar/Automated-Video-GeneratorpaperRemember When It Matters: Proactive Memory Agent for Long-Horizon AgentspaperLightMem-Ego: Your AI Memory for Everyday Liferepofajarhide/omnirepoNovasPlace/CSMpaperSparse Evidence Can Suffice: Agentic Evidence Seeking for Multimodal Video Misinformation Detectionrepoarchit15singh/memorirepomemvid/memvidpaperSearching Videos as Trees: Self-Correcting Agents for Grounded Long Video QApaperThinking Beyond Videos: Unifying Video Reasoning and Deep Research for Open-World Video Agentsreposandst1/remindpaperHuman-AI Agent Interaction as a Neuroplastic Training Environmentreporaiyanyahya/recallreponossa-y/activity-framespaperUtility Under Attack: Agent Memory Poisoning and the Limits of Content Screening and Provenance RankingpaperStreaming Multi-Agent Autoregressive Diffusion Model with World State Registers
