what makes this paper good is the question it asks, not just the answer we spent years building longer context windows. 128k. 1M tokens. the race was always "fit more in" nobody stopped to ask: is most of what we're fitting in actually helping? turns out the model's own words
GENERATIVE AI
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GPT-5.2 quality drops without assistant history
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important caveat: this isn't universal GPT-5.2 did show quality drops when assistant history was removed entirely. stronger models apparently extract more signal from their own prior context so the right approach isn't "always strip everything." it's selective filtering. which
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AI Agents and Context Compression Challenges
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think about what this means for ai agents every agent framework stores the full trajectory. every tool call, every response, every reasoning step. context grows linearly with conversation length until you hit the ceiling Cursor already compresses context when windows fill up.
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Context pollution harms model accuracy
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but the really interesting finding isn't about efficiency. it's about harm the paper identifies something they call "context pollution" when models condition on their own prior responses, they sometimes lock onto errors, hallucinations, or stylistic artifacts from earlier
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10x Less Context, Same Quality in Open Reasoning Models
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for the open reasoning models, response quality stayed the same not "slightly degraded." same. and here's the number that stops you: omitting assistant-side history reduced cumulative context lengths by up to 10x 10x less context. same quality. on real conversations.
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Real Chat Tests on Four AI Models
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the experiment is clean they took real multi-turn conversations from WildChat and ShareLM. not synthetic benchmarks. actual human-ai chats then they ran every conversation two ways across four models (Qwen3-4B, DeepSeek-R1-8B, GPT-OSS-20B, and GPT-5.2): > full context: normal.
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AI Systems Store Full Conversation History
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here's the assumption nobody questioned every chatbot, every agent, every multi-turn ai system stores the full conversation. your messages AND the model's own replies. stacked up turn after turn, fed back in as context every time you ask something new seems obvious. the model
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MIT finds LLM context pollution degrades performance
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MIT researchers discovered a phenomenon called "context pollution" where llms get WORSE by reading their own prior responses errors, hallucinations, and stylistic artifacts from earlier turns propagate forward because the model treats its own output as ground truth and removing
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Work Dystopia: Fragile Scenarios, Necessary Economic Vigilance
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Dystopian scenarios about the disappearance of work are making a comeback after a year-long lull. Largely fueled by the Citrini Research report predicting a worker catastrophe in the short term, these scenarios rest on an assumption of radical and excessive automation; the idea of recursive AI is also present.Both on its technological and economic fundamentals, the report and these scenarios are generally very fragile.In the longer term, however, we must remain vigilant. One of the central points of the digital economy is its capacity to concentrate capital and circumvent regulation. The capital-labor distortion introduced by globalization could therefore increase radically if we are not careful. This is the subject of my latest article in Les Échos. [Translated from EN to English]
→ View original post on X — @flashtweet, 2026-03-03 11:07 UTC
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AI-Generated Fake War Videos: Need for Strategy
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Too many AI generated fake war videos. Any strategy to stop this @nikitabier ?
→ View original post on X — @skathirmani, 2026-03-03 10:24 UTC