论文称上下文质量决定 GPT-6 Sol 等模型智能体成败
一条推文引用 arXiv 论文，主张 92% 的智能体失败在生成首个 token 前就已注定，并给出含 GPT-6 Sol、Luna 的四层上下文工程栈。
EVEN CLAUDE OPUS 5.5 AND GPT-6 SOL WILL CRASH IF YOUR CONTEXT HARNESS IS ROTTEN There is no catch and that scares me Anthropic and OpenAI just dropped Claude Opus 5.5 and GPT-6 Sol: but a new paper [arXiv:2607.14275] proves your agents will still derail across 4,800 production workflows When autonomous agents fail, they do not fail by stopping: they complete 400 wrong steps with absolute confidence, corrupt the database, and burn $1,200 in looping API calls Holding frontier models fixed and varying only context quality proved that 92% of agent failures are locked in before the first token generates: when context is fuzzy, even god-tier models hallucinate tools and leak credentials The 4-tier frontier stack solving zero-drift context engineering: > Claude Opus 5.5: master strategic graph orchestrator with a 1M context window and adaptive thinking that locks execution order > OpenAI GPT-6 Sol: parallel execution workhorse with 1.05M tokens, compiling and dispatching 128 tool schemas at half the cost > OpenAI GPT-6 Luna: sub-10ms lightweight guard verifying schema integrity and halting prompt injection before state commits > AST Checkpoint Tape: durable state rollback buffer enabling 0ms non-blocking resume when an external tool fails The benchmark results when you enforce the 7-dimensional context harness: > Hallucination resistance jumped from 14% to 98.6% > Prompt injection exploits collapsed to absolute zero > Multi-agent schema collisions dropped from 412 down to zero > Operational token spend fell by 40% through deterministic preflight pruning The era of naive prompt engineering is dead: deterministic context engineering is what keeps autonomous agent swarms in production Bookmark this and study the full 60 FPS multi-agent harness connectome in the video below ↓
这条是引用着另一条写的：x.com/mirku21/status/2098836008468992471