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Memory & context
17 articlesFilter articles Memory & context
Ideas to revisit
Memory & context
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Instant agent steering and recoverable tool calls
A close reading of Codex instant_interrupt: ending model output, continuing background tools, and preserving state when users change requirements mid-run.
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Agent 中途改需求:即时转向、抢占与可恢复工具调用
拆解 Codex instant_interrupt:用户在 Agent 运行途中补充要求时,模型输出如何收口,后台工具如何继续,以及状态怎样避免重复或丢失。
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Keep review evidence separate from compacted agent context
Independent Codex Guardian history shows why generation context and review evidence need separate records, with provenance through resume and rollback.
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主 Agent 压缩上下文后,审查证据为什么要独立保存
从 Codex Guardian 历史独立保存出发,解释生成上下文与审查证据为什么不能共用一份压缩摘要,以及 resume、rollback 应如何保留来源。
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Company Brain separates team memory permissions
Company Brain scopes team-agent memory by room and user permissions, with personal authorization, traceable sources, and separate tool-write access.
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Company Brain:团队 Agent 的记忆权限该怎么拆
拆解 Company Brain 的团队记忆与权限设计,重点看个人授权、共享知识、可追溯来源和团队 Agent 的权限边界。
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GitHub Agentic Autofix starts retaining repair experience
GitHub Agentic Autofix reads and writes Copilot Memory. How security repair knowledge crosses agent workflows, and why verification, scope, and expiry matter.
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GitHub Agentic Autofix 开始积累修复记忆
GitHub Agentic Autofix 会读取并写入 Copilot Memory。本文拆解安全修复经验如何跨 Agent 复用,以及验证、范围和过期为什么重要。
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GPT-6 prompt caching becomes an agent state layer
GPT-6 adds cache breakpoints, diagnostics, prewarming, and mutable configuration. Long-running agents need stable shared prefixes and cache-hit monitoring.
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GPT-6 Prompt Cache 变成了 Agent 的状态层
GPT-6 增加 cache breakpoint、诊断、预热和可变配置。长跑 Agent 需要主动保持共享前缀稳定,并监控缓存命中率。
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skill-up: evaluate real stateful multi-turn sessions
Alibaba skill-up resumes real sessions turn by turn, letting skill evaluations test context continuity, intermediate state, and per-turn assertions.
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多轮 Skill Eval 不该把对话压成一包:skill-up 开始续接真实 Session
Alibaba skill-up 的 stateful 多轮模式逐轮恢复真实 Session,让 Agent Skill Eval 能检查上下文连续性、中间状态和逐轮断言。
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Why more agent tools can mean worse tool selection
How Tool Search loads definitions on demand, affects prompt caching, and differs from MCP connections and ARD resource discovery.
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Agent 工具越接越多,为什么反而更容易选错?读懂 Tool Search、MCP 与运行时发现
解释 Tool Search 如何按需加载工具定义,为什么它会影响 Prompt Cache,以及 MCP、Tool Search 和 ARD 分别解决什么问题。
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Should an agent keep going or start a fresh attempt?
Use When Agents Slow Down, Reflexion, Hyperband, and agent evaluation to compare continued revision with independent attempts under reproducible budgets.
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Agent 该继续跑,还是重新开一轮?读懂预算分配、记忆与评测
结合 When Agents Slow Down、Reflexion、Hyperband 和 Agent 评测,说明怎样比较连续修改与独立尝试,并设计可复现的预算分配实验。
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长任务为什么不能只靠一条越来越长的对话
结合 OpenAI Agents API 和 Anthropic Managed Agents,解释 Session、Context Window、Compaction 与持久状态怎样支撑跨上下文窗口的长任务 Agent。
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Why long tasks need more than one growing conversation
How OpenAI Agents API and Anthropic Managed Agents use sessions, context windows, compaction, and persistent state for long-running agents.
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RTK: filter shell output before it reaches a coding agent
RTK filters test, Git, search, and build noise before command output enters model context, applying context engineering at the tool-output boundary.
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RTK:Coding Agent 不该把整段 Shell 输出塞进上下文
介绍 RTK 如何在命令输出进入模型上下文前过滤测试、Git、搜索和构建噪声,把 Context Engineering 做到工具输出边界。
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Free Vision Skill: visual evidence for text agents
VEP/1 turns images into short evidence packets. Provider fallback, caching, and credential storage manage cost and reliability for text agents.
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Free Vision Skill:让文本 Agent 按需读取视觉证据
通过 VEP/1 把图片转换成短证据包,并用 Provider fallback、缓存和凭据存储控制文本 Agent 的视觉输入成本与可靠性。
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Loop Engineering Harness: controlling agent iterations
Break down planning, execution, evaluation, state management, and stopping rules to keep multi-step agent tasks controlled and traceable.
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Loop Engineering Harness:怎样控制 Agent 的迭代循环
把 Harness 作为一种工程模式,拆解计划、执行、评估、状态管理和终止判断,说明 Agent 多轮任务怎样保持可控和可追溯。
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