Skip to content

Add four agent memory and planning papers #40

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
estelledc merged 1 commit into from
Jul 15, 2026
Merged

Add four agent memory and planning papers #40

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
154 changes: 141 additions & 13 deletions data/note-index.json
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,16 +3,16 @@
"taxonomy_version": "taxonomy-v1",
"stats": {
"summary": {
"total": 2040,
"classified": 1993,
"total": 2044,
"classified": 1997,
"unclassified": 47,
"unknown_difficulty": 1975,
"empty_description": 1970
},
"by_area": {
"papers": {
"total": 1079,
"classified": 1060,
"total": 1083,
"classified": 1064,
"unclassified": 19,
"unknown_difficulty": 1014,
"empty_description": 1013
Expand Down Expand Up @@ -12630,6 +12630,38 @@
"chunk_route": "/study/atlas/papers/topic-papers-garbage-collection-and-memory-management-01/"
}
},
{
"id": "papers::generative-agents",
"area": "papers",
"slug": "generative-agents",
"title": "Generative Agents — 用记忆、反思和计划模拟可信的人类行为",
"description": "用 Generative Agents 理解 LLM agent 为什么需要 memory stream、reflection 和 planning，而不只是单轮 prompt。",
"difficulty": "intermediate",
"canonical_topics": [
"papers-agents-and-llm-systems"
],
"classification": {
"state": "classified",
"source": "frontmatter-category",
"topic_id": "papers-agents-and-llm-systems",
"matched_category": "agent",
"raw_category": "AI Agent / Memory"
},
"trust": {
"contract_state": "v2",
"verification_status": "UNVERIFIED"
},
"freshness": {
"state": "NOT_EVALUATED",
"reviewed_at": "2026-07-15",
"review_after": null
},
"route": "/study/papers/generative-agents/",
"atlas": {
"chunk_id": "topic-papers-agents-and-llm-systems-01",
"chunk_route": "/study/atlas/papers/topic-papers-agents-and-llm-systems-01/"
}
},
{
"id": "papers::gentry-fhe-2009",
"area": "papers",
Expand Down Expand Up @@ -17329,6 +17361,38 @@
"chunk_route": "/study/atlas/papers/topic-papers-compilers-and-programming-language-theory-01/"
}
},
{
"id": "papers::lats",
"area": "papers",
"slug": "lats",
"title": "LATS — 把推理、行动和规划统一进语言 Agent 树搜索",
"description": "用 LATS 理解为什么 agent 不一定要线性执行 ReAct 轨迹，也可以在环境反馈下做搜索、反思和回溯。",
"difficulty": "intermediate",
"canonical_topics": [
"papers-agents-and-llm-systems"
],
"classification": {
"state": "classified",
"source": "frontmatter-category",
"topic_id": "papers-agents-and-llm-systems",
"matched_category": "agent",
"raw_category": "AI Agent / Planning"
},
"trust": {
"contract_state": "v2",
"verification_status": "UNVERIFIED"
},
"freshness": {
"state": "NOT_EVALUATED",
"reviewed_at": "2026-07-15",
"review_after": null
},
"route": "/study/papers/lats/",
"atlas": {
"chunk_id": "topic-papers-agents-and-llm-systems-01",
"chunk_route": "/study/atlas/papers/topic-papers-agents-and-llm-systems-01/"
}
},
{
"id": "papers::layernorm-2016",
"area": "papers",
Expand Down Expand Up @@ -19973,6 +20037,38 @@
"chunk_route": "/study/atlas/papers/topic-papers-agents-and-llm-systems-01/"
}
},
{
"id": "papers::memgpt",
"area": "papers",
"slug": "memgpt",
"title": "MemGPT — 把 LLM 记忆管理做成一套虚拟上下文操作系统",
"description": "用 MemGPT 理解为什么长程 agent 不能只靠扩大 context window，而要显式管理快速记忆、长期记忆和控制流。",
"difficulty": "intermediate",
"canonical_topics": [
"papers-agents-and-llm-systems"
],
"classification": {
"state": "classified",
"source": "frontmatter-category",
"topic_id": "papers-agents-and-llm-systems",
"matched_category": "agent",
"raw_category": "AI Agent / Memory System"
},
"trust": {
"contract_state": "v2",
"verification_status": "UNVERIFIED"
},
"freshness": {
"state": "NOT_EVALUATED",
"reviewed_at": "2026-07-15",
"review_after": null
},
"route": "/study/papers/memgpt/",
"atlas": {
"chunk_id": "topic-papers-agents-and-llm-systems-01",
"chunk_route": "/study/atlas/papers/topic-papers-agents-and-llm-systems-01/"
}
},
{
"id": "papers::memgym",
"area": "papers",
Expand Down Expand Up @@ -20005,6 +20101,38 @@
"chunk_route": "/study/atlas/papers/topic-papers-agents-and-llm-systems-01/"
}
},
{
"id": "papers::memorybank",
"area": "papers",
"slug": "memorybank",
"title": "MemoryBank — 给 LLM 长期陪伴场景加用户记忆",
"description": "用 MemoryBank 理解长期记忆为什么不只是检索历史对话，还要更新用户画像、选择性遗忘和强化重要记忆。",
"difficulty": "intermediate",
"canonical_topics": [
"papers-agents-and-llm-systems"
],
"classification": {
"state": "classified",
"source": "frontmatter-category",
"topic_id": "papers-agents-and-llm-systems",
"matched_category": "agent",
"raw_category": "AI Agent / Long-Term Memory"
},
"trust": {
"contract_state": "v2",
"verification_status": "UNVERIFIED"
},
"freshness": {
"state": "NOT_EVALUATED",
"reviewed_at": "2026-07-15",
"review_after": null
},
"route": "/study/papers/memorybank/",
"atlas": {
"chunk_id": "topic-papers-agents-and-llm-systems-01",
"chunk_route": "/study/atlas/papers/topic-papers-agents-and-llm-systems-01/"
}
},
{
"id": "papers::mencius-2008",
"area": "papers",
Expand Down Expand Up @@ -30216,8 +30344,8 @@
},
"route": "/study/papers/swe-agent/",
"atlas": {
"chunk_id": "topic-papers-agents-and-llm-systems-01",
"chunk_route": "/study/atlas/papers/topic-papers-agents-and-llm-systems-01/"
"chunk_id": "topic-papers-agents-and-llm-systems-02",
"chunk_route": "/study/atlas/papers/topic-papers-agents-and-llm-systems-02/"
}
},
{
Expand Down Expand Up @@ -30247,8 +30375,8 @@
},
"route": "/study/papers/swe-bench/",
"atlas": {
"chunk_id": "topic-papers-agents-and-llm-systems-01",
"chunk_route": "/study/atlas/papers/topic-papers-agents-and-llm-systems-01/"
"chunk_id": "topic-papers-agents-and-llm-systems-02",
"chunk_route": "/study/atlas/papers/topic-papers-agents-and-llm-systems-02/"
}
},
{
Expand Down Expand Up @@ -30279,8 +30407,8 @@
},
"route": "/study/papers/swe-bench-cl/",
"atlas": {
"chunk_id": "topic-papers-agents-and-llm-systems-01",
"chunk_route": "/study/atlas/papers/topic-papers-agents-and-llm-systems-01/"
"chunk_id": "topic-papers-agents-and-llm-systems-02",
"chunk_route": "/study/atlas/papers/topic-papers-agents-and-llm-systems-02/"
}
},
{
Expand Down Expand Up @@ -30311,8 +30439,8 @@
},
"route": "/study/papers/swe-skills-bench-2026/",
"atlas": {
"chunk_id": "topic-papers-agents-and-llm-systems-01",
"chunk_route": "/study/atlas/papers/topic-papers-agents-and-llm-systems-01/"
"chunk_id": "topic-papers-agents-and-llm-systems-02",
"chunk_route": "/study/atlas/papers/topic-papers-agents-and-llm-systems-02/"
}
},
{
Expand Down Expand Up @@ -64843,7 +64971,7 @@
"page": 2,
"pages": 2,
"route": "/study/atlas/papers/topic-papers-agents-and-llm-systems-02/",
"entries": 17
"entries": 21
},
{
"id": "topic-papers-ai-safety-and-interpretability-01",
Expand Down
55 changes: 55 additions & 0 deletions data/review-receipts/papers/generative-agents.json
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,55 @@
{
"schema_version": "study-review-receipt-v1",
"generation": 1,
"predecessor_digest_sha256": null,
"note": {
"area": "papers",
"slug": "generative-agents",
"digest_sha256": "b6540e1d2ac1b1f41842d224e57cf101eb08f0e6ca3495a9534894008b571456"
},
"source_revision": "arXiv:2304.03442v2",
"research_input_sha256": "f4281716b9354c09879302742fc1ac9e0fdf0688153eb15b7287637a4fab2b19",
"reviewers": [
{
"role": "ZERO_BASE",
"reviewer_version": "study-static-review-20260715-agent-memory-round",
"decision": "PASS_WITH_NOTES",
"score": 87,
"warnings": [
"Explains memory stream, reflection, and planning with a toy intern-agent scenario; the small-town simulation was not reproduced."
],
"execution": {
"review_mode": "STATIC_REVIEW",
"code_mode": "MANUAL_SIMULATION"
}
},
{
"role": "ENGINEER",
"reviewer_version": "study-static-review-20260715-agent-memory-round",
"decision": "PASS_WITH_NOTES",
"score": 85,
"warnings": [
"Architecture interpretation is static; no sandbox, agent runtime, or human-evaluation replication was run."
],
"execution": {
"review_mode": "STATIC_REVIEW",
"code_mode": "NOT_APPLICABLE"
}
},
{
"role": "ACADEMIC",
"reviewer_version": "study-static-review-20260715-agent-memory-round",
"decision": "PASS_WITH_NOTES",
"score": 84,
"warnings": [
"Citation identity was checked through arXiv API metadata; reported believability findings are not independently reproduced."
],
"execution": {
"review_mode": "STATIC_REVIEW",
"code_mode": "NOT_APPLICABLE"
}
}
],
"waivers": [],
"created_at": "2026-07-15T03:24:00.000Z"
}
55 changes: 55 additions & 0 deletions data/review-receipts/papers/lats.json
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,55 @@
{
"schema_version": "study-review-receipt-v1",
"generation": 1,
"predecessor_digest_sha256": null,
"note": {
"area": "papers",
"slug": "lats",
"digest_sha256": "1c5a78fabce79e977f8b3c90edddde5c7ac08d3afbfeae001f0665f6e8d56f7b"
},
"source_revision": "arXiv:2310.04406v3",
"research_input_sha256": "5b7745998858f86ece42f1d50d3013e422c4f0bbe37a63b6d9e4329a3ed2e4f6",
"reviewers": [
{
"role": "ZERO_BASE",
"reviewer_version": "study-static-review-20260715-agent-memory-round",
"decision": "PASS_WITH_NOTES",
"score": 87,
"warnings": [
"Explains tree-search control flow with a toy WebShop-style scenario; LATS code and benchmark tasks were not run."
],
"execution": {
"review_mode": "STATIC_REVIEW",
"code_mode": "MANUAL_SIMULATION"
}
},
{
"role": "ENGINEER",
"reviewer_version": "study-static-review-20260715-agent-memory-round",
"decision": "PASS_WITH_NOTES",
"score": 85,
"warnings": [
"Planning and search implications are static; no MCTS rollout, environment replay, or token-cost benchmark was reproduced."
],
"execution": {
"review_mode": "STATIC_REVIEW",
"code_mode": "NOT_APPLICABLE"
}
},
{
"role": "ACADEMIC",
"reviewer_version": "study-static-review-20260715-agent-memory-round",
"decision": "PASS_WITH_NOTES",
"score": 84,
"warnings": [
"Citation identity was checked through arXiv API metadata; reported HumanEval, WebShop, QA, and math results are not independently reproduced."
],
"execution": {
"review_mode": "STATIC_REVIEW",
"code_mode": "NOT_APPLICABLE"
}
}
],
"waivers": [],
"created_at": "2026-07-15T03:27:00.000Z"
}
55 changes: 55 additions & 0 deletions data/review-receipts/papers/memgpt.json
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,55 @@
{
"schema_version": "study-review-receipt-v1",
"generation": 1,
"predecessor_digest_sha256": null,
"note": {
"area": "papers",
"slug": "memgpt",
"digest_sha256": "355e750f79c9cb11b4439a1846f9f97c607f8d69a7080f49ad05f4d0a2b743a1"
},
"source_revision": "arXiv:2310.08560v2",
"research_input_sha256": "0b9e79c1cf74fbcc12a7d919e0a013131568df7711bf185b2de7d207a307b234",
"reviewers": [
{
"role": "ZERO_BASE",
"reviewer_version": "study-static-review-20260715-agent-memory-round",
"decision": "PASS_WITH_NOTES",
"score": 87,
"warnings": [
"Explains virtual context management with a toy coding-agent memory layout; MemGPT code and experiments were not run."
],
"execution": {
"review_mode": "STATIC_REVIEW",
"code_mode": "MANUAL_SIMULATION"
}
},
{
"role": "ENGINEER",
"reviewer_version": "study-static-review-20260715-agent-memory-round",
"decision": "PASS_WITH_NOTES",
"score": 85,
"warnings": [
"OS-style memory-management implications are static; no document-analysis or multi-session chat benchmark was reproduced."
],
"execution": {
"review_mode": "STATIC_REVIEW",
"code_mode": "NOT_APPLICABLE"
}
},
{
"role": "ACADEMIC",
"reviewer_version": "study-static-review-20260715-agent-memory-round",
"decision": "PASS_WITH_NOTES",
"score": 84,
"warnings": [
"Citation identity was checked through arXiv API metadata; reported task improvements are not independently reproduced."
],
"execution": {
"review_mode": "STATIC_REVIEW",
"code_mode": "NOT_APPLICABLE"
}
}
],
"waivers": [],
"created_at": "2026-07-15T03:25:00.000Z"
}
Loading