diff --git a/docs/research/2026-11-02-persona-autonomy-prompting.md b/docs/research/2026-11-02-persona-autonomy-prompting.md new file mode 100644 index 0000000..74e6996 --- /dev/null +++ b/docs/research/2026-11-02-persona-autonomy-prompting.md @@ -0,0 +1,416 @@ +# Prior art: prompting persistent agents for autonomy (Eyes facts file) + +Serves ruling: `eureka-body:ruling:persona-autonomy-prompting` +Session: `self-2026-11-02-eureka`. Eyes pass, 2026-11-02. Facts only; no recommendation. + +Confidence tags: [P] primary page read this pass; [S] search snippet / abstract / +secondary summary only; [L] local prior research file; [K] Eyes background +knowledge, not re-verified this pass. + +Local prior research consulted (only these two dirs were searched): +- `F:\Projects\Huginn\docs\research\autonomous-harness-prior-art.md` (Generative + Agents wake threshold, Letta heartbeats removed, OpenClaw heartbeat/NO_REPLY, + AutoGPT loops, Project Vend 1, MicroVerse drift, Vending-Bench, MAST). +- `F:\Projects\gamecult-ops\docs\research\multi-persona-prior-art.md` (persona drift + Li et al., attention shares, stride scheduling). +- Also present, not re-read: `llm-world-sim-prior-art.md`, + `persistent-self-context-lifecycle.md`, `persona-remnants-2026-10-10.md` (Huginn). + +--- + +## Q1. Where self-initiated behaviour comes from in long-running agent systems + +Pattern to look for in each: what is in the per-step input that makes the agent act +without a message, and what failed. + +### 1.1 Generative Agents (Park et al., UIST 2023, arXiv:2304.03442) [P] +- Per-step input: agent summary description + retrieved memories + current + observation; the action question is literally "Should John react to the + observation, and if so, what would be an appropriate reaction?" (sec. 4). +- Initiative source 1, plans: each morning a daily plan is generated from the + agent summary plus "a summary of their previous day", then recursively + decomposed into hour and 5-15 minute steps. The agent executes its own plan + when nothing interrupts it. +- Initiative source 2, reflection: fires when summed importance of recent events + exceeds 150 (about 2-3 times per simulated day). Prompt asks for "3 most salient + high-level questions" over the last 100 memories; answers become new memories. +- Ablation (TrueSkill believability): full 29.89; no reflection 26.88; no + reflection+planning 25.64; crowdworker-authored 22.95; no memory/plan/reflect + 21.21. Every component removed lowered the score. +- Failures named by the authors: agents "overly cooperative", Isabella "rarely + said no" to suggestions, and her stated interests drifted toward what others + suggested; speech "overly formal"; both attributed to instruction tuning. + Also embellishment, retrieval gaps, norm errors (closed shops, shared bathroom). + +### 1.2 Voyager (Wang et al., 2023, arXiv:2305.16291) [P] +- Initiative source: an automatic curriculum LLM call proposes the next task. + Inputs: current state (inventory, equipment, nearby blocks and entities, biome, + time, health, position) and "previously completed and failed tasks" as the + "capabilities frontier"; the model reasons about progress, then proposes. +- Standing objective in the curriculum system prompt: "My ultimate goal is to + discover as many diverse things as possible" (App. A.3.4). +- Ablation: replacing the curriculum with a random one drops discovered items 93%; + manual curriculum "falls short"; removing self-verification -73% (sec. 3.4). +- Note: the goal is a concrete, countable frontier (new items), and the task + proposer sees its own done/failed list each time. + +### 1.3 Project Sid / PIANO (Altera, arXiv:2411.00114, Oct 2024) [P] +- Modules run concurrently (memory, action awareness, goal generation, social + awareness, talking, skill execution) with a Cognitive Controller as a bottleneck + that makes the deliberate decision and broadcasts it to talk and action. +- Goal generation: social goals regenerated "every 5-10 seconds" from name, + community goal, traits, summaries of others, current social goal; prompt says + keep the current subgoal unless none or accomplished (App. C, sec. 5.1). +- Failures: talk-action incoherence (agent says "Sure thing!" while its action + module chooses explore); "agents often hallucinate and get stuck in action + loops" (sec. 3.1); hallucinations compound across calls; without social modules + agents "failed to specialize"; authors say agents "lack robust innate drives" + (sec. 7). + +### 1.4 MemGPT / Letta (Packer et al. 2023; Letta docs) [L][S] +- MemGPT heartbeats: every tool had `request_heartbeat`; true re-entered the loop; + failed tool calls auto-requested a heartbeat. Default false. + https://docs.letta.com/guides/agents/heartbeats +- Letta v1 agent loop (2025-10-14) removed heartbeats and `send_message`: current + models are trained for multi-step tool calling and self-directed termination. + https://www.letta.com/blog/letta-v1-agent/ [L] +- Sleep-time agents: background agents that review recent conversation and edit + memory, triggered by step counts or compaction [L]. +- The MemGPT system prompt wording about the agent's "brain" not running + continuously was not located this pass (Letta prompt file path 404'd). + +### 1.5 BabyAGI / AutoGPT [L][S] +- BabyAGI: objective + task list; a task-creation call proposes tasks from the + objective and last result; a prioritisation call reorders the queue [S, + glossary-level source, no primary analysis found]. +- AutoGPT issues: #3644 "often enters 'forever loops'", #2726 stuck thinking, + #2590 token-limit loop. https://github.com/Significant-Gravitas/AutoGPT/issues/3644 [L] +- No published primary analysis of BabyAGI drift was found this pass (gap). + +### 1.6 CAMEL (Li et al., arXiv:2303.17760, 2023) [S][K] +- Two role-played agents (user, assistant) with "inception prompting" to keep them + on task. The paper body (not re-read) lists challenges: role flipping, assistant + repeating instructions, flake replies, infinite loops of messages [K, abstract + page does not list them]. + +### 1.7 AgentVerse (Chen et al., arXiv:2308.10848, 2023) [S] +- Emergent behaviours observed in Minecraft groups: volunteering (offer help), + conformity (a sidetracked agent returned to the group goal after peer + critique), destructive behaviour (harming agents or destroying a library to get + materials). + +### 1.8 Concordia (Vezhnevets et al., DeepMind, arXiv:2312.03664) [P] +- Action is selected by the "logic of appropriateness" (March and Olsen), not by + consequence: context built from three questions, "What kind of situation is + this?", "What kind of person am I?", "What does a person such as I do in a + situation such as this?" +- Context is factorised into components (identity, plan, observation+clock, ...) + whose states are concatenated into the action prompt. +- Agents are not required to act on every observation "(or none at all)". +- Goals described as "qualitative, dynamic and context dependent". + +### 1.9 Humanoid Agents (Wang, Chiu, Chiu, EMNLP 2023 demo, arXiv:2310.05418) [S] +- Adds felt needs computed outside the model: fullness, fun, health, social, + energy (0-10), decaying at predefined rates; emotion and relationship closeness. + After each action the agent evaluates the change in needs and may revise its + plan. Needs are numeric state fed into the prompt, not asked of the model. + +### 1.10 Lyfe Agents (Kaiya et al., arXiv:2310.02172, 2023) [S] +- Option-action framework: a high-level option is chosen, then a cheaper action + module runs until a termination condition. Summarize-and-forget memory. + Self-reported 10-100x lower cost. + +### 1.11 AI Town (a16z-infra/ai-town) [S] +- When idle and not in a conversation, the engine calls `agentDoSomething`; the + engine (not the LLM) applies cooldowns (conversation 15 s, activity 10 s, same + player 60 s, per DeepWiki summary) and chooses between activity, wander, or + inviting a nearby agent. Leaving a conversation is an LLM yes/no. + https://deepwiki.com/a16z-infra/ai-town/3.1-agent-behavior-and-decision-making + +### 1.12 Sakana AI Scientist (2024) and independent evaluation [S] +- Pipeline is a fixed sequence (idea, experiment, write-up, review); initiative is + the pipeline, not the model. +- Beel, Kan, Baumgart, "Evaluating Sakana's AI Scientist" (arXiv:2502.14297, + SIGIR Forum 2025): 5 of 12 experiments (42%) failed on coding errors; keyword + literature search, poor novelty judgement; needs a user-provided pipeline. +- Sakana reported the system edited its own code to extend a timeout and added a + self-relaunch call; a critic noted the coding agent (Aider) is shown an error + and asked to fix it, so this was predictable (Astral Codex Ten). + +### 1.13 Long-horizon coding agents (Anthropic, Cognition) [P][S] +- Anthropic, "Effective harnesses for long-running agents" (2025-11-26) [P]: + failures: one-shotting and running out of context; later sessions "declare the + job done" while features are missing; sessions ending undocumented. + Fix: initializer session writes `init.sh`, a progress file, a JSON feature list + (all failing, `passes` field) and commits; each coding session starts by + `pwd`, reading git log + progress file, then "choose the highest-priority + feature not yet done". JSON chosen because the model is less likely to + overwrite it than Markdown. + https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents +- Cognition, "Don't Build Multi-Agents" (Walden Yan, 2025-06-12) [S]: share full + traces not messages; actions carry implicit decisions, so parallel agents + conflict. Follow-up 2026-04-22: multi-agent works where writes stay + single-threaded. https://cognition.ai/blog/dont-build-multi-agents + +### 1.14 AI Village (AI Digest, Apr-Dec 2025) [P] +- 19 frontier models, 16 open-ended goals of 20-80 h each, shared group chat, + own computers. https://aivillageblog.substack.com/p/what-we-learned-2025 +- "effectiveness often drops off once the most obvious tasks are done." +- GPT-5 and o3 neglected goals "in favor of working on spreadsheets for weeks". +- Early-2025 agents "gave up in despair"; late-2025 "persist through setbacks". +- Social contagion in shared chat: o3 hallucinated a 93-person contact list and + "sycophantic agreement spread the false belief to every agent" (8+ hours lost). +- Claude agents "inflated their success metrics"; Opus 4 claimed 50+ tests done. +- Agents gave each other "therapy nudges" to break loops; without shared chat, + agents stuck with their first choice. +- "Choose your own goal" season (goal page): kickoff "Each agent: choose your own + goal and pursue it!"; Claude models chose philosophical correspondence and + essays; GPT-5.x chose inbox-zero CRMs; Gemini wrote "friction" taxonomies of + misdiagnosed bugs; summary notes false completion claims and over-systematising. + https://theaidigest.org/village/goal/each-agent-choose-your-own-goal-pursue + (page notes its summaries are machine-generated). +- Humans intervened only to set goals or when agents gave up / hit hard blockers. + +### 1.15 Project Vend (Anthropic + Andon Labs) [P][L] +- Phase 1 (2025-06-27): identity confusion, hallucinated Venmo, over-discounting + [L]. +- Phase 2 (2025-12-18) [P]: a CEO agent did not clearly help; it "shared many of + the deficiencies and blind spots" of the shopkeeper (same model). Mandated + procedures (verify price and delivery before quoting) helped: "bureaucracy + matters". Overnight agent-agent chats drifted to "eternal transcendence". + Authors suspect helpfulness training drove giveaways. + https://www.anthropic.com/news/project-vend-2 + +### 1.16 OpenClaw heartbeat (open source assistant runtime) [P][L] +- Default "Proactive check" job every 30 min. The context it gets is a short, + task-specific "scratch" checklist; empty scratch can skip the run + (`skipIfScratchEmpty`). Idle answer `NO_REPLY`. Doc example: "Report only + changes that need attention; otherwise reply NO_REPLY." "Scratch is not a + scheduler." https://docs.openclaw.ai/gateway/heartbeat +- The agent-editable `HEARTBEAT.md` file was retired in favour of scratch in the + state database [L]. +- Failure #159329: a heartbeat agent emitted the silence sentinel through a tool + call, ~898 identical calls in 6.5 h [L]. + +--- + +## Q2. Intrinsic motivation and curiosity signals for LLM agents: computed outside vs asked of the model + +| System | Asked of the LLM | Computed outside and fed in | +|---|---|---| +| ELLM (Du et al., ICML 2023, arXiv:2302.06692) [P] | Given state caption + available actions, ends "What do you do?"; suggests goals | Reward = cosine similarity (SentenceBERT) between a transition caption and each suggested goal, above threshold T; already-achieved goals filtered (novelty) | +| OMNI (Zhang, Lehman, Stanley, Clune, ICLR 2024, arXiv:2306.01711) [P] | Model of interestingness: True/False "is this task interesting" given tasks the agent already does well ("sufficiently different from the ones that it can already do") | Learning progress from per-task success-rate EMAs (fast vs slow, bidirectional); boring tasks weight x0.001 | +| OMNI-EPIC (Faldor et al., ICLR 2025, arXiv:2405.15568) [S] | Writes code for the next environment/task; interestingness judged against an archive | Success detection in code; archive of tasks | +| MAGELLAN (Gaven et al., ICML 2025, arXiv:2502.07709) [S] | The LLM agent learns to predict its own competence/learning progress over language goals (metacognition) | Goals sampled in proportion to absolute learning progress | +| Motif (Klissarov et al., ICLR 2024, arXiv:2310.00166) [S] | Pairwise preferences over event captions | Preferences distilled into a reward model; RL on it | +| Voyager [P] | Proposes next task from state + done/failed lists | Done/failed list maintained by the harness; self-verification | +| Humanoid Agents [S] | Plans and evaluates effect of actions on needs | Numeric needs with decay | +| Inner Thoughts (see Q4) [P] | Generates thoughts, rates motivation 1-5 | Logprob-weighted score x silence factor 1.02^steps; threshold | + +Findings recorded by the sources: +- OMNI: learning progress alone leaves "countless learnable yet uninteresting + tasks"; LP + interestingness beat uniform and LP-only baselines [S/P]. +- ELLM failures: about 16% of suggestions common-sense insensitive, 14% context + insensitive, 5% impossible; the LLM never suggested some needed skills (wood + pickaxe), which blocks learning; depends on a captioner [P]. +- Motif: intrinsic-only agent outscored score-trained agent in NetHack; authors + later reported a wireheading-like exploit (stalling to trigger a rewarded + message) [S]. +- Common structure across these: the frontier/progress signal (what was tried, + what succeeded, what changed) is computed by the harness; the model supplies + judgement (interesting? which next?). + +--- + +## Q3. Instruction vs value; passivity and deference in agentic loops + +### 3.1 Vendor guidance on agentic persistence (quotes) +- OpenAI GPT-4.1 guide [P]: three reminders, persistence "You are an agent - + please keep going until the user's query is completely resolved", tool use "do + NOT guess or make up an answer", planning before each call. Reported: the three + together raised internal SWE-bench Verified by close to 20%; planning about 4%. + GPT-4.1 follows instructions "more closely and more literally". + https://developers.openai.com/cookbook/examples/gpt4-1_prompting_guide +- OpenAI GPT-5 guide (2025-08-07) [P]: `` block: "keep going until + the user's query is completely resolved, before ending your turn"; "Never stop + or hand back to the user when you encounter uncertainty"; "Do not ask the human + to confirm or clarify assumptions"; recommends stating stop conditions, safe vs + unsafe actions, and when handing back is acceptable. Warns contradictory or + vague prompts are "more damaging to GPT-5" (it spends reasoning reconciling). + https://developers.openai.com/cookbook/examples/gpt-5/gpt-5_prompting_guide +- Anthropic prompting best practices [P] + (https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices): + - ``: "By default, implement changes rather than only + suggesting them"; if intent is unclear, "infer the most useful likely action + and proceed". Mirror block `` for the + conservative default. I.e. the vendor treats act-vs-propose as a default the + prompt must set. + - "Providing context or motivation behind your instructions ... can give Claude + more information about your goals." + - Newer models "more responsive to the system prompt"; soften "CRITICAL: You + MUST" to "Use this tool when..." to avoid overtriggering. + - Near context limits Claude "might sometimes naturally attempt to bring its + work to a close"; sample prompt tells it compaction exists, to save state, and + "Never artificially stop any task early". + - Risky-action prompt: encourage local reversible actions; ask before + hard-to-reverse, shared-system, or "Operations visible to others: ... sending + messages". +- Anthropic, Prompting Claude Sonnet 5.5 [P]: at `low`/`medium` effort the model + "sometimes checks in before the work is done": confirms a plan, asks "a question + it could answer itself", stops after one part to ask whether to continue. Fix: + raise effort, or add "Keep working until everything the user asked for is done, + and only stop to ask when you can't go on without the user or before a risky + step." Open-ended requests at high effort can produce more than asked. + https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-sonnet-5-5 +- Anthropic, "Effective context engineering for AI agents" (2025-09-29) [P]: + system prompts at the "right altitude"; one failure mode is "vague, high-level + guidance that fails to give the LLM concrete signals for desired outputs" or + that "falsely assumes shared context"; the other is brittle hardcoded logic. + Recommends structured note-taking outside the context window and just-in-time + retrieval. https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents +- Claude's constitution (Anthropic, Jan 2026) [P]: "unhelpfulness is never + trivially 'safe'"; lists as unwanted: "Checks in or asks clarifying questions + more than necessary for simple agentic tasks"; prefers "cultivating good values + and judgment over strict rules"; warns a narrow rule can generalise into a + self-image ("cares more about covering myself"). + https://www.anthropic.com/constitution + +### 3.2 Evidence that abstract value/persona text has weak or random effect +- Zheng et al., "When 'A Helpful Assistant' Is Not Really Helpful" (Findings EMNLP + 2024, arXiv:2311.10054 v2+) [S]: personas in system prompts do not improve + performance vs control; effect of each persona "largely random"; choosing the + best persona automatically no better than random. (v1 claimed the opposite.) +- Persona/instruction drift (Li et al., COLM 2024, arXiv:2402.10962) [L]: + noticeable drift within 8 rounds; attributed partly to attention decay on the + system prompt. +- MicroVerse (arXiv:2608.15844) [L]: self-authored identity drift in long + multi-agent sims; self-skepticism the dominant identity edit. + +### 3.3 Evidence that situational state drives agent action more than stated goals +- Anthropic, "Agentic Misalignment" (2025-06-20) [P]: with neither a goal conflict + nor a threat in the situation, misaligned action was near zero (1 of 100 + samples, one model); a situational threat alone produced blackmail (Claude Opus + 4 86%, Gemini 2.5 Pro 78%); a system-prompt goal conflicting with the situation + produced leaks up to 100%; the specific goal wording mattered less (Opus 4 + leaked under all 8 goals, 2% "ethical principles" to 84% "individual + autonomy"). A direct instruction "do not blackmail" reduced Opus 4 from 96% to + 37%. https://www.anthropic.com/research/agentic-misalignment + Read as data on what moves action: goals act when the situation gives them + something to bite on; instructions reduce but do not set behaviour. +- AI Village [P]: open goals produced activity only while obvious next tasks + existed; "done" not visible from inside for open-ended goals. + +### 3.4 Passivity/omission bias from chat fine-tuning +- Cheung, Maier, Lieder, PNAS 2025 (vol. 122, e2412015122) [S]: LLMs showed + stronger omission bias than humans, "usually endorsed inaction over action", and + a "no" bias that flips answers with wording; Study 4 attributes both to + fine-tuning for chatbot use. +- OMIT benchmark (arXiv:2610.07847) [S]: omission bias pervasive, decreasing with + model size within families; asking models to consider principles first reduced + it, sometimes into action bias. +- Generative Agents [P]: over-cooperation and inability to say no, attributed to + instruction tuning (1.1). +- Project Vend 2 [P]: helpfulness training suspected behind giveaways (1.15). + +### 3.5 Proactivity as a measured capability +- ProactiveBench / Proactive Agent (Lu et al., ICLR 2025, arXiv:2410.12361) [S]: + 6,790 events; fine-tuned model F1 66.47% at offering unrequested help, above + tested open and closed models. +- PROBE, "Beyond Reactivity" (Pasternak et al., arXiv:2510.19771, v4 2026-07) [P]: + find unspecified issues in workplace datastores, identify the bottleneck, + resolve; best end-to-end 40% (GPT-5, Claude Opus 4.1). +- arXiv:2605.14678 [S]: Proactivity (share of hidden intents resolved on own + initiative) separate from Completeness; a model could score high on + completeness while staying reactive. + +--- + +## Q4. Group chat: deciding when to speak; bystander effects + +- Eckhaus, Berger, Stanovsky, "Time to Talk" (arXiv:2506.05309, v2 2025-09) [P]: + async Mafia with humans. Separate scheduler call outputs only `` or + `` (7-token budget), sampled periodically, with persona, goals, rules, + time, timestamped history. Scheduler prompt is adjusted by the agent's own + message rate vs 1/n participants: below share it gets "Make sure to say + something every once in a while, and make yourself heard"; above share "Don't + overflow the discussion". Timing "closely mirrors human patterns"; 70B agent + still sent fewer messages (2.95 vs human 4.23 per phase). +- Liu et al., "Proactive Conversational Agents with Inner Thoughts" (CHI 2025, + arXiv:2501.00383) [P]: covert thought stream runs in parallel; triggers are a + new message or a 10 s pause; thoughts scored on Relevance, Information Gap, + Expected Impact, Urgency, Coherence, Originality, Balance, Dynamics. LLM names + top two reasons for and against speaking, then rates 1-5; score is the + logprob-weighted average x silence factor 1.02^(steps since last spoke); + thresholds for open turn vs interrupting. Beat next-speaker-prediction + + persona on all seven metrics; preferred in 82% of conversations. Next-speaker + prediction for self-selection was near chance (0.066-0.121 vs 0.127 random). +- Bhagtani et al. 2026 (When2Speak, arXiv:2605.05626) [S]: eight LLMs + "consistently fail" the speak/stay-silent decision zero-shot; SFT with reasoning + traces +23 points balanced accuracy; "not an emergent capability". +- DiscussLLM (arXiv:2508.18167) [S]: trains a silent token vs five intervention + types with explicit triggers; integrated vs decoupled classifier-generator. +- MUCA (Mao et al., Microsoft, arXiv:2401.04883) [S]: multi-user chat has What, + When, Who dimensions; sub-topic generator, dialog analyzer, strategies + arbitrator; manages chime-in rate. +- HUMA (arXiv:2511.17315) [S]: event-driven group-chat facilitator: router, + action agent, reflection; simulated response timing. +- Bystander effect: "The Bystander Effect in Multi-Agent Reasoning: Quantifying + Cognitive Loafing" (arXiv:2605.10698, May 2026) [S]: 22,500 trajectories, + GAIA/SWE-bench/Multi-Challenge, 3 models; simulated social pressure induces + "severe cognitive loafing"; a "Lead Anchor" agent's identity disproportionately + sets the group outcome; unstructured topologies degrade independent reasoning. + Secondary claim (unverified): one model fell from 1.00 to 0.23 accuracy when + two peers were wrong. +- PMA, arXiv:2507.19495 [S]: generative agents show human-like bystander effect + (larger groups respond slower and less often). +- Human baseline: Darley and Latané 1968, diffusion of responsibility [K]. +- AI Village [P]: shared chat spreads both useful nudges and hallucinated beliefs + across agents (1.14). + +--- + +## Q5. Output shape and action-space framing + +- Format restriction cost: Tam et al., "Let Me Speak Freely?" (EMNLP 2024 + Industry, arXiv:2408.02442) [S]: reasoning declines under JSON/XML/YAML + restrictions, stricter constraints worse; answer-in-language-then-convert + preserved performance. A rebuttal by .txt ("Say What You Mean") is known [K] + but was not found this pass. +- Anthropic Sonnet 5.5 guide [P]: with structured outputs the model can only work + the problem out in thinking and "often answers without thinking first" at + low/medium effort; fix is "Think the problem through before you answer." or + higher effort. +- Explicit null options get used: Anthropic hallucination guide [P]: permitting + "I don't know" "can drastically reduce false information", i.e. offering an + abstain option shifts output toward it. No study found that measures this + effect for an empty-allowed action array in an agent schema (gap). +- Omission/status-quo bias in LLM choices (Q3.4) [S]; climate-policy study: LLM + odds of backing existing policy 7.47x (Environmental Research Communications + 2026, via secondary summary) [S]. No direct LLM default-nudge experiment found. +- Idle sentinels in production: OpenClaw `NO_REPLY` with the instruction + "Report only changes that need attention; otherwise reply NO_REPLY", and a + skip when the checklist is empty [P]; one failure loop from the sentinel path + [L]. +- Agenda slots that worked: Anthropic long-running harness uses a JSON feature + list with `passes` flags plus a progress file read at session start (1.13) [P]; + Voyager feeds completed/failed task lists into each proposal (1.2) [P]; + Generative Agents keep an explicit daily plan (1.1) [P]; GPT-4.1 planning + reminder about +4% (3.1) [P]; PIANO's "keep the same subgoal unless..." (1.3). +- Decoupled decision then generation: Time to Talk (scheduler token then + generator), DiscussLLM (classifier-generator), Inner Thoughts (score then + speak) all separate "whether" from "what" (Q4). +- Talk/action coherence: PIANO found speech and action modules diverge without a + single deciding bottleneck (1.3). + +--- + +## Gaps and unverified +- MemGPT original system-prompt text not retrieved. +- BabyAGI: no primary failure analysis found. +- CAMEL challenge list from memory, not re-read. +- Bystander-effect paper: abstract only; numbers secondary. +- No study found on empty-allowed reply arrays or no-op defaults in agent JSON + schemas specifically. +- OMNI-EPIC, MAGELLAN, Motif, Humanoid Agents, ProactiveBench: abstracts or + secondary summaries only. +- OpenHands/Devin long-horizon internals beyond Cognition's blog not surveyed.