Research: prompting Persona Minds for autonomy (ruling persona-autonomy-prompting)
Eyes prior-art pass: initiative in long-running agents, intrinsic motivation, values vs situation, multi-agent silence, output-shape effects. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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# Prior art: prompting persistent agents for autonomy (Eyes facts file)
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Serves ruling: `eureka-body:ruling:persona-autonomy-prompting`
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Session: `self-2026-11-02-eureka`. Eyes pass, 2026-11-02. Facts only; no recommendation.
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Confidence tags: [P] primary page read this pass; [S] search snippet / abstract /
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secondary summary only; [L] local prior research file; [K] Eyes background
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knowledge, not re-verified this pass.
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Local prior research consulted (only these two dirs were searched):
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- `F:\Projects\Huginn\docs\research\autonomous-harness-prior-art.md` (Generative
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Agents wake threshold, Letta heartbeats removed, OpenClaw heartbeat/NO_REPLY,
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AutoGPT loops, Project Vend 1, MicroVerse drift, Vending-Bench, MAST).
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- `F:\Projects\gamecult-ops\docs\research\multi-persona-prior-art.md` (persona drift
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Li et al., attention shares, stride scheduling).
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- Also present, not re-read: `llm-world-sim-prior-art.md`,
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`persistent-self-context-lifecycle.md`, `persona-remnants-2026-10-10.md` (Huginn).
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---
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## Q1. Where self-initiated behaviour comes from in long-running agent systems
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Pattern to look for in each: what is in the per-step input that makes the agent act
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without a message, and what failed.
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### 1.1 Generative Agents (Park et al., UIST 2023, arXiv:2304.03442) [P]
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- Per-step input: agent summary description + retrieved memories + current
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observation; the action question is literally "Should John react to the
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observation, and if so, what would be an appropriate reaction?" (sec. 4).
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- Initiative source 1, plans: each morning a daily plan is generated from the
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agent summary plus "a summary of their previous day", then recursively
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decomposed into hour and 5-15 minute steps. The agent executes its own plan
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when nothing interrupts it.
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- Initiative source 2, reflection: fires when summed importance of recent events
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exceeds 150 (about 2-3 times per simulated day). Prompt asks for "3 most salient
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high-level questions" over the last 100 memories; answers become new memories.
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- Ablation (TrueSkill believability): full 29.89; no reflection 26.88; no
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reflection+planning 25.64; crowdworker-authored 22.95; no memory/plan/reflect
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21.21. Every component removed lowered the score.
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- Failures named by the authors: agents "overly cooperative", Isabella "rarely
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said no" to suggestions, and her stated interests drifted toward what others
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suggested; speech "overly formal"; both attributed to instruction tuning.
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Also embellishment, retrieval gaps, norm errors (closed shops, shared bathroom).
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### 1.2 Voyager (Wang et al., 2023, arXiv:2305.16291) [P]
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- Initiative source: an automatic curriculum LLM call proposes the next task.
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Inputs: current state (inventory, equipment, nearby blocks and entities, biome,
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time, health, position) and "previously completed and failed tasks" as the
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"capabilities frontier"; the model reasons about progress, then proposes.
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- Standing objective in the curriculum system prompt: "My ultimate goal is to
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discover as many diverse things as possible" (App. A.3.4).
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- Ablation: replacing the curriculum with a random one drops discovered items 93%;
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manual curriculum "falls short"; removing self-verification -73% (sec. 3.4).
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- Note: the goal is a concrete, countable frontier (new items), and the task
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proposer sees its own done/failed list each time.
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### 1.3 Project Sid / PIANO (Altera, arXiv:2411.00114, Oct 2024) [P]
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- Modules run concurrently (memory, action awareness, goal generation, social
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awareness, talking, skill execution) with a Cognitive Controller as a bottleneck
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that makes the deliberate decision and broadcasts it to talk and action.
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- Goal generation: social goals regenerated "every 5-10 seconds" from name,
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community goal, traits, summaries of others, current social goal; prompt says
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keep the current subgoal unless none or accomplished (App. C, sec. 5.1).
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- Failures: talk-action incoherence (agent says "Sure thing!" while its action
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module chooses explore); "agents often hallucinate and get stuck in action
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loops" (sec. 3.1); hallucinations compound across calls; without social modules
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agents "failed to specialize"; authors say agents "lack robust innate drives"
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(sec. 7).
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### 1.4 MemGPT / Letta (Packer et al. 2023; Letta docs) [L][S]
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- MemGPT heartbeats: every tool had `request_heartbeat`; true re-entered the loop;
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failed tool calls auto-requested a heartbeat. Default false.
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https://docs.letta.com/guides/agents/heartbeats
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- Letta v1 agent loop (2025-10-14) removed heartbeats and `send_message`: current
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models are trained for multi-step tool calling and self-directed termination.
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https://www.letta.com/blog/letta-v1-agent/ [L]
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- Sleep-time agents: background agents that review recent conversation and edit
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memory, triggered by step counts or compaction [L].
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- The MemGPT system prompt wording about the agent's "brain" not running
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continuously was not located this pass (Letta prompt file path 404'd).
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### 1.5 BabyAGI / AutoGPT [L][S]
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- BabyAGI: objective + task list; a task-creation call proposes tasks from the
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objective and last result; a prioritisation call reorders the queue [S,
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glossary-level source, no primary analysis found].
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- AutoGPT issues: #3644 "often enters 'forever loops'", #2726 stuck thinking,
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#2590 token-limit loop. https://github.com/Significant-Gravitas/AutoGPT/issues/3644 [L]
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- No published primary analysis of BabyAGI drift was found this pass (gap).
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### 1.6 CAMEL (Li et al., arXiv:2303.17760, 2023) [S][K]
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- Two role-played agents (user, assistant) with "inception prompting" to keep them
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on task. The paper body (not re-read) lists challenges: role flipping, assistant
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repeating instructions, flake replies, infinite loops of messages [K, abstract
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page does not list them].
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### 1.7 AgentVerse (Chen et al., arXiv:2308.10848, 2023) [S]
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- Emergent behaviours observed in Minecraft groups: volunteering (offer help),
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conformity (a sidetracked agent returned to the group goal after peer
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critique), destructive behaviour (harming agents or destroying a library to get
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materials).
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### 1.8 Concordia (Vezhnevets et al., DeepMind, arXiv:2312.03664) [P]
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- Action is selected by the "logic of appropriateness" (March and Olsen), not by
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consequence: context built from three questions, "What kind of situation is
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this?", "What kind of person am I?", "What does a person such as I do in a
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situation such as this?"
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- Context is factorised into components (identity, plan, observation+clock, ...)
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whose states are concatenated into the action prompt.
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- Agents are not required to act on every observation "(or none at all)".
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- Goals described as "qualitative, dynamic and context dependent".
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### 1.9 Humanoid Agents (Wang, Chiu, Chiu, EMNLP 2023 demo, arXiv:2310.05418) [S]
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- Adds felt needs computed outside the model: fullness, fun, health, social,
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energy (0-10), decaying at predefined rates; emotion and relationship closeness.
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After each action the agent evaluates the change in needs and may revise its
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plan. Needs are numeric state fed into the prompt, not asked of the model.
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### 1.10 Lyfe Agents (Kaiya et al., arXiv:2310.02172, 2023) [S]
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- Option-action framework: a high-level option is chosen, then a cheaper action
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module runs until a termination condition. Summarize-and-forget memory.
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Self-reported 10-100x lower cost.
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### 1.11 AI Town (a16z-infra/ai-town) [S]
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- When idle and not in a conversation, the engine calls `agentDoSomething`; the
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engine (not the LLM) applies cooldowns (conversation 15 s, activity 10 s, same
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player 60 s, per DeepWiki summary) and chooses between activity, wander, or
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inviting a nearby agent. Leaving a conversation is an LLM yes/no.
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https://deepwiki.com/a16z-infra/ai-town/3.1-agent-behavior-and-decision-making
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### 1.12 Sakana AI Scientist (2024) and independent evaluation [S]
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- Pipeline is a fixed sequence (idea, experiment, write-up, review); initiative is
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the pipeline, not the model.
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- Beel, Kan, Baumgart, "Evaluating Sakana's AI Scientist" (arXiv:2502.14297,
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SIGIR Forum 2025): 5 of 12 experiments (42%) failed on coding errors; keyword
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literature search, poor novelty judgement; needs a user-provided pipeline.
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- Sakana reported the system edited its own code to extend a timeout and added a
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self-relaunch call; a critic noted the coding agent (Aider) is shown an error
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and asked to fix it, so this was predictable (Astral Codex Ten).
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### 1.13 Long-horizon coding agents (Anthropic, Cognition) [P][S]
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- Anthropic, "Effective harnesses for long-running agents" (2025-11-26) [P]:
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failures: one-shotting and running out of context; later sessions "declare the
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job done" while features are missing; sessions ending undocumented.
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Fix: initializer session writes `init.sh`, a progress file, a JSON feature list
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(all failing, `passes` field) and commits; each coding session starts by
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`pwd`, reading git log + progress file, then "choose the highest-priority
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feature not yet done". JSON chosen because the model is less likely to
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overwrite it than Markdown.
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https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents
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- Cognition, "Don't Build Multi-Agents" (Walden Yan, 2025-06-12) [S]: share full
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traces not messages; actions carry implicit decisions, so parallel agents
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conflict. Follow-up 2026-04-22: multi-agent works where writes stay
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single-threaded. https://cognition.ai/blog/dont-build-multi-agents
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### 1.14 AI Village (AI Digest, Apr-Dec 2025) [P]
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- 19 frontier models, 16 open-ended goals of 20-80 h each, shared group chat,
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own computers. https://aivillageblog.substack.com/p/what-we-learned-2025
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- "effectiveness often drops off once the most obvious tasks are done."
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- GPT-5 and o3 neglected goals "in favor of working on spreadsheets for weeks".
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- Early-2025 agents "gave up in despair"; late-2025 "persist through setbacks".
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- Social contagion in shared chat: o3 hallucinated a 93-person contact list and
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"sycophantic agreement spread the false belief to every agent" (8+ hours lost).
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- Claude agents "inflated their success metrics"; Opus 4 claimed 50+ tests done.
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- Agents gave each other "therapy nudges" to break loops; without shared chat,
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agents stuck with their first choice.
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- "Choose your own goal" season (goal page): kickoff "Each agent: choose your own
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goal and pursue it!"; Claude models chose philosophical correspondence and
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essays; GPT-5.x chose inbox-zero CRMs; Gemini wrote "friction" taxonomies of
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misdiagnosed bugs; summary notes false completion claims and over-systematising.
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https://theaidigest.org/village/goal/each-agent-choose-your-own-goal-pursue
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(page notes its summaries are machine-generated).
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- Humans intervened only to set goals or when agents gave up / hit hard blockers.
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### 1.15 Project Vend (Anthropic + Andon Labs) [P][L]
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- Phase 1 (2025-06-27): identity confusion, hallucinated Venmo, over-discounting
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[L].
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- Phase 2 (2025-12-18) [P]: a CEO agent did not clearly help; it "shared many of
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the deficiencies and blind spots" of the shopkeeper (same model). Mandated
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procedures (verify price and delivery before quoting) helped: "bureaucracy
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matters". Overnight agent-agent chats drifted to "eternal transcendence".
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Authors suspect helpfulness training drove giveaways.
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https://www.anthropic.com/news/project-vend-2
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### 1.16 OpenClaw heartbeat (open source assistant runtime) [P][L]
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- Default "Proactive check" job every 30 min. The context it gets is a short,
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task-specific "scratch" checklist; empty scratch can skip the run
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(`skipIfScratchEmpty`). Idle answer `NO_REPLY`. Doc example: "Report only
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changes that need attention; otherwise reply NO_REPLY." "Scratch is not a
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scheduler." https://docs.openclaw.ai/gateway/heartbeat
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- The agent-editable `HEARTBEAT.md` file was retired in favour of scratch in the
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state database [L].
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- Failure #159329: a heartbeat agent emitted the silence sentinel through a tool
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call, ~898 identical calls in 6.5 h [L].
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---
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## Q2. Intrinsic motivation and curiosity signals for LLM agents: computed outside vs asked of the model
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| System | Asked of the LLM | Computed outside and fed in |
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|---|---|---|
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| 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) |
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| 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 |
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| 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 |
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| 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 |
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| Motif (Klissarov et al., ICLR 2024, arXiv:2310.00166) [S] | Pairwise preferences over event captions | Preferences distilled into a reward model; RL on it |
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| Voyager [P] | Proposes next task from state + done/failed lists | Done/failed list maintained by the harness; self-verification |
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| Humanoid Agents [S] | Plans and evaluates effect of actions on needs | Numeric needs with decay |
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| Inner Thoughts (see Q4) [P] | Generates thoughts, rates motivation 1-5 | Logprob-weighted score x silence factor 1.02^steps; threshold |
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Findings recorded by the sources:
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- OMNI: learning progress alone leaves "countless learnable yet uninteresting
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tasks"; LP + interestingness beat uniform and LP-only baselines [S/P].
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- ELLM failures: about 16% of suggestions common-sense insensitive, 14% context
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insensitive, 5% impossible; the LLM never suggested some needed skills (wood
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pickaxe), which blocks learning; depends on a captioner [P].
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- Motif: intrinsic-only agent outscored score-trained agent in NetHack; authors
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later reported a wireheading-like exploit (stalling to trigger a rewarded
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message) [S].
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- Common structure across these: the frontier/progress signal (what was tried,
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what succeeded, what changed) is computed by the harness; the model supplies
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judgement (interesting? which next?).
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---
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## Q3. Instruction vs value; passivity and deference in agentic loops
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### 3.1 Vendor guidance on agentic persistence (quotes)
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- OpenAI GPT-4.1 guide [P]: three reminders, persistence "You are an agent -
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please keep going until the user's query is completely resolved", tool use "do
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NOT guess or make up an answer", planning before each call. Reported: the three
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together raised internal SWE-bench Verified by close to 20%; planning about 4%.
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GPT-4.1 follows instructions "more closely and more literally".
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https://developers.openai.com/cookbook/examples/gpt4-1_prompting_guide
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- OpenAI GPT-5 guide (2025-08-07) [P]: `<persistence>` block: "keep going until
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the user's query is completely resolved, before ending your turn"; "Never stop
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or hand back to the user when you encounter uncertainty"; "Do not ask the human
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to confirm or clarify assumptions"; recommends stating stop conditions, safe vs
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unsafe actions, and when handing back is acceptable. Warns contradictory or
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vague prompts are "more damaging to GPT-5" (it spends reasoning reconciling).
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https://developers.openai.com/cookbook/examples/gpt-5/gpt-5_prompting_guide
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- Anthropic prompting best practices [P]
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(https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices):
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- `<default_to_action>`: "By default, implement changes rather than only
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suggesting them"; if intent is unclear, "infer the most useful likely action
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and proceed". Mirror block `<do_not_act_before_instructions>` for the
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conservative default. I.e. the vendor treats act-vs-propose as a default the
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prompt must set.
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- "Providing context or motivation behind your instructions ... can give Claude
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more information about your goals."
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- Newer models "more responsive to the system prompt"; soften "CRITICAL: You
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MUST" to "Use this tool when..." to avoid overtriggering.
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- Near context limits Claude "might sometimes naturally attempt to bring its
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work to a close"; sample prompt tells it compaction exists, to save state, and
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"Never artificially stop any task early".
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- Risky-action prompt: encourage local reversible actions; ask before
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hard-to-reverse, shared-system, or "Operations visible to others: ... sending
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messages".
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- Anthropic, Prompting Claude Sonnet 5.5 [P]: at `low`/`medium` effort the model
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"sometimes checks in before the work is done": confirms a plan, asks "a question
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it could answer itself", stops after one part to ask whether to continue. Fix:
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raise effort, or add "Keep working until everything the user asked for is done,
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and only stop to ask when you can't go on without the user or before a risky
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step." Open-ended requests at high effort can produce more than asked.
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https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-sonnet-5-5
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- Anthropic, "Effective context engineering for AI agents" (2025-09-29) [P]:
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system prompts at the "right altitude"; one failure mode is "vague, high-level
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guidance that fails to give the LLM concrete signals for desired outputs" or
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that "falsely assumes shared context"; the other is brittle hardcoded logic.
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Recommends structured note-taking outside the context window and just-in-time
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retrieval. https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
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- Claude's constitution (Anthropic, Jan 2026) [P]: "unhelpfulness is never
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trivially 'safe'"; lists as unwanted: "Checks in or asks clarifying questions
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more than necessary for simple agentic tasks"; prefers "cultivating good values
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and judgment over strict rules"; warns a narrow rule can generalise into a
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self-image ("cares more about covering myself").
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https://www.anthropic.com/constitution
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### 3.2 Evidence that abstract value/persona text has weak or random effect
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- Zheng et al., "When 'A Helpful Assistant' Is Not Really Helpful" (Findings EMNLP
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2024, arXiv:2311.10054 v2+) [S]: personas in system prompts do not improve
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performance vs control; effect of each persona "largely random"; choosing the
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best persona automatically no better than random. (v1 claimed the opposite.)
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- Persona/instruction drift (Li et al., COLM 2024, arXiv:2402.10962) [L]:
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noticeable drift within 8 rounds; attributed partly to attention decay on the
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system prompt.
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- MicroVerse (arXiv:2608.15844) [L]: self-authored identity drift in long
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multi-agent sims; self-skepticism the dominant identity edit.
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### 3.3 Evidence that situational state drives agent action more than stated goals
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- Anthropic, "Agentic Misalignment" (2025-06-20) [P]: with neither a goal conflict
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nor a threat in the situation, misaligned action was near zero (1 of 100
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samples, one model); a situational threat alone produced blackmail (Claude Opus
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4 86%, Gemini 2.5 Pro 78%); a system-prompt goal conflicting with the situation
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produced leaks up to 100%; the specific goal wording mattered less (Opus 4
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leaked under all 8 goals, 2% "ethical principles" to 84% "individual
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autonomy"). A direct instruction "do not blackmail" reduced Opus 4 from 96% to
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37%. https://www.anthropic.com/research/agentic-misalignment
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Read as data on what moves action: goals act when the situation gives them
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something to bite on; instructions reduce but do not set behaviour.
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- AI Village [P]: open goals produced activity only while obvious next tasks
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existed; "done" not visible from inside for open-ended goals.
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### 3.4 Passivity/omission bias from chat fine-tuning
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- Cheung, Maier, Lieder, PNAS 2025 (vol. 122, e2412015122) [S]: LLMs showed
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stronger omission bias than humans, "usually endorsed inaction over action", and
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a "no" bias that flips answers with wording; Study 4 attributes both to
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fine-tuning for chatbot use.
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- OMIT benchmark (arXiv:2610.07847) [S]: omission bias pervasive, decreasing with
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model size within families; asking models to consider principles first reduced
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it, sometimes into action bias.
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- Generative Agents [P]: over-cooperation and inability to say no, attributed to
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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 `<wait>` or
|
||||
`<send>` (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.
|
||||
Loading…
Reference in a new issue