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2309.02427#57 | Cognitive Architectures for Language Agents | 15 Action space: thinking beyond external tools or actions. Although â action spaceâ is a standard term in reinforcement learning, it has been used sparingly with language agents. CoALA argues for defining a clear and task-suitable action space with both internal (reasoning, retrieval, learning) and external (grounding) actions, which will help systematize and inform the agent design. â ¢ Size of the action space. More capable agents (e.g., Voyager, Generative Agents) have larger action spaces â which in turn means they face a more complex decision-making problem. As a result, these agents rely on more customized or hand-crafted decision procedures. The tradeoff of the action space vs. decision-making complexities is a basic problem to be considered before agent development, and taking the minimal action space necessary to solve a given task might be preferred. | 2309.02427#56 | 2309.02427#58 | 2309.02427 | [
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2309.02427#58 | Cognitive Architectures for Language Agents | â ¢ Safety of the action space. Some parts of the action space are inherently riskier. â Learningâ actions (especially procedural deletion and modification) could cause internal harm, while â groundingâ actions (e.g., â rmâ in bash terminal, harmful speech in human dialog, holding a knife in physical environments) could cause external harm. Today, safety measures are typically task-specific heuristics (e.g., remove â osâ operations in Python (Chen et al., 2021), filter keywords in dialog (Chowdhery et al., 2022; Driess et al., 2023), limit robots to controlled environments (Ahn et al., 2022)). However, as agents are grounded to more complex environments with richer internal mechanisms, it may be necessary to specify and ablate the agentâ s action space for worst-case scenario prediction and prevention (Yao and Narasimhan, 2023). Decision making: thinking beyond action generation. We believe one of the most exciting future directions for language agents is decision-making: as detailed in Section 4.6, most works are still confined to proposing (or directly generating) a single action. Present agents have just scratched the surface of more deliberate, propose-evaluate-select decision-making procedures. | 2309.02427#57 | 2309.02427#59 | 2309.02427 | [
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2309.02427#59 | Cognitive Architectures for Language Agents | â ¢ Mixing language-based reasoning and code-based planning may offer the best of both worlds. Existing approaches either plan directly in natural language (Huang et al., 2022c; Ahn et al., 2022) or use LLMs to translate from natural language to structured world models (Wong et al., 2023; Liu et al., 2023a; Zhang et al., 2023a; Li et al., 2023a; Guan et al., 2023; Silver et al., 2022; 2023). Future work could integrate these: just as Soar incorporates a simulator for physical reasoning (Laird, 2022), agents may write and execute simulation code on the fly to evaluate the consequences of plans. See Section 7 for more discussion. | 2309.02427#58 | 2309.02427#60 | 2309.02427 | [
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2309.02427#60 | Cognitive Architectures for Language Agents | â ¢ Extending deliberative reasoning to real-world settings. Initial works have implemented classical planning and tree search (Yao et al., 2023; Hao et al., 2023; Liu et al., 2023a; Dagan et al., 2023), using toy tasks such as game of 24 or block building. Extending these schemes to more complicated tasks with grounding (Qin et al., 2023) and long-term memory is an exciting direction. â ¢ Metareasoning to improve efficiency. LLM calls are both slow and computationally intensive. Using LLMs for decision-making entails a balance between their computational cost and the utility of the resulting improved plan. Most LLM reasoning methods fix a search budget by specifying a depth of reasoning (Yao et al., 2023), but humans appear to adaptively allocate computation (Russek et al., 2022; Lieder and Griffiths, 2020; Callaway et al., 2022; Gershman et al., 2015). Future work should develop mechanisms to estimate the utility of planning (Laidlaw et al., 2023) and modify the decision procedure accordingly, either via amortization (fine-tuning the LLM based on the results of previous actions, e.g. Nguyen, 2023; Hamrick et al., 2019), routing among several decision sub-procedures (e.g., ReAct (Yao et al., 2022b) investigated backing off to CoT (Wei et al., 2022b) and vice versa), or updates to the decision-making procedure. | 2309.02427#59 | 2309.02427#61 | 2309.02427 | [
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2309.02427#61 | Cognitive Architectures for Language Agents | â ¢ Calibration and alignment. More complex decision-making is currently bottlenecked by issues such as over-confidence and miscalibration (Jiang et al., 2021; Braverman et al., 2020; Chen et al., 2022), misalignment with human values or bias (Liang et al., 2021; Feng et al., 2023), hallucinations in self-evaluation (Shinn et al., 2023), and lack of human-in-the-loop mechanisms in face of uncer- tainties (Nguyen et al., 2022a; Ren et al., 2023). Solving these issues will significantly improve LLMsâ | 2309.02427#60 | 2309.02427#62 | 2309.02427 | [
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2309.02427#62 | Cognitive Architectures for Language Agents | utilities as agent backbones. 16 # 7 Discussion Internal vs. external: what is the boundary between an agent and its environment? While humans or robots are clearly distinct from their embodied environment, digital language agents have less clear boundaries. For example, is a Wikipedia database an internal semantic memory or an external digital environment (Yao et al., 2022b)? If an agent iteratively executes and improves code before submitting an answer (Shinn et al., 2023; Yang et al., 2023), is the code execution internal or external? If a method consists of proposal and evaluation prompts (Yao et al., 2023), should it be considered a single agent or two collaborating simpler agents (proposer and evaluator)? We suggest the boundary question can be answered in terms of controllability and coupling. For example, Wikipedia is not controllable: it is an external environment that may be unexpectedly modified by other users. However, an offline version that only the agent may write to is controllable, and thus can be considered an internal memory. Similarly, code execution on a internal virtual environment should be considered an internal reasoning action, whereas code execution on an external machine (which may possess security vulnerabilities) should be considered an external grounding action. Lastly, if aspects of the agent â such as proposal and evaluation prompts â are designed for and dependent on each other, then they are tightly coupled and best conceptualized as components in an individual agent. In contrast, if the steps are independently useful, a multi-agent perspective may be more appropriate. While these dilemmas are primarily conceptual, such understanding can support systematic agent design and help the field align on shared terminology. Practioners may also just choose their preferred framing, as long as it is consistent and useful for their own work. Physical vs. digital: what differences beget attention? While animals only live once in the physical world, digital environments (e.g., the Internet) often allow sequential (via resets) and parallel trials. This means digital agents can more boldly explore (e.g., open a million webpages) and self-clone for parallel task solving (e.g., a million web agents try different web paths), which may result in decision-making procedures different from current ones inspired by human cognition (Griffiths, 2020). Learning vs. acting: how should agents continuously and autonomously learn? | 2309.02427#61 | 2309.02427#63 | 2309.02427 | [
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2309.02427#63 | Cognitive Architectures for Language Agents | In the CoALA framework, learning is a result action of a decision-making cycle just like grounding: the agent deliberately chooses to commit information to long-term memory. This is in contrast to most agents, which simply fix a learning schedule and only use decison making for external actions. Biological agents, however, do not have this luxury: they must balance learning against external actions in their lifetime, choosing when and what to learn (Mattar and Daw, 2018). More flexible language agents (Wang et al., 2023a; Park et al., 2023) would follow a similar design and treat learning on par with external actions. Learning could be proposed as a possible action during regular decision-making, allowing the agent to â | 2309.02427#62 | 2309.02427#64 | 2309.02427 | [
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2309.02427#64 | Cognitive Architectures for Language Agents | deferâ it until the appropriate time. GPT-4 vs GPT-N: how would agent design change with more powerful LLMs? Agent design is a moving target as new LLM capabilities emerge with scale (Wei et al., 2022a). For example, earlier language models such as GPT-2 (Radford et al., 2019) would not support LLM agents â indeed, work at that time needed to combine GPT-2 with reinforcement learning for action generation (Yao et al., 2020); GPT-3 (Brown et al., 2020) unlocked flexible few-shot and zero-shot reasoning for NLP tasks; while only GPT-4 (OpenAI, 2023a) starts to afford more reliable self-evaluation (Saunders et al., 2022; Shinn et al., 2023; Yao et al., 2023) and self-refinement (Madaan et al., 2023; Chen et al., 2023b). Will future LLMs further reduce the need for coded rules and extra-learned models? Will this necessitate changes to the CoALA framework? As a thought experiment, imagine GPT-N could â simulateâ memory, grounding, learning, and decision-making in context: list all the possible actions, simulate and evaluate each one, and maintain its entire long-term memory explicitly in a very long context. Or even more boldly: perhaps GPT-N+1 succeeds at generating the next action by simulating these implicitly in neurons, without any intermediate reasoning in context. While these extreme cases seem unlikely in the immediate future, incremental improvements may alter the importance of different CoALA components. For example, a longer context window could reduce the importance of long-term memory, while more powerful reasoning for internal evaluation and simulation could allow longer-horizon planning. In general, LLMs are not subject to biological limitations (Griffiths, 2020), and their emergent properties have been difficult to predict. Nonetheless, CoALA â and cognitive science more generally â may still help systematically organize tasks where language agents succeed or fail, and suggest code-based procedures to complement a given LLM on a given task. | 2309.02427#63 | 2309.02427#65 | 2309.02427 | [
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2309.02427#65 | Cognitive Architectures for Language Agents | Even in the most extreme 17 case, where GPT implements all of CoALAâ s mechanisms in neurons, it may be helpful to leverage CoALA as a conceptual guide to discover and interpret those implicit circuits. Of course, as discussed in Section 6, agent usecases will also help discover, define and shape LLM capabilities. Similar to how chips and computer architectures have co-evolved, language model and agent design should also develop a reciprocal path forward. # 8 Conclusion We proposed Cognitive Architectures for Language Agents (CoALA), a conceptual framework to systematically understand and build language agents. Our framework draws inspiration from the rich history of symbolic artificial intelligence and cognitive science, connecting decades-old insights to frontier research on large language models. We believe this approach provides a path towards developing more general and more human-like artificial intelligence. | 2309.02427#64 | 2309.02427#66 | 2309.02427 | [
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2309.02427#66 | Cognitive Architectures for Language Agents | # Acknowledgements We thank Harrison Chase, Baian Chen, Khanh Nguyen, Ofir Press, Noah Shinn, Jens Tuyls for proofreading and valuable feedback, and other members from the Princeton NLP Group and Princeton Computational Cognitive Science Lab for helpful discussions. SY and KN acknowledge support from an Oracle Collaborative Research award and the National Science Foundation under Grant No. 2239363. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. SY is also supported by the Harold W. Dodds Fellowship from Princeton. TS is supported by the National Defense Science and Engineering (NDSEG) Graduate Fellowship Program. | 2309.02427#65 | 2309.02427#67 | 2309.02427 | [
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