From Assumptions to Actions: Turning LLM Reasoning into Uncertainty-Aware Planning for Embodied Agents
Authors: SeungWon Seo
SooBin Lim
Seongrae Noh
Haneul Kim
HyeongYeop Kang
Venue: ICLR 2026 Poster (2026)
Keywords: multi-agent system
large language model
human-agent cooperation
Primary area: applications to robotics, autonomy, planning

Abstract: Embodied agents operating in multi-agent, partially observable, and decentralized environments must plan and act despite pervasive uncertainty about hidden objects and collaborators' intentions. Recent advances in applying Large Language Models (LLMs) to embodied agents have addressed many long-standing challenges, such as high-level goal decomposition and online adaptation. Yet, uncertainty is still primarily mitigated through frequent inter-agent communication. This incurs substantial token and time costs, and can disrupt established workflows, when human partners are involved. We introduce PCE, a Planner-Composer-Evaluator framework that converts the fragmented assumptions latent in LLM reasoning traces into a structured decision tree. Internal nodes encode environment assumptions and leaves map to actions; each path is then scored by scenario likelihood, goal-directed gain, and execution cost to guide rational action selection without heavy communication. Across two challenging multi-agent benchmarks (C-WAH and TDW-MAT) and three diverse LLM backbones, PCE consistently outperforms communication-centric baselines in success rate and task efficiency while showing comparable token usage. Ablation results indicate that the performance gains obtained by scaling model capacity or reasoning depth persist even when PCE is applied, while PCE consistently raises the baseline across both capacity and reasoning-depth scales, confirming that structured uncertainty handling complements both forms of scaling. A user study further demonstrates that PCE produces communication patterns that human partners perceive as more efficient and trustworthy. Together, these results establish a principled route for turning latent LLM assumptions into reliable strategies for uncertainty-aware planning.

Published as a conference paper at ICLR 2026
FROM ASSUMPTIONS TO ACTIONS: TURNING LLM REASONING INTO UNCERTAINY-AWARE PLANNING FOR EMBODIED AGENTS
Seung Won SooBin Lim", Scongrae Noh", Haneul Kim Hyeong Kang Department of Computer Science and Engineering, Korea University (ssw03270, dnpcs, rhosunr99, a adsky0309, si aimm z_.kang) (karaang)kkkeee ac. kr
ABSTRACT
Embodied agents operating in multi-agent, partially observable, and decentralized environments must plan and act despite pervasive uncertainty about hidden objects and collaborators' intentions. Recent advances in applying Large Language Mod- els (LLMs) to embodied agents have addressed many long-standing challenges. such as high-level goal decomposition and online adaptation. Yet, uncertainty is still primarily mitigated through frequent inter-agent communication. This incurs substantial token and time costs, and can disrupt established workflows, when hu- man partners are involved. We introduce PCE, a Paanner-Composer--Vaaiiii framework that converts the fragmented assumptions latent in LLM reasoning traces into a structured decision tree. Internal nodes encode environment assump- tions and leaves map actions; each path is then scored by scenario likelihood, goal-directed gain, and execution cost to guide rational action selection without heavy communication. Across two challenging multi-agent benchmarks (C-WAM (C-WAH and TDW-MAT) and three diverse LLM backbones, PCE consistently outperforms communication-centii baselines in success rate and task efficiency while show- ing comparable token usage. Ablation results indicate that the performance gains obtained by scaling model capacity or reasoning depth persist even when PCE is applied, while PCE consistently raises the baseline across both capacity and reasoning-des depth scales, confirming that structured uncertainty handling comple- ments both forms of scaling. A user study further demonstrates that PCE produces communication patterns that human partners perceive as more efficient and trust- worthy. Together, these results establish principled route for turning latent LLM assumptions into reliable strategies for umcertainty-awart planning.
INTRODUCTION
Imagine two houschold agents collaborating to prepare meal: each perceives only part of the kitchen, yet both must coordinate to assemble ingredients and deliver them to a shared workspace. Such agents exemplify embodied agents, which perceive their surroundings, formulate plans, and execute actions to achieve specified goals in dynamic environments (Fung et al. (025). When mul- tiple embodied agents must cooperate in a decentralized setting under partial observability, each agent's 's limited perceptual field produces pervasive uncertainties about unobserved objects well as the intentions and actions of collaborators (Spaan et al.) 22006) [Amato al. 2016 2015). Naively exploring these uncertainties at every planning step is computationally prohibitive. It also tends to produce suboptimal or inconsistent plans, as the combinatorial growth of possible hidden states sig- nificantly increases the difficulty of assessing the relative value of available actions and maintaining a coherent belief.
Agents powered by Large Language Models (LLMs) have achieved notable progress in such set- tings. By leveraging few-shot and zero-shot reasoning, LLM-based planners can decompose high- level goals into executable action sequences, adapt plans to environmental changes, and reliably infer the intentions of the collaborators, enabling robust performance in complex long-houe horizon set- tings. To handle uncertainty, however, most existing systems rely repeated natural-
"These authors contributed equally to this work.
1 'Correponning author.

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Published as a conference paper at ICLR 2026
communication with collaborators to verify plans, exchange information, and iteratively refine joint strategies. This communication-centr paradigm incurs significant costs: frequent dialogue con- sumes large numbers of tokens and time, and when human collaborators involved, continuous questioning and reporting can disrupt established workflows. Moreover, simply increasing L.M capacity deepening its reasoning chains does not inherently resolve uncertainty. Without explicit mechanisms to identify and assess the assumptions that underlie uncertainty in partially observable environments, larger models still struggle to weigh competing hypotheses about the environment, which leads to misaligned priorities and degraded planning quality.
To address these limitations, we leverage two key empirical observations: First, when asked to evaluate or select actions, LLM planners internally generate implicit assumptions about uncertain aspects of the environment in their zero-shot Chain-of-Thouge reasoning traces. For example, they tend to hypothesize the presence of an unseen object or infer the likely action of a collaborator. These assumptions can in turn be used to identify what information missing. Second, we found that such assumptions are invoked locally and referenced implicitly, without being explicitly aggregated for a global decision. This unstructured handling prevents the planner from systematically reconciling multiple assumptions, which hampers its ability to detect logical confticts, compare expected gains, weigh alternative actions consistently, and ultimately achieve higher planning accuracy.
We therefore propose Maaner--ompooet Composer-Evaluutor (PCE) to extract and aggregate these internally generated assumptions into a single structured representation in the form of a decision tree, enabling them to be jointly evaluated for more reliable and uncertainty- each internal node specifies an assumption about the environment, and each leaf node specifies the action considered appropriate under the accumulated assumptions along the path. Thus, each root- to-leaf path represents a particular combination of assumptions culminating in an action trajectory. We then introduce an evaluator that scores each path in terms of its likelihood, gain, and cost, guiding rational action selection without heavy communication. This design transforms the latent knowledge already produced by L.M reasoning into a principled mechanism for uncertainty-awan planning.
action selection. In this tree,
Unlike prior multi-agent planners (Seo et al. 2025 Liu et al. 2025 Zu et al. 2025) that primarily
optimize coordination through iterative communication or joint plan negotiation, PCE operates at a different level of the decision problem. Instead of optimizing over action sequences or communica- tion strategies, PCE explicitly treats environmental assumptions as first-class decision variables and reasons over them before action execution. This shifts the planning paradigm from communication- centric coordination to structured reasoning over uncertainty embedded in the agent's ' own belief state.
Experiments on two challenging multi-agent benchmarks, C-WAH (hhng et al. 2024b) and TDW- MAT (Zhang al. 2024b), show that PCE consistently outperforms communication-cenm
base- lines in success rate, task efficiency, and token usage. Ablation studies reveal that increasing LLM capacity or its reasoning depth without explicit uncertainty handling yields only limited perfor- mance improvements relative to its computational cost. User studies further demonstrate improved reliability in human aggent collaboration. Because the proposed framework operates on generic rea- soning traces rather than model-specifie internals, it can be readily applied to wide range of LLM backbones and multi-agent domains. We demonstrate this generality by running PCE on diverse backbones, including GPT-4o mini, GPT-OSS:20B, and Gemma3:4B, and observe consistent im- provements across all of them. Together, these results shift the focus from communication-heai mitigation to principled exploitation of the assumptions already present in LLM reasoning, opening a new direction for uncertainty-awware planning in embodied systems. Code and project page are available at htttt::// /ttp://swww32000000 qithub. ic/PCE ICLR- 26, Page/
2 RELATED WORK
Embodied Multi-Agent Cooperation. Embodied agents aim to achieve long-term goals in com-
plex environments populated with diverse objects (Song et al. 2023 Li et al.) 2024). Recent work has leveraged LLMs as planners to decompose high-level goals into executable action sequences for
navigation and manipulation tasks (Zhou et al. 2024 Huang et al. 2022 Song et al. 2023 Wang
jet al.) 2023a|c Hwang et al., (025) ) and to enhance planning effectiveness through zero-shot and few-shot reasoning well dynamic replanning (Huang et al. 2022 Song et al. 2023). Beyond
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single-agems agget settings, cooperative planning in multi-agent environments has been studied to address tasks that exceed the capacity of an individual agent (Zhang et al. 2024c Guo et al. 2024).
To study more realistic embodied multi-agent cooperation, researchers focus environments where distributed agents must make decisions based on their own limited observations (Zhang et al. 2024b
Seo et al. 2025 Liu et al. 2025 Zu al. 2025). In these settings, agents cannot acquire information beyond their observation range in real time. They must therefore either move physically to gather information share it through communication with collaborators reduce travel time (Olichoek & 2006; Foerster el al.) 2016).
Spaan 2012 Spaan et al.
Prior work has mitigated uncertainty through intensive communication. For example, ProA- gent (Zhang et al., 20244) explicitly reasons about collaborators' actions and verifies its reasoning through dialogue; CoELA Zhang et al., 2024b) and REVECA (Sco al. (2025) exchange state and plan information to validate and adjust strategies: RoCo (Mandi et al. (024) and CaPo (Liu et al.) 2025) introduce iterative inter-agent debates; and CoTS ZZ et al. 2025) exploits communication- based feedback to refine joint plans. While these approaches improve coordination, they incur high token and time costs and can disrupt workflows when humans are in the loop.
More recently, LLaMAR (Nayak et al. 2024) has introduced long-horizon LLM--ased planning framework that operates in partially observable environments without relying on heavy communi- cation, yet its formulation addresses coordination in centralized multi-agent setting.
Our work takes a different path: instead of relying on heavy communication, we extract and ag- gregate the implicit assumptions in LLM reasoning into decision tree framework that jointly eval- uates them for rational action selection. This enables agents to adaptively balance physical and communicative actions, reducing communication overhead while preserving or improving planning performance.
Scaling LLM for Embodied Agents. Recent advances in LLMs have enabled natural language reasoning and high-level decision making for embodied agents. Scaling model capacity improves performance (Kaplan et al.] 2020) but requires substantial training time and data resources. To complement these costs, reinforcement learning fine-tuning has been proposed at the training meth- (Yao al.) 2023), and Self- Consistency (Wang et al. 2023b have been introduced at inference time (Snell et al. 2025)
stage (Shao et al. 2024 Wang et al.) 2024, Xu et al. 2025), while reasoning-augmentten ods such Chain-of Thouugh (Wei et al. 2022). Tree-of-Thougee
However, merely increasing model capacity reasoning depth does not address the fundamental reliance on knowledge stored within model parameters (Mirzadeh et al., 2024] Huang et al., [023] Yin al. 2024). This limitation is evident in tasks requiring external knowledge (Lewis et al.
2020), where LLMs produce uncertainty-laden outputs with degraded quality (Li et al.) 2025), in
partially observable, decentralized environments where agents' internal memory quickly becomes outdated, amplifying uncertainty and restricting planning performance (Spaan et al.
2006 Amato
al. 2016 2015).
To the best of our knowledge, no prior work has systematically examined whether uncertainty in embodied planning can be resolved simply by sealing L.Ms. Our experiments show that beyond the gains from increasing model capacity or reasoning depth, PCE delivers robust improvements across all tested backbones, indicating that its benefits are additive to and distinct from those of scaling.
Tree-Structured Reasoning and Search. Tree-based frameworks have been widely adopted to enhance decision quality, but PCE is conceptually distinct from existing approaches such as Tree of Thoughts (ToT) (Yao et al., 2023) and CoTS (Zu et al., 2025) in both what the tree represents and how communication is treated. ToT constructs a tree over reasoning steps enhance logical co- herence, but it operates primarily within the internal reasoning space of a single agent. It implicitly assumes a fully observable environment and treats tree nodes as cognitive steps rather than proba- bilistic environmental states, limiting its applicability in dynamic, partially observable multi-agent scenarios. In multi-agent settings, CoTS performs tree search over the space and uses iterative communication to explore and prune candidate plans. This communication- centric formulation makes dialogue a prerequisite for search, resulting in increased latency and significant token consumption, especially under imperfect observability.
maaasoning-and--ee and-action
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Common goal
Context Input
Mossage logs
Available action list
Compuser
Evaluator
Action history
1. [goexplorc] kitchen
2. [gochock] cabinet
Secmario free
Memory Module
Choose best action...
Scenario 1::
"ypee "sseamption"
"Likeliihood" 4,
"lext' "Missing items are kitchen."
"Gainn 3,
Observation Module
Planning Module
"resson:: "Foods are commonly....
'CossPenall'ss :5.
'Action": [goexplore] kischen'
"tyee" "action"
Planner
"action:: [goexplare] kitchen'
Scenatio 2": 'Likelibood":
"Fakee
Reasoning trace
"type "sseamption"
'Gain': 3, 'ContPenalt':: 1,
"lex" "ferma cabiner"
The kitchen likely contains food as objects
'Action" '[gacheck] cabinet" }.
(goexplone] kitchen
Environment
Execution Module
Communication Module
action: [gochock]
Figure PCE employs modular architecture with Planner, Composer, and Evaluator pipeline for planning.
In contrast, PCE structures the decision tree based on uncertain assumptions regarding the envi- ronment rather than just reasoning steps. Crucially, PCE treats communication mechanism itself, but as atomic action within the search space to be evaluated against physical actions. This allows the agent select communication only when yields higher expected utility than acting alone.
the search
PROBLEM DEFINITION
model cooperative embodied agents under costly communication as decentralized partially observable Markov decision process (DEC-POMDP) (Bernstein et al. 2002 Amato et al. (013).
The environment evolves through states s over finite horizon t = 1,..., H. At each time step, n agents execute actions (a (a'....,a") a") with transition probability P(s' ssa'....,a") ....""...")))
Each agent receives an observation of E O', where Oi O O com combines direct envi- ronmental observations with communication-hann baasdd observations. Observation histories evolve as h; of)
The action space = Agyyy U A contains both physical and communication actions. lo- cal policy xí maps the current history to action, a; N rh()). Communication actions transmit messages mi constructed from h; and broadcast to all other agents j o We assume costly com- munication: each a; Arom' consumes time that could be used for A Aphy and messages arrive with a one-step delay, i.e. m appears in o
Our objective is find set of decentralized policies म (M ........"....................... that enables the agents to cooperatively achieve a common goal G composed of multiple sub-goals within horizon H.
4 METHOD
PCE, depicted in Figure adopts the modular design commonly used embodied agent coopera- tion (Zhang et al. 2024b Seo et al. 2025 Liu et al. 2025 Zu al. 2025). It comprises Observa- tion, Memory, Planning, Communication. and Execution Modules. Among these, we redesign the Planning Module into a Planner Comperr-ooooser--ae Evaluuaoo pipeline that explicitly incorporates uncer- tainty handling, enabling more reliable action selection. Detailed prompting strategies for Planner, Composer, and Evaluator provided in Appendix A.12

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(a) Example of Planner rensoning trace
action
assumption
Okny, analyze situation. My goal to put items onto scoffcetable", currently holding anything, need move following items seupeakes?, sapples, swines, and spudding?>
Let's consider available actions and our goal to determine the optimal action.
1. [goexplone] <livingroom> (198) This could potentially lead you to one of the items you need, but it's quite far.
2. [gосжplorc] <kitchen> (56) - The kitchen likely to have food ilems such oupeakes and pudding, This is short distance.
3. [goexplore] <bedroom> (291) This the farthest option and may not yield food items.
[gochock]
ekathroomeabinet- (25)
the closest option you might something useful here, potenrially or items,
[New!] Hoh know where
[New!] Hedroom has food items
U:0.2
cupeake
U: 0.2
Reasoning trcce
[New!] send message
U:10.1
U:0.1
(b) Composer
(c) Evaluator
assumption node
action node
assumption is TRUE
cassumption
Figure 2:
Flow from reasoning trace action selection. (a) The Planner produces a reasoning
trace. (b) The Composer extracts hypotheses from the trace, structures them into a decision tree, and, when needed, generates new assumptions and communication actions to expand unexplored branches. (c) The Evaluator scores each path; The highlighted path indicates the scenario whose leaf node achieves the maximum score (U), determining the agent's 's final selected action.
4.1 OBSERVATION AND MEMORY MODULES
The Observation Module transforms raw envirommental and collaborator signals into structured per- ceptual information. Each agent detects object names/IDs, locations, room associations, available interactions, and states of objects within its perceptual range, as well as the collaborator's held ob- jects and position. Closed containers occlude their contents; for for example, a cabinet's interior remains unknown until a physical action such as opening is performed, after which newly revealed objects (e.g., a cupcake) become observable. Messages issued by the collaborator in the previous step are also included in the observation.
The Memory Module serves as a unified repository that integrates static information with dynam- ically updated data. At initialization, it is populated with the common goal G and the lovelevel action skill book (a set of APIs for performing action). During execution, the memory is incremen- tally updated with inferred task progress, collaborator information, message logs, and the agent's own previous actions. To prevent unbounded growth in long simulations, message logs are trun- cated the most recent K Kessagee entries and action histories the most recent Kaction entries. See Appendix A.3 for details.
4.2 PLANNER: REASONING FOR ACTION SELECTION
The planner forms the first stage of our Planning Module. It receives as context input the common goal G, current progress, message logs, recent actions from memory, and available action list. Us- ing this, it reasons about why each available action could contribute to achieving G and produces an initial action choice accordingly. For example, if the agent has reached the kitchen and ob- served cabinet but not yet interacted with it, the Planner may propose action such as "[gocheck] <kitchencabinet> (78)'. Leveagging the reasoning capabilities of L.LMs, the Planner outputs not only candidate action but also the associated reasoning trace.
Empirically, each candidate action in this trace tends to be grounded a single partial assump- tion about the uncertain environment. For example, shown in Figure 2a(a), the available ac- tion '[gochekk] <bathroomcabinet>> (25)' is grounded solely in the assumption that 'you might find something useful here,' while other assumptions relevant alternative actions remain implicit. These traces therefore reveal isolated assumption-actoo -actiioonnaationn links but do not show how different as- sumptions relate to each other, limiting the Planner's ability rank actions under uncertainty.
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4.3 COMPOSER: FROM REASONING TRACE TO SCENARIO TREE
The Composer organizes an explicit decision tree. It begins by semantically interpreting the context input and the Planner's reasoning trace to explicitly identify key uncertainties (assumptions). Inter- nal nodes represent these assumptions with True/False splits whose branch conditions are inherited along the path. A root-to-leaf path therefore defines a scenario, which is combination of assump- tions culminating in leaf node. Specifically, each Ieaf node is assigned action aimed at handling a physical action for direct interaction or a communication
the uncertainty of the given action for sharing information instructing the collaborator.
Instead of enumerating all assumption-action relationships, the Composer expands the tree top- down. At each node, a local ranking policy seleets the next assumption to branch on, prioritizing those that most reduce uncertainty and most strongly influence subsequent action choice. Rather than computing
probabilities, which would amount solving intractable POMDP, ap- proximate these criteria using LLMs' commonsense reasoning. Only assumptions not yet assigned on the path and consistent with current premises are considered, preventing incompatible scenarios from being generated.
When no suitable assumptions remain, the Composer proposes new atomic assumptions grounded in entities already present in the context (G, message logs, etc.). These candidates are ranked by the same policy and, if selected, inserted as new branches. Expansion is limited at depth D stops early when further splits would not materially affect action choice; the resulting node becomes a leaf and is assigned the most appropriate action. Multiple leaves may map the same action, reflecting its suitability across scenarios, while some initial actions may be dropped if never optimal.
Figure (b) illustrates this process. Given the goal of finding food items, root assumption 'the living room contains them' leads the True branch toward '[goexplore] <livingroom> (198)'. On the False branch, the premise changes, and the Composer generates new assumption that the col- laborator (Bob) may know the cupcake's location, shifting the plan from physical exploration to an information gathering action such as [send message].
4.4 EVALUATOR: SCENARIO LIKELIHOOD, CONDITIONAL GAIN, AND EXECUTION COST
The Evaluator takes the context input and the decision tree. It then scores each leaf to guide action choice under uncertainty. For every root-to-leaf path, estimates 1) how likely the scenario S is, 2) how much the action would advance the goal if the scenario holds, and 3) how costly the action is to attempt. All scores are normalized to [0, 1] for comparability.
Scenario likelihood (C). C(S) is the estimated probability that the premise of the leaf scenario is true, assessed by an LLM against the agent's 's observation and message history. For instance, if the collaborator was last seen in the kitchen, a scenario stating that the collaborator still there receives a higher score than one claiming that the kitchen is empty.
Conditional gain (G). ((a) measures how much executing action would advance the goal given that the scenario is true. This value also estimated by an LLM.
Exploiting L and G, we define the expected gain as
E Elgain| L(S) G(())
where G(a) =0 0 when the scenario is false.
Execution cost (C). C(a) quantifies the immediate resources required attempt a, regardless of its success. We decompose the cost into movement and communication terms using indicator functions:
C(a) a d(a) 1 {mmove(a) + 8 E(a) (c)mmm(a))
where d(a) is the estimated traversing distance, E(a) is the estimated message length, a, 3 > scaling constants that balance the cost of movement and communication, and 1(-) is the in- dicator. This design expresses the mutually exclusive nature of movement and communication: {mooee()) + 11l:++1(coooo (comm((a) =1

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Final score (U). For each leaf action, the Evaluator computes a final score:
U(S,a) a) = Elgain] C
where 0 controlling cost sensitivity. This score integrates scenario probability, goal-directed effectiveness, and execution expense. Ranking leaves by U(S,a) a) yields a rational action choice under uncertainty, as shown in Figure (c). The empirical setting of a, B, and À is discussed in the experiments.
4.5
COMMUNICATION AND EXECUTION MODULES
These modules translate the Planning Module's decisions into concrete behavior. When a communi- cation action is selected, the Communication Module sends or responds to messages from collabora- tors. When a physical action (e.g., moving, grasping an object, transporting a target) selected, the Execution Module carries it out by computing routes with A search and invoking the appropriate low-level Python APIs from the action skill book.
EXPERIMENT
Benchmarks. Under the constraint of partial observability in decentralized control, conduct experiments on two multi-obe mujti-objecctvve household tasks composed of multiple rooms: C-WAH (Zhang al. 2024b) and TDW-MAT (Zhang et al., 2024b). C-WAH consists of 10 episodes, where agents are required to accomplish 3-5 sub-goals in each episode, with the horizon H set to 250 steps. TDW-MAT consists of 24 episodes, where agents are tasked with achieving the goal of transport- ing 10 target objects, with the horizon H set to 3000 steps. Full environment details appear in Appendix A.
Metrics. In C-WAH, we measure Total Steps to evaluate how quickly agents achieve the goal. In TDW-MAT, we measure the proportion of target objects transported by the agents out the total goal objects, grouped as Food, Stuff. and their average Total.
We also evaluate system efficiency using Usages and Comm. Usages denotes the total token con- sumption generated by the entire system. This explicitly includes not only communication tokens but also all internal tokens generated by the LLM modules within the framework, serving as proxy for total computational cost. Comm measures the number of communication actions. Unlike other metries, Comm does not have an intrinsic "better lower" or "better is higher" interpretation. Com- munication actions not direct goal completions but context-dependent interventions: in some cases, they slow progress through unnecessary exchanges, while others, they avert false plans and improve coordination. We therefore treat Comm descriptive measure reported for diagnostic analysis rather than as a success metric, while primary comparisons focus on task performance and token usage.
Baselines. We compared PCE with four representative LLM-based cooperative agent frame- works: CoBLA Zhang al. 2024b), REVECA (Seo et al. 2025), CaPo (Liu et al., 2025). and COTS Zu et al. 2025). CoELA first demonstrated M.L--rrvven cooperation for embodied agents. REVECA introduced memory management, relative proximity-based planning, and plan valida- tion. CaPo designed plan optimization through iterative debates, and CoTS integrated multi-plan exploration with Monte Carlo Tree Search. All baselines are run under identical environmen- tal and communication settings. For our PCE framework, we use the default hyperparameters D 3.a = 1,8 1,1 1, Kaction 10, K Kesssage = 3. Further details of the baselines
can be found in Appendix A.2
LLMs. To test robustness across model backbones, each framework is run on three di- verse LLMs: op--4o-mini-2024-07711 (GPT-40 mini) (Hurst et al. 2024), google/gemma-3-441 3-b-iit (Gemma3:4BB (Team et al. 2025), and openai/gpt-oss- 20b (GPT-OSS:20B) (Agarwal et al. (025). GPT-4o mini is commercial LLM, whereas Gemma3:4B and GPT-OSS:20B are open-source. GPT-OSS:20B is a large reasoning model that performs internal reasoning before generating re- sponses; use its medium reasoning level for comparisons. GPT-4o mini and Gemma3:4B gener- ate outputs without an explicit reasoning module, and we apply zero-shot Chain-of. of-Thought prompt- ing encourage higher quality plans.

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Table 1: Experimental results in C-WAH. Best result in bold: second-best underlined.
PCE
CoELA
REVECA
CaPo
CoTS
Total Steps
42.76
60.40
46,80
60.82
64.00
GPT-4o mini
Comm
1.70
9.88
6.00
8.72
10.24
Usages
44353.56
55467.12
46312.16
41702.00
44628.12
Total Steps
49.60
72.72
53.86
68.34
65.26
GPT-OSS:20B
Comm
9.22
6.49
8.20
8.32
Usages
73535.24
77727.20
74764.24
99810.44
95428.84
Total Steps +
59.20
77.20
62.56
75.88
72.32
Gemma3:4B
Comm
3.02
9,48
9.14
7.92
4.04
Usages
50984.7
49271.24
44637.58
64015.24
51966.64
Table 2: Experimental results in TDW-MAT. Best result in bold; second best underlined.
PCE
CoELA
REVECA
CaPo
CoTS
Total f
87.50
62.50
81,25
73.33
75.00
Food f
89.17
65.83
80.83
82.50
84.17
GPT-4o mini
Stuff
85.83
59.17
81,66
64.17
65.83
Comm
3.58
13.33
43.76
70.79
108.92
Usages
197807.29
113058.83
185453.54
281404.71
411392.08
Total
81.25
55,00
73.33
65.41
59.17
Food f
85.00
50.83
78.33
72.50
70.83
GPT-OSS:20B
Stuff f
77.50
59.17
68.33
58.33
47.50
Comm
13.75
11.62
107.79
43.00
41.83
Usages
337225.12
237498.88
370737.17
348066.92
334912.67
Total t
70.83
45,84
52.09
67.50
63.33
Food
71.66
50.00
56,67
70.83
64,17
Gemma3:4B
Stuff
70.00
41.67
47.50
64.17
62.50
Comm
9.42
27.42
108.00
57.88
55.96
Usages +
184809.08
98350.25
308221.25
217626.50
212029.79
5.1 COMPARATIVE RESULTS
Table I and Table 2 summarize the two-agent cooperation results C-WAH and TDW-MAT. Across all three LLM backbones, our PCE framework consistently achieves the fastest goal completion in C-WAH and the highest success rates in TDW-MAT, outperforming existing approaches Total, Food, and Stuff metrics.
These gains stem from the agent's ability to act under uncertainty with minimal communication. By explicitly structuring assumptions rather than relying on repeated dialogue, our method spends less time message exchanges and more time on physical actions. This behavior effectively suppresses unnecessary communication-drive over, although PCE's three-module LLM architecture incurs higher per-step inference compared with architectures like CoELA that perform two LLM inferences per step, this overhead is offset by PCE's substantial reduction in episode length. Therefore, PCE achieves high performance while maintaining low Usages under decentralized partial observability.
planning cycles, thereby reducing LLM inference cost. More-
In contrast, CoELA, REVECA, CaPo, and CoTS generally rely heavily on communication, which increases the number of steps and delays goal achievement. CoELA lacks systematic communi- cation strategies and mechanisms for evaluating the value communication, making it difficult to recognize when information exchange is needed to resolve uncertainty in long-horizon, multi-room environments. REVECA can request information required to validate goal-directed plans, but has limited ability proactively identify highly uncertain factors and query them. CaPo and CoTS have the advantage of generating multi-ster plans, but in environments with high uncertainty, these plans often become invalid, leading repeated replanning.
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PCB
Planner
Can
(a) Scaling model capucity
(b) Scaling reasoning depth
65
Gemma3
GPI-0SS:208
60
Avenge Scares
Total Steps
Total
40
4B
128
278
Low
Medium
High
Unghineee
Efferency
Thast
Figure 3: Ablation results about LLM Scaling in C-WAH Figure 4: User study results in C-WAH environment.
environment.
Table 3: Ablation results in C-WAH. Best result in bold; second-best underlined.
PCE
w/o Planner
w/o Composer
w/o Evaluator
Total Steps +
42.76
56.46
46.82
47.34
GPT-4o mini
Comm
1.70
9.52
0.26
1.2
Usages
44353.56
139918.56
33347.66
44720.38
By contrast, PCE yields stable improvements across all backbones and environments by separating assumption extraction, scenario structuring, and evaluation. 'his enables the agent detect when information exchange is genuinely useful, select between physical and communication actions in principled way, and maintain high performance under decentralized partial observability.
5.2 ABLATION STUDY RESULTS
LLM Scaling. We first examine whether performance gains come from our structural design rather than simply scaling L.M capacity reasoning depth. To this end, compare PCE with Planner only variant that removes the Composer and Evaluator, eliminating explicit uncertainty handling. As shown in Figure even when the backbone size is increased from Gemma3:4B 12B B2BB or when GPT-OSS:20B 's reasoning depth is raised from Low--eeium--HHige Planner only shows only modest improvements, while PCE consistently achieves faster goal completion. This indicates that fragmented, implicitly handled assumptions persist under mere scaling, leading to inefficient exploration and occasional planning errors. In contrast, PCE's Composer-Evaluale explicitly or- ganizes and scores these assumptions, demonstrating that our structured uncertainty handling syner- gizes with scaling to provide stable performance advantage regardless of the underlying backbone.
Component Analysis. We also ablate individual components of the PCE pipeline using GPT-4o mini. As reported in Table 3 removing any module (w/o Planner, w/o Composer, w/o Evaluator) reduces performance. Without the Planner, scenario trees are built directly from context. This in- creases the difficulty of scenario exploration and often results in incoherent or redundant branches. Without the Composer, the agent relies solely on Planner's reasoning trace for evaluation, which prevents it from accounting for conflicting or complementary relationships among multiple assump- tions and results in incomplete exploration of alternative scenarios. Without the Evaluator, actions selected without quantitative likelihood serthhood-ginn--oo gain-cost assessment, which degrades decision quality. These results confirm that each module contributes essentially to uncertainty- planning.
We performed additional analyses stress-test the structural foundations of PCE and rigorously evaluate its reliability and scalability.
First, constructed physical-only and communication-onle variants of PCE and conducted com- parative experiments, alongside separate comparisons between PCE and reasoning- basclines -Chain-of-Thought, Thoe-hh-- self-Consistency). These analyses confirm that PCE's high

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performance does not stem merely from more sophisticated reasoning traces, but from the explicit structuring and evaluation of uncertainty (Appendix A.5)
Second, assessed the robustness of the framework through series of quantitative and qualitative ovaluations: (1) scalability tests with increasing number of agents to verify cooperative efficiency (Appendix A.9; (2) reliability assessments of the Composer and Evaluator based human- expert correlation studies (Appendix/ [A. 10.0 I.
D (3) hyperparameter sensitivity analyses to examine con- figuration stability (Appendix A.5); and (4) comparisons with MCTS-based planner demon- strate efficiency advantages over traditional tree search (Appendix A.8).
Finally, we present qualitative case studies illustrating how PCE corrects ill-posed plans through its structured decision-making making process, thereby providing an interpretable grounding for the observed quantitative improvements (Appendix A.7)
5.3 USER STUDY RESULTS
In real human collaboration, communication is double-edged sword: excessive messaging dis- rupts workflow, while the absence of necessary exchanges makes intentions opaque and degrades joint performance. We hypothesized that PCE's ability to structure and evaluate assumptions would allow to trigger communication only when genuinely useful, producing behavior that humans perceive as aligned, efficient, and trustworthy. To test this, ran user study in the C-WAH envi- ronment comparing (1) PCE, (2) w/o Com (communication actions removed), and (3) Com always (communication forced before each physical action). Twelve participants (mean age 26.8; 8 male, 4 female) received the same observations and action choices as the agent.
After completing each method, participants answered four questions on 7-point Likert scale (I: strongly disagree, 7: strongly agree): 1) "Did the agent perform actions appropriate to your inten- tions?" (Appropriateness). 2) "Was the agent helpful in collaboration?" (Usefulness), 3) "Did the agent's 's performance contribute to achieving the goal efficiently?" (Efficiency), and "4) Did you feel sense of trust with the agent?" (Trust). They then joined brief qualitative interviews.
As shown in Figure 4 PCE scored highest across all questions, confirming our hypothesis: by reasoning over uncertainties and initiating communication selectively, PCE achieves a balance of clarity and efficiency that users recognize as more cooperative and reliable. Interview feedback noted that Com always disrupted workflows, while w/o Com made the agent's intentions unclear and harder to trust. Detailed results are in Appendix A.6
CONCLUSION
This paper presented PCE, a modular Planner-Comper Composerr framework that extracts and structures implicit assumptions embedded in LLM reasoning traces, enabling embodied agents to plan under partial observability with minimal communication. Across two multi-objective bench- marks (C-WAH and TDW-MAT) and three diverse LLM backbones, PCE consistently outperformed communication-cemi
baselines in success rate and task efficiency while showing comparable to- ken usage. Ablation studies confirmed that each module is indispensable and that explicit uncer- tainty handling unlocks performance gains beyond what model scaling alone provides. A user study further showed that PCE produces communication patterns that humans perceive as efficient and trustworthy.
While our experiments focus on simulated multi-room household tasks, the proposed mechanism is not tied to specific domain. Future work will explore extending PCE to more complex and dynamic environments, larger and more diverse agent teams, and adaptive discovery of new assumptions in real time. These directions aim to test the framework's 's scalability and generality under richer uncertainty, advancing the broader goal of principled, decentralized cooperation with embodied agents.
ACKNOWLEDGMENTS
This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (No. RS-2025-0051 8643, No. R8-20255228022883, and by the ICT
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Creative Consilience Program through the Institute of Information & Communications Technology Planning & Evaluation (IIPP) grant funded by the Korea government (MSIT) (IITP-2026-R) 2--0022---220000 11201819).
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A APPENDIX
A.1 ENVIRONMENT SETTINGS DETAILS
In our experiments, the agent's observations are obtained through the APIs provided by each en- vironment, following previous studies (Zhang et al. 2024b). We perform experiments in partially observable environments, including C-WAH and TDW-MAT. the next section, we present the details of these environments.
aa) C-WAH
(b) TDW-MAT
Figure 5: example environment
A.1.1 COMMUNICATIVE WATCH-AND-HELP
The Communicative Watch-And-Heij (C-WAH) environment extends the original Watch-And-Help challenge (Puig et al. 2020) by introducing a communication functionality between agents. It is implemented on top of VirtualHome (Puig et al. 2018). a platform for multi-agent simulation. We run 10 episodes in C-WAH, where agents are assigned common goal to accomplish in each episode. As summarized in Table 4 the common goals fall into five categories, and agents must select from the eight possible actions listed in Table to achieve them.
In this setting, agents can exchange information with each other through communication while exe- cuting instructions. When entering a room, an agent can observe all objects that are not inside con- tainers such as fridges or microwaves. To inspect objects within containers, the agent must explicitly perform an additional action to open them. To simulate real-world communication constraints, each agent is restricted to 500 characters per frame.
The horizon H is fixed at 250 simulation steps, and each task contains to subgoals (i.e., ob- jects). Failure to achieve the common goal within 250 steps results in an unsuccessful episode. An illustration of the environment layout provided in Figuree (a).
Table 4: The goal specifications of the C-WAH
Goals
Description
Prepare afternoon tea
put [cupcake, pudding, apple, juice, wine] on coffeetable
Wash dishes
put forate. inside dishwasher
Prepare a meal
put [coffeepot, cupcake, pancake, poundcake, pudding, apple, juice,
wine] on dinnertable
Put groceries
put [cupcake, pancake, poundcake, pudding, apple, juice, wine] inside fridge
Set up a dinner table
put Iplate, fork] on dinnertable
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Table 5: The action space in C-WAH
Action
Description
Walk towards
move to an object in the same room with the agents or a room
Turn left
turn left by 30 degrees
Turn right
turn right by 30 degrees grasp an object
Grasp
Open
open a closed container
Close
close an open container put the held objects into open container onto surface send message other agents
Put
Send message
A.1.2 THREEDWORLD MULTI-AGENT TRANSPORT
The ThreeDWorld Multi-Agent Transport (TDW-MAT) environment, extended version of the ThreeDWorid Transport Challenge (Gan et al. 20216), is built on TDW (Gan et al. 2021a). It incorporates more natural object placements and provides richer set of objects and containers that support item transportation. The common goal in TDW-MAT involves transporting items across two categories: Food and Stuff. Each episode consists of 10 target objects and 2-5 containers, which are strategically placed to enable the transport of multiple items at once, detailed in Table 6
Unlike in C- C-WAH, agents in TDW-MAT cannot acquire complete information about room without executing full 360-degree rotation 15-degree increments. Communication is restricted to 500 characters per frame, and the horizon H is set to 3000 simulation steps. The environment layout illustrated in Figure (b), and the complete action space is provided in Table
Table 6: The target objects and containers of the TDW-MAT environments.
Task
Type
Object Name
Containers
bowl, plate, tea tray bread, burger, loaf bread, apple, banana, orange
Food
Objects
Containers
plastic_basker wicker basket, wood basket iPhone, pen, key, iPod, lighter, purse, calculator, pencil bucket, mouse
Stuff
Objects
Table 7: The action space of the TDW-MAT environment.
Action
Description
Move forward
move forward 0.5m turn left by 15 degrees
Turn left
Turn right
turn right by 15 degrees
Grasp
grasp an object
Put In
put the target into the container drop the objects held in hand send a message other agents
Drop
Send message
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A.2 BASELINES
CoELA (Zhagg et al. 2024b) implements a modular LLM-driven multi-agent architecture: each agent has perception, memory, planning, communication, and execution modules. Agents exchange linguistic updates and reason over what to do next. is decentralized and flexible, and with strong LLMs can achieve good multi-agent coordination in embodied tasks.
REVECA (Seo et al. 2025) proposes an LLM-based cooperative agent architecture that leverages information relevance and relative proximity for adaptive planning, and employs trajectory-basee validation to prevent false planning caused by collaborators actions, yielding more efficient memory use and stronger coordination under partial observability.
CaPo (Liu et al. 2025) introduces a collaborative meta-plan generation phase, where agents jointly construct a high-level task decomposition and allocate subtasks before execution. During the task, agents engage in progress-adaptive re- ee-planning. dynamically updating the meta-plan when new in- formation or environmental changes are observed. This two-stage process of structured plan con- struction followed by adaptive revision enhances coordination, reduces redundancy, and improves task efficiency in embodied multi-agent settings.
CoTS (Zu et al., 2025) introduces collaborative tree search framework for multi-agent planning, inspired by Monte Carlo Tree Search. Instead following a single plan path, agents explore mul- tiple candidate branches, evaluate them using ALM-based reasoning heuristic scoring, and then converge on a coherent long-term strategy. dedicated plan evaluation module monitors execution progress and selectively triggers plan updates when necessary, striking a balance between adaptabil- ity and stability. This design provides greater foresight, improves resilience to planning errors, and reduces unnecessary disruptions during collaboration.
A.3 EXAMPLE OF MEMORIES MEMORY MODULES
In this session, describe the information stored in the memory module module-including the common goal, object information, collaborator information, message logs, previous actions, and the low-level action skill book and provide detailed explanations along with illustrative examples for each.
Common goal. The common goal G is expressed in natural language as a description of the house- mustjointly jointly complete the given episode. Below are examples of common hold task that the agents must goals in C-WAH and TDW-MAT.
C-WAH: "Find and put target objects pudding, juice, apple, cupcakes onto the goal location <coffeetable> (268)."
. TDDW-AA: "Transport 2 breads, 2 burgers, 4 apples, loaf of bread, banana to the bed."
Object information. Object IDs and names, 3D positions, room IDs and names, available actions, and object state (e.g.. whether the object is held, inside a container, available to be grasped). An example this is shown in Listing
Collaborator information. Collaborator's 's held objects and 3D position. This information is up- dated when the collaborator enters the observation range, and the module stores the most recently observed values.
Message logs. The messages sent by all the agents. Only the most recent K Kmessage messages are stored, and in our implementation we set Kmessage = 3.
Previous Actions. The actions performed by the agent from the past the present stored. Only the most recent Kaction actions are kept, and in our implementation, we set Kaction = 10.
Low-level action skill book. The low-level action skill book outlines the Python APIs required for interacting with the environment. An example of this is shown in Listing 2
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Listing 1: Example of object information
1 simple example of object information 1ist with one entry. ob dbjett_infomation__sss
"object_id": 21,
4 "object_name": "apple",
5 "position": [13.22, 1.20, 5.411, " devailable_acton"" "gograb", groom_namet "Livingroom",
6
8
"room_i"" ; 198, "states"; [15, "GRABBABLE"!
9
10 )]
Listing 2: Example of low-level action skill book
simple example of low-level action skill book 2 def (eep)ore(sse1f)
3 if current_room == target_room:
4 Nove towards specific room based the plan.
6 def gocheck (self):
7
if TOPEN' in container ner ('statess
8
Check the status of container and attempt to interact with it.
9
def (ograb(self))
11
if target in reachable seachable_objects::
12
Attempt to grab an object, ensuring availability and condi tions
13
def goput (self):
if len
> 0:
16
# Place the grabbed object in the specified location or container.
A.4 DISTANCE-BASED PLANNING
When only an available action list is provided, LLM agents often struggle to account for efficient movement paths or division of labor that enhances cooperative efficiency during planning. There- fore, following prior studies, we also employ physical distance as key cognitive factor. Specifically, the Memory Module computes the geent-object distance and Dollaborator-objer distance based on the location of the target object for each available action, the most recently observed collaborator position. These distance annotations are incorporated into the textual
position, and the agent's descriptions of available actions, thereby guiding the Planner to consider both spatial efficiency and task allocation when selecting action. Ultimately, the Planner takes the augmented text prompt as input and selects the action from the available action list that is most suitable for achieving the common goal.
A.5 ADDITIONAL ABLATION STUDY RESULTS
Uncertainty Handling. We analyze the cost-gain trade-off between physical actions and com- munication actions, which employ contrasting strategies for handling uncertainty. To this end, we compare four baselines: PCE, Phy-act only, Com-act only, and Planner only. Specifically, Phy- act only relies solely on physical actions by the Composer to resolve uncertainty, while Com-act only employs only communication actions. For all baselines, we use GPT-4o mini as the backbone LLM. The results, presented in Table (Uncertaayyy Handling), demonstrate that PCE achieves the joint goal with fewer Steps than the other baselines. In particular, Phy-act only eliminates com- munication costs entirely, but must compensate through physical exploration, resulting in excessive movements and delayed goal achievement. In the Com-act only condition, the agent tries to pri- oritize resolving all uncertainties through communication before executing any physical actions. Specifically, the Evaluator first inspects all scenarios in the decision tree that be clarified via
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Table Ablation results in C-WAH. Best result in bold; second-best underlined.
PCE
Com-act only
Phy-act only
Planner only
Total Steps
42.76
46.69
45.38
46.69
Uncertainty Handling
Comm
1.70
4.78
0.0
2.39
Usages +
44353.56
49051.36
45536.14
26678.88
PCE
Co?
ToT
SC
Total Steps +
42.76
55.40
50.96
54.86
Reasoning Enhancements
Comm
1.70
6,56
1.22
1.94
Usages +
44353.56
130633.3
149666.80
129134.10
Table 9: Hyperparameter sensitivity analysis results.
Variant 1
Total Steps
Variant 2
Total Steps
Tree Max Depth (D)
D = 2
44.6
D 4
42.4
Cost Weight (ax)
a 0.5
50.1
a
45.5
Cost Weight (3)
B = 0.5
48.6
B = 1.5
44.6
Global Penalty (A)
À 0.5
58.7
À 1.5
45.4
Memory History (K acttion)
K Kaction = 5
44.4
K Kaction
44.3
Memory History (K message)
K Kmessage 2
49.5
Kmessage =
49.7
communication. After each communication action, the PCE framework is invoked again, and the Composer reconstructs the decision tree to examine whether additional information can be obtained through dialogue. This iterative process continues until the Composer determines that no further meaningful information can acquired from the collaborator. Only when no scenario in the de- cision can yield additional benefits through communication does the agent proceed to perform physical actions. Consequently, Com-act only enables rapid acquisition of collaborator information, but redundant communication delays goal completion and increases token costs. Moreover, Planner only, lacking any explicit uncertainty handling, exhibits the lowest overall cooperative performance. Taken together, these findings indicate that approaches biased toward a single mode of uncertainty resolution suffer from inherent structural limitations in cost cost-efficiency By contrast, PCE leverages thereby minimizing costs
both strategies in a balanced manner through the Composer-Evallle while simultaneously enhancing uncertainty handling and cooperative performance.
Reasoning Enhancements. We analyze the effectiveness of different reasoning enhancement strategies in cooperative planning under partial observability. To this end, we compare four base- lines: PCE, Chain-of-Thought (Co7), Tree of Thought (ToT), and Self-Consistency (SC). Specif- ically, CoT performs planning through a single linear chain of thought, ToT expands reasoning into multiple branches using tree of thought and explores candidates via tree search, and SC ap- plies self-consistency by sampling multiple reasoning traces and aggregating them through majority voting. For all baselines, ble (Reasonnin Enhancements), demonstrate that PCE consistently achieves the joint goal more efficiently and reliably than the reasoning-only baselines. In particular, CoT provides stable rea- soning but lacks diversity, often leading to brittle plans under uncertainty. ToT increases diversity by exploring multiple branches. but incurs significant token costs and sometimes selects suboptimal branches due to the absence of explicit uncertainty evaluation. SC improves robustness by aggre- gating multiple sampled plans, yet fails to distinguish between conflicting assumptions, resulting in inconsistent decision decision-making. Taken together, these findings indicate that simply enhancing reason- ing through scale or sampling is insufficient for cooperative planning under uncertainty. Moreover, the absence of explicit structuring and evaluation of uncertainty can cause erroneous assumptions to be amplified as reasoning depth increases, thereby leading to the accumulation of hallucinations and the propagation of errors. By contrast, PCE explicitly structures and evaluates assumptions through the Composer-Evaluatoo Evaluator, enabling principled uncertainty-awee planning that balances efficiency, robustness, and cost-effectiveness.
GPT-4o mini the backbone LLM. The results, presented in Ta-
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Table 10: Representative interview responses from user study participants.
Participant Feedback
"There conversation at all, which was convenient in some sense, but it did not feel like collaboration."
w/o Com
"Since the agent did not speak, was difficult know what it was doing, and I could not trust it." "There
collaboration all."
"The agent talked too much, and my workflow constantly interrupted." "Frequent communication had some benefits, but constant talking was uncomfort- able."
Com always
"Division of labor was easier, but frequent conversations made it hard to concen- trate and felt frustrating."
"There was no unnecessary communication, and I did not feel any inconvenience." "The amount of communication was appropriate, and the agent answered kindly
PCE
when asked."
"I think the agent worked efficiently and effectively."
Hyperparameter Sensitivity Analysis. To examine how each hyperparameter influences plan- ning depth, communication frequency, and execution efficiency, conducted comprehensive sensitivity analysis on the C-WAH benchmark. Each parameter was varied independently while keeping all others fixed. The full numerical results are provided in Table Under the default con- figuration (D 3, a = 3 = 1 = 1, Kastion = 10, and K Keessage 3), the model achieves 42.76 total steps. A shallower tree (D 2) limits exploration of alternative assumption scenarios, resulting in degraded performance. Increasing the depth (D 4) provides only marginal gains (42.4 steps) while incurring exponential tree growth the worst case that increases hallucination risk and computational cost. These results identify D = 3 as an effective balance between expressiveness and robustness. The coefficients a and 8 control the relative impact of physical and communicative action costs. Lower higher I causes the agent to underestimate physical actions, leading to excessive exploration. Conversely, higher a or lower 3 biases decisions toward communication- heavy strategies, also degrading performance. The default values 1==1,3=1) provide balanced weighting across modalities. The global penalty regulates the trade-off between expected util- ity and execution cost. A smaller value overemphasizes likelihood and gain, often pushing the agent toward optimistic yet risky branches. A larger value enforces overly conservative behavior. The empirical trend shows that A=1 provides a stable middle ground. Varying the memory-histor windows (Kactions ) shows that both shorter and longer lengths negatively impact per- formance. Shorter histories weaken long-horizon consistency, while longer histories incorporate irrelevant context and inflate input size. Sensitivity is particularly pronounced for K Messagee where deviations from the default disrupt coordination under partial observability.
A.6 ADDITIONAL USER STUDY RESULTS
To conduct comparative analyses, we first tested the normality of the data using Shapiro- Wilk and Kolmogorov-Smiron tests at a 5% significance level. As the results showed that the data were not normally distributed, we employed the Wilcoxon signed-rank test for pairwise comparisons. The results are presented in Figure
The analysis revealed that PCE significantly outperformed w/o Com in terms of Trust and Appropri- ateness, indicating that appropriately timed communication improved user trust and alignment with user intentions. Furthermore, PCE achieved significantly higher scores than Com always in Use- fulness and Efficiency, suggesting that excessive communication hindered collaborative efficiency, while approach enhanced cooperation by providing information only when necessary.
In addition, to provide a richer account, we report selected interview responses from the user study in Table 10, Participants consistently noted that under the w/o Com condition, the absence of di-
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wels Cove
Coex allays
Avenge Scores
Total Steps +
PCE w/o Com
72.42
GPT-4o mini
75.67
Com always
114.25
Appropriatonss
Unefalnns
Effciony
Table 11:
User study results in C-WAH environment. Best result in bold; second-best underlined.
Figure 6: User study results with C- WAH. This figure illustrates the mean scores and associated standard errors for responses to four research questions. Statistical significance was denoted as for p 0.05.
alogue made it difficult to feel as though they were collaborating with the agent, which in turn reduced their trust. Under the Com always condition, participants reported that frequent questions and information requests disrupted the workflow and delayed goal completion. In contrast, with PCE, participants stated that they were able to receive necessary information through appropriately timed dialogue, which facilitated faster goal achievement and resulted in minimal discomfort despite interacting with an agent.
As shown in Table 11. PCE required fewer Total steps than both baselines. In the w/o Com condi- tion, the absence of dialogue not consume time for communication, but the lack of information sharing led to substantially more steps to achieve the goal, resulting in lower efficiency. In contrast, the Com always condition increased the number of steps due to frequent interruptions in the work- flow caused by excessive communication. By comparison, PCE leveraged timely communication to obtain necessary information and reduce redundant movements, thereby enabling more efficient task completion.
The results of the user study revealed that communication actions go beyond mere information ex- change, serving as a key factor in shaping how human experience and trust their interactions with agents. While unnecessary communication hindered collaborative efficiency, contextually ap- propriate communication accelerated goal achievement and strengthened trust in the collaboration.
A.7 QUALITATIVE CASE STUDIES
In this section, we present qualitative case studies to illustrate how the PCE framework operates in practice. Specifically, we analyze scenarios where the baseline Planner suggests suboptimal or illogical actions due to hallucinations, and demonstrate how PCE corrects these through structured assumption generation and multi-criteria evaluation. These cases highlight PCE's versatility han- dling spatial uncertainty, social inference, and cost-benefit analysis.
Case Correction of Illogical Physical Search Scenario: Agent Alice has successfully col- lected most items, with only one target item, a loaf bread, remaining. She is currently in the Liv- ingroom (ID: 4000). The Bedroom (ID: 8000) has already been thoroughly searched and found to contain only bed (no target items). Meanwhile, the Kitchen (ID: 5000) and Office (ID: 7000) remain completely unexplored.
Planner Failure:
Relying simple heuristic that the remaining item must be somewhere, the Planner hallucinates potential gain the previously visited room and proposes returning to the Bedroom to search again, ignoring the negative observation history.
PCE Resolution: The Composer identifies the core uncertainty: "Where is the remaining loaf bread located?" It constructs scenario tree with competing assumptions: (A) "The bread is in the Bed-
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room" and (B) "The bread is in the Kitchen." The Evaluator then scores these paths. The path containing Assumption A receives an extremely low Scenario likelihood score because the history log confirms the Bedroom was already checked. Conversely, Assumption B is assigned high Sce- nario likelihood (as the area is unexplored) and high Conditionl gain. Consequently, PCE overrides the Planner's decision and selects [goexplore] <Kitchen>, ensuring logical consistency in the search strategy.
Optimizing Transport Efficiency Scenario: Alice in the Livingroom holding target item. The final drop-off location (Coffee Table) is within her immediate vicinity. The Kitchen, which typically contains many objects, far away.
Case 2:
Driven by commonsense prior that "Kitchens contain food," the Planner suggests navigating to the distant Kitchen to gather more items before returning. This is greedy and inefficient because (1) Alice can only carry limited items, and (2) the travel cost is high.
Planner Failure:
The Composer expands the reasoning trace by introducing a critical assumption not present in the Planner's trace: "Collaborator Bob may have already cleared the Kitchen." The Evaluator weighs the scenarios. The path to the Kitchen incurs a high Execution cost (travel time) and carries risk that the Conditionl gain is zero (if Bob took the items). In contrast, the path leading to the immediate drop-off has zero risk and guaranteed Conditional gain for the current item. PCE thus pivots the action to [goput], prioritizing the globally optimal behavior of securing the current point over uncertain exploration.
PCE Resolution:
Case 3: Strategic Resource Management Scenario: Agent Bob has finished searching the Bed- room (ID: 5000) and is currently in the Livingroom (ID: 4000). He has spotted two containers (c.g., a wood basker) but no target items yet. Other rooms remain unexplored. The containers are located far away (210 steps), while an unexplored section of the Livingroom (ID: 3000) is much closer (140 steps).
Planner Failure: The Planner focuses on the Long-term utility of carrying more items and decides to execute [gograsp] <wood basket>. However, it fails to account for the immediate travel cost or the uncertainty of whether the container will actually be useful given the unknown item locations.
The Composer explicitly questions the utility of the tool, branching into scenarios where "The container aids collection" versus "Direct exploration yields items faster." The Evaluator conducts a cost-benefit analysis. determines that investing 210 steps for a container of uncertain utility is less efficient than investing 140 steps to resolve the high entropy of the nearby unexplored area. As a result, PCE selects [goexplore) <Livingroom> (3000), favoring immediate information gain and lower cost over speculative long-term investment.
PCE Resolution:
Table 12: Comparison of Average Total Steps on the C-WAH Benchmark. Best result in bold.
Total Steps (4)
MHP MMCTS-based
64.90
PCE
42.76
A.8 ADDITIONAL BASELINES: COMPARISON WITH MCTS-BASED BASED PLANNER
To provide comprehensive evaluation of planning efficiency, we compare PCE against a traditional tree-search baseline: the MCTS-- based Hierarchical Planner (MHP). Adapted from the approachess in the Watch-And-Help challenge (Puig et al. (020), MHP utilizes MCTS to find optimal action trajectories. It functions by minimizing graph traversal costs and step counts, optimizing plans primarily based on the topological connectivity of the environment and predefined action costs.
PCE demonstrates superior efficiency, completing tasks with significantly fewer steps compared to MHP. While MHP is effective at minimizing traversal costs based the topological structure, it struggles with the high uncertainty inherent in partially ob- servable settings. Relying on topological connectivity and action costs, MHP tends to perform inefficient exhaustive searches when target locations are semantically ambiguous. In contrast, PCE
Analysis. As presented in Table 12
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leverages the semantic reasoning capabilities of LLMs to explicitly model likelihoods from context (e.g., inferring that an apple is likely in the kitchen without physical verification) and prioritizes ac- tions based expected utility rather than proximity alone. These results highlight that in embodied DEC-POMDPs, resolving semantic uncertainty through reasoning is a more critical bottleneck for efficiency than topological path optimization.
A.9 SCALABILITY ANALYSIS: IMPACT OF NUMBER OF AGENTS
A fundamental challenge in multi-agent planning is the potential for combinatorial explosion as the number of agents (NV increases. In this section, we analyze the scalability of PCE from both structural and empirical perspectives, demonstrating how the framework manages complexity in teams larger than two agents.
Structural Efficiency via Semantic Aggregation. Extracting uncertainty directly from the full joint state of all agents would lead to combinatorial complexity in decision tree construction. In contrast, our Composer is designed to extract uncertainty solely from the Planner's reasoning trace, observations, and memory. Because the Planner inherently filters out irrelevant details to focus on salient uncertainties, the Composer's search space scales with the complexity of the uncertainty itself, rather than linearly with the number of agents.
We observe that LLMs naturally employ semantic aggregation when reasoning about multiple col- laborators. Rather than generating separate assumptions for each individual agent (c.g.. "Did Agent A check?" vs. "Did Agent В check?"), the Planner tends to abstract these into collective predicates (e.g.. "Has any collaborator checked the kitchen?"). Because the decision tree constructed based these aggregated semantic assumptions, model critical uncertainties without becoming intractable, even as the number of agents grows.
tree depth (e.g.. D 3) remains sufficient to
3, Empirical Validation =N 4). To empirically validate this scalability, we extended our exper- iments the C-WAH benchmark to include scenarios with 3 and agents, maintaining the same tree depth hyperparameter (D 3).
Table 13:
Performance of PCE with increasing number of agents on C- Total Steps decrease as agents are added, indicating effective coordination without planning overhead.
Number of Agents (N)
Total Steps (4)
= 2
42.76
N 3
34.60
= 4
28.50
As shown in Table 13] ically as the number of agents increased from 2 to 4. This improvement indicates that PCE suc- cessfully coordinates labor division among a larger team without being overwhelmed by planning complexity. The framework effectively converts the increased manpower into task efficiency.
the average Total Steps required to complete the task decreased monoton-
Limitations. While PCE scales robustly to maal-to-medium--te teams (up N = 4), we knowledge that simple semantic aggregation may face limitations scenarios involving massive numbers of agents tasks with strict sequential dependencies between heterogeneous agents. We leave the exploration such extreme scaling conditions to future work.
A. ACCURACY AND STABILITY OF LLM--GENERATED SCORES
critical component of PCE is the Evaluator's ability to assign reliable scores to generated sce- narios. To quantitatively verify the stability of these L.LM-generated scores and address concers regarding the reliability of LLM-as-a-Judge, we conducted quantitative evaluation.
Experimental Setup. We sampled execution logs from 10 tasks in the C- C-WAH benchmark. Four domain experts, familiar with the benchmark mechanics, served annotators. These experts were
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provided with the same context imputs and scenario trees the Evaluator and were asked manually assign scores on a discrete scale to for three criteria: Scenario likelihood, Conditional gain, and Execution cost.
Quantitative Results. We measured the agreement between human annotations and the Evalua- tor's outputs using Mean Absolute Error (MAE).
Table 14: Mean Absolute Error (MAE) between Human Experts and the LLM Evaluator on a 5- point scale. Low MAE values indicate strong alignment between the model's judgment and expert intuition.
MAE
Scenario likelihood
0.91
Conditional gain
1.10
Execution cost
0.88
As shown in Table 14 the Evaluator demonstrates strong alignment with human judgment. The Execution cost metric shows the highest precision (0.88), likely because physical distance and time are objective measures. Scenario likelihood also shows high agreement (0.91), as it relies task progress stored in the agent's memory to judge the rationality each scenario. While Conditional gain exhibits slightly higher enror (1.10), this is within acceptable margin given the inherently subjective nature estimating future utility in partial observable settings.
Correlation with Model Scaling. Furthermore, as illustrated in our main experiment results (see Table 1. Table 2). stronger backbone models (e.g.. GPT--4-miini consistently yield superior plan- ning outcomes compared to smaller models (e.g., Gemma). This trend confirms that as the reasoning capability of the underlying LLM improves, the precision of the Evaluator's 's scoring increases, di- rectly translating to higher system stability and more rational decision-making.
A.11 RELIABILITY ANALYSIS OF THE COMPOSER MODULE
The Composer takes the Planner's free-form reasoning trace as input, restructures it into explicit assumptions, and them to construct decision tree. Therefore, its reliability is critical: if the Composer extracts incorrect information or generates hallucinations, the subsequent decision tree becomes invalid. To rigorously assess this reliability, we conducted two complementary analyses: 1) quantitative stress test at the assumption level, and 2) an expert evaluation at the decision -rree level. The first experiment focuses on how effectively the Composer identifies critical assumptions even when the Planner's s reasoning trace is vague. The second experiment evaluates whether these assumptions are organized into a coherent decision-tree structure.
A.11.1 QUANTITATIVE STRESS TEST AT THE ASSUMPTION LEVEL
To evaluate whether the Composer reliably identifies critical assumptions, including in cases where the Planner's reasoning trace vague incomplete, designed quantitative stress test the level of individual assumption nodes. Since the correctness of extracted or generated assumptions is inherently semantic and context-dependent, cannot be reliably validated by rule-based metrics or environment rewards alone. Human experts are therefore required to serve as the only trustworthy reference for judging whether an assumption genuinely reflects the Planner's intent or constitutes reasonable hypothesis in ambiguous contexts. The primary outcome metric for this experiment is the proportion of assumptions judged valid by human experts, which we refer as the Composer's hit rate.
Experimental Setup The experiment utilized execution logs collected from 10 C- tasks. For each log, we modified the Composer's 's prompt so that every assumption node explicitly self-labels as one of the following types:
Extracted: Assumptions directly grounded in the Planner's reasoning trace.
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Assumptions newly created based on the environment, goal, and memory in- formation.
Generated:
Next, four domain experts categorized each instance into easy, medium, hard based on trace clarity. Easy cases contain assumptions explicitly stated with minimal noise. Medium cases involve assumptions that are inferable but embedded in ambiguous or cluttered reasoning. Hard cases correspond to vague traces where the critical assumption is not directly mentioned, requiring implicit inference or hypothesis generation.
Using this categorized dataset, each assumption was independently evaluated by three additional experts. Extracted assumptions were considered valid only if explicitly supported by the trace, while Generated assumptions were considered valid if they formed contextually plausible hy- pothesis given the environment, goal, and agent memory, and collaborator state. Final labels were determined by majority vote among the three evaluators. The resulting hit rate, reported separately for each difficulty level, quantifies how consistently the Composer's ' assumptions align with human judgment.
Table 15: Composer assumption validity across difficulty levels.
Easy
Medium
Hard
Overall
Hit Rate (%)
84.3
77.8
76.7
80.6
Composer Hit Rate Results. Table 13 summarizes the Composer's 's assumption validity across different difficulty levels. Overall, the Composer achieved a validity rate of 80.6%, and maintained a hit rate of 76.7% even in the Hard cases. Although explicit cues diminish and implicit inference becomes increasingly necessary from Easy to Hard, the performance decreases only gradually rather than collapsing. This pattern shows that the Composer is not merely producing plausible-sounding text but instead reliably identifying uncertainties that are genuinely relevant to the task. It also demonstrates that even when the Planner's reasoning trace contains noise, omissions, ambiguity, the Composer can still generate assumptions that remain contextually appropriate and aligned with the agent's goals. In summary, the results of this stress test demonstrate that the Composer remains robust even when the quality its input reasoning trace degraded.
Additional Evaluation for System-level Stability. Additionally, because assumption-level accu- racy alone does not fully explain system-level stability, we conducted a complementary analysis using the same dataset examine how effectively the Evaluator suppresses erroneous assumptions. We separated scenarios into two categories: those in which all assumptions were judged valid by experts, and those that contained at least one invalid assumption. For each scenario, we compared the Evaluator's 's assigned Scenario likelihood and Final score.
Table 16: Evaluator scores for scenarios with and without invalid assumptions.
Scenario likelihood
Final score
Valid assumptions only Contains invalid assumptions
3.28
3.63
2.85
2.51
Table 16) reports the average Scenario likelihood and Final score produced by the Evaluator for each group. Scenarios containing invalid assumptions show clear degradation in both metrics. The Scenario likelihood drops from 3.28 to 2.85, indicating that the Evaluator detects inconsistencies or contradictions in the underlying knowledge state. The Final score shows an even larger decline, from 3.63 to 2.51, which reflects the Evaluator's active suppression of these flawed scenarios during action selection. These results demonstrate that PCE is not a fragile system that relies on perfect as- sumption extraction from the Composer. Instead, the Evaluator functions structural safety layer that prevents unintended emrors from propagating into the final action decision, thereby maintaining stable overall performance.
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Table 17: Expert evaluation of composer-generate decision trees (7-point Likert Scale).
Metric
Composer
Human
Evaluation Focus
Q1: Extraction Accuracy
6.27
6.64
Does the extracted assumption actually exist in the trace?
Q2: Generation Capability
6.39
6.73
Are new assumptions generated appro- priately when the trace is vague?
Q3: Ranking Logic
5.83
6.23
Are nodes ordered by uncertainty re- duction & impact?
Q4: Logical Consistency
6.18
6,55
Are there contradictions in the decision-tree?
Q5: Action Appropriateness
5.98
6.15
Do leaf actions match the scenario path?
A.11.2 EXPERT EVALUATION AT THE DECISION-TREE LEVEL
While the assumption-level analysis shows how accurately the Composer constructs individual nodes, it does not by itself guarantee that these nodes form coherent strategy when assembled into full decision tree. This limitation is particularly important in multi-agent planning: even when each assumption is locally correct, the agent may still choose suboptimal actions if the assumptions misprioritized, placed inconsistent levels of abstraction, actions. To assess this risk, we evaluated the structural and logical quality of the decision trees produced by the Composer by comparing them against those constructed by human experts.
misaligned with corresponding
Using the same execution logs from the 10 C-WAH tasks employed in the stress test, four domain experts independently constructed decision trees based identical inputs (environment state, goal, and the Planner's s reasoning trace). These human- generated trees were then combined to form Decision Trees Dataset, and we conducted a blind cross-review on this dataset using a 7-point Likert scale across five evaluation dimensions.
The results are presented in Table 17
While it is expected that human experts achieve slightly higher scores overall, the Composer attains scores of 5.83 or higher across all dimensions, indicating a level of quality that is broadly comparable to expert performance. The strong results in Generation Ca- pability and Logical Consistency show that the Composer not only faithfully reflects the Planner's s reasoning but also generates new assumptions when necessary while maintaining coherence with previously established assumptions. The Ranking Logic scores illustrate that the Composer effee- tively prioritizes key uncertaintics, enabling efficient uncertainty reduction during decision making. The Action Appropriateness results further suggest that the Composer correctly interprets the mantics of each scenario path and assigns suitable actions to the corresponding leaf nodes.
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A.12 PROMPT TEMPLATE
Communication
I'm SAGENT NAMES. My collaboralor SOPPO SOPPO_NAMES, NAMES just sent messuge musi reply concisely and helpfully
keep us coordinaled on shared goal.
Goal: SGOALS
My Progress: SPROGRESSS
Dialogue history:
SDIALOGUE HISTORYS
Previous actions: SACTION HISTORYS
Last incoming message from SOPPO NAMES:
SLAST SEESSEESSEES
First, directly ANSWER any explicit question in the last message.
Then, provide exactly ONE short follow-up (status, location, or confirmation) that reduces uncertainty most relevant to the goal (c.g,, where an item is, who will grab it, whether room was checked).
If making//confimmiig commitments, reference specific objects with their full notation <name> (id) ONLY that exact id is known from context; otherwise use just the name without inventing ids.
Keep it brief. Be polite, action-oriented, and unambiguous.
OUTPUT FORMAT: return exactly one action string in this format:
[send_ message] <t..'> Do not add any extra text before after.
Answer:
Figure 7: Prompts template for Communication.
Planner
I'm SAGENT NAMES. I'm in hurry to finish the housework with my friend SOPPO NAMES together. Given our shared goal, dialogue history, and my progress and previous actions, please help me choose the best available action to possible. Do not choose actions related goals that have already been achieved or likely to have been achieved by the opponent. Note that I can hold two objects time. All objects denoted <name's (id), such stable> (712).
uchieve the goal
Goal: SGOALS
Progress: SPROGRESSS
Dialogue history:
SDIALOGUE, HISTORYS
Previous actions: J
Available actions:
SAVAILABLE ACTIONSS
Figure 8: Prompts template for Planner.
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Composer
SAGENT NAMES. examining my reasoning trace uncertainties before acting.
Reasoning Trace the planner's internal chain-of-thought the action in the previous planning slep.
Goal: SGOALS
Progress: SPROGRESSS
Dialogue history:
SDIALOGUE HISTORYS
Previous actions: SACTION LACTION_HISTORYS HISTORYS
Available Actions:
SAVAILABLE
AABLEAACTIONSS ACTIONSS
Reasoning Trace:
SREASONING TRACES
Your task is to convert the Reasoning Trace into deeper treo-of-thinking that cnumcrates major uncertaintics (including multi-agent cases), where cach Icaf maps to exactly one next action (copied verbatim from Available Actions). The evaluator will score cach scenario Icaf (Likelihood, Performance) versus cost (distance/communcctionn to select the best action.
Rules:
1. Identify the uncertain statements in the Reasoning Trace that materially affect action choice.
Uncertain assumptions, guesses, speculation, information that be invalid duc collaborators' actions.
Example: 'The apple might be in the kitchen." or 'Bob probably alrcady checked the cabinet.'
2. For each uncertain stalement, create binary branch and limit the maximum depth to "True: the assumption is currect.
'Falsc': assumption incorrect.
Expand in order of higher uncertainty, then greater expected impact on goal success, steps, or usage.
Include multi-agent third-cases (e-g., . the object existed already taken by the collaborator and my information stale).
For each branch, continue the next uncertainty; none remain, attach exactly one action string copied verbatim from Available Actions.
4. Ground leaf nodes using only actions from Available Actions.
Do not invent new objects/IDs. Use only entilies presenl in Rensoning Trace, Progress, or Available Actions.
You may communicate with the collaborator via [send message] <t..>> even if it is not listed in Available Actions.
All other actions must come from Available Actions.
If two different scenarios lead to the sume action, keep separate leaves (the evaluator still needs distinct scenarios for expocted-value computation).
5. Output only the nested JSON object that represents the secnario tree (no extra text).
Keys = uncertain statements (or chosen actions).
Values dicts with "Truc' and 'False' branches OR direct action strings.
Each action must preserve its full format:
<name> (id), copied verbatim.
Example (paired with Reasoning Trace and Available Actions):
Example Reasoning Trace:
You are in the bathroom and holding nothing, Goal: put cutlery forks into the dishwasher.
Distances Kitchen: 7.38m, Living (3,89m, Bedroom: 8.39m.
Checking the bathroom cabinet cheupest (3.35m) and might contain fork.
Available Actions:
[gochock] <bathroomeabinet> (190) my 3.35 meters [goexplore] <kitchem> (1I) my cost: 7.38
[goexplore] <livingroom> (267) my cost: meters
[goexplore] <bedroom>> (172) - my cost: meters
[send message)
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Exumple Oulpul (valid JSON):
'type': 'hypothesis',
"text': 'The <bathroomcabinet (190) contains scuteryforkes
'reason'; 'Nearby low-cost container, utensils may be stored here!,
"Truc::
'type': 'actionn,,
'action': [[gochock] <bathroomcaaiicee> (190) my cost: 3.35 meters'
'False::
type': 'hypothsiss,
ttet": alrcady took <cutleryfork> from the <bathroomeabiner> (190),
'rcason': 'Lack of obscrvation about collaborator's 's past actions; object could have been collected carlier.,
"Tree:
"ype': 'lctionn,
"action': [[send message] <VBob, did you already take a fork from the bathroom cabinet?!'!!
).
Talse::
Ttye': 'hypothesis'.
"tett:: "Forks are in the <Kitchen> (II) (more likely than other rooms)),
'reason': "Utensils are commonly in the kitchen; cost to kitchen (7.38m) is lower than living room (13.89m).,
Truu': {
'type': 'action',
'action': [[goexplore] <kitchen" (11) my cost: 7.38 meters'
1.
"False:
type': 'hypothesis'.
text': 'Next best room explore (by distance) is the <Bedroom> (172) over the <Living room² (267y,
'reason': 'Bedroom cost (8.39m) Living cost (13 89m)); both unexplored!',
'True::
'type:: 'action',
'action': "[ooxpporr|] <bedroom> (172)- my 8.39 meters'
FFalsc:
type': 'action',
aactionn "[goexplrre] <livingroom> (267) my cost: 13.89 meters'
Figure Prompts template for Composer.
Evaluator
I'm SAGENT_NNE NAMES. have received Scenario Tree produced by the checker, Each leaf' corresponds to concrete next-action choice under a specific set of assumptions (including multi-agent cases such as collaborator-tuken objects or stale information).
Tree format you may reocive:
Hypothesis node:
('type:: "hypothesis', 'text': Kassumption>", 'reason': "cwhy this is considered>", 'True: (-), False' (-)
Action node (leaf):
('type: 'action', 'action': "[uction_stringe strinn])
Your task: Evaluate all actionable leaves (cach leaf that contains conerete action) and retur compact mapping that the selector rank. For each scenario (leaf), provide:
Likelihood (1-5):
How plausible it that this branch (set of assumptions) is true.
very unlikely, 5 highly likely.
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Published as a conference paper at ICLR 2026
Gain (1-5):
How much this advancev/satisfie goal under this branch.
5 directly satisfies or the most impactful next step; 3 moderate progress; marginal.
3. CostPenalty (1-5):
How costly, risky, or burdensome this action is perform (in terms of effort, time, or communication overhcad). Consider physical movement, communication, and resource usage
very low cost: communication ([send message]), or short move less than
3 moderate cost: physical move less than 10m, or multiple small actions combined.
5 - very high cost: long movement across multiple rooms (>1 (=15m), heavy resource usage, or actions likely to waste
significant time/effort.
4. Acttion:
Exactly one action from Available Actions that comesponds to this scenario branch. Copy the full action string verbatim (e-g. [gograb] <apple> (36)). If the leaf already contains a concrete action string, use that.
Constraints:
Use only actions that appear in Available Actions (verbatim match; do not invent objects/IDs).
You communicate with the collaborator via [send message] <t...>> if not listed in Available Actions.
Evaluate every Icaf that contains action. If the Secnario Tree has N actionable leaves, return N sccnarios.
Provide Likelihood, Gain, and CosIPenalty defined above. Performance will be computed by the system.
Number scenarios stable order (e.g.. left-to-right depth-first traversal): 'Scenurio I", 'Scenario 2',
Input:
Goal: $GOALS
Progress: SPROGRESSS
Dialogue history:
SDIALOGUE, HISTORYS
Previous actions: SACTION HISTORYS
Available Actions:
SAVAILABLE ACTIONSS
Socnario Tree:
SSCENARIO TREEŞ
Output Format:
Retur a JSON dictionary (no exira lext) where:
Keys - scenario identifiers (e.g.. 'Scenario I', 'Scenario 2', ...).
Values dictionary with:
"Likelihood': integer (1-5)
'Gain': integer (1-5)
'CustPenalty:: integer (1-5)
'Action': string
Example Output:
'Scenario 1': |
'Likelihood": 4,
'Gain': 5,
'CostPenulty':
'Action': [[gograb <cutleryforke> (373)
'Scenario
"Likelihood:: 3,
'Gain': 'CostPenalty': 5,
'Action': [[gocheck] <kitchencabinet> (75) 1.
'Sccnario {
"Likelihood: 4,
'Gain': 2,
'CostPenalty': I,
'Action': "[send message] <"Bob, did you check the kitchen?*>
Figure 10: Prompts template for Evaluator.
30


Reviews:
[{"review_id": "Dcjt0SpM6Q", "reviewer": "Reviewer_5pEK", "summary": "The paper proposes PCE (Planner–Composer–Evaluator) for embodied decision making. PCE is a modular framework that turns implicit assumptions inside an LLM’s reasoning trace into a structured decision-tree for uncertainty-aware planning in partially observable multi-agent environments. Experiments (including human evaluations) on C-WAH and TDW-MAT show that PCE consistently outperforms communication-centric baselines  across three LLMs. This work also provides ablation and user studies.", "questions": "See weaknesses, and\n\n1. Why is the Usage of PCE lower than CoELA? According to my understanding, PCE’s three modules all require LLM inference, which should make the total cost higher than CoELA, which only infers twice. Please clarify this discrepancy.  \n2. How about using MCTS based on distance as a comparison or baseline? It might provide a stronger reference for tree-based planning efficiency.\n\nI am willing to change my score if the concerns are addressed", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. It is interesting to introduce an uncertainty-handling mechanism to this field. PCE explicitly extracts and evaluates the LLM’s latent assumptions and plan with structured decision-tree.\n2. The experimental results show strong gains in success rate and step efficiency on two benchmarks, with comparable computational cost. It is also good to include human evaluations.\n3. The ablations on reasoning and different LLMs also show the consistency of performance gain.", "weaknesses": "1. It is unclear how the hyperparameters were chosen. The authors set D = 3, alpha = 1, beta = 1, lambda = 1, Kaction = 10, Kmessage= 3 empirically, but no according further explaination or ablation is provided.  \n2. The related work section should discuss tree-search-based methods (e.g., CoTS) more clearly.  The authors need to clearly articulate how their method differs conceptually and why PCE is needed beyond existing tree reasoning or search frameworks.\n3. The paper would benefit from more case studies or qualitative analyses to illustrate how PCE behaves in different uncertainty scenarios and to provide deeper insight into its decision-making process.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "MJvrjbXMeE", "reviewer": "Reviewer_FyaB", "summary": "This paper identifies a key challenge for LLM-based embodied agents in partially observable, multi-agent settings: the heavy reliance on inter-agent communication to resolve uncertainty, which incurs significant token, time, and human workflow costs.\n\nTo address this, the authors propose PCE, a Planner-Composer-Evaluator framework. The core idea is to leverage the implicit, fragmented assumptions that LLMs generate in their reasoning traces.\n\n- The **Planner** produces an initial action and its reasoning trace.\n\n- The **Composer** extracts these latent assumptions and structures them into an explicit decision tree (a \"scenario tree\"). Internal nodes represent assumptions (with True/False branches), and leaves represent the final action to be taken under that scenario.\n\n- The **Evaluator** scores each root-to-leaf path (scenario) based on its estimated likelihood (L(S)), conditional gain (G(a)), and execution cost (C(a)) .\n\nThis allows the agent to make a rational, uncertainty-aware choice—including whether to communicate or take a physical action—by selecting the action from the scenario with the highest final utility score, U(S,a)=E[gain]−λC(a). Experiments on two multi-agent benchmarks (C-WAH and TDW-MAT) with three different LLM backbones show that PCE outperforms communication-centric baselines in success rate and efficiency. A user study also suggests that PCE's communication patterns are perceived by humans as more efficient and trustworthy.", "questions": "- **On the Composer's Reliability (W2)**: The Composer's ability to correctly identify assumptions and generate new ones is critical. How robust is this process? What happens if the Planner's reasoning trace is vague or does not contain an obvious, extractable assumption? Is there any quantitative analysis of the Composer's \"hit rate\" for identifying the correct critical uncertainty?\n\n- **On Scalability (W1)**: Could you elaborate on how you expect the PCE framework, particularly the Composer's tree generation, to scale to n>2 agents? Would the tree depth D=3  be sufficient to model the compounded uncertainties from multiple collaborators?\n\n- **Clarification of Figure 2**: The visualization in Figure 2(c) is slightly confusing. It highlights a path corresponding to nodes 1(False) → 5(False) → 4 as the \"best scenario.\" To clarify: at the time of decision-making, the truth values of the assumptions are unknown. Does this highlighted path simply represent the leaf node (action [gocheck] cabinet) that received the highest utility score U from the Evaluator, and the path shown is the scenario (i.e., the set of assumptions) under which that action is optimal?\n\n- **Clarification of the Communication Mechanism**: The user study strongly supports that PCE's communication is more efficient. To confirm my understanding: is communication reduced simply because the [send_message] action is treated as just another potential leaf in the decision tree, which must then \"win\" the U(S,a) competition against all physical actions? This seems to be the implicit gating mechanism, and it's elegant, but I want to ensure I'm not missing a more explicit component. If that is the case, its best to explicitly mention it in the paper.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **Originality and Significance**: The paper's core contribution is novel and insightful. Instead of simply using an LLM's reasoning trace (like Chain-of-Thought), the PCE framework performs meta-reasoning on the trace itself. The idea of \"turning LLM reasoning into... planning\"  by extracting, structuring, and formally evaluating latent assumptions is a clever way to operationalize the implicit knowledge within LLMs for decision-making under uncertainty.\n\n- **Problem Formulation**: The work addresses a well-defined and critical problem. As LLM agents become more capable, their reliance on frequent communication becomes a bottleneck, especially when humans are in the loop. This paper offers a principled alternative to naive, communication-heavy strategies.\n\n- **Methodological Clarity**: The proposed PCE framework is modular, logical, and well-explained. The three-stage pipeline is intuitive, and the Evaluator's scoring function provides a principled mechanism for balancing scenario likelihood, potential gain, and the distinct costs of physical vs. communicative actions.\n\n- **Thorough Empirical Evaluation**: The experimental validation is a significant strength.\n\n  * __Generality__: The method is tested on three diverse LLM backbones (including commercial and open-source models) across two challenging benchmarks , consistently outperforming four representative baselines.\n\n  * __Ablation Studies__: The ablations are comprehensive. The component analysis (Table 3) successfully demonstrates that each part of the PCE pipeline is necessary for good performance .\n\n   * __Scaling Analysis__: The \"LLM Scaling\" study (Figure 3) provides compelling evidence that the performance gains are attributable to the PCE framework itself, not just to using a larger model. It shows that while scaling models (e.g., Gemma3 4B → 27B) improves a \"Planner only\" baseline, PCE reaps greater benefits, widening the performance gap .\n\n   * __User Study__: The inclusion of a user study  is commendable. It directly validates the paper's central hypothesis: that reducing communication intelligently leads to a human-agent collaboration that is not only more efficient but also perceived as more trustworthy and useful.", "weaknesses": "- **Scalability of the \"Multi-Agent\" Claim**: The experiments are exclusively conducted in two-agent settings. While technically \"multi-agent,\" this does not sufficiently support the paper's broader claims of solving uncertainty in \"multi-agent... environments\". The complexity of tracking collaborator intentions and partial observations scales combinatorially with the number of agents. It is unclear how the Composer's decision tree and the Evaluator's scoring would handle branching on assumptions about n−1 other agents without becoming intractable.\n\n- **The Composer**: The entire framework's effectiveness hinges on the Composer module. This module is tasked with complex, non-trivial reasoning steps: (1) semantically identifying the most critical uncertainties from a free-text reasoning trace , (2) ranking them by abstract criteria like \"influence\" , and (3) proposing new atomic assumptions from scratch when needed. The paper states this is approximated using \"LLMs' commonsense reasoning\"  and provides a prompt (Figure 9), but this sweeps a massive amount of complexity under the rug. The paper offers no analysis of the Composer's reliability. If the Composer fails to extract the key assumption or hallucinates an irrelevant new one, the entire decision tree is built on a faulty foundation, and the Evaluator's \"principled\" scoring becomes meaningless. The ablation in Table 3 only shows that no Composer is bad, not that the current Composer is robust.\n\n\n- **Potentially Overstated Scaling Claims**: In the scaling ablation (Figure 3), the performance improvement (i.e., the slope of the line) for \"Planner only\" appears quite similar to that of \"PCE.\" For example, in Figure 3(b), both methods see a drop of ~9-10 steps when moving from \"Low\" to \"High\" reasoning. The paper's claim that PCE \"amplifies the benefits of scaling\"  seems slightly overstated; the data suggests the benefit of the PCE framework is largely additive—PCE starts at a better baseline, and that baseline advantage is maintained or slightly widened as the model scales.\n\n\n- **Missing Related Work**: The related work section  focuses primarily on communication-centric methods (like ProAgent, CoELA, etc.). However, it seems to overlook other recent lines of work on long-horizon LLM planning in partially observable environments that do not rely on heavy communication. For example, [1].\n\n[1] Nayak, Siddharth, et al. \"LLaMAR: Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments.\" arXiv preprint arXiv:2407.10031 (2024).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "B8NECspTNS", "reviewer": "Reviewer_SbnL", "summary": "This paper proposes PCE (Planner-Composer-Evaluator), a framework for uncertainty-aware planning in LLM-based embodied agents. The key idea is to extract implicit assumptions from LLM reasoning traces and structure them into a decision tree, enabling effective multi-agent coordination without heavy communication. However, the technical contribution is limited and differentiation from similar work is unclear.", "questions": "- What is the recovery method when assumptions at decision tree nodes are incorrect?\n- How does PCE perform in environments where communication is completely blocked?\n- How do you evaluate and ensure the quality of assumption extraction?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "- The paper effectively identifies the communication overhead problem.\n- User study results showing that PCE produces more efficient communication patterns demonstrate practical value.\n- Consistent improvements across GPT-4o mini, GPT-OSS:20B, and Gemma3:4B suggest broad applicability.", "weaknesses": "- The distinction between the proposed methodology and existing multi-agent task planning techniques remains unclear. In particular, the paper needs to articulate clear differences from approaches like ProAgent, CoELA, REVECA, and CaPo, which also perform tasks through multi-agent communication.\n- Furthermore, while the main contributions are presented as the Planner-Composer-Evaluator structure and the decision tree-based techniques in the Composer and Evaluator, these appear to be applications of existing methods rather than novel contributions.\n- In the experimental section, the performance degradation when removing the Composer appears minimal. This raises questions: if the Planner-Evaluator structure alone achieves higher performance than baselines that utilize collaborative agents, what accounts for this superiority? The analysis lacks clarity on why performance exceeds baselines even without the Composer, and where specifically the Composer contributes to performance improvements. Such analysis should be included to better understand the contribution of each component.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "knPdlXy85J", "reviewer": "Reviewer_yuvs", "summary": "The paper proposes **PCE (Planner–Composer–Evaluator)**, a modular framework that enhances LLM agents’ ability to plan and act in multi-agent embodied environments. The **Planner** generates potential next steps, the **Composer** extracts hypotheses from plans and structures them into a decision tree, and the **Evaluator** assesses candidate actions based on expected gain and cost of paths on the decision tree. The system aims to make LLM agents more consistent and efficient by explicitly modeling future actions and uncertainty. Experiments conducted on two benchmarks, C-WAH and TDW-MAT, across multiple backbone LLMs demonstrate improvements in task success rate with decreased communication. Furthermore, human studies on PCE show that the system is more helpful and efficient.", "questions": "- Regarding Weakness 1, can the authors analyze the accuracy of the LLM-generated scores? It is important to understand their stability.\n- Regarding Weakness 2, for the metrics, does the **Usage** metric measure only communication tokens or all tokens generated within the system? If it’s the former, could the authors also compare the total generated tokens with the baselines? I believe this would be a good proxy for both the latency and cost of the designed system.\n- From Table 3, we observe an interesting phenomenon: removing the **planner** dramatically increases the number of communication rounds (from 1.70 to 9.52), whereas removing the **composer** decreases them. Could the authors explain why this happens?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The **motivation for PCE is clear and well-grounded**. By designing the planning module to explicitly model upcoming steps and their corresponding confidence scores, analogous to a world model, the approach enables LLM agents to act more consistently and **avoid redundant or unproductive communications**, leading to more efficient collaboration.\n- Across both C-WAH and TDW-MAT benchmarks, **PCE consistently achieves faster goal completion and higher success rates** under all three backbone LLMs (GPT-4o mini, GPT-OSS:20B, and Gemma3:4B). This demonstrates the strong performance of PCE. Meanwhile, smaller communication times make PCE more efficient when cooperating with humans.\n- The authors systematically evaluate the necessity of each component and provide clear explanations of their functionalities. This detailed analysis and transparent modular design make the system **relatively easy to reproduce and adapt** to other tasks or environments.", "weaknesses": "- The designs of the planner, composer, and evaluator in the planning module essentially prompt LLMs to perform planning, extraction, and evaluation. Meanwhile, the evaluation metric is the score output by an LLM judge. Given that LLM scores are not very accurate, it may be challenging to accurately measure the system's stability.\n- As in Tables 1 and 2, regarding the token usage metric, the result is not significant. This makes me think about whether the system is sending too many tokens at one time in the communication channel, or if the system has a higher latency in planning for the next step.\n- Typos:\n    - In the composer part of Fig 1., “assumtion” should be “assumption”.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)