Compositional Diffusion with Guided search for Long-Horizon Planning
Authors: Utkarsh Aashu Mishra
David He
Yongxin Chen
Danfei Xu
Venue: ICLR 2026 Oral (2026)
Keywords: Diffusion Models
Compositional Diffusion
Goal-directed Planning
Primary area: applications to robotics, autonomy, planning
TL;DR: We integrate search into compositional diffusion to scale short-horizon models into long-horizon plans, supporting motion planning, panoramic image synthesis, and long-video generation.

Abstract: Generative models have emerged as powerful tools for planning, with compositional approaches offering particular promise for modeling long-horizon task distributions by composing together local, modular generative models. This compositional paradigm spans diverse domains, from multi-step manipulation planning to panoramic image synthesis to long video generation. However, compositional generative models face a critical challenge: when local distributions are multimodal, existing composition methods average incompatible modes, producing plans that are neither locally feasible nor globally coherent. We propose Compositional Diffusion with Guided Search (CDGS), which addresses this \emph{mode averaging} problem by embedding search directly within the diffusion denoising process. Our method explores diverse combinations of local modes through population-based sampling, prunes infeasible candidates using likelihood-based filtering, and enforces global consistency through iterative resampling between overlapping segments. CDGS matches oracle performance on seven robot manipulation tasks, outperforming baselines that lack compositionality or require long-horizon training data. The approach generalizes across domains, enabling coherent text-guided panoramic images and long videos through effective local-to-global message passing. More details: https://cdgsearch.github.io/

Published as a conference paper at ICLR 2026
COMPOSITIONAL DIFFUSION WITH GUIDED SEARCH FOR
PLANNING
Utkarsh A. Mishra, David He, Yongxin Chen and Danfei Xu
Georgia Institute of Technology ka rssm. robol rowo@gateeeteee.eoooo.o.. gatchgattcheeee
ABSTRACT
Generative models have emerged as powerful tools for planning, with composi- tional approaches offering particular promise for modeling long-horizon task dis- tributions by composing together local, modular generative models. This compo- sitional paradigm spans diverse domains, from multi-step manipulation planning to panoramic image synthesis to long video generation. However, compositional generative models face a critical challenge: when local distributions are mul- timodal, existing composition methods average incompatible modes, producing plans that are neither locally feasible nor globally coherent. We propose Composi- tional Diffusion with Guided Search (CDGS), which addresses this mode averag- ing problem by embedding search directly within the diffusion denoising process. Our method explores diverse combinations of local modes through population- based sampling, enforces global consistency through iterative resampling between overlapping segments, and prunes infeasible candidates using likelihood-basse fil- tering. CDGS matches oracle performance on seven robot manipulation tasks, outperforming baselines that lack compositionality or require long-horizon train- ing data. The approach generalizes across domains, enabling coherent text-guided panoramic images and long videos through effective local-to-globee global message pass- ing. More details: cddsearch. github.io/
INTRODUCTION
Compositional Dilfusion (Prior work)
Construct global dian bution
Sumple sequemoe of Mode averoging louatplara
jumpiig out of distribttion
Synthesizing coherent long sequences is a crucial and challenging task, requiring reasoning over extended horizons. This task arises naturally in various domains: robotic actions must enable future steps, parts of a panorama must align semanti- cally, and subjects in video must remain consistent across hundreds of frames.
Local Plan cistrituriou
Alwergg Preee
Compositional Ditfusion with Search (Ours)
Search
Prune wlanswwth
Select the best global plan
Recent work leverages generative models to learn long sequence distributions [25] Bl with diffusion models [ 21 gain- ing popularity for modeling multi-modal data (9 20 However, full-sequence data
retential
incomsstenncies
jobal plans
is expensive to acquire, and monolithic models fail generalize beyond training horizons [10]. As an alternative, com- positional generation effectively combines short-horizon local distributions to sam- ple long-horizon global plans [66 44 40
Stari
Ptan
Candicite
Sotected
Plan
Distribution
Transition
Figure Compositional Diffusion with Guided Search (CDGS) composes short-horizon plan distri- butions sample long-horizon goal- coal-directed plans di- rectly at inference. Unlike naive compositional sam- pling, explores diverse plans and filters locally in- consistent paths to avoid "mode averaging", yielding globally coherent plans.
e.g. e.g.. chaining skills for task plan- ning, connecting images into panoramas, stitching clips into videos. While this improves data-efficiency and allows ex- trapolation beyond training data, it intro-

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Published as a conference paper at ICLR 2026
pick(hook)
pull/cube, hook)
place(hook)
pick-place(cube)
pick-place(cube)
121 SD2.0 0 Prompt: A photo of the dolomites with red lava flowing through the valley (512 x 4608)
T2V CogVideoX-28 Prompt: A cute happy panda, paws strum acoustic guitar, (350 fremes, 720p)
Figure 2: Applications CDGS. (Top) Long horizon motion planning: CDGS discovers a valid multi-step plan to move the blue cube the green cube's original position via (1) using the hook to pull blue cube in workspace, (2) displace the green cube to make space and (3) moving the blue cube to the target position. (Mid) CDGS generates coherent panoramic images. (Bottom) CDGS can stitch short clips to generate consistent, longer videos.
duces a critical challenge: as local plan distributions become highly multimodal, the distribution of global plans inherits combinatorial multi-modality. For example, in the robotics scenario in Fig. 2 because the robot has a large combination of actions and objects it can act on, the search space of possible plans grows exponentially with the length of the planning horizon.
Existing methods for compositional generation offer a promising approach, using score-averaging to compose modes of local distributions into a global distribution [66. 44 However, these methods have an important limitation: their inability to handle the combinatorial multi-modality leads them to average incompatible local modes (mode-averaging)) ultimately producing invalid global plans. Addressing such complex multi-modal distributions requires inference methods that jointly reason about compatibility between local modes and effectively navigate the exponentially large search space.
To address the challenge and overcome the limitation, we aim to identify compatible sequences of local modes that compose into globally coherent plan. Given the diversity and multi-modality of the search space, we take inspiration from classical search techniques and introduce Compositional Diffusion with Guided Search (CDGS). guided search mechanism integrated into the diffusion denoising process as illustrated in Fig. T To facilitate the search during inference, at each diffusion timestep, method introduces two key components: (i) iterative resampling enhance local- global message passing in compositional diffusion to propose globally plausible candidates, and (i) likelihood-based pruning remove incoherent candidates that fall into likelihoohod regions due to mode-averaging. Together, these components enable CDGS to efficiently sample coherent long-horizon plans. For roboties tasks, our method outperforms or is on par with baselines that lack compositionality or use long-horizon data for training, respectively. We also show the efficacy of our method in long text-to-image and text-to-video tasks (ig. 2), producing more coherent and consistent generations.
2 BACKGROUND
Problem formulation. A long-horizon plan generation problem is characterized by the task of con- structing global plan t = (x),...,XN) as sequence variables x;, by sampling from the joint distribution p(T). The problem becomes goal-directed if must connect a given start x1 = x, to desired goal xxy = Xg. Such problems arise diverse domains: long- morizon manipulation planning, panoramic images, and long videos. While modeling the full joint distribution p p(T) would directly model all dependencies between any x, it usually entails end-to-end learning from long- long-horizon data, which can be infeasible or expensive to obtain. In the absence of long-horizon data, promis-
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31 Ms 34
M6
X1 X2 X4 Xs
X7
X1 X2 X3 X4 X5 X6 X7
X1 X2 X3 X4 X5 X5 X7 (c) with resampling
X1 X2 X3 X4 X5 X6 X7 (d) with resampling + pruning
(a) the planning domain (b) naive composition
Figure 3: Running example. (a) Consider DD-omain of {x1:7 variable distributions and {yr: feasible directed transitions between the variables. There two feasible long-horizon plans from start (xz) to goal (xy): one through the top and one through the bottom. (b) in naive-composition, sampled plans may choose to start in the top and end the bottom, or vice versa. When this happens. the intermediate models {y2.5} will average the modes of intermediate variables (x2:6] to satisfy both constraints, manifesting infeasible transitions (red) (c) adding iterative resampling reduces the frequency of mode-averaging (d) adding pruning eliminates plans with infeasible
ing strategy is to approximate the joint distribution p(t) with a factor graph of overlapping local distributions that can be learned from short-horizon data. For the joint variable t= (x) .WW) we construct a factor graph 30 connecting variable nodes { and factor nodes { where each factor yj represents the joint distribution of contiguous subsequences of t. For example, we represent r = (x) X55..... with factors y1 (x1,22.33).12 (x5. With this, construct the joint distribution p(t) using the Bethe approximation 644
p(t) x2x),.x,.x3)
I p(y,)
p(x3)...
where d; is the degree of the variable node x;. This representation enables sampling from the long- horizon distribution p(T) using only samples drawn from short-horizon distribution p(y).
Diffusion models. Diffusion models are defined by forward process that progressively in- jects noise into the data distribution p(y(0)) and reverse diffusion process that iteratively moves the noise by approximating Vlog to recover the original data distribution. For a given noise injection schedule a,, forward noising adds a Gaussian noise E to clean samples s.t. ylr) vā,y(") VI-a&, GE. With pt being the distribution of noisy samples, the denoising is performed using the score function Vym) lo often estimated by a neural network Ea ()()"), learned via minimizing the score matching loss [23] given by E E,y00 [|e - Eo (ooy(),,))3 Such a score function allows denoising the noise samples via sampling from
P(yl(-1)/0 N (t--)) Va--15 + +V
a-1 a oFee)
where s/(7 11100----- Tae is the Tweedie estimate of the clean sample distribution at denoising
& step and G; controls stochasticity [54]. Several works have leveraged the flexibility of the denoising process in performing post-hoc guidance 200 and plug-and-play generation [37 12
Compositional sampling with diffusion models. Under the diffusion model formulation, we can compositionally sample [II 66 from the factor graph representation of p(t) by calculating the score Vlog p( T) as a sum of factor and variable scores following Eq.
M
N E(1 -1- Vlgg((()
Vlogp(t) = Σ Vlogp(y,)
(3)
In practice, our factor graph is a chain, so overlapping variables (i.c., the ones shared between neighboring factors yj and yj+1) have degree d; = 2 while non-overlapping ones have d; (i.e. their marginals have no contribution to Vlogp(T)). For overlapping variables, we approximate
the marginal scores using the average of the conditional score: Vlogp(x)) (xll +
VlogPy,. (x]]-. where x; E Eyjnyj+1 denotes the overlapping variable between yj and yj+1- This, along with the scores of p(y)) computed from local distribution, allows to formulate the global compositional score Vlog (()) using Eq. 3 While this formulation enables generalization beyond the lengths seen during training, it comes with limitations described in Sec.
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Betch yto
Batch e
Predicted Clean Sample
Local feasiellity
mutrii TO)
min/(>
Eliia Batch gl-nt
Re-popdaaa Batch
((
Compositionl Sampling Coherence
Local fousibility metric with DOUM Inversion Pruning
v00
gll-1)
y:
Lood Pinn Bitaians Mode:
GxOg .0)
Benoiiing
y
y
DDM Inwura on C
xi
y
16
2G0
Figure 4: Compositional diffusion with Guided Search. At each denoising timestep, CDGS it- eratively denoises a batch of noisy candidate global plans by (i) iterative resampling propagate information through averaged scores at overlaps (blue) and (ii) pruning candidates with local in- consistencies based on the predicted clean samples (yellow). This process ensures all local plans align and belong to high- lieeliikel regions of p(y), producing globally coherent plans.
METHOD
Compositional sampling with multi-modal distributions. Solving long-horizon tasks requires constructing a coherent global plan distribution that induces an exponentially large search space and requires reasoning about long-horizon dependencies. Data scarcity prohibits directly learning the target global plan distribution p(T), so a convincing alternative is to approximate it as a composition of local plan distribution p() (using Eq. 1p. Thus, one can sample short-horizon local plans yIM ~ p(y) and compose them with suitable overlaps to form a coherent t. However, as the diversity of feasible local behaviors increases, p(v) becomes highly multi-modal and com- posing such distributions causes p(T) to inherit combinatorial multi-modality-wiie of the global plan distribution corresponds tribution. In this setting, naive compositional methods (E3I that merge distributions yI:M p(y) via score averaging (Eq. 3) often fail due to the mode-averaging issue: selecting high high-likelihood local segments that, while individually plausible, result in incompatible mode sequences leadingg to inconsistent overlaps and incoherent global plans. A natural way to address multi modtiimmddllity is explore diverse modes during sampling, an idea recently explored by inference-time scaling ap- proaches 69 However, these methods are limited to sampling from standalone distributions and not composed sequence of distributions. The key challenge generate feasible sequence of local plans that collectively form coherent global plan-requiring a sampling algorithm that reasons over structured combinations of modes rather than collapsing into incoherent averages.
Challenge:
each mode distinct sequence of modes from the local plan dis-
Compositional Diffusion with Guided Search (CDGS). CDGS is structured inference-time algorithm designed to identify coherent sequences of local modes that form valid global plans. Specifically, CDGS employs a population- based search to explore and select promis- ing mode sequences beyond naïve sampling. To facilitate the search, it: (i) incorporates iterative resampling into the compositional score calculation to enhance information exchange across dis- tant segments, leading to potentially coherent global plan candidates, and (ii) prunes the incoherent candidates by evaluating the likelihood of their local segments with a ranking objective. Note that this is all within a standard denoising diffusion process, making CDGS a plug-and-plas sampler applicable across domains, including robotics planning, panorama image generation, and long video generation. In the following sections, we detail each of these components and demonstrate how their integration enables efficient navigation of the complex multi-modal search space produce coherent long-horizon plans.
Our method:
3.1 COMPOSITIONAL DIFFUSION WITH GUIDED SEARCH
A key challenge with multi-modal modal distributions is that naive compositional sampling can lead to incoherent global plans: since each segment is independently sampled from p(y). they may not align well at their overlaps and potentially lead to mode-averaging issues-where high-likelihood local plans do not combine to form a feasible global plan.
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Algorithm CDGS
Algorithm 2 ComposedScore
Require: Start Ky, Goal zz. Planning horizon H
Require: Noisy sample r0) denoising timestep t,
Require: Diffusion noise schedule.
pretrained local plan score function
Require: Pretrained local plan score function (o(y"), .r).
Require: Start and goal: XG,Ag
Require: number of candidate plans B, number of clite plans K at every step
Require: Number of resampling steps
Initialize global plan candidates: z(T)
1: for a = 1....U do
2: p(²)) (1) ".'")....)) .. N(O..)
2:
Calculate E(T()) using Eq.
3: if < U then
3: for for IT......, do
4:
Calculate zlt using Fg. 2
4: e(r") r. e.o
5:
Add noise xz/xg:
5: +! -((()(((--- (0(T")) .1)l V/
6:
(0-)) 14 -1--))1)
6: Rank plans using ) 5
*/e
7: Inpaint noisy start and goal in rlt
7:
Select best-K global plans
Resampling: r!tl (((1))
8: Repopulate candidates using filtered plans
8:
Tl--1) ~ p( p(= a(t) Eq.
9: end if
9:
10: end
10: end for
11: return (0))
11: return 8(r!),,)
To address this, our approach leverages guided search procedure that explores promising sequences of local modes while filtering out ones that are more likely to result in incoherent global plans.
Method formulation. At each diffusion timestep 1, given a noisy global plan z!), our goal is to sample from an improved next-step distribution over el-1)) that is more likely to yield coherent global plan. To achieve this, we define modified sampling distribution:
ps ( (c))) < p p( exp (-J (-)(+)-)))2)
where (i) ((2())))2()) is the original diffusion transition realized using the compositional score function e(r"), .r), (ii) elr-1) the eweedie-estimate
the clean global plan at timestep / 1, (iii) J(-) is a plan ranking metric we define below, and (iv) b controls the exploration-exxlootatioo tradeoff. We approximate sampling from this distribution using a Monte Carlo search procedure resembling the cross-entropy method: draw batch of noisy global plans from ((r(-1) (())), rank them using and retain a subset of elite global plans that minimizes the evaluation metric ((-) as illustrated in Algorithm [ The number of elites K is tunable parameter of our algorithm, enabling exploration of many possibilities parallel when the planning problem is very large/difficult. Now, just need to ensure that (i) the global plans are ranked appropriately and (ii) the candidate sam- ples proposed by compositional sampling contain informative, globally coherent mode-seqqencees to pursue.
Ranking global plans via local feasibility. To guide the search effectively, we require a mechanism to evaluate the feasibility of candidate plans. Our key insight is that a global plan is feasible iff all of its local transitions are feasible. Since the local model p(y) is trained to model feasible short-horizon behavior, high-likelihood local plans are strong indicators of local feasibility. Therefore, globally feasible plan should consist of high-likelihood local-plan segments throughout. However, computing exact likelihoods in diffusion models is computationally expensive (53), often intractable.
To address this, we leverage DDIM inversion segments y. Each local segment y of sampled global plan goes through forward diffusion using the learned score network (Ee) such that:
54 to approximate the likelihoods of local plan
yM
11
a,1
Eg E (t()))),,))
VaG
Va-i
06
a
ligeliike sample follows low-curvature path, whereas low-likelihood samples exhibit high curvature to bring noisy latents in-distribution when forward noised [[8] (refer App. D). Specifically, we define a smoothness measure based on the curvature of the diffusion trajectory during inversion:
deg
M
J( = exp
I
(1m
(5)
Ji
where e()(0)) measures closeness of ()) to the nearest mode of p(y), intuitively. A higher value of ))) corresponds lower-likelihood local plans. We aggregate s(y(0)) over all local plan
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Published as a conference paper at ICLR 2026
(0) segments YI.M in (0)) to define the global plan ranking metric J( J(+ to measure plan feasibility. Low-quality plans have high / values, making their denoising paths more likely to be pruned.
3.2 ITERATIVE RESAMPLING
To ensure the effectiveness of the guided search, it is not enough to rank global plans correctly- must also promote globally coherent candidate plans. However, standard compositional sampling fails to propagate long-horizon dependencies across overlapping local plans. Consider the running example in Fig. After one denoising step, due to independent sampling of local plans, y1 has no information about yo, and vice versa.
repeatedly alternating between forward noising e!t) ~ -(rr()/((01) and denoising steps. This procedure enables the score network's 's predictions for each segment to incorporate information from distant neighbors via overlapping variables, en- couraging global consistency. Mathematically, this process resembles belief propagation on a chain of factors where each local plan ym E Y1:M in I depends on its neighbors ym-1 and Ym+1 through the respective overlaps (ym mmm-1 and ym (mm+1). During resampling, the belief of Vm is updated p('m Dm 0m- (pm-1)pmmmmm Yym 0ym+1) 1) Following Algorithm 2 after U iter-
To address this, we apply iterative resampling
as: p0Ym|)m-1..---- ations, this iterative resampling ensures that information propagates across the entire aang-hoizoon sequence, producing more globally coherent plan.
Summary of CDGS. We propose guided-searee algorithm by integrating population- prun- ing strategy within compositional sampling. Given local plan score function, our approach samples potentially coherent global plan candidates and filters out plans with locally inconsistent segments. Repeating this throughout the denoising process improves the probability that the retained candi- dates satisfy local feasibility at every segment and are therefore globally feasible plans. Our algo- rithm benefits from adaptive compute at inference time, with the flexibility to scale the batch size and the number of resampling steps U for problems with longer horizons and larger search spaces.
EXPERIMENTAL RESULTS: ROBOTIC PLANNING
In this section, we evaluate the performance of CDGS for long-horizon robotic planning. For all the experiments, we represent inputs with low-dimensional state-space of the system comprising the pose of the end-effector and the objects in the scene in the global frame of reference. For real-world evaluations, we obtain the pose of the objects through perception, more details in App. H]
CDGS can solve learning from play and stitching problems efficiently. We evaluate CDGS for sequential-dccsiinn making tasks using the OGBench Maze and Scene task suite 48 which includes PointMaze and AntMaze along with five tasks for Scene where a robot must manipulate objects (a drawer, sliding window, and cube) reach goal state. The primary challenge is learning from small maze trajcetories or unstructured play data during training, which does not directly solve the target tasks. The diversity of the unstructured plans makes the local distributions highly multi- modal. We hypothesize that CDGS is an ideal method for this problem statement because it can compose short-horizon plans into meaningful moriioorooon plans.
CDGS uses a Diffuser !4 to learn the distribution of local plans (up to 4 secs of trajectory at 20 Hz) represented as sequence of states and actions y "i,.a...hhh.,,,, 4, and then composes them at in- ference for given goal state sample up to 10 secs of motion plans т a (h < IT). We compare the performance of CDGS with inverse reinforcement learning baselines from OGBench, including GCBC 4T 177. GCIVL, GCIQL BII, and HIQL 49 with results presented in Tab. In addition we also include compositional generative baselines like GSC [4] and CompDiffuser [40] should be noted that CDGS with resampling and pruning can scale the performance of naïve com- positional sampling (GSC), in wraining-free manner, to an extent that beats baselines like Com- pDiffuser [40 that use overlap information while training and learn an overlap conditioned score function. Finally, also validate CDGS on composite ball reaching and ball carrying trajectory stitching of AntSoccer in OGBench and show the results in Tab.
CDGS can solve hybrid-planning problems. Task and Motion Planning (TAMP) decomposes robotic planning into a symbolic search for a sequence of discrete high-level skills (c.g.. pick, place, pull) followed by low-level motion planning for each skill [14] Specifically, we formu-

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Published as a conference paper at ICLR 2026
Environment
Type
Size
GCBC
GCIVL
GCIQL
QRL
CRL
HIQL
GSC
CD
Ours PR
Ours
Medium
23 alt
70 =14
21 49
0 =1
80 =12
ss
100 +0
=0
100 =0
100
PointMaze
Stitch
Large
7
12 16
31
0 =0
84 215
13
100
100
100
100
GLant
0.
0.
0.
0 =0
=3
40
29 =2
+3
78 62
Medium
45
44
29
59
94
97
96
97
97
AutMaze
Stitch
Large
3:
18 +2
18 =2
11 =2
67 m5
66 =2
86 =2
86 +2
88 =2
Glant
0.
0
20
65
82
85
Humanoid-
Mediiu
29
12
42
18 42
71 43
96
92 41
91 41
88 43
93 11
Maze
SLitch
Large
6:43
1+1
0.4
3-
6.st
+3
70:43 a3
43
70 =2
m2
Gf ant
0.10
0.40
0.49
0
0.40
12 kz
5
64
47 15
55 15
Scene
Play
1
42 14
51 14
5
19 13
38 13
8 8:
13 11
36 16
51 12
Table I: OGbench [48] learning from stitch and play datasets. With much less training data requirements, CDGS performs on-par with inverse-reinforcement learning baselines and better than generative baselines in a receding horizon control. For GSC, CD and CDGS, we replan based on distance from goal for maze tasks (following CD 40) and sample the complete plan based on the oracle planning horizon for scene task. Success rate averaged over 100 trials and 3 seeds with
randomly chosen task ids. Baseline performance is borrowed from original papers 48
40
Environment
Size
GCBC
GCIVL
GCIQL
QRL
CRL
HIQL
GSC (4D)
(4D)
CD
(17D)
GSC
CD (17D)
Ours wars
(17D)
(17D)
Ours
AntSoccer
Azena
34 24
21
5
2+1
2+1
23 43
41 24
55 =6
66 e3
69 m3
67 43
69
Medium
2
4.40
5
12
16
18
Table 2: Stitching composite task AntSoccer in OGBench 48). We evaluate CDGS on high- dimensional (17D) state space to stitch ball reaching and ball carrying behaviors for two AntSoccer environments. Success rate averaged over 100 trials and 3 seeds with randomly chosen task ids. Baseline performance is borrowed from original papers [8 40
late a losk-and-motion as r : ) where y; {sy. S where a discrete skill n. with motion parameters a; is executed on state to get to state St+1- This entails solving hybrid-planning problem where the chosen discrete skill modes and continuous action modes must simultaneously satisfy symbolic and geometric constraints. We systematically evaluate our method on three suites of TAMP tasks, which are described detail in App.
We compare our method with learning-based TAMP and other compositional methods. Specifi- cally, we consider the following categories: (1) privileged with Planning Domain Definition Lan- guage (PDDL): Random CEM and STAP CEM [2] search for symbolic plans in manually and systematically constructed PDDL domain and apply Cross Entropy Method (CEM) optimization over potential motion plans (2) task information provided via prompting: LLM-T2M [66] prompts an LLM (GFT-4.1) and VLM (VLM-T2M) with descriptions of the scene along with " = 11 in-context examples (w/o and w/ scene images respectively)
generate feasible task plan that
is checked by a geometric motion planner (STAP CEM in this case). (3) compositional diffusion: GSC (no task plan) 43 performs compositional diffusion (equivalent to CDGS w/o RP and PR). Notably, GSC and CDGS are the only methods that do not rely on explicit symbolic search or
Remark (Task information)
Hook Reach
Rearrangement Push
Rearrangement Memory Task
Task
Task
Task I
Task 2
Task I
Task Length
4
4
Random CEM
0.14
0.10
0.08
0.00
0.02
0.02
PDDL + BFS
STAP CEM
0.66
0.70
0.76
0.70
0.00
0.00
LLM-T2M, n II
LM Prior
0.0
0.48
0.72
0.06
0.0
0,0
VL.M-2M, 11
Prompting
0.0
0.42
0.62
0.02
0.0
0.0
GSC (Original)
Oracle task plan
0.78
0.80
0.88
0.64
0.82
0.48
GSC (no task plan)
0.18
0.04
0.00
0.00
0.07
0.00
No PDDL
CDGS (w/o PR)
0.24
0.12
0.12
0.00
0.11
0.00
skill-level data only
CDGS (ours)
0.64
0.58
0.84
0.48
0.42
0.18
Table 3: Evaluation on TAMP task-suite. We compare CDGS with relevant search-based (PDDI. Domain) and prompting based (LLM/VLM) baselines. CDGS performs on-par slightly trails privileged methods on Hook Reach and Rearrangement Push, but substantially outperforms them on Rearrangement Memory, (success rate over 50 trials)
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(a)
Success (H:7)
Infarsibla Transitions
20
60
(b)
(c)
pacethoolt
plocedhock)
pushioubs, hockd
pickihook)
hack)
Motion an Success (H67)
200
100-
10-
pickthoooo
pe pullicutie, fooks
place(hook)
pickto.be)
placejoube)
pict/hook
0.3
10-
0.55
0.00
Figure 5: Left: Visualizing plan pruning, When compositional sampling chooses an infeasi- ble mode sequence, the resulting plan can hallucinate out-of-distribution transitions due mode- averaging explained in Seec. 3 For instance, (a) Infeasible transitions: inhand (hook) precondition never met for place (hook), and (b) State hallucination: cube moves under (rack) as result of averaging toward the goal state, despite being geometrically infea- sible for push (cube, hook). . Our pruning objective (Eq. 5) ensures only feasible plans during denoising, where all transitions are in in-distribution with short-horizon transition diffusion model. Right: Scaling analysis. (H-7) denotes performance averaged over tasks of horizon 7. (c) Task planning success improves with batch size, with larger gains from more resampling steps. (d) Mo- tion planning success improves with resampling steps, but only when batch size is large enough
Note that while GSC (Original) [ leverages skill-level expert diffusion models and oracle task plan, our case it represents naïve compositional sampling with unified model (w/o oracle task plan).
LLM/VLM supervision for the task plan. The results of our evaluation are in Tab. 3
CDGS's performance scales with compute. We hypothesize that CDGS has adaptive inference- time compute, meaning that it benefits from more compute on harder problems. We validate this hypothesis on our most challenging TAMP tasks with a planning horizon of 7. We find that increas- ing batch size (B) and number of resampling steps (U) increases the task planning success FFi.. c)) and motion planning success Fig. d) of CDGS. Interestingly, we find that neither increasing B nor on their own sufficient for overall motion planning success. Thus, both resampling and pruning are essential for long-horizon tasks, as evidenced by the significant improvement of CDGS
Tab.
5 CDGS FOR LONG CONTENT GENERATION
We formulate CDGS with specific design choices that enable (i) efficient message passing for global consistency and (ii) pruning denoised paths that lead to incoherent sequences. While these mecha- nisms are essential for long- horizon planning, we investigate their broader applicability. particularly in long-content generation tasks such text-to-image (T2I) and text-to-video (T2V), which require spatial and temporal coherence over extended horizons. Our framework demonstrates effective im- provement in long-horizon content generation.
CDGS enables coherent panoramic image generation via stitching. We evaluate CDGS on represented as sequence of small images y, each split into three overlapping patches y (2,))),xx). Using Sta- ble Diffusion-2.0 (51, we generate up to 512 x 4608 panoramas by stitching 512 x 512 images. We compare against (i) Multi-Diffusien (MD) 4. which averages scores across overlaps (image-domain analogue of GSC (3) ). and (ii) Sync-Diffussee Diffusion (SD) [33, which enforces LPIPS-based perceptual
panoramic synthesis by composing multiple image patches. A panorama
guidance [67] As shown in Tab. 4 CDGS matches SD without explicit perceptual loss, indicating effective message passing for global style and perceptual transfer while maintaining prompt align-
ment (CLIP 50 Qualitative samples are shown in Fig. 6 with more details in App.
CDGS can sample temporally-consisee longer videos. We follow a setup similar to panorama generation, composing shorter clips along the temporal axis for long-video generation. When short
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SD
CDGS
SD2. Prompt: A photo snowy mountain peak (512 4608)
Back Li
MD
CDGS
121 SD2.0 Prompt: An illustration of beach La La Lend style (512 4608)
Figure Panorama image generation. The above figure shows the qualitative comparison of CDGS with MD 4 and SD 33 We show qualitative intuition behind global coherence and local feasibility: while SD generates smooth panoramas, they fail to satisfy the global context (mountain peak with skiers), the other hand, MD follows the global context (beach in La La Land style) but fails to exhibit local consistency. CDGS excels at both.
Metric
GSC/Multi-Diffusin
Sync-Diffusion
CDGS w/o PR
CDGS
Intra Intra-LPIPS LPIPS $
0.72 2005
0.58 2006
0.61 20.88
0.59 20.04
Intra-Style-Lan Styn--tty--LL
2.96 +0.44
1.39 2012
1.97 10.06
1.38 10.03
Mean-CLIP-ST
31.77 #214
31.77
31.71
32.51
Table 4: Quantitative comparison of panorama generation. We generate 1000 panoramas of dimensions 512 4608 using 14 prompts and compare different methods based on their perceptual similarity (LPIPS 677) style similarity (Style-loss 15 and prompt alignment (CLIP score (50))
sequences of frames stitched make long video, key challenge is maintaining subject sistency and minimizing temporal artifacts. We use Cog VogViXoo222B 62) as the base model, capable of generating 50-frame videos, and extend it up 350 frames at 720p resolution. We use six prompts to generate videos with naïve composition (GSC/Gen-L-Videe positional diffusion with resampling, and CDGS. The results are evaluated with VBench [22 for temporal consistency, subject fidelity, visual quality, and alignment with the prompt (refer Tab. 5
[56] equivalent). com-
Qualitative analysis in Fig. 7 clearly shows multimodal problem where multiple local plans low satisfying the global context, but with CDGS's effect local-to-globe message passing, we see an improvement in subject consistency and temporal smoothness. This comes at a minor acsthetic degradation tradeoff commonly observed in video generation models.
Method
Subject-consistency 95.91
Temporal-ffickerian Aesthetic-uuality f Prompt-alignment f 63.10
Cog VideoX-21B (50 frames)
97.35
25.51
Cog -BB (350 frames)
90. 24
98.44
49.44
21.78
GSC (= Gen-L-Video)
89.51
96.89
60. 12
25.
07.08
Ours
91. 67
97.16
58.90
26. 13
Table S: Quantitative comparison of long-video generation. We evaluate the performance of CDGS based selected metrics from VBench that measure subject consistency, aesthetics, prompt alignment and temporal artifacts, use prompts (refer App. B) and generate videos with 350
frames at 720p resolution. CDGS achieves competitive video quality but for significantly (7x) ex- tended horizons.

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CDGS w/o PR
Frume
Frama 60
Frome 150
Frame 210
Framna 2065
Frama 350
CDCS
CogVideoX Prompt: cat dag baking cake together kitchen. carefully measuring flour, dog stirring the hotter with wooden spoon. The kitchen is with sunlight streaming through the window, (350 fromes, 720p)
Figure 7: Long video generation. CDGS w/ PR (below) maintains subject-consistency while CDGS w/o PR (top) exhibits mode-averaging, resulting in significant changes to the subjects' appearances.
6 RELATED WORK
Long-horizon content generation There are many approaches to generating long-horizon content like panoramas and long videos
26
38 [19] Some assume access long-horizon training data
for end-to-end training [6
5
60
b.. while others with weaker assumptions about training data
will compose the outputs of short-horizon models through outpainting [59] 2X or stitching [66] 29
34
32
40
Our method belongs to the latter, enabling generalization to longer horizons than
seen during training.
Generative planning. Generative models such as diffusion models 53 21 are widely used for
though they struggle with task lengths beyond their training data. Recent works including Diffusion-CCSP 63), GSC [4), and GFC 45] have explored compositional sam- pling
planning 25
8
35
66 but they sidestep the mode-averaging problem via additional mode supervision in the form of task skeletons or constraint graphs. In contrast, approach directly addresses the mode-averaging problem to generate goal-directed long-houe horizon plans from short-horizon models.
37
Inference-time compute. Scaling inference-ting time computation is powerful strategy for improving the performance of generative models [58] 46). For diffusion models [54] 277, recent work has shown the efficacy of scaling inference-tine compute through verifier-guided search during the denoising Our algorithm differs in that it addresses the unique limitation of mode-averaging when sampling from compositional chain of distributions.
process 422 52
68 39).
7 CONCLUSION
We introduce CDGS, framework integrating compositional diffusion with guided search to gener- ate Long-horizon sequences with horrz- models. By embedding search within the denoising process, CDGS can handle composing highly multimodal distributions and sample solutions that are both globally coherent and locally feasible. Qualitative and quantitative results suggest that CDGS general pathway for extending the reach of generative models beyond their training horizons across robotic planning, panoramic images, and video generation.
LIMITATIONS
While CDGS demonstrates strong performance in long-horizon goal-directed planning, relies on a few simplifying assumptions that also suggest directions for future work. We assume the ability to specify a goal state, which simplifies planning but can naturally extended to goal-generation or classifier-guided goal-conditioning methods [1] Similarly, we generate plans for a fixed hori- zon, yet the framework can handle arbitrary horizons given the same start and goal, enabling se- lection among multiple candidate plan lengths. Finally, long-horizon dependencies are communi- cated through score averaging and resampling between adjacent skills; more sophisticated message- passing -aaentt mechanisms could improve efficiency and coherence across entire plans. These assumptions keep the problem tractable while providing a flexible foundation for extending CDGS to more general and complex planning scenarios.
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9 REPRODUCIBILITY STATEMENT
We are committed to ensuring that all the results presented in this paper are repro- ducible. To this end, we have provided pseudocodes in the paper and released the offi- cial code base through our project website: https://cdkssaarch have also provided the hyperparameters table for motion planning (refer App. Fp. for im- age generation (refer App. J) and video generation (refer App. K.. Apart from this our content-generation experiments use open-source models like Stable-Diffusion-2 (refer hhtps: m/
gittiu.. io/ We
ffus innssion22 on- 2 and CogVideoX221 (refer httpp::/ttttggiiii http:////hgggggggc..ooooo /o/ zai wwwpgg/ooViieeoo 2b). For all other robotics setup, we provide more information through appendix and project website.
10 LLM USAGE
LLMs not used in any manner for conceptualization of the idea, key contributions of the pro- posed work and finding relevant prior woks.
ACKNOWLEDGMENTS
This work is partially supported by NSF-2442393, NSF-2409016, NSF-1942523, and Samsung Re- search America.
REFERENCES
[1] Christopher Agia, Toki Migimatsu, Jiajun Wu, and Jeannette Bohg. Taps: Task-agnostic policy sequencing, arXiv preprini arXiv:2210.1223 2022.
[2] Christopher Agia, Toki Migimatsu, Jiajun Wu, and Jeannette Bohg. Stap: Sequencing task- agnostic policies. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 7951-7958. IEEE, 2023.
[3] Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, and Pulkit Agrawal. Is conditional generative modeling all you need for decision making? In The Eleventh International Conference on Learning Representations, 2023. URL https:/7
openrevi ew net. Forummiddaaooo Fo
[4] Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. Multidiffusion: Fusing diffusion paths for controlled image generation. 2023.
[5] Boyuan Chen, Diego Martí Monsó, Yilun Du, Max Simchowitz, Russ Tedrake, and Vincent Sitzmann. Diffusion foreing: Next-token prediction meets full-sequence diffusion. Advances in Neural Information Processing Systems, 37:24081-244
2024.
[6] Chang Chen, Hany Hamed, Doojin Back, Taegu Kang, Yoshua Bengio, and Sungjin Ahn. Extendable long-horizon planning via hierarchical multiscale diffusion. arXiv preprint arXiv:2503.20102 2025.
[7] Xinyuan Chen, Yaohui Wang, Lingjun Zhang, Shaobin Zhuang, Xin Ma. Jiashuo Yu, Yali Wang, Dahua Lin, Yu Qiao, and Ziwei Liu. Seine: Short-to-long video diffusion model for generative transition and prediction. In The Twelfth International Conference on Learning Representations, 2023.
[8] Cheng Chi, Siyuan Feng Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shu- ran Song. Diffusion policy: Visuomotor policy learning via action diffusion. In Proceedings of Robotics: Science and Systems (RSS), 2023.
[9] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems, 34:8780-8794, 2021.
[10] Yilun Du and Leslie Pack Kaelbling. Position: Compositional generative modeling: A single model is not all you need. In Forty-first International Conference Machine Learning, 2024.

---

Published as a conference paper at ICLR 2026
[11] Yilun Du, Shuang Li, and Igor Mordatch. Compositional visual generation with energy based models. Advances in Neural Information Processing Systems, 33:6637-66477 2020.
[12] Yilun Du, Conor Durkan, Robin Strudel, Joshua B Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, and Will Sussman Grathwohl. Reduce, reuse, recycle: Compositional generation with energy- based diffusion models and meme. In International conference on machine learning, pp. 8489-8510. PMLR, 2023.
[13] Carlos Florensa, David Held, Xinyang Geng, and Pieter Abbeel. Automatic goal generation for reinforcement learning agents. In International conference machine learning, pp. 1515-
1528. PMLR, 2018.
[14] Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay, Beomjoon Kim, Tom Silver, Leslie Pack Kaelbling, and Tomás Lozano-Pérez. Integrated task and motion planning. Annual review of control, robotics, and autonomous systems, 4(1):265-293, 2021.
[15] Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. Image style transfer using convolu- tional neural networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2414-2423, 2016. doi: 10.1 101//99/V 2016..201..65
[16] Songwei Ge, Thomas Hayes, Harry Yang, Xi Yin, Guan Pang, David Jacobs, Jia- Baa Huang, and Devi Parikh. Long video generation with time-agnostic vggan and time-sensitive trans- former. In European Conference on Computer Vision, pp. 102-118. Springer, 2022.
[17] Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Devin, Benjamin Eysenbach, and Sergey Levine. Learning to reach goals via iterated supervised learning, arXiv preprint arXiv:1912.06088. 2019.
[18] Alvin Heng, Harold Soh, et al. Out-of-distribution detection with a single unconditional diffu- sion model. Advances in Neural Information Processing Systems, 01144952-439777 2024.
[19] Roberto Henschel, Levon Khachatryan, Hayk Poghosyan, Daniil Hayrapetyan, Vahram Tade- vosyan, Zhangyang Wang, Shant Navasardyan, and Humphrey Shi. Streamingt2v: Consistent, dynamic, and extendable long video generation from text. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. (168-2577,203 2025.
[20] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207. 10007.1230 2299, 2022.
[21] Jonathan Ho. Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Ad- vances in neural information processing systems, 33:6840 1116640-6851,,, 2020.
[22] Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. Vbench: Comprehensive benchmark suite for video generative models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21807-21818, 2024.
[23] Aapo Hyvärinen and Peter Dayan. Estimation of non-normalized statistical models by score matching. Journal of Machine Learning Research, 6(4), 2005.
[24] Michael Janner, Yilun Du, Joshua Tenenbaum, and Sergey Levine. Planning with diffusion for flexible behavior synthesis. In International Conference on Machine Learning, 2022.
[25] Michael Janner, Yilun Du, Joshua Tenenbaum, and Sergey Levine. Planning with diffusion for flexible behavior synthesis. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato (eds.), Proceedings of the 39th International Con- ference Machine Leurning, volume 162 of Proceedings of Machine Learning Research, pp. 9902-9915. PMLR, 17-23 Jul 2022. URL. https::/ ptttps://proceedingg. mtg.eeerisnns..m..rss.... r.press/ v162/janne 2162/janner22a. html
[26] Nikolai Kalischek, Michael Oechsle, Fabian Manhardt, Philipp Henzler, Konrad Schindler, and Federico Tombari. Cubediff:: Repurposing diffusion-based image models for panorama generation. In The Thirteenth International Conference on Learning Represemattinns, 2025.
[27] Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. Advances in neural information processing systems, 35: 26565-26577 2022.
[28] Jihwan Kim, Junoh Kang, Jinyoung Choi, and Bohyung Han. Fifo-diffusion: Generating infinite videos from text without training. Advances in Neural Information Processing Systems, 37:89834 89868, 2024.
12

---

Published as a conference paper at ICLR 2026
[29] Subin Kim, Scoung Wug Oh, Jui-Hsien Wang, Joon- Young Lee, and Jinwoo Shin. Tuning- free multi-event long video generation via synchronized coupled sampling. arXiv preprint arXiv:2503.08005, 2025.
[30] Daphne Koller and Nir Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009.
[31] Ilya Kostrikov, Ashvin Nair, and Sergey Levine. Offline reinforcement learning with implicit q-learning- arXiv preprint arXiv:21 2021.
[32] Kyowoon Lee and Jaesik Choi. State-covering trajectory stitching for diffusion planners. arXiv preprint arXiv:2506.00889 2025.
[33] Yuseung Lee, Kunho Kim, Hyunjin Kim, and Minhyuk Sung. Syncdiffusion: Coherent mon- tage via synchronized joint diffusions. Advances in Neural Information Processing Systems, 36:50648 50660, 2023.
[34] Guanghe Li, Yixiang Shan, Zhengbang Zhu, Ting Long, and Weinan Zhang, Diffstitch: Boost- ing offline reinforcement learning with diffusion- trajectory stitching. arXiv preprint drXiv:2402.02439, 2024.
[35] Wenhao Li, Xiangfeng Wang, Bo Jin, and Hongyuan Zha. Hierarchical diffusion for offline decision making. In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (eds.). Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pp. 20035 20064. PMLR, 23-29 Jul 2023. URL https: /ttpss////ccceeings.coo mir press/v202/
1123ad. html
[36] Kevin Lin, Christopher Agia, Toki Migimatsu, Marco Pavone, and Jeannette Bohg. Text2motion: From natural language instructions to feasible plans. Autonomous Robots, 47 18):1345 2023.
[37] Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, and Joshua B Tenenbaum. Compositional visual generation with composable diffusion models. In European Conference on Computer Vision, pp. 423-439. Springer, 2022.
[38] Yu Lu, Yuanzhi Liang, Linchao Zhu, and Yi Yang. Freelong: Training-free long video gen- eration with spectralblend temporal attention. Advances in Neural Information Processing Systems, 37:131434-131455 2024.
[39] Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1 1166111477,
2022.
[40] Yunhao Luo, Utkarsh A Mishra, Yilun Du, and Danfei Xu. Generative trajectory stitching through diffusion composition. arXiv preprint arXiv:2503.05 153, 2025.
[41] Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, and Pierre Sermanet. Learning latent plans from play. In Conference on robot learning, pp- 1113-1132. PMLR, 2020.
[42] Nanye Ma. Shangyuan Tong, Haolin Jia, Hexiang Hu, Yu-Chuan Su, Mingda Zhang, Xuan Yang, Yandong Li, Tommi Jaakkola, Xuhui Jia, et al. Inference-time scaling for diffusion models beyond scaling denoising steps. arXiv preprint arXiv:2501. 09732, 2025.
[43] Utkarsh Aashu Mishra, Shangjie Xue, Yongxin Chen, and Danfei Xu. Generative skill chain- ing: Long-horizon skill planning with diffusion models. In 7th Annual Conference on Robot Learning, 2023. URL http:::////eeeeiiiee/ee net Forum? JE9155d
[44] Utkarsh Aashu Mishra, Shangjie Xue, Yongxin Chen, and Danfei Xu. Generative skill chain- ing: Long-horizon skill planning with diffusion models. In Conference on Robot Learning, pp- 2905-2925. PMLR, 2023.
[45] Utkarsh Aashu Mishra. Yongxin Chen, and Danfei Xu. Generative factor chaining: Coordi- nated manipulation with diffusion-based factor graph. In 8th Annual Conference on Robot Learning, 2024. URL http::::////peereeeeeee net / forum didi=pip6p6. jHH.
[46] Niklas Muennighoff. Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannanch Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsumori Hashimoto. s1: Simple test-time scaling. arXiv preprint NXiv:25011.1399, 2025.
13

---

Published as a conference paper at ICLR 2026
[47] Zizheng Pan, Bohan Zhuang, De-An Huang, Weili Nie, Zhiding Yu, Chaowei Xiao, Jianfei Cai, and Anima Anandkumar. T-stitch: Accelerating sampling in pre-trained diffusion models with trajectory stitching. arXiv preprint arXiv:2402.14166 2024.
[48] Seohong Park, Kevin Frans, Benjamin Eysenbach, and Sergey Levine. Ogbench: Benchmark- ing offline goal-conditioned rl. arXiv preprint arXiv:2410.20099 2024.
[49] Seohong Park, Dibya Ghosh, Benjamin Eysenbach, and Sergey Levine. Higl: Offline goal- conditioned with latent states as actions. Advances in Neural Information Processing Sys- tems, 36, 2024.
[50] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agar- wal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp. 3748-8763. PMLR, 2021.
[51] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10684-10695, June 2022.
[52] Raghav Singhal, Zachary Horvitz, Ryan Teehan, Mengye Ren, Zhou Yu, Kathleen McKeown, and Rajesh Ranganath. A general framework for inference-time scaling and steering of diffu- 2025.
sion models. arXiv preprint arXiv:2501.0688
[53] Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan. and Surya Ganguli. Deep Un- supervised Learning using Nonequilibrium Thermodynamics, November 2015. URL http:
//axxiv.org/abss 500.. 03585 arXiv:1503.053: [cs].
[54] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXivv2010.005502, 2020.
[55] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score- generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13450 2020.
[56] Fu- Yu-Yun Wang, Wenshuo Chen, Guanglu Song, Han-Jia Ye, Yu Liu, and Hongsheng Li. Gen-I-video: Multi-text to long video generation via temporal co-denoising. arXiv preprint arXiv:2305.18264, 2023.
[57] John Wang and Edwin Olson. Apriltag 2: Efficient and robust fiducial detection. In 2016 JEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp- 4193-4198, 2016. doi: 10. 20117779....11
[58] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoe V Le, Denny Zhou, et al. Chain-of-though prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24837, 2022.
[59] Chenfei WU, Jian Liang, Xiaowei Hu, Zhe Gan, Jianfeng Wang. Lijuan Wang, Zicheng Liu, Yuejian Fang, and Nan Duan. Nuwa-infinity: Autoregressive over autoregressive generation for infinite visual synthesis. arXiv preprini arXiv:2207.098: 099144 2022.
[60] Desai Xie, Zhan Xu, Yicong Hong, Hao Tan, Difan Liu, Feng Liu, Arie Kaufman, and Yang Zhou. Progressive autoregressive video diffusion models. In Proceedings of the Computer Vision and Puttern Recognition Conference, pp. 6322-6333 6332, 2025.
[61] Xin Yan, Yuxuan Cai, Qiuyue Wang. Yuan Zhou, Wenhao Huang. and Huan Yang. Long video diffusion generation with segmented artss-attention and content-rich video data curation. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 3194-3194, 2025.
[62] Zhuoyi Yang. Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video diffusion models with expert transformer. arXiv preprini arXiv:2408.06600
2024.
[63] Zhutian Yang, Jiayuan Mao, Yilum Du, Jiajun Wu, Joshua B Tenenbaum, Tomás Lozano-Pérez, and Leslie Pack Kaelbling, Compositional diffusion-based continuous constraint solvers. arXiv preprint arXiv:2309. 0096009 2023.
14

---

Published as a conference paper at ICLR 2026
[64] Jonathan S Yedidia, William T Freeman, and Yair Weiss. Constructing free-energy approx- imations and generalized belief propagation algorithms. IEEE Transactions on information theory, 51(7):2282-25
2005.
[65] Jaesik Yoon, Hyeonseo Cho, Doojin Back, Yoshua Bengio, and Sungjin Ahn. Monte carlo tree diffusion for system 2 planning. arXiv preprint arXiv:2502.07202, 2025.
[66] Qinsheng Zhang, Jiaming Song, Xun Huang. Yongxin Chen, and Ming- Yu Liu. Diffcollage: Parallel generation of large content with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10188-10198, June 2023.
[67] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unrea- sonable effectiveness of deep features as perceptual metrie. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 586-595, 2018.
[68] Tao Zhang. Jia-Shu Pan, Ruiqi Feng, and Tailin Wu. T-scend: Test-time scalable mets- enhanced diffusion model. arXiv preprint arXIv:2502.01989 2025.
[69] Xiangcheng Zhang, Haowei Lin, Haotian Ye, James Zou, Jianzhu Ma, Yitao Liang, and Yilun
Du. Inference-time scaling of diffusion models through classical search. arXiv preprint arXiv:2505.23611, 2025.
[70] Yifeng Zhu, Abhishek Joshi, Peter Stone, and Yuke Zhu. Viola: Imitation learning for vision- based manipulation with object proposal priors. arXiv preprint arXiv:2210.11339, 2022. doi: 10. 10.4550Warxiv.2100.1333
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Reviews:
[{"review_id": "cqxJX8yTxr", "reviewer": "Reviewer_27Ji", "summary": "The paper presents Compositional Diffusion with Guided Search (CDGS), a generative method that augments compositional diffusion sampling with population-based guided search and likelihood-based pruning to mitigate mode-averaging when composing multimodal local distributions. The proposed method aims to unify diffusion-based planning and long-horizon content generation under a single approach, demonstrating its effectiveness in not only robotic long-horizon manipulation tasks but also in panoramic imaging and video generation. While the motivation is compelling and the experiments effectively highlight the contributions, the work overemphasizes conceptual novelty and lacks rigorous comparisons with modern and traditional planning-based methods.", "questions": "1. Could you position CDGS against modern mode-seeking control/planning frameworks (e.g., Reverse-KL MPPI / SV-MPC / SVG-MPPI) and mode-seeking diffusion methods (e.g., mean-shift/flow-to-mode)? Why is CDGS preferable when the objective is to avoid “mode averaging”?\n2. Why is multiplicative aggregation over segments (Eq. 5) preferable to additive/soft-min aggregations? Any sensitivity analysis to the aggregation choice?\n3. Figure 5 shows the benefits from increasing $B$ and $U$. Where are the diminishing returns, and how do you set $\\lambda_t$ (exploration/exploitation) across timesteps? Provide a schedule and an ablation.\n4. In OGBench (Table 1), please report the number of seeds, confidence intervals, and effect sizes vs. the strongest baseline (not only means). Also, clarify whether CDGS uses less long-horizon supervision than IRL baselines in practice.\n5. Do you enforce hard preconditions/effects (e.g., in-hand (hook)) during sampling to guarantee task-level feasibility, or is feasibility entirely delegated to DDIM-curvature pruning?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The paper identifies a genuine issue in compositional generative modeling: mode-averaging across multimodal local distributions, which degrades global coherence in long-horizon tasks. In addition, CDGS is evaluated across robotics, image synthesis, and video generation, demonstrating domain-agnostic adaptability at the conceptual level.\n\n2. The method is implemented end-to-end with population-based inference and ablations (with/without pruning, resampling, scaling analysis), demonstrating the engineering completeness. Despite limited novelty, the approach achieves reasonable performance improvements on OGBench, TAMP, and long-horizon generative benchmarks.\n\n3. The figures, especially Figure 4, effectively illustrate the proposed resampling and pruning mechanisms in an end-to-end manner. Algorithms and Equations are clearly presented, with a strong emphasis on reproducibility.", "weaknesses": "1. The population-based denoising, iterative resampling, and pruning loops likely incur heavy compute costs. No runtime or complexity analysis is provided to justify practicality. Classical planners, such as SVG-MPPI [1] and Reverse-KL MPPI [2], already produce mode-seeking behavior in closed form, with stronger theoretical rigor and lower computational overhead. Within this realm, CDGS lacks a clear justification for its adoption over these established alternatives.\n\n2. CDGS essentially reuses established ideas, including population-based search, iterative resampling, and likelihood pruning. Indeed, it repackages under the umbrella of diffusion inference without introducing fundamentally new theory or algorithmic principles. The paper lacks direct traditional predecessors such as SVG-MPPI [1], Reverse-MPPI [2], and SV-MPC [3], which already address multimodality via reverse KL minimization and Stein variational guidance. Without such baselines, it is unclear how CDGS advances beyond established mode-seeking control frameworks. More importantly, the paper fails to position itself in relation to modern diffusion-based mode-seeking methods, such as Flow to the Mode [4] and Mean-Shift Distillation [5], which explicitly address mode collapse and mode-averaging in generative diffusion models.\n\n3. The pruning metric J(τ) based on DDIM curvature is heuristic and unvalidated. There is no proof linking it to likelihood, feasibility, or global convergence. In contrast, SVG-MPPI provides a clear variational derivation and theoretical backing. Moreover, the stability and safe validation are largely omitted, which might be acceptable for video generation or image synthesis, but not for robot control for long-horizon tasks.\n\n4. The experiments presented are too limited in scope to establish the superiority of CDGS convincingly. The evaluation mainly contrasts against lightweight or outdated baselines (e.g., GSC, Diffuser, CEM-based methods) and omits stronger diffusion-based or traditional mode-seeking approaches, such as Flow to the Mode [4], Mean-Shift Distillation [5], and SVG-MPPI [1]. The claims of generality and cross-domain performance are not substantiated by statistically rigorous analysis or comprehensive comparisons. Without large-scale quantitative studies, ablations on computational trade-offs, or benchmarks against state-of-the-art mode-seeking diffusion and planning frameworks, the empirical validation remains insufficient to justify the claimed novelty or practical advantage of the proposed method.\n\n[1] Honda, Kohei, et al. \"Stein variational guided model predictive path integral control: Proposal and experiments with fast maneuvering vehicles.\" 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024.\n[2] Kobayashi, Taisuke, and Kota Fukumoto. \"Real-time sampling-based model predictive control based on reverse kullback-leibler divergence and its adaptive acceleration.\" arXiv preprint arXiv:2212.04298 (2022).\n[3] Lambert, Alexander, et al. \"Stein variational model predictive control.\" arXiv preprint arXiv:2011.07641 (2020).\n[4] Sargent, Kyle, et al. \"Flow to the mode: Mode-seeking diffusion autoencoders for state-of-the-art image tokenization.\" arXiv preprint arXiv:2503.11056 (2025).\n[5] Thamizharasan, Vikas, et al. \"Mean-Shift Distillation for Diffusion Mode Seeking.\" arXiv preprint arXiv:2502.15989 (2025).", "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": "VPctzm5GkI", "reviewer": "Reviewer_QzLm", "summary": "The paper proposes a novel method, Compositional Diffusion with Guided Search (CDGS), for long-horizon planning tasks. The core idea is to combine the generative power of diffusion models with a structured search algorithm to create complex and coherent plans over long horizons. CDGS works by composing shorter-horizon plans generated by a diffusion model and then using an iterative resampling and pruning mechanism to guide the search towards feasible and high-quality long-horizon plans. The authors demonstrate the effectiveness of their approach on a variety of challenging tasks, including robotic manipulation, long-form video generation, and panoramic image synthesis, showing significant improvements over existing methods.", "questions": "1. The pruning objective $g(x_0^{i})$ is based on the curvature of the denoising path. While intuitive, could the authors provide a more formal justification for why this particular metric is a good proxy for plan feasibility or quality? Have the authors experimented with other pruning criteria, and how sensitive is the performance of CDGS to the choice of this objective?\n2. How does the performance and computational cost of CDGS scale as the planning horizon $H$ increases? Is there a point where the composition of many short-horizon plans leads to a degradation in quality or an explosion in computational requirements?\n3. The paper mentions the use of a Mixture-of-Experts (MoE) layer in the score model. How critical is the MoE component for the success of CDGS, especially in multi-modal tasks?\n4. The results presented are very positive. It would be instructive to see some failure cases of CDGS. In which scenarios does the method struggle to produce coherent long-horizon plans? This could shed light on the limitations of the current approach and point to avenues for future work.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper introduces a highly novel and significant approach to long-horizon planning. The combination of compositional generative models (diffusion models) with explicit guided search is a powerful paradigm that effectively balances generation diversity with goal-directedness. This work has the potential to influence future research in planning, robotics, and generative modeling.\n2. The experimental evaluation is thorough and convincing. The authors demonstrate the superiority of CDGS across multiple, distinct, and challenging domains. The qualitative results, especially in video and panorama generation, are impressive and clearly showcase the model's ability to maintain long-term coherence. The quantitative results on robotic planning tasks also show clear improvements over state-of-the-art baselines.\n3. The proposed method is well-grounded in mathematical principles. The use of compositional score functions and the DDIM-based pruning objective are elegant and well-motivated. The paper provides sufficient detail to understand the core mechanics of the algorithm, and the mathematical derivations appear to be correct and consistently applied.", "weaknesses": "1. The proposed method seems computationally intensive. The iterative nature of resampling and refining plans at each step of the search could be very time-consuming, which might limit its applicability in real-time or resource-constrained settings. A more detailed analysis of the computational complexity and wall-clock time comparisons with baselines would be beneficial.\n2. The paper could benefit from providing more details on how the states and actions for the robotic planning tasks are represented and fed into the diffusion model. Understanding the specifics of the parameterization would help in assessing the method's applicability to other robotic tasks.\n3. While the paper does present some ablation studies (e.g., in Appendix H), a more in-depth analysis of the contribution of each component of CDGS would strengthen the paper. For instance, a clearer study on the comparison of different pruning objectives could provide valuable insights.", "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": "b2OYs8M2Ki", "reviewer": "Reviewer_Qy6u", "summary": "In this paper, the authors propose Compositional Diffusion with Guided Search (CDGS), a novel framework for compositional generation using diffusion models. The work primarily addresses the \"mode-averaging\" problem inherent in prior score-averaging approaches. This issue arises when composing multi-modal local distributions, where existing methods tend to average incompatible local modes, resulting in globally incoherent and infeasible plans.\n\nTo overcome this limitation, CDGS introduces two key inference-time strategies. First, it employs iterative resampling of local generations based on the compositional score. This process functions as an effective message-passing mechanism to propose globally coherent candidate samples by propagating information across the entire sequence. Second, the framework filters these candidates using a likelihood-based pruning strategy. It generates a diverse population of candidate plans and then selects and re-populates the top-*K* candidates based on a proposed local feasibility metric. The authors offer a novel approach to approximate this metric by leveraging the insight that high-likelihood samples follow low-curvature paths during the DDIM inversion process. Specifically, this curvature is measured by the norm of the time-derivative of the score network's noise prediction, allowing for an efficient, likelihood-informed search.\n\nThe authors empirically validate CDGS across a diverse set of long-horizon tasks, including robotic planning, panoramic image generation, and long video generation. The results consistently demonstrate the superiority of CDGS over previous methods. The paper further substantiates the efficacy of its components through ablation studies and detailed analyses on scaling effects. Notably, the method demonstrates strong performance on complex robotic planning tasks where baseline approaches like GSC fail. Even when provided with the oracle symbolic task plan, GSC struggles with compositional motion planning due to mode-averaging, whereas CDGS successfully finds a coherent solution by integrating task and motion planning within its guided search, highlighting the critical importance of the proposed mechanism. The authors ensure reproducibility by providing code and detailed experimental settings in the appendix, which significantly aids in the concrete understanding of the proposed method.", "questions": "- In the 1D DDIM Inversion study (Appendix E, Fig. 8), the denoising latent paths (blue) appear to converge more strongly to the in-distribution modes compared to the DDIM inversion paths (red). Could you elaborate on the intuition or mechanism behind this visual result?\n- The model utilizes a Mixture-of-Experts (MoE) architecture (Appendix G.1). What was the motivation for this specific choice over other potential architectures, and were any ablation studies performed to validate this design?\n- The sampling strategy (Appendix G.2, Table 6) employs an adaptive schedule for resampling ($U(t)$) and specific ratios for exploration ($k_e$​) and pruning ($k_p$​). Have you experimented with simpler schedules (e.g., uniform resampling) or different values for $k_e$​ and $k_p$​ to test the sensitivity of these crucial hyperparameters?\n- Appendix H.1 presents an ablation, CDGS (BFS-2), which uses system dynamics rollouts and is described as an “upper bound” of the proposed approach. Could you clarify what this result implies?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **Clear Problem Formulation and an Effective Solution**: The paper clearly articulates critical limitations of prior score-averaging based compositional diffusion methods—the mode-averaging and global incoherence problems. It then introduces a well-motivated and effective solution, CDGS, which synergistically combines two key strategies: (1) iterative resampling to enforce global coherence through message passing , and (2) a novel likelihood-based pruning mechanism to filter out locally infeasible candidates. This problem-solution framing is a significant strength of the paper.\n    \n- **Convincing Empirical Validation across Diverse Domains**: The generalizability of CDGS is convincingly demonstrated through extensive experiments on a variety of challenging long-horizon tasks, including robotic planning , panoramic image generation , and long video generation. The consistent and significant performance improvements over baselines across these different domains strongly support the claim that CDGS is a broadly applicable, task-agnostic framework.\n    \n- **Thoroughness and Commitment**: The paper is well-supported and transparent, which significantly strengthens its contribution.\n    \n    - **Reproducibility**: The authors provide a link to the source code and meticulously detail all hyperparameters for the different experimental domains in the appendices (e.g.,  Appendices G, K, L) .\n        \n    - **In-depth Analysis and Discussion**: The appendices offer deep dives into crucial components, providing a detailed derivation of the pruning objective (Appendix E) , a thoughtful comparison with prior methods (Appendix D) , and insightful ablation studies on scaling effects (Appendix H).\n        \n    - **Transparency**: The authors transparently document the full experimental setup, including prompts and task specifications (Appendices C, F, I, J) , and proactively discuss the limitations of their work (Appendix A), demonstrating a mature and thoughtful research approach.", "weaknesses": "- **Lack of Adaptive Computation and Backtracking**: While CDGS effectively overcomes limitations of prior methods through inference-time search, it lacks adaptability.\n\t- **Fixed Computational Overhead**: The proposed method utilizes a fixed computational budget regardless of task difficulty, as determined by hyperparameters (Appendix G.2) . This can lead to inefficient, excessive computation for simpler tasks, or insufficient computation for more complex ones.\n\t- **Absence of Backtracking**: CDGS does not support backtracking, which limits its ability to explore diverse possibilities at various depths—a crucial capability for solving complex reasoning and generation problems. In contrast, several recent inference-time scalable diffusion models [1,2,3] offer both adaptability to solve problems within a given budget and backtracking for effective deep search.\n\n- **Ambiguous Link Between Iterative Resampling and Mode-Averaging**: The paper presents iterative resampling and likelihood-based pruning as solutions to the mode-averaging problem. While both contribute to this goal, their primary roles appear distinct. Likelihood-based pruning is a more direct countermeasure to mode-averaging by filtering out incompatible local modes. In contrast, iterative resampling seems to primarily address a different, albeit related, issue: global incoherence. This problem could arise even in compositional methods that do not use score averaging. The paper would be significantly strengthened by a more precise discussion on how iterative resampling specifically mitigates mode averaging, perhaps by explaining how improving global coherence helps resolve local mode conflicts.\n\n- **Suggestions for Presentation and Clarity**: The paper's presentation could be improved in several areas.\n\t- **Clarity in TAMP Section**: The discussion on Task and Motion Planning (TAMP) in the main paper is brief and could be confusing for readers. Clarifying why it is a “hybrid-planning problem”, defining key terms like PDDL, and explaining the different representations of states/actions ($s_i$,$a_i$​) at high vs. low levels would be beneficial . Crucially, the fact that the GSC baseline is conditioned on an oracle task plan—a key factor for performance analysis—is only mentioned in Appendix D and should be in the main paper .\n\t- **Minor Errors and Formatting**: Several minor issues were noted.\n\t    - Line 316: The index in the set definition appears to have a typo ($s_h,a_h$ → $s_i,a_i$).\n\t    - Appendix G's title, \"more details on robotic planning experiments,\" is misleading as it only covers TAMP, not OGBench.\n\t    - Some qualitative results could be discussed as limitations. For example, in the Appendix B visualization for the prompt “Silhouette wallpaper of a dreamy scene with shooting stars,” some global incoherence is still visible even applying iterative resampling and pruning.\n\t    - Minor capitalization and spacing issues were found in Appendices G.2 lines 1248, 1251, 1254 and H.1 between the paragraph and table 8 caption.\n- **Compliance with LLM Usage Policy**: The authors do not state whether Large Language Models (LLMs) were used in preparing the manuscript, which is required by the ICLR 2026 Author Guide.\n\n[1] Yoon, Jaesik, et al. \"Monte carlo tree diffusion for system 2 planning.\" _arXiv preprint arXiv:2502.07202_ (2025).\n\n[2] Yoon, Jaesik, et al. \"Fast Monte Carlo Tree Diffusion: 100x Speedup via Parallel Sparse Planning.\" _arXiv preprint arXiv:2506.09498_ (2025).\n\n[3] Lee, Gyubin, et al. \"Adaptive Cyclic Diffusion for Inference Scaling.\" _arXiv preprint arXiv:2505.14036_ (2025).", "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": "0mWyP8ISR1", "reviewer": "Reviewer_xJxE", "summary": "The author introduces Compositional Diffusion with Guided Search (CDGS), a novel framework for generating coherent long-horizon plans and content by addressing the critical challenge of mode-averaging in compositional generative models. Existing compositional approaches, while efficient in leveraging short-horizon local generative models, often average incompatible modes when local distributions are multimodal, leading to globally incoherent or locally infeasible outputs. CDGS integrates a guided search mechanism directly into the diffusion denoising process to overcome this limitation. The authors demonstrate the effectiveness of CDGS across three challenging long-horizon domains: robotic manipulation planning, panoramic image generation, and long video synthesis, showing significant improvements over naive compositional baselines.", "questions": "see weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-structed and easy to follow. The proposed approach is well-grounded in motivation, and the empirical results provide compelling evidence of its effectiveness.\n2. The paper tackles a fundamental and highly significant problem in generative modeling.", "weaknesses": "1. Some implementation details are missing to fully replicate the method. Notably, the candidate \"repopulation\" step lacks clarity on how new batches are generated from elite plans.\n2. CDGS requires additional resampling iterations and pruning steps which can require many additional forward passes of the model. But the paper doesn’t report the wall-clock runtime or compute comparisons, which makes it hard to judge the practical overhead.\n3. Although this paper focuses on inference-time trajectory stitching methods, I suggest the authors include related works on trajectory data augmentation methods (e.g., [1,2]) that address similar problems.\n4. CDGS uses a curvature-based approach to approximate likelihoods for pruning low-quality plans. However, there are existing reconstruction-based methods for evaluating sample quality in diffusion models, such as restoration gap [3] and minority score [4]. Could you clarify the advantages of the curvature-based approach over reconstruction-based alternatives for likelihood approximation?  \n5. (optional) It would strengthen the paper if the authors could provide empirical comparisons demonstrating the effectiveness of the curvature-based metric compared to reconstruction-based approaches for plan pruning.\n\n[1] State-Covering Trajectory Stitching for Diffusion Planners, 2025\n\n[2] Extendable Long-Horizon Planning via Hierarchical Multiscale Diffusion, 2025\n\n[3] Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans, 2023\n\n[4] Don’t Play Favorites: Minority Guidance for Diffusion Models, 2024\n\nNote: I still think this work is promising. Although a borderline accept is given currently, I'd be open to increasing my score upon seeing suitable revisions or clarification addressing my concerns.", "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 (Oral)