CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control
Authors: Zirui Yuan
Siqi Lai
Hao Liu
Venue: ICLR 2026 Poster (2026)
Keywords: Traffic Signal Control
Large Language Model
Multi-Agent System
Intelligent Transportation
Primary area: applications to robotics, autonomy, planning
TL;DR: We introduce CoLLMLight, a cooperative LLM framework that achieves effective and efficient network-wide traffic signal control via spatiotemporal reasoning, asynchronous decision architecture, and cost-aware cooperation optimization.

Abstract: Large Language Models (LLMs) have recently emerged as promising agents for Traffic Signal Control (TSC) due to their strengths in reasoning and generalization. However, current LLM-based approaches treat intersections as independent agents without inter-intersection cooperation, limiting their effectiveness in network-wide optimization. To address this gap, we propose CoLLMLight, the first cooperative LLM agent framework for network-wide traffic signal control. CoLLMLight enables agents to perform in-depth spatiotemporal reasoning for cooperation, while ensuring real-time responsiveness through an asynchronous cooperative decision architecture. The reasoning process runs asynchronously, deriving cooperative control guidance from dynamic interactions among intersections. This guidance is cached and incorporated as contextual input for real-time signal decisions. To enhance cooperation quality while ensuring reasoning efficiency, we propose cost-aware cooperation optimization. It first applies adaptive reasoning chain optimization to enable the LLM to adjust its reasoning depth according to traffic complexity. The model is then refined with reinforcement learning using reward signals that promote network-wide performance while penalizing excessive reasoning. Extensive experiments on four real-world traffic networks demonstrate that CoLLMLight consistently outperforms existing methods, achieving more effective and generalizable cooperation while maintaining real-time responsiveness and efficient token usage.

Published as a conference paper at ICLR 2026
COLLMLIGHT: COOPERATIVE LARGE LANGUAGE MODEL AGENTS FOR NETWORK-WIEE TRAFFIC SIG-
NAL CONTROL
Zirui Yuan Siqi Lai
Hao Liu"
The Hong Kong University of Science and Technology (Guangzhou). Guangzhou, China (zyuan779, (lai125]econnect: .kuss ceaust-g..est www.gg..dd....mm.. cn, Liuh@ust. uustuuuttkkkkk
ABSTRACT
Large Language Models (LLMs) have recently emerged as promising agents for Traffic Signal Control (TSC) due to their strengths in reasoning and generaliza- tion. However, current LLM-based approaches treat intersections as indepen- dent agents without inter-intersection cooperation, limiting their effectiveness in network-wide optimization. To address this gap, we propose CoLLMLight, the first cooperative LLM agent framework for network-wide traffic signal control. Ligh enables agents to perform in-depth spatiotemporal reasoning for cooperation, while ensuring real-time responsiveness through asynchronous cooperative decision architecture. The reasoning process runs asynchronously, deriving cooperative control guidance from dynamic interactions among intersec- tions. This guidance is cached and incorporated as contextual input for real-time signal decisions. To enhance cooperation quality while ensuring reasoning ef- ficiency. we propose cost-aware cooperation optimization. It first applies adap- tive reasoning chain optimization to enable the L.M to adjust its reasoning depth according to traffic complexity. The model is then refined with reinforcement learning using reward signals that promote network-wide performance while pe- nalizing excessive reasoning. Extensive experiments on four real-world traffic networks demonstrate that CiLh consistently outperforms existing meth- ods, achieving effective and generalizable cooperation while maintaining real-time responsiveness and efficient token usage.
INTRODUCTION
Traffic congestion has become a pressing urban challenge, with profound societal and environmental impacts. As cities continue grow due rapid urbanization, the burden on transportation infras- tructure intensifies. In this context, Traffic Signal Control (TSC) plays a pivotal role in improving
traffic efficiency and road safety (Wu al. 2023 Zhang al. 2024 Wei et al. 2019c).
Over the past decades, both transportation-basee and Icarning- based approaches have been explored enhance TSC systems. However, these methods often struggle to generalize across diverse traffic environments. Recently, Large Language Models (LLMs) have attracted increasing attention in TSC and broader urban applications, leading to a series of L.LM-based agents (Lai et al., 2025 Wang et al. 2024 2025 Feng et al. 2024] Lai et al. 2026 Zhou et al., 2026 Han et al. [2025). Compared with prior TSC methods, LLM-based methods leverage language-driven reasoning, which improves interpretability and facilitates generalization across varying traffic conditions.
Despite their advancement, a fundamental limitation of these L.LM-based agents that they lack communication and act independently at single intersection. This design ignores the cooperation across intersections and leads to higher risk of network congestion. An example of this limitation is shown in Figure I (a): the independent agent selects the East- West phase to clear the longest local queue. While this decision appears reasonable from a local perspective, it ultimately causes upstream spillback, severely undermining overall traffic efficiency. In contrast, the cooperative agent (Figure D (b)) can leverage information from adjacent intersections to anticipate oncoming traffic,
"Corresponding author

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Published as a conference paper at ICLR 2026
Reasoning:
The east-side lane has the longest queue.
CD:0 CD
Action: Release the East- West Traffic.
Current Observation
Independent Agent
Signal Decision
Congestion
(a) Independent agent.
This agent clears the longest queue based solely on local observa- tion, leading to upstream blockage.
Reasoning: Heavy traffic from the North is approaching my northside lane.
Action:
41
Release the North- South Traffic.
Current Observation
Cooperative Agent
Signal Decision
Smooth Flow
(b) Cooperative agent. This agent considers upstream traffic to proactively avoid conges- tion and ensure smooth flow.
Figure I: Independent vs. cooperative TSC agents. Vehicles are colored by state: queued (gray), moving (green), and blocked (red). Green arrows indicate the selected signal; shaded areas denote each agent's observation range.
and proactively clears its lane to coordinate with upstream flows, thereby avoiding congestion and maintaining smoother network traffic.
While cooperative LLM-based TSC agents hold great promise, they also face several challenges. First, cooperation is inherently more complex than single-intersection control. Agents must reason about dynamic interactions among intersections and anticipate how local actions influence upstream and downstream flows, making difficult identify effective cooperation signals. Second, current LLMs perform multi-step reasoning for complex problems, which brings significant computational overhead and latency. This conflicts with the strict real-time requirements of traffic signal control, where decisions must be made within few seconds. The third challenge lies in balaneing coopera- tion quality with reasoning efficiency. Traffic scenarios vary greatly in complexity, from low-volume intersections to high-density junctions with highly dynamic flows. A fixed reasoning strategy is in- adequate, as it leads to redundant computation simple cases and insufficient analysis in complex ones, making difficult handle the diverse reasoning demands across traffic environments.
In response to these challenges, we propose CoLLMLight, the first cooperative LLM agent frame- work for network-wide traffic signal control. This framework is supported by an asynchronous cooperative decision architecture. To ensure effective cooperation, the architecture performs spatiotemporal-awee cooperative reasoning, conducting step-by-step analysis of the spatiotemporal context around the target intersection and deriving cooperative control guidance. To meet real-time requirements, the reasoning process is executed asynchronously and results are cached. The deci- sion module then retrieves the latest cooperative guidance contextual input and integrates it with real-time observations to quickly select the optimal signal. Moreover, to enhance both cooperation effectiveness and reasoning efficiency, we propose cost-aware cooperation optimization strategy. first fine-tunes the LLM through adaptive reasoning chain optimization, enabling it to adjust rea- soning depth according to traffic complexity. The model is then further refined with reinforcement learning using tailored reward functions, which guide both reasoning and decision policies toward efficient reasoning and network-wing traffic optimization.
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Published as a conference paper at ICLR 2026
Asynchronous Cooperative Decision Architecture
Cost-Aware Cooperation Optimization
Real-Time Decision
Adaptive Reasoning Optimization
Signal
Cooperative Reasoning
Traces
Road Network (t)
Real-Time Decision
Road Network
GPT-4o
Feecback
Shortes:
Bust
O
Fine-tning
Final
Policy Refinement
Cooperative Control
Guidance
Spatiotemporal
Information Construction
paaiittemporal-Aaar Cooparative Reasoning
State
Traffic
PPO
Sample
Spotial Ralations
Analyss
Qa
Historical
Interactions
Traffic
Temporal
Roflection
Suggestion,
Perf
Utility
Figure 2: The overview of CoI.LMLight framework.
Our contributions summarized as follows: (1) We propose CoLLMLight, the first cooperative LLM agent framework for traffic signal control, enabling agents to achieve network-wide traffic optimization through inter-interseccion cooperation. (2) We design asynchronous cooperative decision architecture that supports real-time decision-maie reasoning over rich spatiotemporal contexts. (3) We introduce cost-aware cooperation optimization strategy that aligns reasoning depth with the demands of different traffic scenarios and improves both efficiency and cooperation quality. (4) Extensive evaluations four real-world datasets demonstrate the effectiveness and superiority of CoLLMLight.
while enabling multi-step cooperative
PROBLEM STATEMENT
In this section, we introduce key concepts related to traffic signal control and the formal problem statement. more detailed illustration of the intersection layout, lane types, and signal phases is provided in the Appendix A.2
Definition (Road Network) The road network directed graph composed of intersections I and lanes L. Lanes be categorized into three types: 1) go-through lanes (Lgo). 2) left-turn lanes (Lteft). 3) right-turn lanes (Eright). Each lane connects two intersections and interacts with adjacent lanes at intersections.
Definition (Traffic Signals) At each signal-switchin time step. the agent assigned to the inter- section selects signal from the predefined signal set A {a1.... am). The traffic signal is represented as a = set( (aallow), where Lallow is a group of allowed-to-go lanes without conflicting movements (i.e., a green light for Lallow and red light for others).
Problem (Cooperative LLM Agent for Network-Wir Wide Traffic Signal Control) Consider road network with multiple intersections, where each intersection i E I controlled by LLM agent that follows a shared control policy и. At each signal-switching timestep i, the i-th agent receives: 1) traffic observations from own and neighboring intersections Oú, 2) spatial relations with nearby intersections G¹ 3) historical traffic interactions involving itself and its neighbors T Based on these inputs, the agent selects the traffic signal action at from the action space A':
af m(OOr, gi A').
The goal is develop cooperative L.MM Agent with policy nt that maps traffic context to signal decisions, aiming to improve wide traffic efficiency, evaluated by metrics such as average queue length, travel time, and waiting time.

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Published as a conference paper at ICLR 2026
3 METHODOLOGY
We present the overview of CoL.LMiight in Figure including: /) Asynchronous Cooperative De- cision Architecture. This component enables effective multi-step cooperative reasoning while decou- pling it from decision-making to ensure real-time signal control. At each timestep t, it executes two modules asynchronously: the spatiotemporal-awee reasoning over historical traffic interactions and spatial relations and caches the results Y and the real-time decision module, which selects the signal based on the current observation O₂ and the latest cooperative control guidance YSR 2) Cost-Aware Cooperation Optimization. This component fine-tunes the L.MM improve cooperation quality while ensuring reasoning efficiency. First, adap- tive reasoning chain optimization collects reasoning traces of varying lengths by prompting GPT-4o in simulation, retaining only those that are both concise and effective for supervised fine-tuning. Then, reinforcement learning with cost-aware reward signals is applied to further refine reasoning and decision policies, promoting efficient reasoning and network-wide traffic performance.
cooperative reasoning module, which performs
3.1 ASYNCHRONOUS COOPERATIVE DECISION ARCHITECTURE
To enable effective multi-step cooperative reasoning over the spatiotemporal context and ensure real- time signal selection, we design an asynchronous decision architecture. At each timestep f, the agent first collects spatiotemporal information from its own intersection and neighboring collaborators. then asynchronously triggers two modules: spatiotemporal -ware cooperative reasoning (SR) and real-time decision (RD).
3.1.1 SPATIOTEMPORAL INFORMATION CONSTRUCTION
At each decision timestep we collect traffic condition features for each lane 1:
(2) where ni muecu is the number of queued vehicles, ni move is the number of moving vehicles, 72 is the average waiting time, and E [0. is the occupancy of lane l. These lane-level features are aggregated construct the traffic observation intersection i timestep t:
of n quene
ni nomoee 7). Pt]
o {or | | L'
where L denotes the of lanes connected to intersection and its neighbors. We represent the spatial relations around intersection i using a directed subgraph G (V", (V", where V includes i and its neighboring intersections. To capture temporal dynamics, we collect a sequence of historical observations and signal actions within a fixed time window At:
Te = (O), a/x) | - At < <t}.
where at. denotes the signal decisions of both intersection i and its neighboring intersections at timestep I'.
3.1.2 SPATIOTEMPORAL-WWARR COOPERATIVE REASONING
Based on the constructed spatiotemporal context (O), Gi, ) each agent performs multi-step rea- soning timestep to analyze traffic dynamics and generate cooperative signal suggestions that consider both local efficiency and network-wide optimization.
Specifically, the spatiotemporal context is transformed into human-readable prompt, which is then processed by the LLM:
SR,
.LLLM (Prompt(O), T
where Y.R.i represents the reasoning outcomes. In this process, the agent adaptively activates the necessary reasoning steps to comprehensively understand the traffic situation. The critical lane iden- rification step determines which movements contribute most to current or emerging congestion and assigns them higher priority. The spatial interaction analysis step examines lane-level dependencies between the target intersection and its neighbors to capture how their traffic conditions mutually in- fluence another. The temporal pattern analysis step identifies short-term traffic evolution trends,
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enabling the agent anticipate near-future congestion. The reflection step evaluates the outcomes of recent signal decisions to avoid repeating ineffective or inefficient control actions. Finally, these analyses collectively enable the agent generate cooperative guidance that provides conditional signal recommendations informed by predicted short-term traffic patterns. The reasoning output produced by SR is cached textual context for later real-time decisions, and the model's ability to adaptively select the necessary reasoning steps is further refined during the optimization stage.
3.1.3 REAL-TIME DECISION
At each decision timestep f, the agent selects the signal phase based on the current traffic observation Oj and the cached reasoning result Y SK.,;
of = JLLM (Prompt(O) Y
(6)
where of denotes the selected signal for intersection at timestep t. In this process, the agent only needs brief analysis to make decisions, thereby enabling real-time response. Notably, the agent does not simply follow the previously reasoned cooperative suggestions Y instead, leverages them contextual guidance and makes judgments based the latest observation Oj. When sudden traffic condition changes occur and the reasoning results derived from historical data offer limited benefit, the agent adaptively prioritizes real-time observations to ensure effective decisions.
3.2 COST-AWARE COOPERATION OPTIMIZATION
To improve cooperation quality while ensuring cost-effective reasoning, we propose cost-aware co- operation optimization, which consists of two stages: adaptive reasoning chain optimization and policy refinement via reinforcement learning.
3.2.
ADAPTIVE REASONING CIIANN OPTIMIZATION
This stage fine-tunes the LLM agent to ensure format-compliant outputs while establishing ability adaptively adjust reasoning depth to traffic conditions. We begin by sampling diverse traffic scenarios from the simulator. For each scenario, we prompt GPT-4o to generate multiple SR traces of varying lengths, each followed by a corresponding RD output. Among these combinations, we identify the SR-RD pair where the shortest SR trace is sufficient to support RD output that achieves the best long-term network-wide traffic performance (i.e., lowest average queue length). The selected SR and RD samples are then compiled into supervised fine-tuning dataset D
We then fine-tune the LLM by minimizing the negative log- likelihood over this dataset:
LsFr(O) =
Σ
Σ
log (M
X, Yzu),
(7)
(X. Y
w=1
where Ty denotes the LLM policy parameterized by 0, X is the input prompt (for either SR or RD), and Y is the corresponding target output.
3.2.2 POLICY REFINEMENT VIA REINFORCEMENT LEARNING
This stage employs RL to jointly refine the SR and RD modules through interactions with the traffic environment. To guide this process, we design specialized reward signals for the reasoning and decision stages.
The reward RRD assigned to the real-time decision module by comparing the agent's selected signal with the long-term optimal signal a":
+1,
i
RRD
-1, otherwise,
where a" is determined by evaluating all candidate signal phases in simulation and selecting the one that minimizes the long-term average queue length.
The reward RSR balances reasoning utility and computational cost. It integrates the downstream RD reward RRD, the reasoning length T., and binary utility score U E {0, 1}. The utility score U is
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assigned by the agent during the RD process, indicating whether the SR provides useful cooperative guidance for its decision. The final reward is defined as:
+ B )
11- B3-
R
RRD.
Lunax
where Lmxx denotes the maximum reasoning length and 8 E [0, controls the trade-off between reasoning conciseness and quality.
We optimize the LLM using Proximal Policy Optimization (PPO) (Schulman et al. 2017 The PPO objective maximizes the following clipped surrogate:
JPO (0)
Ex
min (rx(0) Ås, clip(rx(0),
As
(10)
=o(uulse) where & indexes the sampled steps in the training trajectory. rx( Thuu (Uv]sg) is the probability ratio between the current policy and the previous policy for the generated output ye given state $t, and Ag is the estimated advantage computed from the corresponding reward signal.
4 EXPERIMENTS
4.1 EXPERIMENTAL. SETUP
Datasets. Our experiments were conducted on one synthetic dataset (Syn-Train) and four real- world traffic flow datasets (Jinan, Hangzhou, New York 1, New York 2) (Mei al. 2024). Detailed information about these datasets provided in Appendix A.3 Syn-Train is a dataset that we syn- thesized. In our experiments, all learning-based methods (RL-based, LLMLight, CoLLMLight) are trained on Syn-Train and evaluated for their zero-shot performance on the real-world datasets.
Environment Settings. We utilize City (Zhang al. 2019), widely used open-source sim- ulator, for our experiments. Each intersection operates with four signal phases: ETWT (east-west through), ELWL (cast-west left-turn), NTST (north-south through), and NLSL (north-south left- turn). Each dataset simulates one hour. In the simulation, right-turn movements are allowed at all times. The green signal phase lasts for thirty seconds, followed by three-second yellow phase and a two-second all-red phase (Zhang et al. 2022, Wei et al. 2019b] Lai et al. 2025).
Baselines. We compare our proposed method with two conventional transportation methods: Fixed- 2008) and MaxPressure (Varaiya (013); nine RL-based approaches: MPLight
Time (Koonce et al.
(Chnn al.] (020). Aith (Oroojlooy et al. (020)). PressLight (Wei et al. (019a). Col Light
(Wei et al. 2019b), Efficient-CoLighe (Wu et al.) 2021). Advanced-CoLight (Zhang et al. 2022),
X-Light (Jiang et al., 2024). CosLight (Ruan et al. (2024), and DuaLight (Lu et al. 2024); ; and an
SOTA LLsedbase method: LLMLight (Lai al. 2025). Additionally, assess the performance
of general LLMs integrated within our CoLLMLight framework, including Llama 3.1 (8B and 70B), Qwen 3 (8B and 32B), and the Deepseek-RI R1 distilled models (R1-8B and R1-32B).
Evaluation Metrics. We adopt Average Travel Time (ATT), Average Waiting Time (AWT), and Average Queue Length (AQL. (Wei al., 2002 Zhang et al. 2022 Lai al. 2025) evaluation metrics to assess the performance of different traffic signal control methods. Specifically, ATT measures the average time taken for vehicles to travel from their origins to their destinations. AWT captures the average time vehicles spend waiting at intersections. AQL indicates the average number of vehicles queued each intersection.
4.2 PERFORMANCE COMPARISON
To evaluate CoLLMLight against existing methods under unseen traffic scenarios, adopt zero- shot setting. All learning-basse methods are fully trained on Syn-Train and directly tested on four real-world datasets. The results reported in Table 1
Compared to all existing baselines, CoLLMLight consistently achieves SOTA performance across all datasets and evaluation metrics, significantly outperforming both transportation-bsse and learning- based approaches. This demonstrates not only its strong generalization ability but also the effectiveness of its cooperative decision across diverse urban scenarios.

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Published as a conference paper at ICLR 2026
Table 1: Zero-shot performance comparison across different datasets (lower is better). Best results are shown in bold. Both CoLLMLighh-88 and LLM are finetuned from Llama3.
New York
New York 2
Jinan
Hangzhou
Method
ATT
AWT
AQL.
ATT
AWT
AQL.
ATT
AWT
AQL.
ATT
AWT
AQL.
Transportation Methods
FixedTime
1535.6
290.1
3173.6
1771.7
428.7
4523.1
441.2
66.7
294.1
616.0
74,0
3013
MaxPressure
1223.3
153.3
2410.2
1566.8
255.5
3936.5
273.2
38.2
106.6
325.3
49.6
68.9
RL Methods
MPLight
1492.2
262.2
3054.1
1711.0
364.6
4324.5
482.2
84.6
340.6
496.2
64.7
197.5
AttendLight
1267.7
292.8
2557.2
1755.3
496.8
4571.8
310.2
63.4
144.5
327.2
72.9
69.0
PressLight
1687.2
508.4
3542.7
1894.0
489.7
4820.7
410.1
139.2
270.4
600.0
259 2
301.
CoLight
1427.9
246.7
2923.0
1753.5
457.7
4517.6
474.4
90.8
333.1
530.2
90.3
228.1
E-CoLight
1266.0
281.3
2664.2
1645.7
437.3
4327.5
844.8
489.9
799.2
874.5
8
A-CoLight
1037.9
185.5
1845.8
1428.9
359,8
3665.6
392.9
144.7
242.9
428.0
235.9 9
156.5
CosLight
18514
761.8
3225.
1986.9
816.3
4025.8
875.9
752.5
890.3
848.7
696.5
557.6
X-Light
368.8
661.1
117.3
117.5
DuaLiggt
1102.3
337,3
2026.1
1487.7
467.5
3952.3
425.5
230.7
289.7
558.5
435.1
277.3
ight Framework
Llama3. 1-8B
1289.2
549.1
2551.4
1618.3
582.1
4190.5
332.9
100.3
168.0 08.0
355.0
110.7
91.0
LI.MLiihht-S
1599.4
388.7
4074.6
268.6
39.5
CoLLMMLight Framework (Ours)
Llama3. 1-8B
1196.5
242.3
2342.8
1540.7
388.7
3917.7
281.3
44.7
114.3
333.8
45.6
73.5
Qwen3 8B
1109.2
116.6
2134.1
1435.9
245.0
3666.7
268.8
37.3
100.8
311.1
40.3
$6.5
Qwen3-32B
1939.4
1381.2
237.5
3510.3
277.1
38.5
110.6
314.8
40.9
R1-8B
1253.9
189.1
2455.4
1551.7
321.2
3910.7
329.8
76.9
167.9
372.8
91.5
105.8
R1-32B
1117.2
159.3
2050.2
1375.4
246.3
3463.1
272.6
37.7
105.3
310.9
37.1
56.3
Llama3. 70B
1082.6
1427.6
245.0
3644.8
101.9
313.7
34.8
CoLLMLight-SB
1000.4
90.5
1816.8
1345.1
167.6
3394.2
267.9
34.8
100.4
308.5
33.5
54,9
Among prior methods, MaxPressure shows relatively better generalization, benefiting from its queue-difference pressure heuristic. contrast, RI.-based models suffer from performance incon- sistencies when applied to unseen networks. For example, Wwwaandd-CoLigge performs better in New York, while Aighn excels Jinan and Hangzhou, despite all models being trained solely on the synthetic network. This suggests that these methods are inherently biased toward certain structural or traffic patterns, resulting in uneven performance across test scenarios with varying characteristics, revealing their limited robustness under zero-shot evaluation. Conventional multi- agent RL methods represent traffic states as latent vectors and learn cooperation implicitly, which tends anchor the resulting policies the traffic patterns observed during training. In contrast, CoLLMLight interprets traffic states through explicit semantic reasoning, enabling more generaliz- able cooperative signal control. Additionally, we report the supervised performance of RL-based methods and LoLh on Syn-Train in the Appendix A.6
Moreover, LLMLight, which adopts an independent TSC agent with localized signal selection strategy, performs competitively on simpler networks such as Jinan and Hangzhou. However, its performance drops more complex settings like New York, where densely distributed intersec- tions increase the need for cooperative signal control. This highlights the limitation of purely local makiin. In contrast, CoLLMLight employs asynchronous spatiotemporal reasoning across intersections, allowing agents to anticipate upstream and downstream traffic before making real-time decisions, thereby achieving consistent advantages across all networks.
general-purpose We further evaluate general- purpose LLMs within the CoLLMLight framework. While larger mod- els (32B/70B) achieve reasonable results due to their capacity, they are consistently outperformed by optimized CoLLMLight-888 This confirms the benefit of our optimization strategy over scaling.
4.3 INFERENCE LATENCY ANALYSIS
We evaluate the inference latency of SR and RD modules across 8B-scale LLMs, as shown in Fig- ure Larger models (32B, 70B) are excluded due to higher latency and cost. RD time reflects real-time decision latency, crucial for timely switching. LLMLight lacks an SR module and per- forms all reasoning and decisions in RD, leading to higher latency than most models except R1, which conducts excessive reasoning. In contrast, models in framework achieve low RD latency.

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Published as a conference paper at ICLR 2026
RD (b=1)
RD (b=5)
(b=10)
SR (b=1)
SR (b=5)
SR (b=10)
Inference Latency (s)
30
20
10.40
10
1.021.80 8.8..6 46
1.cl
2.373
1.5y2.66
4.82
3.671.155.00
CoLLMLight
LLaMA3
Qwen3
LLMLight
Figure 3: Inference time comparison of 8B-scale LLMs batch sizes b {1, 10).
Table 2: Impact of SR design average travel time (in seconds) across four datasets.
Method
New York
New York 2 Jinan Hangzhou
Asyne SR
1000.4 1005.7
1345.1
267.9
308.5
Syne SR
1351.0
266.9
307.2
w/o SR 1155.1
1477.3
267.1
308.8
Although SR takes longer than RD due multi-step reasoning, runs asynchronously and does not delay signal decision. Among these, CoLLMLight achieves the lowest latency across all batch sizes and maintains RD latency below the typical yellow-light duration (3-5s) (Koonce et al. 2008), en- suring real-time applicability. Moreover, it shows the lowest SR latency, validating the effectiveness of our cost-aware cooperation optimization in reducing reasoning overhead.
4.4
ABLATION STUDY
4.4.1 IMPACT OF SPATIOTEMPORAI-AWWARR COOPERATIVE REASONING (SR)
We compare three CoILMMigght variants to evaluate SR: (1) Async SR, where SR runs asyn- chronously and RD uses cached SR output YSR and the latest observation Oj for decision-making: (2) Sync SR, where SR runs synchronously and RD uses Y and Oj: (3) w/o SR, where SR is re- moved and RD relies only on Oj As shown in Table 2 removing SR increases average travel time, especially in New York, confirming its importance for cooperation. Asyne SR performs compara- bly to Syne SR across datasets, showing that asynchronous design avoids latency while preserving reasoning effectiveness. Although Async SR omits t-step observations, RD combines its output with the latest Oj maintaining decision quality. Performance differences are smaller in Jinan and Hangzhou, whose sparse topologies make agents rely more real-time observations than historical patterns, reducing SR's s contribution. Despite this, Asyne SR achieves comparable results, suggest- ing that the RD module effectively adapts latest traffic conditions. This highlights RD's flexibility and its ability to operate reliably even when cooperative reasoning offers limited guidance.
4.4.2 EFFECT OF OPTIMIZATION STAGES
We conduct an ablation study on the two stages of our cost-aware cooperation optimization: adaptive reasoning chain optimization (AR) and policy refinement (PR). Table comparee four variants: the full model (Ours), w/o AR, w/o PR, and w/o Both. The results show that removing either AR or PR negatively impacts performance, confirming the effectiveness of both modules, with AR playing dominant role. Specifically, removing AR significantly increases the reasoning chain length, with approximately 52% more tokens on the New York 1 dataset and 55% more tokens on Jinan. It also leads to a notable rise in ATT. This highlights the necessity of AR for adaptive reasoning. On this basis, PR further enhances both traffic performance and reasoning efficiency.
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Table Ablation study of optimization stages on average travel time (ATT, in seconds) and reason- ing length (Token) of the SR module across New York I and Jinan.
New York 1
Jinan
Variant
ATT |
Token 4
ATT +
Token +
Ours
1000.4
484.2
267.9
691.3
w/o AR
1066.3
738.5
270.3
1072.6
w/o PR
1034.2
543.9
269.8
759.2
w/o Both
1196.5
809.2
281.3
1198.5
NewYork1
Jinan
SR Token Lengthh
1500
SR Token Length
1500
1000
1000
500
L3-8B
500
L3-8B
Ours
Ours
R1-88
R1-8B
0
50
100
150
20
40
60
80
Vehicle Count
Vehicle Count
Figure 4: SR token length across traffic conditions with different vehicle counts.
4.5 REASONING BEHAVIOR ANALYSIS
To evaluate reasoning adaptiveness and efficiency, we analyze the token length of spatiotemporal- aware cooperative reasoning under varying traffic conditions. Specifically, we measure the number of vehicles each agent observes during SR, including both its own and neighboring intersections. The results are shown Figure 4 where we compare our method with two baselines: Llama3.1 1- 8B (L.3-BB) and R1-8B. We observe that CoLLh produces shorter reasoning chains under light traffic. When faced with higher vehicle counts, which imply more complex traffic conditions, CoLLMLight dynamically increases its reasoning effort, resulting in increased SR token length. This behavior demonstrates the model's ability to adaptively adjust its reasoning depth according to traffic complexity, validating the effectiveness of cost-aware cooperation optimization strat- egy. In contrast, L3-8B and R1-8B adopt fixed-length reasoning across all traffic conditions, with consistently longer reasoning chains than CoLLMLighht
4.6 ROBUSTNESS EVALUATION
To evaluate the robustness of CoLLMLight under network instability, introduce two perturbed scenarios. The first is Stale SR, where the SR reasoning is delayed by one additional timestep with probability of 50%. The second Communication Failure, where the traffic state randomly selected neighboring intersection fails to be communicated, also with probability of 50%. Ta- deerreprt the ATT performance for New York I and New York For reference, we also report the normal setting, where SR reasoning finishes within one signal timestep and all collaborators communicate successfully. Across both perturbed scenarios, CoLLMLight exhibits only minor per- formance degradation, indicating that the asynchronous cooperative decision architecture remains stable. This robustness supported by the design of the two modules. The SR module reasons over historical traffic states, which reduces sensitivity intermittent communication issues. The RD module makes decisions based on real-time observations rather than blindly following potentially outdated SR guidance.

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Table 4: Robustness test of CoLLMLight (ATT, lower better).
Scenario Dataset
New York 1
New York 2
Normal Stale SR
1000.4
1345.1
1017.7
1380.4
Communication Failure
1028.1
1381.7
RELATED WORK
Traffic signal control presents a long-standing challenge in intelligent transportation systems, with approaches evolving from transportation-batt methods RL-based methods, and more recently, methods such as FixedTime (Koonce et al. 2008), which employs fixed cycle lengths and phase allocations, and MaxPressure (Varaiya 2013) which reduces congestion by minimizing queue imbalance across traffic movements, were widely used in early traffic control systems. However, they rely on manually designed rules and often struggle to adapt in complex and dynamic traffic environments. RL-based methods significantly advanced traffic signal control by introducing innovative neural architectures and refined state representa- tions. On the architectural side, FRAP (Zheng al. 2019) models dynamic phase-level interac- tions; CoLight (Wei et al., (019b) leverages graph attention networks (Veličković et al.) 2017) for inter-intersection coordination; and CosLight (Ruan al. 2024) utilizes multilayer perceptron to construct a collaborator matrix across intersections. In parallel, state representation techniques have become increasingly sophisticated. PressLight (Wei et al. (2099) proposes pressure- based features; Efficient-XLight (Wu et al., (221)) extends this idea by computing pressure in a lane-to-lane manner for finer granularity; and Advanced-Xllight (Zhang al. 2022) further improves performance by incorporating effective vehicle counts. Although RI--based methods have demonstrated strong per- formance, their cooperative behavior encoded implicitly within neural architectures. Moreover, these models often struggle to transfer their learned coordination strategies to unseen road networks, limiting their generalization capability. More recently, LLM-based TSC agents have emerged as a promising new paradigm. Approaches such LLMLight (Lai et al., 2025) and VLMLight (Wang ct al., 2025) exhibit strong generalization and human-like reasoning abilities. However, existing LLM- controllers primarily focus local decision-making and lack explicit mechanisms for inter-intersection cooperation, which is essential for achieving network-lewv coordination. In con- trast, CoLLMLight adopts a multi-agent architecture and enables explicit semantic cooperative rea- soning over spatiotemporal information, while still maintaining real-time responsiveness.
to LLM-based agents. Transportation
CONCLUSION
In this paper, we propose CoLLMLight, a cooperative LLM agent framework for network-wide traffic signal control. Specifically, we design an asynchronous cooperative decision architecture. This architecture executes spatiotemporal-awaan
cooperative reasoning and the real-time decision module asynchronously, enabling effective inter-intersection cooperation while ensuring real-time responsiveness. Moreover, we propose a cost-aware cooperation optimization strategy with two stages. The first stage applies adaptive reasoning optimization, allowing the LLM to adjust its reasoning depth according to traffic conditions. The second stage refines policy using reward signals that promote both decision quality and reasoning efficiency. Extensive experiments four real-world datasets demonstrate the superiority of CoLL.Mighh.
ACKNOWLEDGMENTS
This work supported by the National Natural Science Foundation of China (Grant No. 62572417 and No. 92370204), the National Key R&D Program China (Grant No. 2023YFF0725004), and the CCF-DiDi GAIA Collaborative Research Funds.
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Table Statistics of datasets.
Arrival rate (vehicles/5 min)
Dataset
No. Inter:
No. Veh.
Mean
Std
Max
Min
Syn-Train
16
8000
666.6
32.1
735
621
Jinan
12
4365
362.8
74.8
493
236
Hangzhou
16
2983
247.6
40.4
332
211
New York
196
11058
849.6
174.0
964
382
New York
196
16337
1255.7
264.8
1440
475
A APPENDIX
A.1 THE USE OF LARGE LANGUAGE MODELS (LLMS)
LLMs are a core component of our methodology. We propose LLLh, the first cooperative LLM agent framework for network-wide traffic signal control. In addition, LLMs employed during manuscript preparation to refine grammar and improve clarity.
A.2 SETTINGS OF TRAFFIC SIGNAL CONTROL
We present the most commonly used settings Figure
North
++ 6 OR
West
East
(b) Twelve Lanes
South
NTST
NLSL
ETWT
ELWL
(a) Intersection
(c) Signal Phases
Figure 5: An illustration of intersections, lanes, and signal phases. The signal phases include ETWT (cast-west through movement), ELWL (left-turn from cast and west), NTST (north-south through movement), and NLSL (left-turn from north and south)
A.3 DATASET DESCRIPTION
Jinan: A dataset from the Dongfeng sub-district in Jinan, China, consisting of 12 intersec- tions. Each intersection features 400-meter roads (east-west) and two 800-meter roads (north-south).
Hangzhou: dataset from the Gudang sub-distriet Hangzhou, China, comprising 16 intersections, with each intersection featuring two 800-meter roads (east-west) and two 600-meter roads (north-south).
New York: Collected in Manhattan's Upper East Side using taxi trip data, this extensive dataset encompasses 196 intersections. includes two large-scale traffic flow datasets from different periods.
Syn-Train: A synthetic dataset based a x 4 grid network, where each road segment is 300 meters long. It serves as the primary training set for learning- bearning-based traffic signal control methods in our experiments.
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Comprehensive statistics for these datasets presented in Table 5 Figure 7 presents the road- network layouts of the real-world datasets.
Figure 6 shows the empirical distribution of per-intersection vehicle counts observed in our simu- lations. Although the New York network is far denser than Jinan and Hangzhou, most of its inter- sections still face light demand, while small fraction experience extremely heavy congestion (over 200 vehicles). This wide variance underscores the importance of adaptive reasoning.
35%
NewYork1
Jinan
30%
Hangzhou
25%
Proportion (%)
20%
15%
10%
5%
0%
0
50
100
150
200
250
300
350
400
Vehicle Count
Figure 6: Distribution of per-agent observed vehicle counts across datasets. While most intersections in New York experience low traffic volumes, notable fraction face extremely high traffic.
A.4 COMPARED METHODS
FixedTime (Koonce al. 2008): A traditional signal control method using static, prede- fined cycle lengths and phase times.
MaxPressure (Varaiya 2013): A SOTA transportation method selecting signal phases
based on queue length pressure between upstream and downstream intersections.
MPLight (Chen al. the FRAP.
2020 It utilizes pressure both observation and reward, based
AttendLight (Oroojlooy et al. 2020 It uses attention mechanisms to predict phase tran-
sitions and construct observation features.
PressLight (Wei et al.
2019a: Applies Deep Reinforcement Learning (DRL) to optimize
intersection pressure.
CoLight (Wei et al.
2019b Employs graph attention network (GAT) for inter-intersection
communication.
Efficient-CoLiight (Wu et al. 2021> Enhances CoLight integrating efficient pressure
observations.
Advanced-CoLight (Zhang et al. 2022): SOTA MARL-based method that enhances CoLight by integrating advanced traffic state features, including pressure and effective run- ning vehicles.
X-Light (Tang et al. 2024y: A meta-MARL framework that leverages a transformer-on transformer architecture to extract scenario-shared and scenario-specific knowledge for cross-city transfer.
CosLight (Ruan et al. 2024) collaboration aware method that jointly optimizes collab- orator selection and signal decision-making to enhance inter-intersection cooperation.
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(c) Upper East Side, Manhattan, New York, US.
(a) Dongfeng, Jinan, China.
(b) Gudang, Hangzhou, China.
Figure 7: The road network layouts of the Jinan, Hangzhou, and New York datasets Lai et al, 2025
Table 6: Comparative Performance of Learning- based Methods at Syn-Train
Method
ATT
AWT
AQL
MPLigh
979.03
504.66
568.29
AttendLight
753.09
152.85
502.38
PressLight
715.45
403.20
371.90
CosLight
949.97
646.26
502.24
X-Light
989.18
183.25
525.46
DuaLight
621.63
531.31
451.08
CoLight
826.44
260.96
492.87
E-CoLight
716.40
361.47
$21.49
A-CoLight
671.60
348.82
491.97
LLMLight 1256.93
411.15
790.06
LoLhh
868.81
110.02
579.41
DuaLight (Lu al.
2024): A dual-branch MARL approach that separately models scenario-specifie and scenario-shared knowledge for improved adaptability across diverse traffic conditions.
LLMLight (Lai et al. 2025): :
A SOTA LLM TSC agent that employs reasoning process mimicking human-like intuition to optimize traffic management.
A.5 IMPLEMENTATION DETAILS
We conducted our experiments Linux servers equipped with two A800 GPUs. All RL methods were trained with consistent hyperparameters: learning rate of 1 x 10 a replay buffer size of 12,000, sample size of 3,000, and hidden layer size of 20, for 100 training epochs. For LLMs, the temperature parameter is set to 0.1. We perform LoRA fine-tuning on Llama 3.1 (8B) to obtain LLMLight and CoLLMLight, using LoRA rank of 8, a scaling factor a 16, and learning rate of 1 x 10-4, In CoLLMLight, the historical observation window is set to Al 5, and the reward balancing coefficient in Equation is set to 3 0.5.
A.6 SUPERVISED PERFORMANCE COMPARISON
We present the supervised performance on Syn-Train in Table From these results, we observe that DuaLight and Advanced-CoLighh (A-Light) achieve the lowest ATT, while Presslight yields the low- est AQL. In contrast, our method, CoLLMLight, attains the lowest AWT. The strong performance of RL-based methods on Syn-Train ronment. However, when applied real-world datasets, both RL-based methods exhibit significant performance degradation. contrast, CoLLMLight maintains superior performance, demonstrating its strong generalization capability.
Train can be attributed to their effective training on this synthetic envi-
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A.7 CASE STUDY
To demonstrate the superiority of CoLLMLight, we conduct a case study on the New York-1 dataset. This scenario represents typical spillback dilemma, where locally congested lane is blocked downstream, forcing agents to balance the urgency of local queue discharge against network-level stability. As shown in Table 7
we compare the spatiotemporal reasoning (SR) and real-time deci- sions (RD) of the baseline models with those of CoLLMMighht.
In this scenario, persistent activation of the NTST phase in previous timesteps has caused severe congestion in the downstream lane of NT, while WT is under strong upstream pressure and its downstream section remains relatively free. From the SR-RD outputs, we observe that LLaMA-3.1 8B does not sufficiently account for the downstream blockage of NT and still releasing NTST, which would further worsen the spillback.
Both LLaMA-3.1 70B and CoLLMLight-SB select the ETWT phase. However, CoLLMLight demonstrates more comprehensive reasoning: its SR module not only identifies both upstream pres- sure and downstream blockage, but also performs prediction and enumerates plausible candidate strategies. This structured anticipation enables the RD module to make a fast and accurate decision, ultimately selecting ETWT relieve WT's upstream neighbors and stabilize the network.
A.8 VARIANCE AND STATISTICAL SIGNIFICANCE ANALYSIS
Each model is evaluated using five random seeds (11, 42, 1024, 2025, 20), and the corresponding performance variance summarized in Table To further assess robustness, we conduct Welch's t-tests comparing all baselines against CoLLMLight-SB.
Across nearly all datasets and metrics, LighLM achieves highly significant improvements (p < 0.001). Only few isolated exceptions fall outside this range. Specifically, non-significant differences (p 2 0.05) are observed for A-CoLight on New York (AQL), Qwen3-8B on Jinan (AQL), and R1-32B on Hangzhou (AQL). A small number of moderately significant outcomes (0.01 I < 0.05) appear for R1-32B on New York (AQL) and Llama3. 1-70B on New York (AWT), Additionally, R1-32B shows one intermediate significance case (0.001 p < 0.01) on Hangzhou (ATT).
Aside from these cases, all remaining comparisons consistently demonstrate that CoL.LMLighh- 8B outperforms alternative methods with strong and statistically significant margins.
A.9 COMMUNICATION MECHANISM
the neighborhood structure in a simple 3 grid network. our problem setting, the neighborhood radius is defined based on signal-controlled lanes rather than purely topological one-hop distance. In real-world urban networks, some adjacent intersections include uncontrolled right-tum lanes, and such lanes allow the dependency to extend to the next signal-controlled inter- section located at the corresponding two-hop position. Consequently, the neighborhood of an agent consists of all directly adjacent intersections and small number of two-hop intersections that be- come reachable when a neighboring connection passes through an uncontrolled lane. For example, in simple 3 x 3 grid, if the target intersection is the center, its communication neighborhood includes the surrounding eight intersections because each is reachable through controlled lanes or through connections that extend across uncontrolled right-turns. The lane set used for observation follows the same dependency rules. It includes all controlled lanes of the target intersection, as well as lanes from neighboring intersections that maintain direct upstream or downstream relationships with the target intersection's 's approaches or exits, including those reached through an uncontrolled connector.
A.10 COLLMLIGHT PSEUDOCODE
The procedure in Algorithm describes how LoLLM makes signal decisions at each timestep.
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Table Case Study at Timestep 115. The table provides concise summary the SR-RD process, while a complete compt-response example is included in Appendix A.15
Case Description (Step 114 115)
Historical Information (Brief Summary, T109 + T114):
T109 (NTST): NT has a queue of 36; WT fully congested; upstream saturated. T110 (NTST): NT still 36; WT remains fully congested. T111 (NTST); NT drops slightly (36-+30) WT still 37; NT downstream overloaded. T112 (NTST)): NT drops to 12; WT still 37. T114 (ETWT): WT slightly relieved (37-+36); NT rebounds to 28; upstream still saturated.
Real-Time Observation (Brief Summary, T115):
At T115, both NT and WT remain highly congested. NT's downstream lanes are overloaded (Spillback Risk), while WT's downstream is free. Upstream pressure persists on all major approaches.
Expert Action: KTWT. Rationale: Ratiinaa: Prioritize WT clear unstream congestion. Do release NT to prevent
downstream spillback.
Model
Brief Summary of Spatiotemporal- aware Cooperative Reasoning (SR)
Real-time Decision (RD)
and WT are the critical congested lanes, with spillback affecting their up- stream approaches. The previous sig- nal phase did not mitigate the buildup, and keeping the current phase will likely worsen delays. A phase change is needed: ETWT or NLSL are the most suitable options to relieve NT/WT pres- sure, though ETWT may introduce congestion on the ET approach.
NTST (Wrong). Rationale: The traffic state matches ATR predictions of NT/WT pressure and upstream spill- back. Seleeting NTST is the most suit- able choice to maintain flow on the most critical approaches and aligns with the lo- cal priority ranking. ATR guidance di- rectly supports this decision.
Action:
Llama 3.1 8B
WT and NT are the main congested lanes with spillback from multiple upstream approaches. Previous signals only par- tially relieved pressure, and congestion is expected to persist at the next timestep. Two feasible options emerge: ETWT relieve WT or NTST to relieve NT. The appropriate choice depends on which lane's pressure is prioritized at timestep 115.
Action: ETWT. Rationale: Congestion WT persists predicted by ATR, and ETWT the top-ranked option the Lo- Signal Priority. Since WT's down- stream lanes have sufficient capacity, ac- tivating ETWT is the appropriate choice to relieve WT effectively.
Llama 3.1 70B
and WT remain critical lanes, both under heavy upstream pressure; re- leasing either would help prevent fur- ther spillback. The previous phase par- tially eased NT but left WT congested, indicating additional release needed. Without intervention, queues on both lanes especcalpyy their upstream feed- ers likely worsen. A conditional strategy recommended: use NTST if NT's downstream path is clear, otherwise select ETWT to relieve WT and its criti- cal upstream neighbors.
Action: ETWI. Rationale: WT remains heavily congested with critical upstream pressure, and releasing it would prevent further spillhack. ATR identifies ETWT appropriate given that WT's down- stream clear, this matches Lo- cal Signal Priority ranking, where ETWT offers the greatest waiting-time reduc- tion.
CoLLMLight 8B
A.11 SPATIOTEMPORAL ABLATION STUDY
We conducted ablation experiments on the New York dataset to examine whether CoLLMLight relies on spatial and temporal context during decision-making. In each setting, specific components of the input prompts were removed to isolate their contribution. Table 9 reports the ATT results for New York 1 and New York 2. The results show that removing either temporal or spatial information leads to a noticeable drop performance, and the degradation becomes more severe when both are removed. In particular, the absence of spatial context produces substantial decline in accuracy,
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Table 8: Zero-shot performance comparison across different datasets (lower is better). Best results are shown in bold. The lower block shows LLMs evaluated with the CoLLMLight agent framework.
Maddl:
AWT
AgC.
ATT
AWT
Pradt
1335 pvsts
2001 Luh
422.2um
44: k
09:
2063 cn
0160
331 Jsk
362
1400 14003.xxt.00
502.3
33561
1711.0 90701
3545
433.5 (40
4822
496.
(677u5.5
157.5
Alkultigs
120 (10303)
0023 (1540
(730 J033
436 15
4371
146.3 2530
2273 (0277
Coligit
1423 (457
366.7
30350
175555
457- 147777
10175 06
476
93 m4
3014 00.0
536. (47777
00.3.9922
238:1 1-200
4200
253.
8567 1977
(4977)
1575
Dualigk
(10
33733 mm-00
31361 J0764
140777 J1155
401.5 (6330
3332 104603
4333 1313 90
23077
2007 (33
330. A68.5)
453.1 168-33
277.3 14R
Uibend 1:85
362-5 J036
30173 (33
116. 21:00
255.51300
036-1018
75.3 6000
81:00
(2519
1501 0m.3
3055-
15517 mc
311.3 (400
3803 (100
1385
1079 mm
375.8 1m:00
91.
125 (0)1)
277 1a
25.
1106
RI-125
(11122666)
1352
19734
1963
3003 mm
1177 mm
315 (467
786 2-
101.
11..7 (m1
Figure 8: Communication neighborhood of agent (blue) in 3 3 grid network. The eight surrounding intersections (green) serve as its cooperative partners. Blue lanes denote the agent's controlled lanes, green lanes represent upstream downstream lanes from neighboring intersec- tions, and gray lanes indicate uncontrolled right-turn lanes.
indicating that the agent makes active use of information from neighboring intersections rather than behaving an isolated controller.
Table 9: Ablation study results (ATT, lower is better).
Method
New York 1
New York 2
CoLLMLighh (Full)
1000.4
1345.1
w/o Temporal Context
1155.1
1477.3
w/o Spatial Context
1112.6
1443.5
w/o Both
1168.7
1542.2
A.12 HUMAN EVALUATION
To assess whether CoLLMLight's decisions are interpretable and safe, we conducted an expert in- spection study with four transportation researchers experienced in traffic-signal control. The evalua-
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Algorithm I: CoLLMLight Procedure for Intersection at Timestep /
Input: Traffic observation Oj, spatial relations G historical traffic interactions T previous coop- crative guidance y (if available)
Output: Signal action dz: new cooperative guidance y.
Asynchronous Execution: Initiate the following two processes (RD and SR) parallel:
2: Process 1: Real-Time Decision (RD)
3: Construct decision prompt D; based on current observation Oj and cached guidance yRRR.
cache
4: Query RD module to select signal phase: oj JEL. (LLM (D:).
5: Execute action a; at intersection i.
6: Process 2: Spatiotemporal-Awaan
Cooperative Reasoning (SR) 7: Construct reasoning context C; based on Oj, g", and T
8: Query SR module with C to adaptively select and execute necessary reasoning steps (skip- ping unnecessary ones in simple scenarios), such as:
9:
ddnttify critical lanes (local and neighboring).
10:
Analyze spatial interactions (upstream/ddownstraan dependencies).
11:
Analyze temporal patterns from history T:
12:
Reflect on previous decisions.
Generate cooperative control guidance: Y.R..i
S I.L.M (C)).
y-RR,
14:
tion was performed on 100 sampled SR-RD outputs from CoLLMLight-8B, Each sample was rated on a 1-5 scale across five dimensions: interpretability, internal consistency, decision soundness, rea- soning validity, and risk-awareness. Table reports the mean and standard deviation of the scores. The results indicate that .ight provides clear and coherent reasoning traces, reliably iden- tifies potential safety risks such downstream blockage, and selects signal phases consistent with established engincernggg principles.
Table 10: Human evaluation results.
Metric
Mean + Std
Interpretability
4.78 + 0.17
Consistency
4.59 +'2 0.26
Decision soundness
4.71 + 0.12
Reasoning validity
4.74 + 0.14
Risk-awareness
4.83 + 0.11
Overall
4.73 + 0.13
A.13 EXPERIMENT ON LARGE ROAD NETWORKS
CoLLMLight is designed to maintain efficient communication and reasoning as the network grows, since its overhead scales linearly with the number of upstream and downstream collaborators rather than with the size of the entire road network. We evaluate CoLLMLight on a large Manhattan road network consisting of 1176 intersections and an hourly demand of 46,541 vehicles. Following the same protocol as in our main experiments, all baaned methods are trained on the Syn- Train dataset and evaluated zero-shot on this unseen large-scale network. Table [1 reports the ATT, AWT, and AQL results. CoLLMLight-ST achieves the best performance across all three metrics, with a consistently larger improvement margin compared to conventional MARL approaches. This suggests that the semantic reasoning mechanism remains effective and robust even under highly complex urban traffic conditions.
A.14 FEASIBILITY OF WORLD DEPLOYMENT
The real-time feasibility of CoLLMLight well within the capabilities of modern traffic-signal in- frastructures. The communication payload is lightweight, each agent transmits less than 1 KB
19

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Published as a conference paper at ICLR 2026
Table 11: Zero-shot performance comparison on the large Manhattan road network (lower is better).
Method
ATT
AWT
AQL
FixedTime
1901.82
885.05
14891 ,94
MaxPressure
1165.76
119.38
9223.00
MPLight
1392.39
189.82
12013.66
AdvancedCoLight
1195.48
333.31
9395.88
CoLight
1380.24
207.46
11872.34
LLMLight-8B
1111.76
144.98
8257.82
CoLLMLight-SB
1008.10
86.28
6902.18
of structured numerical lane features per signal interval, resulting in minimal bandwidth require- ments. Although our experiments do not explicitly model communication latency, deployed intelli- gent transportation systems already achieve delays that are sufficient for real-time coordination; for example, the FHWA feasibility study (Rayamajhi et al. 2020 reports that Dedicated Short-Range Communications can achieve point-to-point delays below ms. The inference time of CoLLMLight meets real-time operational needs as well. As shown in Figure 3 the RD module exhibits low la- tency with average delays of 1.02, 1.80, and 2.46 seconds when jointly processing batches of 1, 5, and 10 intersections on a 2 A800 setup, which remains safely below typical yellow-light intervals of to seconds (McGee Sr al. 2012). Deployment cost also modest when the system is served through an LLM provider using API access. Current reference prices for 8B-scale models are low, with DeepInfra listing approximately S0.02 per million input tokens and $0.03 per million output tokens. Based
measured token usage in the New York experiments (SR: 2331.69 input and 484.20 output tokens; RD: 1403.95 input and 109.69 output tokens; 2880 calls per module per day), the estimated daily cost is roughly $0.27 per intersection. API inference prices have been decreasing over time as model architectures and serving systems improve, suggesting that long-term operational costs will continue decline (Xiao et 2025
A.15 PROMPT AND RESPONSE EXAMPLES
We provide representative prompt-respone examples. In these prompts, "advance traffic reasoning" refers to spatiotemporal-awart cooperative reasoning. Additionally, we instruct the decision module to output the utility score corresponding to the cooperative reasoning result.
20

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Published as a conference paper at ICLR 2026
Prompt: System Prompt
You are a traffic signal control expert responsible for managing a four-way intersection within a larger road network. Your objective is to optimize both local and netwerk-wide traffic flow and safety by selecting appropriate signal phases at each decision point. You must consider at your intersection, but also the influence on upstream and downstream intersections.
only the traffic condition
2
Your decision-making follows two-stage process:
1. **Advance Traffic Reasoning:**
Based on current and historical traffic observations, you should conduct sufficient but necessary analysis understand the key lanes, congestion risks, and propagation effects. goal to provide a well-grounded recommendation and justification that can support the next-stage decision.
7
2. **Reactive Action:*
Given the real-tine observation at timestep t and the prior reasoning, you must promptly determine the optimal signal phase. If significant changes are detected compared to previous reasoning, you should adapt your decision accordingly.
11 Follow all safety principles and ensure that each decision balances local urgency and global impact.
12
# Background Context
An intersection has 12 lanes: [NI, NI, NR, Sh,, ST, SR, Rb, ET, ER, Wh, wT, WR]. Each lane is labeled by direction and movement: N for north, S for south, for east, W for west, for left turn, for through, and for right for the East Through lane, where traffic moves straight ahead from east to west. WL is the West Left-turn lane, where traffic turns left from west to south. Right turns are always allowed. There are four signal options: [ETWT, NTST, ELNL, NLSL] For example, ETWT indicates the release of both the ET and WT lancs. The signal phase curation set to thirty secends.
For instance, ET stands
15
16 # Note:
17
For each Lane X, when considering activating it, keep these in mina
18
NEVER 1et the occupancy of Xss downstream lanes be close to 100% at any risk, as it will cause severe congestion, this is FIRST PRIORITY.
19
the upstrean or downstream information mentioned, means that they are a good state with low occupancy.
isn't
20
You MUST consider how much the occupancy of X's downstream lanes will increase upon releasing lane X. You MUST delay the release occupancy rate.
its downstrean has high
22
If there are many high-occupancy lanes upstrean of X and X occupancy is not lew, you MUST consider releasing so as to help uest lanes release.
You can't t keep a lane waiting for too long. You MUST release the lane with excessive waiting time when the downstream condition allews.
21

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Published as a conference paper at ICLR 2026
Prompt: Spatiotemporal-Aware Cooperative Reasoning -
You are now in the **Avvance Traffic Reasoning** phase (at time
step 114).
Your goal to perform deep but adaptive reasoning based on historical and current traffic conditions. This reasoning will guide the signal decision for the next time step 115.
#4 Historical Observation
Lanes include both signal-controlled lanes at the current intersection (e.g., Ni, NT, Sh, ST, EL, ET, Wh, WT) and upstream /downstream lanes from neighboring intersecticns (e.g., SL's upstrean lane (4, ST)). **Only lanes with vehicles are shown. Values are shown as: value or value (+change from previous timestep).
7
Timestep 109 signal: NTST
Timestep 110 traffic states:
10
[Lane Cars Input |Cars Outpat Iappt//Quele Queued Cars MMvinng Cars (vvragee
Waiting Time (mins) [Occupancy ($)
11
INT 010136 (+0) |0 (+0) 5.63 (109.9990.00
12
WT 010137 (+0) (+0) [+0. (4 (+0.0)
INT'S s upstream lane (58, NT) (410036(+++) 10 (+0) 3. (+0.5) (109.09(+0.0)
14
IN downstream 1ane (28, RT) (3110)0(-155) 221 (+8) 10.0 191.19)
(3..44(22122) I downstream lane (56, NT) 101011 (+0) |0 (+0) 15. (+0.5)
15
(3.03(+0.0)]
16
INL s upstream lane (58, NT) 1010]36 |C (+0) 14.32 (+0.5) (109.09(+0.0)
17
INL's s upstream lane (59, NT) (010135(+0) |0 (+0) 13. (+0.5)
18
WT''s s upstream lane (28, ST) [110]7 11 (+1) 12. (+0.5)
124.24 (+3.033)
19
WT' upstream lane (56, SL) 1010|388 10 (+0) 14 (+0.5)
[115.15(+0.0)
WWT's s upstream lane (29, WT) 1510/366((113) (- (+0.36)
(199.09(455.15))
21
WT's s upstream lane (29, 1010/11144 10 (-1) 12. 04 (+0.38)
(40.42(44.0))
22
Timestep 110 signal: NTST
24
Timestep 111 traffic states:
25
|Lane| Cars Input |Cars / Queued Cars |Moving Cars Average
Waiting Time (mins) |0ccupancy (8)
INT
010136 (+0) (+0) 6.13 (14.0H000))109..09(40..00
27
WT 010137 (+0) 10 (+0) (199
28
IT' upstream lanc (58, NT) (010136(+++) 10 (+0) 84.82 (+0.5)
(109.09
29
INT's downstream lane (28, вT) (810120((22)) (-12) 20.2 .5)
887.88 (+24.24)
30
INT's a downstream lane (56, NT) 1010]1 (+0) 6.28 (+0.5)
3 (+0.0)+
31
NT' downstream lane (29, EL) (+0) [1 (+1) 10.0 (+0.0)
13.03
32
INL'S s upstream lane (58, NT) (010]36 10 (+0) 82 (+0.5)
1199.09
33
INL's upstream lane (59, NT) (010135(40)) 10 (+0) 4. (+0.5)
(+0.00
WTT's s upstream lane (28, ST) [11018 11 (+0) 12.38 (+0 .19)
127.27
35 WT' upstream lanc (56, SL) 1110018 (+0) |0 (+0) |1. 64 ()
1115.1
WTT's upstream lane (29, WT) 1010136((4)) 10 (+0) 1. (+0.5) (109.09(40.0)
37 1 upstream 1ane (29, NI.) 14 (+0) 10 (+0) 12. 54 (+0.5)
(42.42(440.0)
22

---

Published as a conference paper at ICLR 2026
Prompt: Spatiotemporal-Aware Cooperative Reasoning
Timestep 111 signal: NTST
Timestep 112 traffic states:
3 [Lanel Cars Input [Cars Output Queued Cars MMvingg Cars Average
Waiting Time (mins) [Occupancy ($)
INT NNI3112100 (-6) (4 (+4) 5.88 (-0.25) 1103. (6)
010131 (+0) (+0) 2.49 (+0..)) /11.:/11222111111 (1
INT's s upstream lanc (58, (0101336((+)) 10 (+0) 5..32 (+0.5) (109.09(+0.0) I downstream lane (28, BT) [1110)]36(+1)6) 10 10. (+0.31) (
INT's s downstream Lane (56, NT) (01011(++0) |0 (+0) (+0.5) (3.03(+0.0)]
INT's s downstream Lane (28, EL) [11011 12 (+0) 10. 33 (+0.33) .3))
10
I upstream lane (58, NT) (410 10 (+0) 15.32 (+0.5)
1109.09
11
INL's upstream lane (59, (010133HHH+)) (+0) 6.65 (+0.5)
(106.06(+0.0)1
12
WT''s s upstream lane (28, ST) (11910(-8) 00.0 (-2. 38)
(3.03(-24.241
13
WT upstream lanc (56, SL) (018117 /13 (+13) 11. 91 (+0.27)
(-24.
14
WT's upstream lane (29, WT) (+)0 10 (+0) 11.6 (+0.5) (109.09(+0.0) w s upstream 1ane (29, NI.) 1010]14 10 (+0) 13.04 (+0.5)
15
442.42 (+0.0)
16
Timestep 112 signal: NEST
18
Timestep 113 traffic states:
19
[Lane| Cars Input [Cars Output Queued Cars [Moving Cars Average Waiting Time (mins) 1Cccupancy ($)
20
INT 016112 (-18) (16()12) (-4 78) (84.88
WT W 010137 (+0) (+0) (1
22
INT's s upstream lane (58, NT) (010]36(+H))) (C (+0) 55.82 (+0.5)
[109.09
23
INT's downstream lane (28, ET) 11111111 112 (+12) 10.8 (40.24) (-21.222)1
24
INT's s downstream lane (56, NT) 131011 (3(+3) 17.28 (+0.5)
(12.02
25
IN s downstream lane (28, 81.) (0101((())+ (-11))
(6.05(+0.0)
INL'S s upstream lane (58, NT) (010]36(0)) |0 (+0) 15.82 (+0.5)
(103.09(+0.0)
27
NNL upstream lane (59, |0)0113 (+0) 10 (+0) |5.15 (+0.5)
1106.06
WT upstream lane (28, ST) |1/011 11 (+0) 10.08 (+0.
(6.06(33.03)
29
WTT's s upstream lane (56, SL) (+3)) [11 (-2) 22 (-1.69)
(43.00)).33))
30
WT upstream lane (29, WT) 1010 36 (+0) |0 (+0) 22 (+0.5)
(40..09
31
IW''s s upstream lane (29, NL) 1010 14 (+0) 1C (+0) 13.54 (+0.5)
142. 42 (+0.0)
23

---

Published as a conference paper at ICLR 2026
Prompt: Spatiotemporal Aware Cooperative Reasoning 3
Ti Timestep 113 signal: ROWT
Timestep 114 traffic states:
[Lane|Case Cars Input Cars Output Queued Cars IMoving Cars lvverage
Waiting Time (mins) [Occupancy (8)
INT |10/510128 (4 0.28 0.28
|WT] (-13 (0 (-9 (33
INT's s upstream lane (58, NT) (1)101133((113) 18 (+8) (4.01
193.94 (-15.15)
7 INT's downstream lane (28, ET) [1/1|199())) (-0(-2) 10.22 (87.88(+0.0)
INT's downstream lane (56, NT) 101014 (C(-3) (-511
|12.12 (+0.0)
INT's downstream lane (28, EL) (11012(+++) (1(+1) 11. (+0.55
(+.09(+3.03)
10
IN upstream 1ane (58, NT) 10]5|23 (-13) |8 (+8) 04.01 (-1.81)
(-15.111))1
11
IN 8 upstream 1ane (59, NI) 1010013555 (+0) |C (+0) 5.66 65 (+0.5)
1106.06(+0.0)
12
IN''s s downstream lane (85, (11010 12 (+1) 10.0 (+0.0)
(3.03(+3.03)
13 WT'ss s upstream lane (28, ST) 1010)((+1))0 10 10.47 (40. 16.9))6666666666...6669 16.06
14 WT''s upstream lane (56, SL) (410)38(411) 10 (-11) 54..3
W upstream lane (29, WT) (010]36(0)) |0 (+0) 2. (+0.5) [109.09(+0.0))
16 WI''s s upstream lane (29, NL) 1010/14 (+0) 10 (+0) 14.014400 (+0.5)
142.42 +0.0)
WI''s downstream lane (85, WT) 111010(++))) 12 (+1) (4.00 13.03
18
19 4 Current Observation
20
Timestep 114 signal: ETWT
22 # Instruction
23
Please proceed through the following steps:
24
#4# step 1: Identify Critical Lanes
Examine the current Intersection and its upstream/downstroen neighbors to identify critical local and neighbering lanes that are critical or congested and require attenticn; output as:
26 Local Critical Lanes:
27
Neigaboring Critical Lanes:
29 #+ Step 2: Perform Adaptive Reasoning
30 Select and apply only the **eccssaryy reasoning steps** to support effective and fast decision the next time step 115.
Suggested reasoning topics include (but are not limited to):
33
Local queue and waiting time analysis
34 Upstream/downa rewnstreawntttnnnnnnnmmm traffic influence
35 Reflection on previous signal effectiveness
Prediction the traffic state at timestep 115
37 Anticipated effects of activating specific signal phases (e.g., NTST, EIWI, NLSL, ELML) at 115
38
39 ++ Step Conditional Signal Suggestions
Provide one or more si ginll strategies that may be appropriate at time step 115.
41 For each **suggested signal+*, specify the situations under which it would be effective, including reasoning and expected effects.
24

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Published as a conference paper at ICLR 2026
Prompt: Spatiotemporal-Awarre Cooperative Reasoning
Structure each strategy as:
2
**Suggested Signal-x: The signal phase that may be activated
time step 115
**Applicable Conditions*:; what kind of traffic pattern or development this signal is suitable for
**Rationslexs: Why this signal is an appropriate choice under those conditions
**Expected Effect**: The anticipated outcome if this signal is activated
7
#+# Step Summary
10
# Notes
11
Your reasoning should d be adaptive: lorger and deeper if traffic
condicions complex, shorter if simple.
12
You may skip Steps and 3 is the scenario is simple\u2014for example, when there are no critical neighboring lanes.
13
14 4F Output Format
15
Your output should follow the structure below:
16
** Identify Critical Lanes
18
19 # Reasoning Topic 1
20
# Reasoning Topic
22
23
24
25
Conditional Signal Suggeations
27
# Summary
25

---

Published as a conference paper at ICLR 2026
Response: Spatiotemporal-Awarr Cooperative Reasoning
# Identity Critical Lanes
Local Critical Lanes: N Neighboring Critical Lanes: NT's upstream lane (58, NT), WT's upstream lane (56, SL), wT's upstream lane (29, WT)
#* Reasoning Topic 1: Local Queue and Waiting Time Analysis
NT has a high queue of 28 vehicles and an occupancy of 100%, indicating ing significant congestion. The average waiting time has decreased to 0.28 minutes, suggesting requires attention.
relief but still
WTl has a queve of 36 vehicles and an occupancy of 109.098, with a waiting time of 1.9 minutes. This 1ane is alse critical and needs to be addressed soon.
10
* Reasoning Topic 2: Upstzeam/Downstreen Traffic Influence
12
13 NT's upstream lane (58, NT) has reduced its queue to 23 vehicles, but still has high occupancy of 93.948. This indicates that needs
cleared prevent upstream congestion.
WT's upstream lane (56, SL) has high queue of 38 vehicles and an occupancy of 115.158, which is critical. Releasing WT could help alleviate this upstream congestion.
15
WT's upstream lane (29, WT) has queue of 36 vehicles and occupancy of 109.098, also critical. Releasing WI could help here as well.
16
17
4 Reasoning Topic 3: Reflection Previous Signal Effectivenes:
18
19 The previous signal phase ETTT was effective in reducing queue in NT's s downstream lane (28, ET) and NT's s upstream lane (58, NT). However, WT's s queue remains high, indicating that further release of WT is necessary.
20
# Reasoning Topic 4: Prediction Traffic State at Timestep 115
22
23 If NT is not released, the queue will likely increase, exacerbating upstream congestion.
wT is not released, the queue will remain high, and upstream congestion will worsen.
25
26 44 Conditional Signal Suggestions
Suggestted Signal..: NTST
29
AApplicable Conditionss:: Suitable if NT's downstream lanes are not critical, allowing for safe release of NT without causing downstream congestion.
30
**Rationglox:: NT has a high queue and occupancy, and its upstream lane is critical. Releasing NT will help alleviate upstream congestion and prevent further buildup. Rxpected Rffect**: Reduction in NT queue and occupancy, relief for reat congestion, and improved flow through the intersection.
31
26

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Published as a conference paper at ICLR 2026
Response: Spatiotemporal-Awwrre Cooperative Reasoning 2
Suggesttdd Signal*x: ETWT
**Applicable Conditionse:: Suitable if WT's downstream lanes are not critical, allowing for safe release of WT without causing downstream congestion.
Rationalee:: WT has high queue and occupancy, and its upstream lanes are critical, Releasing WT will help alleviate upstream congestion and prevent further buildup. **Expected Effect**: Reduction in WT's queue and occupancy, relief for upstream congestion, and improved flow through the intersection.
4
44 Summary
7
The current traffic conditions the intersection indicate critical congestion NT and WT lanes, with sigmificant upstrean congestion affecting these lanes. The NIST signal phase is recommended if NT's downstream lanes are clear, to alleviate upstream congestion and reduce NT's queuc. Alternatively, the ETWT signal phase is recemmended if WT's downstream lanes are clear, to alleviate upstream congestion and reduce WT's queue. Careful monitoring of downstrean conditions is essential to prevent cascading congestion.
27

---

Published as a conference paper at ICLR 2026
Prompt: Teme-Time Decision
You are now in the R*Rcct Action** phase time step 115.
Your goal is to quickly and accurately select the signal phase to activate at this moment, based on:
The prior **Advance Traffic Reasoning (ATR) conducted at time step 114
The **Current Observation at time stop 115
The Locall Signal Friority** ranking based on estimated waiting time reduction
10
[Copperative Reasoning Result]
11
12
13
14 Current Observation
15
Lanes include both signal-controlled lanes at the current intersection (e.g., NL, NT, SL, ST, EL, WL, and upstream /donnstream lanes from neighboring intersections (e.g., SL's upstroam lane (4, ST)) **Only lanes with vchicles are shown. Values are shown as: value or value (+change from previous timestep).
16
17
Timestep 114 signal: ROWT
19
Timestep 115 traffic states:
20
|aane| Input [Cars Output Queued Cars |Moving Cars Average
Waiting Time (mins) [Occupancy ($)
INT 310136 (+8) (-() 0.7 (19..9949909))
22
INL (+) (+1)) 10.01H0.00)33.03443..00.
23
WT 01 (+0)
24
INT'S 5 upstream lane (58, NT) 121311888 (12 10.11
190.91(-3.03)1
INT'S downstream lane (28, вT) 1710136(+17) 10. (+0.31)
[103.09
26
INT's downstream lane (56, NT) (010 (0(+)) 12. (+0.55
[12.12 INT's s downstream lane (28, EL) 131014 (+2) (2(+1) (0..2
(18.18(49.09)
28
INI s upstream 1ane (58, NI) /2|3 (-5) 112 (+4) 10. (-3.9)
29
INL upstream lane (59, NT) 1013 225 (-10) (7())) 06 (+0.41)
196.97(-9.09)
30
NN s downstream lane (85, WT) 101011 (+2) [0 33
(3.03(+0.0)
31
WT upstream lane (28, ST) [1]012 (+1) 10.97 (+0. 5) 19. (43.004
32
WI'' s upstream lane (56, SL) 1017]19 |12 (+12) 00. 787.222.55)
143.94 (-)1.21) WT' upstream lane (29, 1010036 |0 (+0) 3.1 (+0.5)
33
1109.09
WTT'S upstream lane (29, NL) 11/0/15 10 (+0) 14.24 (+0.2)
145..5043.055
35 wI'ss downstream lane (85, WI) 1010001220 (+1) 13.03
(+1) 10 (-1) 10. 33 (+0..33)
28

---

Published as a conference paper at ICLR 2026
Prompt: Real-Time Decision 2
Instruction
Please:
5 1. Compare the ATR suggestions with the current traffic state.
2. If the current situation aligns with ATR predictions, select the recommended signal accordingly.
3. the current situation *ddeviates.. from ATR, make necessary adjustments to respond to new patterns.
4. If
influence is minimal negligiblers, directly select the top-ranked signal from the Local Signal Priority table.
5. Do not congest downstzeam lanes. When the occupancy of Lane X, plus that of any other x's downstream lanes, is close to 1008, you must delay the release of X.
10
6. Decide whether the ATR output was exactually uxe your decision using the 'used_atr' flag:
11
"used atr 1': The ATR output was referenced and influenced the decision.
12
lused atr 0): The decision made independently of the ATR output.
13
14
15
16
Output Format
18
Return your result in the following #*structured JSON format**:
19
20
"jsonn
21
\"signal\"; \"<Selected signal phase, e.g., ETWT, NTST, NISH, BLWI>\",
23
\rreason\;: \"<Brief justification: why this signal is chosen based traffic observation, local signal ranking, and (if applicable) the ATR suggestion.>\", \u <0 or i>
25
26
Response: Real-Time Decision
'jsoon
2
3 \Msignal\V \ \"ETWT>",
\ \reesson\"; \"The current observation shows that WT has high queue and occupancy, and its upstream lanes are critical. Releasing WT will help alleviate upstream congestion and reduce W queue. The AIR suggested ETWT as viable option if W downstream lanes the current state. Additionally, ETWT is the top-ranked signal in the local Signal Priority table, offering the highest waiting time reduction.\", "used atr
not critical, which aligns with
29


Reviews:
[{"review_id": "fltk7bzZWf", "reviewer": "Reviewer_jThr", "summary": "The paper proposes CoLLMLight, a cooperative large language model framework for network-wide traffic signal control. Unlike existing LLM agents that manage intersections independently, CoLLMLight enables inter-intersection cooperation through an asynchronous decision architecture. It performs spatiotemporal reasoning to guide real-time signal decisions, ensuring both cooperation and responsiveness. A cost-aware optimization strategy further balances reasoning depth and efficiency using adaptive reasoning and reinforcement learning. Experiments on four real-world traffic networks show that CoLLMLight significantly outperforms existing rule-based, RL-based, and LLM-based methods, achieving better traffic flow and faster decision-making.", "questions": "1. Can you provide clearer methodological details or examples/intuitions on how Cooperative Reasoning and Reflection are implemented within the pipeline?\n\n2. Can you discuss and, if possible, test larger networks?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Novel Contribution: This paper proposes the first cooperative LLM-based framework for network-wide traffic signal control. It shows a new perspective for the TSC community.\n2. Innovative Architecture: The asynchronous cooperative decision architecture cleverly decouples reasoning from real-time control, ensuring both deep cooperation reasoning and real-time responsiveness.\n3. Adaptive and Efficient Reasoning: The introduction of cost-aware cooperation optimization and adaptive reasoning chain optimization enables the model to balance reasoning depth with computational cost, improving both efficiency and scalability.", "weaknesses": "1. Clarification issue: The paper should clearly explain how Cooperative Reasoning and Reflection are implemented. How the implementation fits the need of Cooperation and Reflection function, is not described.\n\n2. The neighborhood radius (e.g., one-hop) and the precise set of lanes included are not fully specified; bandwidth, latency and the practical deployment cost, especially across large city-scale networks with many intersections, are not quantified or discussed.", "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": "b8RUuf49nR", "reviewer": "Reviewer_Vb9x", "summary": "This paper introduces CoLLMLight, the first framework that uses cooperative large language model agents for managing traffic signals across entire road networks. Unlike existing LLM-based controllers that operate intersections independently, CoLLMLight enables intersections to share spatiotemporal information and coordinate decisions asynchronously, improving network-wide traffic flow. The model includes a cost-aware cooperation optimization mechanism that balances reasoning depth and computation efficiency through adaptive reasoning and reinforcement learning. Experiments on four real-world traffic networks show that CoLLMLight outperforms traditional, reinforcement learning, and prior LLM-based methods in all evaluation metrics, while maintaining real-time responsiveness.", "questions": "Please see the weakness part", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. The problem discussed in the paper is a very important and interesting problem.\n\n2. The ablation study of the work is comprehensive enough to understand how different parts of the framework contribute, especially in the inference time part.", "weaknesses": "1. Some important details are missing in the paper. For example, what is the base model that the authors try to optimize.  And the introduction to the framework is somehow vague, lacking a case study to show how the system really works.\n\n2. The experimental setting is not very standard for RL-based baselines. Normally, in the original paper, RL-based methods are trained on the same map as the evaluation setup, with a different traffic scenario. However, this paper uses a transfer learning style evaluation. Though I understand LLM-based methods have a better generalization on this transfer style evaluation, which is also one of the advantages of the LLM-based method,  I hope to see a comparison for a normal setting for the RL-based method to better understand the performance.\n\n3. This paper lacks solid proof of the effectiveness of the proposed framework. In Table3, the results show that the proposed method will perform worse than LLMLight-8B (From Table 1) without any policy refinement. Therefore, I doubt that the performance gain is coming from the training process in policy refinement instead of the multi-agent framework.", "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": "UU1DjbYHLZ", "reviewer": "Reviewer_9P3T", "summary": "The paper addresses a relevant and emerging topic in large-scale urban traffic signal control by extending LLM-based single-intersection agents to a cooperative multi-agent framework.\n\nThe proposed CoLLMLight architecture integrates asynchronous reasoning–decision decoupling with cost-aware reinforcement optimization, aiming to balance coordination performance and reasoning efficiency.\n\nThe idea is interesting and experimentally validated across multiple city networks.\n\nHowever, several aspects—including the theoretical motivation, reward design rigor, and consistency of coordination—require further clarification and analysis to fully establish the framework’s soundness and generality.", "questions": "1.The paper does not clearly explain why large language models (LLMs) are more suitable for multi-interaction cooperation than conventional multi-agent reinforcement learning (MARL) frameworks. A theoretical or conceptual discussion on the limitations of existing RL-based coordination strategies is necessary to justify the shift toward LLM-based control.\n\n\n2. Comparing more recent baselines will enhance the persuasiveness of the experimental results.\n\n\n3. The paper should analyze how asynchronous reasoning affects network-wide coordination and feedback consistency.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.Explores a novel and timely research direction by bridging large language models (LLMs) with multi-agent coordination for network-wide traffic signal control.\n\n2.The proposed Spatiotemporal Reasoning–Real-Time Decision (SR–RD) decoupling effectively mitigates LLM latency issues in real-time control.\n\n3.Experiments on multiple city-scale networks demonstrate the scalability of the approach beyond toy environments.\n\n4.The paper is generally well-written and clearly organized, making it accessible to both MARL and LLM audiences.", "weaknesses": "1.The paper does not clearly explain why large language models (LLMs) are more suitable for multi-interaction cooperation than conventional multi-agent reinforcement learning (MARL) frameworks. \n\n2.There are fewer recent baselines in the comparative experiments.\n\n3.The SR (Spatiotemporal Reasoning) and RD (Real-Time Decision) modules operate asynchronously, and the reasoning latency of SR is not fixed. This could lead to desynchronized decisions among intersections. \n\n4.Cooperation among intersections is primarily achieved through prompt aggregation and message sharing at the text level. The framework would benefit from a more explicit or interpretable mechanism for cooperative policy modeling to reveal how coordination behaviors emerge across agents.", "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": "Ph2bVV6SWP", "reviewer": "Reviewer_TspF", "summary": "This paper, CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control, proposes a framework where multiple LLM-based agents collaboratively manage a network of intersections. The key ideas are an asynchronous cooperative decision architecture that separates reasoning from real-time control, and a cost-aware cooperation optimization mechanism that balances reasoning depth and computational efficiency. Experiments on several real-world traffic datasets show promising improvements over both reinforcement learning (RL) and LLM-based baselines.", "questions": "Besides weakness, I also have the following questions:\n\nHow are LLM reasoning traces (SR) concretely represented and reused during decision making? Are they natural-language summaries or latent embeddings?\n\nHow is the asynchronous communication among intersections implemented? Do agents share text tokens, numeric signals, or both?\n\nHow robust is CoLLMLight to communication failures or stale reasoning caches in dynamic conditions?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Novel problem framing: The paper extends prior LLM-based traffic control works (e.g., LLMLight) to a cooperative multi-agent setting, which is a meaningful and realistic step toward network-level optimization.\n\nAsynchronous architecture: Separating reasoning and decision modules to maintain real-time control is a practical and well-motivated design.\n\nCost-aware optimization idea: Introducing adaptive reasoning and reinforcement learning to regulate reasoning depth shows awareness of the token and latency constraints in LLM systems.", "weaknesses": "- The proposed method appears as an incremental extension of LLMLight with cooperation and token-efficiency modules. Both the asynchronous design and adaptive reasoning could be considered engineering optimizations rather than fundamentally new learning mechanisms.\n\n- The cooperative reasoning process relies on textual prompting, but there is little analysis of why or how the LLM reasoning benefits coordination beyond the cached contextual hints. No ablation or visualization clarifies what the LLM is learning about spatial-temporal dependencies.\n\n- Although the asynchronous module mitigates latency, the experiments rely on simulation rather than real-time control. It’s unclear whether the system can truly operate under realistic time constraints with communication and inference overhead among dozens of intersections.\n\n- The zero-shot evaluation uses synthetic training and real-world testing but does not report variance or statistical significance.\n\n- No human or expert evaluation validates the interpretability or safety of the LLM’s decisions.\n\n- Comparison with strong graph-based MARL methods (e.g., multi-agent transformers) is missing.\n\n- The paper devotes extensive space to performance tables and prompts but offers limited insights into why the model works, missing a deeper understanding of LLM-agent cooperation dynamics.", "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)