AgentComm-Bench: Stress-Testing Cooperative Embodied AI Under Latency, Packet Loss, and Bandwidth Collapse

Aayam Bansal; Ishaan Gangwani

cs.AIMar 18, 2026

AgentComm-Bench: Stress-Testing Cooperative Embodied AI Under Latency, Packet Loss, and Bandwidth Collapse

Authors: Aayam Bansal, Ishaan Gangwani

Organizations: Synthetic Sciences

Abstract

Cooperative multi-agent methods for embodied AI are almost universally evaluated under idealized communication: zero latency, no packet loss, and unlimited bandwidth. Real-world deployment on robots with wireless links, autonomous vehicles on congested networks, or drone swarms in contested spectrum offers no such guarantees. We introduce AgentComm-Bench, a benchmark suite and evaluation protocol that systematically stress-tests cooperative embodied AI under six communication impairment dimensions: latency, packet loss, bandwidth collapse, asynchronous updates, stale memory, and conflicting sensor evidence. AgentComm-Bench spans three task families: cooperative perception, multi-agent waypoint navigation, and cooperative zone search, and evaluates five communication strategies, including a lightweight method we propose based on redundant message coding with staleness-aware fusion. Our experiments reveal that communication-dependent tasks degrade catastrophically: stale memory and bandwidth collapse cause over 96% performance drops in navigation, while content corruption (stale or conflicting data) reduces perception F1 by over 85%. Vulnerability depends on the interaction between impairment type and task design; perception fusion is robust to packet loss but amplifies corrupted data. Redundant message coding more than doubles navigation performance under 80% packet loss. We release AgentComm-Bench as a practical evaluation protocol and recommend that cooperative embodied AI work report performance under multiple impairment conditions.

Figures & tables

Figure 1: Overview of AgentComm-Bench . The benchmark evaluates cooperative methods across three task families under six communication impairment dimensions, producing robustness profiles via standardized metrics.

Figure 2: Communication corruption pipeline. Each message passes through the configurable impairment channel before reaching the receiver. The six dimensions are applied independently by default.

Figure 3: ResilientComm architecture. Redundant message coding sends two copies; staleness-aware fusion weights received messages by estimated age. When both copies are lost, the agent falls back to its most recent received state.

Method	CP (F1)	NAV (WP%)	Search (Rec.%)
No-Comm	95.8	3.6	20.7
Event-Triggered	95.8	96.7	26.8
Compressed	95.8	96.7	26.8
ResilientComm (Ours)	95.8	96.7	26.8
Full-Comm	95.8	96.7	26.8

Table 1: Clean-condition performance (

\sigma{=}0

). Communication provides massive benefit for navigation (waypoint coordination) and moderate benefit for search.

Figure 4: Robustness curves across all task–impairment combinations. Each subplot shows performance (mean

\pm

std over 30 episodes) as communication impairment severity increases. Navigation shows the most dramatic degradation, with all six impairments causing monotonic performance drops from near-perfect coordination to near-random-walk levels. Shaded regions indicate

\pm 1

standard deviation.

Method	CP	NAV	Search	Avg $\downarrow$
No-Comm	0.0	0.0	0.0	0.0
ResilientComm (Ours)	28.5	63.6	31.4	41.1
Event-Triggered	28.5	68.6	31.1	42.7
Compressed	28.5	68.4	31.5	42.8
Full-Comm	28.5	68.4	31.5	42.8

Table 2: Mean Normalized Performance Drop (%) across all impairments at maximum severity. Lower is more robust. No-Comm is trivially robust (0% NPD) because it has no communication to degrade.

Figure 5: Normalized Performance Drop (%) at maximum impairment severity. Each cell shows the percentage of clean performance lost. Navigation dominates: stale memory and bandwidth collapse cause $>$ 96% NPD, effectively reverting coordinated agents to random walks. CP shows extreme sensitivity only to content corruption (stale/conflict $>$ 85%).

Figure 6: Method rankings across conditions. Rank 1 is best. Under clean communication, all communicating methods tie. Under impairments, ResilientComm shows rank-1 advantage for NAV packet loss and async; No-Comm dominates CP under content corruption.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Parameter	Value
Number of agents	4
Episodes per condition	30
Grid size (all tasks)	20 $\times$ 20
Message dimension	400 (flattened grid)
Impairment levels per sweep	11
Random seeds	42–71 (per-episode)

Appendix

Table 3: Full benchmark configuration.

Method	Msgs/step	Bits/step	Relative
No-Comm	0	0	0 $\times$
Full-Comm	12	153,600	1 $\times$
Compressed	12	19,200	0.125 $\times$
Event-Triggered	7–10	89,600–128,000	0.6–0.8 $\times$
ResilientComm	24	307,200	2 $\times$

Appendix

Table 4: Communication load per step (4 agents, 20

\times

20 grid).

Channel	Method	0%	40%	80%
Bernoulli	Full-Comm	96.7	55.0	10.0
( $b{=}0$ )	ResilientComm	96.7	86.1	21.9
Bursty	Full-Comm	96.7	58.3	8.3
( $b{=}0.9$ )	ResilientComm	96.7	68.3	17.5
No-Comm (constant)		3.6

Appendix

Table 5: NAV waypoint completion (%) under Bernoulli vs. Gilbert-Elliott bursty channel (30 episodes). ResilientComm ’s redundancy advantage is dramatic: at 40% loss, ResilientComm retains 86.1% vs. Full-Comm’s 55.0% under Bernoulli.

Budget	Method	dim	0%	40%	80%
$1\times$	Full-Comm	400	96.7	55.0	10.0
	Compressed	400	96.7	55.0	10.0
	ResilientComm	200	34.2	31.9	11.4
$2\times$	Full-Comm	400	96.7	55.0	10.0
	Compressed	400	96.7	55.0	10.0
	ResilientComm	400	96.7	86.1	21.9

Appendix

Table 6: NAV waypoint completion (%) under bandwidth constraints. At

1\times

budget, ResilientComm ’s 200-dim truncation loses waypoints indexed

>

200, severely hurting performance. At

2\times

budget, ResilientComm dominates via redundancy.

$\lambda$	stale=0	stale=4	stale=8	stale=12	stale=20
0.0	95.8	14.0	14.0	14.0	14.0
0.3	95.8	14.0	14.0	14.0	14.0
1.0	95.8	14.0	14.0	14.0	14.0
2.0	95.8	14.0	14.0	14.0	14.0

Appendix

Table 7: Staleness decay ablation: ResilientComm CP F1 (%) across

\lambda

values under stale memory (30 episodes). The fusion mechanism dominates;

\lambda

has no effect.

Method	CP	NAV	Search	Avg $\uparrow$
No-Comm	100.0	100.0	100.0	100.0
ResilientComm (Ours)	73.0	69.8	84.9	75.9
Event-Triggered	73.0	63.4	84.3	73.5
Full-Comm	73.0	63.3	83.0	73.1
Compressed	73.0	63.3	83.0	73.1

Appendix

Table 8: AURC (% of max) averaged across 6 impairment dimensions. No-Comm achieves 100% trivially (flat curves). ResilientComm shows highest AURC among communicating methods due to redundancy.

Figure 7: Sensitivity analysis. Radar plots showing NPD (%) for each method across impairment dimensions. The dominant vulnerability for all communicating methods is to stale memory and conflicting evidence, which corrupt the information being shared.

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