Source-linked AI summary
Beyond Task Relevance: Interaction-Aware Semantic Communication for Wireless Agent Collaboration
Yiru Wang, Pengxin Guan, Xiumei Deng, Zehui Xiong
TL;DR
Task-relevant messages can still change inter-agent dependence and the team’s best available solution. This paper models that team-level value to guide communication decisions; across evaluated power budgets, its policy achieves higher net surplus with lower energy than semantic-aware and full-solution baselines.
Problem
Task relevance and receiver-local gain do not determine whether a message improves expected best-of-team utility, because sharing can change both candidate quality and inter-agent dependence.
Source: §I · p. 1Method
The paper relates communication-induced dependence to best-of-team utility, assigns semantic units a team-level value, and selects at most one unit and its power—or no transmission—to maximize predicted net surplus.
Source: §I · pp. 1–2, B. Interaction-Aware Transmission Policy · p. 4Results
Full-solution exchange induces stronger convergence and more adverse team-level effects, while method hints show direction-dependent effects; across evaluated power budgets, the policy achieves higher net surplus with lower energy than semantic-aware and full-solution baselines.
Source: §I · p. 1, §VI · p. 5Takeaways & Limitations
Semantic abstraction and communication direction matter to team outcomes: method hints exhibit directionality, whereas full-solution exchange has more adverse team-level effects.
Source: §I · p. 1, §VI · p. 5
Abstract
from arXiv · showhide
Semantic communication in multi-agent systems often assumes that task-relevant information benefits the team once delivered. Yet semantic sharing can alter the receiver's decision and increase inter-agent dependence, potentially reducing team utility. Motivated by this phenomenon, we develop an interaction-aware framework for wireless collaboration between heterogeneous agents. We analytically relate communication-induced dependence to expected best-of-team utility and define a team-level semantic value for candidate messages. We consider semantic units at different abstraction levels and formulate a deadline- and power-constrained problem that maximizes predicted team-level net surplus. We derive an interaction-aware policy that selects at most one unit and its transmit power, or chooses not to transmit. Experiments on two verifier-scored optimization tasks show that full-solution exchange induces stronger structural convergence and more adverse team-level effects, whereas method hints exhibit pronounced directionality. Across the evaluated power budgets, our policy achieves the highest net team-level surplus while consuming less energy than semantic-aware and full-solution baselines.
I. INTRODUCTION
Task relevance and receiver-local gain alone do not establish whether a message improves the team, because sharing can change dependence and expected best-of-team utility. The paper develops an interaction-aware framework and studies how semantic abstraction and communication direction shape team outcomes.
I. INTRODUCTION
- Task relevance and receiver-local gain alone do not determine team value because sharing can alter both receiver quality and dependence on the sender.Source: §I · p. 1
- The framework relates interaction-induced dependence to expected best-of-team utility and defines semantic value by a message’s downstream team-level effect.Source: §I · p. 1
- Full-solution sharing induces the strongest convergence and most adverse team-level effects, while method-level guidance exhibits pronounced directionality.Source: §I · p. 1
- Across evaluated power budgets, the interaction-aware policy achieves higher net team-level surplus with less energy than semantic-aware and full-solution baselines.Source: §I · p. 2
A. Collaborative Multi-Agent System
- Agents exchange at most one semantic unit, ranging from verifier-derived feedback or method hints to partial or complete solutions, and the receiver submits one final candidate.Source: A. Collaborative Multi-Agent System · p. 2
B. Wireless Link
The wireless-link model connects transmit power and channel conditions to achievable rate, transmission latency, and energy use.
B. Wireless Link
- Under block fading, the channel coefficient remains constant during one semantic-unit transmission, and transmit power and channel gain determine received SNR.Source: B. Wireless Link · p. 2
- Achievable transmission rate is modeled as bandwidth times log2(1 + received SNR).Source: B. Wireless Link · p. 2
- Transmission latency depends on payload size and rate, while energy consumption is transmit power multiplied by latency.Source: B. Wireless Link · p. 2
III. SEMANTIC INTERACTION COST
The interaction model separates a message’s quality benefit from its influence on receiver behavior and resulting dependence between agents. It uses these effects to evaluate the team-level consequences of semantic sharing.
III. SEMANTIC INTERACTION COST
- The model separates receiver improvement from behavioral coupling: α captures following the transmitter’s candidate, while δ captures residual semantic benefit beyond that influence.Source: A. Communication-Induced Correlation · pp. 2–3
- Semantic units with similar task relevance can have different effects: a failure indication may add quality with weak steering, while a complete solution may couple agents more strongly.Source: A. Communication-Induced Correlation · p. 3
- The receiver’s expected quality gain combines α-weighted difference between agents’ candidate means with the residual semantic benefit δ.Source: A. Communication-Induced Correlation · p. 3
- Because α and δ are latent and cannot be separately inferred from one event, the framework estimates group-level effects from comparable historical interactions.Source: A. Communication-Induced Correlation · p. 3
- Under 0 ≤ ρij < 1 and 0 ≤ αij,k ≤ 1, stronger semantic influence increases post-interaction dependence.Source: A. Communication-Induced Correlation · p. 3
B. Team-Level Interaction Cost
The section models how communication-induced dependence changes expected best-of-team utility, then defines an interaction cost that isolates this effect while holding post-interaction marginal scores fixed.
B. Team-Level Interaction Cost
- The analysis approximates the interacting score pair as jointly Gaussian to obtain a closed-form expected maximum.The expression uses the post-interaction means and standard deviations, their correlation, and the standard Gaussian CDF and PDF.Source: B. Team-Level Interaction Cost · p. 3
- Stronger statistical dependence reduces expected best-of-team utility when individual quality distributions are fixed.This holds for positive score standard deviations and correlations between -1 and 1.Source: B. Team-Level Interaction Cost · p. 3
- The team-level interaction cost measures the utility difference attributable to changed correlation while holding post-interaction marginal score distributions fixed.The section identifies Gaussian approximations in aggregate language-model score inference as related work [12].Source: B. Team-Level Interaction Cost · p. 3
A. Net Semantic Value
The section defines semantic value at the team level: it measures the expected pairwise utility change after successful reception, accounting for both receiver score changes and altered score correlation.
A. Net Semantic Value
- Communication can improve receiver marginal quality while simultaneously reducing the value of independent exploration.The paper frames this trade-off as a reason to evaluate messages by their net collaborative contribution.Source: A. Net Semantic Value · p. 3
- Interaction-aware semantic value is the expected pairwise utility change after successful reception, combining changes in receiver score distribution and score correlation.Its prediction uses estimates of interaction effects obtained before transmission.Source: A. Net Semantic Value · p. 3
B. Interaction-Aware Transmission Policy
At each opportunity, the policy selects at most one available semantic unit and its power, or chooses no transmission, to maximize predicted team-level surplus under deadline and power constraints.
B. Interaction-Aware Transmission Policy
- The policy jointly chooses a semantic unit and transmit power to maximize predicted team-level net surplus under deadline and power-budget constraints.Net surplus subtracts energy cost, weighted by λ, from predicted semantic value; no transmission has zero surplus.Source: B. Interaction-Aware Transmission Policy · p. 4
- For each unit, the minimum power that meets the deadline minimizes transmission energy among feasible powers.This follows because transmission energy increases with power under the stated rate model.Source: B. Interaction-Aware Transmission Policy · p. 4
- The policy can select no transmission, represented by unit index k = 0 and power p = 0; ties at zero surplus favor that choice.The selected unit and power are denoted k⋆ and p⋆.Source: B. Interaction-Aware Transmission Policy · p. 4
A. Experimental Setup
The setup evaluates semantic interactions between heterogeneous agents on two verifier-scored optimization tasks, using held-out interaction estimates and wireless transmission constraints.
A. Experimental Setup
- The evaluation uses Knapsack-50 and MaxCut with GPT-4.1-mini and Gemini-2.5-Flash as heterogeneous agents.The candidate units are verifier-derived Outcome Feedback (S1), a high-level Method Hint (S2), and a Full Solution with verifier score (S3).Source: A. Experimental Setup · p. 4
- 180 interaction records from 30 task instances are split by instance: 20 instances (120 records) for fitting and 10 (60 records) for held-out evaluation.Group estimates from training records are used for held-out transmission decisions without receiver post-interaction scores.Source: A. Experimental Setup · p. 4
- Fig. 1 maps the semantic interaction-tax landscape for Knapsack-50 and MaxCut, with shading marking structural convergence alongside team-value degradation.Source: Figure 1 · p. 4
- Candidate quality is normalized to the certified task optimum on a 0–100 scale, while structural convergence uses Jaccard distance for Knapsack-50 and normalized Hamming distance for MaxCut.Realized team-value change compares the best candidate before and after interaction.Source: A. Experimental Setup · p. 4
- The wireless setup uses a 2.4 GHz carrier, 100 kHz bandwidth, a 10 ms deadline, and links 50–100 m long with path loss and block Rayleigh fading.A message counts as delivered when transmission time meets the deadline.Source: A. Experimental Setup · p. 4
B. Performance Analysis
The analysis finds that interaction effects depend on semantic unit, direction, and wireless reliability, while the proposed policy delivers positive net surplus with lower energy than specified baselines at the highest power budget.
B. Performance Analysis
- S3 enters the structural-convergence and team-utility-degradation regime more often than S1 and S2 and induces significantly stronger convergence (Holm-adjusted p < 0.01).The dominant harmful direction differs across tasks.Source: B. Performance Analysis · p. 4, Figure 2 · p. 5
- Higher SNR amplifies intrinsic interaction effects rather than necessarily improving team utility; S3 becomes increasingly detrimental while S1 and S2 remain closer to neutral.Fig. 2(a) plots delivered team-value change against SNR for S1–S3.Source: B. Performance Analysis · p. 4, Figure 2 · p. 5
- For S2, expected delivered value is nonnegative when the sender's pre-interaction score exceeds the receiver's, but becomes more negative with SNR when the sender's score is lower.The sender's candidate remains available in the first ordering; harmful updates are delivered more reliably in the second.Source: B. Performance Analysis · pp. 4–5
- A dependence–utility check finds 0.977 correlation and 1.8 score-point mean absolute difference between closed-form and empirical utilities; stronger dependence lowers empirical best-of-team utility in all 12 groups under marginal-preserving permutations.Source: B. Performance Analysis · p. 4
- At 15 dBm, Interaction-Aware reaches mean net team-level surplus of 0.5187 and uses 82.2% and 91.8% less energy than Semantic-QoS-Aware and Task-Similarity-Aware, respectively.Fig. 3 compares average net surplus and physical transmission energy across maximum power budgets.Source: B. Performance Analysis · p. 5, Figure 3 · p. 5
VI. CONCLUSION
The conclusion presents an interaction-aware framework that models communication-induced dependence and optimizes team-level net value. Experiments find adverse effects from full-solution exchange and higher net surplus with lower energy for the proposed policy than the stated baselines.
VI. CONCLUSION
- The framework analytically characterizes how communication-induced dependence affects expected best-of-team utility.Source: §VI · p. 5
- The policy optimizes predicted team-level utility gain net of weighted transmission energy under deadline and power constraints.It may remain silent or select a semantic unit from different abstraction levels and an appropriate transmit power.Source: §VI · p. 5
- Full-solution exchange induces the strongest structural convergence and more adverse team-level effects, whereas method hints show direction-dependent effects while better preserving solution diversity.Source: §VI · p. 5
- Across evaluated power budgets, the policy achieves higher net team-level surplus with lower energy consumption than semantic-aware and full-solution baselines.Source: §VI · p. 5