Towards Communication-Efficient Social Intelligence in Language Agents

Linxiao Gong; Yijie Xu; Tianfu Wang; Yin Wu; Yili Wang; Xingbo Yao; Huizai Yao; Xilin Xia

cs.CLSep 28, 2026

Towards Communication-Efficient Social Intelligence in Language Agents

Authors: Linxiao Gong, Yijie Xu, Tianfu Wang, Yin Wu, Yili Wang, Xingbo Yao, Huizai Yao, Xilin Xia, +2 more

Organizations: The Hong Kong University of Science and Technology (Guangzhou) · University of Science and Technology of China · The Hong Kong University of Science and Technology

Abstract

Socially intelligent language agents must negotiate, coordinate, and resolve conflicting preferences while respecting the time and attention of both participants. Balancing these demands is challenging because agents must convey enough to address a partner's constraints and advance their goals without adding words that do not help the interaction. In this paper, we propose Teacher-Assisted Communication Training (TACT) to improve social goal attainment while reducing communication cost, making interactions with agents more productive and less demanding. We first characterize communication efficiency in terms of action strategy and expression, whose effects extend beyond the current utterance to the partner's response and subsequent exchanges. We design TACT to revise student-generated actions, test the revisions through partner responses, and distill useful feedback into the student. An expression specialist removes unnecessary detail while preserving the intended action, while a strategy specialist proposes alternatives that may better address the partner's constraints. To determine which revision helps, TACT samples a partner response for each candidate and selects a teacher reference by balancing local goal support against action-token cost. That reference guides on-policy distillation on the student's own generation prefixes, allowing the student to act independently at deployment. We evaluate TACT on SOTOPIA and AgentSense. On SOTOPIA, it achieves the highest Goal among the evaluated methods on All and Hard while using substantially fewer target tokens than SFT+SDPO. On AgentSense, it improves goal success over the initial student while reducing target tokens and interaction messages.

Figures & tables

Figure 1: Communication efficiency depends on the whole interaction. (A) A shorter request can require additional clarification, whereas a more informative strategy revision reaches the same goal in fewer exchanges. Dialogues are illustrative, not experimental outputs. (B) TACT lies on the empirical Goal–Cost Pareto frontier among the methods shown on both SOTOPIA-All and Hard.

Figure 2: Overview of TACT. Expression and strategy specialists propose revisions to student actions. TACT selects a reference using local interaction feedback and token cost, then conditions the teacher on this reference to guide on-policy distillation.

SOTOPIA-All ( $n=450$ )
Method	Goal $\uparrow$	Rel. $\uparrow$	Avg $\uparrow$	Action tokens $\downarrow$	Turns $\downarrow$
Initial student	4.327	-0.193	2.090	300.3	13.06
Concise prompt	4.691	-0.009	2.232	235.0	13.04
Teacher SFT	4.840	0.476	2.513	293.4	10.95
Vanilla OPD	4.960	0.580	2.555	275.4	11.10
Prompted OPD	5.151	0.680	2.631	275.1	10.61

Table 1: Social performance and communication cost on SOTOPIA. Avg is the seven-dimension mean. Bold and underlined values indicate the best and second-best results, respectively. Full results appear in Table 12 .

SOTOPIA-All ( $n=450$ )
Variant	Goal $\uparrow$	Avg $\uparrow$	Action tokens $\downarrow$	Turns $\downarrow$
Expression only	5.327	2.645	250.1	14.60
Strategy only	5.073	2.583	310.6	13.02
IG-only ranking	5.093	2.587	279.8	12.57
Token-only ranking	5.009	2.528	256.2	13.17
Random ranking	5.049	2.560	252.0	12.74

Table 2: Specialist, selection, and supervision comparisons. All/Hard panels contain 450/70 cases. TACT uses the same selected 2,970-node checkpoint as Table 1 . Full scores and configuration details appear in Table 13 ; intervals appear in Appendix F.2 .

Method	Goal (%) $\uparrow$	Rel. $\uparrow$	Action tokens $\downarrow$	Messages $\downarrow$
Initial student	48.4	0.391	899.7	14.25
Concise prompt	45.2	0.328	621.6	9.92
Vanilla OPD	54.6	0.435	841.8	13.27
TACT	54.2	0.432	789.6	12.47

Table 3: Transfer to the AgentSense two-person subset. Each method uses the same 500 instances from 100 templates. Goal is whole-goal-list success (%); Rel. is mean relationship change on

[-1,1]

; Action tokens is the target agent’s generated-token count; Messages is the number of utterances from both participants. Bold and underlining mark the best and second-best displayed means, not statistical significance.

Method	Initial Qwen3.5-4B	Llama-3.1-8B	Self-play
Initial	4.327 / 3.457	4.987 / 3.400	4.576 / 3.729
Concise prompt	4.691 / 3.857	4.682 / 3.586	4.771 / 3.843
Vanilla OPD	4.960 / 3.686	5.547 / 3.371	5.251 / 2.986
TACT	5.611 / 4.371	5.807 / 3.900	6.031 / 4.214

Table 4: Goal across interaction partners. Each cell reports All / Hard Goal (

n=450/70

). Fixed-partner columns hold the partner policy constant across methods; self-play uses the evaluated policy for both roles. Full score and cost profiles appear in Appendix F.6 .

SOTOPIA-All ( $n=450$ )
	DeepSeek-v4-pro		Kimi K2.6		GLM-5.2
Method	Goal $\uparrow$	Avg $\uparrow$	Goal $\uparrow$	Avg $\uparrow$	Goal $\uparrow$	Avg $\uparrow$
Initial	4.327	2.090	3.080	1.211	4.244	1.944
Concise prompt	4.691	2.232	3.340	1.342	4.404	2.052
Vanilla OPD	4.960	2.555	3.636	1.768	4.769	2.370
TACT	5.611	2.711	4.191	1.874	5.251	2.500

Table 5: Cross-judge consistency on identical dialogues. Each judge scores the same 450 dialogues per method (70 Hard). Goal and seven-dimension Avg are reported; bold and underlining mark the best and second-best means. Full scores and protocols appear in Appendix F.6 .

Appendix figures & tables19 assets

Supplementary material from the paper’s appendix.

Appendix

Setting	Value
Student / frozen specialist backbone	Qwen3.5-4B / Qwen3.5-27B
Training partner	Student snapshot for the collection batch
Evaluation partner	Initial Qwen3.5-4B
Final dialogue judge	DeepSeek-v4-pro
OPD implementation	Sampled-token K0, upstream actor update
LoRA rank / scaling / dropout	32 / 64 / 0

Appendix

Table 6: Implementation settings. Training-generation and evaluation temperatures are distinct. Thinking generation is disabled.

Computation	Input and permitted use
Student / source partner	Own visible observations, own goal, and outward interaction; no teacher reference or partner-private goal.
Specialist proposal	Learning role’s visible messages and complete original action; its expression or strategy instruction.
Branch partner	Partner’s own visible messages after the hypothetical action; no specialist instructions or identity.
Goal-support scorer	Learning role’s before/after visible messages; its unchanged goal text as the scoring target.
OPD teacher	Original student messages plus selected reference in the final user message; original student token prefix. No branch reply.
OPD student	Original student messages and token prefix only; no selected reference.

Appendix

Table 7: Information available to each computation. “Visible history” is role-specific. The final judge has retrospective evaluation access; generated actions do not.

Metric	Range	Evaluation question
Goal	$[0,10]$	To what extent was the role’s stated goal achieved?
Rel.	$[-5,5]$	Did the interaction improve or damage relationships or standing?
Kno.	$[0,10]$	Did the role acquire new and relevant information?
Bel.	$[0,10]$	Was behavior natural and consistent with the character?
Sec.	$[-10,0]$	Were private information and secret intentions protected?
Rules	$[-10,0]$	Were moral rules or laws violated?

Appendix

Table 8: SOTOPIA dimensions and native score ranges. Higher is better for every dimension. Avg is the unnormalized arithmetic mean of all seven scores; it does not define a social-sufficiency threshold.

Role	Model and information used
Candidate specialists	Frozen Qwen3.5-27B with expression or strategy instructions.
IG scorer	Batch-frozen Qwen3.5-4B student; scores the target goal before and after the action and partner reply.
Training/branch partner	Same batch-frozen student snapshot in the partner role.
Distillation teacher	Frozen Qwen3.5-27B; conditions on the selected reference and scores original student tokens.
Evaluation partner	Fixed initial Qwen3.5-4B.
Outcome judge	DeepSeek-v4-pro; evaluates completed dialogues with the seven-dimensional SOTOPIA rubric.

Appendix

Table 9: Model roles in TACT. Specialists share a frozen backbone; the IG scorer and distillation teacher are distinct roles.

Method	Goal (%) $\uparrow$	Rel. $\uparrow$	Tokens $\downarrow$	Messages $\downarrow$
Initial	48.4 [42.4, 54.2]	0.391 [0.330, 0.452]	899.7 [844.7, 950.9]	14.25 [13.40, 15.04]
Concise prompt	45.2 [39.4, 51.0]	0.328 [0.265, 0.388]	621.6 [574.7, 667.1]	9.92 [9.18, 10.63]
Vanilla OPD	54.6 [48.8, 60.4]	0.435 [0.374, 0.492]	841.8 [791.0, 890.9]	13.27 [12.48, 14.04]
TACT	54.2 [48.0, 60.2]	0.432 [0.370, 0.487]	789.6 [736.4, 842.3]	12.47 [11.63, 13.30]

Appendix

Table 10: AgentSense means and 95% template-cluster intervals. All four panels contain 500 instances in the same 100 templates.

Comparator	$\Delta$ Goal (pp) $\uparrow$	$\Delta$ Rel. $\uparrow$	$\Delta$ Tokens $\downarrow$	$\Delta$ Messages $\downarrow$
Initial	5.8 [0.8, 10.8]	0.040 [-0.001, 0.081]	-110.1 [-156.9, -62.1]	-1.78 [-2.52, -1.02]
Concise prompt	9.0 [3.8, 14.2]	0.104 [0.063, 0.145]	168.0 [130.6, 206.2]	2.56 [1.97, 3.16]
Vanilla OPD	-0.4 [-4.8, 4.2]	-0.003 [-0.040, 0.034]	-52.2 [-91.1, -13.3]	-0.80 [-1.42, -0.18]

Appendix

Table 11: Paired AgentSense differences: TACT minus each comparator. Goal differences are percentage points; relationship differences retain the native scale. Brackets give 95% template-cluster intervals.

SOTOPIA-All ( $n=450$ )
Method	Goal $\uparrow$	Rel. $\uparrow$	Kno. $\uparrow$	Bel. $\uparrow$	Sec. $\uparrow$	Rules $\uparrow$	Fin. $\uparrow$	Avg $\uparrow$	Tokens $\downarrow$	Turns $\downarrow$
Initial student	4.327	-0.193	3.707	7.800	-0.442	-0.669	0.102	2.090	300.3	13.06
Concise prompt	4.691	-0.009	3.687	7.827	-0.311	-0.476	0.216	2.232	235.0	13.04
Teacher SFT	4.840	0.476	3.944	8.431	-0.213	-0.251	0.367	2.513	293.4	10.95
Vanilla OPD	4.960	0.580	4.060	8.376	-0.171	-0.224	0.307	2.555	275.4	11.10
Prompted OPD	5.151	0.680	4.122	8.413	-0.160	-0.207	0.420	2.631	275.1	10.61

Appendix

Table 12: Full social performance and communication cost on SOTOPIA. Full-dimensional results for Table 1 . Bold and underlined values indicate the best and second-best results, respectively.

SOTOPIA-All ( $n=450$ )
Method	Goal $\uparrow$	Rel. $\uparrow$	Kno. $\uparrow$	Bel. $\uparrow$	Sec. $\uparrow$	Rules $\uparrow$	Fin. $\uparrow$	Avg $\uparrow$	Tokens $\downarrow$	Turns $\downarrow$
Expression only	5.327	0.869	4.082	8.289	-0.147	-0.338	0.433	2.645	250.1	14.60
Strategy only	5.073	0.764	4.067	8.218	-0.162	-0.238	0.356	2.583	310.6	13.02
IG-only ranking	5.093	0.631	3.987	8.384	-0.147	-0.213	0.373	2.587	279.8	12.57
Token-only ranking	5.009	0.611	3.978	8.216	-0.162	-0.289	0.331	2.528	256.2	13.17
Random ranking	5.049	0.613	4.027	8.276	-0.136	-0.258	0.351	2.560	252.0	12.74

Appendix

Table 13: Full specialist, selection, and supervision comparisons. Full-dimensional results for Table 2 . TACT uses the same selected 2,970-node checkpoint and original evaluation cohort as Table 1 ; checkpoint details appear in Appendix D.2 . All populated rows use complete 450/70 panels after missing-case recovery. No reference retains candidate selection but removes selected-reference conditioning from the scoring teacher; its checkpoint uses 2,818 training nodes, 74 updates, and learning rate

10^{-5}

. Intervals for the full-method and single-specialist rows appear in Appendix F.2 .

SOTOPIA-All
Method	Goal $\uparrow$	Rel. $\uparrow$	Kno. $\uparrow$	Avg $\uparrow$	Tokens $\downarrow$	Turns $\downarrow$
Initial student	[3.90, 4.77]	[-0.48, 0.09]	[3.45, 3.96]	[1.94, 2.24]	[282.7, 318.7]	[12.30, 13.82]
Concise prompt	[4.20, 5.19]	[-0.32, 0.30]	[3.43, 3.95]	[2.07, 2.39]	[222.4, 247.9]	[12.33, 13.75]
Teacher SFT	[4.36, 5.32]	[0.19, 0.75]	[3.66, 4.22]	[2.38, 2.65]	[276.2, 310.5]	[10.26, 11.66]
Vanilla OPD	[4.46, 5.46]	[0.27, 0.89]	[3.78, 4.34]	[2.41, 2.70]	[257.0, 294.3]	[10.29, 11.93]
SFT+SDPO	[4.77, 5.79]	[1.21, 1.77]	[4.05, 4.54]	[2.66, 2.93]	[381.4, 523.0]	[13.65, 15.14]

Appendix

Table 14: 95% intervals for complete-panel means. Full point estimates appear in Tables 12 and 13 .

SOTOPIA-All
Comparator	$\Delta$ Goal $\uparrow$	$\Delta$ Tokens $\downarrow$	$\Delta$ Turns $\downarrow$
Initial student	+1.284 [0.947, 1.631]	-19.7 [-33.8, -5.5]	+1.158 [0.573, 1.738]
Concise prompt	+0.920 [0.616, 1.231]	+45.6 [33.6, 57.6]	+1.182 [0.609, 1.742]
Teacher SFT	+0.771 [0.456, 1.107]	-12.8 [-26.7, 1.0]	+3.269 [2.669, 3.873]
Vanilla OPD	+0.651 [0.331, 0.976]	+5.2 [-8.0, 18.9]	+3.122 [2.540, 3.720]
SFT+SDPO	+0.336 [0.027, 0.647]	-155.1 [-234.9, -107.2]	-0.182 [-0.805, 0.413]

Appendix

Table 15: Paired differences: main-table TACT minus each comparator. Entries are mean differences [95% scenario-cluster interval]. Positive Goal and negative cost differences favor TACT. Comparators retain their recorded training pipelines.

SOTOPIA-All: development ( $n=83/84$ , respectively)
	Social performance $\uparrow$								Communication cost $\downarrow$
LR / nodes	Goal	Rel.	Kno.	Bel.	Sec.	Rules	Fin.	Avg	Tokens	Turns
$10^{-6}$ / 3,118	5.07	0.33	3.66	8.14	-0.30	-0.36	0.37	2.42	251.6	12.22
$5\!\times\!10^{-6}$ / 3,606	5.32	0.51	3.90	8.29	-0.20	-0.25	0.38	2.56	293.5	13.67
SOTOPIA-All: main-table reference ( $n=450$ )
$10^{-5}$ / 2,970	5.611	0.818	4.171	8.356	-0.278	-0.229	0.527	2.711	280.6	14.22

Appendix

Table 16: Development endpoints and the selected main-table checkpoint. Development rows use baseline-paired valid cases from the 90-case panel. The

10^{-5}

rows reproduce the complete 450/70-case evaluation in Table 12 . Nodes count admitted training actions at the evaluated checkpoint, not the final size of its training run. Panels have different evaluation coverage and checkpoint selection; no cross-panel ranking is applied.

Run	Unique configs.	Nodes	Updates
Reference, $10^{-5}$	931	3,000	59
Reference, $5\times 10^{-6}$	1,129	3,606	71
Reference, $10^{-6}$	1,129	3,118	71

Appendix

Table 17: Training exposure. Reference-conditioned runs at the reported training endpoints. A selected training node, a dialogue configuration, and an optimizer update are distinct units.

Stage	Expression	Strategy
Proposed	6,624 (100.0%)	6,624 (100.0%)
Scorable	6,039 (91.2%)	5,665 (85.5%)
Eligible	2,405 (36.3%)	1,370 (20.7%)
Selected	2,036 (30.7%)	934 (14.1%)
Used for OPD	2,036 (30.7%)	934 (14.1%)
Share of used references	68.6%	31.4%

Appendix

Table 18: Specialist proposal funnel and OPD adoption. Same 2,970-node checkpoint as the main results. Scorable requires both the original and candidate branches to be scorable. Funnel percentages use each specialist’s 6,624 proposals; the final row instead uses all 2,970 references actually used for updates. Counts describe supervision frequency.

Diagnostic	Count	Percentage
No eligible candidate	3,755 / 6,725	55.8%
Exactly one eligible candidate	2,165 / 6,725	32.2%
Both candidates eligible	805 / 6,725	12.0%
Disagreement with IG-only ranking	165 / 805	20.5%
Disagreement with shortest-eligible ranking	315 / 805	39.1%

Appendix

Table 19: Selection opportunities and ranking disagreements. Eligibility percentages use all 6,725 target actions. Ranking disagreements use only the 805 dual-eligible nodes and count a disagreement when the selected candidate falls outside the alternative rule’s best set; tied optima are not disagreements.

Candidate group	Pairs	Nodes	$\Delta$ Goal [95% interval]
All comparable candidates	351	218	$+0.103$ [ $-0.012$ , $+0.223$ ]
Positive IG gain	187	149	$+0.147$ [ $+0.017$ , $+0.277$ ]
Upper quartile of positive gains	45	38	$+0.244$ [ $-0.073$ , $+0.605$ ]
Remaining positive gains	142	122	$+0.116$ [ $-0.021$ , $+0.255$ ]

Appendix

Table 20: Candidate–student IG gains and complete-continuation outcomes. Exploratory groups within the random 300-node cohort. Goal changes compare a teacher candidate with the original student action; brackets give 95% scenario-cluster intervals. Counts are paired comparisons, not independent training runs; one node may contribute two pairs.

Randomly sampled nodes ( $n=153$ )
Selection	$\Delta$ Goal [95% interval]	$\Delta$ Tokens	$\Delta$ Turns
Shortest legal action	$-0.023$ [ $-0.159$ , $+0.114$ ]	$-17.03$	$-0.157$
IG-only, eligible	$+0.023$ [ $-0.101$ , $+0.145$ ]	$-9.20$	$+0.020$
Random, eligible	$+0.025$ [ $-0.103$ , $+0.149$ ]	$-10.35$	$-0.026$
TACT selection	$+0.020$ [ $-0.106$ , $+0.144$ ]	$-9.19$	$+0.065$
Large teacher–teacher IG gaps ( $n=43$ )

Appendix

Table 21: Selector outcomes on common complete training nodes. Differences are relative to freshly continuing the original student action. Goal brackets are 95% scenario-cluster intervals; cost columns are mean differences. Positive Goal and negative cost differences favor the selector.

Comparison	Pairs	Nodes	Spearman $\rho$ [95% interval]
Randomly sampled nodes
Candidate minus student	351	218	$+0.030$ [ $-0.088$ , $+0.144$ ]
Eligible candidate minus student	230	193	$+0.084$ [ $-0.048$ , $+0.212$ ]
Expression minus strategy	163	163	$-0.001$ [ $-0.163$ , $+0.163$ ]
Large teacher–teacher IG gaps
Candidate minus student	111	68	$+0.002$ [ $-0.195$ , $+0.202$ ]

Appendix

Table 22: IG–Goal rank associations in the two continuation cohorts. Spearman correlations use paired differences, with fixed expression-minus-strategy direction for teacher–teacher comparisons. Brackets give pointwise 95% scenario-cluster intervals; exploratory comparisons are not multiplicity-adjusted. Each row uses the complete pairs required for that comparison.

SOTOPIA-All ( $n=450$ ) — Fixed initial Qwen3.5-4B
Method	Goal $\uparrow$	Rel. $\uparrow$	Kno. $\uparrow$	Bel. $\uparrow$	Sec. $\uparrow$	Rules $\uparrow$	Fin. $\uparrow$	Avg $\uparrow$	Tokens $\downarrow$	Turns $\downarrow$
Initial	4.327	-0.193	3.707	7.800	-0.442	-0.669	0.102	2.090	300.3	13.06
Concise prompt	4.691	-0.009	3.687	7.827	-0.311	-0.476	0.216	2.232	235.0	13.04
Vanilla OPD	4.960	0.580	4.060	8.376	-0.171	-0.224	0.307	2.555	275.4	11.10
TACT	5.611	0.818	4.171	8.356	-0.278	-0.229	0.527	2.711	280.6	14.22
SOTOPIA-All ( $n=450$ ) — Fixed Llama-3.1-8B-Instruct

Appendix

Table 23: Sensitivity to the interaction partner. The same four methods are compared with the initial Qwen3.5-4B partner, Llama-3.1-8B-Instruct, and a partner using the evaluated policy (self-play). Each block uses the same 450 scenario–character configurations, including 70 Hard cases, and DeepSeek-v4-pro scoring. Fixed-initial results are reproduced from Tables 1 and 12 . Bold and underlining mark the best and second-best means among the four methods within each partner–subset block.

SOTOPIA-All ( $n=450$ ) — DeepSeek-v4-pro
Method	Goal $\uparrow$	Rel. $\uparrow$	Kno. $\uparrow$	Bel. $\uparrow$	Sec. $\uparrow$	Rules $\uparrow$	Fin. $\uparrow$	Avg $\uparrow$
Initial	4.327	-0.193	3.707	7.800	-0.442	-0.669	0.102	2.090
Concise prompt	4.691	-0.009	3.687	7.827	-0.311	-0.476	0.216	2.232
Vanilla OPD	4.960	0.580	4.060	8.376	-0.171	-0.224	0.307	2.555
TACT	5.611	0.818	4.171	8.356	-0.278	-0.229	0.527	2.711
SOTOPIA-All ( $n=450$ ) — Kimi K2.6

Appendix

Table 24: Full cross-judge score panels. Seven native-scale SOTOPIA dimensions and their arithmetic mean for the identical dialogues in Table 5 . Best and second-best displayed means are marked separately within each judge–subset block; markings do not imply statistical significance.

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What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems

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Open paper AskPDF arXiv

Multi-agent systems (MAS) built on large language models are typically organized around roles, pipelines, and turn schedules, while the content that agents pass to one another is often left as unconstrained natural language. However, this free-form communication can rapidly inflate token usage, consume the shared context window, and ultimately affect both system performance and inference cost. We analyze five common inter-agent communication strategies across two MAS topologies, finding that no fixed strategy is universally optimal. Instead, effective inter-agent messages consistently preserve action-centered information needed by downstream agents. Building on this, we propose the PACT (Protocolized Action-state Communication and Transmission), which treats inter-agent communication as a public state-update problem and projects each raw agent output into a compact action-state record before it enters shared history. Across different MAS topologies, PACT consistently improves the performance-cost trade-off, achieving comparable or stronger task performance with substantially fewer tokens. The gains extend to production coding harnesses: PACT lifts OpenHands' resolve rate at -10% tokens-per-resolved, and is resolve-neutral on SWE-agent while halving input tokens. Our code is publicly available at https://github.com/iNLP-Lab/PACT.

Abstract

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