Prospective Interpretation Risk: Principled Communication Control Between LLMs

Wanrong Yang; Rehan Deen; Julian Ma; Yuheng Fan; Yaoyu Jin; Taher Jafferjee; Ziquan Liu; Dominik Wojtczak

cs.MASep 27, 2026

Prospective Interpretation Risk: Principled Communication Control Between LLMs

Authors: Wanrong Yang, Rehan Deen, Julian Ma, Yuheng Fan, Yaoyu Jin, Taher Jafferjee, Ziquan Liu, Dominik Wojtczak, +2 more

Organizations: University of Liverpool · Independent Researcher · University College London · Queen Mary University of London

Abstract

Large language model (LLM) agentic systems increasingly rely on models communicating with one another, yet existing uncertainty and multi-agent methods rarely estimate how a particular receiver will interpret a message before it is sent. This matters in heterogeneous systems, where capable receivers can reconstruct different tasks from the same message. We model this as a sender-receiver problem with a latent receiver type and define prospective interpretation risk (PIR): the probability that a receiver reconstructs a task other than intended. Rather than model an LLM's full input-output behaviour, we use black-box probes relating messages, intended tasks, and receiver-specific reconstructions, yielding scalable supervision while separating interpretation from downstream capability failure. Offline, heterogeneous frozen receivers provide supervision for receiver-conditioned risk and the effects of predefined mutable message features. At deployment, history induces a posterior over receiver types, guiding message revision and selection. We introduce value of interpretation information (VoII), querying for receiver information only when its expected communication benefit exceeds its cost. Our theory characterises when receiver information has decision value and bounds such queries. Empirically, interpretation-failure rates vary by 4-13x across receivers. Receiver information reduces PIR calibration error by 68% relative to a receiver-agnostic predictor, largely by correcting receiver-specific risk levels. PIR-guided revision reduces interpretation failure by 44% relative to the original message and 40% relative to a generic rewrite, mostly through a repair that helps every receiver. VoII outperforms information-gain and random querying at matched cost on the interpretation objective it optimises, lowering interpretation failure from 3.84% to 3.79% while querying 18.2% of episodes.

Figures & tables

Figure 1: Framework overview. Top: receivers read one message as different tasks (1), PIR separates interpretation from capability risk (2), and the sender infers the receiver and queries only if worthwhile (3). Bottom: offline probes train the PIR model, which picks the verified rewrite to send.

Panel of Figure 2 — Figure 2: Interpretation failure is separate from execution failure and depends on the receiver (test split). (a) $Y^{A}$ after a correct read ( $\circ$ ) and a misread ( $\bullet$ ) of the probe. (b) $Y^{I}$ per receiver. Bands span the lowest to the highest receiver.

	Per receiver $\uparrow$		Across receivers $\uparrow$		Calibration $\downarrow$
Method	AUROC	AUPRC	AUROC	AUPRC	Brier	NLL	ECE
A0 Global prior	.500	.045	.500	.045	.0428	.180	.029
A0 Receiver prior	.500	.045	.697	.086	.0415	.168	.002
A1 Message judge	.552	.057	.540	.055	.0428	.180	.031
A2 Equiv. judge	.500	.045	.500	.045	.0428	.180	.029
A4 $Y^{A}$ predictor	.511	.048	.530	.050	.4229	1.329	.540

Table 1: Prospective PIR prediction (test split, six-dataset means, seed 0). Ours is best on all seven metrics (bold, second best underlined, A0 excluded).

Condition	Gen.	Sel.	(%)	points [95% CI]	(%)
			$Y^{I}\downarrow$	$\Delta Y^{I}$ (ours $-$ row)	$Y^{A}\downarrow$
Original message	–	–	4.16	$-1.85$ [ $-2.09$ , $-1.62$ ] ‡	59.33
Generic rewrite	–	–	3.85	$-1.54$ [ $-1.78$ , $-1.30$ ] ‡	58.15
Selection only	–	✓	3.47	$-1.15$ [ $-1.41$ , $-0.93$ ] ‡	58.20
Posterior only	$\pi_{t}$	✓	3.43	$-1.11$ [ $-1.33$ , $-0.90$ ] ‡	58.59
Guidance only	✓	–	2.29	$+0.03$ [ $+0.01$ , $+0.05$ ]	58.33

Table 2: Message revision on 1,579 held-out questions: ours beats every pre-registered reference on

Y^{I}

( ‡ Holm

p<0.001

). Gen.: learned risk guides generation (

\pi_{t}

: posterior only). Sel.: it selects the sent message.

	Primary cohort				Replication
	$Y^{I}$	Gap	Net	Ident.	$Y^{I}$	Gap
Policy (query rate)	(%)	(%)	utility	(%)	(%)	(%)
No query (0%)	3.84	0	.9616	24.6	3.93	0
Exact 20% quota
Random (3 seeds)	3.84	3	.9614	25.7	3.93	4
IG	3.83	5	.9615	28.5	3.94	${<}0$

Table 3: Query policies (interpretation objective,

c=0.001

). Gap: share of the no-query to true-identity

Y^{I}

gap that is closed. Ident.: posterior mode is correct. IG: information gain. † Secondary comparator. Bold: best deployable value.

Appendix figures & tables22 assets

Supplementary material from the paper’s appendix.

Appendix

Symbol	Meaning	Defined in
General
$\mathbb{E}$ , $\mathbb{P}$ (or $P$ ), $\operatorname{Var}$	Expectation, probability, and variance	–
$\mathbf{1}\{\cdot\}$	Indicator: 1 if the condition holds and 0 otherwise	–
$H(\cdot)$ , $I(\cdot\,;\cdot\mid\cdot)$	Shannon entropy and conditional mutual information, in nats	–
$D_{\mathrm{KL}}(\cdot\\|\cdot)$	Kullback–Leibler divergence	–
$\mathrm{Ber}(p)$	Bernoulli distribution with mean $p$	–

Appendix

Table 4: Notation, grouped by the part of the framework that introduces each symbol. The last column gives where the symbol is defined, and – marks standard notation.

Receiver	First	Second	Last	All	First or second [95% CI]
Qwen3.6	1.28	1.61	2.33	1.74	1.45 [1.26, 1.65]
Gemma	1.59	2.49	2.57	2.22	2.04 [1.81, 2.28]
gpt-oss	3.27	2.08	1.63	2.33	2.68 [2.42, 2.95]
Nemotron	3.14	4.51	24.49	10.72	3.83 [3.57, 4.09]
Mistral	5.26	5.74	4.45	5.16	5.50 [5.15, 5.85]
Highest/lowest	4.1	3.6	15.0	6.2	3.8 [3.4, 4.3]

Appendix

Table 5: Probe error rate (%) by the position of the intended task (test split, parsed probes). Last column: probes with the intended task first or second, with question-cluster bootstrap 95% CIs.

ID	Method	Definition
A0	Base-rate priors	Global $P(Y^{I}=1)$ and receiver-specific $P(Y^{I}=1\mid R)$ estimated on training data.
A1	Message-only ambiguity	Fixed strong judge sees $m$ only and scores whether materially different task interpretations are possible.
A2	Semantic equivalence	Judge sees $(m,z^{\star},x_{S})$ and predicts whether $m$ faithfully specifies the intended task, with no receiver information.
A3	Receiver-agnostic PIR	Same learned PIR architecture but remove $e_{R}$ / receiver posterior information.
A4	Action-failure predictor	Same architecture/input budget trained against $Y^{A}$ rather than $Y^{I}$ , evaluated against both targets.
A5	Listener/ToM	Predict $P(\widehat{Z}_{R}=z\mid m,x,e_{R})$ over probe task identities and use $1-P(\widehat{Z}_{R}=z^{\star})$ as risk.

Appendix

Table 6: Baseline definitions.

Figure 3: Interpretation failure depends on the receiver and the dataset (test split). (a) $Y^{I}$ (%) per receiver and dataset on a log colour scale. Boxes mark the least risky receiver in each dataset, and the bottom row gives the ratio of the highest to the lowest receiver. (b) Misread pairs split by the answer check. Labels give the share of misreads that pass.

		Marginal rates (%)		Off-diagonal cells (%)
Dataset	Pairs	$Y^{I}$ [95% CI]	$Y^{A}$ [95% CI]	misread, pass	read, fail
FreebaseQA	6,550	2.5 [1.7, 3.4]	24.0 [21.7, 26.4]	1.8	23.3
WebQSP	1,095	5.5 [2.5, 8.6]	54.1 [47.5, 60.7]	1.8	50.3
GrailQA	6,430	7.9 [6.5, 9.4]	89.6 [87.9, 91.3]	0.8	82.4
SQ-WD	7,090	5.2 [4.1, 6.4]	61.9 [59.3, 64.4]	2.2	58.8
PopQA	7,255	3.3 [2.4, 4.2]	75.7 [73.5, 77.9]	0.8	73.2

Appendix

Table 7: Interpretation (

Y^{I}

) and execution (

Y^{A}

) failures per dataset (test split, pooled over five receivers, 95% question-level normal intervals).

	Per receiver $\uparrow$		Across receivers $\uparrow$		Calibration $\downarrow$
Method	AUROC	AUPRC	AUROC	AUPRC	Brier	NLL	ECE
A3 (no receiver)	.729	.128	.701	.115	.0414	.168	.032
A3 + recv. intercept	.729	.128	.792	.158	.0401	.156	.015
A3 + recv. calib.	.728	.128	.796	.158	.0399	.155	.012
A3 + recv. map ∗	.728	.128	.797	.160	.0398	.154	.010
Ours	.732	.135	.800	.164	.0398	.154	.010

Appendix

Table 8: Receiver levels or interaction (Experiment 2, test split, six-dataset means, seed 0). Maps are refitted on the validation questions used by the full model ( ∗ on all validation data). Difference: ours minus A3 + recv. calib., with paired question-group bootstrap 95% CIs.

Figure 4: Receiver information calibrates the predicted risk (test split, seed 0 in (a, b), three-seed means in (c)). (a) Within-receiver ECE per dataset. (b) Mean predicted versus measured

Y^{I}

in each of the 30 receiver–dataset cells. (c) AUROC across versus within receivers, with arrows from A3 to ours.

Dataset	Method	AUROC [95% CI]	AUPRC	Brier $\downarrow$	NLL $\downarrow$	ECE $\downarrow$
FreebaseQA	A0 global base rate	0.500	0.025	0.0246	0.118	0.024
	A0 per-receiver base rate	0.500	0.025	0.0238	0.105	0.003
	A1 message-only judge	0.529 [0.481, 0.578]	0.029	0.0246	0.118	0.024
	A2 equivalence judge	0.500	0.025	0.0246	0.118	0.024
	A3 receiver-agnostic PIR	0.690 [0.645, 0.730]	0.085	0.0243	0.113	0.024
	A4 action-failure predictor	0.558 [0.518, 0.599]	0.032	0.115	0.377	0.234

Appendix

Table 9: Experiment 2 results per dataset (test split, seed 0, with metrics computed within each receiver and macro-averaged). AUROC has a paired 95% bootstrap CI. Bold: best value per dataset among learned methods.

FreebaseQA
Variant	AUROC (pooled) $\uparrow$	AUROC (macro) $\uparrow$	Brier $\downarrow$	NLL $\downarrow$	ECE (macro) $\downarrow$
Full model (with $e_{R}$ )	0.810	0.698	0.0234	0.100	0.005
Receiver removed (A3)	0.663	0.691	0.0243	0.113	0.024
Receiver shuffled	0.661	0.691	0.0243	0.113	0.024
$\Delta$ full $-$ A3	$+$ 0.147	$+$ 0.008	$-$ 0.0009	$-$ 0.013
95% CI	[0.124, 0.170]		[ $-$ 0.0012, $-$ 0.0007]	[ $-$ 0.015, $-$ 0.011]

Appendix

Table 10: Receiver-information ablation (test split, three-seed means).

\Delta

: full minus receiver-agnostic, with paired 95% CIs in the row below. Last row per dataset: A4 scored on

Y^{I}

, with its AUROC on

Y^{A}

in parentheses.

Figure 5: Behavioural history sharpens the receiver belief (960 audit histories per length). (a) Accuracy and (b) calibrated NLL by history length for genuine and shuffled history, with binomial 95% CIs in (a). (c) One-vs-rest AUROC at

|H_{t}|=5

against each receiver’s pooled

Y^{I}

, with 95% bootstrap CIs.

	Calibrated, genuine history				Raw	Shuffled history
$\|H_{t}\|$	Accuracy $\uparrow$	NLL $\downarrow$	Brier $\downarrow$	ECE $\downarrow$	NLL $\downarrow$	Accuracy	NLL
0	20.0%	1.609	0.800	0.000	–	–	–
1	22.6%	1.634	0.811	0.056	1.626	20.9%	1.625
3	23.5%	1.604	0.798	0.013	1.603	18.4%	1.633
5	24.9%	1.593	0.794	0.014	1.594	21.3%	1.617
10	26.6%	1.591	0.793	0.031	1.593	19.9%	1.628

Appendix

Table 11: Receiver-type inference by history length

|H_{t}|

(960 audit histories per length). Length 0 is the uniform prior over five receivers, and “calibrated” uses temperature scaling fitted on the calibration split.

Dataset	Accuracy $\uparrow$	NLL $\downarrow$	Brier $\downarrow$	ECE $\downarrow$
FreebaseQA	21.5%	1.604	0.798	0.025
WebQSP	24.6%	1.598	0.796	0.028
GrailQA	20.5%	1.608	0.799	0.040
SQ-WD	18.3%	1.610	0.801	0.051
PopQA	25.9%	1.583	0.790	0.008
Mintaka	25.9%	1.588	0.792	0.017

Appendix

Table 12: Receiver-type inference at

|H_{t}|=5

: by dataset of the calibration tasks (top, 960 histories each) and row-normalised confusion matrix (bottom, %, true receiver in rows).

Figure 6: Risk-guided revision lowers measured interpretation failure on the 1,579 held-out questions. Paired differences in

Y^{I}

, ours minus each reference (points, 95% CIs, negative favours ours). (b) Per dataset against the original (filled) and the generic rewrite (hollow), post hoc.

Figure 7: One risk feature is shared by all receivers, and guidance removes it. (a) Learned scores $s_{rk}$ and intercepts $b_{r}$ (points of $Y^{I}$ ). Boxed: the score and its 2.5% bootstrap quantile are both positive, the guidance rule for a known receiver. (b) $Y^{I}$ per receiver. (c) Form of all sent messages (unchanged originals included) and $Y^{I}$ per condition, with guided conditions in blue.

Condition	$Y^{I}$	$Y^{A}$	None	Distr.	Changed	Val. $Y^{I}$
Original message	4.16	59.33	0.93	3.20	0.0	5.01
Generic rewrite	3.85	58.15	0.88	2.95	97.2	4.97
Selection only	3.47	58.20	0.86	2.59	86.7	4.60
Posterior only	3.43	58.59	0.81	2.60	86.1	4.23
Ours	2.31	58.33	0.72	1.56	90.0	2.83
Guidance only	2.29	58.33	0.70	1.54	91.5	2.66

Appendix

Table 13: Experiment 4 results by condition.

Y^{I}

Y^{A}

, None , and Distr. (probes that choose the contrast task) are percentages on the 1,579 held-out questions, and Changed is the share of messages rewritten. Val.

Y^{I}

is on the 300 validation questions of the check before the main run.

Receiver	Original	Ours	original [95% CI]	generic	sel. only	post. only	original	Value
	$Y^{I}$ (%)		Ours minus				Generic minus
Nemotron	8.73	3.76	$-4.96$ [ $-5.34$ , $-4.59$ ]	$-3.54$	$-2.70$	$-2.66$	$-1.42$	$-5.60$
Gemma	2.84	2.16	$-0.69$ [ $-1.04$ , $-0.35$ ]	$-0.88$	$-0.55$	$-0.42$	$+0.20$	$+0.07$
gpt-oss	1.91	1.51	$-0.40$ [ $-0.62$ , $-0.17$ ]	$-0.56$	$-0.63$	$-0.52$	$+0.16$	$+0.01$
Mistral	5.27	2.65	$-2.62$ [ $-3.00$ , $-2.25$ ]	$-2.04$	$-1.50$	$-1.60$	$-0.58$	$-4.09$
Qwen	2.07	1.50	$-0.57$ [ $-0.85$ , $-0.31$ ]	$-0.66$	$-0.40$	$-0.36$	$+0.09$	$-0.38$

Appendix

Table 14: Experiment 4 results per receiver on the held-out questions. Ours minus each reference and the generic rewrite minus the original are paired differences in

Y^{I}

(points), and Value is ours minus the original on the 275 value-versus-attribute questions.

Figure 8: NetVoII allocates queries by decision value (primary cohort, interpretation objective,

c=0.001

). (a)

Y^{I}

against the share of episodes queried. (b) Paired differences at matched quotas with 95% CIs, equal to the net-utility contrasts with the sign reversed. (c) Share of episodes whose posterior mode is the true receiver. (d) Strict rule at six query costs

c

: measured

Y^{I}

(top) and share of episodes queried (bottom).

Figure 9: Information is not value (primary cohort). (a) Expected IG of the most informative query (nats) and share of episodes with positive predicted VoII, per dataset. (b) Share of episodes whose best query is worth more than a cost

c

, with pooled values marked at four of the tested costs.

Primary cohort (test split). No query: 3.84 (0.9616), true identity: 3.77 (0.9623), adapted VoI † without queries: 4.42 (0.9558).
Policy	10%	20%	30%	50%	Always
Random (3-seed mean)	3.84 (0.9615)	3.84 (0.9614)	3.83 (0.9614)	3.83 (0.9612)	3.84 (0.9606)
IG	3.83 (0.9616)	3.83 (0.9615)	3.83 (0.9614)	3.82 (0.9613)	3.80 (0.9610)
Adapted VoI †	4.42 (0.9557)	4.42 (0.9556)	4.42 (0.9555)	4.42 (0.9553)	–
NetVoII (ours)	3.80 (0.9619)	3.79 (0.9619)	3.78 (0.9619)	3.78 (0.9617)	–
Replication cohort. No query: 3.93 (0.9607), true identity: 3.91 (0.9609), adapted VoI † without queries: 4.27 (0.9573).

Appendix

Table 15: Experiment 5 budget grid (interpretation objective): measured

Y^{I}

(%) by exact query quota, with net utility at

c=0.001

in parentheses. Bold: net-utility contrasts against both IG and random querying survive Holm correction. † Secondary comparator: its estimates never let a query change the message.

Primary cohort (test split)
Cost $c$	Queried (%)	$Y^{I}$ (%)	$1-Y^{A}$ (%)	Net utility
0	32.9	3.78	35.66	0.9622
0.0001	30.2	3.78	35.68	0.9622
0.0005	24.0	3.79	35.62	0.9620
0.001 (main)	18.2	3.79	35.68	0.9619
0.005	0.8	3.83	35.72	0.9617

Appendix

Table 16: Strict rule of Experiment 5 across query costs

c

(interpretation objective, net utility at each row’s cost). The last two rows of each block re-run IG and random querying at the query count of the

c=0.001

rule.

		$Y^{I}$ at the 20% quota			Queried by NetVoII (%)
Unit	No query	NetVoII	Inf. gain	Random	20% quota	Strict	$Y^{I}$ strict	True identity
FreebaseQA	2.16	2.16	2.15	2.16	1.5	1.1	2.16	2.10
WebQSP	5.07	5.07	5.07	5.07	0.0	0.0	5.07	5.14
GrailQA	6.96	6.96	6.96	6.96	0.0	0.0	6.96	6.96
SQ-WD	4.53	4.51	4.50	4.53	4.9	4.1	4.51	4.45
PopQA	2.61	2.42	2.62	2.60	49.8	45.1	2.42	2.52

Appendix

Table 17: Experiment 5 results per dataset and per receiver (primary cohort, interpretation objective):

Y^{I}

(%) at the pooled 20% quota and under the strict rule, with the share of episodes that NetVoII queries.

Figure 10: NetVoII spends a pooled quota where predicted value is high (primary cohort, interpretation objective). (a) Share of each dataset’s episodes queried at pooled quotas of 10–50% and under the strict rule. (b) Change in $Y^{I}$ against no query at the 20% quota.

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Multi-agent LLM systems rely on message passing among specialized agents to accomplish complex tasks. However, an upstream agent may provide useful information or an incorrect answer that causes a downstream agent to override a correct answer supported by its own evidence. Prior work has not clearly separated the benefits of communication from the damage caused by incorrect messages. We study this problem with controlled experiments across five benchmarks and five receivers, keeping the downstream task and evidence fixed while comparing answers under three conditions: no message, the upstream agent's original message, or a message with the opposite conclusion. Our experiments reveal three key findings. First, messages often help when the downstream agent would otherwise answer incorrectly. Second, messages can also hurt: when the downstream agent would answer correctly without a message, an incorrect upstream message changes the answer in up to 32% of cases. Third, in 94% of audited harmful cases, the downstream agent copies the upstream's specific wrong answer--a pattern we term answer substitution. Removing unreliable messages recovers part of the lost accuracy, suggesting that communication should be selective based on upstream reliability and the evidence already available to the downstream agent.

Abstract

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