Word-Level Text Unmixing via Evidence-Preserving Ownership Routing with Language Models

Jinglin He; Siyang Jiang; Lixing He; Guoliang Xing; Hongkai Chen

cs.CLOct 5, 2026

Word-Level Text Unmixing via Evidence-Preserving Ownership Routing with Language Models

Authors: Jinglin He, Siyang Jiang, Lixing He, Guoliang Xing, Hongkai Chen

Organizations: The Chinese University of Hong Kong

Abstract

Text from multiple sources can become interleaved into a single sequence when attribution metadata is lost, such as overlapping speech transcripts, document reading flows, or concurrent agent streams. We formalize this challenge as Word-Level Text Unmixing: given an interleaved lexical stream and source count K, recover the original source sequences while preserving every word occurrence and its within-source order exactly. Directly generating separated texts with LLMs can omit, duplicate, or hallucinate words, violating this exact-reconstruction objective. We therefore propose Evidence-Preserving Ownership Routing (EPOR), which decouples source-ownership prediction from reconstruction. EPOR adapts a causal LLM to predict canonical ownership routes conditioned on the mixed stream and prior routing decisions. At inference, completion-safe constrained decoding is combined with deterministic indexed reconstruction, yielding structurally valid K-source partitions that preserve every observed occurrence exactly once. We also introduce UNMIXBENCH, covering controlled synthetic mixtures, timestamp-derived speech from AMI and ICSI, layout-derived document streams from ReadingBank, and simulated concurrent digital outputs. Across five evaluation tracks, a 4B EPOR model achieves the lowest mean minimum-permutation word error rate among finetuned baselines, reducing the five-track mean by 22.3% relative to compact source-array generation and remaining competitive with zero-shot frontier LLMs. These results show that when lexical evidence is fully observed, separating ownership inference from lexical regeneration provides a reliable alternative to direct generation.

Figures & tables

Figure 1: The Word-Level Text Unmixing problem. Temporal, spatial, and concurrent serialization motivate recovery from an interleaved lexical stream. Given the observed words and known source count $K$ , recover the latent sources while preserving every occurrence and its within-source order.

Figure 2: Evidence-Preserving Ownership Routing ( EPoR ). Training: the causal LLM learns canonical ownership from the complete mixed input and gold prefixes. Inference: completion-safe beam search scores candidate ownership routes, and then indexed reconstruction gathers the original occurrences into sources.

Track	Corpus	Examples / groups	$K$	$T$	Interleaving
Controlled	MultiNLI	2,640 / 240	2–4	16/24/32	Controlled intensity
Temporal	AMI	1,000 / 59	2/3	64	Word timestamps
Temporal	ICSI dev	400 / 15	2/3	64	Word timestamps
Spatial	ReadingBank	833 / 828	2–4	32/64	Reading-flow metadata
Concurrent	WikiText	1,200 / 400	2–4	32/64	Virtual event times

Table 1: The UnMixBench evaluation tracks. Counts give examples / provenance groups.

K

is the source count and

T

the number of observed word occurrences.

Generative baselines
	MNLI			AMI			ICSI-dev			RB			DIG
System	MP $\downarrow$	Own. $\uparrow$	Ev. $\uparrow$	MP $\downarrow$	Own. $\uparrow$	Ev. $\uparrow$	MP $\downarrow$	Own. $\uparrow$	Ev. $\uparrow$	MP $\downarrow$	Own. $\uparrow$	Ev. $\uparrow$	MP $\downarrow$	Own. $\uparrow$	Ev. $\uparrow$
Compact source-array generation (4B)	0.6696	–	0.0845	.7275	–	.0423	.7439	–	.0400	.8415	–	.0240	.7310	–	.0464
Evidence-explicit source-array generation (4B)	0.6747	–	0.0814	.7418	–	.0343	.7530	–	.0317	.8547	–	.0272	.7314	–	.0544
Turn-marker generation (4B)	0.4271	0.6991	0.3957	0.6928	0.4441	0.6377	0.6690	0.4805	0.6600	0.6862	0.4959	0.3325	0.6449	0.5426	0.7769
Structured baselines

Table 2: Main results across five evaluation tracks on UnMixBench . Each block reports MP-WER (MP), permutation-aligned ownership accuracy (Own.), and evidence preservation (Ev.). Turn-marker ownership uses lexical alignment (Appendix C.2 ). Lower MP-WER is better; higher values are better for the other metrics. Best and second-best results are bold and underlined. Turn-marker uncertainty is given in Table 12 , and Appendix D.5 reports hosted-model settings.

Condition	Adapt.	Legal mask	MP-WER $\downarrow$	Evidence $\uparrow$	Complete $K$ -way $\uparrow$	Ownership acc. $\uparrow$	Switch F1 $\uparrow$
EPoR	$\checkmark$	$\checkmark$	0.3757	1.0000	1.0000	0.7457	0.8514
w/o task adaptation	$\times$	$\checkmark$	0.6842	1.0000	1.0000	0.5278	0.5333
w/o legality masking	$\checkmark$	$\times$	0.3819	1.0000	0.9721	0.7429	0.8492
w/o adaptation and legality masking	$\times$	$\times$	1.2008	1.0000	0.1473	0.3830	0.0894
Route-independent fixed- template history	$\checkmark$	$\checkmark$	0.4486	1.0000	1.0000	0.7113	0.7603
Representation control

Table 3: EPOR component and representation ablations on MultiNLI. Adapt. denotes task adaptation; Legal mask denotes completion-safe decoding. PI-Route is a matched representation control with noncanonical legal support, rather than a component-removal condition.

Figure 3: Recovery across controlled mixing intensities. The same source groups are evaluated at eleven target values of

M

. Points average three training runs and 80 source groups per

(K,T)

condition; shaded regions show source-group bootstrap 95% confidence intervals.

Appendix figures & tables23 assets

Supplementary material from the paper’s appendix.

Appendix

System	Scoring / state	Trainable head	Loss
Free generation	causal source-text likelihood	none beyond QLoRA	assistant-token LM
Turn-marker generation	causal marked-stream likelihood	none beyond QLoRA	assistant-token LM
Global Transformer	bidirectional contextual emissions	256-d projection; 2-layer, 8-head encoder; linear emission	occurrence CE
EMA prototype	emission plus cosine to per-source EMA ( $\lambda=.75$ , scale 4)	256-d projection/query; shared biases	occurrence CE
RGS-CRF	global emissions plus $4\!\times\!4$ transitions	Global head plus transition matrix	exact legal-route CRF NLL
Legal projection	edit cost to generated source arrays; EMA tie guide	none in projection	none

Appendix

Table 4: Implementation summary and trainable parameter counts for the comparison baselines.

Corpus	Role	Examples / groups	Source unit and preprocessing
SNLI 1.0	Train	10,000 / 10,000	Premise and hypothesis spans; deduplication across held-out splits, NFKC normalization, whitespace collapse, and deterministic regex unitization.
SNLI 1.0	Development	1,000 / 1,000	Same preprocessing as training; source/text-disjoint from training and test data.
MultiNLI 1.0	Controlled evaluation	2,640 / 240	Premises only; NFKC normalization, whitespace collapse, and eight whitespace units per source. Prompt and document identifiers define provenance.
MultiNLI 1.0	Paired construction	480 / 240	Same fixed-length preprocessing; identical source word occurrences under controlled and independent-clock mixing.
MultiNLI 1.0	Unequal-length stress	540 / 180	Variable source spans; independent-clock, long-run, and bursty mixing. Prompt and document identifiers define provenance.
WikiText-103	Domain transfer	1,448 / 1,448	Length-filtered sentence spans materialized with the deterministic regex unitizer.

Appendix

Table 5: Corpora, roles, and preprocessing used in the experiments.

Split	Corpus	Source unitization	Examples / groups	$K$	$T$ /length	Interleaving	Disjointness	Seed
Train	SNLI	NFKC + regex words	10,000 / 10,000	2–4	variable	controlled IID	held-out source/text	2027
Development	SNLI	NFKC + regex words	1,000 / 1,000	2–4	variable	controlled IID	train/test source/text	2027
Controlled evaluation	MultiNLI	first 8 whitespace units/source	2,640 / 240	2–4	16/24/32	11 controlled intensities	source + document	2701
Paired construction	MultiNLI	first 8 whitespace units/source	480 / 240	2–4	16/24/32	controlled + renewal clock	source + document	13013
Unequal-length stress	MultiNLI	variable source spans	540 / 180	2–4	variable	clock + long-run + bursty	source + document	20270905

Appendix

Table 6: Core UnMixBench specification. All held-out groups are provenance-disjoint from training/development; the paired and stress sets are also disjoint from the controlled MultiNLI evaluation.

Track	Serialization mechanism	Examples	Groups	$K$ counts	$T$ counts	Mean $M$
AMI	annotated word-time merge	1,000	59	2:667; 3:333	64:1,000	0.275
ICSI dev	annotated/repaired word-time merge	400	15	2:200; 3:200	64:400	0.290
ReadingBank	metadata-derived reading flows	833	828	2:300; 3:300; 4:233	32:383; 64:450	0.176
Digital–smooth	virtual event-time merge	400	400	2:134; 3:133; 4:133	32:201; 64:199	0.582
Digital–bursty	virtual event-time merge	400	400	2:134; 3:133; 4:133	32:201; 64:199	0.266
Digital–unequal-rate	virtual event-time merge	400	400	2:134; 3:133; 4:133	32:201; 64:199	0.531

Appendix

Table 7: Serialization-transfer extension. Temporal and spatial tracks use real metadata; digital profiles simulate a concurrent-production mechanism with virtual event-time schedules and reuse the same source groups. Counts describe frozen evaluation views before model inference.

Condition	Beam	Score normalization	Generated length	EOS	Codec validation
Adapted, constrained (EPOR)	4	LM beam score	exactly $T$	excluded as route action	atomic; reject unsupported
Frozen, constrained	4	LM beam score	exactly $T$	excluded as route action	atomic; reject unsupported
Adapted, unconstrained	4	LM beam score	exactly $T$	suppressed before $T$	retain as invalid ( $-1$ )
Frozen, unconstrained	4	LM beam score	exactly $T$	suppressed before $T$	retain as invalid ( $-1$ )

Appendix

Table 8: Search settings for the adaptation-by-support ablations. “LM beam” uses the decoder’s original beam-score convention; no legal-subset renormalization is added.

Method	MP-WER $\downarrow$	Ownership acc. $\uparrow$	Switch F1 $\uparrow$	Evidence $\uparrow$
Compact source-array generation	0.6696 $\pm$ 0.0608	–	–	0.0845
Evidence-explicit source-array generation	0.6747 $\pm$ 0.0242	–	–	0.0814
Turn-marker generation	0.4271 $\pm$ 0.0096	0.6991	0.8081	0.3957
Global Transformer	0.5368 $\pm$ 0.0192	0.6658	0.6847	1.0000
EMA prototype	0.5165 $\pm$ 0.0140	0.6703	0.7390	1.0000
RGS-CRF	0.4799 $\pm$ 0.0063	0.6917	0.7001	1.0000

Appendix

Table 9: Full results on the controlled MultiNLI UnMixBench test set. MP-WER is mean

\pm

sample SD over three training runs; remaining entries are three-run means, with corresponding SDs provided in the supplementary results.

Prompt	Parse	Limit hit	Conditional MP-WER	Conditional Evidence
Compact	0.6972 $\pm$ 0.1147	n/a	0.5272 $\pm$ 0.0108	0.1239 $\pm$ 0.0259
Evidence-explicit	0.6852 $\pm$ 0.0274	0.0034 $\pm$ 0.0023	0.5256 $\pm$ 0.0225	0.1189 $\pm$ 0.0029

Appendix

Table 10: Generation-pipeline diagnostics, mean

\pm

sample SD over three runs. Conditional metrics use each method’s successfully parsed subset.

Metric	Turn-marker generation	EPoR $-$ Turn-marker	Turn-marker $-$ source-array generation
MP-WER $\downarrow$	0.4271 $\pm$ 0.0096	$-0.0515$ $[-0.0636,-0.0395]$	$-0.2476$ $[-0.2720,-0.2238]$
Ownership acc. $\uparrow$	0.6991 $\pm$ 0.0068	$+0.0466$ $[0.0381,0.0554]$	–
Switch F1 $\uparrow$	0.8081 $\pm$ 0.0032	$+0.0432$ $[0.0338,0.0530]$	–
Evidence $\uparrow$	0.3957 $\pm$ 0.0131	$+0.6043$ $[0.5617,0.6461]$	$+0.3143$ $[0.2778,0.3524]$

Appendix

Table 11: Task-matched turn-marker generation on the 2,640-example MultiNLI evaluation. The turn-marker column is mean

\pm

sample SD over three training runs. Difference columns give paired point estimates with 95% source-group bootstrap intervals.

Track	EPoR MP	Turn-marker MP	Paired difference [95% CI]	Ownership acc.	Evidence
MultiNLI	0.3757	0.4271 $\pm$ 0.0096	$-0.0515$ $[-0.0636,-0.0395]$	0.7457 / 0.6991	1.0000 / 0.3957
AMI	0.6320	0.6928 $\pm$ 0.0312	$-0.0608$ $[-0.0724,-0.0481]$	0.6205 / 0.4441	1.0000 / 0.6377
ICSI development	0.6161	0.6690 $\pm$ 0.0185	$-0.0529$ $[-0.0704,-0.0353]$	0.6228 / 0.4805	1.0000 / 0.6600
ReadingBank	0.6156	0.6862 $\pm$ 0.0154	$-0.0706$ $[-0.0859,-0.0556]$	0.6224 / 0.4959	1.0000 / 0.3325
Digital	0.6474	0.6449 $\pm$ 0.0088	$+0.0026$ $[-0.0067,0.0120]$	0.5912 / 0.5426	1.0000 / 0.7769

Appendix

Table 12: EPOR and turn-marker generation across five tracks. Turn-marker MP-WER is mean

\pm

sample SD over three runs. Paired differences are EPoR minus turn-marker, with 95% group-bootstrap intervals; negative values favor EPoR . Ownership accuracy and Evidence columns give EPoR / turn-marker means.

Track	Ownership difference [95% CI]	Switch F1: EPoR / turn-marker	Switch F1 difference [95% CI]
MultiNLI	$+0.0466$ [0.0381, 0.0554]	0.8514 / 0.8081	$+0.0432$ [0.0338, 0.0530]
AMI	$+0.1765$ [0.1632, 0.1891]	0.2567 / 0.4469	$-0.1902$ [-0.2090, -0.1714]
ICSI development	$+0.1423$ [0.1258, 0.1572]	0.3018 / 0.5168	$-0.2150$ [-0.2379, -0.1885]
ReadingBank	$+0.1264$ [0.1121, 0.1412]	0.4459 / 0.4161	$+0.0298$ [0.0148, 0.0449]
Digital	$+0.0486$ [0.0406, 0.0568]	0.6764 / 0.7136	$-0.0372$ [-0.0500, -0.0246]

Appendix

Table 13: Ownership and boundary recovery after lexical alignment. Differences are EPoR minus turn-marker, with paired 95% group-bootstrap intervals. Positive differences favor EPoR . All examples, including strict parsing failures, contribute to the reported scores.

Table row / scope	Provider	Requested model ID	Thinking	Completion-token budget
Qwen3.8-Flash / all	Alibaba Cloud Model Studio	qwen3.8-flash	off	$\max(8192,64T)$
Qwen3.8-Max / all	Alibaba Cloud Model Studio	qwen3.8-max	off	$\max(8192,64T)$
DeepSeek-V4-Flash / MNLI	DeepSeek API	deepseek-v4-flash	off	$\max(256,16T)$
DeepSeek-V4-Flash / AMI, ICSI-dev, RB, DIG	Alibaba Cloud Model Studio	deepseek-v4-flash	off	$\max(8192,64T)$
DeepSeek-V4-Pro / MNLI	DeepSeek API	deepseek-v4-pro	off	$\max(256,16T)$
DeepSeek-V4-Pro / AMI, ICSI-dev, RB, DIG	Alibaba Cloud Model Studio	deepseek-v4-pro	off	$\max(8192,64T)$

Appendix

Table 14: Hosted-model execution configuration for Table 2 . “Scope” identifies the cells supplied by each run: MNLI, AMI, ICSI-dev, ReadingBank (RB), or the three-profile digital aggregate (DIG).

System	JSON parse $\uparrow$	Refusal $\downarrow$
DeepSeek-V4.1-Flash	0.9284	0.0000
DeepSeek-V4-Pro	0.8811	0.0000
Qwen3.8-Flash	0.8023	0.0004
Qwen3.8-Max	0.9826	0.0000

Appendix

Table 15: MultiNLI compliance diagnostics for four hosted evaluations under the generous

\max(8192,64T)

budget. Metrics include all examples. Parse failures and provider refusals receive no repair.

Condition	MP-WER $\downarrow$	Ownership acc. $\uparrow$	Switch F1 $\uparrow$	Evidence $\uparrow$	Complete $K$ -way $\uparrow$
Unmodified history	0.3729	0.7457	0.8514	1.0000	1.0000
Route-independent template	0.9126	0.4722	0.5057	1.0000	1.0000
25% eligible-label rotation	0.4761	0.6702	0.7763	1.0000	1.0000
50% eligible-label rotation	0.5335	0.6263	0.7080	1.0000	1.0000
100% eligible-label rotation	0.5941	0.5796	0.5692	1.0000	1.0000

Appendix

Table 16: Inference-time ownership-history interventions on MultiNLI. The checkpoint is fixed; only the prefix shown to the scorer changes. The controller uses the actual candidate prefix, leaving output legality intact.

Figure 4: Sensitivity to model-visible ownership history. Points report one adapted model; bands are 95% source-group bootstrap intervals over 240 groups, retaining all eleven interleavings within each group. Corruption begins only after all

K

labels have appeared; the decoder’s true legal prefix is unchanged.

Representation	MP-WER $\downarrow$	LM train sequences	Rel. train time $\downarrow$
Canonical ( EPoR )	0.3757	60,000	1.00 $\times$
Arbitrary labels (PI-Route)	0.3782	602,608	4.08 $\times$

Appendix

Table 17: Matched representation comparison. Recovery values are three-run means; both models have 64,929,792 trainable parameters.

Method	MP-WER $\downarrow$	Ownership acc. $\uparrow$	Switch F1 $\uparrow$	Evidence $\uparrow$	Structure $\uparrow$
EPoR	0.3757 $\pm$ 0.0056	0.7457 $\pm$ 0.0039	0.8514 $\pm$ 0.0022	1.0000	1.0000
PI-Route	0.3782 $\pm$ 0.0012	0.7438 $\pm$ 0.0037	0.8521 $\pm$ 0.0023	1.0000	1.0000

Appendix

Table 18: Full matched representation comparison on MultiNLI, mean

\pm

sample SD over three training runs.

Similarity band	MP-WER $\downarrow$	Ownership acc. $\uparrow$	Switch F1 $\uparrow$
Low	0.3419 [0.3227, 0.3619]	0.7747 [0.7610, 0.7882]	0.8657 [0.8515, 0.8790]
Medium	0.3775 [0.3513, 0.4035]	0.7494 [0.7317, 0.7673]	0.8451 [0.8259, 0.8641]
High	0.3888 [0.3665, 0.4115]	0.7323 [0.7151, 0.7491]	0.8487 [0.8358, 0.8613]
Extreme	0.3944 [0.3730, 0.4163]	0.7266 [0.7100, 0.7428]	0.8461 [0.8298, 0.8616]

Appendix

Table 19: Recovery across matched source-similarity strata. Entries are matched means with 95% source-group bootstrap intervals. Lower MP-WER is better; higher values are better otherwise.

Construction	Mixing $M$	Switches	Mean run	Run var.	Ambiguity
Controlled	0.4953	12.4542	2.6454	0.4096	0.1208
Independent clock	0.5651	14.4542	1.7185	1.0254	0.1583

Appendix

Table 20: Route statistics for paired controlled and independent-clock interleavings.

Backbone	Output	Controlled MP-WER $\downarrow$	Clock MP-WER $\downarrow$	Evidence C/I $\uparrow$
Qwen3.5-4B	EPoR	0.4163	0.5189	1.0000/1.0000
	RGS-CRF	0.5344	0.6075	1.0000/1.0000
	Free generation	0.6012	0.6480	0.1708/0.1792
Qwen3.5-9B	EPoR	0.3625	0.4459	1.0000/1.0000
	Free generation	0.5368	0.5886	0.2125/0.2333
Gemma-7B-IT	EPoR	0.4630	0.5488	1.0000/1.0000

Appendix

Table 21: Full cross-backbone comparison on 240 paired source groups. Each backbone uses one training run. Evidence reports controlled/clock; route methods have structural validity 1.0 in both conditions.

Method	Clock MP-WER	Long-run MP-WER	Bursty MP-WER	Min. evidence $\uparrow$
EPoR	0.500	0.288	0.469	1.000
RGS-CRF	0.568	0.347	0.612	1.000
Task-finetuned source-array generation	0.623	0.658	0.592	0.139
Random legal route	0.726	0.775	0.697	1.000
Equal contiguous blocks	0.683	0.278	0.759	1.000
Gold-length contiguous blocks †	0.668	0.000	0.740	1.000

Appendix

Table 22: Unequal-length construction comparison on 180 paired source groups. † The final row receives gold source lengths and is a privileged reference rather than a deployable baseline.

Domain	RGS-CRF MP-WER	EPoR MP-WER	$\Delta$ MP-WER [95% CI]
WikiText	0.3583 $\pm$ 0.0081	0.2506 $\pm$ 0.0136	-0.1077 [-0.1174, -0.0982]
AG News	0.6187 $\pm$ 0.0220	0.7322 $\pm$ 0.0228	0.1135 [0.1056, 0.1213]
AMI	0.6483 $\pm$ 0.0103	0.6320 $\pm$ 0.0086	-0.0163 [-0.0257, -0.0062]
ICSI reserve	0.6581 $\pm$ 0.0036	0.6283 $\pm$ 0.0057	-0.0297 [-0.0457, -0.0113]

Appendix

Table 23: Domain transfer, mean

\pm

SD over three training runs. WikiText and AG News intervals resample examples; AMI and ICSI-reserve intervals resample meetings. AMI is the same frozen 1,000-window view used in the temporal-serialization analysis; ICSI here denotes the separate 300-window consumed reserve.

\Delta

is EPoR minus RGS-CRF.

View	Free-c	Free-e	Global	EMA	RGS-CRF	Projection	EPoR	$\Delta$ to best alternative [95% CI]
AMI	0.7275	0.7418	0.6406	0.6596	0.6483	0.6431	0.6320	$-0.0086$ [ $-0.0175$ , $+0.0003$ ]
ICSI development	0.7439	0.7530	0.6389	0.6674	0.6495	0.6483	0.6161	$-0.0228$ [ $-0.0377$ , $-0.0078$ ]
ReadingBank	0.8415	0.8547	0.6820	0.7427	0.6555	0.6843	0.6156	$-0.0399$ [ $-0.0517$ , $-0.0281$ ]
Digital–smooth	0.7193	0.7283	0.7718	0.7326	0.7467	0.6741	0.6750	$+0.0009$ [ $-0.0128$ , $+0.0152$ ]
Digital–bursty	0.7431	0.7307	0.6763	0.6794	0.6406	0.6764	0.6156	$-0.0251$ [ $-0.0409$ , $-0.0090$ ]
Digital–unequal-rate	0.7306	0.7352	0.7482	0.7114	0.7223	0.6788	0.6517	$-0.0270$ [ $-0.0413$ , $-0.0130$ ]

Appendix

Table 24: Source-array and structured baselines by serialization view. Entries are mean MP-WER over three training seeds. “Free-c” and “Free-e” denote compact and evidence-explicit task-finetuned generation.

\Delta

compares EPoR with the lowest-mean source-array or structured baseline shown in that view; 95% CIs resample provenance groups after averaging seeds. Negative differences favor EPoR . Best and second-best means are bold and underlined.

Panel of Figure 5 — Figure 5: Candidate quality, selection, and cost. Selected and candidate-oracle MP-WER are shown across legal beam sizes, together with gold-route coverage, runtime, and peak allocated memory. Points average three trained models; intervals resample source groups.

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