FocusVTC: Efficient and High-Performance Visual Text Compression with Adaptive Resolution

FangZhi Zhong; Xuerui Qiu; Yuqi Pan; Ya Liu; Shaowei Gu; Bo Xu; Guoqi Li

cs.CVSep 29, 2026

FocusVTC: Efficient and High-Performance Visual Text Compression with Adaptive Resolution

Authors: FangZhi Zhong, Xuerui Qiu, Yuqi Pan, Ya Liu, Shaowei Gu, Bo Xu, Guoqi Li

Organizations: Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · Zhongguancun Academy · Shanghai Jiao Tong University

Abstract

Long-context reasoning in large language models incurs substantial computation and memory costs. Visual text compression (VTC) reduces input length by rendering text as images, but fixed-resolution rendering creates a compression-performance trade-off: low DPI saves tokens at the expense of legibility, whereas high DPI spends tokens on irrelevant content. We introduce FocusVTC, which breaks this trade-off through adaptive resolution while preserving general multimodal capabilities. It combines compressed low-DPI global views with selective region enhancement, integrating enhanced views into ongoing reasoning. We construct 29.4K high-quality Reasoning-Evidence Localization (REL) chain-of-thought examples (REL-CoT) that link reasoning traces to page indices and bounding boxes. Multi-resolution REL supervised fine-tuning (REL-SFT) teaches the model to localize relevant regions, and Group Relative Policy Optimization learns when to enhance resolution and how to use the resulting observations, without a separate continual-pretraining stage. At 72 DPI on RULER v1, FocusVTC scores 87.4 at $2.9\times$ input compression, including tool observations, versus 57.5 for Glyph at $3.0\times$ input compression. It surpasses its text-input backbone on LongBench (56.40 versus 55.86), improves the MRCR macro-average by 13.91 points, and achieves a 51.19 macro-average on VTCBench. The MRCR latency evaluation also shows a $2.79\times$ online end-to-end speedup over Text. General multimodal capabilities are preserved, with MMMU increasing from 65.12 to 66.73 and MME from 2424.02 to 2457.62.

Figures & tables

Figure 1: Breaking the compression–performance trade-off through selective enhancement. (a) Fixed-resolution VTC ties local legibility to whole-page token cost. (b) FocusVTC retains a compressed global view and selectively enhances relevant regions. (c) On RULER v1, it sustains high scores at low initial DPI, while Glyph needs higher DPI and more tokens for comparable performance. (d) These gains coexist with preserved general capabilities across six benchmarks.

Figure 2: Overview of FocusVTC data construction and two-stage training. After calibrating font and point size, text contexts are rendered at multiple DPIs and annotated with REL-CoT. REL-SFT teaches low-DPI evidence localization, while GRPO trains the model to iteratively acquire and use high-resolution observations through selective region enhancement.

Figure 3: Font and point-size selection at 72 DPI. (a) Random-text CER across point sizes, with one-standard-error bars; references mark 5% CER and 9 pt. (b) 9 pt cost–CER comparison; the star indicates the preferred direction. (c) Confusable-unit errors, DejaVu Sans minus Verdana, with 95% paired-bootstrap intervals.

Model / stage	Input	Single-doc QA			Multi-doc QA		Summarization		Few-shot		Synthetic		Overall
		QP	MF-En	MF-Zh	DuR	2Wiki	MNews	QMSum	SAMSum	Trivia	PR-En	PR-Zh	Avg
LLaMA-3.1-8B ( Grattafiori et al., 2024 )	Text	44.56	44.61	41.26	19.06	46.67	25.30	23.28	35.46	89.12	99.50	62.20	48.27
Qwen2.5-7B ( Yang et al., 2025b )	Text	45.29	43.44	42.12	16.55	40.51	24.94	22.95	34.59	86.93	100.00	98.50	50.53
Qwen3-8B ( Yang et al., 2025a )	Text	44.67	47.73	45.21	21.19	73.92	21.30	19.60	35.01	87.98	97.26	100.00	53.99
GLM-4-9B ( Team GLM et al., 2024 )	Text	43.75	45.21	41.23	20.79	50.89	24.82	22.84	35.84	90.07	99.50	100.00	52.27
Qwen3.5-9B ( Qwen Team, 2026 )	Text	47.23	49.32	56.58	25.25	61.82	23.79	22.28	38.02	90.16	100.00	100.00	55.86

Table 1: LongBench task scores (%), grouped by task family. Avg is the unweighted arithmetic mean of the 11 displayed non-code task scores. Vision denotes 72-DPI pages.

Figure 4: MRCR results: (a) two needles; (b) four needles; (c) eight needles; (d) prompt-plus-observation compression. Text is dashed/hollow; VTC is solid/filled. Context axes use bin upper bounds on a base-two scale. Compression divides Text tokens by the initial prompt plus tool observations. Full scores: Appendix C.3 .

Model / stage	Input	Retrieval					Reasoning					Memory
		8	16	32	64	Avg	8	16	32	64	Avg	8	16	32	64	Avg
Qwen3-VL-8B ( Bai et al., 2025 )	Vision	90.50	77.90	77.01	76.36	80.44	18.83	12.16	11.11	1.52	10.91	21.08	20.18	24.03	20.00	21.32
Qwen3.5-9B ( Qwen Team, 2026 )	Vision	89.14	78.45	79.21	78.23	81.26	62.97	55.41	34.26	13.64	41.57	32.50	22.75	18.42	10.10	20.94
GLM-4.1V-9B ( V Team et al., 2025 )	Vision	86.20	85.25	82.76	58.01	78.06	31.59	10.14	22.22	9.09	18.26	25.10	21.89	13.16	7.07	16.81
Glyph ( Cheng et al., 2026 )	Vision	92.08	87.85	80.51	77.16	84.40	22.18	6.08	15.74	10.61	13.65	22.11	17.11	21.47	19.00	19.92
FocusVTC (Ours)	Vision	98.39	93.37	85.34	86.89	91.00	45.24	43.24	35.19	21.33	36.25	32.98	22.07	24.64	25.64	26.33

Table 2: VTCBench performance (%) across Retrieval, Reasoning, and Memory. Length labels are bin upper bounds (K tokens). Each Avg is the unweighted arithmetic mean of the four displayed length-bin scores. Vision denotes 72-DPI pages.

Model	REL-SFT	GRPO	Tools	RULER		LongBench	MRCR (needles)			VTCBench
				v1	v2	Avg	2	4	8	Retrieval	Reasoning	Memory
FocusVTC (Ours)	$\checkmark$	$\checkmark$	$\checkmark$	87.38	75.94	56.40	60.76	45.21	30.71	91.00	36.25	26.33
FocusVTC (Verdana) (Ours)	$\checkmark$	$\checkmark$	$\checkmark$	85.13	75.49	54.93	60.26	43.11	27.34	89.21	35.93	24.23
FocusVTC w/o tools (Ours)	$\checkmark$	$\checkmark$	–	36.60	44.82	36.82	52.00	37.65	23.50	67.25	3.11	22.95
FocusVTC w/o SFT (Ours)	–	$\checkmark$	$\checkmark$	73.21	62.77	49.30	57.05	39.36	26.66	87.17	38.15	23.56
FocusVTC w/o GRPO (Ours)	$\checkmark$	–	–	32.28	43.58	37.86	44.94	28.83	21.30	80.27	20.37	17.68

Table 3: Training stages and tool access at 72 DPI (scores in %). Best and second-best scores are bold and underlined, respectively. +tools uses tool access without additional training. Detailed results are provided in Appendices C.3 and C.4 .

Figure 5: Training behavior and checkpoint scores. (a) Tool calls; (b) response length; (c) grounding IoU; (d) benchmark scores at 0/50/100/150 steps. S1–S4 mark exploration, frequent tool use, fewer calls with longer responses, and shorter responses with higher IoU.

Figure 6: RULER results across DPI: (a) v1 scores; (b) v2 scores; (c) v1 input token costs; (d) v2 token costs. FocusVTC shows prompt and prompt-plus-observation costs. Glyph uses prompt tokens for v1 and v2. Dashed references show Text prompts. Token accounting: Appendix C.1 ; task-level scores at 72 DPI: Appendix C.4 .

Appendix figures & tables18 assets

Supplementary material from the paper’s appendix.

Appendix

Parameter	Setting
Font family	DejaVu Sans (selected from 15 candidates)
Font size	9 pt (selected from 4, 5, 6, 7, 8, 9, 11, 13 pt)
Initial training DPI	48, 60, 72, 84, 96, 120, 144 for REL-SFT; 72, 96, 144 for GRPO
Default evaluation DPI	72, except for the resolution sweep
Enhancement source DPI	144, with aligned page layout and coordinates
Font-selection page layout and preprocessing

Appendix

Table 4: Rendering settings. The first block lists the selected font and resolution settings; page-layout and image-processor values in the second block apply to the font-selection diagnostics.

Font	Family	CER (%)	Threshold pt [95% CI]	Pages	Visual tokens
Liberation Sans Narrow	Sans	17.53	11.69 [11.41, 11.91]	12.18	6,016.9
Times New Roman	Serif	13.15	10.87 [10.76, 11.01]	13.54	6,688.8
Liberation Serif	Serif	12.94	10.67 [10.56, 10.80]	13.54	6,688.8
FreeSans	Sans	9.30	10.60 [10.44, 10.77]	14.50	7,163.0
Georgia	Serif	10.76	10.73 [10.54, 10.90]	14.82	7,321.1
Arial	Sans	8.39	10.39 [10.19, 10.59]	14.86	7,340.8

Appendix

Table 5: All font candidates at 72 DPI, sorted by visual-token cost. CER uses the 400-passage 9 pt set; threshold sizes and 95% intervals use the separate 30-passage sweep. Pagination and visual tokens are means over the same 50 long passages. Bold identifies the selected font.

Model / font	Single-1	Single-2	Single-3	MKey-1	MKey-2	MKey-3	MValue	MQuery	QA-1	QA-2
FocusVTC (Ours)	98.00	100.00	72.00	97.00	96.00	46.00	96.25	98.50	89.00	81.00
FocusVTC (Verdana) (Ours)	98.00	99.00	63.00	94.00	96.00	40.00	95.80	98.50	87.00	80.00

Appendix

Table 6: FocusVTC font comparison on RULER v1 at 72 DPI (scores in %).

Figure 7: REL-CoT source families (left) and the Multi-hop breakdown (right), before resolution expansion. Percentages use each panel’s total; the right panel combines long and original variants of each dataset.

Global batch size	Training steps	Learning rate	Weight decay	LR schedule	Warm-up steps	Visual encoder	Max. packed seq. length
8	7,000	$10^{-6}$	0.01	Cosine	325	Frozen	32,768

Appendix

Table 7: Stage 1: REL-SFT training hyperparameters.

Panel of Figure 8 — Figure 8: SFT sequence packing. Gray spans denote conditioning inputs; teal spans denote assistant targets.

Global batch	Train steps	LR	Weight decay	LR schedule	Warmup steps	Visual encoder	Max. prompt / response	Max. gen. / turn	Rollouts / prompt	PPO mini-batch	PPO micro- batch / GPU	Temp. / top- $p$	Max. turns / tool calls	$g$ exponent $\gamma$	Clip $\epsilon$
64	150	$10^{-6}$	0.01	Constant	0	Trainable	8K / 10K	2K	8	64	1	1.0 / 1.0	9 / 8	2	0.2

Appendix

Table 8: Stage 2: GRPO training hyperparameters.

Situation	Consequence
Fully correct answer, no tool calls	$R_{\mathrm{acc}}=R_{\mathrm{fmt}}=1$ and $R_{\mathrm{tool}}=0$ , giving $R=1$ .
Answer not fully correct	The tool-bonus term is gated off.
Full-page request	$s_{i}=1$ : the area factor is zero, so this call contributes zero to the matching sum.
Repeated requests for the same evidence	Each annotated evidence region can be matched at most once.
Excess or malformed tool calls	Both set $R_{\mathrm{fmt}}=0$ . For $N>E$ , $E_{\mathrm{call}}=E/N<1$ ; malformed calls count in $N$ but are excluded from matching.
Initial view at 144 DPI	$g(144)=0$ , giving $R=0.8R_{\mathrm{acc}}+0.2R_{\mathrm{fmt}}$ .

Appendix

Table 9: Representative cases under the reward function. Unless otherwise stated, terminal answer formatting is valid.

		Glyph ( Cheng et al., 2026 )		FocusVTC (Ours)
Benchmark	DPI	P	Text/P	$P$	$O$	$P+O$	Text/ $(P+O)$
RULER v1	48	1,491	5.6 $\times$	1,218	1,768	2,986	2.8 $\times$
	60	2,061	4.1 $\times$	1,655	1,392	3,047	2.8 $\times$
	72	2,765	3.0 $\times$	2,154	723	2,877	2.9 $\times$
	84	3,517	2.4 $\times$	2,729	698	3,427	2.5 $\times$
	96	4,522	1.9 $\times$	3,544	681	4,225	2.0 $\times$

Appendix

Table 10: Token costs and compression.

P

and

O

denote initial prompt and extra-observation tokens. Text references are 8,400/9,201/11,313 tokens for RULER v1/v2 and LongBench.

Model	Latency (s)	Initial prefill (s)	Tool-round prefill (s/sample)
Text (Qwen3.5-9B)	187.09	6.42	–
FocusVTC (Ours)	67.06	4.14	1.34

Appendix

Table 11: End-to-end latency on 300 paired MRCR four-needle examples with 64K–128K contexts. Values exclude recorded page-rendering time but include all online inference and tool rounds. Prefill timings are measured separately; tool-round prefill is cumulative per sample. Lower is better.

Model / stage	Input	Single-doc QA			Multi-doc QA		Summarization		Few-shot		Synthetic		Overall
		QP	MF-En	MF-Zh	DuR	2Wiki	MNews	QMSum	SAMSum	Trivia	PR-En	PR-Zh	Avg
FocusVTC w/o SFT (Ours)	Vision	43.77	46.73	53.80	26.74	61.26	24.07	23.95	31.56	85.31	72.32	72.74	49.30
FocusVTC w/o GRPO (Ours)	Vision	34.52	39.94	31.77	14.73	56.54	17.47	16.69	31.64	89.23	67.12	16.85	37.86
FocusVTC w/o GRPO+tools (Ours)	Vision	16.46	32.70	23.16	8.42	49.70	2.96	3.79	3.88	56.42	65.19	16.83	25.41
FocusVTC-50 (Ours)	Vision	47.61	41.73	49.21	29.80	52.73	21.47	12.15	30.78	87.21	94.05	93.66	50.95
FocusVTC-100 (Ours)	Vision	46.90	45.06	54.22	30.10	61.14	22.47	20.06	32.76	90.10	95.73	96.00	54.05

Appendix

Table 12: Detailed LongBench scores (%) for training-stage variants. Avg is the unweighted arithmetic mean of the 11 displayed non-code task scores. The final FocusVTC row is included as reference. Vision denotes 72-DPI pages. +tools uses tool access without additional training.

Model / stage	Input	Retrieval					Reasoning					Memory
		8	16	32	64	Avg	8	16	32	64	Avg	8	16	32	64	Avg
FocusVTC w/o SFT (Ours)	Vision	99.83	85.55	82.22	81.07	87.17	50.88	49.09	33.96	18.65	38.15	31.24	22.21	21.57	19.20	23.56
FocusVTC w/o GRPO (Ours)	Vision	87.78	79.56	80.21	73.52	80.27	40.17	25.68	12.04	3.57	20.37	12.12	17.98	20.60	20.00	17.68
FocusVTC w/o GRPO+tools (Ours)	Vision	66.28	59.67	51.72	39.34	54.25	7.82	1.35	0.93	0.00	2.53	7.50	4.23	6.18	9.79	6.93
FocusVTC-50 (Ours)	Vision	96.96	80.65	81.97	65.56	81.29	38.51	29.37	14.22	7.00	22.28	17.63	13.65	18.06	24.99	18.58
FocusVTC-100 (Ours)	Vision	97.94	87.85	85.34	77.05	87.05	39.53	29.73	16.67	11.21	24.29	25.53	17.84	20.85	25.64	22.47

Appendix

Table 13: Detailed VTCBench results (%) for training-stage variants. Length labels are bin upper bounds (K tokens). Each Avg is the unweighted arithmetic mean of the four displayed length-bin scores. The final FocusVTC row is included as reference. Vision denotes 72-DPI pages. +tools uses tool access without additional training.

Model / stage	Input	2 needles							4 needles							8 needles
		8	16	32	64	128	256	Avg	8	16	32	64	128	256	Avg	8	16	32	64	128	256	Avg
LLaMA-3.1-8B ( Grattafiori et al., 2024 )	Text	54.27	53.21	51.05	29.81	24.98	20.90	39.04	33.42	25.97	22.73	26.97	12.68	6.00	21.30	23.80	17.69	19.85	17.72	11.79	7.80	16.44
Qwen2.5-7B ( Yang et al., 2025b )	Text	45.92	51.07	46.97	34.67	37.57	37.60	42.30	25.96	20.13	19.93	24.25	17.29	12.30	19.98	17.64	19.48	12.41	14.80	14.24	13.70	15.38
Qwen3-8B ( Yang et al., 2025a )	Text	58.95	41.18	36.18	24.99	20.89	17.50	33.28	29.34	22.67	20.34	23.63	19.11	15.50	21.77	18.75	19.69	16.81	17.86	15.00	12.60	16.79
GLM-4-9B ( Team GLM et al., 2024 )	Text	39.77	15.87	18.42	18.63	18.42	18.20	21.55	15.17	13.78	9.18	20.27	15.05	11.20	14.11	14.55	9.65	9.34	9.47	8.97	8.50	10.08
Qwen3.5-9B ( Qwen Team, 2026 )	Text	54.96	51.59	48.59	43.44	41.48	39.08	46.52	36.72	35.02	34.55	25.44	25.82	21.66	29.87	23.04	20.50	21.53	12.68	21.13	12.48	18.56

Appendix

Table 14: MRCR performance (%) for two, four, and eight needles. Length labels are bin upper bounds (K tokens). Avg is the unweighted arithmetic mean of the six length-bin scores. Vision denotes 72-DPI pages. +tools uses tool access without additional training.

Model	Single-1	Single-2	Single-3	MKey-1	MKey-2	MKey-3	MValue	MQuery	QA-1	QA-2	Avg
Qwen3.5-9B	54.00	48.00	0.00	43.00	40.00	1.00	37.25	42.75	62.00	46.00	37.40
GLM-4.1V-9B	61.00	26.00	0.00	30.00	19.00	0.00	19.50	33.25	50.00	48.00	28.68
Glyph	74.00	76.00	44.00	69.00	37.00	2.00	70.75	74.50	64.00	64.00	57.53
FocusVTC w/o GRPO (Ours)	50.00	39.00	0.00	29.00	37.00	0.00	24.25	24.50	65.00	54.00	32.28
FocusVTC w/o GRPO+tools (Ours)	43.00	29.00	1.00	24.00	37.00	1.00	23.75	23.00	22.00	26.00	22.98
FocusVTC-50 (Ours)	82.99	82.73	46.03	73.50	66.15	37.59	54.36	71.10	77.27	68.58	66.03

Appendix

Table 15: RULER v1 task scores (%) at 72 DPI. Avg is the unweighted arithmetic mean of the 10 displayed task scores. Best and second-best scores are bold and underlined, respectively. Qwen3.5-9B+tools uses tool access without additional training.

Model	MK-NIAH				MV-NIAH				QA				Overall
	Basic	Easy	Medium	Hard	Basic	Easy	Medium	Hard	Basic	Easy	Medium	Hard	Avg
Qwen3.5-9B	19.00	79.44	70.00	46.00	10.75	8.65	23.36	23.45	69.00	34.00	71.00	81.00	44.64
GLM-4.1V-9B	8.00	51.12	29.00	25.00	3.50	9.67	10.64	18.24	53.00	32.00	53.58	53.50	28.94
Glyph	16.00	67.15	45.00	43.00	9.25	5.72	17.02	34.05	86.00	93.00	78.35	80.36	47.91
FocusVTC w/o GRPO (Ours)	18.00	59.96	51.00	39.00	7.25	7.05	16.79	27.24	74.00	83.00	68.50	71.17	43.58
FocusVTC w/o GRPO+tools (Ours)	20.00	71.32	45.00	33.00	10.75	7.03	13.04	29.78	72.00	36.00	63.27	71.68	39.41

Appendix

Table 16: RULER v2 task scores (%) at 72 DPI. Avg is the unweighted arithmetic mean of the 12 displayed task scores. Best and second-best scores are bold and underlined, respectively. Qwen3.5-9B+tools uses tool access without additional training.

Model	OCRBench	DocVQA	MMMU	MME	ChartQA	InfoVQA
Qwen3.5-9B ( Qwen Team, 2026 )	851	92.38	65.12	2424.02	85.96	74.76
Glyph ( Cheng et al., 2026 )	799	91.75	57.67	2253.13	72.76	71.89
GLM-4.1V-9B ( V Team et al., 2025 )	820	91.34	64.33	2392.53	70.76	70.52
FocusVTC (Ours)	860	92.43	66.73	2457.62	86.28	76.20
Model	VRAG	VH	NIAH image	NIAH text	Summ	DocQA
Qwen3.5-9B ( Qwen Team, 2026 )	61.12	56.10	45.04	71.82	29.19	68.96

Appendix

Table 17: General multimodal scores from Figure 1 (d) (top) and MMLongBench results (bottom). DocVQA and InfoVQA use ANLS; other entries retain their benchmark scales.

Figure 9: A factual evidence chain in REL-CoT. The question and target are adapted; source pages and boxes are unchanged. (A) A factual question over 22 pages (72-DPI thumbnails). (B) 144-DPI excerpts linking Merlin, Arthur, and Sir Ector; box coordinates are normalized to $[0,1000]$ . (C) Initial question analysis followed by sentence-embedded page–box citations and a short, directly verifiable answer.

Figure 10: An adapted MuSiQue illustration of selective enhancement. The question, source pages, reasoning excerpts, and answer are retained from the recorded Verdana example; tool-call boxes and observations are adapted for illustration. Enhance_Region reads local passages on pages 3 and 6 from aligned 144-DPI sources, using original-page coordinates in $[0,1000]$ . Insets enlarge text within these enhanced regions.

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LensVLM: Selective Context Expansion for Compressed Visual Representation of Text

Roy Xie, Dan Friedman, Donghan Yu, Bowen Pan+6

Apple · Duke University

Vision Encoders Visual Representations Lens Text Analysis and Detection

Open paper AskPDF arXiv

Vision Language Models (VLMs) offer the exciting possibility of processing text as rendered images, bypassing the need for tokenizing the text into long token sequences. Since VLM image encoders map fixed-size images to a fixed number of visual tokens, varying rendering resolution provides a fine-grained compression knob. However, accuracy deteriorates quickly as compression increases: characters shrink below the vision encoder's effective resolution, making them indistinguishable. To address this, we propose LensVLM, an inference framework and post-training recipe that enables VLMs to scan compressed images, then selectively expand only the relevant images to their uncompressed form via learned tools. Building on Qwen3.5-9B-Base, LensVLM maintains accuracy comparable to the full-text upper bound at 4.3

\times

effective compression and outperforms retrieval-based, text- and visual-compression baselines up to 10.1

\times

effective compression across seven text QA benchmarks. LensVLM also generalizes to multimodal document and code understanding tasks, with the accuracy gain over baselines growing as compression increases. Our analysis validates this approach: training makes visual compression robust to rendering choices, and as compression grows the model increasingly relies on expanded content rather than unreliable visual reading. The analysis also yields practical tool-choice guidance: text expansion is preferable for rendered text, while high-resolution image expansion suits native documents whose layout cues carry task-relevant information.

Abstract

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