71% Fewer Tokens in One Harness, 14.5% in Another

A paper called Read Less, Solve More turned up on r/LocalLLaMA this morning. It’s from a group at the Institute of Computing Technology in Beijing and the University of Chinese Academy of Sciences. It describes SparseRead, a layer that sits between an agent and the files, PDFs, tables and web pages it opens, and decides how much of each one the model actually gets to see. The abstract says that across six models, Claude Opus 5 among them, and five scenarios, it “reduces total tokens by up to 92.9% and wall time by up to 89.0%, preserving or improving task quality in 97% settings.”

The problem it targets is real. When an agent opens a 200-page report to find one figure, the whole report often lands in context and stays there for every later call. I read version 2 of the paper (7 November) and the abstract of version 1 (23 August). I came away thinking the mechanism is sound and the paper is more candid than most about where it loses. The headline numbers mostly measure how wasteful the default agent was, though, and that varies a lot more than the abstract lets on.

What it does

SparseRead has three parts. The agent states what it needs from an artifact (a “Read Intent”) and gets back an “EvidencePack”: anchored snippets, a record of which parts of the request are covered and which are still open, and the size of what was exposed. Swappable “Readers” decide how to find the evidence. That might be BM25 or hybrid retrieval for text, column queries for tables, key-path lookup for JSON. A “Budgeted Read Gate” caps how much sparse content one reading episode can admit before it gives up and hands over the full view.

The Gate is the part I like most, because it comes with a bound you can check. If sparse reads are capped at the length L of the cheapest faithful full view, and the fallback delivers that view at most once, an episode exposes at most 2L. The proof in Appendix A is a few lines and it holds. The authors are also clear about its limits: it is “not a bound on replay-weighted context, billed tokens, task latency,” and it applies per episode, not per session. So the worst case is reading the document twice. That is a reasonable price for a chance of reading 5% of it.

The case for bothering rests on how sparse useful evidence is. In LooGLE long-document QA, the annotated supporting evidence covers a median of 3.17% of source tokens per question; for Qasper it’s about 5.8%. The paper also cites earlier work saying external observations make up 60 to 80% of the tokens in agent trajectories. And there is a quality argument as well as a cost one. Liu et al. showed in 2024 that models use information at the start or end of a long context much better than information buried in the middle, so stuffing a whole document into context doesn’t guarantee the model finds the part that matters.

Where 92.9% comes from

The 92.9% comes from a single cell, Kimi-K2.5 on the NanoBot harness. Table 1 lists that cell as 92.9% fewer tokens with a score change of +0.598. In Figure 6, Kimi’s long-context bar has a score delta of +80 percentage points next to it.

A gain that size means the unmodified agent was mostly failing. Whatever Kimi-K2.5 was doing with NanoBot’s native reading tools, it scored badly and burned tokens doing it. That’s a fair result to report, and fixing a broken reading loop is useful. But “up to 92.9%” in an abstract reads like the size of the efficiency win on working agents, and it isn’t that.

Token reduction against score change, six models on the NanoBot harness From Table 1 of the SparseRead paper, NanoBot harness. Qwen3.6-Plus: 69.0% fewer tokens, score change plus 0.039. DeepSeek-V4-Flash: 22.4%, plus 0.140. DeepSeek-V4-Pro: 71.3%, minus 0.015. GLM-5.1: 61.0%, plus 0.012. Claude Opus 5: 58.6%, plus 0.024. Kimi-K2.5: 92.9%, plus 0.598. Four models cluster between 58.6% and 71.3% savings with score changes within 0.04 of zero; Kimi-K2.5 sits alone in the top-right corner. TOKEN REDUCTION VS SCORE CHANGE · NANOBOT · SPARSEREAD TABLE 1 0 +0.2 +0.4 +0.6 0% 25% 50% 75% 100% Fewer total tokens than the unmodified agent Score Δ Qwen3.6-Plus — 69.0% fewer tokens, +0.039 score (SparseRead Table 1, v2, 7 Nov 2026) DeepSeek-V4-Flash — 22.4% fewer tokens, +0.140 score (Table 1) DeepSeek-V4-Pro — 71.3% fewer tokens, −0.015 score (Table 1) GLM-5.1 — 61.0% fewer tokens, +0.012 score (Table 1) Claude Opus 5 — 58.6% fewer tokens, +0.024 score (Table 1) Kimi-K2.5 — 92.9% fewer tokens, +0.598 score (Table 1) Kimi-K2.5 · 92.9%, +0.598 DeepSeek-V4-Flash · 22.4%, +0.140 Four models · 58.6–71.3%, within ±0.04 Score Δ is on the paper's 0–1 task-score scale. The four clustered points are Qwen3.6-Plus, DeepSeek-V4-Pro, GLM-5.1 and Opus 5.
The headline 92.9% comes from the one model that also gained 0.6 on score. Its baseline was mostly failing. The working agents cluster around 60–70% savings with almost no score change.

The cluster is the more useful result, and it’s still a good one. Four of the six models save between 58.6% and 71.3% of tokens on NanoBot, with score changes between −0.015 and +0.039. Across all 30 model-scenario cells in Figure 6 the median saving is 61.4% of tokens and 53.7% of wall time. If I were quoting a number, I’d quote those.

The harness is half the measurement

Every saving in the paper is measured against “Naive,” which the authors define as the unmodified framework’s own behavior, “including autonomous search and partial reads through native tools.” So the result is SparseRead minus whatever reading habits that harness already has. Table 1 runs the same six models through three harnesses: NanoBot v0.2.0, OpenCode v1.17.14 and OpenClaw v2026.6.11. The spread is wide.

Same model, different harness: SparseRead token reduction Token reduction from SparseRead, by agent harness, from Table 1. NanoBot: median across six models 65.0%; DeepSeek-V4-Pro 71.3%; Claude Opus 5 58.6%. OpenCode: median 65.2%; DeepSeek-V4-Pro 58.5%; Opus 5 46.8%. OpenClaw: median 30.6%; DeepSeek-V4-Pro 14.5%; Opus 5 32.5%. TOKEN REDUCTION VS EACH HARNESS'S OWN AGENT · SPARSEREAD TABLE 1 NanoBot OpenCode OpenClaw median of six: 65.0% median of six: 65.2% median of six: 30.6% DeepSeek-V4-Pro Claude Opus 5 DeepSeek-V4-Pro Claude Opus 5 DeepSeek-V4-Pro Claude Opus 5 71.3% — DeepSeek-V4-Pro on NanoBot, SparseRead Table 1, 7 Nov 2026 58.6% — Claude Opus 5 on NanoBot, Table 1 58.5% — DeepSeek-V4-Pro on OpenCode, Table 1 46.8% — Claude Opus 5 on OpenCode, Table 1 14.5% — DeepSeek-V4-Pro on OpenClaw, Table 1 32.5% — Claude Opus 5 on OpenClaw, Table 1 71.3% 58.6% 58.5% 46.8% 14.5% 32.5% Each saving is relative to that harness's unmodified agent, which already has its own search and partial-read tools.
One model, one reading layer, three harnesses: DeepSeek-V4-Pro saves 71.3% in NanoBot and 14.5% in OpenClaw. How much you save depends mostly on how much the harness was already wasting.

The authors present this table as evidence of portability, and in one sense it is: tokens and wall time went down in all 18 settings, with score kept or improved in 15. What it doesn’t support is a single savings figure. OpenClaw’s median saving is half of NanoBot’s, so OpenClaw’s own reading tools are probably less wasteful to begin with. This is the same thing I found in the 72% claim from Lance Martin’s talk: a percentage saving tells you about the baseline as much as the method. If your agent already reads files in chunks and greps before opening them, expect the OpenClaw column, not the NanoBot one.

How 97% was counted

Version 1 of the abstract said SparseRead reduces tokens “while preserving or improving task quality,” with no qualifier. Version 2 says “in 97% settings.” That is a more honest sentence, and it’s worth checking what it counts.

In the version 2 main matrix, Figure 6, score is kept or improved in 29 of 30 cells, which is 96.7%. The one regression is GLM-5.1 on the AI-for-Science scenario. But Appendix C says that in “the original five-scenario matrix,” one of the five scenarios was a set of “native-fit controls”: finding the largest file in a directory, writing a discount calculator, writing a SPARQL query. These are tasks where a short native read is already the right move. In that matrix, score held in 26 of 30 cells, or 87%. For the final version, those controls went to the appendix and Web deep search took their place, and every model gained 5 to 14 points on Web deep search.

The appendix states all of this plainly, and says the controls probe settings where “direct tool use or a short native read can already be appropriate.” Still, it means the 97% describes tasks chosen to suit sparse reading. On tasks that don’t suit it, the layer is a cost, and the paper’s own appendix shows how much.

What forcing it costs

Appendix C runs eight matched tasks three ways: the native agent, sparse reading forced on every access, and the Gate deciding. Three of the tasks sit on the boundary: a difference-in-differences regression (T58), SPARQL generation (T67) and a discount calculation (T59).

Forced sparse reading against the Read Gate on three boundary tasks Token change relative to the native agent, from Appendix C of the SparseRead paper. Difference-in-differences regression, T58: forced sparse reading uses 49.0% more tokens; the Read Gate uses 45.0% fewer. SPARQL generation, T67: forced uses 309.0% more; the Gate uses 22.5% fewer. Discount calculation, T59: forced uses 210.5% more; the Gate uses 54.0% fewer. TOKEN CHANGE VS NATIVE AGENT · THREE BOUNDARY TASKS · SPARSEREAD APPENDIX C Difference-in-differences regression (T58) SPARQL generation (T67) Discount calculation (T59) Forced sparse reading Read Gate Forced sparse reading Read Gate Forced sparse reading Read Gate +49.0% tokens — forced sparse reading, T58, SparseRead Appendix C −45.0% tokens — Read Gate, T58 +309.0% tokens — forced sparse reading, T67 −22.5% tokens — Read Gate, T67 +210.5% tokens — forced sparse reading, T59 −54.0% tokens — Read Gate, T59 +49.0% 45.0% fewer +309.0% 22.5% fewer +210.5% 54.0% fewer ← fewer tokens than native more tokens than native → Eight-task supplementary comparison on NanoBot, earlier Gate policy. Bars are proportional to the reported percentages.
Sparse reading applied everywhere can quadruple the bill. The Gate is what turns a 309% overrun into a 22.5% saving, so it is the part of the system doing the most work.

With the Gate, those three tasks scored 0.80, 0.50 and 0.69, against the native agent’s 0.52, 0.43 and 0.65. So it beat the baseline on both cost and score. The admission control is doing the heavy lifting here, more than the sparse reader underneath it. A team that copies the retrieval part and skips the admission control will get the 309% row.

I found two loose ends. The Figure 9 caption says the experiment ran on “Deepseek-v4 Pro,” while the appendix text describing the same comparison says DeepSeek-V4-Flash. The text also says these results come from “the earlier Gate policy,” and the current Gate’s budget enforcement was tested separately, on five tasks with three repeats each. Neither undoes the result. Both mean the boundary evidence is thinner than the main tables.

What I’d take from it

The idea holds up. Agents read far more than they use, a reading layer can cut that without retraining anything, and the 2L bound is a good model for building that kind of layer: try the cheap path, cap the overrun, fall back cleanly. The comparison with retrieval baselines is also worth a look. Static RAG scored below the native agent in nine of 15 settings, and ACON’s compression lost score in five and raised both costs in six. Deciding what enters the context works better here than compressing it after it’s in.

The numbers I’d repeat are the medians: about 61% fewer tokens and 54% less wall time on tasks suited to sparse reading, in a harness that wasn’t already careful. I’d leave “up to 92.9%” out, because that figure describes fixing a failing agent. And before expecting any of it, I’d look at how my own harness reads. On OpenClaw, the same model saved 14.5%.

References

  1. Liu, Z., Wu, J., Ma, X., Xu, L., Wang, K., Hu, Y., Tao, D. and Tan, G. (2026). Read Less, Solve More: Token-Efficient Sparse Reading of External Content for AI Agents. arXiv:2608.22237, version 2, 7 November 2026.
  2. Liu, Z. et al. (2026). Read Less, Solve More, version 1 abstract. arXiv:2608.22237v1, 23 August 2026.
  3. Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F. and Liang, P. (2024). Lost in the Middle: How Language Models Use Long Contexts. Transactions of the Association for Computational Linguistics 12, 157-173, 2024.
  4. r/LocalLLaMA. [Paper] Read Less, Solve More: Token-Efficient Sparse Reading of External Content for AI Agents. Posted 14 November 2026.