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REVIEW 1 major objections 3 minor 23 references

LLM agent runs leave a persistent disk footprint that is as informative as accuracy and inference cost, and it spans 15.7x across equal-accuracy configurations once measured at the logical-stream level.

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2026-08-02 06:58 UTC pith:QP5HLF4N

load-bearing objection Storage footprint is a real, measurable evaluation axis; the disclosed boundary caveat is a soft spot, not a fatal one. the 1 major comments →

arxiv 2607.11149 v2 pith:QP5HLF4N submitted 2026-07-13 cs.AI

The Hidden Footprint: Making Storage a First-Class Metric for LLM Agent Evaluation

Chenglin Yu , Hongquan Gui , Ying Yu , Hongxia Yang , Tao Zeng , Ming Li This is my paper
classification cs.AI
keywords LLM agentsstorage footprintpersistent statebenchmarkingduplicationserializationreconstructabilitycontent addressing
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper argues that the bytes an LLM agent run leaves on disk—logs, checkpoints, snapshots, traces—are a resource dimension as worth reporting as accuracy and inference cost, and it builds a benchmark, AgentFootprint, to measure them. The benchmark's serialization-aware meter reads logical content streams rather than raw files, so duplication hidden by database paging and JSON escaping becomes visible. Across eight representative frameworks on identical tasks, the paper finds documented configurations with perfect accuracy retain between 0.32 and 5.10 MB per task—a 15.7x spread—and that replaying one fixed trajectory through different persistence layers alone produces a 6.7x spread. Full-history checkpointing grows superlinearly with repeated observations, while a content-addressed store cuts retention 4.8-32.7x without changing any reconstructability score. The upshot: storage can be reported as a first-class metric, and exact history reconstruction does not require megabytes.

Core claim

The central claim is that persistent storage footprint is a meaningful, orthogonal resource axis for LLM-agent evaluation, and that it has been systematically under-measured because serialization masks duplication. Measuring SQLite cells and JSONL records as logical streams instead of raw bytes raises a naive duplication factor from 1.01 to 12.1 on one store, and content probes confirm the repetition. With this measurement, equal-accuracy configurations differ by 15.7x in retained bytes, the persistence layer alone accounts for a 6.7x spread on identical content, full-history frameworks grow with exponent up to 1.95 on a repeated-observation task, and a content-addressed store reduces retent

What carries the argument

The central object is the six-metric storage-footprint suite (total retention, channel composition, duplication factor D, growth exponent alpha, compressibility C, and 0-3 reconstructability score R), built on a per-run sandbox delta and a serialization-aware meter. The meter extracts logical content streams—SQLite cells and JSONL records—before fingerprinting, so duplication masked by page fragmentation and JSON escaping becomes visible, and content probes estimate how many times input content is stored. A fixed-trace control replays an identical trajectory through each framework's persistence adapter to isolate persistence-layer amplification from agent behavior.

Load-bearing premise

The load-bearing premise is that the fresh-sandbox diff captures every byte the run leaves behind; if significant persistence lands outside the workspace and home boundary (system temp, remote telemetry), the reported totals and spreads shift.

What would settle it

Run the same eight configurations with a full-filesystem container audit covering system temp and remote endpoints; if a configuration's per-task retained bytes outside the declared boundary exceed the reported delta by a meaningful fraction, the metric's attribution boundary fails and the headline spreads need revision.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Benchmark authors can report retained bytes, duplication, growth, compressibility, and reconstructability alongside accuracy and inference cost.
  • Equal-accuracy configurations can differ by 15.7x in retained bytes, so a success-only leaderboard hides a large operational difference.
  • Full-history persistence grows superlinearly on repeated observations (up to a 1.95 exponent), implying long-running agents need archival policies or windowed context to bound disk usage.
  • Content addressing removes 4.8-32.7x of retention while preserving exact conversation-history reconstruction, showing most retained bytes are removable redundancy.
  • Exported trajectory volume in the wild shows no detectable correlation with task resolution, so storage volume is not a proxy for quality.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If storage becomes a standard reporting axis, frameworks that bundle recovery, audit, and workflow-state capabilities will need capability-normalized efficiency metrics, not just raw byte counts, to be compared fairly.
  • The serialization-masking effect generalizes beyond agents: any system persisting structured data via SQLite or JSON is vulnerable to order-of-magnitude duplication that raw byte measurement misses.
  • A testable extension: run the same metric suite on long-horizon production workloads with different archival policies to see whether the superlinear growth exponent predicts actual retention obligations at fleet scale.
  • The content-addressed reference store suggests delta encoding, not just exact-match deduplication, could close the remaining gap between opaque compression and schema-preserving stores.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 3 minor

Summary. This paper proposes AgentFootprint, a benchmark and metric suite for measuring persistent storage left by LLM agent runs. The six metrics (Stotal, composition, D, α, C, R) are applied in 1,061 sandboxed runs across eight agent frameworks. The controlled study reports a 15.7× spread in retained bytes among 100%-accuracy default configurations, a 6.7× spread under fixed-trace replay, superlinear retention growth for full-history frameworks, zero retention for an in-memory framework, and a 1,617× spread in exported trajectory volume across 108 SWE-bench Verified submissions with no detectable resolve-rate correlation. A content-addressed store reduces retention 4.8–32.7× while preserving reconstructability scores. The paper includes extensive reproducibility artifacts, calibration studies, and sensitivity analyses.

Significance. If the measurements hold, this paper makes a strong case for reporting storage as a resource axis in agent evaluation. Its main methodological contribution is serialization-aware measurement: logical-stream chunking recovers D=12.1 where raw-file chunking sees 1.01, and calibration on synthetic stores confirms the meter on known ground truth. The fixed-trace control cleanly isolates persistence-layer amplification from agent behavior. The empirical effort is unusually careful: exhaustive per-run accounting, a bootstrap CI for the headline spread, two backend replications with rank correlations 0.96/0.89, threshold-sensitivity audits, and an automated verification suite in the artifact. The central claim is credible within the declared sandbox boundary; the open question is whether that boundary fully captures the storage phenomenon the title promises.

major comments (1)
  1. [§3.2 / Appendix N] The headline numbers all reduce to Stotal, but Stotal is measured only from workspace/HOME snapshots. Appendix N explicitly concedes that the redirected boundary does not cover system temp directories or remote telemetry. The only container validation (§4.3) is LangGraph on three tasks (0–8.5% deviation). For the other seven frameworks, and for LangGraph on the growth stress task, there is no check for writes to /tmp, /var/tmp, SQLite journal directories, or unredirected XDG state. If any framework leaves a few percent of its retention outside the boundary, the 15.7×, 6.7×, and 3.9× spreads and the CAS factors could shift. Because the paper identifies this as an 'extension path,' it is an acknowledged limitation, but it is load-bearing for the central claims. Please extend the container audit to all eight adapters on a representative task set, or provide explicit evidence that none of th
minor comments (3)
  1. [Appendix B / §3.1] The statement that OpenAI Agents' hosted tracing is disabled because it is remote retention should be moved to §3.1 next to the boundary definition; otherwise readers may not realize Stotal is local-only.
  2. [§4.2 / references] InfiAgent is the authors' own prior framework; while it is cited, an explicit sentence in the benchmark description would remove any appearance of conflict.
  3. [Table 1 caption] The caption defines α as a growth exponent but does not state that the value for non-full-history frameworks is a single-run fit while the full-history trio has three seeds; this distinction appears only in the text.

Circularity Check

0 steps flagged

No circular derivation; measurement-based benchmark with only a minor non-load-bearing self-citation.

full rationale

The paper's derivation chain is self-contained and empirically grounded. The central quantity S_total is defined operationally (§3.1) as bytes present after the run, attributable to it, and absent before it, and is measured by sandbox diffing rather than fitted to any target conclusion. The metrics D, α, C, and R are descriptive or verification-oriented: α is explicitly labeled a descriptive statistic with per-horizon retention primary, and the CAS-reduction claim is verified by re-scoring restored stores rather than assumed. The fixed-trace control replays identical content through seven frameworks and compares measured retained bytes, so the 6.7× spread is a direct measurement, not a consequence of a definition. The in-the-wild trajectory-volume analysis uses public submission metadata and reports a lack of correlation, again purely empirical. There is no fitted parameter that is later renamed as a prediction, no uniqueness theorem imported from the authors' prior work, and no ansatz smuggled in via citation. The only self-citation is the use of InfiAgent, the authors' own framework, as one of eight measured frameworks (§4.2); its footprint is measured in this paper, not taken from the cited prior work, and the headline equal-accuracy 15.7× spread is between LangGraph and CrewAI and does not depend on InfiAgent. The acknowledged boundary limitation in App. N—that the redirected boundary excludes system temp directories and remote telemetry—is a measurement-scope caveat, not a circular step: it affects completeness of S_total but does not make any result equivalent to its inputs by construction. No specific reduction of a claimed result to its own definition or to a self-citation chain can be exhibited.

Axiom & Free-Parameter Ledger

4 free parameters · 5 axioms · 0 invented entities

The central claims rest on measurement definitions rather than physical postulates. The key choices are the sandbox boundary, the 64B stream threshold, the CAS 1KB blob cutoff, the zstd configuration, and the fitted α. No ad hoc conceptual entities are introduced; the content-addressed store is an implementation, not a new physical or conceptual entity.

free parameters (4)
  • α growth exponent = LangGraph 1.74±0.01, AutoGen 1.95±0.18, LlamaIndex 1.88±0.10, Agno 0.98, OpenAI Agents 0.73, etc.
    Fitted power-law exponent log S_total ∼ α log T over T ∈ {25,50,100,200} on the repeated-observation stress task. The paper explicitly treats it as a descriptive statistic, not a validated scaling law.
  • 64B logical-stream length threshold = 64 bytes
    Streams shorter than 64B are excluded from the numerator and denominator of D. The paper audits thresholds 0–128B and reports no framework's D changes by more than 0.1.
  • CAS blob size threshold = 1 KB
    The content-addressed compactor moves strings/blobs above 1KB into the hash store. This design choice affects the reported 4.8–32.7× reduction factors.
  • zstd compression configuration for C = zstd-19 with 128MB long-range window
    Chosen as the syntactic compressibility baseline. Different compression settings would shift C values, though the paper uses it only as a bound.
axioms (5)
  • domain assumption Per-run fresh-sandbox workspace/home diff captures all task-attributable bytes.
    Used throughout §4–§5. App. N audits the delta accounting but explicitly excludes system temp directories and remote telemetry from the boundary.
  • domain assumption A baseline file with unchanged size is treated as unchanged.
    App. N: pre-existing bytes in changed files total 0.0022% of aggregate S_total, and byte-comparison found zero same-size in-place rewrites. Still an assumption for the meter's accounting.
  • domain assumption Serialization semantics are invariant across tasks at a fixed framework version and configuration.
    App. O states this assumption explicitly. It underpins the generalization of R scores beyond the tasks used for reconstruction validation.
  • domain assumption Each framework's documented durable-session configuration represents its default persistence behavior.
    §4.2 selects configurations from documentation rather than tuning for storage. The paper discloses that results describe versioned configurations, not frameworks in the abstract.
  • standard math FastCDC with SHA-256 exact-match fingerprinting is a sufficient definition of duplication for the paper's claims.
    D and echo rely on content-defined chunking and hashing. Near-duplicates and compression-wrapped redundancy are intentionally invisible to exact-match analysis, as the calibration in App. K shows.

pith-pipeline@v1.3.0-alltime-deepseek · 23028 in / 12950 out tokens · 115079 ms · 2026-08-02T06:58:38.136622+00:00 · methodology

0 comments
read the original abstract

LLM agent benchmarks measure task completion, reliability, and inference cost, but not the persistent data an agent run leaves on disk, including logs, context snapshots, checkpoints, and debug traces. We introduce AgentFootprint, a cross-framework benchmark of post-run agent storage footprint. Its serialization-aware metric suite measures total retention, channel composition, duplication, growth, compressibility, and conversation-history reconstructability. It addresses a measurement trap: naive byte-level measurement understates duplication by an order of magnitude because database paging and JSON escaping obscure repeated content. A fixed-trace control separates agent-generated logical volume from persistence-layer amplification: replaying the same trajectory through seven persisting frameworks yields a 6.7x spread. Under identical models, tools, and tasks, configurations with 100% accuracy differ by 15.7x in retained bytes, although their defaults support different recovery and audit capabilities. Three full-history configurations grow superlinearly on a repeated-observation stress task. Exported trajectories from 108 instance-normalized SWE-bench Verified submissions span three orders of magnitude per instance, with no detectable correlation with resolve rate. A content-addressed store reduces retention by 4.8x-32.7x while preserving every reconstructability score. These results establish persistent storage as a resource metric to report jointly with accuracy and reconstructability.

Figures

Figures reproduced from arXiv: 2607.11149 by Chenglin Yu, Hongquan Gui, Hongxia Yang, Ming Li, Tao Zeng, Ying Yu.

Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Retention growth on the repeated-observation stress [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 2
Figure 2. Figure 2: Retention growth on the repeated-observation stress [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: SWE-bench Verified, 108 instance-normalized sys [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 1
Figure 1. Figure 1: Composition of retained bytes on the file-QA [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Reconstructability versus retention (discussed in §5.4 of the main paper). Filled: default retention per task; hollow: [PITH_FULL_IMAGE:figures/full_fig_p017_2.png] view at source ↗

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