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arxiv: 2606.22953 · v1 · pith:GTX37APVnew · submitted 2026-06-22 · 💻 cs.AI · cs.CL

Plans Don't Persist: Why Context Management Is Load Bearing for LLM Agents

Aman Mehta , Anupam Datta This is my paper

Pith reviewed 2026-06-26 08:25 UTC · model grok-4.3

classification 💻 cs.AI cs.CL
keywords LLM agentscontext managementplan persistencehidden statesreplay pairingcontext compressionagent evaluation
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The pith

LLM agents do not keep plans as persistent internal state and instead need the plan text to stay in context.

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

The paper tests whether plans written early in long tasks remain active inside the model or must be preserved in the prompt window. It introduces replay pairing to run identical trajectories once with the plan present and once without, then measures the drop in hidden-state similarity. On standard models the plan signal falls sharply within one or two steps, and removing the plan from context cuts success rates on ALFWorld by more than 30 points. The work also shows that reasoning models re-derive plan content inside their thinking traces, which requires a stricter stripping method to measure the effect cleanly. The central result is that context management must protect plan information because the model does not internalize it.

Core claim

Standard LLM agents do not carry plans forward as persistent state, and instead depend on the plan remaining in context. Replay pairing on Llama-3.1-70B shows plan signal in hidden states reaching 0.453 one step after the plan then dropping 4.1 times in a single action-observation step. A compression stress test finds that naive plan eviction lowers ALFWorld success by 34.7 percentage points. Reasoning models require strict stripping of prior thinking traces to isolate plan effects, recovering over 150 percent of the signal while leaving non-reasoning models largely unchanged.

What carries the argument

Replay pairing, a diagnostic that runs matched trajectories differing only in whether the plan appears in history and measures cosine distance between the resulting hidden states.

If this is right

  • Plan signal decays rapidly after the plan is written, so context windows must retain it for continued use.
  • Naive eviction of plans from context produces large drops in downstream task success.
  • Reasoning models need strict removal of prior thinking traces before plan effects can be measured accurately.
  • Probe transfer between models is only partial, indicating plan information may be encoded in different directions.

Where Pith is reading between the lines

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

  • Long-horizon agent systems will need explicit detection and protection of plan-like information during compression.
  • The observed decay suggests plans act more like temporary prompts than stored knowledge the model can recall unaided.
  • Extending the same pairing method to other critical information such as goals or constraints could reveal similar context dependence.

Load-bearing premise

The cosine distance between hidden states isolates the plan's own contribution rather than other differences between the two runs.

What would settle it

Hidden-state cosine distances between the paired runs stay large across many subsequent steps even when the plan has been removed from context.

Figures

Figures reproduced from arXiv: 2606.22953 by Aman Mehta, Anupam Datta.

Figure 1
Figure 1. Figure 1: Replay pairing. The same trajectory is run twice: A keeps the plan in history; B [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Plan signal is localized in depth but stable across tasks. The peak layer is [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Plan signal (mean ± SE, 80 tasks) as a function of step offset from guard injection. Pre-plan signal (s<0) is near zero (validation). Signal jumps to 0.453 at step +1 then drops 4.1× in one step. On ALFWorld, the probe predicts plan-signal magnitude at R 2 = 0.875 ± 0.016, well below the R 2 = 1 a trivially-leaked feature would produce. The induced binary decision at τ = 0.15 reaches AUROC 0.999 with balan… view at source ↗
Figure 4
Figure 4. Figure 4: Context-compression stress test on 30 ALFWorld tasks and 5 runs per task. Naive [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Step-index leakage at L8. Real binary AUROC (1.000) drops to chance (0.297) under label shuffling. The Ridge regression target at L32 (continuous plan-signal magnitude) sidesteps this confound. A.4 Probe Calibration Brier score and 10-bin Expected Calibration Error (ECE) at L32 are in [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Reliability diagram for the L32 probe. Diagonal indicates perfect calibration. A.5 Strict-Strip Per-Task Recovery Per-task strict-strip recovery on R1-Distill-Llama-70B is in [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Multi-model panel. Llama-3.1-70B: full replay-paired decay curve, peak [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
read the original abstract

Long-horizon agents depend on context management: systems compress, summarize, and evict old tokens so tasks can continue beyond finite windows. That is safe only when dropped information is no longer needed or has been internalized. Plans are the stress case: they are written early, used for many steps, and first to be evicted. We introduce replay pairing, a diagnostic that runs the same trajectory with and without the plan in history and measures hidden-state cosine distance. On Llama-3.1-70B, plan signal spikes to 0.453 one step after the plan, then falls 4.1x in a single action-observation step; HotpotQA falls 12.4x. This is evidence that standard LLM agents do not carry plans forward as persistent state, and instead depend on the plan remaining in context. A layer-L32 probe detects this decay as a diagnostic, not as proof that it reads plan content itself. Reasoning models add a measurement confound: their `<think>` traces re-derive plan content, so standard stripping leaves plan evidence in the stripped condition. We name this the reasoning-trace confound and fix it with strict stripping, which removes prior `<think>` blocks from the stripped run only. It recovers +163% of the step+1 signal in-sample and +153% held out, while not meaningfully changing non-reasoning Llama (+4.8%). On DeepSeek-R1-Distill-Llama-70B, a Llama-trained probe transfers at AUROC 0.748 (p=6e-4), while R1-specific probes reach 1.000, suggesting R1 encodes plan signal in a different hidden-state direction. Finally, a compression stress test shows the practical cost: naive plan eviction cuts ALFWorld success by 34.7pp, while probe-gated re-surfacing does not recover it. The contribution is a measurement and stress-test framework showing that agent-critical information can be context-resident rather than persistent. Context management is load bearing, but plan protection alone is not enough.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript claims that plans in standard LLM agents do not persist as internal state but remain context-resident, demonstrated via replay pairing: identical trajectories run with/without the initial plan yield hidden-state cosine distance that spikes to 0.453 one step after the plan then decays 4.1× (Llama-3.1-70B) or 12.4× (HotpotQA) after a single action-observation step. A layer-L32 probe serves as a diagnostic; reasoning models introduce a trace confound addressed by strict stripping (+153% held-out recovery); probe transfer reaches AUROC 0.748 on DeepSeek-R1-Distill-Llama-70B; and a compression stress test shows naive plan eviction drops ALFWorld success by 34.7pp while probe-gated resurfacing does not recover it. The contribution is a measurement framework showing context management is load-bearing for agent-critical information.

Significance. If the central measurements hold, the work supplies a concrete diagnostic (replay pairing + probe) for distinguishing context-resident versus internalized information in LLM agents, with direct implications for compression and eviction policies. Credit is due for the use of held-out data, external task-success metrics (ALFWorld), the explicit handling of the reasoning-trace confound, and the probe-transfer result across model families. These elements make the empirical case stronger than purely in-sample observations.

major comments (2)
  1. [replay pairing setup] Replay pairing (abstract and §3): the claim that cosine-distance decay demonstrates plan non-persistence specifically requires that the measured difference isolates the plan token sequence rather than any early-context token. No control is reported that replaces the plan prefix with a matched-length non-plan sequence while keeping subsequent tokens identical; without it the observed 0.453 spike and rapid fall after one step remains consistent with generic early-context overwriting.
  2. [compression stress test] Compression stress test (abstract): the reported 34.7pp ALFWorld drop under naive eviction is load-bearing for the practical claim, yet the manuscript provides no variance, number of episodes, or statistical test; this weakens the assertion that probe-gated resurfacing “does not recover it” relative to the baseline.
minor comments (2)
  1. [probe interpretation] The abstract states the probe is “a diagnostic, not … proof that it reads plan content itself,” but the main text should explicitly restate this caveat when interpreting the L32 probe and transfer AUROC numbers.
  2. [methods] Notation for the strict-stripping procedure and the reasoning-trace confound should be introduced with a short equation or pseudocode block to make the +163% / +153% recovery numbers easier to replicate.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the strengths of the measurement framework, including the use of held-out data, external metrics, and handling of the reasoning-trace confound. We address each major comment below, indicating revisions where appropriate.

read point-by-point responses

  1. Referee: [replay pairing setup] Replay pairing (abstract and §3): the claim that cosine-distance decay demonstrates plan non-persistence specifically requires that the measured difference isolates the plan token sequence rather than any early-context token. No control is reported that replaces the plan prefix with a matched-length non-plan sequence while keeping subsequent tokens identical; without it the observed 0.453 spike and rapid fall after one step remains consistent with generic early-context overwriting.

    Authors: We agree that a matched-length non-plan prefix control would better isolate plan-specific effects from generic early-context overwriting. The current design holds all post-prefix tokens fixed, so observed differences are due to the prefix, but this does not rule out that any distinctive early prefix could produce similar dynamics. In the revised manuscript we will add results from such a control (neutral instructions and random token sequences of matched length) and report the resulting cosine-distance trajectories to quantify specificity. revision: yes

  2. Referee: [compression stress test] Compression stress test (abstract): the reported 34.7pp ALFWorld drop under naive eviction is load-bearing for the practical claim, yet the manuscript provides no variance, number of episodes, or statistical test; this weakens the assertion that probe-gated resurfacing “does not recover it” relative to the baseline.

    Authors: We agree that variance, episode count, and statistical testing are required to support the practical claim. The ALFWorld results were obtained on a fixed evaluation set; the revised manuscript will report the exact number of episodes, per-condition success rates with standard deviation, and an appropriate statistical comparison (e.g., McNemar’s test) between naive eviction and the probe-gated condition. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical measurements are self-contained

full rationale

The paper's central claims rest on replay-pairing experiments that directly compare hidden-state cosine distances and probe AUROCs between matched trajectories (with/without plan prefix) on held-out data and external task success rates (ALFWorld). No equations, fitted parameters, or self-citations are used to derive the reported decay factors (4.1×, 12.4×) or the +163% recovery; these are raw measurements. The reasoning-trace confound is addressed by an explicit control (strict stripping) whose effect is quantified on the same data. No load-bearing step reduces to a definition, prior self-citation, or ansatz smuggled in; the work is a measurement framework against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Review based solely on abstract; no explicit free parameters, axioms, or invented entities beyond the introduced diagnostic method itself.

invented entities (1)
  • replay pairing no independent evidence
    purpose: diagnostic to measure plan persistence via hidden-state comparison
    New measurement technique introduced by the paper; no independent evidence outside this work.

pith-pipeline@v0.9.1-grok · 5914 in / 1155 out tokens · 24653 ms · 2026-06-26T08:25:44.773417+00:00 · methodology

discussion (0)

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