When Can Agents Forget Their Reasoning? ICLR for Long-Horizon Agent Context Compression
Mingxuan Wang, Fei Luo, Bo Wang, Guorun Yao, Yinglong Guo, Chao Ning, Hongyue Chen, Yanbiao Ma, et al.
cs.AI · cs.CV
Quick verdict
ICLR prunes low-entropy agent reasoning online, cutting tokens and lifting reward, but gains hinge on one domain, delete-all nearly matches, stats thin.
Summary
The paper asks whether an LLM agent's past reasoning can be deleted from context once its actions have been executed. It proposes ICLR, a training-free online compressor that partitions each step's new reasoning into blocks, scores them with predictive entropy from a frozen proxy model (Qwen3.5-9B), and replaces the lowest-entropy 80% with a [SKIP] placeholder while preserving actions, tool calls, and observations. On 260 WorkBuddyBench tasks with DeepSeek-V4-Flash as the acting agent, average reward rises from 0.699 to 0.718 while input, output, and cache-read tokens fall 25.5%, 14.4%, and 33.3%. Ablations reveal 'trajectory amplification': local deletion strength and total computation are nonmonotonic (random 10% deletion saves more input than random 20%). Probing (AUROC 0.844 under task-level nested cross-validation), activation patching (KL repair rising from 0.047 to 0.978 across depth), and controlled interventions support the central claim that reasoning becomes replaceable once task-relevant state is externalized into code, files, tool outputs, or feedback (future risk 67.3% unexternalized vs. 36.2% externalized). Main limitations: the aggregate reward gain is driven by the Security domain while Code, Office, and Web decline; a delete-all-reasoning baseline nearly matches ICLR at far larger token savings; there are no significance tests or repeated runs; and no code is released.
Review complete
Assessment at a glance
6
risk flags identified
● high 2
● medium 3
● low 1
4 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- Online removal of low-entropy reasoning blocks (ICLR) raises average reward from 0.699 to 0.718 on 260 WorkBuddyBench tasks while cutting input, output, and cache-read tokens by 25.5%, 14.4%, and 33.3%.
- Local reasoning deletion and total trajectory computation are nonmonotonic ('trajectory amplification') because edits change later decisions and the number of subsequent requests.
- Current hidden states predict future reasoning reuse (probe AUROC 0.844 under strict task-level nested evaluation), and deleted-history information is progressively restorable via activation patching as depth increases.
- Historical reasoning becomes replaceable once task-relevant derived state is externalized into code, files, tool outputs, or environmental feedback (future behavioral risk 67.3% vs. 36.2%).
Strengths
Closed-loop framing and trajectory amplification
Moving CoT compression from static post-hoc editing into the live agent loop is well-motivated, and the amplification effect is concretely demonstrated: random 10% deletion saves 60.5% total input vs. 44.0% for random 20% (Table 10), formalized by the exact token decomposition (Prop B.5) and the perturbation bound (Thm B.3).
Disciplined probing protocol
Future-necessity probing uses strict task-level nested cross-validation with layer/pooling/PCA selection confined to training tasks (App G.2), reaching AUROC 0.844 and AP 0.811 on 265 boundary samples from 31 tasks — hygiene many probing studies lack.
Activation patching with a real positive control
Triangulated externalization account
Weaknesses
Headline gain is single-domain
Entropy ranking's marginal value unproven
No statistical machinery for main results
Mechanistic evidence is on the wrong model and small cohorts
Key label and hyperparameter opacity; no release
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Scope & Claims
The paper asks when a long-horizon LLM agent can delete its own past reasoning without harming future behavior. It contributes (1) ICLR, a training-free online compressor that scores reasoning blocks with proxy predictive entropy (frozen Qwen3.5-9B) and replaces the lowest-entropy 80% with a [SKIP] placeholder while never touching actions, tool calls, or observations; (2) the trajectory-amplification observation that local deletion strength and total computation decouple (Table 10: random 10% deletion saves 60.5% total input vs. 44.0% for random 20%; delete-all saves 63.6%); (3) a mechanistic account (probing AUROC 0.844; layerwise KL repair 0.047 to 0.978; externalization gap 67.3% vs. 36.2%) arguing reasoning is replaceable once derived state lives in code, files, tool outputs, or feedback. The central empirical claim — reward improves while tokens fall — needs qualification: the aggregate gain is concentrated in a single domain.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 4 strengths & 5 weaknesses, analyzed in detail
- 6 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
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Frequently asked questions
What does ICLR stand for in this paper (it shares a name with a famous conference)?
Here ICLR means Interaction Aware Compression for Long-Horizon Reasoning — a training-free method that deletes low-entropy reasoning blocks from an LLM agent's interaction history while keeping actions, tool calls, and observations intact. It has no relation to the ICLR machine-learning conference; the shared acronym is coincidental.
Does the ICLR compression method improve agent accuracy on all task types?
No. Across the full 260-task WorkBuddyBench evaluation the average reward rises from 0.699 to 0.718, but the gain is driven by the Security domain (+22.9 points); Code, Office, and Web scores all decline. The paper's own ablations also show that a simpler 'delete all reasoning' baseline nearly matches the full method at much larger token savings.
What is trajectory amplification in agent context compression?
It is the paper's term for the finding that deleting a small fraction of reasoning text can change the agent's later decisions, so total computation changes nonlinearly. For example, randomly deleting 10% of reasoning tokens reduced total input tokens by 60.5%, while randomly deleting 20% reduced them by only 44.0%.
Is code or data available for this paper?
No public code, prompts, or trajectory data release is stated. The appendices document the protocol in detail (block segmentation, entropy formulas, runtime settings, cohort sizes), but the WorkBuddyBench tasks and the DeepSeek-V4-Flash and Qwen3.5-9B models would need to be obtained separately to reproduce results.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figures 1-4 are only partially recoverable from the extracted text; qualitative figure claims were cross-checked against the corresponding tables where possible.
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- arXiv categories include cs.CV, but the extracted text contains no vision content.
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