Writing study: MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent

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← ICLR 2026 oral writing corpus

Hongli Yu, Tinghong Chen, Jiangtao Feng, Jiangjie Chen, Weinan Dai, Qiying Yu, Ya-Qin Zhang, Wei-Ying Ma, Jingjing Liu, Mingxuan Wang, Hao Zhou · Official ICLR 2026 oral record

Observed writing choices, not acceptance rules. This study examines the inspected manuscript version. It does not establish why the paper was accepted or that its presentation choices should be copied unchanged.

Figure, table and section ledger

Editorial contribution type: Learning & optimization algorithms (primary) · Architecture & representation design.

Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.

Canonical PDF

29 physical pages

Before references / appendix

≈ 11.11 page-equivalents

Main figures / tables

9 / 4

Attached appendix figures / tables

1 / 7

Attached appendix span

≈ 12.89 page-equivalents

Before references / appendix: ≈ 38.31%References / intervening material: ≈ 17.24%Attached appendix: ≈ 44.45%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Section-by-section lengths and source pages

The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.

Top-level heading	Starts on PDF page	Estimated span	Share of entire PDF
1 INTRODUCTION	1	≈ 1.89 pp.	6.52%
2 METHODOLOGY	3	≈ 2.74 pp.	9.45%
3 EXPERIMENTS	6	≈ 3.45 pp.	11.9%
4 RELATED WORK	9	≈ 0.89 pp.	3.07%
5 CONCLUSION	10	≈ 0.35 pp.	1.21%
REPRODUCIBILITY STATEMENT	11	≈ 1 pp.	3.45%
REFERENCES	12	≈ 5 pp.	17.24%
A IMPLEMENTATION DETAILS	17	≈ 2.41 pp.	8.31%
B COMPUTATION COMPLEXITY	19	≈ 0.8 pp.	2.76%
C CASE STUDY	20	≈ 1.79 pp.	6.17%
D SUPPLEMENTARY EXPERIMENTS	22	≈ 1.72 pp.	5.93%
E LLM USAGE	23	≈ 0.28 pp.	0.97%
F FALIURE PATTERN STUDY	24	≈ 5.89 pp.	20.31%

All detected figure and table locations

Main text

• Figure 1 · p. 1
• Figure 2 · p. 3
• Figure 3 · p. 3
• Figure 4 · p. 4
• Table 1 · p. 5
• Table 2 · p. 6
• Table 3 · p. 6
• Figure 5 · p. 7
• Figure 6 · p. 8
• Figure 7 · p. 8
• Figure 8 · p. 9
• Figure 9 · p. 9
• Table 4 · p. 10

Attached appendix

• Table 5 · p. 17
• Table 6 · p. 18
• Table 7 · p. 19
• Figure 10 · p. 19
• Table 8 · p. 22
• Table 9 · p. 22
• Table 10 · p. 23
• Table 11 · p. 23

Argument and organization

1. Long-document bottleneck and memory workflow

   Separate input length from the model's active context

2. Multi-conversation outcome-reward optimization

   Explain how final-answer reward trains intermediate memory updates

3. Length sweeps, ablations and failure traces

   Test retention and disclose recurrent-memory failure modes

Problem, claim and contribution

The central distinction is processing millions of input tokens through repeated bounded-context calls, not creating a million-token attention window. The 1,024-token memory is overwritten after each 5,000-token chunk; the question remains available throughout processing.

Sections 2.1 and 3.1, pages 3–6 · Main paper

Argument and section order

Figure 2 contrasts monolithic attention with recurrent textual memory; Figure 3 then introduces a conversation axis into policy optimization. Keeping workflow and training diagrams separate makes the final-outcome credit assignment legible.

Page 3, Figures 2–3; equations 1–3 · Main paper

Figures, captions and visual explanation

The NIAH heatmap exposes task variants and context lengths instead of hiding them in an average. At 512K the 14B average is 98.18, but Level 2 is 94.53; the headline above 95% therefore does not describe every variant.

Page 7, Figure 5 · Main paper

Tables, comparisons and evidence

RULER-HQA decreases from 80.47 to 71.09 for 14B between 7K and 3.5M: 9.38 percentage points, not less than 10% relative degradation. The 128-question synthesized test and shared construction with training constrain extrapolation claims.

Table 1, page 5; Appendix A.3, page 18 · Main paper

Appendix and reproducibility

Memory-difference traces show overwrite, missing prerequisite information and primacy bias. These concrete failures substantially qualify the main-text near-lossless language. Complexity uses an estimated FLOP model, while prompt and reward details support implementation checking.

Appendices A–B and F, pages 17–20 and 24–29 · Main paper

Clarity and writing conventions

Reproducibility is helped by explicit templates but hurt by inconsistent pseudocode and arithmetic: Algorithm 1 concatenates histories despite context-independent prose, and Table 11's RAG averages do not equal means of its five displayed task scores.

Page 17, Algorithm 1; page 23, Table 11 · Main paper

What to reuse—and what not to infer

Transferable lesson: For recurrent agents, document the complete state transition and show failure-state diffs, not only successful final answers or nominal context capacity.

Boundary: Arbitrary stream length is not guaranteed lossless memory or constant total runtime. Fixed-window linear scaling and estimated FLOPs do not establish latency; appendix inconsistencies require implementation verification before reproducing the method.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked

2026-09-19

Reading status

Full text + selected visuals inspected

Main text

Pages 1–11, Sections 1–5 and reproducibility statement; references 12–16

Appendix / supplement

Pages 17–29, Appendices A–F including algorithm, training/evaluation, complexity estimate, case study, supplementary baselines, AI disclosure and failure traces

Visually inspected PDF pages

3, 7

• official ICLR 2026 proceedings PDF · 29 pages