In-Place Feedback: Reliable Refinement for Multi-Turn Expert-LLM Collaboration

Youngbin Choi; Minjong Lee; Saemi Moon; Seunghyuk Cho; Chaehyeon Chung; MoonJeong Park; Dongwoo Kim

cs.LGOct 1, 2025

In-Place Feedback: Reliable Refinement for Multi-Turn Expert-LLM Collaboration

Authors: Youngbin Choi, Minjong Lee, Saemi Moon, Seunghyuk Cho, Chaehyeon Chung, MoonJeong Park, Dongwoo Kim

Organizations: Graduate School of Artificial Intelligence, POSTECH · Department of Computer Science and Engineering, POSTECH

Abstract

LLM-generated drafts often contain subtle factual or logical errors, yet prior work shows that models struggle to reliably integrate multi-turn feedback aimed at fixing them. We propose in-place feedback, an interaction paradigm in which the user directly edits the model's previous response and the model continues generation from the edited context. In-place feedback consistently outperforms standard multi-turn feedback across five reasoning-intensive benchmarks while requiring fewer tokens, and our fine-grained analysis shows that it applies corrections more reliably and propagates them to subsequent reasoning. A user study with domain experts refining LLM-generated summaries corroborates these findings: participants report higher final-output satisfaction and substantially lower fatigue with in-place feedback, and a mixed strategy combining in-place and multi-turn feedback improves every dimension we measure. These results suggest that editing errors in place is a more reliable way to incorporate expert corrections than multi-turn feedback, while combining the two acts complementarily to form a more effective collaboration than either mechanism alone.

Figures & tables

Figure 1: Illustration of common failure cases in multi-turn conversational feedback and in-place feedback. After in-place feedback, the LLM continues generation from the green word “ requires ”.

Figure 2: Representative examples of in-place feedback on a toy problem. Red marks incorrect reasoning in the original response, blue indicates user corrections with in-place feedback, and green shows the subsequent reasoning based on the corrected context. Additional examples are provided in Section I.1 .

Figure 3: Performance comparison across five datasets. Across all datasets and LLMs, in-place feedback consistently outperforms the multi-turn and memory-based feedback approaches. The points with black borders represent the baselines’ performance at the last turn.

Figure 4: The number of input and generated tokens across multiple turns for Gemma-3-4b-it . In-place feedback consistently requires fewer tokens than multi-turn feedback. The token counts for the other models and datasets can be found in Appendix H .

Dataset	Model	Method	Number of turns
Dataset	Model	Method	$0$	$1$	$2$	$3$	$4$	$5$	$6$	$7$	$8$	$9$
MATH-hard	Qwen	Multi-turn	$0.550$	$0.684$	$0.738$	$0.766$	$0.792$	$0.816$	$0.838$	$0.850$	$0.858$	$0.862$
		Localized MT	$0.550$	$0.682$	$0.758$	$0.790$	$0.808$	$0.830$	$0.838$	$0.848$	$0.852$	$0.858$
		In-place	$0.550$	$\mathbf{0.714}$	$\mathbf{0.766}$	$\mathbf{0.812}$	$\mathbf{0.852}$	$\mathbf{0.888}$	$\mathbf{0.904}$	$\mathbf{0.918}$	$\mathbf{0.924}$	$\mathbf{0.928}$
	Gemma	Multi-turn	$0.492$	$0.606$	$0.684$	$0.730$	$0.760$	$0.776$	$0.792$	$0.804$	$0.820$	$0.828$
		Localized MT	$0.492$	$0.612$	$0.692$	$\mathbf{0.750}$	$0.776$	$0.794$	$0.808$	$0.824$	$0.838$	$0.846$

Table 1: Cumulative accuracy on MATH-hard and GPQA with Qwen-2.5-7B-Instruct and Gemma-3-4B-it . Localized MT uses the same in-place agent as in-place feedback, but the edited response is delivered as a multi-turn user message with the full dialogue history.

Figure 5: CPR, FAR, and CTRR for 10-turn. The points with a black border represent the second response of the LLMs (i.e.,

y_{1}

), and the subsequent responses across turns are connected by lines.

Figure 6: Impact of accumulated dialogue history. For CPR, FAR, and CTRR, we report the difference between the multi-turn feedback rates and the rates under pruned-history settings. Negative values indicate performance degradation due to accumulated history.

Method	Strengths	Weaknesses
Multi-turn	“Generates natural sentences that reflect my intended nuance when I know the direction but not the exact wording.”, “Good at revising the overall flow.”	“Fatigue becomes severe when the model repeats the same mistake after correction.”, “Untargeted parts get modified, and previously accepted edits are undone.”, “Waiting for full regeneration every turn is tiring.”
In-place	“I can quickly convey my intent without making the model understand it first.”, “Convenient for fixing a word or phrase directly.”, “Useful when the opening of a sentence is fixed and I want the model to continue naturally.”	“The model’s drafting ability is hard to leverage when I don’t know how to fix it myself.”, “Sentences after the edited region sometimes change slightly.”, “Tedious when many small items need to be removed at once.”
Mixed	“Restructuring with multi-turn first and then polishing details with in-place was the most efficient.”, “Combines the advantages of both methods.”, “Lowest fatigue and highest satisfaction overall.”	“Choosing which mode to use for each edit is itself a small burden.”, “I tended to use in-place as the default and multi-turn only as a fallback.”

Table 2: Summary of user experience across interaction modes. Representative strengths and weaknesses of three interaction modes from open-ended responses collected in our user study.

Figure 7: User study results across the five questionnaire items, scored on a 1–10 scale (higher is better; for fatigue , 10 corresponds to not fatigued ). In-place feedback outperforms standard multi-turn feedback on every dimension, and a mixed strategy that combines both paradigms is rated highest overall.

Appendix figures & tables73 assets

Supplementary material from the paper’s appendix.

Appendix

Figure D.1: Grid and cell accuracy on the ZebraLogic dataset. Top-

4

feedback is used.

Figure D.2: Grid and cell accuracy on the ZebraLogic dataset. Top-

2

feedback is used.

Figure D.3: CPR, FAR, and CTRR for 10-turn with top-2 feedback. Filled points denote in-place feedback, and hollow points denote multi-turn feedback. The points with a black border represent the second response of the LLMs (i.e.,

y_{1}

), and the subsequent responses across turns are connected by lines.

Figure D.4: CPR, FAR, and CTRR for 10-turn conversations of LLMs on the ZebraLogic dataset. Top-

4

feedback is used. The points with a black border represent the second response of the LLMs (i.e.,

y_{1}

), and the subsequent responses across turns are connected by lines.

Figure D.5: CPR, FAR, and CTRR for 10-turn conversations of LLMs on the ZebraLogic dataset. Top-

2

feedback is used. The points with a black border represent the second response of the LLMs (i.e.,

y_{1}

), and the subsequent responses across turns are connected by lines.

Figure D.6: Grid and cell accuracy of LLMs on the ZebraLogic dataset. In-place feedback consistently outperforms multi-turn feedback under an oracle setting.

Figure D.7: CPR, FAR, and CTRR for 10-turn conversations of LLMs on the ZebraLogic dataset. The oracle feedback function is used. Filled points denote in-place feedback, and hollow points denote multi-turn feedback. The points with a black border represent the second response of the LLMs (i.e.,

y_{1}

), and the subsequent responses across turns are connected by lines.

Model	Method	0	1	2	3	4	5	6	7	8	9
Qwen	Multi-turn	0.005	0.009	0.010	0.014	0.016	0.017	0.017	0.019	0.021	0.024
Qwen	In-place	0.005	0.009	0.017	0.028	0.037	0.049	0.059	0.068	0.079	0.086
Gemma	Multi-turn	0.000	0.002	0.003	0.005	0.009	0.010	0.017	0.019	0.021	0.023
Gemma	In-place	0.000	0.009	0.018	0.034	0.042	0.044	0.055	0.067	0.076	0.090

Appendix

Table E.1: Grid accuracy on ZebraLogic under incomplete top-4 feedback. Feedback indicates only that a cell is incorrect, without providing the correct value.

Model	Method	0	1	2	3	4	5	6	7	8	9
Qwen	Multi-turn	0.274	0.284	0.290	0.296	0.295	0.297	0.296	0.299	0.300	0.299
Qwen	In-place	0.274	0.304	0.341	0.372	0.395	0.414	0.434	0.444	0.453	0.449
Gemma	Multi-turn	0.273	0.283	0.307	0.319	0.333	0.344	0.346	0.346	0.354	0.361
Gemma	In-place	0.273	0.309	0.336	0.371	0.392	0.414	0.424	0.449	0.461	0.472

Appendix

Table E.2: Cell accuracy on ZebraLogic under incomplete top-4 feedback.

Model	Method	Number of turns
Model	Method	0	1	2	3	4	5	6	7	8	9
Qwen	Multi-turn	0.550	0.596	0.614	0.622	0.624	0.626	0.632	0.634	0.634	0.634
Qwen	In-place	0.550	0.606	0.632	0.646	0.650	0.660	0.666	0.668	0.676	0.686
Gemma	Multi-turn	0.492	0.566	0.610	0.630	0.646	0.650	0.658	0.664	0.670	0.670
Gemma	In-place	0.492	0.600	0.638	0.658	0.684	0.694	0.698	0.710	0.714	0.722

Appendix

Table E.3: Incomplete-feedback ablation on MATH-hard. Feedback only identifies the erroneous part of the previous response, without specifying how to correct it.

Model	Method	Number of turns
Model	Method	0	1	2	3	4	5	6	7	8	9
Qwen	Multi-turn	0.103	0.119	0.119	0.143	0.143	0.151	0.151	0.151	0.159	0.159
Qwen	In-place	0.103	0.119	0.119	0.151	0.191	0.198	0.214	0.230	0.230	0.230
Gemma	Multi-turn	0.056	0.079	0.079	0.087	0.103	0.103	0.103	0.103	0.111	0.111
Gemma	In-place	0.056	0.087	0.087	0.111	0.127	0.127	0.135	0.143	0.143	0.151

Appendix

Table E.4: Incomplete-feedback ablation on GPQA. Feedback only identifies the erroneous part of the previous response, without specifying how to correct it.

Category	Sub-type	MATH (%)	Zebra (%)	Description
Appropriate	–	40.6	53.9	Correct localization, minimal fix, proper pruning.
Incomplete	Over-prune	18.2	0.7	Valid reasoning discarded beyond what feedback necessitated.
	Under-prune	3.8	39.9	Erroneous downstream content retained.
	Partial address	33.4	–	Feedback partially incorporated.
	Misapplied	2.4	4.2	Feedback misunderstood or not reflected in the edit.
Excessive	–	1.6	1.3	Rewriting valid content beyond feedback scope.

Appendix

Table E.5: Reliability analysis of the in-place agent on MATH-Hard (Gemma-3-4b-it) and ZebraLogic (top-4 feedback), evaluated by Claude 4.5 Sonnet as LLM-judge. Most edits are appropriate or incomplete; aggressive over-rewriting (excessive) is rare.

Figure G.1: Performance comparison in MATH-hard, MMLU-pro, GPQA, and LiveCodeBench. Across all datasets and large-scale LLM models, in-place feedback consistently outperforms the multi-turn and memory-based feedback approaches. Tabulated results are provided in Tables G.1 , G.2 , G.3 and G.4 .

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	31.2	52.6	63.2	71.0	74.6
	Memory	31.2	51.6	62.2	67.0	72.4
	In-place	31.2	53.6	64.6	71.8	76.8
Gemma-3-27B	Multi-turn	78.2	86.6	89.2	90.4	91.0
	Memory	78.2	86.2	87.8	89.8	90.8

Appendix

Table G.1: Performance comparison on MATH-hard.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	54.8	70.2	75.3	77.1	77.9
	Memory	54.8	68.8	76.5	78.5	80.2
	In-place	54.8	70.6	77.7	79.1	81.3
Gemma-3-27B	Multi-turn	61.3	73.4	76.3	79.3	80.5
	Memory	61.3	74.6	78.7	80.3	81.4

Appendix

Table G.2: Performance comparison MMLU-pro.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	18.3	29.4	42.1	46.0	50.8
	Memory	18.3	30.2	40.5	46.6	50.8
	In-place	18.3	31.8	42.9	46.8	53.2
Gemma-3-27B	Multi-turn	17.5	34.1	42.9	48.4	51.6
	Memory	17.5	33.3	39.7	44.4	47.6

Appendix

Table G.3: Performance comparison on GPQA.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	49.1	62.3	68.9	73.1	76.1
	Memory	49.1	56.9	63.5	66.5	70.1
	In-place	49.1	65.3	71.9	74.3	77.3
Gemma-3-27B	Multi-turn	54.5	76.1	80.8	84.4	85.6
	Memory	54.5	76.1	80.8	84.4	86.8

Appendix

Table G.4: Performance comparison on LiveCodeBench.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	231.0	715.1	1373.4	2070.4	2902.1
	Memory	231.0	740.4	1059.7	1301.0	1340.6
	In-place	231.0	378.0	472.7	534.1	610.2
Gemma-3-27B	Multi-turn	177.9	1833.3	3527.8	4934.1	6250.7
	Memory	177.9	1897.3	1769.4	1958.0	2020.5

Appendix

Table G.5: Number of input tokens on Math-hard.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	165.1	747.6	1342.3	1987.7	2608.3
	Memory	165.1	776.8	954.3	1187.8	1210.4
	In-place	165.1	451.9	515.8	560.3	595.4
Gemma-3-27B	Multi-turn	149.0	894.6	1672.0	2454.1	3209.8
	Memory	149.0	924.6	1117.7	1315.8	1314.0

Appendix

Table G.6: Number of input tokens on MMLU-pro.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	224.1	998.2	1720.3	2405.4	3099.9
	Memory	224.1	1027.5	1138.1	1309.9	1307.2
	In-place	224.1	585.9	636.0	694.4	752.2
Gemma-3-27B	Multi-turn	205.0	1085.9	1903.6	2754.2	3606.0
	Memory	205.0	1115.9	1230.8	1380.2	1414.2

Appendix

Table G.7: Number of input tokens on GPQA.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	584.3	1317.0	2055.4	2835.1	3598.0
	Memory	584.3	1342.2	1439.8	1648.5	1662.7
	In-place	584.3	931.8	1007.7	1080.3	1186.6
Gemma-3-27B	Multi-turn	594.3	1251.5	1922.7	2529.5	3266.9
	Memory	594.3	1571.1	1914.6	2182.0	2254.8

Appendix

Table G.8: Number of input tokens on LiveCodeBench.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	915.3	2488.4	4109.8	5844.1	7626.1
	Memory	915.3	2454.8	2250.5	2460.7	2558.0
	In-place	915.3	2147.0	3037.9	3612.3	4322.2
Gemma-3-27B	Multi-turn	961.4	2269.8	2971.6	3663.9	4401.4
	Memory	961.4	2245.8	2133.8	2421.5	2599.9

Appendix

Table G.9: Number of input tokens on ZebraLogic.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	279.8	380.0	482.5	522.4	593.4
	Memory	279.8	434.3	555.5	615.1	592.4
	In-place	279.8	249.2	239.0	315.2	300.5
Gemma-3-27B	Multi-turn	927.4	1134.5	1238.2	1154.2	1098.9
	Memory	927.4	969.4	1134.5	1222.9	1204.4

Appendix

Table G.10: Number of generated tokens on Math-hard.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	310.1	385.4	423.6	467.4	477.6
	Memory	310.1	432.1	515.1	575.7	623.8
	In-place	310.1	153.9	160.8	166.7	147.7
Gemma-3-27B	Multi-turn	420.3	511.5	561.6	561.3	605.3
	Memory	420.3	586.8	680.9	712.1	723.7

Appendix

Table G.11: Number of generated tokens on MMLU-pro.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	597.8	549.2	508.2	515.6	571.9
	Memory	597.8	574.9	607.8	600.5	620.3
	In-place	597.8	280.1	229.7	232.3	259.7
Gemma-3-27B	Multi-turn	738.9	641.1	643.1	666.4	679.0
	Memory	738.9	683.3	728.2	751.3	749.8

Appendix

Table G.12: Number of generated tokens on GPQA.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	467.9	488.3	513.9	553.0	517.2
	Memory	467.9	369.2	392.5	420.2	429.3
	In-place	467.9	233.2	217.7	238.8	212.5
Gemma-3-27B	Multi-turn	449.1	573.4	499.5	472.8	522.9
	Memory	449.1	552.9	704.2	835.5	895.1

Appendix

Table G.13: Number of generated tokens on LiveCodeBench.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	1429.6	1444.6	1439.1	1431.2	1445.6
	Memory	1429.6	1126.5	1200.4	1178.2	1216.5
	In-place	1429.6	1069.6	791.1	762.5	666.8
Gemma-3-27B	Multi-turn	1191.3	575.1	590.1	585.2	605.5
	Memory	1191.3	985.0	1153.2	1250.3	1313.4

Appendix

Table G.14: Number of generated tokens on ZebraLogic.

Figure G.2: Grid and cell accuracy on the ZebraLogic dataset with large-scale LLMs. Tabulated results are provided in Tables G.15 , G.17 , G.16 and G.18 .

Figure G.3: CPR, FAR, and CTRR for five-turn conversations of large-scale LLMs on the ZebraLogic dataset with top-

4

feedback. The points with a black border represent the second response of the LLMs (i.e.,

y_{1}

), and the subsequent responses across turns are connected by lines.

Figure G.4: CPR, FAR, and CTRR for five-turn conversations of large-scale LLMs on the ZebraLogic dataset with top-

2

feedback. The points with a black border represent the second response of the LLMs (i.e.,

y_{1}

), and the subsequent responses across turns are connected by lines.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	0.031	0.140	0.314	0.550	0.691
	Memory	0.031	0.134	0.326	0.489	0.674
	In-place	0.031	0.131	0.330	0.574	0.740
Gemma-3-27B	Multi-turn	0.042	0.103	0.204	0.330	0.482
	Memory	0.042	0.122	0.241	0.384	0.518

Appendix

Table G.15: Grid accuracy of large-scale LLMs on ZebraLogic with top-

4

feedback.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	0.031	0.096	0.195	0.337	0.492
	Memory	0.031	0.108	0.234	0.360	0.455
	In-place	0.031	0.096	0.201	0.335	0.475
Gemma-3-27B	Multi-turn	0.042	0.094	0.166	0.234	0.314
	Memory	0.042	0.099	0.180	0.262	0.344

Appendix

Table G.16: Grid accuracy of large-scale LLMs on ZebraLogic with top-

2

feedback.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	0.354	0.554	0.713	0.819	0.890
	Memory	0.354	0.559	0.714	0.816	0.871
	In-place	0.354	0.565	0.731	0.832	0.890
Gemma-3-27B	Multi-turn	0.392	0.564	0.698	0.800	0.875
	Memory	0.392	0.567	0.684	0.774	0.834

Appendix

Table G.17: Cell accuracy of large-scale LLMs on ZebraLogic. Top-

4

feedback is used.

Model	Method	Number of turns
Model	Method	0	1	2	3	4
Llama-3.1-70B	Multi-turn	0.354	0.480	0.590	0.694	0.756
	Memory	0.354	0.485	0.580	0.640	0.696
	In-place	0.354	0.487	0.599	0.677	0.730
Gemma-3-27B	Multi-turn	0.392	0.499	0.586	0.651	0.711
	Memory	0.392	0.496	0.578	0.645	0.699

Appendix

Table G.18: Cell accuracy of large-scale LLMs on ZebraLogic with top-

2

feedback.

	Method	Number of turns
	Method	0	1	2	3	4	5	6	7	8	9
Qwen	Multi-turn	55.0	68.4	73.8	76.6	79.2	81.6	83.8	85.0	85.8	86.2
	Memory	55.0	62.6	66.2	69.2	71.2	72.8	74.2	75.0	76.4	77.4
	In-place	55.0	71.4	76.6	81.2	85.2	88.8	90.4	91.8	92.4	92.8
Gemma	Multi-turn	49.2	60.6	68.4	73.0	76.0	77.6	79.2	80.4	82.0	82.8
	Memory	49.2	60.4	65.0	68.0	70.6	72.6	74.6	76.4	77.2	78.0

Appendix

Table H.1: Performance comparison on Math-hard.

	Method	Number of turns
	Method	0	1	2	3	4	5	6	7	8	9
Qwen	Multi-turn	56.2	68.0	72.6	75.1	77.3	78.1	79.1	79.3	80.1	80.5
	Memory	56.2	67.1	70.8	73.4	75.7	77.7	78.5	79.7	80.7	80.9
	In-place	56.2	69.8	78.5	80.1	81.9	83.0	83.8	84.6	85.0	85.4
Gemma	Multi-turn	41.4	54.0	60.0	65.9	68.4	69.8	71.2	72.0	72.8	73.4
	Memory	41.4	52.9	57.2	63.5	66.3	68.0	69.6	70.4	71.6	72.2

Appendix

Table H.2: Performance comparison on MMLU-pro.

	Method	Number of turns
	Method	0	1	2	3	4	5	6	7	8	9
Qwen	Multi-turn	10.3	17.5	27.8	38.1	40.5	42.9	43.7	43.7	43.7	46.0
	Memory	10.3	19.0	27.0	29.4	34.9	36.5	37.3	38.1	38.1	38.9
	In-place	10.3	25.4	36.5	48.4	51.6	57.9	63.5	64.3	66.7	69.0
Gemma	Multi-turn	5.6	9.5	11.9	14.3	15.1	19.0	22.2	23.0	24.6	27.0
	Memory	5.6	8.7	10.3	13.5	15.9	17.5	18.3	22.2	22.2	23.8

Appendix

Table H.3: Performance comparison on GPQA.

Model	Method	Number of turns
Model	Method	0	1	2	3	4	5	6	7	8	9
Qwen	Multi-turn	0.033	0.033	0.067	0.067	0.067	0.067	0.100	0.100	0.100	0.100
	Memory	0.033	0.033	0.067	0.100	0.100	0.100	0.100	0.100	0.100	0.100
	In-place	0.033	0.200	0.233	0.300	0.333	0.433	0.433	0.467	0.467	0.467
Gemma	Multi-turn	0.100	0.133	0.133	0.167	0.167	0.167	0.167	0.167	0.167	0.167
	Memory	0.100	0.100	0.200	0.233	0.267	0.300	0.300	0.333	0.333	0.333

Appendix

Table H.4: Cumulative accuracy on AIME 2025.

Model	Method	Number of turns
Model	Method	0	1	2	3	4	5	6	7	8	9
Qwen	Multi-turn	0.067	0.100	0.100	0.100	0.100	0.133	0.133	0.133	0.133	0.133
	Memory	0.067	0.067	0.067	0.067	0.067	0.067	0.067	0.067	0.067	0.067
	In-place	0.067	0.133	0.233	0.267	0.333	0.467	0.533	0.533	0.533	0.533
Gemma	Multi-turn	0.067	0.133	0.167	0.167	0.167	0.167	0.167	0.200	0.200	0.200
	Memory	0.067	0.067	0.133	0.133	0.200	0.200	0.200	0.200	0.200	0.200

Appendix

Table H.5: Cumulative accuracy on AIME 2026.

	Method	Number of turns
	Method	0	1	2	3	4	5	6	7	8	9
Llama	Multi-turn	231.0	800.9	1572.4	2453.3	3513.4	4433.9	5513.2	6621.6	7854.8	8949.1
	Memory	231.0	826.2	1073.4	1319.3	1361.2	1414.1	1413.2	1468.8	1515.9	1566.9
	In-place	231.0	373.3	456.5	533.1	608.1	666.8	733.2	790.0	825.0	872.1
Gemma	Multi-turn	165.9	1822.6	3247.8	4640.9	5850.4	7188.1	8483.4	9833.5	11076.5	12444.9
	Memory	165.9	1898.6	1537.5	1712.3	1717.8	1737.9	1712.2	1744.5	1739.2	1720.0

Appendix

Table H.6: Number of input tokens on Math-hard.

	Method	Number of turns
	Method	0	1	2	3	4	5	6	7	8	9
Llama	Multi-turn	165.1	800.4	1473.2	2164.4	2871.0	3594.4	4274.1	4962.2	5578.6	6248.8
	Memory	165.1	829.6	973.4	1157.5	1209.8	1224.9	1256.0	1237.0	1249.4	1279.0
	In-place	165.1	422.1	487.9	534.0	592.6	625.2	665.0	638.6	677.9	670.8
Gemma	Multi-turn	149.0	975.2	1770.5	2545.2	3392.5	4187.6	4961.9	5791.9	6656.0	7565.4
	Memory	149.0	1005.2	1078.5	1191.2	1210.4	1217.3	1256.2	1272.8	1267.5	1285.5

Appendix

Table H.7: Number of input tokens on MMLU-pro.

	Method	Number of turns
	Method	0	1	2	3	4	5	6	7	8	9
Llama	Multi-turn	224.1	1054.1	1859.6	2744.6	3635.5	4503.7	5393.5	6302.9	7250.4	8134.1
	Memory	224.1	1083.4	1181.7	1394.7	1389.8	1360.0	1377.9	1354.2	1319.9	1340.8
	In-place	224.1	522.0	611.1	689.5	725.9	740.0	770.9	806.7	890.8	876.4
Gemma	Multi-turn	205.0	1197.2	2138.4	3073.1	3996.3	4913.9	5850.2	6865.6	7829.9	8823.9
	Memory	205.0	1227.2	1322.4	1482.0	1466.4	1508.8	1522.3	1498.5	1524.4	1503.4

Appendix

Table H.8: Number of input tokens on GPQA.

	Method	Number of turns
	Method	0	1	2	3	4	5	6	7	8	9
Llama	Multi-turn	932.3	2748.8	4089.0	5760.6	7508.6	9251.7	11123.1	12914.4	14775.8	16653.6
	Memory	932.3	2700.9	2275.6	2312.4	2370.3	2402.9	2433.7	2439.4	2434.0	2386.2
	In-place	932.3	2166.4	2709.9	3171.8	3641.0	4069.7	4551.9	5079.0	5519.2	5878.4
Gemma	Multi-turn	1004.4	2249.6	3464.7	4709.9	5963.8	7295.7	8601.6	9843.2	11092.1	12269.7
	Memory	1004.4	2171.6	2341.8	2414.9	2479.8	2470.6	2490.3	2520.4	2534.1	2525.8

Appendix

Table H.9: Number of input tokens on ZebraLogic.

	Method	Number of turns
	Method	0	1	2	3	4	5	6	7	8	9
Llama	Multi-turn	395.2	504.4	587.3	661.0	730.8	734.3	792.9	809.8	852.6	923.6
	Memory	395.2	479.1	588.1	647.8	696.7	705.3	743.1	790.1	844.8	861.5
	In-place	395.2	294.1	307.8	331.9	328.6	351.5	373.8	388.6	364.8	432.4
Gemma	Multi-turn	1138.3	1152.2	1098.2	1074.0	1070.4	1087.0	1063.4	1091.2	1081.9	1119.2
	Memory	1138.3	907.0	961.6	986.1	987.7	984.3	999.7	1041.3	1013.6	992.2

Appendix

Table H.10: Number of generated tokens on Math-hard.

	Method	Number of turns
	Method	0	1	2	3	4	5	6	7	8	9
Llama	Multi-turn	381.4	432.4	477.4	480.0	499.4	537.3	537.0	514.7	518.2	546.3
	Memory	381.4	429.7	515.0	574.6	603.1	627.7	617.2	650.2	662.3	642.3
	In-place	381.4	236.2	236.7	206.9	214.0	212.8	188.0	249.9	223.5	253.1
Gemma	Multi-turn	551.7	567.9	562.3	585.7	603.2	635.0	641.1	662.3	683.4	683.2
	Memory	551.7	564.8	600.9	593.8	614.2	655.1	664.4	667.3	688.2	696.4

Appendix

Table H.11: Number of generated tokens on MMLU-pro.

	Method	Number of turns
	Method	0	1	2	3	4	5	6	7	8	9
Llama	Multi-turn	665.7	644.4	659.2	683.2	721.5	699.2	697.8	689.6	697.8	690.8
	Memory	665.7	614.8	671.1	678.5	667.1	673.4	660.3	646.5	651.6	627.4
	In-place	665.7	421.3	351.0	283.8	312.3	297.0	292.7	306.9	241.3	266.8
Gemma	Multi-turn	822.6	811.8	756.1	731.8	773.5	736.9	796.8	809.3	806.9	820.0
	Memory	822.6	796.5	797.6	774.2	815.5	837.2	816.6	835.6	823.3	815.7

Appendix

Table H.12: Number of generated tokens on GPQA.

	Method	Number of turns
	Method	0	1	2	3	4	5	6	7	8	9
Llama	Multi-turn	1685.8	1237.7	1502.3	1597.5	1535.5	1558.8	1558.4	1623.8	1572.2	1600.6
	Memory	1685.8	1181.6	1118.8	1164.6	1182.5	1176.5	1175.7	1169.2	1133.1	1144.5
	In-place	1685.8	746.2	566.9	523.3	501.3	528.7	529.6	563.8	582.7	662.8
Gemma	Multi-turn	1123.1	1073.6	1090.5	1096.2	1104.9	1118.8	1129.5	1140.4	1147.1	1143.0
	Memory	1123.1	1207.0	1194.1	1263.0	1251.1	1271.5	1299.5	1313.1	1304.8	1318.6

Appendix

Table H.13: Number of generated tokens on ZebraLogic.

	Method	Number of turns
	Method	0	1	2	3	4	5	6	7	8	9
Gemma	Multi-turn	0.000	0.028	0.078	0.148	0.271	0.421	0.556	0.650	0.728	0.771
	Memory	0.000	0.007	0.028	0.052	0.078	0.105	0.135	0.167	0.199	0.234
	In-place	0.000	0.031	0.115	0.265	0.441	0.582	0.712	0.816	0.868	0.906
Qwen	Multi-turn	0.003	0.022	0.071	0.150	0.249	0.356	0.468	0.566	0.645	0.690
	Memory	0.004	0.025	0.073	0.166	0.251	0.348	0.434	0.520	0.600	0.665

Appendix

Table H.14: Grid accuracy of LLMs on ZebraLogic with top-

4

feedback.

	Method	Number of turns
	Method	0	1	2	3	4	5	6	7	8	9
Gemma	Multi-turn	0.000	0.013	0.035	0.070	0.109	0.156	0.208	0.266	0.325	0.390
	Memory	0.000	0.005	0.018	0.033	0.044	0.058	0.078	0.089	0.101	0.120
	In-place	0.000	0.013	0.057	0.103	0.167	0.252	0.334	0.421	0.494	0.582
Qwen	Multi-turn	0.004	0.013	0.029	0.059	0.093	0.136	0.188	0.229	0.276	0.322
	Memory	0.004	0.015	0.032	0.071	0.119	0.157	0.217	0.278	0.342	0.395

Appendix

Table H.15: Grid accuracy of LLMs on ZebraLogic with top-

2

feedback.

	Method	Number of turns
	Method	0	1	2	3	4	5	6	7	8	9
Gemma	Multi-turn	0.272	0.435	0.567	0.681	0.779	0.848	0.896	0.927	0.947	0.959
	Memory	0.272	0.350	0.411	0.464	0.505	0.528	0.548	0.571	0.586	0.603
	In-place	0.272	0.432	0.573	0.698	0.794	0.859	0.914	0.944	0.963	0.972
Qwen	Multi-turn	0.263	0.417	0.543	0.652	0.736	0.806	0.859	0.891	0.921	0.935
	Memory	0.264	0.406	0.518	0.618	0.690	0.750	0.794	0.835	0.866	0.891

Appendix

Table H.16: Cell accuracy of LLMs on ZebraLogic with top-

4

feedback.

	Method	Number of turns
	Method	0	1	2	3	4	5	6	7	8	9
Gemma	Multi-turn	0.272	0.367	0.441	0.511	0.575	0.629	0.679	0.723	0.761	0.793
	Memory	0.272	0.311	0.347	0.376	0.401	0.428	0.445	0.461	0.482	0.501
	In-place	0.272	0.359	0.434	0.510	0.580	0.642	0.702	0.754	0.793	0.832
Qwen	Multi-turn	0.264	0.350	0.421	0.482	0.538	0.585	0.629	0.666	0.698	0.730
	Memory	0.264	0.339	0.410	0.471	0.529	0.581	0.628	0.669	0.703	0.735

Appendix

Table H.17: Cell accuracy of LLMs on ZebraLogic with top-

2

feedback.

Panel of Figure 61 — Figure I.1: Prompt format used for memory-based feedback in MATH-hard, MMLU-pro free-form, GPQA free-form, and LiveCodeBench. The system prompt is identical to that in multi-turn feedback.

Panel of Figure 62 — Figure I.2: Prompt format used for LLM-as-a-judge in MATH-hard, MMLU-pro free-form, and GPQA free-form.

Panel of Figure 63 — Figure I.3: Prompt format used for the feedback agent in empirical experiments.

Panel of Figure 64 — Figure I.4: Prompt format used for in-place feedback agent in MATH-hard, MMLU-pro free-form, and GPQA free-form.

Panel of Figure 65 — Figure I.5: Prompt format used for in-place feedback agent in LiveCodeBench.

Panel of Figure 66 — Figure I.6: Input template used for ZebraLogic.

Panel of Figure 67 — Figure I.7: Rule-based feedback template in ZebraLogic.

Panel of Figure 68 — Figure I.8: Prompt format used for in-place feedback agent in ZebraLogic.

Panel of Figure 69 — Figure I.9: In-place feedback example for the MATH-hard dataset.

Panel of Figure 70 — Figure I.10: In-place feedback example for the MATH-hard dataset.

Panel of Figure 71 — Figure I.11: In-place feedback example for the GPQA free-form dataset.

Panel of Figure 72 — Figure I.12: In-place feedback example for the MMLU-pro free-form dataset.

Panel of Figure 73 — Figure I.13: Failure example of multi-turn refinement on the MATH-hard dataset: corrupting correct content.

Panel of Figure 74 — Figure I.14: Failure example of multi-turn refinement on the MMLU-pro free-form dataset: ignoring feedback.

Panel of Figure 75 — Figure I.15: Failure example of multi-turn refinement on the MMLU-pro free-form dataset: introducing a new error.

Figure J.1: Screenshot of the summarization page. Participants paste a passage from their chosen paper and click Summarize to obtain an initial summary from the model.

Figure J.2: Screenshot of the edit decision page. Participants record the edits they plan to make. This serves as a memo to help them stay organized during the subsequent interaction.

Figure J.3: Screenshot of the mode selection page, where participants choose the editing mode for the upcoming task.

Figure J.4: Screenshot of the multi-turn mode interface, used when participants provide feedback through standard multi-turn feedback.

Figure J.5: Screenshot of a model response in the in-place feedback mode. Clicking the button below the response opens an editor that allows participants to perform an in-place edit on the response.

Figure J.6: Screenshot of the in-place feedback interface. Participants directly modify the model’s response and click Continue Generation to resume generation from the edited context.

Figure J.7: Screenshot of an example response produced under in-place feedback. The blue span indicates the participant’s in-place edit, and the green span indicates the continuation subsequently generated by the model.

LLM Reliability

Large language model reliability studies whether outputs remain correct, calibrated, robust, and controllable across prompts, contexts, and deployment conditions.

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Inject a Turn, Not a Token: Learning Where to Redirect LLM Reasoning for Self-Correction
Oct 9, 2026Jiwon Jeon, Myungsik Cho, Jeonghye Kim +1LLM Self-Correction LLM Reasoning
Korea Advanced Institute of Science and Technology (KAIST)
Exact Feedback Is Not Control: Evaluating Text-based Closed-Loop Revision in LLMs
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College of Computer Science and Software Engineering, Shenzhen University
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Jun 4, 2026Kuan-Yen Chen, Fang-Yi Su, Jung-Hsien ChiangLLM Self-Correction LLM Prompting
National Cheng Kung University

Oct 9, 2026J/K move · Enter open · S save

Inject a Turn, Not a Token: Learning Where to Redirect LLM Reasoning for Self-Correction

Jiwon Jeon, Myungsik Cho, Jeonghye Kim, Youngchul Sung

Korea Advanced Institute of Science and Technology (KAIST)

LLM Self-Correction LLM Reasoning LLM Prompting

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Large language models struggle to reliably identify and correct errors in their own reasoning. Unlike prior training-based approaches that fine-tune an LLM to generate revisions or feedback, we learn where to intervene in failed rollouts and determine how to redirect them. To choose where to intervene, we train a 130K-parameter MLP head on a frozen encoder using only final-answer correctness. For redirection, we find that under a fixed generation budget, the tested inline injections fail to reliably redirect reasoning, whereas presenting the same instruction in a new user turn changes the model's course. The resulting correction framework, UPTURN (User-Prompt TURN injection at a learned position), requires no LLM fine-tuning: it retains a selected prefix, injects an instruction as a new user turn, and regenerates the suffix. Across four reasoners and six math benchmarks, UPTURN improves average accuracy in every setting over a 32-rollout baseline. It outperforms self-correction and reflection-token injection baselines under matched correction budgets, transfers across reasoners and to GPQA-Diamond without retraining, and complements GRPO and self-correction methods.

Abstract

LLM Reliability

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Inject a Turn, Not a Token: Learning Where to Redirect LLM Reasoning for Self-Correction

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