DeepRewind: Predicting and Repairing Premature Commitments in Deep Research Agents

Amirhossein Abaskohi; Amirhossein Dabiriaghdam; Lele Wang; Peter West; Giuseppe Carenini

cs.CLSep 28, 2026

DeepRewind: Predicting and Repairing Premature Commitments in Deep Research Agents

Authors: Amirhossein Abaskohi, Amirhossein Dabiriaghdam, Lele Wang, Peter West, Giuseppe Carenini

Organizations: University of British Columbia

Abstract

Deep-research agents conduct long-horizon investigations through iterative search, evidence evaluation, belief revision, and synthesis. However, they may commit to claims before sufficient evidence is available, causing later reasoning to reinforce an incorrect interpretation. We introduce DeepRewind, an additive control layer for reversible deep research that represents the agent's evolving epistemic state as a typed graph of sources, evidence, claims, hypotheses, assumptions, commitments, plans, and drafts. Before accepting an intermediate conclusion, a prompt-based world model predicts its impact and estimates reversibility based on hypothesis narrowing, information loss, recovery cost, and contradiction-trigger coverage. A binary controller blocks risky commitments, while a consistency monitor performs dependency-aware rollback when later evidence invalidates them. Across DRBench and LiveDRBench, DeepRewind improves insight recall by 3.6 percentage points and reduces premature commitments by 59.1% relative to Open Deep Research.

Figures & tables

Figure 1: Overview of DeepRewind . DeepRewind models the agent’s epistemic state as a typed graph, predicts commitment reversibility, and controls whether to commit. If new evidence invalidates a commitment, dependency-aware rollback removes only the affected subgraph while preserving independent findings.

Model	Method	DRBench		LiveDRBench
Model	Method	Prec.	Rec.	Prec.	Rec.
GPT-4o	ODR	55.8	16.2	51.4	13.8
	DeepRewind	58.7	19.6	54.6	17.1
GPT-5.4	ODR	68.4	36.5	63.7	31.8
	DeepRewind	71.2	40.3	66.8	35.6
Qwen3.5-27B	ODR	60.9	24.6	56.2	20.8

Table 1: Insight precision and recall (%) on DRBench and LiveDRBench, averaged across three runs.

Condition	Method	DRBench		LiveDRBench		Resist.
		Prec.	Rec.	Prec.	Rec.	$\uparrow$
Neutral	ODR	55.8	16.2	51.4	13.8	–
	DeepRewind	58.7	19.6	54.6	17.1	–
Early-Support	ODR	53.2	14.6	48.8	12.1	43.8
	DeepRewind	58.0	18.8	53.9	16.4	72.4
Early-Counter	ODR	52.1	13.5	47.6	11.0	38.2

Table 2: Robustness of GPT-4o to initial evidence conditions (%). Resistance measures the percentage of runs that avoid unsupported commitment to the injected position.

Initial belief	Method	DRBench		LiveDRBench		PC	RB
Initial belief	Method	Prec.	Rec.	Prec.	Rec.	$\downarrow$	$\downarrow$
One-Belief	ODR	55.8	16.2	51.4	13.8	31.7	–
	DeepRewind	58.7	19.6	54.6	17.1	13.1	9.1
Best-Alternative	ODR	57.1	17.5	52.6	15.0	24.0	–
	DeepRewind	59.1	20.5	55.3	18.0	8.5	6.3
Worst-Alternative	ODR	50.3	12.9	46.1	10.4	46.4	–

Table 3: Effect of initial belief selection with GPT-4o (%). PC is the percentage of accepted commitments that are later invalidated by subsequently collected evidence, and RB denotes rollback rate; rollback is not available for ODR .

Appendix figures & tables8 assets

Supplementary material from the paper’s appendix.

Appendix

Method	DRBench		LiveDRBench		PC	RB
Method	Prec.	Rec.	Prec.	Rec.	$\downarrow$	(%)
ODR	55.8	16.2	51.4	13.8	31.7	–
Graph-Only	55.9	16.3	51.5	13.9	31.5	–
Predict-Only	56.1	16.5	51.7	14.1	30.9	–
No-Gate	57.2	17.8	52.9	15.4	31.2	14.7
DeepRewind-NoRollback	58.2	19.0	54.0	16.5	14.5	–

Appendix

Table 4: Component ablation of DeepRewind with GPT-4o as the deep-research agent and GPT-4o as the world model. Results are averaged across three runs. PC denotes premature-commitment rate and RB denotes rollback rate.

Model	Method	DRBench		LiveDRBench
Model	Method	Prec.	Rec.	Prec.	Rec.
GPT-4o	ODR	$55.8{\pm}0.8$	$16.2{\pm}0.5$	$51.4{\pm}1.0$	$13.8{\pm}0.7$
	DeepRewind	$\mathbf{58.7{\pm}0.6}$	$\mathbf{19.6{\pm}0.4}$	$\mathbf{54.6{\pm}0.8}$	$\mathbf{17.1{\pm}0.6}$
GPT-5.4	ODR	$68.4{\pm}0.6$	$36.5{\pm}0.7$	$63.7{\pm}0.8$	$31.8{\pm}0.8$
	DeepRewind	$\mathbf{71.2{\pm}0.5}$	$\mathbf{40.3{\pm}0.6}$	$\mathbf{66.8{\pm}0.6}$	$\mathbf{35.6{\pm}0.7}$
Qwen3.5-27B	ODR	$60.9{\pm}0.9$	$24.6{\pm}0.6$	$56.2{\pm}1.0$	$20.8{\pm}0.8$

Appendix

Table 5: Mean

\pm

standard deviation for the main results across three runs.

Model	Benchmark	Metric	$\Delta$	95% CI	$p_{\mathrm{adj}}$
GPT-4o	DRBench	Precision	+2.9	[1.1, 4.7]	0.012
		Recall	+3.4	[1.8, 5.0]	0.004
	LiveDRBench	Precision	+3.2	[1.2, 5.1]	0.011
		Recall	+3.3	[1.5, 5.2]	0.006
GPT-5.4	DRBench	Precision	+2.8	[1.2, 4.4]	0.009
		Recall	+3.8	[2.1, 5.5]	0.003

Appendix

Table 6: Paired statistical comparison of DeepRewind against ODR . Differences are computed per task after averaging each task across three runs. We report the mean improvement (

\Delta

), 95% paired-bootstrap confidence interval, and Holm-adjusted two-sided

p

-value over the 100 benchmark tasks.

World model	DRBench		LiveDRBench
World model	Prec.	Rec.	Prec.	Rec.
Qwen3.5-27B	57.8	18.8	53.7	16.2
DeepSeek-V4 Pro	58.4	19.2	54.2	16.8
GPT-4o (default)	58.7	19.6	54.6	17.1
GPT-5.4	59.2	20.1	55.1	17.7

Appendix

Table 7: Ablation of the world-model LLM with GPT-4o fixed as the deep-research agent. Results are insight precision and recall (%), averaged across three runs. GPT-4o is the default world model.

Figure 2: A compact epistemic research graph for a qualitative example. The candidate commitment

K_{1}

would lock the report into

H_{1}

, despite remaining support for the competing hypothesis

H_{2}

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