TreeSeeker: Tree-Structured Trial, Error, and Return in Deep Search

Dongmei Zhang; Fangkai Yang; Lu Wang; Mingzhe Ma; Pu Zhao; Qingwei Lin; Saravan Rajmohan; Wei Zhang; Yiming Guan; Youling Huang

arxiv: 2606.11662 · v1 · pith:LZKQE54Lnew · submitted 2026-06-10 · 💻 cs.AI

TreeSeeker: Tree-Structured Trial, Error, and Return in Deep Search

Zhuofan Shi , Mingzhe Ma , Lu Wang , Fangkai Yang , Pu Zhao , Yiming Guan , Youling Huang , Wei Zhang

show 3 more authors

Qingwei Lin Dongmei Zhang Saravan Rajmohan

This is my paper

Pith reviewed 2026-06-27 10:11 UTC · model grok-4.3

classification 💻 cs.AI

keywords deep searchtree searchtrial and errorUCB signalsweb agentsbranch and returninference-time controlevidence memory

0 comments

The pith

TreeSeeker structures deep search as tree branches and uses textual UCB signals to decide when to exploit, explore, or prune paths.

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

The paper presents TreeSeeker as an inference-time method that turns multi-step web search into a controlled process of trying directions and returning from dead ends. Search states form a tree in which each branch stands for one tentative sub-goal. At every step the system computes textual signals of value, uncertainty, and risk from branch descriptions, then chooses to keep extending a strong path, test an uncertain one, or abandon a failing continuation and backtrack. TreeMem stores the evidence, conflicts, and outcomes attached to each branch so past results inform future choices. Experiments on three benchmarks indicate this explicit control loop improves results over baselines that lack the same branch discipline.

Core claim

TreeSeeker organizes search as branch-and-return search over tree-structured states, where each branch is a tentative direction for a sub-goal. At each round, TreeSeeker reads all sub-goal trees, identifies active goals, and uses textual UCB signals of value, uncertainty, and risk to select among exploiting a promising branch, exploring an uncertain alternative, or pruning an unproductive continuation and returning to an earlier branch point. TreeMem supports this control loop by keeping evidence, uncertainty, conflicts, progress, and failure cues attached to the branches that produced them, so trial outcomes can guide later decisions.

What carries the argument

Tree-structured states with branch-and-return control driven by textual UCB signals of value, uncertainty, and risk, backed by TreeMem for attaching evidence to branches.

If this is right

TreeSeeker outperforms strong open-source baselines on XBench-DeepSearch, BrowseComp, and BrowseComp-ZH.
Explicit branch-and-return control complements stronger reasoning and tool execution at inference time.
Agents can prune unproductive continuations and return to earlier branch points using risk signals.
Trial outcomes stored in TreeMem allow past evidence to inform later exploit/explore choices without retraining.

Where Pith is reading between the lines

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

The same textual-signal approach could be tested on non-web tasks such as multi-step planning or code search where numeric rewards are unavailable.
If branch descriptions alone suffice for UCB-style decisions, agents might reduce reliance on separately trained value heads.
The tree-plus-return pattern offers a concrete way to add backtracking to chain-of-thought or tool-use loops that currently lack it.
A direct comparison replacing textual UCB with numeric Monte-Carlo estimates on the same benchmarks would clarify how much the language-based encoding contributes.

Load-bearing premise

Textual UCB signals computed from branch descriptions can reliably encode value, uncertainty, and risk to guide decisions on when to exploit, explore, or prune without further training or external calibration.

What would settle it

An experiment that replaces the textual UCB selection rule with uniform random branch choice on the same three benchmarks and finds no drop in performance would show the signals are not carrying the claimed decision value.

Figures

Figures reproduced from arXiv: 2606.11662 by Dongmei Zhang, Fangkai Yang, Lu Wang, Mingzhe Ma, Pu Zhao, Qingwei Lin, Saravan Rajmohan, Wei Zhang, Yiming Guan, Youling Huang, Zhuofan Shi.

**Figure 1.** Figure 1: Overview of TreeSeeker. TreeSearch is the central trial-and-error controller. It tests uncertain branches, continues promising ones, and prunes weak or misleading attempts. TreeMem provides the branch-local state required to recognize useful and failed attempts, while the goal DAG determines which goals are eligible for trial-and-error control. puts one decision for each active sub-goal tree (EXPLORE/EXPLO… view at source ↗

**Figure 2.** Figure 2: An example of TreeMem. node stores a branch state, including evidence, uncertainty, progress, and failure cues. Deeper nodes store the recent trace, including the latest tool calls and returned observations [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: A TreeSearch pruning example. Given the current TreeMem state, TreeSearch uses textual UCB signals to [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: Cumulative success rate (%) versus action [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

read the original abstract

Deep search requires agents to answer complex questions through multi-step web search, browsing, evidence comparison, and synthesis. A central challenge is deciding how to search when several directions look plausible but only some will later lead to reliable evidence. If an agent greedily follows the current best-looking direction, it may keep extending a weak continuation. If it explores without discipline, it may waste budget on disconnected trials. We propose TreeSeeker, an inference-time framework for controlled trial-and-error in deep search. TreeSeeker organizes search as branch-and-return search over tree-structured states, where each branch is a tentative direction for a sub-goal. At each round, TreeSearch reads all sub-goal trees, identifies active goals, and uses textual UCB signals of value, uncertainty, and risk to select among exploiting a promising branch, exploring an uncertain alternative, or pruning an unproductive continuation and returning to an earlier branch point. TreeMem supports this control loop by keeping evidence, uncertainty, conflicts, progress, and failure cues attached to the branches that produced them, so trial outcomes can guide later decisions. Experiments on XBench-DeepSearch, BrowseComp, and BrowseComp-ZH show that TreeSeeker consistently outperforms strong open-source baselines, suggesting that explicit branch-and-return control complements stronger reasoning and tool execution.

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.

Desk Editor's Note private letter to a colleague

TreeSeeker introduces a tree-structured branch-and-return loop with textual UCB signals for agent search control, but the abstract supplies no numbers, baselines, or computation details to support the outperformance claim.

read the letter

TreeSeeker organizes multi-step web search as a tree where each branch represents a sub-goal direction, then uses LLM-generated textual signals for value, uncertainty, and risk to pick between exploiting a branch, exploring another, or pruning and returning. TreeMem stores evidence, conflicts, and failure notes tied to those branches so outcomes inform later choices. This inference-time setup without extra training is the main new element; it does not collapse into standard tree search or UCB variants from prior agent work.

The framing of the core problem—greedy continuation versus undisciplined exploration—is clear and the branch-and-return mechanism plus memory attachment addresses it directly. The suggestion that explicit control can complement stronger base reasoning and tools is a reasonable incremental point for the agent search subfield.

The soft spot is the complete absence of supporting data. The abstract states consistent gains on XBench-DeepSearch, BrowseComp, and BrowseComp-ZH but lists no scores, error bars, baseline names, or implementation specifics. There is also no description of the prompt or rule used to produce the textual UCB signals, which is the part that has to carry the decision logic. If those signals turn out noisy or surface-level, any observed gains could trace to the tree structure alone rather than the control loop. That matches the stress-test concern and leaves the central claim unverified from what is shown.

The paper is aimed at researchers building web agents and multi-step tool users. A reader already working on inference-time search methods could extract the tree-plus-memory pattern as a concrete idea to test, even without the results.

I would send it to peer review because the problem is real and the framework is stated cleanly, but the experimental section would need to be present and detailed for the paper to hold up.

Referee Report

3 major / 1 minor

Summary. The manuscript proposes TreeSeeker, an inference-time framework for deep search that structures agent behavior as branch-and-return search over tree-structured states. Each branch represents a tentative sub-goal direction; at each step the system uses textual UCB signals (value, uncertainty, risk) computed from branch descriptions to decide whether to exploit a promising branch, explore an uncertain one, or prune and return to an earlier point. TreeMem maintains attached evidence, conflicts, and failure cues to support these decisions. The central empirical claim is that TreeSeeker consistently outperforms strong open-source baselines on XBench-DeepSearch, BrowseComp, and BrowseComp-ZH.

Significance. If the claimed outperformance is reproducible and attributable to the UCB-guided control rather than the tree structure or base model alone, the work would demonstrate a practical inference-time mechanism for disciplined trial-and-error in multi-step web search and synthesis without additional training or external calibration.

major comments (3)

[Abstract] Abstract: the claim that TreeSeeker 'consistently outperforms strong open-source baselines' is stated without any numerical results, error bars, baseline names, or implementation details. This absence makes the central empirical claim unverifiable from the manuscript as presented.
[Framework description] Framework description (paragraphs introducing textual UCB): no explicit computation rule, prompting template, or formula is supplied for deriving the value, uncertainty, and risk components from branch descriptions. Because the paper attributes performance gains specifically to the UCB-driven exploit/explore/prune loop, the lack of this operational definition is load-bearing for the central claim.
[Experiments] No ablation isolating the textual UCB component versus the tree structure or TreeMem alone is described. Without such a control, outperformance on the three benchmarks could be explained by the base reasoning model or tree organization rather than the proposed control loop.

minor comments (1)

[Abstract] The abstract refers to 'TreeSearch' when describing the control loop; this appears to be a typographical inconsistency with the title and defined term TreeSeeker.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments on our manuscript. We address each of the major comments below and outline the revisions we will make to strengthen the paper.

read point-by-point responses

Referee: [Abstract] Abstract: the claim that TreeSeeker 'consistently outperforms strong open-source baselines' is stated without any numerical results, error bars, baseline names, or implementation details. This absence makes the central empirical claim unverifiable from the manuscript as presented.

Authors: We agree that including quantitative results in the abstract would make the central claim more verifiable. In the revised manuscript, we will update the abstract to include specific performance numbers (e.g., accuracy improvements on XBench-DeepSearch, BrowseComp, and BrowseComp-ZH), names of the strong open-source baselines used, and references to error bars or standard deviations from our experimental results. revision: yes
Referee: [Framework description] Framework description (paragraphs introducing textual UCB): no explicit computation rule, prompting template, or formula is supplied for deriving the value, uncertainty, and risk components from branch descriptions. Because the paper attributes performance gains specifically to the UCB-driven exploit/explore/prune loop, the lack of this operational definition is load-bearing for the central claim.

Authors: We acknowledge that the manuscript would benefit from a more explicit description of how the textual UCB signals are computed. We will add the specific prompting templates used to elicit value, uncertainty, and risk scores from the branch descriptions, along with any aggregation rules or decision criteria for the exploit/explore/prune loop, in the revised framework description section. revision: yes
Referee: [Experiments] No ablation isolating the textual UCB component versus the tree structure or TreeMem alone is described. Without such a control, outperformance on the three benchmarks could be explained by the base reasoning model or tree organization rather than the proposed control loop.

Authors: The referee raises a valid point regarding the need for ablations to isolate the contribution of the textual UCB component. Our current experiments demonstrate outperformance over baselines that do not employ the full TreeSeeker framework, but to more rigorously attribute gains to the UCB-guided control, we will include additional ablation studies in the revised manuscript comparing the full system against variants that use the tree structure and TreeMem without the UCB signals. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper introduces TreeSeeker as a new inference-time tree-structured search framework that organizes branches with textual UCB signals for exploit/explore/prune decisions and validates it via experiments on external benchmarks (XBench-DeepSearch, BrowseComp, BrowseComp-ZH). No load-bearing step reduces by construction to a fitted parameter, self-citation chain, or self-definitional loop; the method description and performance claims remain independent of any prior author results or internal re-labeling of inputs as outputs. The derivation chain is therefore self-contained against external evaluation.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The central claim rests on the assumption that textual UCB signals can be computed from branch text to represent value, uncertainty, and risk, plus the modeling choice that search can be usefully organized as a tree of sub-goals with return mechanics. No free parameters or invented physical entities are mentioned.

axioms (2)

domain assumption Textual UCB signals derived from branch descriptions can be computed to represent value, uncertainty, and risk for selection decisions.
Invoked to justify the control loop that selects exploit/explore/prune actions.
domain assumption Search states can be effectively organized and controlled as a tree of tentative sub-goal branches with return capability.
Foundational modeling choice for the entire TreeSeeker architecture.

invented entities (2)

TreeSeeker no independent evidence
purpose: Inference-time framework that organizes deep search as branch-and-return over tree-structured states.
New system proposed to solve the trial-and-error control problem.
TreeMem no independent evidence
purpose: Memory module that attaches evidence, uncertainty, conflicts, progress, and failure cues to the branches that produced them.
Component required to support the textual UCB control loop.

pith-pipeline@v0.9.1-grok · 5795 in / 1456 out tokens · 40263 ms · 2026-06-27T10:11:21.300357+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

27 extracted references · 2 canonical work pages

[1]

arXiv preprint arXiv:2506.13651 , year=

Finite-time analysis of the multiarmed ban- dit problem.Mach. Learn., 47(2–3):235–256. Guoxin Chen, Zile Qiao, Xuanzhong Chen, Donglei Yu, Haotian Xu, Xin Zhao, Ruihua Song, Wenbiao Yin, Huifeng Yin, Liwen Zhang, Kuan Li, Minpeng Liao, Yong Jiang, Pengjun Xie, Fei Huang, and Jingren Zhou. 2026a. Iterresearch: Rethinking long-horizon agents with interactio...

work page arXiv 2025
[2]

Hongli Yu, Tinghong Chen, Jiangtao Feng, Jiangjie Chen, Weinan Dai, Qiying Yu, Ya-Qin Zhang, Wei- Ying Ma, Jingjing Liu, Mingxuan Wang, and Hao Zhou

Agentfold: Long-horizon web agents with proactive context management.arXiv preprint arXiv:2510.24699. Hongli Yu, Tinghong Chen, Jiangtao Feng, Jiangjie Chen, Weinan Dai, Qiying Yu, Ya-Qin Zhang, Wei- Ying Ma, Jingjing Liu, Mingxuan Wang, and Hao Zhou. 2026. Memagent: Reshaping long-context LLM with multi-conv RL-based memory agent. In The Fourteenth Inter...

work page arXiv 2026
[3]

integrates reasoning, acting, and planning in an MCTS-style framework, providing a tree- search baseline for agentic reasoning. • Reported open-source model systems.We also include reported results for open-source model or agent systems such as DeepSeek-R1 (Guo et al., 2025), Qwen3 (Yang et al., 2025), K2 (Team et al., 2025a), Search-o1 (Li et al., 2025b)...

2025
[4]

Goal Decomposition: Break the task into 1-5 goals
[5]

Goals with no dependencies can execute in parallel

Dependency Declaration: For each goal, explicitly state which other goals it depends on (DAG structure). Goals with no dependencies can execute in parallel. Goals with dependencies must wait until ALL their dependencies are completed. ,→ ,→ ,→ ,→
[6]

Path Diversity: For each goal, design 1-5 ALTERNATIVE execution paths (any single path succeeding completes the goal) ,→ ,→
[7]

Goal 2 (Search Y's population) depends on Goal 1 (Search X's population)

Path Specificity: Each path must specify: - Core approach/technique to achieve the goal - Success criteria ### DAG Design Principles: **CRITICAL RULE: Goal B depends on Goal A ONLY when Goal B literally CANNOT START without the OUTPUT/RESULT produced by Goal A. If Goal B can begin independently -- even if thematically related -- it MUST have Dependencies:...
[8]

**exploit**: Use when one path has the strongest overall promise and uncertainty is low enough to commit to it for now. ,→ ,→
[9]

,→ ,→ ,→

**explore**: Use when several paths remain plausible, uncertainty is still high, and trying multiple paths in parallel is likely to reveal which one is best. ,→ ,→ ,→
[10]

If a path has been selected 8+ times without escaping the loop, you may **consider** backtracking; if 12+ times, you **MUST** backtrack

**backtrack**: Use when a path has been tried multiple times and got stuck — likely because earlier rounds followed an unreliable source, clicked a misleading link, or anchored on a noisy intermediate result, causing this path to loop on the same dead-end information. If a path has been selected 8+ times without escaping the loop, you may **consider** bac...
[11]

can the original task be answered NOW with the evidence already collected across ALL goals?

**finished**: Use when ALL goals have gathered enough evidence to answer the original task. You do NOT need perfect or exhaustive evidence — if the key constraints of the question are supported by findings, finish immediately. Continuing to search after you have a well-supported answer wastes budget and risks overwriting correct conclusions with noise. ,→...
[12]

Briefly explain the original plan's goals and their corresponding execution paths,→
[13]

Goal X: resolved, result is [result summary]

Analyze the completion status of each goal's execution paths:,→ - For completed goals: "Goal X: resolved, result is [result summary]",→ - For partially completed goals: "Goal Y: completed up to path n, previous path results: [summary of results]" ,→ ,→ - For blocked or inefficient paths: Optimize the behaviors of such paths (including tool selection and t...
[14]

Determine the next parallel sub-paths to solve based on current information,→ Pay special attention to:
[15]

Using the execution trajectory to accurately judge whether each goal's paths are completed, blocked, or in progress ,→ ,→
[16]

Prioritizing adjustment of stagnant paths if trajectories show loops or inefficiency in certain goals ,→ ,→
[17]

Consolidating facts derived from completed paths to support unresolved goals,→
[18]

Identifying dependencies between goals and paths that may affect parallel execution,→ **Part 2 (Progress JSON — strict JSON after the separator):**,→
[19]

Extract new facts, revise existing ones, and track candidate answers.,→
[20]

For each goal, estimate a completion_ratio (0.0-1.0) based on evidence collected.,→ - 0.0 = no progress, 0.5 = about half done, 1.0 = effectively complete,→ - If completion_ratio >= 0.9, the goal will be auto-marked as completed.,→
[21]

1.1", "1.2

**CRITICAL: For each goal, write COMPREHENSIVE per-path summary reports (path_summaries).**,→ Each path_summary is a self-contained progress report for that specific path.,→ **PATH_ID keys MUST be the numeric path identifier exactly as shown in the plan (e.g., "1.1", "1.2", "2.1"). Do NOT use path names, descriptions, or any other format. Only paths that ...
[22]

The player was drafted in the first round

**For each goal, also write a brief goal_summary** — a 2-4 sentence high-level synthesis of the goal's overall progress across ALL paths. This should capture the key conclusion or current status of the entire goal, not repeat per-path details. ,→ ,→ ,→ ,→ ,→ ## CRITICAL: Precision Rules for Facts - ALWAYS preserve exact values: numbers, dates, names, epis...

2015
[23]

completed

If a goal is completed, mark as "completed" and summarize the result,→
[24]

If a path of a goal is blocked or inefficient, update this path and conclude the past paths,→ 18
[25]

Ensure the next parallel paths are directly derived from unresolved goals in the execution trajectory ,→ ,→
[26]

goals": {

Consider dependencies between goals when suggesting parallel paths,→ ** Output Format — TWO parts separated by `---PROGRESS_JSON---`**:,→ ## Plan Summary [Provide a brief summary of the original plan's goals and their execution paths],→ ## Execution Status Analysis ### Goal 1: [Goal Name] - Status: [Completed/In Progress/Blocked] - Path Analysis: [Analyze...

2012
[27]

three-stage develop- ment

Goal 4 merges the evidence and selects the final answer. Unlike Flash-Searcher, the early execution does not immediately commit to one locally plausible answer. Early UCB-guided deci- sions use exploration to spread the search over several evidence entrances: which activ- ities have a three-stage-development account, which are performed during Chinese New...

2000

[1] [1]

arXiv preprint arXiv:2506.13651 , year=

Finite-time analysis of the multiarmed ban- dit problem.Mach. Learn., 47(2–3):235–256. Guoxin Chen, Zile Qiao, Xuanzhong Chen, Donglei Yu, Haotian Xu, Xin Zhao, Ruihua Song, Wenbiao Yin, Huifeng Yin, Liwen Zhang, Kuan Li, Minpeng Liao, Yong Jiang, Pengjun Xie, Fei Huang, and Jingren Zhou. 2026a. Iterresearch: Rethinking long-horizon agents with interactio...

work page arXiv 2025

[2] [2]

Hongli Yu, Tinghong Chen, Jiangtao Feng, Jiangjie Chen, Weinan Dai, Qiying Yu, Ya-Qin Zhang, Wei- Ying Ma, Jingjing Liu, Mingxuan Wang, and Hao Zhou

Agentfold: Long-horizon web agents with proactive context management.arXiv preprint arXiv:2510.24699. Hongli Yu, Tinghong Chen, Jiangtao Feng, Jiangjie Chen, Weinan Dai, Qiying Yu, Ya-Qin Zhang, Wei- Ying Ma, Jingjing Liu, Mingxuan Wang, and Hao Zhou. 2026. Memagent: Reshaping long-context LLM with multi-conv RL-based memory agent. In The Fourteenth Inter...

work page arXiv 2026

[3] [3]

integrates reasoning, acting, and planning in an MCTS-style framework, providing a tree- search baseline for agentic reasoning. • Reported open-source model systems.We also include reported results for open-source model or agent systems such as DeepSeek-R1 (Guo et al., 2025), Qwen3 (Yang et al., 2025), K2 (Team et al., 2025a), Search-o1 (Li et al., 2025b)...

2025

[4] [4]

Goal Decomposition: Break the task into 1-5 goals

[5] [5]

Goals with no dependencies can execute in parallel

Dependency Declaration: For each goal, explicitly state which other goals it depends on (DAG structure). Goals with no dependencies can execute in parallel. Goals with dependencies must wait until ALL their dependencies are completed. ,→ ,→ ,→ ,→

[6] [6]

Path Diversity: For each goal, design 1-5 ALTERNATIVE execution paths (any single path succeeding completes the goal) ,→ ,→

[7] [7]

Goal 2 (Search Y's population) depends on Goal 1 (Search X's population)

Path Specificity: Each path must specify: - Core approach/technique to achieve the goal - Success criteria ### DAG Design Principles: **CRITICAL RULE: Goal B depends on Goal A ONLY when Goal B literally CANNOT START without the OUTPUT/RESULT produced by Goal A. If Goal B can begin independently -- even if thematically related -- it MUST have Dependencies:...

[8] [8]

**exploit**: Use when one path has the strongest overall promise and uncertainty is low enough to commit to it for now. ,→ ,→

[9] [9]

,→ ,→ ,→

**explore**: Use when several paths remain plausible, uncertainty is still high, and trying multiple paths in parallel is likely to reveal which one is best. ,→ ,→ ,→

[10] [10]

If a path has been selected 8+ times without escaping the loop, you may **consider** backtracking; if 12+ times, you **MUST** backtrack

**backtrack**: Use when a path has been tried multiple times and got stuck — likely because earlier rounds followed an unreliable source, clicked a misleading link, or anchored on a noisy intermediate result, causing this path to loop on the same dead-end information. If a path has been selected 8+ times without escaping the loop, you may **consider** bac...

[11] [11]

can the original task be answered NOW with the evidence already collected across ALL goals?

**finished**: Use when ALL goals have gathered enough evidence to answer the original task. You do NOT need perfect or exhaustive evidence — if the key constraints of the question are supported by findings, finish immediately. Continuing to search after you have a well-supported answer wastes budget and risks overwriting correct conclusions with noise. ,→...

[12] [12]

Briefly explain the original plan's goals and their corresponding execution paths,→

[13] [13]

Goal X: resolved, result is [result summary]

Analyze the completion status of each goal's execution paths:,→ - For completed goals: "Goal X: resolved, result is [result summary]",→ - For partially completed goals: "Goal Y: completed up to path n, previous path results: [summary of results]" ,→ ,→ - For blocked or inefficient paths: Optimize the behaviors of such paths (including tool selection and t...

[14] [14]

Determine the next parallel sub-paths to solve based on current information,→ Pay special attention to:

[15] [15]

Using the execution trajectory to accurately judge whether each goal's paths are completed, blocked, or in progress ,→ ,→

[16] [16]

Prioritizing adjustment of stagnant paths if trajectories show loops or inefficiency in certain goals ,→ ,→

[17] [17]

Consolidating facts derived from completed paths to support unresolved goals,→

[18] [18]

Identifying dependencies between goals and paths that may affect parallel execution,→ **Part 2 (Progress JSON — strict JSON after the separator):**,→

[19] [19]

Extract new facts, revise existing ones, and track candidate answers.,→

[20] [20]

For each goal, estimate a completion_ratio (0.0-1.0) based on evidence collected.,→ - 0.0 = no progress, 0.5 = about half done, 1.0 = effectively complete,→ - If completion_ratio >= 0.9, the goal will be auto-marked as completed.,→

[21] [21]

1.1", "1.2

**CRITICAL: For each goal, write COMPREHENSIVE per-path summary reports (path_summaries).**,→ Each path_summary is a self-contained progress report for that specific path.,→ **PATH_ID keys MUST be the numeric path identifier exactly as shown in the plan (e.g., "1.1", "1.2", "2.1"). Do NOT use path names, descriptions, or any other format. Only paths that ...

[22] [22]

The player was drafted in the first round

**For each goal, also write a brief goal_summary** — a 2-4 sentence high-level synthesis of the goal's overall progress across ALL paths. This should capture the key conclusion or current status of the entire goal, not repeat per-path details. ,→ ,→ ,→ ,→ ,→ ## CRITICAL: Precision Rules for Facts - ALWAYS preserve exact values: numbers, dates, names, epis...

2015

[23] [23]

completed

If a goal is completed, mark as "completed" and summarize the result,→

[24] [24]

If a path of a goal is blocked or inefficient, update this path and conclude the past paths,→ 18

[25] [25]

Ensure the next parallel paths are directly derived from unresolved goals in the execution trajectory ,→ ,→

[26] [26]

goals": {

Consider dependencies between goals when suggesting parallel paths,→ ** Output Format — TWO parts separated by `---PROGRESS_JSON---`**:,→ ## Plan Summary [Provide a brief summary of the original plan's goals and their execution paths],→ ## Execution Status Analysis ### Goal 1: [Goal Name] - Status: [Completed/In Progress/Blocked] - Path Analysis: [Analyze...

2012

[27] [27]

three-stage develop- ment

Goal 4 merges the evidence and selects the final answer. Unlike Flash-Searcher, the early execution does not immediately commit to one locally plausible answer. Early UCB-guided deci- sions use exploration to spread the search over several evidence entrances: which activ- ities have a three-stage-development account, which are performed during Chinese New...

2000