arxiv: 2605.05191 · v1 · submitted 2026-05-06 · 💻 cs.AI

Recognition: 3 theorem links

· Lean Theorem

LongSeeker: Elastic Context Orchestration for Long-Horizon Search Agents

Jiajun Wang, Rui Ye, Siheng Chen, Songhua Liu, Yijun Lu, Yuwen Du

Pith reviewed 2026-05-08 17:42 UTC · model grok-4.3

classification 💻 cs.AI

keywords long-horizon searchcontext managementagentic paradigmContext-ReActsearch agentsadaptive orchestrationtool-use agents

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The pith

Search agents improve long-horizon performance by dynamically reshaping their context with five atomic operations.

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

The paper claims that long-horizon search agents suffer from context overload when they accumulate every intermediate step, and that making context management adaptive solves this. It introduces Context-ReAct, a single loop that combines reasoning, tool calls, and five operations to keep only relevant details at the right level of abstraction. LongSeeker is the resulting agent, fine-tuned on 10,000 synthetic trajectories from Qwen3-30B-A3B, and it reaches 61.5 percent on BrowseComp and 62.5 percent on BrowseComp-ZH while beating prior systems. The authors show that the Compress operation is expressively complete and the others reduce cost and error risk. A reader would care because the method offers a concrete way for agents to scale reasoning length without proportional increases in memory or mistakes.

Core claim

The paper establishes that effective context management for long-horizon search must be elastic, with parts of the trajectory kept at different levels of detail according to current relevance. Context-ReAct unifies reasoning, context management, and tool use in one loop and supplies five atomic operations: Skip, Compress, Rollback, Snippet, and Delete. The Compress operator is proven expressively complete, while the others supply efficiency and fidelity guarantees. LongSeeker, built by fine-tuning on 10k synthesized trajectories, demonstrates the approach by achieving 61.5% on BrowseComp and 62.5% on BrowseComp-ZH, substantially above Tongyi DeepResearch and AgentFold.

What carries the argument

Context-ReAct, the unified agentic loop that adds five atomic context operations (Skip, Compress, Rollback, Snippet, Delete) to the standard ReAct cycle so the agent can reshape its working context on the fly.

If this is right

Agents preserve key evidence while summarizing resolved steps and discarding dead branches.
Context size stays bounded, lowering generation cost and latency.
Hallucination risk drops because the model attends only to currently relevant material.
The same operations transfer across four different search benchmarks without task-specific redesign.
Fine-tuning on 10k trajectories is sufficient to instill the elastic management behavior.

Where Pith is reading between the lines

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

The five operations could be added to non-search agents such as code or planning systems that also face growing histories.
Combining Context-ReAct with external retrieval might further reduce reliance on the model's internal memory.
Real-world tests on live web sessions would reveal whether synthetic training data generalizes when tool outputs contain noise or change over time.
Future agent designs may need built-in meta-reasoning to decide when to apply each operation automatically.

Load-bearing premise

The 10k synthesized trajectories are representative of real long-horizon search tasks and the observed gains come primarily from the Context-ReAct operations rather than other training details or the base model.

What would settle it

Train two otherwise identical agents on the same data, give one the five Context-ReAct operations and the other only standard context accumulation, and run both on the BrowseComp benchmarks; equal or lower scores for the Context-ReAct version would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.05191 by Jiajun Wang, Rui Ye, Siheng Chen, Songhua Liu, Yijun Lu, Yuwen Du.

**Figure 1.** Figure 1: LongSeeker-30B delivers strong results on challenging long-horizon benchmarks, matching or view at source ↗

**Figure 2.** Figure 2: Overview of the Context-ReAct paradigm. Unlike standard ReAct, which passively accumulates history, and unlike prior proactive curation methods (Ye et al., 2025; Yao et al., 2026) that operate at a coarse granularity, Context-ReAct introduces a complete and fine-grained meta-action layer. At each step, the agent co-generates meta-operations (SKIP, COMPRESS, ROLLBACK, SNIPPET, DELETE) alongside standard too… view at source ↗

**Figure 3.** Figure 3: Managed context and structured output at a single Context-ReAct step. Left: The curated context after applying meta-operations to the raw trajectory. Steps 1–4 are consolidated via COMPRESS into a summary preserving essential findings. Step 5 remains unchanged via SKIP. Step 6 is DELETEd to eliminate noise from a redundant query. The trajectory is ROLLBACKed to Step 7, discarding unproductive exploration. … view at source ↗

**Figure 4.** Figure 4: Analysis of LongSeeker’s context management on 200 trajectories sampled from BrowseComp. view at source ↗

**Figure 5.** Figure 5: Effectiveness of the Context-ReAct paradigm on BrowseComp compared to other context view at source ↗

**Figure 6.** Figure 6: Complete case study showing managed context at a reasoning step. The trajectory demonstrates view at source ↗

**Figure 7.** Figure 7: Complete structured output from LongSeeker, including reasoning, meta-tool calls, motivation, and standard tool call. 15 view at source ↗

read the original abstract

Long-horizon search agents must manage a rapidly growing working context as they reason, call tools, and observe information. Naively accumulating all intermediate content can overwhelm the agent, increasing costs and the risk of errors. We propose that effective context management should be adaptive: parts of the agent's trajectory are maintained at different levels of detail depending on their current relevance to the task. To operationalize this principle, we introduce Context-ReAct, a general agentic paradigm for elastic context orchestration that integrates reasoning, context management, and tool use in a unified loop. Context-ReAct provides five atomic operations: Skip, Compress, Rollback, Snippet and Delete, which allow the agent to dynamically reshape its working context, preserving important evidence, summarizing resolved information, discarding unhelpful branches, and controlling context size. We prove that the Compress operator is expressively complete, while the other specialized operators provide efficiency and fidelity guarantees that reduce generation cost and hallucination risk. Building on this paradigm, we develop LongSeeker, a long-horizon search agent fine-tuned from Qwen3-30B-A3B on 10k synthesized trajectories. Across four representative search benchmarks, LongSeeker achieves 61.5% on BrowseComp and 62.5% on BrowseComp-ZH, substantially outperforming Tongyi DeepResearch (43.2% and 46.7%) and AgentFold (36.2% and 47.3%). These results highlight the potential of adaptive context management, showing that agents can achieve more reliable and efficient long-horizon reasoning by actively shaping their working memory.

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

LongSeeker gives a clean set of five context operations inside the ReAct loop and reports solid benchmark lifts, but the gains are hard to attribute without ablations on the synthesized data.

read the letter

The paper's main move is to fold context management directly into the agent's step-by-step loop instead of treating it as a separate post-processing step. Context-ReAct adds Skip, Compress, Rollback, Snippet, and Delete as explicit actions the model can choose at each turn. This lets the agent decide on the fly what to keep at full detail, what to summarize, and what to drop as the working memory grows during long search tasks. The claim that Compress is expressively complete is the part that could matter most if the proof holds up in the full text, because it suggests the other operators are mainly for efficiency rather than new power.

Referee Report

3 major / 2 minor

Summary. The paper proposes Context-ReAct, a unified agentic loop integrating reasoning, tool use, and five atomic context-management operations (Skip, Compress, Rollback, Snippet, Delete) to enable elastic orchestration in long-horizon search agents. It claims that Compress is expressively complete and that the other operators provide efficiency and fidelity guarantees. LongSeeker is obtained by fine-tuning Qwen3-30B-A3B on 10k synthesized trajectories and is reported to reach 61.5% on BrowseComp and 62.5% on BrowseComp-ZH, substantially outperforming Tongyi DeepResearch (43.2%/46.7%) and AgentFold (36.2%/47.3%) across four benchmarks.

Significance. If the performance deltas can be causally attributed to the Context-ReAct operators rather than the synthesis procedure or other training choices, the work would offer a practical and theoretically grounded approach to context management that could reduce token costs and error accumulation in long-horizon agents. The claimed expressiveness result for Compress, if accompanied by a formal model, would be a notable theoretical contribution.

major comments (3)

[Experiments] Experiments / Results: The headline scores (61.5% BrowseComp, 62.5% BrowseComp-ZH) are obtained after fine-tuning on trajectories that already incorporate the five Context-ReAct operators, yet no ablation is reported that trains an otherwise identical model on the same 10k trajectories using only standard ReAct. Without this control, attribution of the observed gains to elastic orchestration remains unverified.
[Method] Method / Theoretical claims: The statement that Compress is 'expressively complete' is presented without the underlying formal model of agent expressivity, the precise definition of completeness, or any proof sketch. Consequently the claimed efficiency and fidelity guarantees for the other operators cannot be evaluated.
[Data Synthesis] Data synthesis: The 10k trajectories are central to the empirical result, but the manuscript provides no statistics on their horizon lengths, branching factors, or tool-use distributions, nor any quantitative check that they are representative of real long-horizon search tasks. This leaves open the possibility that the synthesis procedure itself embeds the advantages being tested.

minor comments (2)

[Abstract] Abstract: Only two of the four 'representative search benchmarks' are named; the remaining two should be identified so readers can assess coverage.
[Method] Notation: The five operators are introduced without a compact tabular summary of their preconditions, effects, and cost/fidelity trade-offs; such a table would improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important areas for strengthening the empirical attribution, theoretical presentation, and data transparency. We address each major comment below and commit to revisions that directly respond to the concerns raised.

read point-by-point responses

Referee: The headline scores (61.5% BrowseComp, 62.5% BrowseComp-ZH) are obtained after fine-tuning on trajectories that already incorporate the five Context-ReAct operators, yet no ablation is reported that trains an otherwise identical model on the same 10k trajectories using only standard ReAct. Without this control, attribution of the observed gains to elastic orchestration remains unverified.

Authors: We agree that the current results do not isolate the contribution of the Context-ReAct operators from the synthesis procedure. In the revised manuscript we will add an ablation study that fine-tunes an otherwise identical Qwen3-30B-A3B model on the exact same 10k trajectories but using only standard ReAct actions (no Skip/Compress/Rollback/Snippet/Delete). The new results will be reported alongside the original numbers to enable direct causal attribution. revision: yes
Referee: The statement that Compress is 'expressively complete' is presented without the underlying formal model of agent expressivity, the precise definition of completeness, or any proof sketch. Consequently the claimed efficiency and fidelity guarantees for the other operators cannot be evaluated.

Authors: We acknowledge that the main text does not supply the formal model or proof details. The revised version will include a dedicated subsection (or appendix) that (i) defines the formal model of agent expressivity, (ii) states the precise definition of expressive completeness, and (iii) provides a proof sketch that Compress is expressively complete, together with the efficiency and fidelity arguments for the remaining operators. revision: yes
Referee: The 10k trajectories are central to the empirical result, but the manuscript provides no statistics on their horizon lengths, branching factors, or tool-use distributions, nor any quantitative check that they are representative of real long-horizon search tasks. This leaves open the possibility that the synthesis procedure itself embeds the advantages being tested.

Authors: We will expand the data-synthesis section with a new table and accompanying text that reports (a) distributions of trajectory horizon lengths and branching factors, (b) tool-use frequency statistics, and (c) quantitative comparisons against characteristics of real long-horizon search tasks drawn from the evaluation benchmarks. These additions will allow readers to assess representativeness directly. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical results and stated proof do not reduce to self-definition

full rationale

The paper's central claims rest on an empirical pipeline: synthesis of 10k trajectories, fine-tuning of Qwen3-30B-A3B, and benchmark evaluation yielding 61.5% / 62.5% on BrowseComp variants. The abstract states a proof that the Compress operator is expressively complete, yet no equations, formal model, or derivation steps are supplied that would allow inspection for self-referential reduction. No fitted parameters are relabeled as predictions, no self-citation chain is invoked to justify uniqueness or ansatz, and the performance numbers are direct experimental outcomes rather than quantities algebraically forced by the input definitions. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Central claim rests on the unverified effectiveness of the five Context-ReAct operations and the assumption that synthesized trajectories capture real task distributions; no free parameters or external axioms are explicitly listed in the abstract.

invented entities (1)

Context-ReAct paradigm no independent evidence
purpose: Unify reasoning, context management, and tool use in one loop with elastic operations
Introduced as the core new framework in the abstract.

pith-pipeline@v0.9.0 · 5600 in / 1341 out tokens · 97444 ms · 2026-05-08T17:42:44.847300+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

Cost.FunctionalEquation (washburn_uniqueness_aczel) washburn_uniqueness_aczel unclear
Theorem 3.1 (Expressive Completeness). The meta-action set O = {SKIP, COMPRESS, ROLLBACK, SNIPPET, DELETE} is expressively complete... COMPRESS(H,1,|H|,Σ) replaces the entire history H with an arbitrary string Σ.

Reference graph

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name": "search_web

for each candidate landlocked African country with at least two landlockedneighbors. The results show that for most countries, the increase from 2014 to 2022 is not 1.16%. However, Zimbabwe's literacy rate increased from 88.7% in 2014 to 89.85% in 2022, a rise of 1.15% (very close to 1.16%, possibly rounding). Other candidates like Uganda, Rwanda, Zambia,...

2014
[18]

it is home to a species from the Family Chamaeleonidaenamed after the individual who collected its holotype

[Chamaeleodilepis-The Reptile Database -Reptarium] Taxonomic database that provides basic information about all living reptile species, such as turtles, snakes, lizards, and crocodiles, as well as tuataras ... Managed Context Figure 6: Complete case study showing managed context at a reasoning step. The trajectory demonstrates the combined effect of COMPR...

2014
[19]

Find literacy rate data for Zimbabwe in 2014 and 2022 to verify 1.16%increase

2014