AgentFold: Long-Horizon Web Agents with Proactive Context Folding
Authors: Rui Ye
Zhongwang Zhang
Kuan Li
Huifeng Yin
Zhengwei Tao
Yida Zhao
Liangcai Su
Liwen Zhang
Zile Qiao
Xinyu Wang
Pengjun Xie
Fei Huang
Jingren Zhou
Siheng Chen
Yong Jiang
Venue: ICLR 2026 Poster (2026)
Keywords: Web Agent
Context Management
AI Agent
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: LLM-based web agents show immense promise for information seeking, yet their effectiveness on long-horizon tasks is hindered by a fundamental trade-off in context management. Prevailing ReAct-based agents suffer from context saturation as they accumulate noisy, raw histories, while methods that fixedly summarize the full history at each step risk the irreversible loss of critical details. Addressing these, we introduce AgentFold, a novel agent paradigm inspired by the human cognitive process of retrospective consolidation. AgentFold treats its context as a dynamic cognitive workspace to be actively sculpted, rather than a passive log to be filled. At each step, it learns to execute a folding operation, which manages its historical trajectory at multiple scales: it can perform granular condensations to preserve vital, fine-grained details, or deep consolidations to abstract away entire multi-step sub-tasks. The results on prominent benchmarks are striking: our AgentFold-30B-A3B agent achieves 36.2% on BrowseComp and 47.3% on BrowseComp-ZH. Notably, this performance not only surpasses or matches open-source models of a dramatically larger scale, such as the GLM-4.5-355B-A32B and the DeepSeek-V3.1-671B-A37B, but also surpasses leading proprietary agents like OpenAI's o4-mini.

Published as a conference paper at ICLR 2026
AGENTFOLD: LONG-HORIZON WEB AGENTS WITH PROACTIVE CONTEXT FOLDING
Rui Ye! Zhongwang Zhang Kuan Li Huifeng Yin
Zhengwei Tao", Yida Zhao?, Liangeai Su², Liwen Zhang', Wenbiao Yin", Zile Qiao²
Xinyu Wang', Pengjun Xie", Fei Huang'. Jingren Zhou', Siheng Chen * Yong Jiang
'Shanghai Jiao Tong University, 'Alibaba Tongyi Lab
"Equal Contribution, *Corresponding Author siihengc@sjtu.eduuu.u
Code: https:/gttttbbooomm/liiaialllllt hag.a//geeessuudddddooooaannnnno jiangyong.mml @gmail.com)
Model: http:///weccooooccooooddiiiicccc
ABSTRACT
LLM-based web agents show immense promise for information seeking, yet their effectiveness on long-horizon tasks is hindered by fundamental trade-off in con- text management. Prevailing ReAct-based agents suffer from context saturation as they accumulate noisy, raw histories, while methods that fixedly summarize the full history at each step risk the irreversible loss of critical details. Addressing these, we introduce AgentFold, novel agent paradigm inspired by the human cognitive process of retrospective consolidation. AgentFold treats its context as dynamic cognitive workspace to be actively sculpted, rather than passive log to be filled. At each step, learns to execute a folding operation, which man- ages its historical trajectory multiple scales: it can perform granular conden- sations to preserve vital, fine-grat fine-grained details, or deep consolidations to abstract away entire multi-step sub-tasks The results prominent benchmarks are strik- ing: our Saa--oodd-33---33- agent achieves 36.2% on BrowseComp and 47.3 % BrowseComp-ZH. Notably, this performance not only surpasses matches open-source models of a dramatically larger scale, such as the GLM-4.5-355B. A32B and the DeepSeck-V3., 13.1--61117-1--5-1 but also surpasses leading proprictary agents like OpenAl's 04-mini.
BrowseComp
BrowseComp.2z
Context Length v.S. Turn Index
40
36.2
10
47.3
75c
Rocet
20-
35.3
M
30
40
34.6
MN
50c
195 Tolvors 093
by
Reaching Contoel Livit
161
13.
20
25k
10
ApontFald--n 2008
Geepdinnt odd 1:7788
Opesdr colenii
BLA 9.300a
claiteed Ohil
Komi As feshrach
Demuftive Mi0
Mioo Momii-d---00 3/1
Agunttiad- 2018
Derptiinal 13, 14778
Opinit minl
Cleaded Dpes
Powytwerr 230000
Westaelee77 7.0
Instrict
Moothiiker
AgentFoid prouctively folds its working context during complex task-soliinng
Figure 1: Our AgentFold-30B-
agent demonstrates remarkable performance on challenging long- Long-horizon benchmarks, matching or surpassing agents with significantly larger model sizes. This enabled by its proactive context folding, which maintains a highly concise and focused context that reaches only 7k tokens after 100 turns of interaction and is capable of scaling 500+ turns.
INTRODUCTION
The ability to effectively seek and synthesize web information (Marchionini 1995 Given et al., 2023) is foundational to modern progress. This critical process, however, fundamentally con- strained by inherent human limitations in cognitive capacity and endurance. The advent of LLM- based web agents marks paradigm shift, offering systems that transcend these boundaries to tire- lessly navigate the digital landscape and dramatically enhancing the efficiency and effectiveness of complex iiformatioon-eeeiiie tasks (OpenAl 2025a Comanici al. 2025).

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However, critical challenge for contemporary web agents lies in striking an effective balance be- tween context comprehensiveness and conciseness, a trade-off off that significantly impacts their per-
formance, especially on long- long-horizon tasks (Wei et al., 2025 Wong et al., 2025). (1) Prevailing
ReAct-based agents (Yao et al. 2023] Wu et al. 2025 Li et al. 2025b), which accumulate the entire history of cassoning-aciionoosseee
triplets in their context, preserve informational integrity but severely suffer from the overwhelming noise of raw web data, leading to suboptimal actions. (2) Conversely, recent approaches (Zhou et al., 2025b Yu et al. 2025 Wang et al. 2025) that mechan- ically summarize the full history at every step maintain a clean context but risk the premature and irreversible loss of crucial details during any single summarization phase. These fundamental lim- itations reveal a critical gap in current methodologies, signaling the necessity for next-generation agent paradigm with advanced context management.
In this paper, we posit that an ideal agent should manage its internal context like a human's 's men- tal scratchpad: a workspace to be actively managed, not passively filled (Miller) 1956). Human problem-solving entails neither the exhaustive retention of all information nor its rigid, step-wise summarization. Instead, it is a process of disciplined, retrospective consolidation performed at crit- ical points. This involves a dynamic 'look-back' mechanism: after several actions, irrelevant steps are discarded, intermediate findings are distilled, and key insights are abstracted (Newell et al.) 1972). This self-correcting act of consolidation is what enables effective and sustained reasoning, capability we believe is essential for effective long-horizon reasoning and exploration in an agent.
Following this spirit, we introduce AgentFold, agent architected to proactively and intelligently 'oold' segments of context during task execution. It operates not on a monolithic log, but on a dynamic trajectory composed of Multi-Scale State Suamaarie--svverall distilled records of past events-and the Latest Interaction, which is the complete record of the most recent action and ob- servation. At each intermediate step of task-solving trajectory, AgentFold conducts deep reasoning that leads to two concurrent outputs: folding directive and a tool call. This folding directive has a dual (two-scale) character: (1) as a granular condensation, crystallizes the Latest Interaction into a new state summary, appending it to the sequence of State Summaries; (2) or as a deep consolidation, it fuses the Latest Interaction with a chain of prior summaries, retracting these specific entries and replacing them with single abstraction at coarser strategic scale. This is powerful for maintaining logical coherence and conciseness, for instance, by packaging a completed sub-investigation into its final conclusion. Simultaneously, the resulting observation from the executed tool call then, com- bined with the action, constitutes the new Latest Interaction for the subsequent cycle. By choosing what and how much to fold, AgentFold transcends the brutal trade-off between retaining noisy de- tails and risking catastrophic information loss. This capability equips AgentFold with focused and deeply informed reasoning process, essential for conquering long-horte long-horizon challenges.
Training AgentFold requires a dataset that does not yet exist: trajectories that demonstrate so- phisticated interplay of situational action and strategic context curation. To this end, we develop Feld-Gen,rator, specialized LLM-oriented data collection pipeline that can automatically generate trajectories for training. Recognizing that even the most advanced LLMs cannot reliably produce AgentFold's 's structured, multi-part responses through prompt engineering along, we leverage series of rejection sampling mechanism and finally fine-tunes AgentFold based on open-source LLMs.
To validate our folding paradigm, we implement AgentFold by fine-tuning the Qwwn3-30B-AA model (Yang et al., 2025). The results on prominent information-sseking benchmarks are strik- ing. Our resulting AgenlFold-30B-A33 achieves state-of-the-arr performance, scoring 36.2% on BrowseComp and 47.3% % on BrowseComp-ZH. Notably, this performance not only surpasses lead- ing proprietary agents like OpenAl's ss 04-mini (OpenAl 20256) but also matches or surpasses open- source models of a dramatically larger scale, such as the 4.5 (Zeng et al. 2025) and the DeepSeek- 1ww.//eek-33.-771----
(DeepSeek Team 2025).
2 RELATED WORKS
Web Agents. The advent of LLM-based web agents marks paradigm shift how human seeks in- formation, as these agents could tirelessly and broadly search and synthesize web information. Pio- neering efforts such as OpenAl's deep research (OpenAl 2025a) have demonstrated their promising potential, attracting massive interests from both academia and industry. The majority of contempo- rary web agents are architected upon the influential ReAct paradigm (Yao et al. 2023), where an
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agent iteratively interacts with an environment in a reasoning wtwp:iinnnnnntiooooooootttteeiie loop. Examples
include WebThinker (Li et al. 2025d), WebDancer (Wu et al. 2025). WebSailor (Li et al. 2025b),
WebSailor- V2 (Li et al. 2025a WebShaper (Tao et al., 2025 WebExplorer (Liu et al. (025) that
focus on dataset construction; X-Master (Chai et al. 2025) and BrowseMaster (Pang et al., (025) that focus on test-time scaling (Ye et al. 2025). However, the append-only context inherent to the ReAct paradigm leads to context saturation on long-horizon tasks, impairing reasoning as critical signals become buried in noise. Our work addresses this vulnerability by empowering AgentFold to proactively sculpt its cognitive workspace, ensuring the context remains focused and efficient.
Context Management. Context management, or context engineering, is an emerging research topic aiming to provide LLM agents with an appropriate and effective context (Mei et al.] [202]] Yang
et al. 2026 Li et al. 2025ch. significant line of research focuses on External Context Augmen- as user profiles or past conversations to provide a richer, more personalized context (Li et al. 2025e]
Iation, which injects relevant knowledge from sources outside the current task trajectory-
Chhikara al. 2025: Xu et al. 2025 Yang al. 2024). Our work. in contrast, pursues Intra- Task Context Curation, which focuses on managing the context generated within the task itself to maintain relevance and efficiency over long horizons. Along this line, MEMI (Zhou et al. 2025b) and MemAgent YYu et al.) (2025) are two recent attempts that compress the full history at each step. However, these methods employ rigid, step-wise summarization policy and have been primarily evaluated on simpler, retrieval-focused tasks like HotpotQA (Yang et al. (018). Unlike these meth- ods, AgentFold introduces a flexible look-back mechanism that avoids rigid, step-wise compression by retrospectively evaluating and selectively folding multi-step interactions at different scales, a capability crucial for complex, long-horizon tasks (Wei al. 2025 Zhou et al. 2025a).
3
AGENTFOLDD: WEB AGENT WITH PROACTIVE CONTEXT FOLDING
3.1 OVERVIEW
AgentFold is novel web agent designed tackle complex, long-horizon tasks by emulating a key aspect of human cognition: proactive and structured context/memory management. At its core, AgentFold makes two primary designs: first, defines the agent's context not as a monolithic log. but as dynamic cognitive workspace. Second, it empowers the agent to proactively operate upon and sculpt this workspace as an intrinsic part of its reasoning process.
AgentFold's workspace (i.e., context) is explicitly partitioned into the invariant user question, the curated Multi-Scale State Summaries representing long-term memory, and the high-fidelity Latest Interaction serving the immediate working memory. Based on this workspace, the agent's op- erational process unfolds iteratively. In a typical step, its reasoning yields a multi-part response comprising folding directive to manage historical state summaries, explanation of its thought process, and the next action. The folding directive is immediately applied to update the Multi-Scale State Summaries for future steps, while the explanation, the executed action and its resulting obser- vation form the new Latest Interaction for the subsequent cycle. This process repeats until the agent determines it has gathered sufficient information to provide an accurate final answer, with the initial step being a special case that omits the folding directive due to the absence of prior history.
This operational cycle establishes a powerful perceive -> reason -> fold -> act loop, where context curation is an explicit, learned step rather than a passive byproduct. By syner- gizing a well-defined cognitive workspace with the agent's autonomy to manipulate it, AgentFold directly resolves the critical trade-off between retaining granular details and preventing context infla- tion, enabling a more focused and efficient reasoning process for complex, long horgzoo challenges.
3.2 AGENTFOLD'S CONTEXT: MULTI-SCALE STATE SUMMARIES, LATEST INTERACTION
The performance of a web agent is critically dependent on the quality and structure of the context receives. To this end, design AgentFold's context as dynamic cognitive workspace partitioned into four distinct components (i.e., user question, available tools, multi-scale state summaries, and latest interaction) to enable both strategic longerange planning and precise situational action.
(1) The question serves as an anchor, constantly reminding the agent of its ultimate objective. (2) The list of available tools defines the agent's capacity for action within its environment. This com-
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Context
Question
AgentFold
Response
Context Management
Qusstion Available Tools
Tools
Think
Granular
[Folded Step I to 3]
[Folded Step to 7]
[Folded
Multi-Scale
Folding
Condensation: Fold Step
[Folded Step 8]
R]
Summaries
State
Explanation
[Folded Step 9] A
[Sup 9) Explenation:
Tool
Latest
Tool Call
Consolidation:
@ Deep
[Folded Step to 9]
[Folded Step to
Tool Response
Interaction
Tool Response
Fold Step 4
Update for Next Stop
Figure 2: Overview of AgentFold at an intermediate step. The two key parts in AgentFold" context are: Multi-Scale State Summaries (several folded blocks recording previous information) and Latest Interaction (a full record of the latest step). AgentFold responds with four blocks: thinking, folding, explanation, and tool call (which leads to appended tool response). The folding directive has two operation modes: granular condensation that folds one single step with useful information reserved and deep consolidation that folds several steps with a coarse summary especially when these steps complete a sub-task and the intermediate details are not critical for further task-solving.
ponent provides detailed schema for each tool incluuncu its name, description, and required parameters outlining Summaries function as the agent's curated long-term memory. This component preserves the se- quential, logical flow of the trajectory, with past steps recorded at different scales based on their perceived utility for future actions. This multi-scale structure allows critical findings to be retained as distinct, fine-grained summaries, while less critical intermediate steps can be consolidated into coarser, more abstract blocks. Consequently, retains a coherent historical narrative while mini- mizing informational noise. (4) The Latest Interaction acts as high-fidelitt working memory. It provides the complete, complete record of the most recent transaction www.cciiiiiinnnninnnnngn the agent's ' brief thinking (explanation), the executed tool call, and the resulting observation. This full transparency into the immediate past is crucial for providing the situational awareness needed to make sound de- cision, which involves both how selectively fold this new information and what action generate next. The entire architecture mirrors how humans leverage a stable goal, consolidated knowledge, and volatile working memory.
outtiiin the agent's 's entire suite of executable operations. (3) The Multi-Scale State
Specifically, the context C₁ provided to the agent at step t is a triplet:
C. = (Q,
(1)
where Q and are the invariant user question and tools, respectively. St_2 represents the Multi- Scale State Summaries, dynamically updated sequence of condensed summaries from previous steps. Formally, we represent S as an ordered sequence of summary blocks:
S = (Sel.y) i 822 Spm. 18m
where each 82.y is textual summary of the contiguous block of steps from x y. The step ranges partition the entire history up to the previous step, such that 21 = 1, Vm = t t- 2, and 1i+1 = 5/1 + for all i. This notation explicitly captures the multi-scale property: a summary of a single, independent step is denoted as sæ.e (where consolidation of multi-step process (e.g., verifying a condition over several steps) is denoted as Sz.y (where y > z). The third component, I,-1 is the Latest Interaction, a verbose, complete record of the previous step's 's full transaction. It is formed by concatenating the explanation, action, and
I), whereas a summary representing the
observation from step ( - 1: 1-1 = (E,-1, (4-1, 0t-1). For the initial step (1 = 1), the context is initialized as C = (Q, T, 0, 0), containing only the user's question; while for step 2, the context set as C2 (Q, T, 0. L1), containing the user question and latest interaction.
This structured context design offers the best of both worlds. The Latest Interaction provides the raw, granular detail necessary for the agent make informed, short-term decisions without any loss of information. Simultancously, the Multi-Scale State Summaries offer a noise-free, abstracted overview of the mission far, preventing the agent from getting lost in irrelevant details and en- abling coherent long-tena term reasoning. This structure directly mitigates the trade-off between context comprehensiveness and conciseness that hinges contemporary web agents.
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3.3 AGENTFOLD'S s RESPONSE: THINKING, FOLDING, EXPLANATION, ACTION
Complementing its structured cognitive workspace, AgentFold's response is not monolithic com- mand but a multi-faceted output that reflects its dual role as both a situational problem-solver and strategic context manager. At each step, the agent generates a single, coherent block of text that is parsed into three components, each designed to operate on its context: a directive to fold its long-term memory, an explanation to articulate its motivation behind the following action, and an action to propel the task forward. This design integrates context management as a core, learnable component of the agent's 's reasoning process, rather than treating
passive byproduct.
Specifically, at each step I, AgentFold generates a response Rg based on the context C2 and model 0. This response is single, coherent block of text designed be parsed into a quadruplet:
R = Agent 0) - (th,, fe. c,,
Here, th, is the thinking process, detailed chain-of meain-of-thouue of-thought monologue where the agent analyzes its context (C₂) and weighs options for both context folding and the subsequent action. From this internal deliberation, the other three structured components are derived. (1) The folding directive (f+) is the agent's s explicit command for sculpting its Multi-Scale State Summaries St-2. It takes the form of a JSON object: ft { ("range",, [k, "summary" "'rr" " where is the starting ID for folding and oe is the replacement summary text that can be proactively determined by AgentFold itself. This single format supports two modes of context management that operate at different scales:
1): This operation folds only the Latest Interaction into new, fine-grained summary. It is used for incremental steps, preserving the highest resolution of the historical trajectory by converting a single verbose record into a compact summary block (e.g.. '[Compressed Step 5] Found new candidate XYZ that needs further explo- ration.). ) This is the case where agent decides to only fold the latest interaction while keeping the remaining context the same (i.e., folding one single step).
Granular Condensation (i.e., the agent sets 1t
Deep Consolidation (i.e., the agent assigns a & smaller than t - 1): This operation performs a change of scale by fusing the Latest Interaction with chain of prior summaries into single, coarse-grained summary. This is powerful for abstracting away noisy, intermediate steps once a sub-task is complete. For instance, an agent might spend multiple steps verifying single fact, navigating through irrelevant websites or encountering failed tool calls. Deep consolidation allows the agent to retract this entire verbose sequence and replace it with a single, conclusive summary (e.g.. '[Compressed Step to 9] Confirmed that XYZ does not fit all criteria after checking several sources]'). ). This the case where agent decides fold the latest interaction together with several previous steps (i.e., folidng several steps). See concrete examples in Figure 5 and
This directive transforms Multi-Scale State Summaries S.-2 into S by retracting all summary blocks whose steps fall within the range [k:, - 1] and replacing them with a single new summary block, Sk,I-1 ot. (2) The explanation (e,) is concise summary of the key insights from the thinking process, articulating the motivation for the chosen action. (3) Finally, a, is the agent's s chosen external action, which is either tool call with specified tool name and tool arguments, or the final answer if the agent deems that no further interaction is required. When another tool call invoked, the tool will be executed obtain the observation o, from the environments. Finally, the question Q, tools T', the new Multi-Scale State Summaries S,-1 and Latest Interaction 4 (et, at o,) constitute the AgentFold's context C++1 = T, S,-1, for the next step t 1.
This structured response architecture engenders a powerful cognitive symbiosis between the agent's two core deliberations: planning the next action and curating its own context. (1) The explicit requirement to formulate a folding directive compels the agent to critically evaluate its trajectory and distill the most salient information from its historical context. This act of reflection inherently sharpens its understanding of the current state, leading to more informed and effective subsequent action. (2) Conversely, the process of planning a new action necessitates a purposeful interrogation
of its recent history to identify pivotal clues. This very process of determining what is immediately relevant provides a perfect, real-time signal for what is worth preserving in a folded summary. This tight coupling of acting and reflecting ensures that AgentFold's behavior is both purposeful and efficient, creating a regu-aegu loop that simultancously enhances the quality its actions and the coherence of its context memory.
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3.4 AGENTFOLD'S s TRAINING: DATA TRAJECTORY COLLECTION
Training AgentFold requires dataset that does not yet exist: trajectories that demonstrate phisticated interplay of situational action and strategic context curation. To this end, we develop Fold-Generaton a specialized data collection pipeline built upon powerful open-source Large Lan- guage Models (LLMs) to generate the necessary trajectory training data. To ensure a fair and direct comparison with prior work, utilize the same question set as the recent WebSailor work (Li et al.) 2025b). We find that even the most advanced LLMs cannot reliably produce AgentFold's 's accurate, structured, multi-part responses through prompt engineering alone. To relieve the effects of this, we leverage a rejection sampling mechanism, discarding any generated step that fails to strictly adhere required formats, or any trajectory that contains too many environmental errors. This ensures every data point in our collection is clear example of the desired reasoning process.
Specifically, the output of the Fold-Generator is collection of high-quality interaction pairs, {(C,, R,)},N N. where each C₁ is the structured context, R2 is the validated, gold-standard response, and N is the total number of interaction steps across all questions. This curated dataset is then used for conducting conventional Supervised Fine-Tuning (SFT) on open-source LLMs. The training ob- jective is to distill the complex, multi-step, validated reasoning of our pipeline into a single, efficient forward pass, thereby teaching the model to produce the entire structured output intrinsically.
This training methodology is not merely an implementation choice but a necessity that yields critical advantages. Primarily, it transforms the agent's 's ability to 'fold' from fragile, prompt-dependent instruction into a robust, internalized skill. Furthermore, the SFT process effectively distills the com- putationally intensive "generate-ane and-filter' strategy into the weights of the final AgentFold model. This results in a specialized agent that is not only highly capable but also significantly more efficient inference time than the general-purpose models used for data collection. Finally, by building this entire pipeline on open-source models, we maintain full transparency and control over the data and training process, enabling detailed inspection and future iteration.
3.5 DISCUSSIONS
Comparisons with existing methods. AgentFold's design offers a novel approach context man- agement, resolving the trade-off between the append-only history of ReAct, which leads to context saturation, and uniform full-history summarization, which risks irreversible information loss. The primary advantage lies in the agent's s ability to adapt its folding strategy. It can employ Granular Condensation to preserve a potentially vital, fine-graine detail, protecting from the indiscrimi- nate compression of full-history summarizer. Conversely, it can Deep Consolidation to prune an entire concluded sub-investigation. combating the noise accumulation found in ReAct. Crucially, the ability to delay consolidation until sub-task's outcome is clear allows for more informed and less sight-sighted curation decisions.
This represents conceptual leap from agents with static, predefined context policies to those self- aware knowledge managers. By integrating context curation as a learnable, core action, AgentFold learns sophisticated, task-specific strategies for what remember, what to abstract, and what to discard. This ability to actively shape its own informational workspace is the key to its enhanced robustness and efficiency, enabling to dynamically balance the need for granular detail with coherent long-term plan on complex, long- long-horizon challenges.
Efficiency and KV cache reuse. AgentFold is its highly compatible with KV cache reuse, which substantially reduces computational overhead. In the standard ReAct paradigm (Yao et al.) 2023), context is appended incrementally, allowing for near-total cache reuse but leading to context infla- tion. AgentFold strikes a superior balance: since context updates are localized to specific folding operations, much of the historical prefix remains untouched.
Specifically, when the agent employs Granular Condensation at step t to compress the f - 1 interac- tion, the prefix comprising steps through t 2 remains identical. Consequently, at step t + 1, the KV cache for this large prefix can be fully reused. Even during Deep Consolidation, when the agent decides to fold a range from step & to - 1, the prefix from step to l - is preserved and its cache remains valid. While Deep Consolidation presents trade-off by reducing the reusable cache length compared to simple appending, it compensates by drastically shrinking the total context length, which lowers the memory floor for subsequent steps. This architecture is far more efficient than

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Published as a conference paper at ICLR 2026
Table I: Main results. AgentFold-30B- achieves remarkable performance, surpassing open- source agents with much larger model size such as DeepSeek-V3. V3.1-671B-AS3 and matching pro- prietary agents such as OpenAl-o4-mini, indicating the potential of this new paradigm.
Agent
BrowseComp
BrowseComp-ZHH
WideSearch
GAIA
Proprictary Agents
Claude-4-onmee
14.7
22.5
62.0
68.3
Claude-4-Opus (anthopicc 2025
18.8
37.4
OpenAT-o4-man (OpenA1 2025b
28.3
44.3
OpenAT-o3 (OpenAl) 2025b
49.7
58.1
OpenAl Deep Reseurch UpenAl 2025a
51.5
42.9
67.4
Open-Source Agents
Qwen3-30B-A3B
Yang 2025
0.5
13.5
35.9
WcbThinker:32B
2.8
7.3
48.5
WchDancer-32B
Wü al.
24E5
3.8
18.0
51.5
WcbSailor-32B
cf.
2025b
10.5
25.5
53.2
WehSailln-72B [I. al. 20725
12.0
30.1
Searcher-Web-3221 (Gao
5.2
15.6
MiroThinkar:32B-DPOxx1222 MMrnaa MindMMinn Team 2025
13.0
17.0
64.1
WchFxplorer-8B [[.iu al. 2076)
15.7
32.0
50.0
DeepDive-32B (Lu al A0%5)
14.8
25.6
DeepDiver-V2-Sot Uanen Team 2025
13.4
34.6
Kimi-K2-Instruct-IT [Team al 2025
(Ieam
14.1
28.8
59.9
GL.M-4.5-35BB-A32FF
/Zeng al.
26.4
37.5
DeepSeet-V3.1-671I--X1//
(DeepSeek (eee-SeeeSDDDeeeeess
Team 2025
30.0
49.2
63.1
AgentFold-30B-A3B (Ours)
36.2
47.3
62.1
67.0
methods that perform uniform full-history summarization at every step (Zhou et al. 2025b Qiao ct al. 2025);; because those methods typically rewrite the entire historical narrative, they risk inval- ddttnng the KV cache every turn, forcing costly full re-computation. In contrast, AgentFold's prefix-preservie nature ensures significantly higher inference efficiency.
EXPERIMENTS
Implementation. We train our AgentFold based on open-source LLM Breen3-30B-ABB-Imee 2507 (Yang et al. 2025) with 30B parameters in total and 3B activated during prediction. We set the max tool call number as 100, any trajectory beyond this number will be forcibly terminated. We leverage GLM-4.5 Zeng al. 2025) and DeepSeek-V3. V3.1 (DeepSeek Team 2025) as the LLMs for driving Fold-Generator due to their high instruction-followie JSON format errors, unexpected tool call arguments, incorrect ending are filtered out. Meanwhile, we downsample those trajectories with fewer than 10 tool calls.
capabilities. Trajectories that involve
Benchmarks. We consider information-seeking benchmarks including BrowseComp (Wei al., 2025), BrowseComp-ZIr (Zhou et al., 2025a), and WideSearch-en (the most detailed metric: Item-
F1) (Wong al., 2025); and general benchmark: GAIA (text-only subset) (Mialon et al. 2023 For benchmarks with less than 200 samples, we report the averaged results on 3 trials.
Baseline Agents. We comprehensively compare our AgentFold-30--AA with representative open-
source agents including WebThinker (Li et al. 2025d). WebDDancrr (Wu et al. 2025), WebSailor (Li
al. 2025b). ASearcher (Gao et al. al.] 2025), MiroThinker (MiroMind Team 2025). Web
plorer (Liu et al. 2025). DeepDive (Shi et al. 2025). DeepDiver-V V2 (OpenPangu Team 2025).
Kimi-K2-Instruet (Team et al. 2025). GLM-4.5 Zeng et al. (2025), and DeepSeek- V3.1 (DeepSeck
Team 2025). We also report performances of several proprietary agents for reference, including
Claude 4-Soneet-Son (anthropic, 2025). OpenAAlo4-mmniio (OpenAl (0255) and OpenAl Deep
Research (OpenAl 2025a). Some results are taken from corresponding papers leaderboards.
4.1 RESULTS AND ANALYSIS
Main results, presented Table demonstrate that AgentFold 30B-A3B establishes a new state of the art for open-source agents and is highly competitive with leading proprietary systems. Notably,

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Published as a conference paper at ICLR 2026
The Grown Curve Agentiollds Context
The AgentFold's
the mnjestrries
Aveel Token Number
Agentfoll
Avarag
10
20
20
400
50
00
70
50
03
100
20
35
69
70
80
90
100
Turn dex
Turn
(a) Growth curve of AgentFold's context
(b) Number of blocks in AgentFold's is context
Figure 3: Analysis of AgentFold's context trajectories sampled from BrowseComp. (a) Agent- Fold's context length grows at remarkably slow, sub-linear rate, less than doubling from approx- imately 3.5k to 7k over 100 turns. As our model's max context is 128k, this indicates promising potential for AgentFold for tackling complex and long-horizon tasks. (b) Our Deep consolidation operation in AgentFold merges multiple past steps into single summary, thereby maintaining a significantly more structural and concise context compared to the popular ReAct.
solidifies its dominance in the open-source landscape by outperforming models up to 20 times its size, scoring 36.2% on BrowseComp against the 671B DeepSeck-V3. V3.1's 30.0%. Furthermore, AgentFold proves its capability at the highest level by achieving the best overall score of 62.1 % on WideSearch, surpassing all proprietary agents including OpenAl-o3 and Claude-4-Sonnet. These results underscore the profound impact of our architectural innovations, showcasing how effective context management can bridge the performance gap with dramatically larger models.
Dynamics of AgentFold's context: token count. To empirically validate AgentFold's context management, we analyze 200 trajectories from the BrowseComp benchmark (Figure Bap. We plot the number of surviving trajectories at each turn ([Tel, grey bars) alongside the average context token count (A,, blue curve) for those same trajectories. Specifically, A, is formally defined as: A, IT, Σjet; TokenCount(G;, where Ti is the set of surviving trajectories that are consisted of more than turns, and C3.t is the context of trajectory:
turn t.
The figure reveals that AgentFold maintains an exceptionally concise context. The average token count grows at remarkably slow, sub-linear rate, less than doubling from approximately 3.5k to 7k over 100 turns, proving the efficacy of the 'fold' operation in preventing context inflation.
When observing the survival curve, notice that over 20% of tasks being forcibly terminated experimental limit of 100 turns, which are typically marked as failure. Crucially, at this termination point, the agent's context is only m/7k tokens-a minor fraction of the underlying model's 128k capacity. This vast remaining capacity points to two promising conclusions. (1) First, it suggests that simply scaling the number of allowed interactions could unlock higher success rates. (2) Second, and more broadly, it demonstrates AgentFold's ' significant potential for tackling extremely complex and long-horizen problems. We provide a conceptual verification the following Figure 4 but defer detailed explorations to future work due to time constraints.
Dynamics of AgentFold's context: block count. To analyze the structural complexity of the con- text, we measure the number of discrete 'blocks' in the agent's workspace at each turn. A block is defined as any single entry in the Multi-Scale State Summaries (e.g., '[Compressed Step 52 to 67]' is one block) plus the one Latest Interaction. For an append-only method like ReAct, this count necessarily increases linearly with each turn (refcrence line in Figure (bb.. The analysis of Agent- Fold's block count reveals two key conclusions. (1) Sub-linear growth and structural simplicity. In stark contrast to the linear explosion expleeioooooooo
ReAct, AgentFold's block count grows at a slow, sub-linear rate. This efficiency is driven by the Deep Consolidation operation, which merges multiple past steps into a single summary, thereby maintaining a structurally simple and cognitively manageable context. (2) Compounding efficiency over time. The growing divergence between the curves highlights the compounding advantage of proactive curation. While ReAct's 's append-only policy leads runaway structural complexity over long horizons. AgentFold's consolidation ensures the context remains controlled, so its efficiency advantage over ReAct grows larger on longer tasks.
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Scaling Properties of Interaction Turns.
Effects of Scaling Number of Turns
Building on our finding of its compact context
Accuracy on BrowsoCoom Comp 6
in Figure Bal we test AgentFold's 's performance
35
AgentFold Can
when scaling the number of interaction turns, a
Further Scale
primary bottleneck for conventional agents. As
shown in Figure 4 we evaluate on BrowseC-
Limited Context
omp with a turn limit up to 256, comparing our 30B model against a much larger 355B GLM- 4.5 baseline. The results show two clear advan- tages. First, our smaller model consistently out- performs the 355B baseline at all comparable turn limits. Second, the GL.M-455 agent's 's per- formance saturates and fails beyond 64 turns as its append-only context fills, while AgentFold's accuracy continues to improve steadily up to 256 turns, showing promising scaling property.
15
10
GLMM-45-3558--433
AgentFald.308 308-A38
8116
32
64
128
256
Number of Turn Limit
Figure 4: Scaling properties of interaction turns (tool calls). This demonstrates the profound po- tential of AgentFold to tirelessly and robustly work for hundreds of steps for humans.
This experiment confirms that AgentFold's s proactive context management is the key to unlocking long-horizon task-solving. It demonstrates the profound potential for agents engage in truly ex- tended potential lasting for hundreds of steps-to perform the kind of broad and deep web exploration required for complex research and analysis tasks that remain far beyond the reach of conventional agent architectures.
Context comparison between AgentFold and ReAct. To provide a more direct and intuitive com- parison, we plot the average context length (in tokens) of AgentFold against standard ReAct base- line across the same set of trajectories. As illustrated on the right of Figure I the contrast is stark. The ReAct agent's 's context exhibits an uncontrolled, near-linear growth, accumulating massive to- ken count as the task progresses. In contrast, AgentFold's context size remains remarkably flat and controlled due to its proactive folding mechanism.
By the 100th turn, this architectural difference results in a dramatic quantitative advantage: Agent- Fold's context is, average, over 84k tokens (92%) smaller than ReAct's. This token reduction also has profound implications for computational resource requirements, translating to an estimated memory saving of nearly 7GB per inference instance at this trajectory length. This analysis demon- strates not only the conceptual benefits of our approach but also its immense practical value in mak- ing long-horizon agents more efficient, scalable, and cost-effective.
Case study. To directly illustrate AgentFold's operational intelligence, we provide a case study in Figure (with more in Appendix A.2).. The figure captures the agent at step 17 of complex task, where its context already showcases its multi-scale structure, comprising both fine ane-grained, single- step summaries (e.g.. [Compressed Step 5)) and previously consolidated blocks (e.g., [Compressed Step 6 to 8)). The figure showcases a critical moment of reflection and re-planning. Recognizing a long and unsuccessful series of attempts (from step 6 to 16) dead end, AgentFold executes a decisive, strategic move. First, it performs a Deep Consolidation, folding the entire 1 11-step failed sequence into a single, conclusive summary. This operation distills the valuable lesson from the ttat this approach is unworkable-whin pruning the noisy and now-irrelevant procedural details from its context. Informed by this newly consolidated insight, the agent then dynamically plans (in the motivation block, equals to explanation) to shift towards new line of investigation, which is immediately reflected in its subsequent tool call. This example powerfully demonstrates AgentFold's ability to reason about its own trajectory, learn from extended failures, and strategically re-plan by actively curating its cognitive workspace.
CONCLUSIONS
This paper introduces AgentFold, a novel agent paradigm that resolves the fundamental trade-off between context saturation in append-only agents (e.g.. ReAct) and irreversible information loss from uniform summarization. We move beyond these static policies by empowering the agent to act self-aware knowledge manager, equipped with proactive 'fold" operation to dynamically sculpt its context at multiple scales. This mechanism allows the agent to preserve fine- fine- grained details via Granular Condensation while abstracting away irrelevant history with Deep Consolidation. Our

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Published as a conference paper at ICLR 2026
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Figure 5: Case study for illustration of AgentFold. See detailed content in Table 2 Figure 0 and 7 After series of failure attempts happened (steps 6 to 16), AgentFold notices that this direction might be a dead end, folds these intermediate steps into one conclusion, plans to switch to other search directions, and decides the new search queries.
experiments validate the power of this approach: the AgemtFold-30B-ABB model establishes new state of the art for open-source agents, outperforming models over 20 times its size like DeepSeek- V3.1-671B and proving highly competitive against leading proprietary agents such OpenAl's 04- mini Furthermore, its exceptional context efficiency enables truly ang-horizon problem-solving by supporting hundreds of interaction steps within manageable context.
What's next. In this work, we prioritize demonstrating the potential of the AgentFold paradigm, thus employing straightforward SFT approach without extensive optimization. The clear next step is to leverage reinforcement learning (RL) to enable the agent to autonomously discover optimal and potentially non-obvious folding policies by directly optimizing for task success.
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A APPENDIX
A.1 LLM USAGE
In this paper, leverage Gemini-2.5-Pro to improve the writing grounded human-written content. The usage is limited to language polishing. We are responsible for any contributions generated by L.MSs.
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A.2 CASE STUDY
We provide two cases here. See case 1 in Table 2
Figure 6 and 7
See case in Table 3 Figure g
and 9
Table 2: Case Study
Turn ID
Context
Turn I
Turn 2
[Step 0]
Turn 3
[Compressed Step O], [Step 1]
Turn 4
[Compressed Step 0], [Compressed Step 1J. [Step
Turn 5
[Compressed Step 0], [Compressed Step to 2], [Step 3]
Turn 6
[Compressed Step 0), [Compressed Step to 2), [Compressed Step 3), [Step 4]
Turn 7
[Compressed Step 0J. [Compressed Step to 2). [Compressed Step 3), [Com-
pressed Step 4), [Step
Turn 8
[Compressed Step 0], [Compressed Step to 2]. [Compressed Step 3], [Com- pressed Step 4], [Compressed Step 5], [Step 6]
Turn 9
[Compressed Step 0]. [Compressed Step to 2]. [Compressed Step 31, [Com- pressed Step 4], [Compressed Step 5), [Compressed Step 6]. [Step 7]
Turn 10
[Compressed Step Oj. [Compressed Step to 2]. [Compressed Step 3]. [Com- pressed Step 4]. [Compressed Step 5], [Compressed Step 6]. [Compressed Step 7], [Step 8]
Turn 11
[Compressed Step 0], [Compressed Step to 2]. [Compressed Step 31. [Com- pressed Step 4]. [Compressed Step 5), [Compressed Step to 8]. [Step 9]
Turn 12
[Compressed Step 0], [Compressed Step to 2), [Compressed Step 3], [Com- pressed Step 4]. [Compressed Step 5], [Compressed Step 6 8), [Compressed Step 9]. [Step 10]
Turn 13
[Compressed Step 0], [Compressed Step to 2]. [Compressed Step 3], [Com- pressed Step 4], [Compressed Step 5], [Compressed Step to 8), [Compressed Step 9]. [Compressed Step 10], [Step 11]
Turn 14
[Compressed Step 0], [Compressed Step to 2). [Compressed Step 3], [Com- pressed Step 4), [Compressed Step 5], [Compressed Step 6 to 8), [Compressed Step 9], [Compressed Step 10], [Compressed Step 11], [Step 12]
Turn 15
[Compressed Step 0], [Compressed Step to 2], [Compressed Step 3], [Com- pressed Step 4), [Compressed Step 5], [Compressed Step 6 to 8), [Compressed Step 9]. [Compressed Step 10), [Compressed Step 11], [Compressed Step 12), [Step 13]
Turn 16
[Compressed Step 0], [Compressed Step to 2). [Compressed Step 3), [Com- pressed Step 4], [Compressed Step 5], [Compressed Step 6 to 8), [Compressed Step 9], [Compressed Step 10], [Compressed Step 11], [Compressed Step 12], [Compressed Step 13], [Step 14]
Turn 17
[Compressed Step 0], [Compressed Step to 2). [Compressed Step 3), [Com- pressed Step 4), [Compressed Step 5], [Compressed Step 8), [Compressed Step 9]. [Compressed Step 10], [Compressed Step 11]. [Compressed Step 12], [Compressed Step 13), [Compressed Step 14], [Step 15]
(Continued on next page)
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Table 2: (Continued) Case Study
Turn ID
Context
Turn 18
[Compressed Step 0], [Compressed Step to 2]. [Compressed Step 3], [Com- pressed Step 4], [Compressed Step 51. [Compressed Step 6 to 8), [Compressed Step 9]. [Compressed Step 10), [Compressed Step 11]. [Compressed Step 12], [Compressed Step 13), [Compressed Step 14], [Compressed Step 15], [Step 16]
Turn 19
[Compressed Step 0], [Compressed Step to 2]. [Compressed Step 31, [Com- pressed Step 4]. [Compressed Step 5]. [Compressed Step to 16]. [Step 17]
Turn 20
[Compressed Step 0j. [Compressed Step to 2]. [Compressed Step 3], [Com- pressed Step 4], [Compressed Step 5], [Compressed Step 6 to 16], [Compressed Step 17], [Step 18]
Turn 21
[Compressed Step 0]. [Compressed Step to 2]. [Compressed Step 3), [Com- pressed Step 4], [Compressed Step 5], [Compressed Step 6 to 16), [Compressed Step 17]. [Compressed Step 18], [Step 19]
Turn 22
[Compressed Step 0], [Compressed Step to 2], [Compressed Step 3]. [Com- pressed Step 4], [Compressed Step 5]. [Compressed Step 6 to 16], [Compressed Step 17]. [Compressed Step 18], [Compressed Step 19], [Step 20]
Turn 23
[Compressed Step 0], [Compressed Step to 2], [Compressed Step 3), [Com- pressed Step 4]. [Compressed Step 5], [Compressed Step 6 to 16]. [Compressed Step 17], [Compressed Step 18), [Compressed Step 19], [Compressed Step 20],
[Step 21]
Turn 24
[Compressed Step 0], [Compressed Step to 2], [Compressed Step 3]. [Com- pressed Step 4], [Compressed Step 5]. [Compressed Step 6 to 16], [Compressed Step 17]. [Compressed Step 18), [Compressed Step 19]. [Compressed Step 20], [Compressed Step 211, [Step 22]
Turn 25
[Compressed Step 0], [Compressed Step to 2]. [Compressed Step 3], [Com- pressed Step 4], [Compressed Step 5], [Compressed Step 6 to 16), [Compressed Step 17]. [Compressed Step 18), [Compressed Step 19]. [Compressed Step 20], [Compressed Step 21], [Compressed Step 22), [Step 23]
Turn 26
[Compressed Step 0]. [Compressed Step to 2]. [Compressed Step 3]. [Com- pressed Step 4], [Compressed Step 5], [Compressed Step to 16], [Compressed Step 17]. [Compressed Step 18), [Compressed Step 19]. [Compressed Step 20], [Compressed Step 213, [Compressed Step 22], [Compressed Step 23), [Step 24]
Turn 27
[Compressed Step 0], [Compressed Step to 2]. [Compressed Step 3], [Com- pressed Step 41, [Compressed Step 5], [Compressed Step 6 to 16), [Compressed Step 17]. [Compressed Step 18), [Compressed Step 19), [Compressed Step 20), [Compressed Step 21], [Compressed Step 22], [Compresssed Step 23), [Com- pressed Step 24), [Step 25]
Turn 28
[Compressed Step 0], [Compressed Step to 21. [Compressed Step 31, [Com- pressed Step 4], [Compressed Step 5], [Compressed Step to 16). [Compressed Step 17). [Compressed Step 18), [Compressed Step 19), [Compressed Step 20], [Compressed Step 21J. [Compressed Step 22]. [Compressed Step 23), [Com- pressed Step 24], [Compressed Step 25], [Step 26]
Turn 29
[Compressed Step 0], [Compressed Step to 2). [Compressed Step 3). [Com- pressed Step 4], [Compressed Step 5], [Compressed Step 6 to 16], [Compressed Step 17], [Compressed Step 18), [Compressed Step 19], [Compressed Step 20], [Compressed Step 211, [Compresseed Step 22], [Compressed Step 23], [Com- pressed Step 24], [Compressed Step 25), [Compressed Step 26), [Step 27]
(Continued on next page)
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Table 2: (Continued) Case Study
Turn ID
Context
Turn 30
[Compressed Step 0], [Compressed Step to 2), [Compressed Step 3], [Com- pressed Step 4], [Compressed Step 5]. [Compressed Step 6 to 16], [Compressed Step 17]. [Compressed Step 18], [Compressed Step 19]. [Compressed Step 20], [Compressed Step 21 to 27]. [Step 28]
Turn 31
[Compressed Step 0], [Compressed Step to 2]. [Compressed Step 31, [Com- pressed Step 4], [Compressed Step 5]. [Compressed Step 6 to 16), [Compressed Step 17]. [Compressed Step 18), [Compressed Step 19]. [Compressed Step 20], [Compressed Step 21 to 27], [Compressed Step 28). [Step 29]
Turn 32
[Compressed Step 0], [Compressed Step to 2]. [Compressed Step 3), [Com- pressed Step 4], [Compressed Step 5]. [Compressed Step to 16), [Compressed Step 17]. [Compressed Step 18), [Compressed Step 19], [Compressed Step 20], [Compressed Step 21 to 27], [Compressed Step 28). [Compressed Step 29], [Step 30]
Turn 33
[Compressed Step 0], [Compressed Step to 2], [Compressed Step 3). [Com- pressed Step 4], [Compressed Step 5], [Compressed Step 6 to 16). [Compressed Step 17]. [Compressed Step 18), [Compressed Step 19], [Compressed Step 20], [Compressed Step 21 to 27]. [Compressed Step 28). [Compressed Step 29], [Com- pressed Step 30), [Step 31]
Turn 34
[Compressed Step 0], [Compressed Step to 2]. [Compressed Step 3], [Com- pressed Step 4], [Compressed Step 5], [Compressed Step 6 to 16). [Compressed Step 17]. [Compressed Step 18), [Compressed Step 19]. [Compressed Step 20], [Compressed Step 21 to 27], [Compressed Step 28), [Compressed Step 291, [Com- pressed Step 30), [Compressed Step 31]. [Step 32]
Turn 35
[Compressed Step 0]. [Compressed Step to 2]. [Compressed Step 3], [Com- pressed Step 4], [Compressed Step 5], [Compressed Step to 16], [Compressed Step 17]. [Compressed Step 18), [Compressed Step 19]. [Compressed Step 20], [Compressed Step 21 to 27]. [Compressed Step 28), [Compressed Step 29], [Com- pressed Step 30), [Compressed Step 31], [Compressed Step 32), [Step 33]
Table 3: Case Study 2
Turn ID
Context
Turn 1
Turn 2
[Step 0]
Turn 3
[Compressed Step 0), [Step]] 1]
Turn 4
[Compressed Step to 1], [Step 2]
Turn 5
[Compressed Step to 2), [Step 3]
Turn 6
[Compressed Step 0 to 2), [Compressed Step 3), [Step 4]
Turn 7
[Compressed Step 0 to 21, [Compressed Step 3], [Compressed Step 4], [Step 5]
Turn 8
[Compressed Step 0 to 2], [Compressed Step 3], [Compressed Step 4), [Com-
pressed Step 5], [Step 6]
Turn 9
[Compressed Step 0 to 2]. [Compressed Step 3], [Compressed Step 4), [Com- pressed Step 5). [Compressed Step 6), [Step 7]
Turn 10
[Compressed Step to 2). [Compressed Step 3], [Compressed Step 4], [Com- pressed Step 5], [Compressed Step 6), [Compressed Step 7], [Step 8]
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Table 3: (Continued) Case Study 2
Turn ID|
Context
Turn 11
[Compressed Step to 2}, [Compressed Step 3), [Compressed Step 4], [Com- pressed Step 5], [Compressed Step 6], [Compressed Step 7]. [Compressed Step 8), [Step 9]
Turn 12
[Compressed Step to 9], [Step 10]
Turn 13
[Compressed Step to 9], [Compressed Step 10), [Step 11]
Turn 14
[Compressed Step 0 to 9], [Compressed Step 10), [Compressed Step 1 11], [Step 12]
Turn 15
[Compressed Step 0 to 91. [Compressed Step 10), [Compressed Step 11]. [Com- pressed Step 12], [Step 13]
Turn 16
[Compressed Step 0 to 9]. [Compressed Step 10), [Compressed Step 11], [Com- pressed Step 12], [Compressed Step 13), [Step 14]
Turn 17
[Compressed Step 0 to 9], [Compressed Step 10), [Compressed Step 11], [Com- pressed Step 12], [Compressed Step 13), [Compressed Step 14), [Step 15]
Turn 18
[Compressed Step 0 to 9]. [Compressed Step 10), [Compressed Step 11]. [Com- pressed Step 12], [Compressed Step 13], [Compressed Step 14], [Compresssed Step 15], [Step 16]
Turn 19
[Compressed Step 0 to 9]. [Compressed Step 10), [Compressed Step 11], [Com- pressed Step 12], [Compressed Step 13], [Compressed Step 14), [Compressed Step 15], [Compressed Step 16), [Step 17]
Turn 20
[Compressed Step 0 to 9]. [Compressed Step 10), [Compressed Step 11], [Com- pressed Step 12], [Compressed Step 13], [Compressed Step 14], [Compressed Step 151, [Compresssed Step 16], [Compresseed Step 17], [Step 18]
Turn 21
[Compressed Step 0 to 9], [Compressed Step 10), [Compressed Step 11], [Com- pressed Step 12], [Compressed Step 13], [Compressed Step 14], [Compressed Step 15), [Compressed Step 16]. [Compressed Step 17]. [Compressed Step 18), [Step 19]
Turn 22
[Compressed Step 0 to 9]. [Compressed Step 10), [Compressed Step 11], [Com- pressed Step 12], [Compressed Step 13], [Compressed Step 14), [Compressed Step 15), [Compressed Step 16), [Compressed Step 17], [Compressed Step 18), [Com- pressed Step 19], [Step 20]
Turn 23
[Compressed Step 0 to 9]. [Compressed Step 10), [Compressed Step 11], [Com- pressed Step 12], (Compressed Step 13], [Compressed Step 14], [Compressed Step 15), [Compressed Step 16), [Compressed Step 17), [Compressed Step 18), [Com- pressed Step 19), [Compressed Step 20), [Step 21]
Turn 24
[Compressed Step 0 to 9]. [Compressed Step 10), [Compressed Step 11], [Com- pressed Step 12), [Compressed Step 13], [Compressed Step 14), [Compressed Step 15], [Compressed Step 16], [Compressed Step 17], [Compressed Step 18), [Com- pressed Step 19], [Compressed Step 20], [Compressed Step 21], [Step 22]
Turn 25
[Compressed Step 0 to 22], [Step 23]
Turn 26
[Compressed Step to 22], [Compressed Step 23), [Step 24]
Turn 27
[Compressed Step 0 to 22], [Compressed Step 23], [Compressed Step 24], [Step
25]
Turn 28
[Compressed Step 0 to 22], [Compressed Step 23], [Compressed Step 24), [Com- pressed Step 25), [Step 26]
Turn 29
[Compressed Step 0 to 22), [Compressed Step 23], [Compressed Step 24], [Com- pressed Step 25), [Compressed Step 26), [Step 27]
(Continued on next page)
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Table 3: (Continued) Case Study 2
Turn ID
Context
Turn 30
[Compressed Step 0 22), [Compressed Step 23], [Compressed Step 24), [Com- pressed Step 25], [Compressed Step 26], [Compressed Step 27], [Step 28]
Turn 31
[Compressed Step 0 to 22]. [Compressed Step 23], [Compressed Step 24), [Com- pressed Step 25], [Compressed Step 26], [Compressed Step 27], [Compressed Step 28], [Step 29]
Turn 32
[Compressed Step 0 22], [Compressed Step 23], [Compressed Step 24), [Com- pressed Step 25], [Compressed Step 26]. [Compressed Step 27], [Compressed Step 28], [Compressed Step 29], [Step 30]
Turn 33
[Compressed Step 0 to 22], [Compressed Step 23], [Compressed Step 24), [Com- pressed Step 25]. [Compressed Step 26), [Compressed Step 27J. [Compressed Step 28], [Compressed Step 29). [Compressed Step 30). [Step 31]
Turn 34
[Compressed Step 0 to 22). [Compressed Step 23], [Compressed Step 24), [Com- pressed Step 25], [Compressed Step 26), [Compressed Step 27], [Compressed Step 281, [Compressed Step 29], [Compressed Step 30]. [Compressed Step 31], [Step 32]
Turn 35
[Compressed Step to 22). [Compressed Step 23], [Compressed Step 24), [Com- pressed Step 25], [Compressed Step 26], [Compressed Step 27J. [Compressed Step 281. [Compressed Step 29], [Compressed Step 30J, [Compressed Step 31], [Com- pressed Step 32], [Step 33]
Turn 36
[Compressed Step 22], [Compressed Step 23], [Compressed Step 24), [Com- pressed Step 25], [Compressed Step 26], [Compressed Step 27], [Compressed Step 28], [Compressed Step 29]. [Compressed Step 30], [Compressed Step 31], [Com- pressed Step 32), [Compressed Step 331, [Step 34]
Turn 37
[Compressed Step to 34), [Step 35]
Turn 38
[Compressed Step to 34), [Compressed Step 35), [Step 36)
Turn 39
[Compressed Step 0 to 34], [Compressed Step 35], [Compressed Step 36), [Step 37]
Turn 40
[Compressed Step 0 to 34], [Compressed Step 35], [Compressed Step 36), [Com- pressed Step 37], [Step 38]
Turn 41
[Compressed Step 0 to 34), [Compressed Step 35J, [Compressed Step 36), [Com- pressed Step 37], [Compressed Step 38), [Step 39]
Turn 42
[Compressed Step to 34), [Compressed Step 35), [Compressed Step 36), [Com- pressed Step 37), [Compressed Step 38), [Compressed Step 39], [Step 40]
Turn 43
[Compressed Step 0 34), [Compressed Step 35), [Compressed Step 36), [Com- pressed Step 37], [Compressed Step 38), [Compressed Step 39), [Compressed Step 40), [Step 41]
Turn 44
[Compressed Step 34), [Compressed Step 351, [Compressed Step 36), [Com- pressed Step 37], [Compressed Step 38), [Compressed Step 39], [Compressed Step 40), [Compressed Step 41], [Step 42]
Turn 45
[Compresseed Step 0 to 34], [Compressed Step 35], [Compressed Step 36), [Com- pressed Step 37], (Compressed Step 38), [Compressed Step 391, [Compressed Step 40), [Compressed Step 41), [Compressed Step 42), [Step 43)
Turn 46
[Compressed Step 0 to 34], [Compressed Step 351, [Compressed Step 36), [Com- pressed Step 37], [Compressed Step 38), [Compressed Step 39], [Compressed Step 40], [Compressed Step 41], [Compressed Step 42], [Compressed Step 43), [Step
44]
(Continued on next page)
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Table 3: (Continued) Case Study 2
Turn ID
Context
Turn 47
[Compressed Step 44), [Step 45]
Turn 48
[Compressed Step to 44), [Compressed Step 45], [Step 46]
Turn 49
[Compressed Step 0 to 44], [Compressed Step 45], [Compressed Step 46], [Step
47]
Turn 50
[Compressed Step 44], [Compressed Step 45]. [Compressed Step 46], [Com- pressed Step 47], [Step 48]
Turn 51
[Compressed Step 0 to 44], [Compressed Step 45], [Compressed Step 46], [Com- pressed Step 47], [Compressed Step 48], [Step 49]
Turn 52
[Compressed Step to 44], [Compressed Step 45], [Compressed Step 46), [Com- pressed Step 47], [Compressed Step 48], [Compressed Step 49], [Step 50]
Turn 53
[Compressed Step 44), [Compressed Step 45], [Compressed Step 46], [Com- pressed Step 47], [Compressed Step 48), [Compressed Step 49), [Compressed Step 50), [Step 51]
Turn 54
[Compressed Step to 44], [Compressed Step 45], [Compressed Step 46), [Com- pressed Step 47], [Compressed Step 48), [Compressed Step 49], [Compressed Step 50), [Compressed Step 51]. [Step 52]
Turn 55
[Compressed Step 0 to 44). [Compressed Step 45], [Compressed Step 46), [Com- pressed Step 47], [Compressed Step 48], [Compressed Step 49), [Compressed Step 50], [Compressed Step 51], [Compressed Step 52], [Step 53]
Turn 56
[Compressed Step 0 44], [Compressed Step 45], [Compressed Step 46], [Com- pressed Step 47], [Compressed Step 48], [Compressed Step 49], [Compressed Step 50]. [Compressed Step 51], [Compressed Step 52], [Compressed Step 53], [Step 54]
Turn 57
[Compressed Step 0 to 44], [Compressed Step 45], [Compressed Step 46), [Com- pressed Step 47], [Compressed Step [Compressed Step 49), [Compressed Step 50], [Compressed Step 51], [Compressed Step 52], [Compressed Step 53), [Com- pressed Step 54], [Step 55]
Turn 58
[Compressed Step 0 to 44), [Compressed Step 45], [Compressed Step 46), [Com- pressed Step 47], [Compressed Step 48), [Compressed Step 49], [Compressed Step 50). [Compressed Step 51], [Compressed Step 52], [Compressed Step 53), [Com- pressed Step 54), [Compressed Step 55J, [Step 56]
Turn 59
[Compressed Step to 44), [Compressed Step 45), [Compressed Step 46), [Com- pressed Step 47), [Compressed Step 48), [Compressed Step 49), [Compressed Step 50], [Compressed Step 51]. [Compressed Step 52], [Compressed Step 53), [Com- pressed Step 54], [Compressed Step 55), [Compressed Step 56), [Step 57]
Turn 60
[Compressed Step 0 44), [Compressed Step 45], [Compressed Step 46), [Com- pressed Step 47], [Compreeeesee Step 48), [Compressed Step 49], [Compressed Step 50], [Compressed Step 51], [Compressed Step 52], [Compressed Step 53), [Com- pressed Step 54], [Compressed Step 55], [Compressed Step 56), [Compressed Step 57], [Step 58]
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Published as a conference paper at ICLR 2026
##*h Question
am looking for specialty food shop that least one location both Mexico and California of December I. 2023. One of the locations in Mexico a 7.8kiloometer walk from a McDonald's located at Anillo Perif. 137, Polanco, Polanco I Secc, Miguel Hidalgo, 11510 Ciudad México, CDMX, Mexico December 1, 2023. The location California located within a market Roral Arrangement Class event in November 2023 as of December 2023. What is the name of the specialtt food shop?
Thanksgvving
#HH Previous Steps
"[[ooppessed Step 0jts
Search results identified Northgate Marker California market that held Thanksglving Floral Arrangement Class November 20, 2023, making it strong candidate for the California market mentioned in the question.
"[[oompressed Step to 27
Stapa 1-2 Invertigated Northgate Market Mercado Gonzälez concept. Northgate's homepage not reveal locations confirm. cross border specialty shop. The Mercado Gonzälez visit confirned it is thhemed specialty food section within Northgate stores Califoria, but evidence a physical location Mexico was found; references Mexico were cultural, not goographical.
"[[ompressed Step 31
Search for alternative Thanksgiving Floral Arrangement Class' and cross-border specialty food shops yielded results confirming Northgate Market's event but no definitive cross-border shop. Searches for Palanco specialty food listed local candidares like Que Bol on Yelp.
"[Compressed Step 4)00
Visited Yelp page for specialty food shops in Polanco (using 'gourmee' filter), extracted list shops: Que (chocolatier), Araao Pasteleria (baleery/cafe). Le Fromager (chesse/meat shop). Popalort (candy store), Tamales Flor de Lis (samales). Pescadenia & Salchichoneria de
Mar (seafoodlmeat) Contral Cacao (chocolate). Mora Mora (juice bar). Cafete (candy), Yucatoco (spices). None explicidly indicate a California location, but some (e.g. Amado Pasceleria, Central Cacao) may have chains requiring further verification.
*[[ooppesseed Step 5
Scap searched for the McDonald's address details and cross-barden der spocialty food shops. Results confirmed the McDonald's at Bivd. Manuel Avila Caracho No. 137, Polanco, 11510 Mexico City (aligning with 'Anillo Perif. 137). Searches for cross-border specialty food yielded limited direct hits, with Mercado Gonzälez being U.S.-based concept under Northgate Market rather than cross-border operation. Supermercado Gonzalez was identified Mexican chain in Chihuahua, not Mexico City.
""[Compressed Stap 8)
Steps 6-8 focused obtaining GPS coordinates for the McDonald's Blvd. Manuel Ávila Camacho 137 Polanco, Mexico City, to define the 7.8 km radius around it. Step attempeed to extract coordinates from the official McDonald's webpage without success. Scep 7 searched for coordinates and cross-border specialty food shops, idenalying Bazzar del Mundo pocential candidate but not verifying it. Step 8 visited a business directory (infobelpro) but did not find the coordinates. The coordinates remain unknown, and the cross-border shop investigation preliminary.
""[oompressed Step 91
Scep searched for McDonald's coordinates and Bazzar del Mundo. The search for 'McDonalds Polanco Mexico City coordinates' did not yield Manuel Avila Camacho 137, but
the exact GPS coordinates search for 'Bazaar del Mundo specialty food California' confrrmed it is specialty shop chain with locations in San Diego CA, but did not confirm a physical presence Mexico. The search for 'specialty food shop Mexico and California locations' did not identily any definitive cross- border candidates beyond previous leads.
(Instagram) provided a direct
specific restaurant. The
**[Compressed 10
Scap 10 attempted to visit the McDonald's Instagram page and Bazaar del Mundo's website to extract GPS coordinates and verily cross-barder presence. The Instagram page not scrapable, Bazzar Mundo's confirmed only one California location no mention Mexico. Polanco, or McDonald's coordinates.
""Cooppeeseed Step 113e
Scep II searched for McDonald's GPS coordinates using queries like 'McDonalds Polanco Mexico City GPS coordirates' and 'Blvd. Manuel Avila Carracho 137 coordinates', Results did not yield the GPS coordinates for the specific McDonald's at Blvd. Manuel Avila Camacho 137, Polanco. General Polanco coordinates were found, but not for the target location.
"[Coompressed Step 12)
Scap 12 attempted information was obtained.
TripAdvisor page for McDenald's Polanco GPS coardinates, but the URL inaccessible,
""[Compeessed Step 133=s
Step 13 scarched for McDonald's coordinates using querles targtiing the exact coordinates for the specific McDonald's location. Instead, general coordinates for Manuel Ávila Camacho town or Polanco were found, along with confinmation of the address via Yelp and Infobelpro listings.
Ávila Camacho 137, Pelanco, results noc yleld
""[Compressed Stop 1470
Scep 14 actempped to visit the Yelp page for McDonald's Polanco to GPS coordinates failed due an inaccessible webpage.
**[Compressed Step 15
Stap 15 scarched for McDonald's GPS coordinates and cross-border specialty food shops. The search for exact coordinates of McDorald's at Manuel Ávila Camacho Polanco, did not yield precise GPS, identified posential source: an Infobelpro business details page http:///rcchineeeerr..o///////ee
listing the address. Searches for cross-border specialty food shops reiterated Bazzar del Mundo (San Diego locations) but no confirmation of Mexican presence, and other results were inconclusive or unrelated.
"*[Scap 16]* <lengthy web page content amitzed for visualization>
Figure 6: Context of case at step 17.
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<think>
First. need to recall user's question: They're looking for specialty food shop with at least one location both Mexico California as December 2023. One location in Mexico 7.8 from McDonald's at 137, Polanco, Polanco Secc, Miguel Hidalgo, 11510 Ciudad México, CDMX, Mexico. The Califoria within market that held Thanksglving Floral Arrangement Class in November 2023.
From previous steps:
Northgate Market was identified as the market in California that held the Thanksgiving Floral Arrangement Class November 20, 2023.
Bazaar Mundo was mentioned as a potential specialty food shop with California locations, no confirmation of Mexican presence.
Various specialty food shops Polanco listed, clearly had cross-border presence.
We've been trying get GPS coordinates for the McDonald's to define the 7.8 km radius, but all actempts failed, including visiting the Infobelpro page which didn't have the coordinates.
The latest step (Step 16) tried to visit the Infobelpro URL to get coordinates, but the page was inaccessible or didn't contain the data; was just a terms service page.
Now,
solving this. Since can't get coordinates directly, might need alternative approach.
Alternative approach suggestions:
Use different method find the GPS coordinates for the McDonald's. Perhaps map service or reliable source.
Focus on the specialty food shap candidates that might have cross-barder locations.
Re-evaluate the California market: Northgate Market seems confirmed, but are there others? The question says market," and Northgate strong candidate, but maybe should double-check there are other markets in California that held event.
From Step the search confirmed Northgate Market held the Thanksgiving Floral Arrangement Class, Sa, probably Northgate correct.
For the Mexico side, the shop must be within 7.8 km of the McDonald's in Polanco.
Since we can't get the coordinates, parhaps can estimace the location or use the address to find nearby shops.
<tool long, omitted for visualization>>
Hopefully, this will yield results. </think>
<folding>
"range": [6. 16).
"summary": "Multiple attempts (Steps 6-16) to obtain coordinates for McDonald's at Blvd. Manuel Ávila
Camacho 137, Polanco, Mexico City, searches visiting Infobelpro. Yelp, and TripAdvisor pages were unsuccessful. coordinates were found, hindering identification the 7.8 km radius around McDonald's.
</ folding
<motivation>
Since direct methods to retrieve the GPS coardinates have failed, I am shifting focus to identifying the specialty food shap by searching for potential candidates that operate in both Mexico and Califoria. This includes verifying if known candidates like Bazaar del Mundo have Mexican location, and checking specific Polanco shops (e.g, Amado Pasteleria, Le Fromager) for California presence, as well as exploring concepts linking Northgate Market to cross-border operations. </motivation>
<tool_ <all>
("name": "search". "arguments": ("query": ["Bazaar del Mundo Mexico location", "specialty food shop Polanco Mexico
California locations". 'Northgate Market specialty food shop California Mexico". "Amado Pasteleria California,. "Le Fromager California "I])
</tool_call>
Figure 7: Response of case at step 17.
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#1F Question
person was born in the 1930s. blog post from July 7, 2009, says their true revolution was the way they addressed their audiences by appealing to their intelligence, which they have been doing since the late sixties. post also mentions that animals played important part their creations (some featured turtles, cats, cicadas, cows), and some subversive outlook on reality or moral messages that deviated from traditional paths. One of the pieces they authored was featured the New York Festival of Song. They were also paid tribute a public celebration at cultural center that began at 19:30, according to article published January 28, 2021. Who was the first artist to introduce themselves at that event, as written in the said article
had
A15 Previous Steps
"[[ompressed Step 0 to 37]
Excensive actempts to identify the artist and locate the tribute event based on clues: born in 1930s, blog post July 7. 2009 discussing revolution appealing to audiences' intelligence since sixties, use of animals (turtles, cats, cicadas, cows) with subversive messages, featured NYFOS, and tribute event on January 28, 2021, at cultural center starting at 19:30.
Searches for the blog post and tribute event yielded irrelevant results, including an inaccessible PDF for the blog post. events Clemente Soto Vélez and Flushing Town Hall, and unrelated content. Potential artists like Shel Silverstein (born 1932) and Tomi Ungerer (born I 1)31) were explored but did not fully fit; searches for NYFOS connections were unsuccessful. Visits to key URLs (including keyofstrawberry.com and (ilance-faancaise.e)) confirmed no relevant blog post or tribute event. The only partial lead was a mention of 'THE PICKPOCKETS DAUGHTER event at The Studios of Key West in January 2021 referencing Shel Silverstein, but with a 6PM start time, not 19:30.
**[Compressed Step 38)**
Searched for blog post using queries targeting Artsjournal with key phrases 'july 7 2009', 'true revolution', 'appealing intelligence'. Results did not directly find the blog post but included various Artsjournal posts from 2009-2012, none matching the description. No relevant artist tribute event information found.
**[Compressed Step 39)**
Searched for the blog post using three queries including exact phrases from the question (July 7 2009' 'true revolution' 'appealing to their intelligence', etc.). but results were unrelated PDFs and general content, not the blog post.
"[[ooppresedd Step 40]
Searched tribute event using queries including "public celebration" "cultural center" "January 28, "19:30", found no results. Also searched for "tributee event" "January 2021" "7:30 PM finding unrelated events, "January 28, 2021" "cultural center" "artists introduction" finding various cultural center events none matching the cribute description. "New York Festival of Song" "artist" "curtles" yielding irrelevant results, and "artist" "cicadas" "subversive" "turtles" "cats" "cows"" also not helpful.
**[Compressed Step 41]**
Searched for the blog post using exact phrases and for Shel Silverstein and Tami Ungerer regarding 'true revolution' and 'appealing to intelligence', but results were unrelated (egg, a PDF on avoidant personality disorder, general articles about Shel Silverstein's poetry). Searches for Shel Silverstein Tomi Ungerer tribute events at cultural centers with '19:30' start time found no relevant results, only mentions of other events or institutions.
**[Compressed Step 42]ss
Searched for the blog post using site-specific queries on Artsjournal.com, but exact match found. Also searched for the tribute event with various queries, finding two potential Eventbrite links: one for Dublin, Ireland ('Tribute Event January 28 2021 Cultural Center 7:30 PM") and one for Toronto, Canada (CCultural center tribute event 19:30 January 28 202 artist'), as well as an Easy link potentially referencing the article. Searches for NYFOS connections to 'turtles' or 'cicadas' did not yield relevant artist information.
"CCompressed Step 43Jss
Visited two Eventbrite URLs tribute events January 2021, starting 19:30, found details about the artist, cultural center, first introducer, as both pages only listed categories and navigation without specific article content.
*"Step 44]**
**Motivatio: The previous visit to Eventbrice URLs did not yield the necessary details about the tribute event the artist. Therefore, I am performing new web searches to locate the blog post from July 7, 2009, which may identify the artist, and the article from January 28, 2021, that describes the cribute event. These searches target precise phrases from the question to improve the chances of finding relevant results, including potential leads for Dublin or Toronto cultural centers based on earlier hints.
*HTool call: ("name": "search", "arguments": ("query": ["Njuly 20091" Y'tuee revalution!" 'lappealing to their intelligence!"
blog". "VJuly 20091" V"true revolution!" l'appealing their intelligencel" V'blog posti"". "Vtrue revolution!" l'appealing their intelligence!" V'since the lace sixties!" July 2009", "V'January 28, 202 l'cultural centeri" 1"19:30"" l'tribute event"" \"article!", "I"January 28, 202 ('public celebration"" l'cultural center!" 177:30 PMI" V"article!"", "'"Janaryy 28, 202 l'cultural centerl" 1"19:301" l'artist cribute'" ""article)" "V'Dublin cultural cencer\" \"January 28, 202 1'19:00" l'oribute event!"" "("Toronto cultural centerl" l'January 28, 20211" 1"19:30!" l"tribute event!"]]] *"Tool respanse:" <B*10 searched items amitted for visualization>
Figure 8: Context of case 2 at step 45.
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Published as a conference paper at ICLR 2026
<think>
Hmm, let's tackle this step by step. The user provided a detailed question with specific clues about an artist born the 1930s. The key points are: a blog post from July 7, 2009, mentioning their revolutionary approach to audiences, use of animals like turtles, cats, cicadas, cows, subversive messages, featured at NYFOS, and tribute event on January 28, 2021, at cultural center starting 19:30.
First, need compress previous steps. The last few steps (38-44) searches for the blog post and tribute evenc. but they didn't yield relevant results. The compression should summarize these attemptss without lasing critical info like the mentions of Shel Silverstein and Tomi Ungerer, even though they didn't fit perfectly.
Next, the motivation. The previous searches didn't find the blog post or the tribute event article. The blog post is crucial because it might idencify the artist. Since direct searches failed, maybe searching for artists who worked with NYFOS and used those animals could help. Also, tribute event details are sparse, so focusing on the artist's characteristics might
For the tool call, I should search for NYFOS artists associated with animals like turcles, cicadas, and also try find the blog post using different phrases. Maybe the artist isn't Shel Silverstein or Tomi Ungerer, so broadening the search to other creators like Maurice Sendak or Dr. Seuss, but checking if they fit all criteria. Also, considering the cultural center's location-maylo maybe European, given the Dublin lead earlier.
Wair, blog post mention was accessible one the search results (the PDF), but it's about avoidant personality disorder. That might be herring. Alternatively, maybe the artist is less mainstream. Another angle: the subversive messages and animal characters sound kids' authors or satirical just literature.
NYFOS suggests musical or performance
Perhaps the artist someone like William Steig or Edward Gorey! But Gorey wasn't born the 1930s (born 1925). Or Sal Amendola? Born 1939, did work with animals and subversion, but not sure about NYFOS.
Maybe the blog past is on a niche site. Searching for "true revolution appealing intelligence" might work better without the date. Also, tribute event: the article might be on a cultural center's website, Dublin was hinted earlier, maybe checking Irish cultural centers.
But the current search results didn't help. Need try queries: NYFOS artists with turtles cicadas, and the blog post with exact phrases without the date. Also, consider that. "true revolution" might refer specific term the artist's biography.
Finally, the first introducer at the tribute event-the the answer depends on finding the article, which requires identifying the artist first. prioritize finding the artist via blog post or NYFOS connection, </think>>
<folding>
("range" [38, 44]. "summary": "Multiple searches for the July 2009 blog post using
phrases ('true revolution', 'appealing their intelligence', 'since the late sixties') consistently returned single PDF avoidant personality disorder, but actual blog post. Searches for the January 28, 2021 cribute event also failed to find the specific article, yielding irrelevant events and results. A visit to Eventbrite links from previous search found no details. A final comprehensive search using all key phrases and potential locations (Dublin, Toranto) did not locate the blog post or the tribute event article, only unrelated content.") </folding>
<motivation>
The persistent failure locate the specific blog post the tribute article through direct phrase matching suggests different stracegy is needed. The blog post remains the promising lead for identifying the artist, contains unique descriptors (animals, subversive messages, NYFOS feature). I will now search for artists who have been featured at the New Yark Festival of Song (NYFOS) and who also incorporate animals like turtles, cicadas, or cows into their work, as this may directly idencify the correct person. Once the artist is identified, finding the tribute event article should be more straightforward. </motivation>
<tool call>
("name": 'search", "arguments": ("query": ("New York Festival of Song artists turtles cicadas cows". "NYFOS composer turtle cicada cow", "I'true revolution!" l'appealing intelligencel" artist V'since the late sixties!"" "Vartistl V'turdles'' V'cars!" I"cicadasl" l'cowsl" \"subversive!" l"moral messages!".. "Shel Silverstein New York Festival of Song", "Tomi Ungerer New York Festival of Song'Ji)
</cool call>
Figure 9: Response of case 2 at step 45.
23


Reviews:
[{"review_id": "PefW8YoEeN", "reviewer": "Reviewer_C2c8", "summary": "This paper introduces AgentFold, a new paradigm for long-horizon web agents that addresses a persistent trade-off in context management: ReAct-style agents accumulate noisy, ever-growing logs, while fully summarizing agents lose key details prematurely. AgentFold proposes a proactive “context folding” mechanism inspired by human cognitive consolidation—learning when to condense or merge past interactions into structured multi-scale summaries. The system dynamically curates its own context via two operations—granular condensation (folding a single step) and deep consolidation (folding multiple steps). It is trained through a specialized Fold-Generator pipeline producing structured reasoning trajectories, and fine-tuned on Qwen3-30B. Across benchmarks like BrowseComp, BrowseComp-ZH, and WideSearch, AgentFold achieves strong performance (36.2%, 47.3%, and 62.1% respectively), outperforming much larger open-source and even proprietary agents such as OpenAI’s o4-mini, while maintaining sublinear context growth over hundreds of steps", "questions": "N/A", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The conceptual clarity and human-analogy framing are exceptional. The authors identify context saturation as a bottleneck and present a clean, modular solution that integrates memory curation into the agent’s reasoning loop. The paper demonstrates strong experimental rigor: detailed comparisons with major baselines (DeepSeek-V3.1-671B, GLM-4.5-355B, WebSailor, etc.), concrete scaling analyses, and insightful case studies that show the agent’s ability to self-reflect and prune unproductive trajectories. The writing is polished, the methodology well-motivated, and the results both impressive and interpretable. The sub-linear token and block growth analyses convincingly validate the practical efficiency of the folding mechanism.", "weaknesses": "Overall solid paper. No significant weakness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "C3tyGcszGF", "reviewer": "Reviewer_ZzZg", "summary": "This paper introduces AgentFold, a web agent architecture designed to handle long-horizon tasks by proactively managing its interaction history. Instead of either storing the entire trajectory (ReAct-style) or repeatedly summarizing it at every step (which risks losing crucial details), AgentFold performs learned context folding. It decides when to keep high-resolution details, when to condense recent actions into smaller summaries, and when to merge longer sub-trajectories into coarse abstractions. This yields a multi-scale memory representation that scales more gracefully with task length. Experiments are conducted on BrowseComp and BrowseComp-ZH, where the approach shows state-of-the-art performance among open-source agents of comparable and even substantially larger parameter sizes. The approach is technically sound and frames its memory representation rigorously. This paper presents an idea is both intuitive and practically impactful, and the experimental results substantiate its benefits. Some further analysis, like training pipeline cost and folding behavior interpretability, would strengthen the contribution.", "questions": "Have the authors considered adding this folding mechanism to web agents in general and evaluating it on Online-Mind2Web?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The idea of proactively managing its interaction history and dynamic consolidation of interactions is intuitive.\n2. The results on BrowseComp and BrowseComp-zh are promising.", "weaknesses": "1. There is no discussion of the cost of the folding procedure and its associated overhead. \n2. The evaluation on BrowseComp* is rather limited. How about adding this folding mechanism to web agents in general and evaluating on Online-Mind2Web?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "j6k98C4I9G", "reviewer": "Reviewer_nh9H", "summary": "This paper proposes AgentFold, an agent for information seeking tasks which proactively manages its context, enabling more efficient processing and efficacy for long-horizon tasks. The context management is achieved via context folding approach which leverages two types of operations, 1) Granular Condensation that preserves fine-grained details of single steps, 2) Deep Consolidation that merges multiple steps into coarse summaries. To train the model to effectively leverage folding, a dataset is generated using an LLM, which the final model is tuned on with SFT. The experimental results on information-seeking benchmarks performance show matching or exceeding much larger models, while maintaining efficient context usage.", "questions": "- Have the authors tried adding the folding mechanism to baseline and proprietary LLMs? \n- What are the failure modes of the proposed approach? Are there cases where folding can impact performance due to information loss?\n- Is the approach generalizable to other long-horizon tasks, or applicable to other web automation tasks such as WebArena?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The proposed approach demonstrates strong empirical results, consistent across several benchmarks and compared against several baseline approaches.\n- The problem of context explosion in ReAct agents is well-motivated, and the design of folding mechanisms are intuitive.\n- The proposed approach shows significantly enhanced context efficiency, and shows better long-horizon scaling properties.", "weaknesses": "- Missing details of Fold Generator: It is not specified which LLM is used, which prompts were used, and how many trajectories were generated and leveraged for the training data generation. Without these details, it's difficult to assess whether the improvements come from the proposed folding method or superior training data.\n- Lack of ablations: Similarly, the authors do not provide any ablation studies of folding components, and it is difficult to understand how the proposed components contribute to performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "MVbpuiihgc", "reviewer": "Reviewer_mj6d", "summary": "This paper addresses the important long-history handling issue among agents, by proposing AgentFold, which consolidates the latest interactions into fine- and coarse-grained memory. Experiments across different benchmarks demonstrate its empirical advantages among open-source LMs.", "questions": "1. From how humans manage memory, it seems there should be some transition between/from fine-grained memory to coarse-grained memory, yet that is not presented in the method. Why? Both intuitively and empirically.\n\n2. How to ensure the quality of the synthesized training data for AgentFold. While the method is proposed to emulate the human memory consolidation process, it is unclear if the synthesized data ensures this critical property.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "**1. Importance of Problem and Novel Approach.**\n> This paper tackles an important and prevailing issue among agents – their inability to process long contexts as tasks grow more complex. This paper proposes a new angle to actively manage, instead of passively log, past interaction history.\n\n**2. Substantial Experimental Improvements.**\n> Results in Table 1 show the empirical effectiveness of the proposed AgentFold method, among open-source models. Further analysis of the number of turns provides additional insights.", "weaknesses": "**1. Lack of Clarity in Method Description.**\n> While Section 3 describes the two condensation methods at a high level, it is still unclear what counts as a “fine-grained” summary (in granular condensation) and what counts as a “coarse-grained” summary (dense condensation). It would be helpful if Figure 2 (or an additional figure) could show a few concrete examples of both.\n\n**2. Lack of Hyper-Parameter Choices.**\n> Several values could be critical to the empirical performance of the proposed method, but are not reported, justified, or hard to find. These values include: (i) number of steps to trigger consolidation, (ii) number of fine- and coarse-grained memory entries to maintain, (iii) number of steps of the “latest interaction”. Further, if these values need to be adjusted when applying the methods to different benchmarks, providing information on how these values are determined, either empirically or automatically, is important.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)