WALT: Web Agents that Learn Tools
Authors: Viraj Prabhu
Yutong Dai
Matthew Fernandez
Krithika Ramakrishnan
Jing Gu
Yanqi Luo
silvio savarese
Caiming Xiong
Junnan Li
Zeyuan Chen
Ran Xu
Venue: ICLR 2026 Poster (2026)
Keywords: web agents
tool use
LLMs
agentic reasoning
Primary area: foundation or frontier models, including LLMs
TL;DR: Web Agents that Learn Tools autonomously that leads to improved success rate and efficiency

Abstract: Web agents promise to automate complex browser tasks, but current methods remain brittle -- relying on step-by-step UI interactions and heavy LLM reasoning that break under dynamic layouts and long horizons. Humans, by contrast, exploit website-provided functionality through high-level operations like search, filter, and sort. We introduce WALT (Web Agents that Learn Tools), a framework that reverse-engineers latent website functionality into deterministic, callable tools. Rather than hypothesizing ad-hoc skills, WALT exposes robust implementations of automations already designed into websites, spanning discovery (search, filter, sort), communication (post, comment, upvote), and content management (create, edit, delete). Tools abstract away low-level execution: instead of reasoning about how to click and type, agents simply call search(query) or create(listing). This shifts the computational burden from fragile step-by-step reasoning to reliable tool invocation. On VisualWebArena and WebArena, WALT achieves state-of-the-art success rates (52.9% on VisualWebArena, 50.1% on WebArena) with fewer steps and less LLM-dependent reasoning. On Online-Mind2Web, a benchmark of 139 real-world websites, WALT autonomously discovers 252 tools and improves success rate by 20.5% over a tool-free baseline, establishing a robust and generalizable paradigm for browser automation. Code: https://github.com/SalesforceAIResearch/WALT

Published as a conference paper at ICLR 2026
WALT: WEB AGENTS THAT LEARN TOOLS
Viraj Prabhu, Yutong Dai, Matthew Fernandez, Krithika Ramakrishnan, Jing Gu, Yanqi Luo, Silvio Savarese, Caiming Xiong, Junnan Li, Zeyuan Chen, Ran Xu Salesforce Research
(viraj.prabhu, ran. man.xx)@saleesorce.co
ABSTRACT
Web agents promise to automate complex browser tasks, but current methods remain britlle-relying on step-by-step UI interactions and heavy LLM reason- ing that break under dynamic layouts and long horizons. Humans, by contrast, exploit website-provided functionality through high-level operations like search, filter, and sort. We introduce WALT (Web Agents that Learn Tools), frame- work that reverse-engineers latent website functionality into reusable invocable tools. Rather than hypothesizing ad-hoe skills, WALT exposes robust imple- mentations of automations already designed into websites-Spanning discovery (search, filter, sort), communication (post, comment, upvote), and content agement (create, edit, delete). Tools abstract away low-level execution: instead of reasoning about how to click and type, agents simply call search (query) create (1 i st i ng). This shifts the computational burden from fragile step- by-step reasoning to reliable tool invocation. On WesualWebArena and WebArena, WALT achieves tate-of-thhe-a success rates (52.9% on VisualWebAAens 50.1 % on WebArena) with fewer steps and less LLM--dependent reasoning. On Online- Mind2Web, a benchmark of 139 real-world websites, WALT autonomously dis- covers 252 tools and improves success rate by 20.5% over a tool-free baseline, establishing robust and generalizable paradigm for browser automation.
Code: otteg//tteeeeettttacccooalllllee
INTRODUCTION
Task: find me the cheapest blue kayak
search(suary= kayakk, category Boats sort by='price" order= 'asc]]
goooo chase utb:/psgeueeerehf www.ee/st....kkkkkssskk... byupricesord-aa
stop
focusf (eerch)
- ttyee [ksyak]
hover hover (erddoonn--eleeee Boats']
hover dropdown cllick 'Price)) scroll down
flind color= blue
Figure 1: WALT transforms browser agent automation from brittle step-by-step reasoning to efficient tool-based abstraction. For the task "find the cheapest blue kayak," traditional web agents exccute lengthy sequence of primitive UI actions focusing search boxes, hovering drop- downs, clicking categories, and sorting and scanning results. Our method WALT (Web Agents that Learn Tools), designs deterministie tool that exposes this website-provide functionality to the agent: search (query- 'blue kayak', category- 'Boats', scrt.by= 'price'), reducing execution from 8+ fragile UI steps to robust operation.
Consider searching for the cheapest blue kayak on a classifieds page (Fig. [[: existing web agents reason through each step-how to interact with the search box, locate filter controls, determine the correct sort option-wiie while simultaneously handling implementation details like element selection and timing. In contrast, humans naturally think about this task in terms of website functionality: "search for kayaks, filter by price, identify the first blue one." They abstract away the implementation details and focus what they want accomplish, not how the interface mechanics work.

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Published as a conference paper at ICLR 2026
This human capability stems from recognizing reusable patterns across websites not only in search and filtering, but also in content creation and management (e.g., creating, editing, delet- ing listings) and social interactions (e.g.. commenting, messaging, upvoting). Humans leverage this prior knowledge to quickly adapt their interaction strategies to new websites. In the web agent context, this intuition has inspired work on discovering "skills" (Wang al.. (0225) for web agents- reusable action sequences that encapsulate common interaction patterns and can be applied across similar website elements or tasks.
However, existing skill discovery approaches suffer from two key limitations in which skills are dis- covered and how they are implemented. First, they either mine skills only from successful trajecto- ries (Wang et al.] 2025b] |aarch et al., [024] Waan et al. (2225)) codifying existing behaviors- or require agents to hypothesize useful automations (Zheng al., 2025), often yielding unintuitive, overly specific, or irrelevant skills. Second, both approaches implement skills as brittle UI action sequences, highly sensitive to dynamic elements and design changes.
We propose WALT (Web Agents that Learn Tools). Unlike prior "skills" or "workflows," which are agent-induced action sequences, our tools correspond to webxite-provided juniiiion bars, filters, sorting mechanisms, commenting systems, and navigation controls- site designers have already engineered as robust automations. Each tool is exposed to the agent as a high-level deterministic call, with an underlying implementation discovered and validated through reverse- engineering. This reframing shifts the agent's capability frontier: instead of learning brittle approx- imations of interaction patterns, WALT surfaces the functionality already embedded in websites as reliable, reusable tools.
loop for each identified tool: (1) a web agent comprehensively demonstrates the functionality (e.g., all filters and sort options for search); (2) tool generation agent maps execution traces to structured tools with validated input schemas, prioritizing deterministic actions but allowing agentic steps for dynamic elements; (3) the tool is optimized by replacing UI sequences with more robust URL manipulation through API reverse-engineering: (4) a test agent verifies functionality against pretve test inputs.
On each website, WALT follows a
This abstraction transforms the agent's computational burden: instead of reasoning about "how do I search for X, then filter by Y, then sort by Z" through complex UI sequences, the agent simply calls search (X), filter (Y), , sort (Z) and focuses on higher-level planning. Tool discovery and optimization happen offline during website exploration, ensuring both efficiency and reliability.
We benchmark our method on Vee (Koh et al. 2024a) and WebArena (Zhou et al. 2024), discovering over 50 reusable tools spanning search and filtering, content creation and man- agement (e.g., create, edit, delete listings), and communication or social interactions (e.g., comment- ing, messaging, upvoting). WALT achieves state-of-ard art success rates of 52.9% on VisualWebb and 50.1% % on WebArena, significantly outperforming prior work. We further validate real-world generalizability on Onldne-Mind2Web (Xue et al., 2025), a benchmark of 139 live websites, where WALT discovers 252 tools and improves success rate by 20.5% relative over a controlled tool-free baseline, achieving near-parity with the Claude Computer- Use Agent without any specialized train- ing. Ablation studies further reveal that our proposed contributions - discovered tools, multimodal DOM parsing, and external verification yield gains in both success rates (10%-30% across splits) and efficiency (1.3-1.4x fewer steps on average). Overall, WALT transforms browser agent automa- tion from brittle step-by-step reasoning to efficient tool-based abstraction.
2 RELATED WORK
Web Agents. Agents capable of directly operating a browser to perform tasks hold promise for automating online tasks. Prior work advances web agents along four axes: Perception concerns what the agent sees and how grounds elements: some methods parse raw HTML (Gur et al. 2022
Deng et al. 2023). others process full-page screenshots with vision-language models (Furuta et al. 2023 He et al., 2024), often augmented by Set-of-Mark (SoM) visual prompts (Yang et al. 2023):
recent work improves page understanding and grounding via prompting (Zheng et al.) 2024 Yao et al. 2023) and task-specific training (Furuta et al., 2023 Zhang et al. 2025al Pahuja et al. 2025
Qi et al. 2024). Planning scales test-time exploration with search (e.g.. MCTS and related vari-
ants) to choose better action sequences (Koh et al.) 2024b] Putta et al. 2024 Yu et al. 2024 Gu et al. 2024). Reasoning enhances step selection through
and ReAct-style prompt-
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2023) Action execution determines how decisions touch the page: agents that use HTML or SoM predict DOM targets, whereas screenshot-only agents act via pixel- space coordinates (Xu al. 2024), which can be more brittle to layout change. Our approach targets the action-execution and planning axes by mining reusable, efficient tooly offiine encapsulaaa site functionality with validated schemas, URL-level operations, and targeted agentic fallbacks agents solve tasks faster and more reliably than step-by-step UI policies.
ing (Wei al. 2022 Yao et al.
Benchmarks for Web Agents. Benchmarks for web agents expanding rapidly and span simu- lated and real environments. Early simulated testbeds (Shi et al. 2017: Liu et al. 2018) emphasize basic navigation such as clicking and form filling, whereas Yao et al. (2022) focused on e-commerce tasks. ZZhou et al. (2024) introduced WebArena, realistic web simulation environment with repli- cas of various website types (e.g. shopping, public forum, maps, etc.), with rich functionality and realistic underlying databases, and set of complex natural language tasks paired with robust rule- based evaluators. VisualWehArena (Koh et al.) 2024a) further extended this environment to include Aisuall-ggrunde tasks that require rich multimodal understanding, along with supporting website and evaluator additions. complementary direction evaluates agents on real websites or produc- tion sandboxes (He et al. 2024 Zhang al., 2025b; Drouin et al., 2024 Boisvert et al. 2024), covering e-commerce, enterprise software, and everyday workflows. We evaluate on WebArena, Vi- SeerWae, and Online- Mindde-innWWe (139 live websites), and propose method to autonomously discover and construct reusable, website-specifft tools that significantly improve agent performance and efficiency.
Web Agents. While UI-level actions are the default interface to the Web, they can be inefficient and brittle. Accordingly, some works exploit API documentation actions from APIs and thus augment or bypass UI interactions (Song et al. [024] Ni et al. 2025). In contrast, we do not assume any API documentation which is often undocumented or proprietary and instead attempt to reverse-engineer website- provided functionality into callable tools with validated input schemas, Uar.-aarreterr promotion, and agentic recovery, all learned autonomously via systematic exploration.
APl-using
design high-level
website-provided
Skill Discovery for Web Agents. Some recent works focus on discovering skills for web agents by mining successful agent trajectories: SkillWeaver (Zheng et al., 2025) produces unit-tested Python functions from successful attempts, whereas AWM Wang et al. (2025b) and ASI (Wang et al. 2025a) induce skills online (represented text and programs, respectively) by prompting an agent duce skills from action subsequences in successful trajectories. Both lines of work typically mine only from successful executions and implement skills by composing primitive actions, which can be brittle and effectively codify current behavior without expanding capability. By contrast, we sys- tematically explore website-specific functionality and exploit observable regularities and site infras- tructure; our learned tools are stress-tested and iteratively optimized for reliability and modularity. Unlike prior work that composes longer UI sequences, we discover and implement new, website- grounded tools with schema validation, selector stabilization, URL rewerse-engineerinee and targeted agentic fallbacks. See Appendix Table for detailed comparison.
APPROACH
We frame browser automation as the discovery and use of tools: high-level, callable operations that abstract away fragile low-level interactions. Unlike prior work that induces ad-hoc skills or scripted action sequences, WALT treats websites as sources of structured functionality (c.g., search, filter, post). Each tool is backed by validated action script - primarily deterministic URL/DOM opera- tions with targeted agentic steps Figure 2 of tool candidates followed by their construction and validation.
summarizes the two-stage pipeline: strategic discovery
3.1 PROBLEM FORMULATION
Let W = [1, W2..... wo) denote a set of websites, and T = {t1, to..t...tt.. fmm) denote a set of tasks. A browser agent Blowser typically solves these tasks using primitive actions Aprim (aziicks Orype anavi gates.s......................... .... Our goal is to discover and implement tools that can be invoked as high-level actions A at runtime for more efficient and reliable task execution.
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Strategic Exploration
Multi-level Exploitation
candiciate workloows
Ul
seanch bay
Tocus
auto-ompletee ccalegorys
clok submit
generate
propose
www
goal driven
URL
reusable
search workflow
[Inpute] queryt stc, caterozy: optional [ptional[enunt-i] register
composable
[Outgut] results: ts [scr)
[Deacription] Pe-form xearchh by entering query and opt ophictnally specifying callgary
diverse
Figure 2: Overview of WALT. Left Discovery: the browser agent explores key site sections to pro- pose tool candidates. Right- Construction: Tools are learned for each candidate via a demonstrate- generate-optimize-tes loop: (i) a browser agent first demonstrates the tool's underlying functionality and records a detailed execution trace, (ii) a tool builder agent then synthesizes and optimizes an exe- cutable tool-a validated input schema and action script of UI, navigation, extraction, and agentic the trace, (iii) the tool is registered and tested end to end. Feedback refines selectors, schema, and script until robust single-call tool is produced.
We define a tool u as a callable high-level action u S Goal where S specifies structured input parameters and Goal is the target outcome. Once validated, tools exposed to the agent as atomic actions that augment its existing action space. Our approach involves two stages: strategic exploration discover tool candidates, and multi-level exploitation construct and validate them.
3.2 STAGE CANDIDATE DISCOVERY VIA STRATEGIC EXPLORATION
In this phase, we task browser agent Boroweer with systematically exploring user-facing website sections to identify reusable functionality patterns. We prompt it to navigate to key areas (content browsing, discovery/search, communication interfaces) and discover interactive elements through targeted interactions (e.g., hovering over dropdowns to reveal options, clicking menus to expose navigation structures, interacting with forms to understand input fields). The agent then strategically proposes list of tool candidates with clear user intent, optimizing for coverage (diverse func- tionality) and minimizing redundancy (avoid overlapping tools). Each candidate = (8,, Eg, G,) specifies start URL s;, relevant interactive elements E,, and the specific goal G2 to accomplish.
3.3 STAGE 2: TOOL CONSTRUCTION VIA MULTI-LEVEL EXPLOITATION
This stage transforms tool candidates ū from Stage I into validated, executable tools through httprrrtteeeeeeeaa--opptiiieeeee
loop.
Demonstration. For each tool candidate, first prompt browser agent Borowser demonstrate the tool's underlying functionality and record a detailed execution trace X, consisting of primitive actions (clicks, typing), DOM states (element selectors with fallback alternatives), URL changes, and realistic test inputs Iest We execute robust DOM parsers that extract stable selectors for inter- acted elements that allow for reliable replay of logged trajectories. We prompt the agent com- prehensively explore underlying functionality e.g. to log multiple trajectories with different input combinations, which helps reverse-engineer the latent functionality of the tool (e.g. determining whether an input is required or optional, and what values it can take).
b Generation. Next, this rich trace is analyzed systematically by specialized tool builder agent B,ou to synthesize an executable tool, represented by: structured input schema with validated datatypes (e.g. enums for dropdowns), optional fields, and usage examples.
fii A detailed tool description specifying its purpose, usage preconditions, and expected outcomes. iii) An action script of steps to be executed sequentially to accomplish the goal Goal. Steps fall into four types: a) navigation (for URL/route changes), b) extraction (for capturing DOM state), c) Ul interaction (to click, type, etc.), and d) agentic (for dynamic interactions). We deliberately bias Booo towards deterministic operations (navigation and interaction) to improve robustness and efficiency, but permit agentic steps when interfaces are dynamic or ambiguous (e.g. lazy-loading or uploads).
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Optimization. After generating executable action script, Buol selectively attempts to optimize it by engenernngin parameterizable URL routes (e.g. eateery=xccateooooo sible, replacing mulp UI sequences with single navigations for improved efficiency.
where pos-
> Validation. We register (u, Iyes) as a callable action and execute end-to-end with a fresh Browser over pre-vetted Tea- Failures yield structured feedback F:: selector drift, uncovered enum values, timing issues, or semantic mismatches. Biool then refines selectors (preferring stable hashes), amends S (e.g,, adding missing options), or edits the action script (e.g., backing off over-aggressive URL promotion). This iterative loop systematically improves correctness and robustness- one-shot script extraction in prior work.
Formally, in stage 2 we iteratively optimize:
given ū (8i; Ei, Gi)
(1)
execute & generate Brown X Bunl (u, Iwst
(2)
minimize FailRate(u, Iuett + StepCount(u) + AgenticRatio(u))
(3)
Here, FailRate is the fraction of failing test cases (measuring correctness), StepCount is the number of primitive operations the implementation executes (measuring efficiency), and AgenticRatio is the fraction of steps that require LLM-dependent reasoning (measuring determinism). The process iterates upratinguudating the tool and test set with feedback- until validated u" is obtained or the attempt budget is exhausted.
Only tools passing validation are exposed runtime. As a final failsafe against unanticipated fail- ures (e.g,, major UI changes), we equip the agent with agentic fallback spawning fresh agent to handle failing scripts on the fly. Additionally, we expose two generic tools: multimodal DOM parser (converting HTML to interleaved input for cross-modal reasoning) and an external verifica- tion tool (corroborating self-reported outcomes, following Andrade et al. (2025) to further improve the agent's perception and reflection capabilities.
3.4 WALT IN ACTION: LEARNING A SEARCH TOOL ON VISUALWEBARENA
To ground our approach, we present a real-world example of the learned search tool introduced in Fig. [ Proposal: The browser agent explores the site and proposes a search tool based on the search interface. Demonstration: Boroser executes a sample search (e.g., query="bicycle", cate- gory="bikes"), recording DOM interactions (typing into search box, clicking category dropdown, submitting form) and observing URL changes. Generation (Phase 2): Bool analyzes the trace, gen- erates initial Ul-interaction based action script and then uses URL promotion to yield a more efficient implementation based on a parameterizable URL route. It also induces an input schema with validated category enums (Bikes=7, Cars+trucks=10, etc.) extracted from the dropdown menu. Validation (Phase 3): The tool is tested with diverse inputs; failures (e.g., missing category options) trigger schema refinement until tests pass. JSON representation the tool is shown below.
Tool: search. ssst wistinggs ings (...): Keyword search with optional refinements
Precondition: None (callable from any page)
Outcome: Navigate to search results page
Input Schema: (l]=optional)
Action Script: (URL promotion):
sPattern: string [24 chars]
to search base URL:
IsCategory]: enum[-.] (Boats=8,..)
goto (base (base_ur1 / nndex.e.... wwwp:.ppppppaaaeeeaeeeee
[bPic]: boolean
2. Append query params:
[sPriceMi noaain/Man n/Max]: float
geta (current url www.
In this manner, WALT turns complex website functionality into simple tool calls. By pairing grounded interaction (Boowser) with schema-checked, URL-optimized executors (Byoo), delivers robust tools across discovery, content, and communication that run faster and with fewer L.M calls.
4 EXPERIMENTS
We first comprehensively evaluate WALT on two established web agent benchmarks: Visual bArena (Koh al. 2024a) and WebArena (houu et al. 2024). Our experiments demonstrate that
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WALT achieves significant improvements over prior state-of-the-art methods by leveraging website- provided tools rather than brittle UI interaction sequences, improving success rates while reducing action steps. We then conduct comprehensive ablation studies to validate the contribution of each component. Next, we evaluate WALT on 139 live websites, to demonstrate its generalizability to real-world websites. Finally, we conduct fine-grained analysis of when and why WALT succeeds.
(Xue et al. 2025). a benchmark of
4.1 BENCHMARKS
Visual contains 910 sauually-grouded and human-annotated web-tasks instantiated in three highly-realistic and fully-featured websites Classifieds (234), Shopping (466), and Reddit (210). WebArena includes 812 more general tasks spanning five websites (two of which overlap with VisuaaWWehAreaaa- GitLaa (180), Map (109), Shopping (187), CMS (also referred to as Shop- ping Admin 182), Reddit (106), and Multi-site (48). Tasks are defined by numan-annotater intent (e.g. "find the cheapest blue kayak and return its URL") and evaluator functions (e.g. "assert URL == <XYZ>"). Besides robust set of (exact, inclusion, and fuzzy) string and URL matching, the benchmarks also support sophisticated evaluators based on parsing page HTML and image con- tents. Agents are evaluated by their binary success rate- stringent metric that only considers task completion rather than partial success, and is measured objectively by the evaluator function rather than subjective LLM judgement.
4.2 IMPLEMENTATION DETAILS
with browser action executor (GPT- 5 5-mini) with a standard action space (click, type, navigate, etc.). Observations include a page screen- shot with indexed Set-of-Mark (SoM) boxes and list of interactive elements keyed by the same indices. State is maintained via a multimodal message queue. For retrieval, we store trajectory sum- maries in a vector database keyed by task intent; at run time we embed the current intent and append the nearest summary in the DB (with similarity threshold 0.3) as context. Agents authenticate to each site before execution, run for at most 30 steps, and replan every 15. We use GPT-5-mini as the verification LLM following the design of WebJudge (Xue al. 2025). The multimodal DOM parser converts a markdown dump of the page into an interleaved representation. Implementations build browser-use (Browser-Uu Use Team 2024a) and workflow-use (Browser-Us) Use Team 2024b).
Our base agent pairs a VLM planner (GPT-5 (OpenAl 2025)
4.3 BASELINES
We compare against representative set of state-of-the-art methods. Skill-based web agents: Specifically, on WebArena we benchmark against SkillWeaver (Zheng et al. 2025). AWM (Wang) al., 2025b), and ASI (Wang et al., 2025a On VesualWebArena,
benchmark against concur- rent world in tool-oriented web agents Yu et al. (2025). Web agents with test-time scaling: We benchmark against methods that use MCTS (Koh al., 2024b) and reflective-MCTS (Yu et al. 2024). as well as one that uses masel-based planning (Gu et al. 2024). API-using web agents: We benchmark against Hybrid Agent (Song al. 2024), which generates actions from API docu- mentations curated for WebArena. Computer-UUse Use Agents: Specifically, we benchmark the Claude Computer-Use Agent (Anthropic) 2024), implementation details in Appendix AA.3
Additionally, we benchmark against SGV (Andrade et al.) 2025), which proposes using external verification module to mitigate LLM agreement bias. Finally, we include strong baselines from the original benchmark papers as well as human performance as upper bound.
4.4 MAIN RESULTS
We report performance VisualWebArena and WebArena in Figure Table 1 We find:
> WALT achieves state-of-thheart success rates. WALT attains the best average score (52.9%), with large gains Classifieds (64.1%, +12.1 absolute over SGV) and Reddit (39.0%, +6.0 ab- solute), while remaining competitive on Shopping (53.4% vs. 57.0% for SGV). Further, it nearly doubles the success rate of the Claude Computer Use baseline (which uses image-based obser-

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Method
Classifieds
Shopping
Reddit
Avg.
Tesk Success
GPT-4V+SoM (Koh et al. 2024a
9.8
17.1
19.3
-2..5%
1.3k
TreeSearch Koh at 2124h
26.5
29.0
20.5
26.4
213
WebDreamer [ul ac a.
2024
25.0
26.3
15.9
23.2
Computer-Use (Anhhropic 2024
36.7
21.9
27.5
27.0
ExaCT (Yu al. 2024
41.0
32.3
28.7
33.7
higher
Pyster
AWorld Yu al 2025
36.5
SGV (Anorade
52.0
57.0
33.0
42.2
WALT
64.1
53.4
39,0
52.9
gher
Syteer
Boad
Human Kohh et al. 2024a
91.7
88.4
87.1
88.7
Figure 3: Results on VisualWebArena. Left. weighted average. Right. We compare WALT's performance and efficiency with baseline implementation as control.
We report success rate (%) on each split as well
Method
Gitlab
Map
Shopping
CMS
Reddit
Multi
Avg.
GPT Zhou ct al.
2024
14.4
SkillWeaver (Zheng et al. 2025
22.2
33.9
27.2
25.8
50.0
29.8
AWM (Wang et al. 2025b
28.9
39.4
34.8
39.0
51.9
18.8
35.5
ASI (Wang al.
2025a
32.2
43.1
40.1
44.0
54.7
20.8
40.4
Hybrid Agent (ong ct al.
2024
44.4
45.9
25.7
41.2
51.9
16.7
38.9
WALT (Ours)
57.0
58.7
41.2
56.2
48.5
20.8
50.1
Human (Zhou et al. 2024
78.2
Table 1: Performance comparison on WebArena benchmarks showing success rates (%) across dif- ferent domains. Bold values indicate best performance in each column.
vation space), also outperforms strong baselines based on test-time search and tool use by 15-20 points.
On WebArena, WALT again achieves the highest overall average success rate on 5 of 6 splits (tied on the sixth), outperforming prior work in all domains by large margin, and outperforming the best-performing Akill-nnduction based method (ASI) by 9 points.
Tools improve both success rates and efficiency. In Figure (right), we demonstrate both the performance (measured by success rate) and efficiency (measured by average # steps) of WALT on each WeburlWe split. As a control, we benchmark our baseline implementation which uses an identical architecture but does not use tools. As seen, tools are crucial, improving performance by as much as 30.7% (relative) and efficiency by 1.4x. The baseline agent's significantly lower success rates also validate that gains not due to stronger underlying LLM (GPT-5) alone.
Performance ablations. We ablate WALT on VisualWebArena Classifieds (Table 2). chosen for its rich tool diversity tools spanning discovery, content management, and communication) while remaining computationally tractable for multi-backhone evaluation; cross-domain validation is pro- vided by the agents equipped with discovered tools are consistently more accurate and efficient (e.g. GPT-S-mini: 7% higher success rate, 27% fewer steps). Stronger backbones benefit more, indicating that better reasoning improves tool selection and composition rather than low-level manipulation. Notably, all backbones use the same tool set discovered with GPT-5, demonstrating that learned tools transfer across models and are not backbone-span backbone-specifie. Finally, we also benchmark human demo strategy as a performance upper bound, wherein the authors manually demonstrate a set of tools rather than having the agent discover them tools generated thus yield the highest success rate (66.0%). Im- pressively, however, WALT is able recover most of this performance fully autonomously (64.1% %), with 5% fewer steps.
experiments above. We first vary the LLM execution agent, and find
Next, we ablate the two ancillary method components: we find that both multimodal DOM parsing (+2.6%) and external verification (+3.3%) yield modest performance gains, with the latter coming the cost of extra checks (more steps). Combining all components yields the highest success (64.1 %), still with substantially fewer actions than baseline policies (21.3% fewer steps).

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Table Ablations on Sew.-Webrrrna-aaasssiieiiee showing the impact of different components on success rate (SR) and average number of steps. Results shown for different LLM backbones.
browser LLM
tools
dom-parser
verify
avg #steps (L)
SR (%) f
gpt-4.1
none
text self
7.6
34.9
gpt-4.1
discovered
text self
6.6_13.19%
36.4--.3%
gemini-2..-flash none
text self
10.5
52.6
gemini-2.5-flas
discovered
text self
8.3 -6._2
55.3 46.1%
gpt-5-mini none
text self
8.9
57.5
gpt-S-mini
discovered
text
self
6.5 27.0%
61.5 47.0%
gpt-5-mini
human demo
text self
7.4-16.9%
66. 66.0
gpt-5-mini
none
multimodal self
7.5 15.7%
59.0 2..6%
gpt-5-mini none
text external
11.0+23.66
59.4 19.44.3.390
gpt-5-mini
discovered
multimodal
external
7.0
21.3%
64.1 11.8%
Type
Count
%
Method
SR
Steps
fardens F1
URL Promotion
80
31.7
Baseline
42.9
10.8
UI Only
38
15.1
WALT
51.2
8.2
Agentic
60
23.8
A
+8.8
-23.3%
Mixed
74
29.4
luyas
Total
252
100.0
Table 3: Results on Online-Mind2Weh. Left. WALT improves success rate and efficiency against tool-free baseline. Center. Tool composition 252 learned tools. Right. Tool "wins" span diverse domains, demonstrating versatile real-world generalization.
4.5 REAL-WORLD EVALUATION ON ONLINE-MINDA WEB
To demonstrate generalizability beyond simulated benchmarks, evaluate WALT Online- Mind2Web (Xue et al.) 2025). benchmark comprising 139 real-world websites spanning e- commerce, healthcare, travel, education, and government domains. We first WALT discover 2-3 tools per website (to keep costs reasonable). We then provide these tools to the agent at runtime over the 300 benchmark tasks.
We find that: WALT learns useful tools. WALT autonomously discovers 252 validated tools on Online- Mind2Web. Over 238 tasks that it completes without environment errors, compared to con- trolled tool-free baseline, WALT (with GPT-5-mini) improves both success rates (+20.5% relative, 42.9-+51.2) and efficiency (+23.3% relative, 10.8 steps).
Results. We report success rate evaluated by WebJudge (Xue et al. 2025) in Table 3
> tasks show "tool wins": cases where baseline failed but WALT used learned tools to succeed, spanning 24 different websites.
Learned tools boost performance specialized CUA model levels. WALT achieves near-parity with Claude Computer Use's official leaderboard performance (51.2% 51.7%, -0.5% lower) without any specialized training for computer use tasks demonstrating that tool discovery can rival specialized model training.
b Real-world limitations persist: 62 tasks fail either due to bot detection (35) or timeout errors (27), affecting both methods similarly. In total, 22 websites are completely untestable due strong bot detection
reality of measures (e.g., apartments com, cars com, UPS. com), highlighting the messy automation.
& Complementarity of benchmarks: Online-Mind2Wad validates real-world generalization across diverse live sites but is limited to read-only tasks (browsing, search), as authenticated write op- erations would be unsafe. VisualWebArena and WebArena enable evaluation of the full task contruum-contet management (create/cdit/delltte) communication (post, upvote), and complex authenticated workflows- for which WALT discovers bespoke tools (e.g., create. -istingg, post.cooment).
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Humber Shapa
15.0
Step Typas
Actions (successful tasks)
Action distribution
10.5
200
tool post, commen ommentt to
walt to
tool search, by. city lp
click element extract content
done tool search instings
tool sort, results
go back
switch tab
go unl
Scroll down
select dropdoow, option
tool delete listing
scroll text
scroll up
sand heys
Input,
open,
or
Content Manugament
Search
(a) Task type and complexity breakdown
Action distribution
Listnn
1074
Actions (failed tasks)
Listing
200
Post
Lizting
(162)
113
switch, tab is
send teys d
extract, content
click element
tool search listings
tool sort, results
donte
Imput fost
walt
scroil, down
search by city
Back
toel post, comment
go to url
tool create greate_listing Jisting
open tab
By Region
20
00
80
100
Success Rate (16
(b) Success rate analysis by task complexity
(c) Action type analysis
Detailed analysis of the composition, success rates, and runtime invocations of tools discovered on the VeeArraa Classifieds split.
Figure 4:
4.6 ANALYZING WALT
Step distribution. In Figure we perform fine-grained analysis of our method on the Classi- fieds split. First, in Figure 4a we break down each discovered tool by the total step count of its action script and its distribution across step types and functionalities. We make the following ob- servations: i) tools span range of functionalities across communication, content management, and search, ii) tools with the shortest action scripts correspond to URL promotions (typically discover- oriented), whereas those with longer scripts skew heavily towards deterministic UI interactions (typ- ically content-managemenn
e.g. form-filling). iii) Agentic steps are rare: In fact, only 3 out of the tools have least one agentic step.
Tool construction and costs. On Online-M,nd2Webb, WALT attempts 305 tool candidates across 139 websites, of which 252 are successfully validated (82.6% success rate) in average of 1.75 attempts per tool (avg. 1.81 tools/site). Per-tool generation cost (using GPT-5 pricing as an exam- ple) is $1.67, comprising: proposal ($0.26, amortized across tools per site), demonstration ($0.87), generation ($0.46), and testing ($0.08). With baseline inference costing $0.1 1/task, break-even oc- curs after ~4 per tool. Notably, WALT prompts the discovery agent design "minimal but ficxible API" (system prompt in Appendix), yielding average of just 1.81 tools per website. Tools are learned once and reused indefinitely, providing sustained gains: for websites with 220 tasks, total tool cost is less than cumulative baseline inference cost.
Tool-use success rates. In Figure 4b we analyze the success rates of each of these tools, measured by the ratio of successful tool invocations by the agent during the entire evaluation run. Tools are used frequently (e.g. search listings invoked 262 times) and achieve nearly perfect success rates, attesting to high reliability. Finally, Figure 4c breaks down the action type distribution of each tool for successful and failed agent trajectories as seen, the agent uses both primitive and tool actions extensively in both cases.
Qualitative examples. Figure 5 demonstrates how WALT generalizes across diverse real-world websites on Online-Mind2 healthcare (provider search and filtering), finance (retirement planning with calculator tools), and travel (road trip planning with map-based search). Across these domains, WALT composes learned tools to solve heterogeneous tasks efficiently: discovery tools jump directly to filtered result sets via URL parameters, extraction steps parse structured content, and action tools complete interactions
The examples span classifieds (listing search and commenting),

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[PASS] recall seeing this exact item on the site, tell me the email of the most recent lster the itom. recall it was in the Art crafts section and between $600-$1000.
[PASS] Find the closes S-stor rated dentist to zip code 96011.
[PASS] latest listng of white Google Poxel phone and pasl a comment offering $10 less than their asking price.
[PASS] I'm plan al can save $300/mondh w 3% annual return 13% tax rate and 24% refirmant tax rata, compere Tradiional and Roth IRA.
[PASS] What is the second best stop along the road from Yellowstone Vegas?
$500
Figure Qualitative rollouts of WALT on Online-Mind2Web. Each column shows a task with tiled screenshots (left to right) and the agent's actions at each step (gray bars). Top row, left: [PASS] "Recall exact item and return the most recent lister's email." The agent chains search.listings
sort.results to narrow search, then navigates to extract the email. Top row, middle/right: [PASS] Healtheare queries on Healthgrades (dentist search, provider pro- files) demonstrate cross-site generalization with tool-based search and filtering. Bottom row, left: [PASS] "Latest white Google Pixel; post a $10-under offer." The agent locates the listing and uses a tool post the comment. Bottom row, middle: [PASS] Financial planning task comparing Tradi- tional vs Roth IRA using calculator tools with structured inputs. Bottom row, right: [PASS] Travel planning query finding road trip stops between Yellowstone and Vegas using map-based search tools. Across diverse real-world sites, trajectories leverage discovered tools for efficient task completion.
(e.g., v.st..ommentt. The traces show short programs (2-5 calls) with minimal UI clicking, demonstrating how tool reuse enables step-count reductions even on previously unseen websites. The panel illustrates the key design goal deterministic navigation and schema-checker operations for speed and robustness, with targeted agentic steps when needed for complex reasoning.
5 DISCUSSION
In this work, we reframe browser automation around tools - callable abstractions reverse- -ngiieeen from website functionality rather than egent-imagined skills implemented as a brittle sequence of UI actions. Our method WALT exposes existing website functionality as robust tools that accept a validated input schema and accomplish a specific goal via sequence of UT interaction, extraction, agent, and navigation steps, each with strong failsafes built in. WALT achieves state-of-the-an performance on challenging web automation benchmarks while requiring fewer LLM interventions.
Our method has certain limitations. Offline tool discovery incurs an exploration and validation cost per-website, and the type and quality of the tools discovered is function both of what our exploration uncovers and what the site exposes. Highly dynamic interfaces, A/B experiments, CAPTCHAS, and heavy anti-automation can reduce determinism or block URL promotion.. Schemas may still miss rare parameter values; selector stabilization can drift after major redesigns; and some interactions (e.g., complex editors, file uploads) still require agentic steps.
These limitations also present opportunities for future work. Online tool patching when selectors and schemas drift over time can improve robustness. Extracting canonical web patterns for common functionalities (e.g. search, filter, sort) can aid generalization. Hybrid integration with official APIs when available, external MCP servers (Luo et al. 2025). and more agent-accessible observation spaces (Lu et al.) 2025) can help further expand capabilities. Overall, our tool abstraction paradigm suggests a practical path for safe, auditable automation: tools carry explicit contracts, examples, and validation traces, making web agents easier monitor, share, and maintain sites evolve.
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Ethics Statement. All authors have read and agree to the ICLR Code of Ethies. The benchmarks used VisualWebAArems WebArena, and Mindde-)))) are publicly available testbeds; the first two simulate interactions with websites, while the third uses live websites for read-only evaluation. No experiments were conducted with human subjects. Our method is designed for research pur- poses; however, with any browser automation technique, misuse (e-g.. for scraping or spam) is possible. We emphasize that WALT is intended to improve robustness and reproducibility of aca- demic benchmarks, not to enable malicious automation. All data handling follows the licenses of the underlying benchmarks, and no private user-sensitigg data is involved.
Reproducibility Statement. We have made efforts to ensure reproducibility. The paper provides full details of the tool discovery and construction pipeline (Sec3) and (A). optimization objec- tives and algorithmic design (Sec3). and benchmark setups (S Implementation details, includ- ing model choices, observation formats, step limits, and verification procedures, are described in Sec[f Appendix materials include pseudocode, algorithm tables, and ablation analyses. Our code will be made publicly available.
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A APPENDIX
A.1 COMPARISON WITH PRIOR WORK
Table provides a detailed comparison between WALT and prior approaches to web automation. Core insight: Humans use providdd functionality (search, filters, forus)-robust by design. Prior "skill" approaches solve an artificial problem: they induce ad-hoc patterns from agent behavior rather than leveraging this infrastructure. WALT's paradigm is to build robust and efficient tools that exploit website-providee functionality. Key differences: i) WALT discovers what websites provide, not what agents did mirroring human web use, ii) No documentation required; autonomous reverse- engineering, iii) Optimized for robustness via schema validation, selector stabilization, and URL inference. These distinctions mark a paradigm shift from mining agent behavior to surfacing site functionality.
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Table 4: Detailed comparison of WALT with prior approaches for web automation.
Aspect
SkillWeaver ASI
AWM / Hybrid Agent
WALT (Ours)
Approach
Agent-induced from suc- cessful trajectories
Curated API documenta-
Systematic exploration of website functionality Reverse-engineer site in- frastructure
tion Reliant on human- written docs API calls (when avail- able)
Consequence
Codify existing agent be- havior
Implementation
Brittle UI action replay
URL promotion val- idated schemas + fall- backs
Validation
Unit tests on synthetic inputs
N/A
Stress-testing on pre- vetted inputs
Key Distinction: WALT exploits functionality web designers already built (search, filters, forms)-featuree features robust by design. Prior "skill" approaches solve artificial problem by induc- ing ad-hoc patterns from agent behavior rather than leveraging thoughtfully-designee infrastructure. This mirrors how humans use websites: they exploit designed functionality, not invent workarounds.
A.2 ANALYSTS
Tools. In Figure we include list of all tools discovered across the WebArena and VisualWe- bArena benchmarks, as well as the number of attempts required to obtain a validated implementa- tion. As seen, most tools are discovered on the first attempt, but a few more nuanced functionalities (e.g. post a comment on Gitlab issue, searching on OpenStreetMaps, and estimating shopping on Shopping) require as many as attempts.
Performance. In Figure 9a we include additional fine- ine-grained performance analysis of our method on the Classifieds benchmark. First, we analyze the frequency and average length of successful and failed trajectories, segmented by the agent's own assessment of the task outcome in concurrent work (Andrade et al., 2025), web agents suffer from an "agreement bias" and fre- quently rationalize even failed trajectories as successful. Our approach mitigates this bias by using an external verifier to corroborate the agent's assessment.
found
In Figure 9b
we segment performance based on task difficulty (visual, reasoning, and overall), annotations for which are available in the benchmark. Unsurprisingly, failure rates increase with increasing difficulty of any type impressively though, WALT's failure rate does not cross 50% even on the most difficult tasks.
Figures 7 show additional qualitative examples of trajectory rollouts from both WishArWeerrena. and Maline-Mind2Weal including successful and failure cases. Key findings: i) Visual grounding: WALT successfully handles challenging visual matching tasks across sites (e.g., finding items from thumbnail images, matching characters between Reddit and classifieds Fig. top row). ii) Cross-domain generalization: Tools enable diverse real-world tasks spanning travel deals, visual product search, apartment rentals, and pet adoption (Fig. 8). iii) Long-horizon tasks: The apartment search example (Fig. middle) shows WALT composing tools across 10+ steps involving map interactions and filtering. iv) Failure modes: Complex tasks with compound constraints (e.g., "most expensive boat with image showing on water, then rate it"") still exceed the agent's capabilities, particularly when requiring both global optimization and fine-grained visual predicates combined with gated side-effect actions (Fig. 7 bottom; Fig. 8 bottom-right). We note that some such failures stem from benchmark-specifi, quirks-for instance, VisualWebArena Classifieds, searching for "boats" within the "boats" category yields different results than searching without a category filter, and only one path returns the correct item. By contrast, successful com- pound reasoning demonstrated on real-world sites in Online-Mind2Ww (e.g., healthcare provider matching with filters, multi-constraint travel planning: see Fig.
Qualitative Examples.
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Attempts Until Succe
Domain
clessifieds
redeit
giöab
shopping
map
shoppinggadmin
Workflow
Figure 6: Number of tries until successful.
[PASS] the roceers the carsitede name the movie that they
[PASS] Find me the chrerpest blue kayalk. l'iiish the jakk by ravigsiing Bat Zarila pagga and returnnng me the at Kal.
[PASS] What is I'e erril of the sel of the berm with the coffee mag in the p chura on this page?
[FAIL] Which pelens molua--ooo--neyyy price per color? maka comment with
[FAIL] recent lva plam lising from the classileds piant type plant shown mage OnoStopMaricc Thar, are least those plants att product imag commentt "intersled"
"ooldd buy you sccept Pay)all
Figure 7: Qualitative examples showing WALT tool discovery and execution on representative tasks from VisualWebArena and Online-Mind2Web.
A.3 IMPLEMENTATION DETAILS
Tool Creation Agent Algorithm and System Prompt. We include the system prompt for the tool discovery agent in Listing A.4 and algorithm and system prompt of the tool creation agent Booo in Algorithm and Listing A.4
Baseline Implementation Details. We use the Claude Computer-Uss Use Agent with a dedicated desk- top environment setup similar to OS-World (Xie et al. 2024). Each task initializes with a Chrome browser and task-specific web pages. The agent receives desktop screenshots as observations, pre- dicts OS-level actions, and executes them via pyautogui commands. Task completion is determined by either reaching the maximum step limit or agent prediction, with evaluation based on the final active webpage and parsed response.
We use c claude-44sonne- -2025505- with thinking mode enabled (temperature=1), Due to Bedrock API limits, all screenshots and task images are resized to 1280x720, with a maximum of 30 steps per task. Note that active webpage detection relies on heuristic algorithms using Playwright and Chrome DevTools Protocol, which may incorrectly identify the current page in edge cases. Reported accuracies should be viewed as lower bounds rather than exact measurements.
A.4 USE OF LARGE LANGUAGE MODELS
Large language models (LLMs) were used to polish (proofreading, revising, and compressing) the writing, specifically Claude-4-Sonnet and GPT-5.
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[PABS] Find dhaipest travll doal or disooun Thalland and titt lasto more than drys, departs noxt morth, and show the tota price?
[PASS]] Show me the chaapeot wall mack betweeen $30-40 from the dinedlleds ehte that anace Herras image CrwsStaplartet
SPASS Find chappet bed and apartment New Work.
rabbbt
Add & sew minn with tiee "Awssome boat' and and "Mine simi art
Figure 8: Qualitative examples showing WALT tool discovery and execution on representative tasks from VisualWebArena and
classifleds by difficuity
(reasoning samiccity)
classifieds apt-5-mini-walt-ff
100
10.
65
successful reported success
failed reported surceess
failed, reported failure
successful reported fallura
meeiium
difficulty
airiicutt))
to
25
classifieds gpt-5-min apt-5-mini-wwe mini-walt-r]
avg. length
20
fard
23.92
17.0
7.72
clessifieds diffiully
feiled reporteed fallure
execessfful L reported failure
failed reported success
successful reported, success
(overall altficcits)
%/ 5
44.3
herd
(a) Self-reporting analysis
(b) Difficulty analysis
Figure 9: Analysis of classifieds task performance across different dimensions.
System Prompt Tool Discovery Agent
You base_url) discover usez-facine functionality offezed by the websita. Next, you use infermation to design ninimel but fiexible API specification that captures these coze user functions.
automation designer, Your goal eystenstically
Stage 1: Exploraticn
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Algorithm WALT: Two-Agent Tool Construction (Appendix)
Require: Candidate ü (s,, Eg, Gi), attempt budget Nmax
Ensure: Validated tool u* E Amols or FAIL
1: attempts
2: while attempts < Maxx do
attempts < attempts
4: Phase 1: Exploration & Stabilization (by Browweer)
5: X < Thowwer-Eeecutee
E,, G;)
6: if X FAIL then
7: continue
b retry with alternate exploration strategy
8: end if
9: X < STABILIESBLEETOOR(()) & resolve to stable DOM hashes/locators; drop unstable
segments
10: if X UNSTABLE then
11:
continue
12: end
Phase II: Synthesis & Optimization (by Biool)
14:
plan 0 0
for each segment
do
16:
step t Booo Classify
> navigation interaction / agentic
plan +- plan {stepp
18:
end for
plan <- ADDAGGENTICFALLIBACCS((paan
D re-query DOM, retry alt selector, etc. promote eligible UI subsequences to URL ops P enums, optionals, descriptions, examples
20:
plan <- REPLACEEWITIURLOPPPpIa
21:
Sip < INFERSCHEMA(2))
22:
Iees < Buoo ExtrracTTsssllnuusxx
23:
u - (plan, Sup))
24:
Phase III: Registration & Validation (by Bhrowser)
25:
RegisterTool(u. Simp, Irea)
result Berowser TestTool Test))
27:
if result- = SUCCESS then
28:
return
validated; added Awass as
29:
else
F GerValidationEroor((esstt))
selector drift, missing enum, timeout, semantic
mismatch
31:
update selectors, schema, or (8i, E., G.) + REFNECANNDIDATE(( E,, G.,G.))F)
plan hints
32:
continue
33:
end if
34:
end while
35:
return FAIL
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17

---

Published as a conference paper at ICLR 2026
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11
System Prompt of the Tool Creation Agent
You ate master at building re-executable tools cron browser sutonation steps, Your tass is to parameterized reuseble tool,
sequeence of fhroweer the agent
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uri: Current URL
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18

---

Published as a conference paper at ICLR 2026
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from
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output: tabel for the captured dzta (uze meaningful names 1ike "liscing_data, "search_resuite"" description: Briet explanation
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description: Brice explenation. the interaction
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Teak Goal: [soal]
Available Actions: [acticns]
geal original giver co the agent. agent acciona paranscerized and ideatity which variables should be extracted, Input session events will follow in subsequent massages.
19


Reviews:
[{"review_id": "PrCHcwEDNo", "reviewer": "Reviewer_LBGY", "summary": "The paper introduces WALT (Web Agents that Learn Tools), a new framework designed to make web agents more robust and efficient. It tackles a core problem: current web agents are \"brittle\" because they rely on step-by-step UI interactions (e.g., click, type) and heavy LLM reasoning for every single action. This approach easily fails when website layouts change or tasks become complex.\n\nWALT’s solution is inspired by how humans browse the web. Instead of thinking about individual clicks, humans use high-level functions a website provides, like \"search,\" \"filter,\" or \"sort\". WALT is a framework that reverse-engineers a website's built-in functionality into a set of reusable, callable \"tools\".\n\nFor example, instead of an agent executing a long, fragile sequence of actions to find the \"cheapest blue kayak,\" the WALT agent can invoke a single, robust tool it learned for that site, such as search(query='kayak', category='Boats', sort_by='price', order='asc'). This \"abstracts away low-level execution\" and shifts the agent's job from fragile, step-by-step reasoning to high-level planning and reliable tool invocation.", "questions": "Evaluation is limited to WebArena and VisualWebArena, which are simulated benchmarks. While these are standard, they may not capture the full variability of real-world websites (CAPTCHAs, A/B testing, anti-bot measures, dynamic content). The paper acknowledges this but does not empirically test robustness outside controlled environments.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.  It proposes a new paradigm for web automation that shifts from brittle, low-level UI sequences to robust, high-level tool invocation.\n\n2. It introduces a \"demonstrate-generate-validate\" loop to autonomously create these tools.  A browser agent explores the site to demonstrate its functionality (e.g., using search with all its filters). A \"tool generation agent\" analyzes these traces to create a structured tool, prioritizing robust URL manipulation (API reverse-engineering) over simple UI replays. A test agent verifies that the newly created tool works correctly before it is registered for use.\n\n3. WALT achieves significantly higher success rates on the Visual WebArena (52.9%) and WebArena (51%) benchmarks, outperforming prior methods.\n\n4. By abstracting complex actions into single tool calls, WALT completes tasks using fewer steps and less LLM-dependent reasoning.", "weaknesses": "1. The paper frames WALT as a paradigm shift from “skills” to “tools,” but the distinction is not always clear. Prior work such as SkillWeaver (Zheng et al., 2025) and Hybrid Agent (Song et al., 2024) already explored higher-level abstractions (skills, APIs) that reduce reliance on brittle UI actions. WALT’s contribution risks being seen as a rebranding of “API induction” or “workflow abstraction” unless the conceptual boundary is sharpened.\n\n2. While ablations are provided, they mostly show aggregate improvements (e.g., +2.6% from multimodal DOM parsing). The analysis does not deeply isolate why certain components matter or how they interact. For example, how much of the gain comes from URL promotion vs. schema validation vs. fallback strategies?\n\n3. WALT excels at deterministic, schema-driven tasks (search, sort, CRUD operations) but struggles with compound reasoning tasks (e.g., “find the most expensive boat with an image showing it on water and then rate it”). These failures highlight that WALT’s abstraction layer may not handle tasks requiring joint optimization across structured and perceptual constraints.", "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": "H6pOiRv6ZV", "reviewer": "Reviewer_yMR2", "summary": "The paper proposes WALT, a novel framework that reverse-engineers website functionalities into deterministic, invocable tools. This abstract away frafile, step-by-step UI interactions, making browser automation more reliable. One core contribution is to replace brittle click-based sequences with robust URL manipulations through API reverseengineering. Evaluation was run on VisualWebArena and WebAreana. WALT achieves state-of-the-art success rates with fewer steps than the baselines.", "questions": "1. Why does the ablation study focus on a single split (VisualWebArena Classifieds) but not on other splits or WebArena?\n2. There is a large focus on promoting eligible UI chains to URL operations. What is the frequency of failure for URL promotion or schema inference? How does WALT handle this?\n3. **Fairness of comparison.** Some baselines like Claude Computer-Use Agent may operate with different observation spaces/limits/paradigm. How do you ensure comparison with baselines is fair with no hidden advantages for WALT?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- **Originality:** WALT is novel in that it reframing browser automation as demonstrate-generate-validate high-level tools corresponding to website-provided functionalities, which is intuitive and robust. \n- **Significance:** WALT shows SOTA performance on the two evaluaiton benchmarks, with higher efficiency with a controlled baseline approach without tools [Figure 3].\n- **Robustness and generalizability:** Tools span multiple categories (search, filter, content creation, communication) and remain reliable under diverse layouts.\n- **Great qualitatitive analysis:** Detailed analysis and observation of composition, success rates, and action type of discovered tools [Section 4.5].", "weaknesses": "- **Cost not quantified.** The paper does not specify the cost of offline exploration/validation. Please report discovery time distributions and other costs per validated tool.\n- **Generalizability / Practicality beyond benchmarks.** WALT's generalization to live, frequently changing websites (e.g., with CAPTCHA or A/B testing) remains untested; a small study on production sandboxes or WorkArena++ tasks would be better.\n- **Presentation**\n  - Citation format: Line 299", "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": "5IuOIGhsYq", "reviewer": "Reviewer_CcSC", "summary": "This paper presents WALT, a framework for constructing web “tools” from the functional elements of websites. The central idea is to enable an agent to broadly explore and interact with a website, identify its reusable functionalities (such as search, filtering, or posting), and abstract them into high-level tools that can be invoked directly in subsequent tasks. These tools allow the agent to operate at a more semantic level, avoiding fragile, step-by-step UI manipulations. Experiments on the VisualWebArena and WebArena benchmarks demonstrate that WALT achieves substantial improvements in both success rate and execution efficiency compared with previous web-agent baselines.", "questions": "1. If the system detects or constructs a very large number of tools, how does it manage them efficiently? Would the growing tool set cause degradation in planning or selection performance?\n2. It remains unclear how this method was used for the benchmark experiments. Were the same websites used in both the construction and evaluation phases? If so, could there be potential leakage or overfitting to specific site structures?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "1. The core idea is novel and intuitive and abstraction is conceptually sound and potentially impactful.\n2. The empirical results show consistent improvement over prior methods across two benchmarks.", "weaknesses": "1. The paper is difficult to follow, particularly in the method section. Many paragraphs are either overly verbose or lack essential details, making it challenging to reconstruct the full workflow. Logical connections between subsections are weak, and the role of each module is not clearly articulated. For example, the implementation and workflow of B_browser, which generates demonstrations, are insufficiently described.\n2. The proposed approach requires significant offline effort to explore and build a tool set for each website. And these tools are effective only for one website. If the websites change, or when the agent encounters unseen sites, this method does not work.\n3. While the offline stage costs a lot, the experiment section does not analyze the time cost, success rate, or resource consumption of tool construction.\n4. The paper does not include experiments that directly evaluate the Tool Construction procedure itself (e.g., URL promotion, schema synthesis, validation loops). The current experiments merely show that “using tools” improves results but do not demonstrate that the proposed construction method is necessary or superior to simpler alternatives. There is no analysis of whether this complex construction pipeline is justified in terms of accuracy, efficiency, or cost.\n5. The labels of Figure 4 overlap and the text is unreadable.", "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": "nVQE7oaKbF", "reviewer": "Reviewer_FkiG", "summary": "The research paper introduces WALT (Web Agents that Learn Tools), a novel framework designed to overcome the brittleness and inefficiency of web agents. The core problem is that existing agents rely on step-by-step UI interactions, which frequently fail on dynamic websites or over long task horizons. In contrast, WALT proposes a paradigm shift from learning agent-centric \"skills\" to reverse-engineering a website's functionalities—spanning discovery, communication, and content management—into reusable \"tools\". The methodology follow the demonstrate-generate-validate loop, where a browser agent first demonstrates a function, a tool-generation agent then maps the interaction trace to a structured tool, and a test agent rigorously validates it offline. Optimize multi-step UI sequences into single, robust URL manipulations. This abstraction change the agent's role from a low-level manipulator to a high-level planner. \nOn the VisualWebArena and WebArena benchmarks, WALT achieve (52.9% and 51%, respectively) and fewer action than previous methods, demonstrating a more robust approach to browser automation.", "questions": "When real use want to delegate a task on general websites, it is hard to predict the performance on unseen website. If focus on a small range of websites, some prepopulate specific api rules in prompt/tools may generate better performance. Hope the author can share some experiments or analysis about the WALT test on any real world senoria.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Shift from inducing brittle, low-level click/browse action to discovering environment-centric \"tools.\" Address the cause of fragility in web automation. \n2. Change some of the UI interactions to direct URL manipulations, which can directly reduce the long-horizon UI interaction with better robustness.\n3. Achieves state-of-the-art performance on two challenging benchmarks, with comprehensive ablation studies on gpt-4.1, gemini-2.5-flash and gpt5-mini that convincingly attribute these gains to the tool-based framework itself, rather than the powerful LLM.", "weaknesses": "1. Significant practical limitations on scalability and long-term maintenance. The discovery process need upfront computational and time cost. It is impractical to apply at the scale of the entire web.\n2. It require the server store all the website which have explored before. Also, if the some pre-extract website changed. The agent still need update the tools. Still need a efficient way to detect the website change and update the knowledge.\n3. The paper proposed that convert several UI action into one URL operation. It is hard to gurantee all action can change to URL operation. The methods lack some generalization. \n4. The evaluation is limit to a small number of research benchmarks. The framework's effectiveness against real-world adversarial challenges, the sophisticated anti-automation measures in real world still can be improved. \n\nThe main concerns lie in the generalizability, cost-effectiveness, and long-term maintainance of the WALT approach in production environments beyond simulated academic settings.", "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)