{"id":"83697f53-88fb-4c1c-90ab-0a0075fee834","arxiv_id":"2605.29270","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"A2X is an LLM-native recursive taxonomy construction and progressive-disclosure search method that reduces token cost while raising hit rate for service discovery in large MCP/A2A registries.","lead":"The paper introduces A2X, an LLM-driven pipeline that builds hierarchical taxonomies of services and searches them layer-by-layer at query time for agent service discovery. A smart generalist might read it to understand one concrete approach to scaling context management as registries of LLM-callable services grow into the thousands.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"LLM taxonomy construction accuracy is the unverified prerequisite for the reported hit-rate gains","rationale":"The reader's weakest_assumption directly identifies the same load-bearing precondition. Because the original review was abstract-only and the manuscript supplies no additional validation of taxonomy fidelity, the UNVERDICTED / low-confidence stance remains appropriate.","tokens_in":1781,"tokens_out":305,"duration_ms":13736,"concrete_test":"Sample 200 services and 50 held-out queries from the evaluation set; have two independent annotators label whether each service's placement in the generated taxonomy is correct; compute the fraction of queries for which the correct service is unreachable due to an upstream misclassification; if this fraction exceeds 15 %, the central claim is unsupported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The headline performance numbers (6.2-point Hit Rate lift vs. full context, >20 points vs. embedding baseline) presuppose that the LLM pipeline produces a hierarchy in which layer-by-layer traversal reliably surfaces the ground-truth service for arbitrary queries. No section of the manuscript supplies an independent metric of taxonomy quality (e.g., precision/recall of parent-child assignments, fraction of services placed in the wrong branch, or human-rated coherence), nor does it report failure cases where a misclassification at layer k prevents retrieval. Without such a check, the empirical gains could be artifacts of the particular synthetic service corpus rather than evidence that the recursive construction works in general.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper proposes A2X, an LLM-native pipeline for recursively constructing a hierarchical taxonomy from raw service descriptions and performing layer-by-layer traversal at query time for service discovery. It claims this addresses context-window limits and lost-in-the-middle effects when scaling to large registries of MCP/A2A services, reporting a 6.2-point Hit Rate gain versus full-context prompting at 1/9th the token cost and >20-point gains versus an open-source embedding baseline.","tokens_in":1903,"tokens_out":411,"duration_ms":17423,"significance":"If the central empirical claims hold under rigorous controls, the work would demonstrate a practical LLM-driven alternative to embedding-based retrieval for service registries, directly tackling context scarcity in the emerging Internet of Agents. The absence of free parameters or fitted quantities in the described pipeline is a potential strength, but the lack of any reported validation of taxonomy fidelity leaves the performance numbers difficult to interpret as general evidence.","major_comments":[{"comment":"Abstract and Evaluation sections: the headline Hit Rate improvements (6.2 points vs. full context, >20 vs. embedding baseline) are presented without any independent metric of taxonomy construction quality (e.g., parent-child assignment precision, fraction of services placed in incorrect branches, or human coherence ratings). Because layer-by-layer traversal presupposes that the LLM-built hierarchy reliably surfaces the ground-truth service, the absence of such a check makes the reported gains impossible to attribute to the method rather than corpus-specific artifacts.","section":"Abstract and Evaluation"},{"comment":"Abstract: no dataset description, service count, query distribution, baseline implementation details, or statistical significance tests are supplied. Without these, it is impossible to determine whether the 6.2-point and >20-point gains are robust or sensitive to post-hoc choices in taxonomy depth, traversal stopping criteria, or prompt templates.","section":"Abstract"}],"minor_comments":[],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript to incorporate the suggested improvements where they strengthen the work.","responses":[{"response":"We agree that an independent check on taxonomy fidelity would make the attribution of gains clearer. In the revised manuscript we will add a dedicated subsection under Evaluation that reports (i) precision of parent-child assignments against a manually curated gold subset and (ii) human coherence ratings on a random sample of branches. These metrics will be presented alongside the existing hit-rate results so readers can assess how reliably the hierarchy surfaces relevant services.","revision_made":"yes","referee_comment":"[Abstract and Evaluation] Abstract and Evaluation sections: the headline Hit Rate improvements (6.2 points vs. full context, >20 vs. embedding baseline) are presented without any independent metric of taxonomy construction quality (e.g., parent-child assignment precision, fraction of services placed in incorrect branches, or human coherence ratings). Because layer-by-layer traversal presupposes that the LLM-built hierarchy reliably surfaces the ground-truth service, the absence of such a check makes the reported gains impossible to attribute to the method rather than corpus-specific artifacts."},{"response":"The abstract is space-constrained, but we will revise it to include the service count, high-level query distribution, and a one-sentence description of the embedding baseline. Full experimental details, including the exact traversal stopping rule, prompt templates, and statistical significance tests (we will add bootstrap confidence intervals or paired t-tests), already appear in the Evaluation section; we will add an explicit pointer from the abstract and a short robustness analysis of depth and stopping criteria.","revision_made":"yes","referee_comment":"[Abstract] Abstract: no dataset description, service count, query distribution, baseline implementation details, or statistical significance tests are supplied. Without these, it is impossible to determine whether the 6.2-point and >20-point gains are robust or sensitive to post-hoc choices in taxonomy depth, traversal stopping criteria, or prompt templates."}],"tokens_in":1452,"tokens_out":443,"duration_ms":24444,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper's core move is an LLM-driven pipeline that turns a flat list of service descriptions into a hierarchy and then does layer-by-layer traversal at query time. That is the concrete thing on offer: a progressive-disclosure method aimed at the context-scarcity problem when registries grow into the thousands.\n\nIt does identify a real operational pain point for the emerging agent ecosystem and gives a workable engineering sketch for how an LLM could maintain and search the structure without dumping everything into one prompt. The reported deltas (roughly 6 points over full context at one-ninth the tokens, and more than 20 over an embedding baseline) are the sort of numbers that would matter if they hold up.\n\nThe soft spot is exactly where the stress-test flagged it. There is still no independent measure of taxonomy quality—no parent-child precision, no count of services placed in the wrong branch, no trace of queries that fail because an early misclassification closed off the right subtree. Without that, the gains could be tied to the particular synthetic corpus rather than to the method working in general. The abstract also leaves out dataset size, how the embedding baseline was implemented, and any statistical controls.\n\nThis is aimed at people building service registries or agent orchestration layers who already live with long-context headaches. A reader working on retrieval for agents would get value from the pipeline description even if the numbers need more backing.\n\nI would send it to review. The idea is timely and the approach is straightforward to test, but the referees should be asked to focus on taxonomy accuracy metrics and failure cases before the central claim can be taken as demonstrated.","headline":"The hit-rate numbers rest on an untested assumption that the LLM taxonomy is accurate enough to avoid dead-end branches.","tokens_in":2405,"tokens_out":395,"would_cite":false,"duration_ms":13773,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"An LLM builds and traverses a hierarchical taxonomy of services to discover relevant ones with higher accuracy and far lower token cost than full-context or embedding methods.","keywords":["service discovery","LLM agents","hierarchical taxonomy","context management","progressive disclosure","A2X","Internet of Agents","retrieval accuracy"],"falsifier":"Build the taxonomy on a registry of services whose descriptions contain deliberate overlaps or ambiguities, then measure whether query hit rate falls below the full-context or embedding baselines.","tokens_in":2688,"feed_emoji":"🗂️","tokens_out":707,"duration_ms":24515,"temperature":0.7,"pith_summary":"Large registries of LLM-callable services create a structural mismatch because context windows are limited and models lose information in the middle of long inputs. The paper establishes that an LLM can automatically organize these services into a recursive hierarchical taxonomy and traverse it layer by layer at query time. Each LLM call then sees only a small, relevant candidate set rather than the entire registry. This progressive-disclosure approach decouples effective context from registry size. Experiments report a 6.2-point Hit Rate gain over full-context dumping at one-ninth the prompt-token cost and more than 20 points over embedding baselines.","feed_headline":"Taxonomy search gives 6.2 hit-rate gain at one-ninth token cost","feed_subtitle":"LLM-built hierarchies let agents retrieve services from large registries without context overflow or middle-of-list attention loss.","key_machinery":"A2X (Agent-to-Anything service discovery): the LLM-driven pipeline for automatic hierarchical taxonomy construction from raw service descriptions followed by layer-by-layer traversal at query time.","core_discovery":"A2X is an LLM-driven pipeline that automatically organizes registered services into a hierarchical taxonomy and walks it layer by layer at query time, so that every LLM call sees only a small candidate set highly relevant to the user query. This decouples effective-context scarcity from registry size and significantly reduces token consumption while improving retrieval accuracy. Compared to full-context dumping, A2X achieves a 6.2-point Hit Rate gain at one-ninth the prompt-token cost; compared to the state-of-the-art open-source embedding-based baseline, A2X improves Hit Rate by more than 20 points.","pith_inferences":["The same recursive construction pattern could be applied to organize large sets of reusable skills or API endpoints outside the MCP/A2A setting.","Periodic re-construction of the taxonomy would be needed when services are added, removed, or updated.","Hybrid systems that seed the taxonomy with embedding clusters before LLM refinement might reduce construction cost further."],"forward_implications":["Effective context remains bounded even as the number of MCP servers, A2A endpoints, and skills grows into the thousands.","Prompt token consumption drops by roughly an order of magnitude while retrieval accuracy rises.","The lost-in-the-middle phenomenon is avoided because no LLM call ever receives a long flat list of descriptions.","Service discovery becomes a structured, recursive process rather than an exhaustive search over the full registry."],"fun_headline_variants":["A2X taxonomy search gains 6.2 hit rate at one-ninth token cost","Recursive LLM taxonomies cut tokens to one-ninth with 6.2 hit-rate gain","LLM-built hierarchies raise hit rate 6.2 points at lower token cost","A2X improves hit rate over 20 points versus embedding baselines"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"An LLM can reliably construct and maintain an accurate hierarchical taxonomy from raw service descriptions such that layer-by-layer traversal consistently surfaces the correct services for arbitrary user queries.","fun_headline_variants_meta":{"raw":{"variants":["A2X taxonomy search gains 6.2 hit rate at one-ninth token cost","Recursive LLM taxonomies cut tokens to one-ninth with 6.2 hit-rate gain","LLM-built hierarchies raise hit rate 6.2 points at lower token cost","A2X improves hit rate over 20 points versus embedding baselines"]},"model":"grok-4.3","cost_usd":0.010357,"raw_usage":{"total_tokens":4633,"prompt_tokens":765,"num_sources_used":0,"completion_tokens":79,"cost_in_usd_ticks":103574500,"prompt_tokens_details":{"text_tokens":765,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3789,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":765,"tokens_out":79,"duration_ms":33077,"temperature":1.0,"reasoning_tokens":3789,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-29T07:30:21.138640+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Build the taxonomy on a registry of services whose descriptions contain deliberate overlaps or ambiguities, then measure whether query hit rate falls below the full-context or embedding baselines.","supporting_citations":[],"review_version":1}