{"id":"ac9b3657-1edb-481a-89b3-d5b81923c970","arxiv_id":"2606.13120","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":7.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"EvoBrowseComp is an auto-updatable benchmark of 800 complex questions for search agents, synthesized via a three-agent live-web framework to ensure temporal freshness and block parametric shortcuts.","lead":"The paper presents EvoBrowseComp, a benchmark of 800 contamination-free questions in English and Chinese created by pulling fresh information from the live web. It aims to test whether search agents truly retrieve and reason rather than recall memorized facts from static datasets.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Popularity filter may not block parametric shortcuts, as popular facts are likelier to be memorized","rationale":"The reader's weakest assumption is precisely the load-bearing step; the popularity mechanism is the concrete point where the shortcut-blocking claim is least secured by the given description.","tokens_in":1719,"tokens_out":266,"duration_ms":13976,"concrete_test":"Sample 50 English questions from EvoBrowseComp; evaluate a frontier LLM (e.g., GPT-4o or Claude-3.5) on them with search tools disabled and temperature 0; if accuracy exceeds 20 % or matches reported search-agent scores, the popularity filter did not achieve its stated goal.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim requires that the three-agent pipeline produces questions free of parametric shortcuts and that demand broad horizontal search. The information filtering agent is described as blocking shortcuts specifically by filtering on credibility and popularity. Popularity, however, is a direct proxy for frequency in web corpora and therefore in LLM training data; nothing in the abstract indicates an orthogonal mechanism (e.g., recency-only or adversarial construction) that would decouple popularity from memorization risk. The high-level guidance agent only reduces logical redundancy, leaving the memorization pathway unaddressed.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper introduces EvoBrowseComp, a benchmark consisting of 400 English and 400 Chinese complex questions synthesized from live-web traversal. It employs a three-agent collaborative framework: a QA synthesis agent to generate pairs from fresh web knowledge, an information filtering agent that applies credibility and popularity criteria to block parametric shortcuts, and a high-level guidance agent that constructs reasoning graphs to minimize logical redundancy. The automated synthesis enables regular updates to maintain temporal freshness and avoid contamination. Experiments are reported to confirm that the benchmark requires broad horizontal search rather than fact recall.","tokens_in":1820,"tokens_out":404,"duration_ms":29829,"significance":"If the synthesis pipeline reliably produces questions free of parametric shortcuts, this work would establish a scalable, auto-updatable paradigm for benchmarking search agents that remains relevant as world knowledge evolves and model capabilities advance. The fully automated nature of the framework is a clear strength for reproducibility and ongoing maintenance, directly addressing contamination vulnerabilities in static benchmarks such as BrowseComp.","major_comments":[{"comment":"§3 (information filtering agent): The claim that filtering on popularity blocks parametric shortcuts lacks an orthogonal mechanism; popularity is a direct proxy for frequency in web corpora and LLM training data, and the manuscript provides no validation (e.g., shortcut ablation, recency-only filtering, or empirical test of memorization resistance) to show the filter decouples from memorization risk.","section":"§3"}],"minor_comments":[{"comment":"The abstract states that 'extensive experiments confirm its great difficulty' but does not include specific quantitative metrics, baseline comparisons, or error analysis in the high-level description.","section":null},{"comment":"Consider including one or two example synthesized questions (with reasoning graphs) in the main text or appendix to illustrate the output of the three-agent process.","section":null}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback. Below we respond to the single major comment.","responses":[{"response":"We agree that the manuscript currently lacks explicit validation (such as an ablation study or memorization-resistance test) demonstrating that the popularity criterion provides an orthogonal safeguard against parametric shortcuts. The filter is applied jointly with credibility scoring on live-web content to favor less frequently encountered yet verifiable facts; the automated synthesis from recent pages is intended to further reduce overlap with training data. Nevertheless, no dedicated empirical check is reported. In revision we will add to §3 a controlled comparison of question sets generated with and without the popularity filter, together with a small-scale test measuring model shortcut rates on the resulting items.","revision_made":"yes","referee_comment":"[§3] §3 (information filtering agent): The claim that filtering on popularity blocks parametric shortcuts lacks an orthogonal mechanism; popularity is a direct proxy for frequency in web corpora and LLM training data, and the manuscript provides no validation (e.g., shortcut ablation, recency-only filtering, or empirical test of memorization resistance) to show the filter decouples from memorization risk."}],"tokens_in":1315,"tokens_out":257,"duration_ms":23971,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main thing here is a benchmark construction method that pulls fresh web content and runs it through three agents—one to synthesize QA pairs, one to filter on credibility and popularity, and one to build reasoning graphs—so the resulting 800 questions (400 each language) are meant to demand real search rather than recall.\n\nThe paper does a reasonable job stating the contamination problem with static sets like BrowseComp and giving a practical, automated way to keep regenerating questions as knowledge and models change. The pipeline is described clearly enough that someone could try to replicate the synthesis process.\n\nThe soft spots are the lack of any reported numbers on question difficulty, filtering rejection rates, or tests that the final questions cannot be answered from model parameters alone. The popularity filter is presented as the main guard against shortcuts, yet popularity tracks directly with training-data frequency, and the abstract gives no sign of an extra check that would break that link. The stress-test concern therefore lands: without evidence that the questions force horizontal search, the central claim stays unverified.\n\nThis is for groups building or testing web-augmented agents who care about evaluation staying current. A reader focused on benchmark design would get a usable idea from the three-agent setup even if the validation is still missing.\n\nIt should go to peer review because the problem is real and the proposed method is concrete enough for referees to assess and suggest fixes.","headline":"EvoBrowseComp proposes a three-agent live-web pipeline to generate contamination-resistant questions, but the description supplies no results showing the filters actually prevent memorization shortcuts.","tokens_in":2279,"tokens_out":357,"would_cite":false,"duration_ms":21224,"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":"A three-agent framework synthesizes live-web questions into an evolving benchmark that tests search agents on genuine retrieval and reasoning.","keywords":["search agents","evolving benchmark","live-web synthesis","data contamination","three-agent framework","QA generation","reasoning graphs","temporal freshness"],"falsifier":"A model achieving high accuracy on the benchmark questions using only its internal parameters and no search tool calls, or answering correctly from knowledge available before the question synthesis date.","tokens_in":2623,"feed_emoji":"🔍","tokens_out":681,"duration_ms":25659,"temperature":0.7,"pith_summary":"Static benchmarks allow models to score high by recalling facts from training data instead of performing actual searches. EvoBrowseComp generates 800 complex questions by pulling fresh content from the live web and running it through a three-agent process that filters for credibility, removes popular knowledge shortcuts, and structures each item into a reasoning graph. The entire synthesis runs automatically, so the benchmark can be refreshed on a schedule to stay ahead of both new world events and model improvements. Experiments indicate that the resulting questions demand wide-ranging retrieval rather than single-fact lookups. This setup is presented as a way to measure real browsing competence that does not degrade over time.","feed_headline":"Three-agent system builds evolving benchmark to test real search skills","feed_subtitle":"Live-web synthesis and filtering block memorization paths so agents must retrieve and reason over current information.","key_machinery":"The three-agent collaborative framework (QA synthesis agent, information filtering agent, and high-level guidance agent) that automatically generates questions from live web data while blocking memorization shortcuts.","core_discovery":"EvoBrowseComp is an evolving benchmark of 400 English and 400 Chinese contamination-free complex questions synthesized via live-web traversal. It is created using a three-agent collaborative framework where a QA synthesis agent retrieves fresh knowledge, an information filtering agent blocks parametric shortcuts by checking credibility and popularity, and a high-level guidance agent formalizes questions into reasoning graphs to reduce logical redundancy. This setup enables regular updates to prevent data contamination and maintain freshness, establishing a scalable paradigm for high-difficulty benchmarking.","pith_inferences":["The same synthesis loop could be applied to other languages or specialized domains such as code or scientific literature.","Repeated updates over months or years would create a longitudinal record of how quickly agent capabilities improve.","Design choices in the filtering and graph-formalization steps might generalize to auto-generation of other evaluation tasks that resist memorization.","Agents optimized against this benchmark may develop search strategies that transfer better to real-world, time-sensitive queries."],"forward_implications":["Search agents must execute broad, multi-source retrieval and multi-step reasoning to reach high scores.","The benchmark can be regenerated on a regular schedule to remain temporally fresh.","Performance gaps between models will reflect differences in retrieval and reasoning rather than training-data overlap.","The automated pipeline provides a repeatable method for producing high-difficulty, reasoning-intensive questions without manual curation."],"fun_headline_variants":["Three agents create evolving benchmark for search agents","Live-web synthesis blocks memorization in agent evaluations","EvoBrowseComp delivers 800 fresh complex search questions","Three-agent system automates contamination-free benchmarking","Reasoning graphs reduce shortcuts in synthesized QA pairs"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The three-agent collaborative framework successfully produces questions that are free of parametric shortcuts and genuinely require broad horizontal search and reasoning.","fun_headline_variants_meta":{"raw":{"variants":["Three agents create evolving benchmark for search agents","Live-web synthesis blocks memorization in agent evaluations","EvoBrowseComp delivers 800 fresh complex search questions","Three-agent system automates contamination-free benchmarking","Reasoning graphs reduce shortcuts in synthesized QA pairs"]},"model":"grok-4.3","cost_usd":0.004232,"raw_usage":{"total_tokens":2063,"prompt_tokens":687,"num_sources_used":0,"completion_tokens":61,"cost_in_usd_ticks":42315500,"prompt_tokens_details":{"text_tokens":687,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1315,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":687,"tokens_out":61,"duration_ms":10021,"temperature":1.0,"reasoning_tokens":1315,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-27T06:59:50.671651+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A model achieving high accuracy on the benchmark questions using only its internal parameters and no search tool calls, or answering correctly from knowledge available before the question synthesis date.","supporting_citations":[],"review_version":1}