{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/from-passive-to-active-reasoning-can-large","title":"From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?","arxiv_id":"2506.08295","date":"2025-06-09","proceeding":null,"authors":["Zhanke Zhou","Xiao Feng","Zhaocheng Zhu","Jiangchao Yao","Sanmi Koyejo","Bo Han"],"abstract":"While existing benchmarks probe the reasoning abilities of large language models (LLMs) across diverse domains, they predominantly assess passive reasoning, providing models with all the information needed to reach a solution. By contrast, active reasoning-where an LLM must interact with external systems to acquire missing evidence or data-has received little systematic attention. To address this shortfall, we present AR-Bench, a novel benchmark designed explicitly to evaluate an LLM's active reasoning skills. AR-Bench comprises three task families-detective cases, situation puzzles, and guessing numbers-that together simulate real-world, agentic scenarios and measure performance across commonsense, logical, and symbolic reasoning challenges. Empirical evaluation on AR-Bench demonstrates that contemporary LLMs exhibit pronounced difficulties with active reasoning: they frequently fail to acquire or leverage the information needed to solve tasks. This gap highlights a stark divergence between their passive and active reasoning abilities. Moreover, ablation studies indicate that even advanced strategies, such as tree-based searching or post-training approaches, yield only modest gains and fall short of the levels required for real-world deployment. Collectively, these findings highlight the critical need to advance methodology for active reasoning, e.g., incorporating interactive learning, real-time feedback loops, and environment-aware objectives for training. The benchmark is publicly available at: https://github.com/tmlr-group/AR-Bench.","url_abs":"https://arxiv.org/abs/2506.08295v1","url_pdf":"https://arxiv.org/pdf/2506.08295v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"from-passive-to-active-reasoning-can-large","repo_url":"https://github.com/tmlr-group/ar-bench","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2506.08295","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.08295"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/tmlr-group/AR-Bench","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/tmlr-group/ar-bench","reach":{"status":"ok"}}],"summary":{"ran_violates":2,"ran_draft_wrong":3,"ran_honours":1},"by_repo_kind":{"official":{"samples":6,"ran":6,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":6,"samples":[{"code_sha256_prefix":"1b7b0c56a59f7432","entry":"check_outline_keys","repo":"tmlr-group/ar-bench","repo_kind":"official","path":"arbench/data_generation/dc/dc_generator.py","file_url":"https://github.com/tmlr-group/ar-bench/blob/HEAD/arbench/data_generation/dc/dc_generator.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"1b7b0c56a59f7432"}},{"code_sha256_prefix":"4ff37020aef04164","entry":"convert_init_info","repo":"tmlr-group/ar-bench","repo_kind":"official","path":"arbench/data_generation/dc/dc_generator.py","file_url":"https://github.com/tmlr-group/ar-bench/blob/HEAD/arbench/data_generation/dc/dc_generator.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4ff37020aef04164"}},{"code_sha256_prefix":"002d57caba925b23","entry":"extract_answer_choice","repo":"tmlr-group/ar-bench","repo_kind":"official","path":"arbench/reasoner/dc/dc_evaluator.py","file_url":"https://github.com/tmlr-group/ar-bench/blob/HEAD/arbench/reasoner/dc/dc_evaluator.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"002d57caba925b23"}},{"code_sha256_prefix":"e5ea55c6392b39a5","entry":"load_existing_logs","repo":"tmlr-group/ar-bench","repo_kind":"official","path":"arbench/data_generation/dc/dc_generator.py","file_url":"https://github.com/tmlr-group/ar-bench/blob/HEAD/arbench/data_generation/dc/dc_generator.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e5ea55c6392b39a5"}},{"code_sha256_prefix":"8fb299ebb83347d3","entry":"parse_keypoints","repo":"tmlr-group/ar-bench","repo_kind":"official","path":"arbench/reasoner/dc/dc_evaluator.py","file_url":"https://github.com/tmlr-group/ar-bench/blob/HEAD/arbench/reasoner/dc/dc_evaluator.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8fb299ebb83347d3"}},{"code_sha256_prefix":"040af084f5c82b6a","entry":"remove_commas","repo":"tmlr-group/ar-bench","repo_kind":"official","path":"arbench/reasoner/dc/dc_evaluator.py","file_url":"https://github.com/tmlr-group/ar-bench/blob/HEAD/arbench/reasoner/dc/dc_evaluator.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"040af084f5c82b6a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}