AIHOT 于 2026-08-17 收录了“R^3-Bench：LLM 在共享预算下的资源理性推理仍显吃力”这一公开动态。以下先呈现从来源页面抓取的正文，再给出 AIHOT 摘要与 TopoReduce 编辑解读。
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[2608.16033] $R^3$-Bench: LLMs Struggle with Resource-Rational Reasoning under Shared Budgets
Computer Science > Computation and Language
arXiv:2608.16033 (cs)
-
[Submitted on 17 Aug 2026]
Title:$R^3$-Bench: LLMs Struggle with Resource-Rational Reasoning under Shared Budgets
Authors:Peisong Wang, Zhiwei Ma, Bowen Liu, Feixue Liu, Aochuan Chen, Chenyi Zi, Hongchuan Zeng, Yuhan Li, Jia Li
View a PDF of the paper titled $R^3$-Bench: LLMs Struggle with Resource-Rational Reasoning under Shared Budgets, by Peisong Wang and 8 other authors
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Abstract:In cognitive science, resource rationality asks how an agent should allocate limited computation to maximize expected value. Most reasoning and agent benchmarks use independent per-task budgets; existing shared-budget studies do not calibrate suite performance against the same model's demonstrated single-problem competence. We introduce $R^3$-Bench, which evaluates six-problem suites under shared budgets across mathematics, competitive programming, and abstract reasoning in tool-free and agentic settings. Matched single-problem response curves define an offline empirical oracle over observed successes. Across 72 main-table cells for six models, the oracle mean matches or exceeds the contest mean in all cells and is strictly higher in 71. Under moderate tool-free pressure, equal-allocation replay also exceeds contest performance for four of six models. Trajectory diagnostics reveal limited strategy updating and pressure-dependent failure patterns. In a three-model diagnostic under strong agentic pressure, at least one fixed scheduler exceeds the contest mean in six of nine cells, but no policy dominates across domains. These results expose a persistent gap between demonstrated competence and shared-budget realization.
Comments:
Code is available at this https URL . The dataset is available at this https URL
Subjects:
Computation and Language (cs.CL)
Cite as:
arXiv:2608.16033 [cs.CL]
(or
arXiv:2608.16033v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.16033
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Peisong Wang [view email]
[v1]
Mon, 17 Aug 2026 02:59:58 UTC (2,385 KB)
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arXiv:2608.16033 (cs)
-
[Submitted on 17 Aug 2026]
Title:$R^3$-Bench: LLMs Struggle with Resource-Rational Reasoning under Shared Budgets
Authors:Peisong Wang, Zhiwei Ma, Bowen Liu, Feixue Liu, Aochuan Chen, Chenyi Zi, Hongchuan Zeng, Yuhan Li, Jia Li
View a PDF of the paper titled $R^3$-Bench: LLMs Struggle with Resource-Rational Reasoning under Shared Budgets, by Peisong Wang and 8 other authors
View PDF
HTML (experimental)
Abstract:In cognitive science, resource rationality asks how an agent should allocate limited computation to maximize expected value. Most reasoning and agent benchmarks use independent per-task budgets; existing shared-budget studies do not calibrate suite performance against the same model's demonstrated single-problem competence. We introduce $R^3$-Bench, which evaluates six-problem suites under shared budgets across mathematics, competitive programming, and abstract reasoning in tool-free and agentic settings. Matched single-problem response curves define an offline empirical oracle over observed successes. Across 72 main-table cells for six models, the oracle mean matches or exceeds the contest mean in all cells and is strictly higher in 71. Under moderate tool-free pressure, equal-allocation replay also exceeds contest performance for four of six models. Trajectory diagnostics reveal limited strategy updating and pressure-dependent failure patterns. In a three-model diagnostic under strong agentic pressure, at least one fixed scheduler exceeds the contest mean in six of nine cells, but no policy dominates across domains. These results expose a persistent gap between demonstrated competence and shared-budget realization.
Comments:
Code is available at this https URL . The dataset is available at this https URL
Subjects:
Computation and Language (cs.CL)
Cite as:
arXiv:2608.16033 [cs.CL]
(or
arXiv:2608.16033v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.16033
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Peisong Wang [view email]
[v1]
Mon, 17 Aug 2026 02:59:58 UTC (2,385 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled $R^3$-Bench: LLMs Struggle with Resource-Rational Reasoning under Shared Budgets, by Peisong Wang and 8 other authors
- View PDF
- HTML (experimental)
- TeX Source
view license
Current browse context:
cs.CL
< prev
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next >
new
|
recent
| 2026-08
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cs
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- Google Scholar
- Semantic Scholar
export BibTeX citation
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BibTeX formatted citation
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Data provided by:
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Bibliographic and Citation Tools
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alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
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GotitPub Toggle
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ScienceCast Toggle
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About arXivLabs
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AIHOT 摘要
新基准 R^3-Bench 在数学、竞赛编程和抽象推理中，以共享预算评估六题套件，覆盖无工具与智能体两种场景。对六款模型的 72 个主表单元，离线经验 oracle 均值在所有单元达到或超过竞赛均值，其中 71 个单元严格更高；中等无工具压力下，四款模型的均等分配回放也超过竞赛表现。轨迹诊断显示策略更新有限且失败模式随压力变化，揭示模型在共享预算下实现能力与表现之间存在持续差距。
为什么值得关注
用单题响应曲线作为对照，区分能力不足与预算分配失败，让多任务智能体的调度设计从黑盒经验变成可检验的实验问题。
工程化解读
从 TopoReduce 的工程视角看，这条信息属于“论文与研究”主题。它的价值不只在于一个新产品或新观点本身，还在于说明 AI 系统正在如何影响模型接入、智能体协作、研发流程、基础设施和团队决策。实际采用前，应结合原文确认版本、适用范围、价格和运行条件。
- 发布时间：2026-08-17；AIHOT 分类：论文与研究。
- AIHOT 标签：
- AIHOT 判断：用单题响应曲线作为对照，区分能力不足与预算分配失败，让多任务智能体的调度设计从黑盒经验变成可检验的实验问题。
- AIHOT 评分：49；评分用于站内排序，不等同于独立评测结论。
TopoReduce 编辑观察
当 AI 动态进入真实生产环境，团队需要同时关注能力边界、数据来源、调用成本、权限控制和可回滚性。把单条新闻放回完整工程链路中阅读，比只看标题更有助于判断它是否适合自己的产品和工作流。