Generative models increasingly expose a reasoning effort setting that lets users scale inference-time compute, trading higher cost for potentially improved performance. Users must choose an effort level before knowing whether extra reasoning will help, and even a successful run does not reveal whether the effort purchased was necessary. Reasoning effort is therefore a credence good: buyers observe the outcome but cannot assess the marginal value of the computation they paid for. In this work, we measure the counterfactual effect of effort levels on both proprietary and open-weight models on four benchmarks, spanning single-call reasoning and long-horizon agentic tasks. Effort, it turns out, does not consistently preserve tasks that lower effort solves, can overspend, and can change trajectory behavior. First, we find that although higher effort can raise accuracy in aggregate, it frequently fails on tasks that lower effort solved. Second, on the subset of easy tasks that every effort level solves, higher effort is generally more costly, with the highest effort level costing up to 42x as much as the lowest. Lastly, effort changes not only how many tokens are used, but also how agents solve tasks: at higher effort, agents verify their own work more heavily and more often search for solutions than work through problems directly, both of which lengthen trajectories without necessarily altering the outcome. These findings show that naively choosing the highest level is not only cost-inefficient but also offers no guarantee of the best instance-level performance. A simple alternative, starting at the lowest effort and escalating only after a verified failure, matches or improves on fixed top effort across all models and benchmarks at lower cost for most proprietary models.
Jinchang Zhu, Jindong Li, Cheng Zhang Jilin University, Jiahong Liu The Chinese University of Hong Kong, Menglin Yang HKUST(GZ)
Long-term memory is a critical challenge for Large Language Model agents, as fixed context windows cannot preserve coherence across extended interactions. Existing memory systems represent conversation history as unstructured embedding vectors, retrieving information through semantic similarity. This paradigm fails to capture the associative structure of human memory, wherein related experiences progressively strengthen interconnections through repeated co-activation. Inspired by cognitive neuroscience, we identify three mechanisms central to biological memory: association, consolidation, and spreading activation, which remain largely absent in current research. To bridge this gap, we propose HeLa-Mem, a bio-inspired memory architecture that models memory as a dynamic graph with Hebbian learning dynamics. HeLa-Mem employs a dual-level organization: (1) an episodic memory graph that evolves through co-activation patterns, and (2) a semantic memory store populated via Hebbian Distillation, wherein a Reflective Agent identifies densely connected memory hubs and distills them into structured, reusable semantic knowledge. This dual-path design leverages both semantic similarity and learned associations, mirroring the episodic-semantic distinction in human cognition. Experiments on LoCoMo demonstrate superior performance across four question categories while using significantly fewer context tokens. Code is available on GitHub: https://github.com/ReinerBRO/HeLa-Mem
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Tian Lan Alibaba Group, Yiqian Yang, Qianghuai Jia, Li Zhu Alibaba Group, Hui Jiang, Hang Zhu, Weihua Luo Alibaba International Digital Commerce Group, Longyue Wang Alibaba Group
Despite recent progress, existing agent benchmarks neglect a fundamental real-world capability: hierarchical rule application, a critical requirement in fields such as law and medicine where agents must reason from broad categories down to specific exceptions to reach rule-compliant decisions.This introduces significant challenges in resolving logical dependencies and disambiguating vague boundaries.To bridge this gap, we introduce HSCodeComp, a novel benchmark derived from e-commerce, requiring agents to assign a unique 10-digit Harmonized System (HS) Code to products by aligning their fuzzy attributes with strict tariff classification rules.HSCodeComp comprises 632 realistic products across 32 categories, featuring detailed yet noisy product information (titles, attributes, and images). The HS Codes are annotated by a panel of 26 tariff experts, strictly adhering to official rules and an empirical knowledge base, both of which we jointly open-source.Through a comprehensive evaluation of 23 LLMs, VLMs, and agents on HSCodeComp, we demonstrate that: 1) a substantial performance gap remains between state-of-the-art agents and human experts (46.8% vs. 95.0%); and 2) test-time scaling fails to close this gap. Further analysis reveals that 1) excessive reasoning steps frequently induce “reasoning drift,” which degrades accuracy; and 2) agents are prone to premature decisions on high-level categories and reasoning hallucinations that lack factual grounding.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表