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Motivated by recursive self-improvement (RSI), we study how LLM agents can learn through external memory without updating model parameters. Extracting useful experience from a single attempt is difficult: flawed reasoning can produce success, while poor execution can obscure a sound strategy. Preserving its value over time is also difficult: our accumulation baseline without consolidation initially improves but eventually performs below a no-memory baseline. Our key idea is to compare attempts at the same task to identify reusable decisions and refine when they apply. We introduce Hippo, a memory framework inspired by selective hippocampal encoding and replay that learns from these comparisons when shortcomings are diagnosed. It compares the resulting experience with existing memory to merge overlaps and resolve conflicts through explicit applicability conditions. This allows prior guidance to be revised without storing every lesson as a separate entry. Experiments on SWE-bench Verified, WebArena, and Mind2Web show performance gains in all three settings and substantially fewer retained experiences than the accumulation baseline. The gains extend across the evaluated base models of varying capabilities, indicating that memory-based adaptation remains useful even for highly capable models.

新论文 · v1arXiv:2610.40249

更新于 2026-10-29

Reaction precedents extend retrosynthetic prediction beyond training coverage

Jiaxing Sun, Xuli Shen, Jiang Wu, Ruiyi Ding, Ruikang Zhang, Xiao An, Xibao Li, Yubin Wang 等 20 位作者

原文摘要

Reaction precedents guide retrosynthesis, but how far they can compensate for gaps in task-specific training coverage remains unclear. Using 2.71 million reactions, we restricted training and retrieval coverage by transformation or time, then expanded only the retrieval corpus with model parameters fixed. For transformations excluded from training, mean Top-1 accuracy increased from 13.0% to 58.0% on a historical test set and from 4.9% to 23.1% on a temporally held-out test set. For temporal coverage gaps, retrieval expansion enabled a model trained on records before 2017 to match or exceed a non-retrieval model trained on records before 2025 on both test sets. Without task-specific fine-tuning, precedents improved all 14 representative general-purpose models on both test sets and USPTO-50K; the strongest with precedents surpassed all eight representative specialized models. These findings show that inference-time reaction precedents can extend retrosynthetic prediction beyond task-specific training coverage without parameter updates.

cs.AI

版本更新 · v6arXiv:2602.15620

更新于 2026-10-28

STAPO: Stabilizing Reinforcement Learning for LLMs by Silencing Rare Spurious Tokens

Shiqi Liu, Zeyu He, Guojian Zhan, Letian Tao, Zhilong Zheng, Jiang Wu, Yinuo Wang, Yang Guan 等 13 位作者

原文摘要

Reinforcement Learning (RL) has significantly improved large language model reasoning, but existing RL fine-tuning methods rely heavily on heuristic techniques such as entropy regularization and reweighting to maintain stability. In practice, they often suffer from late-stage performance collapse, leading to degraded reasoning quality and unstable training. We identify a key factor behind this instability: a small fraction of tokens, termed spurious tokens (around 0.01%), which contribute little to the reasoning outcome but receive disproportionately amplified gradient updates due to inheriting the full sequence-level reward. We present a unified framework for evaluating token-level optimization impacts across spurious risk, gradient norms, and entropy changes. Building on the analysis of token characteristics that severely disrupt optimization, we propose the Silencing Spurious Tokens (S2T) mechanism to efficiently suppress their gradient perturbations. Incorporating this mechanism into a group-based objective, we propose Spurious-Token-Aware Policy Optimization (STAPO), which promotes stable and effective large-scale model refinement. Across six mathematical benchmarks and three model scales, STAPO consistently demonstrates superior entropy stability and achieves average accuracy improvements over baselines including GRPO, 20-Entropy, and JustRL under two widely-adopted evaluation settings.

cs.CL · cs.AI

新论文 · v1arXiv:2610.34789

更新于 2026-10-26

ISB-NMPC: Interception-Oriented Spherical-Bearing Nonlinear Model Predictive Control for Quadrotors

Li Hong, Linkai Liu, Xiangyu Ren, Shuli Lv, Chen Min, Chunjiang Wu, Yujun Zhang, Dingchen Zeng 等 9 位作者

原文摘要

Quadrotor interceptors equipped with strapdown cameras offer a low-cost solution for physically countering unauthorized UAVs. However, this configuration inherently couples vehicle maneuvering and camera pointing, creating a conflict between rapid closing and maintaining target visibility. We present interception-oriented spherical-bearing nonlinear model predictive control (ISB-NMPC), which couples spherical-bearing prediction with nonlinear quadrotor dynamics to jointly optimize target-directed closing motion and visibility. An attitude-compensated extended Kalman filter on S^2 estimates target bearing motion, compensates for perception delay, and predicts target bearings to guide scale-adaptive detection. Across 30 paired simulation trials, ISB-NMPC achieves an interception success rate of 86.7%, the highest among the evaluated controllers, with a mean per-trial target-visible time of 99.11%. Outdoor flights demonstrate acquisition of a 30 cm target at approximately 150 m, with interceptor speed reaching 17 m/s in the long-range flight and approximately 14 m/s while pursuing a quadrotor-towed maneuvering target. The software and hardware are open source at https://github.com/HOLYNOTBAD/ISB-NMPC and https://github.com/HOLYNOTBAD/holydrone, respectively.

cs.RO

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.3ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing

Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He 等 59 位作者

原文摘要

We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational efficiency. Our approach employs a coarse-to-fine, two-stage parsing strategy that decouples global layout analysis from local content recognition. In the first stage, the model performs efficient layout analysis on downsampled images to identify structural elements, circumventing the computational overhead of processing high-resolution inputs. In the second stage, guided by the global layout, it performs targeted content recognition on native-resolution crops extracted from the original image, preserving fine-grained details in dense text, complex formulas, and tables. To support this strategy, we developed a comprehensive data engine that generates diverse, large-scale training corpora for both pretraining and fine-tuning. Ultimately, MinerU2.5 demonstrates strong document parsing ability, achieving state-of-the-art performance on multiple benchmarks, surpassing both general-purpose and domain-specific models across various recognition tasks, while maintaining significantly lower computational overhead.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1273ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

Achieving Multi-Hop Calculation and Safe Abstention in Financial Numerical Reasoning by Metric Graph Constrained LLMs

Aoyuan Jiang Wuhan University, Liang Hong Wuhan University, Haoxuan Liu, Rui Wang

原文摘要

Financial numerical reasoning demands rigorous adherence to domain-specific logic and precise evidence foundation. However, large language models (LLMs) are prone to forced generation when confronting ambiguous evidence or complex recursive dependencies, often hallucinating values to bridge information gaps. To address this, we propose graph-bounded financial reasoning (GBFR), a neuro-symbolic framework that imposes semantic and structural constraints via a financial metric knowledge graph (FMKG). Unlike sequential generation paradigms, our approach employs a parallel graph-constrained reasoning algorithm that orchestrates specialized operators to simultaneously explore heterogeneous derivation paths of complex financial metrics. Through cross-path verification, the framework aggregates only semantically consistent results, ensuring reasoning is bounded by available context. Crucially, this approach enables safe abstention by distinguishing genuine data absence from retrieval failure, thereby preventing ungrounded fabrication. To evaluate this capability, we further construct counterfactual samples by perturbing entities, times, and metrics to synthesize unanswerable scenarios. Empirical evaluations on standard benchmarks demonstrate that GBFR significantly outperforms state-of-the-art baselines.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1864ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

AdaMix: Adaptive Mixing for Short and Long Reasoning Adapters

Hao Luo Wuhan University, Xiao Yan Wuhan University, Xinyan Li, Qiming Zeng Wuhan University, Yuhao Lin, Shanshan Feng Wuhan University and The Agency for Science, Technology and Research, Hao Wang Wuhan University, Jiawei Jiang Wuhan University

原文摘要

Large Reasoning Models (LRMs) have achieved remarkable success on complex tasks by generating detailed Chain-of-Thought (CoT) reasoning. However, they tend to apply a uniform, computation-intensive deep reasoning strategy to all problems, leading to unnecessary overhead on simple tasks. This significantly hinders their efficiency in real-world applications. While existing methods have improved reasoning efficiency to some extent, they still face critical challenges such as conflicting objectives, limited adaptability. To address these limitations, we propose AdaMix, an adaptive reasoning framework via decoupled optimization. To mitigate optimization conflicts, AdaMix first constructs two specialized adapters: an efficiency-oriented short adapter and an accuracy-oriented long adapter. It then incorporates a difficulty-aware routing model that assesses problem complexity to predict a reasoning intensity coefficient. This coefficient is used to dynamically interpolate a mixed adapter from the two base adapters, enabling fine-grained reasoning control. Our experiment demonstrates that our AdaMix reduces the average response length of DeepSeek-R1-Distill-Qwen-7B by 54.9% while improving accuracy by up to 4.8% on five mathematical datasets, thus indicating a favorable accuracy-efficiency trade-off.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2106ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

JARVIS or Ultron? A Survey on the Safety and Security Threats of Computer-Using Agents

Ada Chen CMU, Carnegie Mellon University, Yongjiang Wu, Junyuan Zhang, Jingyu Xiao The Chinese University of Hong Kong, Shu Yang, Jen-tse Huang, Kun Wang, Wenxuan Wang Renmin University of China 等 9 位作者

原文摘要

Recently, AI-driven interactions with computing devices have advanced from basic prototype tools to sophisticated, LLM-based systems that emulate human-like operations in graphical user interfaces. We are now witnessing the emergence of Computer-Using Agents (CUAs), capable of autonomously performing tasks such as navigating desktop applications, web pages, and mobile apps. However, as these agents grow in capability, they also introduce novel safety and security risks. Vulnerabilities in LLM-driven reasoning, with the added complexity of integrating multiple software components and multimodal inputs, further complicate the security landscape. In this paper, we present a systematization of knowledge on the safety and security threats of CUAs. We conduct a comprehensive literature review and distill our findings along four research objectives: (i) define the CUA that suits safety analysis; (ii) categorize current safety threats among CUAs; (iii) propose a comprehensive taxonomy of existing defensive strategies; (iv) summarize prevailing benchmarks, datasets, and evaluation metrics used to assess the safety and performance of CUAs. Building on these insights, our work provides future researchers with a structured foundation for exploring unexplored vulnerabilities and offers practitioners actionable guidance in designing and deploying secure Computer-Using Agents.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表