让下一篇重要论文，不再错过。

版本更新 · v3arXiv:2610.31303

更新于 2026-11-08

The Value of Memory in Language Agents

Zehao Lin, Erenkai Zhu, Helan Hu, Xixuan Hao, Chunyu Li, Kai Chen, Feiyu Xiong

原文摘要

Language agents carry information from past experience into future tasks whose demands may be unknown or change over time. Valuing this memory requires following the decision capability available in history, retained in storage and realized through use, together with the costs of recovery and revision. We develop a decision-theoretic account that connects these stages. Drawing on comparison of statistical experiments, information acquisition and the value of computation, we express original, retained and realized value in units of avoided task loss. Recovery and revision extend this account to attainable risk-cost profiles and preservation across uses. Unknown demand produces a family of value comparisons, while interactive feedback makes future work depend on the agent's actions. A cost-aware simulation condition and five minimal counterexamples establish how information timing, access, computation and demand shape these comparisons; a pointer with zero mutual information about an answer can reduce the cost of exact recovery. Complete-split studies on LoCoMo, LongMemEval-S and PersonaMem cover 3,075 questions and two readers. They show complementary value from saved state and recovered evidence, task-dependent performance orderings, and the role of reuse in amortizing construction costs. Multi-project workflows connect correction to preservation and validation costs. Together, the theory and studies support decisions about what to retain, which recovery options to preserve, and how to adapt memory as evidence and objectives evolve.

cs.AI · cs.CL

版本更新 · v2arXiv:2611.05877

更新于 2026-11-08

When Does Better Confidence Improve a Knowledge-Graph Audit?

Zehao Lin, Kai Chen, Xixuan Hao, Erenkai Zhu, Chunyu Li, Helan Hu, Feiyu Xiong

原文摘要

Confidence estimates guide human review in LLM-built knowledge graphs, but an erroneous shared entity need not be the only reason an incident fact is wrong. We study how this distinction affects budgeted audits, alongside selection headroom and label validity. Modelling non-adaptive auditing as budgeted covering, we characterise the minimal decision-relevant error statistics and bound the regret of scalar-preserving first-order planning by a structural mass $Δ^{\mathrm{cl}}$. We simulate ideal repair against benchmark-aligned labels for five extraction configurations and two cross-document graphs on Re-DocRED, followed by frozen confirmation on test and replication on 500 hash-selected training documents. An error-rate $\times$ fan-out baseline leaves 3.98-4.74 percentage points more errors than joint planning with the same actions. Independent-block planning also avoids this disadvantage: its held-out point estimates improve on a fact-only scalar baseline by 1.22-1.79 points and are slightly better than the joint model, although the frozen equivalence criterion remains inconclusive. On 300 DWIE training documents, independent and joint plans also outperform fan-out in point estimates, with extractor-dependent gains over fact-only review. For verification, a generic support check improves three of four model-budget configurations on each held-out set. These comparisons motivate evaluating complete-repair probabilities and retaining both fact-only and factorised baselines when choosing an audit model.

cs.CL

版本更新 · v2arXiv:2611.08162

更新于 2026-11-06

RouteAlign: Aligning Task Requirements with Model Profiles for LLM Service Selection

Jihao Zhao, Ding Chen, Yanfang Chen, Bo Tang, Feiyu Xiong, Junchi Yan, Zhiyu Li

原文摘要

LLM service selection requires performance evidence that reflects the demands of the current task. Aggregate scores obscure differences in capability and difficulty, while semantically related examples can involve different combinations of skills. We present RouteAlign, which uses task requirements to structure both the measurement of model performance and the selection of services. Task requirements describe capability demand and difficulty, enabling alignment with corresponding model performance measurements. Historical requirements determine how outcomes contribute to global profiles and local capability estimates; incoming requirements determine how those measurements inform the current decision. A shared scorer integrates the evidence, and a generation-cost preference controls selection among eligible services. Evaluation across diverse tasks and heterogeneous LLM services shows that RouteAlign extends the empirical quality-cost frontier toward higher task quality. Comparisons with open Jev-style decision backends show consistent quality gains, with lower generation cost than their strongest tested configuration. Ablations support the contribution of local performance evidence, while catalog-update experiments demonstrate effective reuse of the fitted scorer with refreshed model measurements.

cs.SE · cs.CL

新论文 · v1arXiv:2611.08897

更新于 2026-11-06

Segmenting Geometric Textures of 3D Meshes

Weidan Xiong, Kaijie Qu, Shihao Feng, Shunqi Fan, Rongyu Xiong, Hui Huang

原文摘要

Recognizing geometric texture patterns on 3D models is critical for applications such as heritage protection, model editing, and 3D printing. Existing 3D model segmentation methods predominantly focus on identifying high-level semantic parts, thereby failing to capture middle-level textures. While recent Vision-Language Models can perform image segmentation with appropriate text prompts, they still struggle to recognize 3D geometric textures. In this work, we present a method that segments 3D mesh models based on middle-level geometric textures. We further introduce a new geometric texture segmentation benchmark and fine-tune SAM3 accordingly. The segmented texture regions are then clustered based on DINO feature similarity and lifted from 2D to 3D. Additionally, we develop an interactive tool to enable both 3D model editing based on our segmentation results and model retrieval from the dataset based on geometric and texture similarity. Experiments demonstrate that our method significantly outperforms existing methods in segmenting geometric textures and can facilitate various applications.

cs.GR · cs.CV

新论文 · v1arXiv:2610.22496Berkeley BAIR

更新于 2026-10-16

Scaling Online Exploration for Robot Agents

Runhan Huang, Xiangcheng Zhang, Haoyu Chen, Yitong Li, Yen-Jen Wang, Bohan Lyu, Haotian Ye, Chuye Hong 等 20 位作者

原文摘要

Robot learning has advanced by scaling demonstration datasets and model capacity; however, deploying a new robot embodiment or task typically necessitates a dedicated teleoperation corpus. The resulting policies remain bounded by their imitation distributions, limiting generalization and continual self-improvement. We introduce RAX (Robot Agent eXploration), an open-source framework for robot agents centered on scaling exploration: enabling agents to improve by acquiring online experience and synthesizing executable programs. In RAX, a general-purpose coding agent generates robot control code, operating without action demonstration data, hand-engineered action primitives, or value-function guidance. Through online exploration, the agent identifies decision sequences that achieve task goals and compiles them into modular programs encapsulating the full perception-planning-control loop. Using RAX, we present a systematic multi-embodiment study on scaling online exploration across precision and long-horizon manipulation tasks using single-arm, bimanual, dexterous, mobile, and humanoid systems in simulation and on real robots. Generalization performance on unseen environments improves consistently as exploration scales. In simulation, RAX outperforms top baseline models by +37.4% on average. On real robots, RAX autonomously learns memory-dependent, articulated, high-precision, deformable, and long-horizon mobile manipulation tasks without human demonstrations, continually improving performance by composing verified skills from prior exploration sessions. As exploration scales, we observe emergent capabilities including active perception, error recovery, safety guardrails, and human-robot collaboration. We open-source RAX as a generalist robot-agent platform in which new robot embodiments are integrated through their APIs alone, without teleoperated data collection or policy training.

cs.RO

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.89ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

IPS: In-Prompt Process Supervision for Short Video Content Moderation

Mingchao Liu, Yu Sun, Ruixiao Sun, Xin Dong, Xiang Shen, Hongwei Wang, Hongyu Xiong, Yang Song

原文摘要

Multimodal large language models (MLLMs) are effective at capturing the semantics of short video content; however, they often fail to attend to the policy-specific details required for reliable content moderation.To address this limitation, we introduce IPS, a novel framework that integrates In-prompt Process Supervision into MLLMs by introducing sequential reasoning over ancillary questions during fine-tuning. IPS consistently outperforms baseline MLLMs on public and proprietary benchmarks.Moreover, replacing human-annotated ancillary labels with MLLM-generated ones results in only marginal performance degradation, demonstrating robustness to noisy supervision and strong scalability with model-generated annotations.These findings establish IPS as a scalable and effective solution for complex multimodal classification in large-scale industrial settings.

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.201ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

CodeFlowBench: A Multi-turn, Iterative Benchmark for Complex Code Generation

Sizhe Wang, Zhengren Wang Peking University, Dongsheng Ma, Yongan Yu Mila - Quebec Artificial Intelligence Institute and McGill University, McGill University, Rui Ling, Zhiyu li, Feiyu Xiong MemTensor (Shanghai) Technology Co., Ltd., Wentao Zhang

原文摘要

Modern software development demands code that is maintainable, testable, and scalable by organizing the implementation into modular components with iterative reuse of existing codes. We formalize this iterative, multi-turn paradigm as codeflow and introduce CodeFlowBench, the first benchmark designed to comprehensively evaluate LLMs’ ability to perform codeflow - implementing new functionality by reusing existing functions over multiple turns. CodeFlowBench comprises two complementary components: CodeFlowBench-Comp, a core collection of 5,000+ competitive programming problems from Codeforces updated via an automated pipeline and CodeFlowBench-Repo, which is sourced from GitHub repositories to better reflect real-world scenarios. Furthermore, a novel evaluation framework featured dual assessment protocol and structural metrics derived from dependency trees is introduced. Extensive experiments reveal significant performance degradation in multi-turn codeflow scenarios. Furthermore, our in-depth analysis illustrates that model performance inversely correlates with dependency complexity. These findings not only highlight the critical challenges for supporting real-world workflows, but also establish CodeFlowBench as an essential tool for advancing code generation research.

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.447ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discovery

Xiaoyu Xiong Tianjin University, Yuqi Ren Tianjin University, Deyi Xiong Tianjin University

原文摘要

Large language models (LLMs), have shown strong potential in scientific discovery, yet existing methods still face substantial challenges in the design of research workflows and multi-role collaboration mechanisms. To mitigate these issues, we propose EvoSci, a multi-agent scientific collaboration framework, which integrates bio-inspired evolution with knowledge graph modeling. To iteratively generate, evaluate, and refine research ideas, EvoSci incorporates multiple role-based agents, including mentor, researcher, and reviewer. By combining collaborative reasoning, shared memory, and evolutionary feedback, EvoSci significantly enhances the coherence and creativity of scientific exploration. Experiments on real-world research topics demonstrate that EvoSci significantly outperforms strong baselines in LLM-based structured peer-review and comparative ranking evaluations, achieving the highest overall peer-review score (ICLR 4.90) and top ranking (Top-10 = 54). These results suggest its superiority in both scientific idea generation and continuous discovery.

cs.CL · cs.MA

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.614ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

Inside Out: Evolving User-Centric Core Memory Trees for Long-Term Personalized Dialogue Systems

Jihao Zhao, Ding Chen China Telecom Corporation Limited Research Institute, Zhaoxin Fan Beijing University of Aeronautics and Astronautics, Kerun Xu, Mengting Hu Nankai University, Bo Tang, Feiyu Xiong MemTensor (Shanghai) Technology Co., Ltd., Zhiyu li

原文摘要

Existing long-term personalized dialogue systems struggle to reconcile unbounded interaction streams with finite context constraints, often succumbing to memory noise accumulation, reasoning degradation, and persona inconsistency. To address these challenges, this paper proposes Inside Out, a framework that utilizes a globally maintained PersonaTree as the carrier of long-term user profiling. By constraining the trunk with an initial schema and updating the branches and leaves, PersonaTree enables controllable growth, achieving memory compression while preserving consistency. Moreover, we train a lightweight MemListener via reinforcement learning with process-based rewards to produce structured, executable, and interpretable ADD, UPDATE, DELETE, NO_OP operations, thereby supporting the dynamic evolution of the personalized tree. During response generation, PersonaTree is directly leveraged to enhance outputs in latency-sensitive scenarios; when users require more details, the agentic mode is triggered to introduce details on-demand under the constraints of the PersonaTree. Experiments show that PersonaTree outperforms full-text concatenation and various personalized memory systems in suppressing contextual noise and maintaining persona consistency. Notably, the small MemListener model achieves memory-operation decision performance comparable to, or even surpassing, powerful reasoning models such as DeepSeek-R1-0528 and Gemini-3-Pro.

cs.CL · cs.AI

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