cs.AI · 2026-07-21 · No. 60
Artificial Intelligence, 2026-07-21.
44 new papers in cs.AI. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
44 entries-
01
Logical Judgments Under Pressure: Diagnosing Syllogistic Stability with Learned Soft Prefixes
Brian K Chen
cs.AI · cs.CL
To test how correct logical judgments respond to learned context, we prepend a soft prefix to an exactly labeled syllogistic reasoning benchmark while keeping the model fixed. Soft prefixes are opaque continuous vectors, so we characterize them through the behavior they induce across controlled variations in logical form and interface. By studying which prefixes succeed and how their effects generalize, we characterize how learned contextual...
-
02
SGA: Plug&Play Geometric Verification for Educational Video Synthesis
Lopez Jhon, Hinojosa Carlos, Ghanem Bernard
cs.AI · cs.CV · cs.GR · cs.MA · cs.MM
Recent work leverages Large Language Models (LLMs) to generate executable code for pedagogical animations using libraries such as Manim. However, ensuring spatial correctness and visual legibility remains challenging, as existing frameworks emphasize pedagogical content while overlooking geometric occlusions. We propose the Symbolic Geometric Agent (SGA), a plug-and-play module for code-centric animation pipelines that intercepts...
-
03
Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering
Sheldon Yu, Tong Yu, Xunyi Jiang, Rohan Surana, Gagan Mundada, Sungchul Kim, Lina Yao, Julian McAuley, Junda Wu
cs.AI
Extended reasoning has become standard for frontier Large Language Models (LLMs), yet the trajectories these models produce remain largely uncontrollable. Existing methods for shaping how a model reasons are prompt based approaches and operate at the input level, offering no fine-grained control over the reasoning process itself. Related work analyzes and discovers latent transition dynamics in the reasoning traces from Large Language Models....
-
04
Judge-dependent safety gains and model-specific helpfulness costs of evidence-sufficiency prompting in clinical LLMs
Koyar Afrasyab
cs.AI
Background: LLM judges increasingly score whether clinical language models give overconfident answers under incomplete evidence, yet whether a measured "safety gain" reflects real behavior change or the judge's calibration is unresolved. Using a structured evidence-sufficiency prompt as a test case, we asked whether it reduces unsafe overconfident answers, how far that effect depends on the scoring judge, and what it costs in helpfulness....
-
05
WorldCupArena: Fine-Grained Evaluation of Language Models and Deep-Research Agents on Football Forecasting
Zhaokai Wang, Tianlin Gui, Jiayuan Rao, Shangzhe Di, Yihong Tang, Dingli Liang
cs.AI · cs.CL · cs.LG
Predicting a football match before kickoff requires more than knowing past results: a model must use changing information and make a clear prediction before the answer is available. We present WorldCupArena, a dynamic benchmark for language models and deep-research agents. The 2026 FIFA World Cup is its first evaluation, and the same process can be reused for future leagues and cups. Before each match, a model either receives a common...
-
06
The Shared Discovery Paradox: How a One-Answer Rule Turns Better Information into Worse Search
Yohei Nakajima
cs.AI · cs.GT · cs.MA
Organizations often pool dispersed information into one ranking and then allow many agents to act on that shared view. In a discovery problem, this can improve beliefs while reducing coverage. We develop an exactly solvable benchmark with sixteen boxes, one target, eight searchers, and noisy private clues. Pooling raises the accuracy of the best single recommendation from 0.20 to 0.3835, but repeating that recommendation lowers group...
-
07
AdaHome: An Adaptive Smart Home Assistant using Local Small Language Models
Eu Jin Lim, Zhaoxing Li, Sebastian Stein
cs.AI
Smart home assistants interpret a wide range of user commands, from explicit device control to underspecified and preference dependent requests. While recent systems based on Large Language Models (LLMs) improve this capability, they often rely on heavyweight reasoning pipelines and cloud-based deployment, limiting their efficiency and suitability for resource-constrained environments, and raising privacy concerns. In addition, existing...
-
08
Rethinking Heterogeneous LLM Merging: A Weighted Model Averaging Perspective
Jiahe Fan, Yinghao Hou, Si Chen, Aiyuan Zhang, Hong Xie, Defu Lian
cs.AI
Can large language models with substantially different parameter spaces be merged by direct weighted averaging, without training or semantic alignment? Existing heterogeneous fusion methods typically introduce distillation, adapters, learned latent spaces, routing, or feature alignment, leaving open whether a simpler recipe can work for genuinely different billion-parameter checkpoints. We revisit this counterintuitive question through...
-
09
PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning
Daegyeong Roh, Juho Bae, Han-Lim Choi
cs.AI · cs.LG
Many visual reinforcement learning (RL) algorithms learn representations by matching latent distances to a behavioral distance induced by reward and transition similarity. In practice, the choice of the latent distance can strongly affect performance: using a fixed, pre-specified global norms (e.g., $\ell_p$ norms or other hand-designed metrics) may be overly restrictive to capture the behavioral distance. In contrast, unconstrained pairwise...
-
10
Do Maps Still Matter for Machines: Revisiting the Role of Choropleth Maps in Foundation Model Spatial Understanding
Zhiwei Wei, Yonghe Sun, Zhenjia Liu, Wenjia Xu, Chao He, Weihua Dong, Chunbo Liu, Hua Liao
cs.AI · cs.CV
Spatial understanding is crucial for foundation models (FMs), and maps have long helped humans organize and reason about geographic information. This study examines whether choropleth maps remain useful for machine spatial understanding when models can directly process structured geodata. We introduce ChoroplethMap-Bench, a controlled benchmark containing 2,400 synthetic choropleth maps, corresponding GeoJSON data, and 12,000 questions across...
-
11
SAGE: Subgoal-Conditioned Action Generation for Latent World Model Planning
Letian Cheng, Qi Zhang, Yisen Wang
cs.AI
Latent world models have emerged as a powerful planning paradigm by learning action-conditioned predictive dynamics and using them as internal simulators to imagine and evaluate candidate action sequences. However, as the planning horizon grows, performance becomes increasingly constrained by proposal quality: a fixed candidate budget must search an exponentially larger action space, making it difficult to expose the world model to...
-
12
OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs
Anna Sofia Lippolis, Mohammad Javad Saeedizade, Stefan Schmid, Simon Blattner, Robin Keskisärkkä, Aldo Gangemi, Eva...
cs.AI
Ontology extension refers to the process of enriching an existing ontology in response to emerging requirements, making it more complete. This task is a resource-intensive and error-prone process. Large Language Models (LLMs) have shown promising performance on generating ontologies from scratch, but current approaches rarely tie ontology extension explicitly to requirements or reusable core models, and offer limited, systematic evaluation of...
-
13
Towards Agentic Agent-based Models: Feasibility, Performance, and Statistical Model Checking
Stefano Blando, Emanuele Guerrazzi, Riccardo Porcedda, Giuseppe Squillace, Max Tschaikowski, Andrea Vandin
cs.AI · cs.MA
Agent-based models (ABMs) rely on simple, explicit and reproducible rules for individual decision making, while complex collective behavior emerges from interactions among agents. Recent advances in large language models (LLMs) make it tempting to replace, enrich, or perturb these rules with LLM-based agentic capabilities. However, this raises a methodological question: how does introducing LLM-driven decisions affect the reliability,...
-
14
The Autonomous Agency Scale: A Behavioral Framework for Measuring Self-Directed Behavior in AI Systems
Samuel Presgraves
cs.AI · cs.CY · cs.HC
Existing AI measurement frameworks quantify cognitive capability, task automation, or catastrophic risk, but none measure autonomous agency: the extent to which a system behaves in a self-directed way. A system can saturate capability benchmarks while remaining entirely reactive, acting only when prompted and ceasing all activity when a task completes. We introduce the Autonomous Agency Scale (AAS), a behavioral framework that scores AI...
-
15
PEARL: Auditable Repair for Scientific Reasoning Graph Extraction
Bohan Su, Pengze Li, Yuchen Lu, Xi Chen
cs.AI
Scientific Reasoning Graph Extraction (SRGE) aims to recover explicit links among observations, evidence, intermediate claims, and paper-level conclusions. LLMs can produce graph-like scientific explanations, but their outputs often mix malformed syntax, drifting edge labels, incorrectly oriented roots, and weak source anchors. We propose PEARL (Peircean Extraction via Abstraction and Repair Layer), a training-free framework that turns noisy...
-
16
Stress Testing Concept Erasure with Large Language Model Agents
Yuyang Xue, Feng Chen, Zhihua Liu, Edward Moroshko, Jingyu Sun, Steven McDonagh, Sotirios A. Tsaftaris
cs.AI
Concept erasure aims to remove semantic concepts from a trained generative model and is increasingly important for responsible AI deployment. However, verifying whether a model has robustly removed targeted concepts remains a critical challenge. Existing evaluation methods are typically pre-defined and static, failing to expose vulnerabilities under diverse natural-language probes and challenging conditions. Moreover, manually designed...
-
17
ST-Veto: Spatio-Temporal Token Veto for Diffusion MLLMs via Taylor Prediction and Visual Grounding
Keuntae Kim, Beomseok Lee, Hyunwoo Kim, Yong Suk Choi
cs.AI
Vision Language Models (VLMs) achieve strong reasoning with Chain-of-Thought (CoT) prompting but incur high sequential-generation cost, error accumulation, and limited self-correction. Diffusion Multimodal Large Language Models (dMLLMs) unmask tokens in an order-agnostic process, improving efficiency and enabling iterative refinement, yet their reasoning and how to enhance it remain underexplored. We propose a training-free method,...
-
18
Exploratory and Assimilating Reflection: Reflective Recall Cycle for Long-term Memory
Ganesh Senrayan, Moyuru Yamada, Ishan Jindal, Kiran Purohit
cs.AI
LLM-based autonomous agents require external memory to overcome their statelessness and limited context window for long-term interaction and dynamic knowledge reasoning. However, existing memory retrieval methods often lack adaptability and sample efficiency, and struggle to retrieve the right mixture of memories from heterogeneous stores. We propose Exploratory-Assimilating Reflection (EAR), a framework for high initial retrieval performance...
-
19
A Hardware-oriented Approach for Efficient Bayesian Inference Computation and Deployment
Nikola Pižurica, Matteo Risso, Nikola Milović, Alessio Burrello, Igor Jovančević, Conor Heins, Miguel de Prado
cs.AI
Bayesian inference provides a principled foundation for reasoning under uncertainty, but its computational cost hinders deployment on resource-constrained edge devices. In this paper, we present a hardware-oriented methodology for accelerating discrete Bayesian inference on commercial off-the-shelf embedded GPUs. We identify that the latency of a broad class of variational message-passing algorithms is dominated by tensor contractions. Our...
-
20
PGN: Design and Implementation of a Vision-Language Navigation System Based on Pangu Multimodal Foundation Model
Li Xian, Mingxi Li, Yizheng Wang, Yiming Shen, Qi Chen, Zhuoling Xiao
cs.AI
Vision-Language Navigation (VLN) requires an embodied agent to interpret a natural-language instruction and predict actions from temporally ordered visual observations. Adapting a multimodal large language model to VLN requires visual-language alignment, compact temporal inputs, action-space grounding, and stable training on the target hardware. This technical report presents PGN (Pangu Navigator), an offline VLN action-prediction system...
-
21
Financial Audit Assistance using Misinformation Detection and Explanation
Kshitij Madhav Jadhav, Sushodhan Vaishampayan, Manoj Apte, Sachin Pawar, Nitin Ramrakhiyani, Girish Keshav Palshikar
cs.AI
Financial statements (FS) such as Balance Sheet (BS), Income Statement (IS) and Cash-flow Statement (CS) summarize the annual financial performance of a company. FS are widely used for evaluating corporate governance, credit appraisal, risk analysis, validate taxation, make investment decisions etc. Financial auditing is a complex and knowledge-intensive discipline whose one important aim is ensuring integrity, accuracy, fairness and absence...
-
22
Dynamic Defense Profiling Enables Cognitive Jailbreak of Text-to-Image Models
Dongdong Yang, Deyue Zhang, Zhao Liu, Zonghao Ying, Wenzhuo Xu, Jiankai Jin, Xiangzheng Zhang, Quanchen Zou
cs.AI
Text-to-Image (T2I) generative models have achieved remarkable progress in synthesizing high-quality visual content, yet they remain vulnerable to adversarial misuse, particularly in generating Not-Safe-For-Work (NSFW) images. Most existing jailbreak attacks primarily rely on heuristic prompt engineering or black-box optimization, treating model feedback as a binary signal (success or failure). This coarse-grained paradigm overlooks the rich...
-
23
WuYu-EnvLE-Bench: A Benchmark for Evaluating Large Language Models in Environmental Law Enforcement
Ziliang Yang, Yi Zhang, Kaijun Lin, Jiachao Ke, Haihong Xu, Zongguo Wen
cs.AI
Large language models (LLMs) are increasingly considered for environmental enforcement, but their ability to produce traceable enforcement decisions remains unclear. We introduce WuYu-EnvLE-Bench, a benchmark built from real enforcement cases, regulatory standards, and expert review. It contains 2,521 benchmark instances, 14 tasks, and 12 pollution-medium subdomains across pre-enforcement, in-enforcement, and post-enforcement workflows. Using...
-
24
Semantically Similar, Logically Distinct: Diagnosing the Semantic-Answerability Gap in Table RAG
Jiaming Tian, Liyao Li, Wentao Ye, Haobo Wang, Lihua Yu, Zujie Ren, Gang Chen, Junbo Zhao
cs.AI
Tables are a critical knowledge source in retrieval-augmented generation (RAG), but a retrieved table may lack sufficient evidence to answer a query, a property we call answerability. While answerability broadly concerns whether a source or collection of sources contains sufficient evidence, retrieval models optimized for semantic relevance do not guarantee it even in the single-source case, creating a fundamental mismatch. To study this, we...
-
25
SR-Agent: An Experience-Driven Agentic Framework for Post-Ranking Strategies Refinement in E-Commerce Recommendation
Hanchen Yang, Kaiwen Yang, Junpeng Zhuang, Yang He, Keting Cen, Bochao Liu, Zhongbo Sun, An Liu, Zhongteng Han, Chenyi Lei
cs.AI · cs.MA
User experience is a first-class objective in industrial e-commerce recommender systems (RS). Post-ranking strategies, which govern diversity, similarity, and exposure over a ranked list, are widely deployed in industrial RS for their simplicity and low serving cost. However, as the online recommendation environment evolves continuously, these statically configured strategies gradually become stale, degrading the user experience. Refining...
-
26
Learning to Detect Cross-Modal Negation: An Analysis of Latent Representations and an Attention-Based Solution
Ali AbuSaleh, Leon Hammerla, Alexander Mehler
cs.AI
Detecting high-level semantic concepts like negation across modalities remains a challenge for current multimodal systems. We analyze this as a fundamental representation learning problem, providing the first evidence that negation does not form a linearly or non-linearly separable class in the latent spaces of standard vision-language models (VLMs). We demonstrate that pretrained embeddings primarily encode modality-specific features,...
-
27
LaT: LLM-as-Trainer for Multi-Task Vehicle Routing Solvers
Yang Wang, Ya-Hui Jia, Wei-Neng Chen, Yi Mei, Wen Song, Zhiguang Cao
cs.AI
Multi-task neural solvers aim to handle multiple Vehicle Routing Problem (VRP) variants within a unified model, avoiding separate training for each constraint combination. However, VRP variants differ in optimization difficulty, while existing methods lack stage-wise feedback on their training status, making the model biased to some specific variants. Although meta-learning can support adaptive training, it typically requires bi-level...
-
28
ProEvent: An Event-centric Benchmark for Proactive Agents
Guanzhen Li, Liangming Pan, Leye Wang
cs.AI
Proactive agents are expected to anticipate user needs and provide autonomous assistance by perceiving environmental context without explicit instructions. A fundamental capability of such agents is to identify and track users' upcoming events, enabling continuous and event-specific assistance. For example, by recording the time and location of a planned hike, an agent can deliver weather reminders in advance or provide navigation support...
-
29
Artificial Intelligence for Understanding and Managing Transportation Behavior in Sustainable Smart Cities
Junbiao Pang, Muhammad Ayub Sabir, Fatima Ashraf
cs.AI
Urban transportation systems generate heterogeneous data, yet these data do not automatically become actionable management intelligence. This chapter adopts a behavior-centered perspective on artificial intelligence (AI), treating mobility records and passenger-generated text as behavioral evidence rather than behavioral truth. It examines four directions: bus arrival prediction for service reliability, taxi mobility pattern discovery for...
-
30
OrientSAM: Mitigating Camera-Centric Shortcut in Multimodal Spatial Reasoning via Orientation-Aware Spatial Alignment
Wenxiao Fan, Hang Yin, Kan Li
cs.AI · cs.CV · cs.MM
Multimodal large language models (MLLMs) still struggle with spatial reasoning that requires perspective transformation. In particular, they often rely on camera-centric cues rather than reasoning from the reference object's viewpoint, leading to systematic errors in non-camera reference settings. In this paper, we first analyze this failure mode and show that object orientation is a key factor underlying such camera-centric shortcut...
-
31
FlowBlock: Wavefront-Parallel Decoding for Self-Correcting Diffusion Language Models
Bing Tian, Haikun Liu, Xiaocheng Zhong, Zhuohui Duan, Zhaokai Luo, Huayi Jin, Zhiyong Wang, Xiaofei Liao
cs.AI
Block-wise diffusion large language models (dLLMs) decode sequentially at the block level, enabling effective KV-cache reuse across blocks but making inter-block decoding strictly serial. Prior work has attempted to unlock inter-block parallelism through post-training methods, but achieves only modest speedups and often degrades accuracy. We observe that self-correcting dLLMs offer a training-free alternative: token-to-token (T2T) editing can...
-
32
Verify, Repair, Repeat, or Stop? Robust Stopping for Noisy Verify-Repair Loops in LLM Agents
Yitao Wu, Si Shen, Rui Yang, Hong Peng, Bin Hu
cs.AI · cs.SE
Verify-repair loops are a standard means for large language model (LLM) agents to correct faulty plans in code generation, mathematical reasoning, and tool use. When both the verifier and the repairer are noisy, repair can damage already-correct plans, and reported acceptance keeps rising while true validity falls, so existing methods lack a principled basis for deciding when repair should stop. We propose VRR-Stop, a robust stopping...
-
33
Mechanistic Attention Guidance for Agent Memory Refinement
Yechao Hong, Haiquan Qiu, Yaqing Wang, Quanming Yao
cs.AI
Existing self-evolving memory systems mainly improve agent memory based on textual outputs, such as task trajectories and reflections. However, this text-based paradigm rarely incorporates internal mechanistic signals, leaving how retrieved memory is actually utilized during task execution underexplored. This limitation can lead to unreliable error attribution and hallucinated memory modifications. In this work, we show that retrieval-head...
-
34
Is Progressive Disclosure All You Need for Long-Context Agents?
Yifeng He, Yinzhe Zhao, Jicheng Wang, Hao Chen
cs.AI · cs.CL · cs.SE
Long-document question answering usually forces a choice between loading the whole document into the context window and bolting on a separate retriever. Agentic AI suggests a broader option, giving the agent the document path and letting it decide how and what to read. Agent Skills, a standard for packaging expertise into folders an agent loads on demand, supply a ready mechanism: progressive disclosure, which exposes only what a query needs,...
-
35
A Dual-Hypothesis Reasoning Framework for LLM Guardrails
Md Asiful Islam, Mihai Surdeanu
cs.AI
We propose ARBITER, a novel LLM guardrail framework that introduces two key ideas: (i) dual-hypothesis reasoning, a reasoning method for LLM guardrails that explicitly considers both safe and unsafe interpretations of a prompt before making a safety decision, and (ii) multi-component supervised fine-tuning (MC-SFT), a structured training loss for reasoning-based guardrails that decomposes LLM outputs into logical components and weights them...
-
36
ZifaMem: Structured Memory for Persona, Preference, and Emotional Continuity in AI Companions
Jingzhe Fang, Guozhi Xu, Yunfan Cui, Xiaochen Yang, Zhangyu Hua
cs.AI · cs.LG
AI companions are judged not only by single-turn fluency but by whether they sustain emotional continuity: remembering who the companion is, what the user prefers, and how the relationship has felt. We present ZifaMem, a structured memory system that organizes dialogue into session summaries, episodic memories, and a consolidated user model. Against a deployment-honest comparator that supplies the full raw dialogue history, and under a fixed...
-
37
Reinforcement Learning: From Algorithms To Foundation Models
Zihan Ding
cs.AI
Reinforcement learning (RL) provides a framework for sequential decision making under explicit objectives. In its classical form, RL studies how an agent should act to maximise long-term reward in a dynamic environment. In richer settings, the problem extends beyond a single agent and fixed environment: intelligent behavior may require strategic interaction, adaptation to uncertainty, and reasoning over high-dimensional worlds. This thesis...
-
38
Why Does Feedback-Augmented Self-Distillation Fail to Improve Retrieval-Interleaved Search Agents?
Fan Yang, Rui Meng, Yuxin Wen
cs.AI
On-policy self-distillation (OPSD) offers a promising approach for training large language models without relying on a separate teacher model. However, its effectiveness on complex agentic tasks remains largely unexplored. In this work, we instantiate Feedback-Augmented Self-Distillation (FA-SD), a self-distillation algorithm for agentic search that leverages successful demonstrations as privileged information. We identify that models can...
-
39
Retain or Consolidate? Budget-Dependent Operator Selection for Language Agent Memory
Qingcan Kang, Mingyang Liu, Shixiong Kai, Kaichao Liang, Zhentao Tang, Yuqi Cui, Tao Zhong, Mingxuan Yuan
cs.AI
Language agents depend on memory across interactions. However, the limited context windows of large language models (LLMs) and their inference costs constrain how much memory can be used at once. Existing systems mainly follow two strategies: memory retention and memory consolidation. Retention keeps raw records and preserves exact details, but relevant evidence may not fit under a tight budget; consolidation compresses and combines records,...
-
40
The Curvature Shadow: An Apparent Failure of Maximum-Entropy Equilibrium Selection is a Removable Artifact
Luis Leal
cs.AI · cs.GT · cs.LG · cs.MA
In two-player zero-sum games whose Nash equilibria form a convex set, regularized solvers such as Regularized Nash Dynamics (R-NaD) empirically select the maximum-entropy member: the information projection (I-projection) of a uniform reference onto the Nash set. On a panel of small games this match is exact, with one apparent exception: in Kuhn poker R-NaD lands at bluff coordinate 0.180 while the maximum-entropy member sits at 0.201, a...
-
41
Can AI Agents Really Complete RTL-to-GDS? Lessons from Benchmarking Tool-Interactive EDA Workflows
Jinyuan Deng, Zhengrui Chen, Xufeng Wei, Tianyu Xing, Chenyi Wen, Cheng Zhuo
cs.AI · cs.AR · cs.LG
LLM-driven agent systems have emerged as a promising paradigm for electronic design automation (EDA), demonstrating strong potential for automating complex design workflows. However, existing evaluations primarily examine individual language models on isolated EDA tasks, providing limited insight into how different agent systems perform across complete EDA flows. In this work, we present FluxBench, a systematic evaluation of AI agents on...
-
42
Pailitao-MMSearch: Building Native E-Commerce Multimodal Search Foundation
Xiaohan Ye, Xu Chen, Zihan Gong, Jian Ding, Lianyu Du, Baicheng Chen, Yunmeng Shu, Jingqian Zhao, Zhixiang Zhao,...
cs.AI
The evolution of e-commerce has fundamentally transformed how users search for products, shifting from simple text-based keyword queries to complex multimodal interactions that seamlessly combine product images, natural language descriptions, and mixed-intent instructions. However, existing approaches face a critical dilemma: single-modal specialist models, deployed independently for text retrieval, visual search, and voice recognition,...
-
43
Panache: One-Pass Motif Discovery at Every Window Length
Tej Sanibh Ranade
cs.AI · cs.DB · cs.LG
Motif discovery, the search for recurring patterns within a time series, is a core primitive of exploratory data analysis. A pattern, however, is defined by its duration, which analysts rarely know in advance. To resolve this unknown duration, an interval of window lengths is defined, and the accepted method is to try every length in that interval. Existing pan matrix profile (PMP) methods compute one z-normalized matrix profile per length,...
-
44
AEC-DS: Adaptive Erasure Coding with PDP-Triggered Reputation and QoS-Aware Migration for Decentralized Storage
Shuaiwen Li, Weihang Yu, Ke Wang, Meng Han
cs.AI
In decentralized storage systems, audit results are often not used directly to guide later redundancy and shard-placement decisions, which can lead to inefficient resource allocation and delayed recovery. We propose AEC-DS, a closed-loop adaptive erasure coding mechanism driven by Provable Data Possession (PDP) feedback. PDP audits continuously update node reputation, while a QoS-aware migration policy adjusts shard placement according to...
This edition is part of The Daily Abstract — cs.AI archive. Subscribe to receive these in your inbox each morning, automatically translated to Spanish, with reply-to-PDF: arxivdaily.ignorelist.com.
#D99C5E. Built and served on an always-free VM. The masthead is set 14% letterspaced because newspapers do that and it works.