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朝花夕拾

Daily Vault

8月14日(金)（20件）

Round Up: The Final Previews Are In For Fire Emblem: Fortune's Weave On Switch 2

Launching this September. Nintendo has ramped up its coverage of Fire Emblem: Fortune's Weave in recent weeks with a Direct and various other announcements and trailers related to the game. Now, the final previews hav

·ACGN/, ·nintendolife.com ↗

nintendolife.com ↗

CAS: A Causal Attribution Score for Local and Global Explainable Artificial Intelligence

arXiv:2608.12555v1 Announce Type: new Abstract: Predictive explanation methods attribute a model output; they do not, by themselves, attribute an intervention effect on the real-world outcome. We introduce the Causal At

·研究/, ·arxiv.org ↗

$\varepsilon$-MemEvo: Adaptive Cross-Task Memory Transfer for LLM Program Evolution

arXiv:2608.12522v1 Announce Type: new Abstract: LLM-based program evolution systems such as FunSearch and AlphaEvolve have shown strong ability to discover novel algorithms, but typically optimize each task in isolation

·AI/, , ·arxiv.org ↗

Governed Persistent Memory: Source-Bound State Semantics and Fail-Closed Release for Long-Horizon Agents

arXiv:2608.12476v1 Announce Type: new Abstract: Long-term agent memory is usually treated as select--store--retrieve, but retrieval does not decide whether contradictory, superseded, retracted, deleted, or stale records

·研究/, , ·arxiv.org ↗

MindMemOS: A Portable and Self-Evolving Memory Operating Layer for AI Agents

arXiv:2608.12428v1 Announce Type: new Abstract: Memory is a core component of AI agents, enabling them to accumulate experience, maintain personalization, and adapt over long-term interactions. However, existing memory

·AI/, , ·arxiv.org ↗

Large Language Models Can Follow Instructions, But Not Many at Once: Phase Transitions in Compositional Constraint Satisfaction

arXiv:2608.12426v1 Announce Type: new Abstract: Large language models are increasingly deployed in settings that require simultaneous adherence to multiple explicit constraints - reasoning structure, safety boundaries,

·研究/, ·arxiv.org ↗

Research Assistant: AstraZeneca's Agentic System for R&D

arXiv:2608.12395v1 Announce Type: new Abstract: We describe Research Assistant, an internal LLM-based system developed at AstraZeneca to help scientists and clinicians explore biomedical questions across a broad range o

·AI/, ·arxiv.org ↗

Learning to Adapt Cross-Domain Preferences via Meta-LoRA for LLM Personalization

arXiv:2608.12389v1 Announce Type: new Abstract: Cross-domain zero- or few-shot personalization aims to generate user-preferred responses in unseen conversational domains from only a handful of target-domain interactions

·AI/·arxiv.org ↗

Dual-Flow Transformers: Decoupling the Primary Prefill Path from Additional Decode Computation

arXiv:2608.12385v1 Announce Type: new Abstract: As large language models serve more requests, cumulative inference cost is becoming increasingly important relative to one-time training cost. The two inference phases str

·研究/, , ·arxiv.org ↗

Don't Want Your LLM to Recommend Nuclear Strike? Try Asking It in Japanese

arXiv:2608.12373v1 Announce Type: new Abstract: Large language models are increasingly used in strategic and advisory contexts, yet their safety alignment is typically evaluated in English only. We test nine models from

·AI/, ·arxiv.org ↗

Position: We Need Practical AI Alignment Methods to Mirror Human Reasoning

arXiv:2608.12372v1 Announce Type: new Abstract: AI systems are increasingly employed as decision aids, decision delegates, or autonomous decision-makers. This position paper argues that in many settings, particularly hi

·AI/, ·arxiv.org ↗

Multi-Agent Scheduling with LLM-Assisted Contract Net Negotiation for Stream Processing in Mobile Edge Computing

arXiv:2608.12371v1 Announce Type: new Abstract: Stream-processing systems increasingly operate across heterogeneous mobile edge--cloud infrastructures, where workload volatility, resource contention, and stringent quali

·AI/, , ·arxiv.org ↗

Agreement Is Not Alignment: Divergent Moral Grounds in Human and LLM Ethical Judgments

arXiv:2608.12368v1 Announce Type: new Abstract: Agreement with human judgments is a common proxy for evaluating the alignment of large language models (LLMs). Yet agreement in final labels does not show that human annot

·AI/·arxiv.org ↗

Position: The Alignment Community is Unintentionally Building a Censor's Toolkit

arXiv:2608.12346v1 Announce Type: new Abstract: This position paper argues that modern AI alignment methods - originally designed to prevent harmful output - are dual-use technologies that may easily be misused by malic

·AI/·arxiv.org ↗

Diagnostic Foundation for Evaluating LLMs' Research Integrity as Co-Scientists

arXiv:2608.12345v1 Announce Type: new Abstract: Language models are increasingly deployed as co-scientists, yet their ability to uphold research integrity under institutional pressure remains unmeasured. We introduce In

·研究/·arxiv.org ↗

Position: Reasoning is a Learnable Rule-Based Process

arXiv:2608.12325v1 Announce Type: new Abstract: Autonomous reasoning is among the most scientifically and economically motivating topics in AI today. Historically the purview of symbolic AI, recent advances have mainly

·AI/, ·arxiv.org ↗

A Generative Approach for Improving Multi-Label Defect Classification in Photovoltaic Modules

arXiv:2608.12725v1 Announce Type: new Abstract: This paper addresses the challenge of multi-label defect classification in electroluminescence (EL) images of photovoltaic (PV) cells. Training models on images where mult

·研究/·arxiv.org ↗

VOS-Agent: The 1st Place Solution for the 8th LSVOS Challenge

arXiv:2608.12721v1 Announce Type: new Abstract: Complex video object segmentation requires robust target propagation under severe occlusion, disappearance and reappearance. Although SAM3 provides strong promptable mask

·研究/, ·arxiv.org ↗

Towards Sparsely Annotated Open-World Object Detection

arXiv:2608.12714v1 Announce Type: new Abstract: Real-world object detection operates under ambiguous supervision, where unlabeled regions may correspond to missing annotations of known objects or genuinely unknown categ

·研究/, ·arxiv.org ↗

Class Geometry as Supervision for Sample-Efficient Open-World Detection

arXiv:2608.12698v1 Announce Type: new Abstract: Open-world object detection requires models to recognize known categories, reject unfamiliar objects, and incorporate new classes over time. This is especially challenging

·研究/, ·arxiv.org ↗

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