2026-02-11 AI News 每日简报
日期: 2026-02-11 09:50:20 来源: arXiv CS.AI 覆盖范围: 过去48小时 语言: 中英文混合 🌐
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LLM-FSM: Scaling Large Language Models for Finite-State Reasoning in RTL Code Generation
来源: arXiv CS.AI
时间: Tue, 10 Feb 2026 00:00:00 -0500
链接: https://arxiv.org/abs/2602.07032
摘要: arXiv:2602.07032v1 Announce Type: new Abstract: Finite-state reasoning, the ability to understand and implement state-dependent behavior, is central to hardware design. In this paper, we present LLM-FSM, a benchmark that evaluates how well large language models (LLMs) can recover finite-state machi...
ST-Raptor: An Agentic System for Semi-Structured Table QA
来源: arXiv CS.AI
时间: Tue, 10 Feb 2026 00:00:00 -0500
链接: https://arxiv.org/abs/2602.07034
摘要: arXiv:2602.07034v1 Announce Type: new Abstract: Semi-structured table question answering (QA) is a challenging task that requires (1) precise extraction of cell contents and positions and (2) accurate recovery of key implicit logical structures, hierarchical relationships, and semantic associations...
DLLM-Searcher: Adapting Diffusion Large Language Model for Search Agents
来源: arXiv CS.AI
时间: Tue, 10 Feb 2026 00:00:00 -0500
链接: https://arxiv.org/abs/2602.07035
摘要: arXiv:2602.07035v1 Announce Type: new Abstract: Recently, Diffusion Large Language Models (dLLMs) have demonstrated unique efficiency advantages, enabled by their inherently parallel decoding mechanism and flexible generation paradigm. Meanwhile, despite the rapid advancement of Search Agents, thei...
Aster: Autonomous Scientific Discovery over 20x Faster Than Existing Methods
来源: arXiv CS.AI
时间: Tue, 10 Feb 2026 00:00:00 -0500
链接: https://arxiv.org/abs/2602.07040
摘要: arXiv:2602.07040v1 Announce Type: new Abstract: We introduce Aster, an AI agent for autonomous scientific discovery capable of operating over 20 times faster than existing frameworks. Given a task, an initial program, and a script to evaluate the performance of the program, Aster iteratively improv...
Theory of Space: Can Foundation Models Construct Spatial Beliefs through Active Exploration?
来源: arXiv CS.AI
时间: Tue, 10 Feb 2026 00:00:00 -0500
链接: https://arxiv.org/abs/2602.07055
摘要: arXiv:2602.07055v1 Announce Type: new Abstract: Spatial embodied intelligence requires agents to act to acquire information under partial observability. While multimodal foundation models excel at passive perception, their capacity for active, self-directed exploration remains understudied. We prop...
ANCHOR: Branch-Point Data Generation for GUI Agents
来源: arXiv CS.AI
时间: Tue, 10 Feb 2026 00:00:00 -0500
链接: https://arxiv.org/abs/2602.07153
摘要: arXiv:2602.07153v1 Announce Type: new Abstract: End-to-end GUI agents for real desktop environments require large amounts of high-quality interaction data, yet collecting human demonstrations is expensive and existing synthetic pipelines often suffer from limited task diversity or noisy, goal-drift...
PreFlect: From Retrospective to Prospective Reflection in Large Language Model Agents
来源: arXiv CS.AI
时间: Tue, 10 Feb 2026 00:00:00 -0500
链接: https://arxiv.org/abs/2602.07187
摘要: arXiv:2602.07187v1 Announce Type: new Abstract: Advanced large language model agents typically adopt self-reflection for improving performance, where agents iteratively analyze past actions to correct errors. However, existing reflective approaches are inherently retrospective: agents act, observe ...
Is there "Secret Sauce'' in Large Language Model Development?
来源: arXiv CS.AI
时间: Tue, 10 Feb 2026 00:00:00 -0500
链接: https://arxiv.org/abs/2602.07238
摘要: arXiv:2602.07238v1 Announce Type: new Abstract: Do leading LLM developers possess a proprietary ``secret sauce'', or is LLM performance driven by scaling up compute? Using training and benchmark data for 809 models released between 2022 and 2025, we estimate scaling-law regressions with release-dat...
From Out-of-Distribution Detection to Hallucination Detection: A Geometric View
来源: arXiv CS.AI
时间: Tue, 10 Feb 2026 00:00:00 -0500
链接: https://arxiv.org/abs/2602.07253
摘要: arXiv:2602.07253v1 Announce Type: new Abstract: Detecting hallucinations in large language models is a critical open problem with significant implications for safety and reliability. While existing hallucination detection methods achieve strong performance in question-answering tasks, they remain l...
Incentive-Aware AI Safety via Strategic Resource Allocation: A Stackelberg Security Games Perspective
来源: arXiv CS.AI
时间: Tue, 10 Feb 2026 00:00:00 -0500
链接: https://arxiv.org/abs/2602.07259
摘要: arXiv:2602.07259v1 Announce Type: new Abstract: As AI systems grow more capable and autonomous, ensuring their safety and reliability requires not only model-level alignment but also strategic oversight of the humans and institutions involved in their development and deployment. Existing safety fra...
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