Full stream

全部 AI 动态

按来源渠道和内容类型浏览完整公开信息流。

38 条结果
来源
类型

10月7日

星期三 · 30 条
Sreedevi Pandiyath Viswambaran论文

Adaptive Workflow Intelligence: A Cognitive Architecture for Context-Driven Enterprise Automation

arXiv:2610.08793v1 Announce Type: new Abstract: Enterprise systems increasingly rely on automated workflows, yet many AI-driven solutions remain brittle under non-stationary conditions, evolving policies, and delayed operational feedback. While reinforcement learning and large language model (LLM) agents offer partial adaptability, they do not by themselves provide persistent reflection mechanisms or straightforward …

Yuanzhuo Zhang论文

Accelerating Floating-Point Satisfiability Solving via Gradient Normalization

arXiv:2610.08808v1 Announce Type: new Abstract: Satisfiability Modulo Theories (SMT) solvers are foundational to software verification, program analysis, and compiler testing, particularly over the theory of Quantifier-Free Floating-Point (QF_FP). While recent optimization-based SMT solvers have successfully applied gradient descent to continuous relaxations of logical formulas, they are fundamentally bottlenecked by…

Xinchen Xiao论文

Route-Verify-Vote: Procedure-Conditioned Self-Consistency for Mixed-Domain Reasoning

arXiv:2610.08814v1 Announce Type: new Abstract: Compositional generalization remains challenging when language models must combine familiar reasoning operations in unfamiliar ways. The Scenario-Based Commonsense Reasoning Evaluation (SCoRE) 2026 tests this ability on three mixed domains absent from training and requires models to identify the complete set of correct options for each question. We introduce Route-Verif…

Yu Cheng, Dehai Zhao, Zhongxin Liu, Qing Huang, Zhenchang Xing, Xiaoxue Ren论文

An Empirical Study of Agent Skills' Downstream Utility

arXiv:2610.08875v1 Announce Type: new Abstract: Agent Skills package procedural guidance and resources for reuse, but a relevant Skill does not necessarily improve task performance. Existing studies characterize Skill content and evaluate downstream performance, yet provide limited explanations of how utility depends on content, execution configuration, and multi-Skill organization. We conduct an empirical study on 8…

Miko{\l}aj Sienicki, Krzysztof Sienicki论文

How Could AI Eliminate Humanity? A Failure-Mode Analysis of Civilizational Risk

arXiv:2610.08878v1 Announce Type: new Abstract: This article develops a failure-mode framework for analyzing how advanced artificial intelligence could contribute to human extinction, irreversible civilizational collapse, or permanent human disempowerment. The central thesis is that catastrophic AI risk does not require consciousness, hostility, or an explicit intention to harm humanity. Instead, risk may arise throu…

Sihao Liu, Ligeng Zhu, Zijian Zhang, Dongyun Zou, Zhengyang Zhang, Changye Li, Song Bian, Song Han, Tony Nowatzki论文

Humanize: Judgement Engineering for Agentic Coding

arXiv:2610.08900v1 Announce Type: new Abstract: Agentic coding makes code generation cheap, but reliable completion remains difficult: the agent that writes the code is a weak judge of whether it is done. We present Humanize, a multi-agent orchestration workflow for agentic coding built around judgement engineering: explicit, mechanically enforced decisions at the boundaries between planning, implementation, review, …

Tunyu Zhang, Zihao Zhao, Yusong Zhao, Haizhou Shi, Zhuohang Li, Haoxian Chen, Hao Wang, Dimitris N. Metaxas论文

Sequential Probabilistic Uncertainty Estimation for Parallel Multi-Agent Reasoning Systems

arXiv:2610.08901v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) have attracted growing attention for improving reasoning through interaction among multiple agents. In this work, we focus on parallel multi-agent reasoning systems, where several agents solve the same problem over multiple rounds and aggregate their outputs into a final answer. Despite their strong reasoning performance, uncertainty …

Harman Singh, Anton Bakhtin, Rulin Shao, Gabriel Synnaeve, Ilia Kulikov, Rob Fergus, Sanjeev Arora, Kurt Keutzer, Jason Weston, Anuj Mahajan, Anirudh Goyal论文

Agent Plasticity: Measuring Self-Improvement Through Experience

arXiv:2610.08902v1 Announce Type: new Abstract: AI agents increasingly operate in environments where they can diagnose failures and improve through experience, yet existing evaluations largely measure what an agent can do at a fixed point in time rather than how effectively it learns. Evaluating self-improvement requires answering three questions: does future performance improve and generalize beyond the interactions…

Melissa Kazemi Rad, Sihui Dai, Isha Slavin, Kushal Chawla, Mann Patel, Jian Ni, William M. Campbell, Stephen Rawls, Sambit Sahu论文

AdaGuard: Enhancing Safety and Policy Compliance with Reasoning-Enabled LLM-As-A-Judge Guardrails

arXiv:2610.08923v1 Announce Type: new Abstract: Enterprise generative AI applications require robust safety mechanisms that can accommodate diverse risk postures, evolving policies, and varying latency constraints. Current guardrail solutions often suffer from rigidity, relying on fixed policy sets and offering limited transparency or reasoning flexibility. We present Adaguard, an adaptive LLM-as-a-Judge framework de…

Wenyu Du, Stephen Chung论文

Can AI Agents Make Open-Ended Scientific Discovery? Evidence from Station

arXiv:2610.08927v1 Announce Type: new Abstract: Recent AI systems have made rapid progress in scientific discovery when given well-defined metrics, but whether they can autonomously undertake open-ended scientific discovery remains unclear. We investigate AI's ability to tackle open-ended tasks in Station, an open-world environment in which multiple agents simulate a scientific ecosystem. To tackle challenges specifi…

Xingang Guo, Jing Gu, Brian Jang, Renxiong Wang, Utkarsh Tyagi, Daniel Quigley, Steven Li, David Yan, Daniel Yue Zhang, Darvin Yi, Forrest Huang, HiJae Kim, Tianyi Zhang, Jared Lichtarge, Jihua Huang, Le Xue, Manan Tomar, Qiuyi Richard Zhang, Ruofei Yu, Seth Neel, Yaning Hu, Marcella Valentine, Xinzhe Jiang, Daniel Evans, Chenguang Wang, Dustin Tran, Tong Zhao, Yinfei Yang, Yunzhong He论文

Humanity's Sixth Sense: Benchmarking Intuitive Visual Reasoning in Multimodal Models

arXiv:2610.08966v1 Announce Type: new Abstract: Humans perceive far more in a scene than what is explicitly depicted: a single glance captures past causes and future trajectories; a quick peek determines if a vehicle can fit between two parked cars; a few seconds of video reveals who holds authority in a room; and a fleeting clip highlights subtle abstract patterns like unwritten rules or hidden labels. This capacity…

Liang Wang, Wenxuan Xie, Xinyi Mou, Yixin Luo, Zhongyu Wei论文

Socio-Foundation: A Model for Generalizable Individual Behavior Simulation via Hierarchical Capability Distillation

arXiv:2610.08967v1 Announce Type: new Abstract: Simulating individual behavior requires large language models (LLMs) to preserve persona traits while adapting to dynamic social contexts. However, general-purpose LLMs often flatten distinct personas, while task-specific tuning suffers from fragmentation and generalization. To overcome these challenges, we organize individual simulation into the \textbf{FONTS Taxonomy}…

Jiamu Bai, Lizhu Zhang, Xin Yu, Yanhong Wu, Zellux Wang, Serena Li, Weiwei Li, Zhuokai Zhao, Lingzhou Xue, Kiwan Maeng, Xiangjun Fan, Bo Peng论文

Verify Less, Evolve More: Training Idea-Level Critics for Verification-Efficient ML Evolving Agents

arXiv:2610.08993v1 Announce Type: new Abstract: As large language models become more powerful, self-evolving agents are able to tackle challenging tasks including AI for machine learning (AI4ML). In AI4ML, while empirical verification is available, it often requires computationally costly model training and evaluation, limiting the speed and scale of agent evolution. Yet verification efficiency remains under-explored…

Muhammad Zeeshan Karamat, Christiana Chamon Garcia论文

How Fragile Is On-Device Language Model Safety? Localizing Safety-Critical Parameters for Sparse Fault Analysis

arXiv:2610.09000v1 Announce Type: new Abstract: As small language models (SLMs) are increasingly deployed on resource-constrained and on-device platforms, including as components of agentic systems, the integrity of locally stored model parameters becomes an important safety concern. We investigate whether safety-sensitive behavior in LLaMA-2-7B-Chat is concentrated within a sparse subset of parameters, creating a re…

Avyay M. Casheekar, Hariganesh Tangirala论文

Learning to Report Unsafe Tasks in a Multi-Agent Game

arXiv:2610.09002v1 Announce Type: new Abstract: When agents share a reward for completed tasks, reporting unsafe work can reduce the reporter's reward by stopping a task. Audits can make reporting optimal without ensuring that further training teaches a silent team to report. We study this learning problem in a game where any witness can stop a task by reporting. With $k$ witnesses per task sharing a policy and drawi…

Kemal Davaslioglu, Sastry Kompella论文

Sigma-Hunter: A Domain-Specific Language Model for Threat Hunting and Detection Engineering

arXiv:2610.09007v1 Announce Type: new Abstract: Detection engineers must translate threat reports, forensic observations, and hunt hypotheses into precise, testable rules. General-purpose large language models (LLMs) can draft such rules, but often produce invalid YAML, incorrect log sources, unsupported fields, or overly broad detection logic. This paper presents \emph{Sigma-Hunter}, a domain-adapted LLM for analyst…

Hongyu Gu, Xinchang Li论文

Whose Memory Is It? Scope-Aware Commit Rules for Long-Term LLM Memory

arXiv:2610.09008v1 Announce Type: new Abstract: Persistent memory allows an LLM agent to carry experience across conversations, but it also turns a local reasoning mistake into a durable one. During deliberation, an agent may consider a plan, simulate a tool result, report another speaker's belief, and then reject all of them. If memory retains only the resulting sentences, those once-useful possibilities can later r…

Aayush Mishra, Arnau Padr\'es Masdemont, Victor Conchello Vendrell, Jordi Ros Giralt, Arash Behboodi, Fabio Valerio Massoli论文

Enabling Dynamic Computation in Looped LMs

arXiv:2610.09013v1 Announce Type: new Abstract: Looped LMs are parameter efficient and promise dynamic computation (saving memory and FLOPs on easy tokens). However, state-of-the-art open Looped LMs trained with this dynamic computation capability (Ouro models) do not realize it in practice as each loop iteration (depth) requires its own level of KV-cache, necessitating all loop computations. Moreover, Ouro's early-e…

Kaixi Feng, Guoheng Sun, Ang li论文

PAIR: Bridging Perception and Action in Vision-Language-Action Models

arXiv:2610.09016v1 Announce Type: new Abstract: Vision-language-action (VLA) models map visual observations and language instructions to continuous robot actions. This task requires a transition from representations that describe the scene and instruction to representations that support action generation. Many continuous-action VLAs leave this transition implicit and supervise it mainly through the final action-predi…

Panagiotis Kasnesis, Christos Chatzigeorgiou, Lazaros Toumanidis, Amalia Contiero Syropoulou论文

Not Every Call Needs a Frontier Model: Per-Call-Site Evaluation of Small Language Models in a Deployed Agentic Home-Automation System

arXiv:2610.09021v1 Announce Type: new Abstract: An agentic system issues several structurally different kinds of LLM calls. It routes intent, classifies actions, grounds language in a device registry, plans multi-agent pipelines and writes the Python code those pipelines run. The difficulty of these call sites varies by an order of magnitude, yet in practice a single model, chosen for the hardest site, serves all of …

Kemal Davaslioglu, Nathan Conger, Sastry Kompella, Yalin E. Sagduyu, Nathaniel D. Bastian论文

BEACON-SP: Ontology-Grounded GraphRAG Framework for Clinical Suicide Risk Assessment

arXiv:2610.09026v1 Announce Type: new Abstract: We present BEACON-SP, an ontology-grounded Graph Retrieval-Augmented Generation (GraphRAG) framework for clinician-facing decision support in behavioral health settings such as suicide prevention, where effective assessment requires integrating heterogeneous clinical, behavioral, social, and temporal evidence. BEACON-SP combines patient knowledge graphs with ontology-gu…

Brandon Ho, Nikola Rogers, Seung-Kyum Choi论文

Shared-Roadmap Generation and Evaluator for Multi-Agent Path Planning Using Heterogeneous Graph Neural Network

arXiv:2610.09034v1 Announce Type: new Abstract: Multi-agent path planning (MAPP) in continuous environments often relies on roadmaps to balance safety and search efficiency. However, traditional roadmap generation methods, such as lattice grids or standard sampling-based approaches, frequently face a trade-off between graph density and the likelihood of finding feasible, high-quality solutions. In this paper, we prop…

Veronique Ziegler论文

When the Governor Becomes the Disturbance: Control-Generated Disturbance and Cost-Aware Backoff in Governed Tool-Using Agents

arXiv:2610.09037v1 Announce Type: new Abstract: Supervisory governors can interfere with the tool-using agents they regulate. We study this possibility in a controlled file-recovery environment where increases in regulatory intensity trigger experimentally imposed tool failures. A cost-blind governor can turn these failures into persistent blocking that prevents task completion. We compare this governor with a backof…

Mahesh Viswanathan, Joan Rossello, Leticia Fernandes, Paul Mutawe论文

From High Recall to High Utility: Dataset-Adaptive Post-Processing of LLM-Generated Customer Intents

arXiv:2610.09039v1 Announce Type: new Abstract: Large language models can extract useful signals from heterogeneous enterprise data, but high-recall extraction often produces outputs that are duplicated, uneven in granularity, semantically overlapping, or too numerous for downstream systems and human reviewers to use effectively. We present a dataset-adaptive post-processing architecture developed for Customer Intent…

W. Russell Neuman论文

Justice After Identity: Large Language Models and the View from Everywhere

arXiv:2610.09053v1 Announce Type: new Abstract: The search for a common view of justice and fairness has challenged human collective activity, as our diverging judgments are unavoidably shaped by the self-interests of social position, personal benefit, cultural inheritance, and historical circumstance. John Rawls famously attempted to overcome this limitation through popularizing a philosophical tradition known by th…

Raham A. Butt, Marco Romanelli, Roche C. de Guzman论文

Epistemic Uncertainty-Aware Defect Detection for Quality Control in Medical Device Manufacturing

arXiv:2610.09057v1 Announce Type: new Abstract: Objective: We investigate whether accounting for epistemic uncertainty can improve the reliability of automated defect detection in medical device manufacturing. Methods: We consider a machine learning framework that operates on heterogeneous manufacturing and device-report data represented with Knowledge Graphs. To mitigate errors arising from uncertainty in the decisi…

Mayur Akewar, Ravi Ranjan论文

RippleCP: Measuring Counterfactual Checkpoint Advantage in Coding Agents

arXiv:2610.09088v1 Announce Type: new Abstract: Agent checkpoint systems decide what state is recovery-relevant, how to snapshot it, and whether rollback is admissible. None decides which of the safe boundaries they expose are worth materializing. We formulate this as counterfactual checkpoint advantage, the reduction in future recovery cost obtained by checkpointing a candidate rather than skipping it, and measure i…

Shaoang Li, Daniel R. Jiang, Jian Li论文

Constraint Tree Exploration for Learning from Language Feedback

arXiv:2610.09107v1 Announce Type: new Abstract: Natural-language feedback in interactive learning often explains why an action failed by pointing to violated requirements. Misinterpreting this feedback can lead an agent to rule out valid solutions. We study this setting by modeling user intent as latent constraints over an action space and formulating learning from language feedback as pure exploration over feasible …

Gabriel Diaz-Ireland, Diego Prieto-Herr\'aez, Mario Garc\'ia Peces, Javier Vel\'azquez, Benjamin Zaitchik, Devika Jain论文

GeoNatureAgent (GNA): A Framework and Benchmark for Pre-Production Evaluation of Tool-Using Agents on Geospatial and Environmental Tasks

arXiv:2610.09112v1 Announce Type: new Abstract: Before tool-using LLM agents are deployed in environmental and geospatial workflows, teams need evidence that an agent reliably selects the right operations against real APIs. We introduce GeoNatureAgent (GNA), a framework for pre-production evaluation of tool-using agents: a fixed sixteen-tool geospatial interface published as a Model Context Protocol (MCP) server, so …

Hanwen Li, Jinhao Duan, Guanhua Zhu, Junchi Lu, Bo Shen, Chenxi Yuan, Kaidi Xu论文

From Uncertainty to Action: Learning to Steer LLM Agents

arXiv:2610.09115v1 Announce Type: new Abstract: Steering an LLM agent means deciding whether to correct it, at which step, and with which mechanism. Uncertainty is often used to decide when to correct an agent, but whether it can guide these decisions remains unclear. We steer agent trajectories separately at every non-terminal step with each of four mechanisms and run each continuation to completion. The resulting s…

10月5日

星期一 · 1 条

9月21日

星期一 · 1 条

9月20日

星期日 · 1 条

9月18日

星期五 · 1 条

9月10日

星期四 · 1 条

9月9日

星期三 · 1 条

9月8日

星期二 · 1 条

9月7日

星期一 · 1 条