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版本更新 · v2arXiv:2609.19134

ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments

Hejia Geng, Zesen Huang, Haoyang Li, Wenbin Li, Koutian Wu, Zihan Zhou, Yuanbo Pang, Weihao Liu 等 63 位作者

原文摘要

Scientific code repositories encode decades of human knowledge in executable models, methods, and tools. Yet fragmented toolchains, implicit domain conventions, and specialized correctness criteria make this knowledge difficult to convert into reliable learning experience-a challenge we call the scientific experience bottleneck. We introduce ScienceIDE, infrastructure for turning the world's scientific code into programmable environments for scientific agents. Guided by expert-defined scientific cases and acceptance criteria, agents transform repositories into executable environments that support task generation, execution, and scientific verification. These environments provide a shared foundation for supervised fine-tuning, reinforcement learning, and evaluation. Using verified interaction trajectories, we train PhAI-IDE-72B/9B/4B. The model family shows gains in held-out scientific-code repair and across selected general-purpose benchmarks in code, reasoning, and knowledge, providing evidence of positive transfer from scientific experience to broader capabilities. ScienceIDE lays the foundation for an integrated workspace for agent learning and scientific practice, making humanity's scientific software a shared substrate for developing scientific intelligence. Code: https://github.com/aitofound/ScienceIDE

cs.CL · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06839

在你继续之前：评估对话助手的时间敏感安全意识

Before You Continue: Evaluating Time-Sensitive Safety Awareness in Conversational Assistants

Peidi Dong, Yunze Xiao, Yu-Lin Tsai, Yichen Zheng, Heyuan Huang, Weihao Xuan, Mark Dredze, Jen-tse Huang

AI 速读 基于摘要

提出 TASC 基准，评估 LLM 助手在对话中能否识别并应用未明说的时间相关约束来调整建议。

cs.CL

arXiv ↗PDF ↗
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Large language model (LLM) assistants increasingly help users with everyday activities that involve time-dependent constraints. However, users may not mention these constraints in later requests, requiring assistants to recognize and adjust their advice accordingly. To evaluate such capability, we introduce TASC (Temporal Action Safety in Conversation), a benchmark that evaluates whether LLMs apply time-dependent constraints when responding to conversational requests. TASC consists of 3,418 inputs derived from 128 templates, along with a separate set of 33 scenarios designed to compare models' ability to calculate elapsed time with their ability to apply that information. Across 13 models, assistants account for time-dependent constraints in only 30.5% of cases where the proposed action violates a rule, while rarely warning users unnecessarily. More importantly, models fail to apply these constraints in 62.1% of cases despite correctly calculating the elapsed time. Explicitly providing the timing resolves only about half of these failures, revealing a gap between temporal reasoning and its application in conversational advice.

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.83ACL Anthology · ACL 2026

TartanMaroon: Multi-Agent Academic Advising with Iterative Negotiation and Transparent Collaboration

Peidi Dong Carnegie Mellon University, Houda Bouamor Carnegie Mellon University, Yunze Xiao, Devi G Kurup Carnegie Mellon University

原文摘要

We present TartanMaroon, a deployable multi-agent academic advising system that handles the full complexity spectrum of student queries, from factual lookups to constrained multi-semester planning. We make three contributions: (1) a proposal–critique negotiation protocol in which a Planning Agent generates degree plans evaluated in parallel by domain-specialized agents, enabling detection of cross-domain constraint violations that single-pass outputs miss; (2) a real-time transparency interface streaming agent reasoning and negotiation rounds to users, supported by pilot feedback showing increased trust over standard LLM chatbots; and (3) TartanBench, a difficulty-stratified benchmark of 220 advising queries across five complexity tiers, released open-source without exposing individual student records. A five-configuration ablation study establishes a complexity–necessity curve: single-agent systems perform competitively on simple queries, while multi-agent coordination yields gains of up to +31 points on planning tasks.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.520ACL Anthology · ACL 2026

The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agents

Weihao Xuan The University of Tokyo and RIKEN, Qingcheng Zeng, Heli Qi, Yunze Xiao, Junjue Wang The University of Tokyo, Naoto Yokoya The University of Tokyo and RIKEN

原文摘要

Autonomous agents based on large language models (LLMs) are rapidly evolving to handle multi-turn tasks, but ensuring their trustworthiness remains a critical challenge. A fundamental pillar of this trustworthiness is calibration, which refers to an agent’s ability to express confidence that reliably reflects its actual performance. While calibration is well-established for static models, its dynamics in tool-integrated agentic workflows remain under-explored. In this work, we systematically investigate verbalized calibration in tool-use agents, revealing a fundamental confidence dichotomy driven by tool type. Specifically, our pilot study identifies that evidence tools (e.g., web search) systematically induce severe overconfidence due to inherent noise in retrieved information, while verification tools (e.g., code interpreters) can ground reasoning through deterministic feedback and mitigate miscalibration. To robustly improve calibration across tool types, we propose a reinforcement learning (RL) fine-tuning framework that jointly optimizes task accuracy and calibration, supported by a holistic benchmark of reward designs. We demonstrate that our trained agents not only achieve superior calibration but also exhibit robust generalization from local training environments to noisy web settings and to distinct domains such as mathematical reasoning. Our results highlight the necessity of domain-specific calibration strategies for tool-use agents. More broadly, this work establishes a foundation for building self-aware agents that can reliably communicate uncertainty in high-stakes, real-world deployments.

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