paper.dinq.meAI 与前沿科学顶会 · 学术新星雷达
登录 DINQ
返回新星榜
FB

Farima Fatahi Bayat

ACL 2026×2推理对齐与偏好评测与基准强化学习与后训练
新星分新星
59
一作贡献30
核心/参与5
方向广度8
合作网络6
1 一作 · 2 篇 · ACL 2026

获取完整研究档案

首次获取该候选人的完整档案及 StartLux 评估共 20 积分;每位用户单独解锁,之后查看免费。主动刷新 20 积分。

联系方式

实时查找电子邮箱,费用不超过20积分;未找到邮箱不扣费。

2026 顶会论文

2
一作ACL 2026Poster· Natural Language Processing

From Proof to Program: Characterizing Tool-Induced Reasoning Hallucinations in Large Language Models

Farima Fatahi Bayat · Pouya Pezeshkpour · Estevam Hruschka

摘要

Tool-augmented Language Models (TaLMs) can invoke external tools to solve problems beyond their parametric capacity. However, it remains unclear whether these tool-enabled gains reflect trustworthy reasoning. Focusing on the Code Interpreter tool, we show that even when tools are selected and exe...

推理对齐与偏好评测与基准OpenReview 项目页
作者位次 #7/8ACL 2026Findings· Findings of ACL

Logit Arithmetic Elicits Long Reasoning Capabilities Without Training

Yunxiang Zhang · Muhammad Khalifa · Lechen Zhang · Xin Liu · Ayoung Lee · Xinliang Frederick Zhang · Farima Fatahi Bayat · Lu Wang

摘要

Large reasoning models exhibit long chain-of-thought reasoning with complex strategies such as backtracking and self-verification. Yet, these capabilities typically require resource-intensive post-training. We investigate whether such behaviors can be elicited in large models without any gradient...

推理对齐与偏好强化学习与后训练OpenReview 项目页

常见合作者