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Manan Suri

ACL 2026×3NeurIPS 2026推理多模态评测与基准智能体与工具可解释性
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2 一作 · 4 篇 · ACL 2026 / NeurIPS 2026

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2026 顶会论文

4
作者位次 #3/7ACL 2026Workshop· ACL System Demonstrations

DIAGRAMS : A Review Framework for Reasoning-Level Attribution in Diagram QA

Anirudh Iyengar Kaniyar Narayana Iyengar · Tampu Ravi Kumar · Manan Suri · Raviteja Bommireddy · Dinesh Manocha · Puneet Mathur · Vivek Gupta

摘要

Diagram question answering (Diagram QA) requires reasoning-level attribution that links each question-answer pair to all visual regions needed to derive the answer, rather than only the region containing the final response. Creating such structured evidence across diagrams, charts, maps, circuits...

多模态推理可解释性OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

Structured Uncertainty guided Clarification for LLM Agents

Manan Suri · Puneet Mathur · Nedim Lipka · Franck Dernoncourt · Ryan A. Rossi · Dinesh Manocha

摘要

LLM agents with tool-calling capabilities often fail when user instructions are ambiguous or incomplete, leading to incorrect invocations and task failures. Existing approaches operate in unstructured language spaces, generating clarifying questions through prompting strategies that lack principl...

智能体与工具强化学习与后训练效率与系统OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

C ode S cout: Contextual Problem Statement Enhancement for Software Agents

Manan Suri · Xiangci Li · Mehdi Shojaie · Songyang Han · Chao-Chun Hsu · Shweta Garg · Aniket Anand Deshmukh · Varun Kumar

摘要

Current AI-powered code assistance tools often struggle with ambiguous problem statements that lack sufficient task context and requirements specification. Recent analysis of software engineering agents reveals that failures on such ambiguous requests are highly correlated with longer trajectorie...

代码智能体与工具评测与基准OpenReview 项目页

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