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Anisha Gunjal

ACL 2026×2ICLR 2026推理强化学习与后训练代码效率与系统评测与基准
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1 一作 · 3 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

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作者位次 #2/4ACL 2026Poster· Natural Language Processing

Agentic Rubrics as Contextual Verifiers for SWE Agents

Mohit Raghavendra · Anisha Gunjal · Bing Liu · Yunzhong He

摘要

Verification is critical for improving agents: it provides the reward signal for Reinforcement Learning and enables inference-time gains through Test-Time Scaling (TTS). Despite its importance, verification in software engineering (SWE) agent settings often relies on code execution, which can be...

代码效率与系统推理OpenReview 项目页
作者位次 #6/24ACL 2026Poster· Natural Language Processing

PRB ench: Large-Scale Expert Rubrics for Evaluating High-Stakes Professional Reasoning

Afra Feyza Akyürek · Advait Gosai · Chen Bo Calvin Zhang · Vipul Gupta · Jaehwan Jeong · Anisha Gunjal · Tahseen Rabbani · Maria Mazzone · David Randolph IV · Mohammad Mahmoudi Meymand · Gurshaan Chattha · Paula Rodriguez · Diego A. Mares Buendia · Pavit Singh · Michael Liu · Subodh Chawla · Peter Cline · Lucy Ogaz · Ernesto Gabriel Hernández Montoya · Zihao Wang · Pavi Bhatter · Marcos Ayestaran · Bing Liu · Yunzhong He

摘要

Frontier model progress is often measured using academic benchmarks that provide a limited view of performance on open-ended, economically consequential tasks in high-stakes professional domains where practical returns matter most. We introduce Professional Reasoning Bench (PRBench), a realistic,...

评测与基准社会与人本推理OpenReview 项目页

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