Papers by Anson Bastos
Learning Optimal Message Representations for Agentic Communication (2026.findings-acl)
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Shashwat Gupta, Anson Bastos, Mayukh Das, Supriyo Ghosh, Nagarajan Natarajan, Chetan Bansal, Saravan Rajmohan
| Challenge: | Existing approaches lack the intelligence necessary to understand, learn or apply optimal communication representations adaptively. |
| Approach: | They propose to dynamically learn the optimal message representations to enhance agentic performance by using an Expanding Markov Decision Process. |
| Outcome: | The proposed framework improves agentic performance while maintaining efficiency. |
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction (2021.findings-acl)
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Abhishek Nadgeri, Anson Bastos, Kuldeep Singh, Isaiah Onando Mulang’, Johannes Hoffart, Saeedeh Shekarpour, Vijay Saraswat
| Challenge: | Existing methods for relation extraction (RE) use only expanded facts from the knowledge graph . |
| Approach: | They propose a method for relation extraction from a single sentence . they use a neural network to expand the context with additional facts from the KG . |
| Outcome: | The proposed method is more accurate than state-of-the-art methods on standard datasets. |
SynthAgent: Adapting Web Agents with Synthetic Supervision (2026.acl-long)
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Zhaoyang Wang, Yiming Liang, Xuchao Zhang, Qianhui Wu, Siwei Han, Anson Bastos, Rujia Wang, Chetan Bansal, Baolin Peng, Jianfeng Gao, Saravan Rajmohan, Huaxiu Yao
| Challenge: | Existing studies have focused on synthetic supervision but have encountered data quality issues. |
| Approach: | They propose a fully synthetic supervision framework that aims at improving data quality via dual refinement of both tasks and trajectories. |
| Outcome: | The proposed framework outperforms existing methods on standardized benchmarks and shows promising results on a standardized test. |