Papers with GAIA
GAIA: A Fine-grained Multimedia Knowledge Extraction System (2020.acl-demos)
Copied to clipboard
Manling Li, Alireza Zareian, Ying Lin, Xiaoman Pan, Spencer Whitehead, Brian Chen, Bo Wu, Heng Ji, Shih-Fu Chang, Clare Voss, Daniel Napierski, Marjorie Freedman
| Challenge: | Open source knowledge extraction tools are used for many real-world applications, but there is no comprehensive system for KE. |
| Approach: | They propose a multimedia knowledge extraction system that takes multimedia data from various sources and languages as input and creates a coherent, structured knowledge base. |
| Outcome: | The system achieves top performance at the recent NIST TAC SM-KBP2019 evaluation. |
MAPS: A Multilingual Benchmark for Agent Performance and Security (2026.findings-eacl)
Copied to clipboard
Omer Hofman, Jonathan Brokman, Oren Rachmil, Shamik Bose, Vikas Pahuja, Toshiya Shimizu, Trisha Starostina, Kelly Marchisio, Seraphina Goldfarb-Tarrant, Roman Vainshtein
| Challenge: | Existing benchmarks do not provide a comprehensive, multi-domain, security-aware evaluation of multilingual agentic AI systems. |
| Approach: | They propose a multilingual benchmark suite to evaluate agentic AI systems across languages and tasks. |
| Outcome: | The proposed framework evaluates agentic AI systems across languages and tasks. |
EvoAgentX: An Automated Framework for Evolving Agentic Workflows (2025.emnlp-demos)
Copied to clipboard
| Challenge: | Existing MAS frameworks often require manual workflow configuration and lack native support for dynamic evolution and performance optimization. |
| Approach: | They propose an open-source platform that automates generation, execution, and evolutionary optimization of multi-agent workflows. |
| Outcome: | The proposed platform automates generation, execution, and evolutionary optimization of multi-agent workflows. |
GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Exploration (2023.acl-demo)
Copied to clipboard
Aleksandra Piktus, Odunayo Ogundepo, Christopher Akiki, Akintunde Oladipo, Xinyu Zhang, Hailey Schoelkopf, Stella Biderman, Martin Potthast, Jimmy Lin
| Challenge: | Using the mature and well-tested methods from the domain of Information Retrieval (IR) we propose to integrate Pyserini with Hugging Face to provide qualitative analysis tools for NLP researchers. |
| Approach: | They propose to integrate Pyserini with Hugging Face to provide qualitative analysis tools for NLP researchers. |
| Outcome: | The proposed tools can be integrated with the Hugging Face ecosystem of open-source AI libraries and artifacts. |
Exploring Reasoning Reward Model for Agents (2026.findings-acl)
Copied to clipboard
Kaixuan Fan, Kaituo Feng, Manyuan Zhang, Tianshuo Peng, Zhixun Li, Yilei Jiang, Shuang Chen, Xiangyu Yue
| Challenge: | Existing methods for agentic reinforcement learning rely on sparse outcome-based reward for training, leading to suboptimal results. |
| Approach: | They propose an agent-based reward model that produces structured feedback for agentic trajectories, including an explicit reasoning trace and a focused critique. |
| Outcome: | The proposed model produces structured feedback for agentic trajectories including an explicit reasoning trace, a focused critique, and an overall score that evaluates process performance. |
AWARE: Agentic Knowledge Warehousing for Contextual Intelligence (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models excel in information seeking tasks, but their knowledge is limited in coverage and timeliness. |
| Approach: | They propose an agentic knowledge warehousing framework that transforms unstructured data into minimal, task-conditioned knowledge representations consumable by LLMs. |
| Outcome: | Experiments on GAIA, WebWalker, and BrowseComp-Plus show improvements over baselines. |
MemoBrain: Executive Memory as an Agentic Brain for Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) are inherently long-horizon, causing reasoning traces and tool artifacts to accumulate and strain the working context of large language models. |
| Approach: | They propose a model that constructs a dependency-aware memory over reasoning steps and captures salient intermediate states and their logical relations. |
| Outcome: | The proposed model prunes invalid steps, folds completed sub-trajectories, and preserves a compact, high-salience reasoning backbone under a fixed context budget. |
COMPASS: Enhancing Agent Long-Horizon Reasoning with Evolving Context (2026.acl-long)
Copied to clipboard
| Challenge: | Long-horizon tasks that require sustained reasoning and multiple tool interactions remain challenging for LLM agents. |
| Approach: | They propose a framework that separates tactical execution, strategic oversight, and context organization into three specialized components. |
| Outcome: | The proposed framework improves accuracy by 20% relative to baselines on GAIA, BrowseComp, and Humanity’s Last Exam tasks. |
Battling against Tough Resister: Strategy Planning with Adversarial Game for Non-collaborative Dialogues (2025.acl-long)
Copied to clipboard
| Challenge: | Non-collaborative dialogue involves two participants with conflicting interests engaging in multiround dialogue to achieve their own goals. |
| Approach: | They propose a Game-based Adversarial self-play InterActive training paradigm which constructs an adversarial two-player (a persuader and a resister) zero-sum game and guides the game to approximate Nash Equilibrium (NE) via reinforcement learning. |
| Outcome: | The proposed model achieves state-of-the-art performance on three datasets. |
Coding Agents with Multimodal Browsing are Generalist Problem Solvers (2026.findings-eacl)
Copied to clipboard
| Challenge: | specialized AI agents with task-specific tools or architectures fail to generalize beyond their intended scope. |
| Approach: | They propose a single-agent system with a modest number of general tools . they propose to generalize across software engineering, deep research and web browsing . |
| Outcome: | The proposed system achieves superior or competitive performance over specialized agents on three benchmarks. |
FlowSearch: Advancing Deep Research with Dynamic Structured Knowledge Flow (2026.acl-long)
Copied to clipboard
Yusong Hu, Runmin Ma, Yue Fan, Jinxin Shi, Zongsheng Cao, Yuhao Zhou, Jiakang Yuan, Shuaiyu Zhang, Shiyang Feng, Xiangchao Yan, Shufei Zhang, Wenlong Zhang, Lei Bai, Bo Zhang
| Challenge: | FlowSearch is a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning. |
| Approach: | They propose a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning. |
| Outcome: | The proposed framework achieves competitive performance on GAIA, HLE, GPQA and TRQA benchmarks and is available to download. |
WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models (2026.acl-long)
Copied to clipboard
Rui Wang, Ce Zhang, Jun-Yu Ma, Jianshu Zhang, Hongru Wang, Yi Chen, Boyang Xue, Tianqing Fang, Zhisong Zhang, Hongming Zhang, Haitao Mi, Dong Yu, Kam-Fai Wong
| Challenge: | Existing agentic systems are retrieval-heavy but reasoning-light . current systems lack compositional reasoning, a key component of deep research . |
| Approach: | They propose a data synthesis pipeline WebAggregator to shift agentic paradigm . they use Proactive Explorer to collect interconnected knowledge and Compositional Logic Proposer to weave knowledge into complex questions . |
| Outcome: | The proposed pipeline surpasses GPT-4.1 and matches Claude-3.7-Sonnet on GAIA, WebWalkerQA, and XBench. |
Cognitive Scaffold: From Fluid Context to Crystallized Memory for Long-Horizon DeepResearch Agents (2026.acl-long)
Copied to clipboard
| Challenge: | Scaling LLM-based agents to long-horizon deep research is constrained by context-noise trade-off . solving a single query may require hundreds of interactions with noisy environments . |
| Approach: | They propose a factorized memory architecture that decouples the cognitive state into a Fluid Working Context for immediate reasoning and a persistent Knowledge Graph for long-term retention. |
| Outcome: | The Cognitive Scaffold outperforms baselines on Xbench-DeepSearch, BrowseComp-ZH, and GAIA . it achieves 74.7% Avg@3 and 87.0% Pass@3 on xbench, browseComp, and 88.3% Pass@3. |
Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification (2026.findings-acl)
Copied to clipboard
Yuxuan Wan, Tianqing Fang, Zaitang LI, Yintong Huo, Wenxuan Wang, Haitao Mi, Dong Yu, Michael R. Lyu
| Challenge: | Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving. |
| Approach: | They propose an inference-time scaling of verification wherein an agent self-improves at test time by evaluating its generated answers. |
| Outcome: | The proposed model outperforms vanilla agent-as-judge and LLM judge baselines by 12%–48% in meta-evaluation F1 score. |
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for designing and optimizing multi-agent systems are static and do not learn from experience. |
| Approach: | They propose a framework that enables a multi-agent system to learn to evolve . they use "textual gradients" to pinpoint failures and suggest granular modifications . |
| Outcome: | a new framework enables a multi-agent system to learn to evolve . it learns from historical optimization experiences to improve its performance . |
PRInTS: Reward Modeling for Long-Horizon Information Seeking (2026.acl-long)
Copied to clipboard
| Challenge: | Existing PRMs cannot capture richer dimensions of information-seeking steps, such as tool interactions and reasoning over tool outputs. |
| Approach: | They propose a generative PRM trained with dual capabilities that compresses the growing context while preserving essential information for step evaluation. |
| Outcome: | PRInTS improves on FRAMES, GAIA, and WebWalkerQA models while preserving essential information for step evaluation. |
EvoRoute: Experience-Driven Self-Routing LLM Agent Systems (2026.acl-long)
Copied to clipboard
| Challenge: | EvoRoute is a self-evolving model routing paradigm that transcends static, pre-defined model assignments. |
| Approach: | They propose a model routing paradigm that transcends static, pre-defined model assignments. |
| Outcome: | Experiments on GAIA and BrowseComp+ show that EvoRoute reduces execution cost and latency by over 70%. |
WebAnchor: Anchoring Agent Planning to Stabilize Long-Horizon Web Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for reinforcement learning (RL)-based agents struggle with long-horizon planning and strategy coherence. |
| Approach: | They propose a reinforcement learning framework that decouples planning and execution. |
| Outcome: | The proposed framework outperforms baseline and first-step RL frameworks on four benchmarks. |