AdaLens is an interactive system designed to monitor and steer long-running, autonomous data analysis workflows powered by large language models. It provides a storyline-based interface that unifies analytical plans, execution progress, intermediate findings, and data-column involvement, enabling analysts to observe evolving reasoning and evidence. The system also offers steering interactions that allow users to redirect low-value directions or deepen promising ones during execution.
By Yangtian Liu, Yan Miao, Shuhan Liu, Yunfan Zhou, Dae Hyun Kim, Di Weng, Yingcai Wu
The paper introduces Chart‑RVR, a reinforcement‑learning framework that trains chart‑reasoning agents to produce monitorable, verifiable outputs. It decomposes reasoning into three auditable stages—Structure, Evidence, and Derivation—allowing stakeholders to trace how the model reads the chart, extracts data, and computes the answer. Experiments on six benchmarks show that Chart‑RVR matches or exceeds state‑of‑the‑art accuracy while delivering rationales that are far more verifiable and evidence‑grounded than existing methods.
By Sanchit Sinha, Oana Frunza, Kashif Rasul, Aidong Zhang
The paper introduces the Procedural Graph, a framework that structures procedural knowledge into (procedure, relation, procedure) triplets to guide large language model agents in planning and tool usage. At each decision point, a guidance model uses the local subgraph to bias the agent’s next action, while an LLM refiner self‑evolves the graph by editing its topology based on successful versus failed trajectories. Experiments across datasets and LLMs show that Procedural Graphs consistently outperform memory‑based baselines, and the self‑evolution mechanism further improves performance without manual engineering.
arXiv:2610.28282v1 Announce Type: new
Abstract: Multimodal large language models (MLLMs) have substantially advanced chart understanding and reasoning, yet their performance remains highly uneven acr...
By Qi Wang, Peijie Wang, Jun Cao, Haoyang Tong, Xiao-Hui Li, Fei Yin, Cheng-Lin Liu
The paper introduces Procedural Graphs, a framework that structures procedural knowledge for large language model agents as (procedure, relation, procedure) triplets, analogous to knowledge graphs for factual data. At each decision point, a guidance model uses the local subgraph to bias the agent’s next action, while an LLM refiner self‑evolves the graph by comparing failed and successful trajectories, editing its topology to improve performance. Experiments across various datasets, tasks, and LLMs show that Procedural Graphs consistently outperform memory‑based baselines, and the self‑evolution mechanism further enhances results without manual engineering.
By Yuxing Lu, Yicheng Chen, Shanchan Wu, Sercan \"{O}. Ar{\i}k
Multimodal large language models (MLLMs) have substantially advanced chart understanding and reasoning, yet their performance remains highly uneven across chart types. While modern MLLMs perform well...
arXiv:2510. 04514v3 Announce Type: replace Abstract: Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-those requiring precise visual interpretation rather than relying on textual shortcuts.
By Rachneet Kaur, Nishan Srishankar, Zhen Zeng, Sumitra Ganesh, Manuela Veloso
RSIV-Harness is a framework that keeps language model weights fixed while recursively improving the surrounding harness based on failure traces, using a four-layer evaluation to ensure stability. It addresses challenges in open-domain conversational text-to-visualization, such as ambiguous user requests, data source discovery, and iterative chart refinement. The introduced ODAC-Vis benchmark evaluates these capabilities across 640 tasks, and experiments show significant gains in executability and chart structure after a single RSIV-Harness iteration.
By Jiawei Lu, Chen Zhang, Jinwei Lu, Yuanwei Song, Yuanfeng Song, Raymond Chi-Wing Wong
arXiv:2606. 06416v1 Announce Type: cross Abstract: Inference-time skill augmentation provides a lightweight way to improve data-analytic agents by injecting reusable procedural knowledge without updating model parameters.
By Zhisong Qiu, Kangqi Song, Shengwei Tang, Shuofei Qiao, Lei Liang, Huajun Chen, Shumin Deng
The paper introduces KNOWS, a benchmark for evaluating web agents that act as assistants by retrieving, synthesizing, and presenting information across complex, multi-step browser tasks. It outlines a task design rubric, evaluation protocol combining deterministic checks with LLM judgments, and reports that current agents achieve only modest success, with the best performing agent succeeding on less than 3% of tasks. The study highlights significant gaps in agents’ tool use, visual understanding, and long‑horizon reasoning.
By Alexander Gill, Md Farhan Ishmam, Xuyen Nguyen, Neha Bhat, Parker Henry DeYoung, Fateme Hashemi Chaleshtori, Nathan Stringham, Kenneth Marino, Ana Marasovi\'c
arXiv:2607. 13884v1 Announce Type: new Abstract: Large Language Model (LLM) agents have shown remarkable capabilities in autonomous decision-making by generating sequential trajectories of states, actions, and observations.
By Wenjun Wang, Yuchen Fang, Fengrui Liu, Zibo Liang, Kai Zheng
arXiv:2606. 13731v1 Announce Type: new Abstract: Business intelligence (BI) increasingly combines dashboard interaction with LLM-based assistance, but these two modes often fall out of sync during multi-step analysis.
By Jisoo Jang Wen-Syan Li