# LexChronos: An Agentic Framework for Structured Event Timeline Extraction in Indian Jurisprudence

arXiv: https://arxiv.org/abs/2603.01651 · HTML: https://mlsanity.com/paper/2603.01651/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 12 of 12)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization](https://mlsanity.com/paper/2311.09184/) (2023)
- [Phi-4-Mini-Reasoning: Exploring the Limits of Small Reasoning Language Models in Math](https://mlsanity.com/paper/2504.21233/) (2025)
- [Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints](https://mlsanity.com/paper/2302.09185/) (2023)
- [Named Entity Recognition in Indian court judgments](https://mlsanity.com/paper/2211.03442/) (2022)
- [Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors](https://mlsanity.com/paper/2110.04399/) (2021)
- [Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies](https://mlsanity.com/paper/2010.12787/) (2020)
- [Aalap: AI Assistant for Legal & Paralegal Functions in India](https://mlsanity.com/paper/2402.01758/) (2024)
- [Large Language Models for Judicial Entity Extraction: A Comparative Study](https://mlsanity.com/paper/2407.05786/) (2024)

## Built on by (most cited first, 1 of 1)

- [LexKairos: Benchmarking Legal Temporal Capabilities in LLMs](https://mlsanity.com/paper/2608.09106/) (2026)

## Abstract (arXiv)

Understanding and predicting judicial outcomes demands nuanced analysis of legal documents. Traditional approaches treat judgments and proceedings as unstructured text, limiting the effectiveness of large language models (LLMs) in tasks such as summarization, argument generation, and judgment prediction. We propose LexChronos, an agentic framework that iteratively extracts structured event timelines from Supreme Court of India judgments. LexChronos employs a dual-agent architecture: a LoRA-instruct-tuned extraction agent identifies candidate events, while a pre-trained feedback agent scores and refines them through a confidence-driven loop. To address the scarcity of Indian legal event datasets, we construct a synthetic corpus of 2000 samples using reverse-engineering techniques with DeepSeek-R1 and GPT-4, generating gold-standard event annotations. Our pipeline achieves a BERT-based F1 score of 0.8751 against this synthetic ground truth. In downstream evaluations on legal text summarization, GPT-4 preferred structured timelines over unstructured baselines in 75% of cases, demonstrating improved comprehension and reasoning in Indian jurisprudence. This work lays a foundation for future legal AI applications in the Indian context, such as precedent mapping, argument synthesis, and predictive judgment modelling, by harnessing structured representations of legal events.