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

Anka Chandrahas Tummepalli, Preethu Rose Anish · Mar 2, 2026 · Citations: 0

Abstract

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.

HFEPX Relevance Assessment

This paper appears adjacent to HFEPX scope (human-feedback/eval), but does not show strong direct protocol evidence in metadata/abstract.

Eval-Fit Score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

HFEPX Fit

Adjacent candidate

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

Human Data Lens

Uses human feedback: No
Feedback types: None
Rater population: Unknown
Unit of annotation: Unknown
Expertise required: Law
Extraction source: Persisted extraction

Evaluation Lens

Evaluation modes: Automatic Metrics
Agentic eval: None
Quality controls: Not reported
Confidence: 0.35
Flags: low_signal, possible_false_positive

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

Research Brief

Deterministic synthesis

We propose LexChronos, an agentic framework that iteratively extracts structured event timelines from Supreme Court of India judgments. HFEPX signals include Automatic Metrics with confidence 0.35. Updated from current HFEPX corpus.

Generated Mar 4, 2026, 4:23 PM · Grounded in abstract + metadata only

Key Takeaways

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…

Researcher Actions

Treat this as method context, then pivot to protocol-specific HFEPX hubs.
Identify benchmark choices from full text before operationalizing conclusions.
Validate metric comparability (f1).

Caveats

Generated from title, abstract, and extracted metadata only; full-paper implementation details are not parsed.
Low-signal flag detected: protocol relevance may be indirect.

Recommended Queries

human-eval protocol design pairwise preference data quality inter-rater agreement adjudication

Research Summary

Contribution Summary

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.
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.

Why It Matters For Eval

We propose LexChronos, an agentic framework that iteratively extracts structured event timelines from Supreme Court of India judgments.
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.

Researcher Checklist

Gap: Human feedback protocol is explicit

No explicit human feedback protocol detected.
Pass: Evaluation mode is explicit

Detected: Automatic Metrics
Gap: Quality control reporting appears

No calibration/adjudication/IAA control explicitly detected.
Gap: Benchmark or dataset anchors are present

No benchmark/dataset anchor extracted from abstract.
Pass: Metric reporting is present

Detected: f1

Category-Adjacent Papers (Broader Context)

These papers are nearby in arXiv category and useful for broader context, but not necessarily protocol-matched to this paper.

Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification Category Neighbor

Citations: 0 Relevance: 4.10
- Shared arXiv category (cs.CL, cs.AI)
- Shared terminology (agentic, framework)
Faster, Cheaper, More Accurate: Specialised Knowledge Tracing Models Outperform LLMs Category Neighbor

Citations: 0 Relevance: 4.00
- Shared arXiv category (cs.CL, cs.AI)
- Shared metric mentions
Contextualized Privacy Defense for LLM Agents Category Neighbor

Citations: 0 Relevance: 3.65
- Shared arXiv category (cs.CL, cs.AI)
- Shared terminology (framework)
Efficient Self-Evaluation for Diffusion Language Models via Sequence Regeneration Category Neighbor

Citations: 0 Relevance: 3.65
- Shared arXiv category (cs.CL, cs.AI)
- Shared terminology (framework)
HateMirage: An Explainable Multi-Dimensional Dataset for Decoding Faux Hate and Subtle Online Abuse Category Neighbor

Citations: 0 Relevance: 2.85
- Shared arXiv category (cs.CL)
- Shared metric mentions
- Shared terminology (framework)
PrivMedChat: End-to-End Differentially Private RLHF for Medical Dialogue Systems Category Neighbor

Citations: 0 Relevance: 2.50
- Shared arXiv category (cs.CL)
- Shared terminology (framework, extraction)
StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning Category Neighbor

Citations: 0 Relevance: 2.50
- Shared arXiv category (cs.CL)
- Shared terminology (agentic, framework)
Evaluating Performance Drift from Model Switching in Multi-Turn LLM Systems Category Neighbor

Citations: 0 Relevance: 2.40
- Shared arXiv category (cs.CL)
- Shared metric mentions

Need human evaluators for your AI research? Scale annotation with expert AI Trainers.

Post a Job Get a Quote