BUILDER & AUTHOR
Shreyas Gowda S
Builder and author of ChronoRAG-G
Designed and built ChronoRAG-G around a simple requirement: temporal AI should show what evidence it used, when that evidence was valid, and where the system failed.
An answer is only as trustworthy
as the evidence behind it.
FROZEN EVALUATION REPLAY · C01
Watch a complex question break into evidence obligations, bind to time-valid records, and rebuild into a grounded answer.
Reading sanitized evidence data from this site only.
ChronoRAG-G reduces temporal hallucination by keeping each requested fact tied to the evidence and period that support it.
THREE THINGS THE TRACE MUST KEEP TRUE
Was this evidence valid for the period being asked about?
ChronoRAG-G keeps temporal meaning attached to evidence instead of treating every similar passage as interchangeable.
What actually supports each part of the answer?
Answer components remain connected to stable evidence records rather than collapsing into one opaque context window.
Can we reconstruct what happened?
The replay exposes what was required, what was retrieved, what was used, and where a failure occurred.
INTERACTIVE CASE VIEWER · C01
Select an obligation, record, answer component, or period. Open the trace when you want the expert view.
No remote request or live model is involved.
MORE THAN RETRIEVAL
A difficult question can require several entities, metrics, and periods at once. ChronoRAG-G keeps those requirements visible, finds evidence for each one, checks when that evidence applies, carries evidence ownership into answer construction, and preserves the trace for audit.
That is how the system reduces temporal hallucination: the answer is built around time-valid evidence obligations instead of treating all relevant-looking context as interchangeable.
What is actually being asked?
Which entities, metrics, operations, and periods matter?
What individual facts must exist before the answer is complete?
Does each record support the requested period and temporal role?
Which evidence belongs to which required answer component?
Construct the result from resolved evidence.
Keep enough trace to inspect success, refusal, or failure.
FROZEN EVALUATION
Evaluated on 1,005 temporal questions over 480 updated earnings-call transcripts.
80.70%
811 accepted answers across the complete 1,005-question evaluation.
90.44%
ChronoRAG-G reached this share of the LLM-GT oracle score while retrieving its own evidence.
101 / 101
Every new-query case that lacked sufficient answer evidence was correctly handled as unanswerable in the frozen evaluation.
Frozen evaluation replay · ECT-QA · updated financial corpus
BUILDER & AUTHOR
Builder and author of ChronoRAG-G
Designed and built ChronoRAG-G around a simple requirement: temporal AI should show what evidence it used, when that evidence was valid, and where the system failed.
RESEARCH MENTORS
Lecturer in Computer Vision
University of Salford, UK
School of Science, Engineering & Environment
Professor & Principal
Nagarjuna College of Engineering and Technology, Bengaluru