Shreyash Gondane
I build and study AI agents for financial research that can be checked: every number traced to its source, only data that existed at the time of a call, and every call recorded before its outcome is known.
Research statement
LLM agents are increasingly asked to read markets and explain their calls, but their outputs are hard to audit: numbers appear in prose with no source, backtests quietly use data that did not exist yet, and track records can be edited after the fact.
My work treats verifiability as the core design constraint. I turn every number an agent reports into a structured claim that points at the exact value in a stored, point-in-time source snapshot, check each claim before anything is published, and record every call in an append-only, hash-chained ledger so the record can be scored forward only.
I also study how to build such systems cheaply and reliably with agents themselves: a builder/critic loop in which low-cost models write code and a stronger model plans and reviews, recording every attempt as labelled data for distillation.
Claim-level provenance
Every reported number is a claim with a JSON pointer into a hashed source snapshot, verified before publication.
Point-in-time evaluation
Agents only see data fetched before their decision time; calls are scored only after they are recorded. No look-ahead leakage.
Tamper-evident records
Append-only, hash-chained call ledgers with database-level guards, so a track record cannot be edited after the outcome.
Distilling coding agents
Builder/critic twin loops that produce labelled trajectories, corrections and failure modes for training smaller models.
Writing
Receipts for every number: claim-level provenance and point-in-time verification for LLM trading agents
Method and early results from GlassBox: the baseline discrepancy audit, the verification gate, and the live forward-only record.
Projects
GlassBox
An evidence-first multi-agent investment-research committee. Release s3-part1 adds a point-in-time snapshot store for every data fetch, structured claims with exact source pointers, a verification gate that re-checks every number (source match, point in time, staleness, snapshot integrity, units), a compliance filter, a gate-health dashboard and an append-only hash-chained ledger. Research preview only; not investment advice.
Twin-agent build lab
Building production software with a free model as builder and a strong model as planner and reviewer. Every run is traced; accepted runs, rejections and before/after correction pairs form a labelled dataset. Includes CPU benchmarks of small local coding models (Qwen2.5-Coder 1.5B at ~42 tokens/s on a 4-core laptop CPU).
Multi-agent trading system
An earlier multi-agent investment system built around a V(n) value function, with dashboards and analytics. The groundwork that GlassBox's verification-first design grew out of.
Scientific discovery agent
Infrastructure for an agent that assists scientific discovery workflows.
Track record
A dated log of releases, results and service, each linked to evidence where it exists. Newest first. Follow via feed.
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Release
GlassBox: gate-health dashboard, stricter verification gate, compliance filter
Snapshot-integrity and unit checks added to the verification gate; an A7 compliance filter with 113 rule tests; a dashboard of the daily verification pass rate.
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Milestone
First verification-gate results on live production data
Every number in the first live committee decisions re-checked against its source snapshot; decisions provably unchanged by the verification layer.
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Release
GlassBox s3-part1: verification foundations
Point-in-time snapshot store, structured claims, A6 verification gate, hash-chained call ledger, feature flags and kill switches.
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Benchmark
Local coding-model benchmark on a laptop CPU
Qwen2.5-Coder 0.5B and 1.5B (Q4_K_M, llama.cpp): ~68 and ~42 tokens/s generation on an i7-1165G7.
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Open source
GlassBox made public under the MIT license
Repository opened with a README comparing it honestly with TradingAgents.
Service
Reviewing and talks will appear here.