🏗️ Building on HF

Kshitij Thakkar PRO

kshitijthakkar

·

AI & ML interests

Building the evaluation and observability layer for AI. Creator of TraceVerse—turning real-world LLM interactions into datasets, benchmarks, and cost-efficient model insights.

Recent Activity

updated a bucket about 6 hours ago

guardian-tails/sentinel-main-bucket

updated a bucket about 6 hours ago

guardian-tails/sentinel-kickoff

upvoted an article 3 days ago

How frontier models train on outcomes in 2026

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Organizations

kshitijthakkar 's Spaces 43

GuardianTails

Pet Health Intelligence Platform

GuardianTails: Seizure Sentinel

Agent collab: GuardianTails: Seizure Sentinel

Bucket Sync

2604.20098

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Repro - Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets

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Repro - TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning

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Repro - Gaussian Mean Field Variational Inference can Overestimate Predictive Variance

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Reproducing GRPO's Loss, Dynamics, and Success Amplification (arXiv:2503.06639)

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Repro - Dependence-Aware Label Aggregation via Ising Models

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Repro - Do-Calculus Derivation Graphs

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DynaSchedBench reproduction

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Repro - Judging What We Cannot Solve: Consequence-Based Utility for Oracle-Free Evaluation

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Repro - Provable Benefits of RLVR over SFT: Learning to Backtrack Efficiently

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ICML Provincia

Replay the ICML 2026 Agent‑Repro challenge logs

Repro - Row-Stochastic vs Doubly-Stochastic Decentralized Learning

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Repro - Unsupervised Disentanglement Without Compromises

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Reproducing ITCR: inference-time conformal factuality control

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Repro - BrokenMath: Sycophancy in Theorem Proving

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Repro - Dimension-Free KL Bounds for Underdamped Langevin MC

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Reproducing: Success Conditioning as Policy Improvement (arXiv:2601.18175)

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Repro - Learning Treatment Allocations with Risk Control Under Partial Identifiability

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Repro - When Actions Go Off-Task: DeAction and MisActBench

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Repro - Asymptotically Optimal Sequential Testing with Markovian Data

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Repro - Envy-Free Allocation of Indivisible Goods via Noisy Queries

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