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Author

Shivam Gupta

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#machine learning Preprint Oct 2026

Exact Memory-Time Optimization for Prefix-Cached Language Model Serving

Retaining language-model prefix states trades recomputation against storage time. Optimizing each cached block independently can overcount savings: a resident block is usable only when the required preceding prefix is also available. We introduce Prefix-Certificate Retention (PCR), an exact finite-trace formulation for...

Shivam Gupta · 0 citations
#machine learning Preprint Sep 2026

Contingent Exposure Routing for Financial AI: Outage Risk and the Cost of Indivisible Decisions

Model failover restores availability, but changes which financial institutions share decision errors. We formulate outage-contingent routing through a local market-impact response matrix and study expected squared price displacement. A symmetric construction shows that a shared backup can leave an order-one concentrati...

Shivam Gupta · 0 citations
#machine learning Preprint Sep 2026

Marginally Correct Tool Caches Can Reverse Group-Normalized Policy Updates

Tool-result caching reduces repeated execution in agent training, but also couples rollout randomness. We study a two-action model in which independent and shared execution preserve every rollout's conditional reward distribution. Despite this marginal agreement, sharing one stochastic result per group can reverse the...

Shivam Gupta · 0 citations
#artificial intelligence Preprint Sep 2026

The Delegation Blind Spot: Auditing Product Decisions from Agent Choices

Successful agent execution need not identify which future product improvement its user would value. We present a decision-specific audit that maps a declared observation channel and product-value contrast to compatible intervals and witness populations. Its foundations are established identification and decision theory...

Shivam Gupta · 0 citations

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