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Pranay M. Mahendrakar

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#reinforcement learning Open access Sep 2026

Scored Before the Question Exists: What a Write-Time Importance Value in LLM Agent Memory Predicts, Why an Additive Term Is Not a Prior, and the Ablation the Canonical Architecture Did Not Run

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. An agent that stores what happens to it must decide what is worth storing and, later, what is worth reading back. The most-copied mechanism for the first decision is a scalar written at storage time: a language model is shown a record and asked how important it is....

Pranay M. Mahendrakar · 0 citations
#reinforcement learning Open access Sep 2026

Scored Before the Question Exists: What a Write-Time Importance Value in LLM Agent Memory Predicts, Why an Additive Term Is Not a Prior, and the Ablation the Canonical Architecture Did Not Run

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. An agent that stores what happens to it must decide what is worth storing and, later, what is worth reading back. The most-copied mechanism for the first decision is a scalar written at storage time: a language model is shown a record and asked how important it is....

Pranay M. Mahendrakar · 0 citations
#small language model Open access Sep 2026

Sufficient for Whose Answer? Three Predicates Under One Word in Selective Retrieval-Augmented Generation, and Why the Only One Tied to Correctness Cannot Be Computed Where the Gate Runs

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. A retrieval-augmented system that answers only when its retrieved context suffices needs a predicate that says when it does. Three incompatible predicates are in circulation under the word "sufficient", and results obtained under different ones are routinely compar...

Pranay M. Mahendrakar · 0 citations
#large language models Open access Sep 2026

Sufficient for Whose Answer? Three Predicates Under One Word in Selective Retrieval-Augmented Generation, and Why the Only One Tied to Correctness Cannot Be Computed Where the Gate Runs

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. A retrieval-augmented system that answers only when its retrieved context suffices needs a predicate that says when it does. Three incompatible predicates are in circulation under the word "sufficient", and results obtained under different ones are routinely compar...

Pranay M. Mahendrakar · 0 citations
#reinforcement learning Open access Sep 2026

The Reassessment That Did Not Travel: What the 2020 Arcade Result About Exploration Bonuses Establishes, the Six Conditions That Made It Informative, and Which of Them the Language-Model Revival Drops

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Exploration bonuses have returned. Between 2025 and 2026 at least six frameworks added an intrinsic novelty or uncertainty term to reinforcement learning with verifiable rewards for language models, each naming a classical antecedent - prediction error, pseudo-coun...

Pranay M. Mahendrakar · 0 citations
#reinforcement learning Open access Sep 2026

The Reassessment That Did Not Travel: What the 2020 Arcade Result About Exploration Bonuses Establishes, the Six Conditions That Made It Informative, and Which of Them the Language-Model Revival Drops

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Exploration bonuses have returned. Between 2025 and 2026 at least six frameworks added an intrinsic novelty or uncertainty term to reinforcement learning with verifiable rewards for language models, each naming a classical antecedent - prediction error, pseudo-coun...

Pranay M. Mahendrakar · 0 citations
#large language models Open access Sep 2026

Consolidation Without Weights: What the Complementary Learning Systems Analogy Licenses in LLM Agent Memory, and Why the Systems That Borrow Its Name Do Not Inherit Its Guarantee

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Memory systems for language-model agents almost all contain a step called consolidation, and almost all of them cite, or gesture at, the complementary learning systems account of hippocampus and neocortex when they name it. In that account consolidation is a specif...

Pranay M. Mahendrakar · 0 citations
#large language models Open access Sep 2026

Consolidation Without Weights: What the Complementary Learning Systems Analogy Licenses in LLM Agent Memory, and Why the Systems That Borrow Its Name Do Not Inherit Its Guarantee

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Memory systems for language-model agents almost all contain a step called consolidation, and almost all of them cite, or gesture at, the complementary learning systems account of hippocampus and neocortex when they name it. In that account consolidation is a specif...

Pranay M. Mahendrakar · 0 citations
#reinforcement learning Open access Sep 2026

Transfer Is a Directed Relation: Four Quantities Under One Word in the Cross-Domain RLVR Debate, and Why the Structural-Similarity Reading Does Not Survive Its Own Evidence

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Reinforcement learning with verifiable rewards is the standard route to reasoning-tuned language models, and the field has split over whether its gains leave the training domain. One body of work reports that most models succeeding at mathematics fail to transfer a...

Pranay M. Mahendrakar · 0 citations
#small language model Open access Sep 2026

A Conflict Is Constructed Before It Is Measured: Seven Design Choices That Set Which Way a Language Model Bends, and Why the Context-Memory Results Do Not Compare

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. The literature on how language models behave when retrieved context contradicts parametric memory contains a flat contradiction, and neither of its two surveys resolves it. One body of results reports over-reliance on memorised information; another reports that mod...

Pranay M. Mahendrakar · 0 citations
#small language model Open access Sep 2026

A Conflict Is Constructed Before It Is Measured: Seven Design Choices That Set Which Way a Language Model Bends, and Why the Context-Memory Results Do Not Compare

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. The literature on how language models behave when retrieved context contradicts parametric memory contains a flat contradiction, and neither of its two surveys resolves it. One body of results reports over-reliance on memorised information; another reports that mod...

Pranay M. Mahendrakar · 0 citations

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