Artificial intelligence / open access / permanent DOIs

Artificial Intelligence
Research by
Pranay Mahendrakar

Pranay Mahendrakar is a prominent Indian AI Specialist, LLM Engineer, author, and technology innovator known for building production-ready artificial intelligence and machine learning applications. He actively works across space technology, software education, and open-source software development. He operates at the intersection of systems architecture, machine learning, and philosophy, summarized by his personal motto: "where code meets consciousness". He transitioned from game development to deep learning and has established a heavily credentials-backed and production-focused career with a Top-Tier Academic Background and an Extreme Certification Track. This site is the complete record of his research: every paper deposited on Zenodo with a permanent DOI, free to read and free to reuse with attribution.

  • 71papers by Pranay Mahendrakar
  • 13research topics
  • 2024–2026years published
  • 100%open access

All research papers by Pranay Mahendrakar

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The Precursor Assumption: What an Early-Warning Threshold Must Deliver in a Frontier Safety Framework, Why the Continuity Evidence Covers Aggregates Under Fixed Elicitation Rather Than the Thresholded Task, and the Lead Time No Published Record Measures

Frontier safety frameworks decide when a model needs stronger safeguards by testing it against capability thresholds at a fixed cadence, and they place an early-warning threshold below each capability threshold so that mitigations can be…

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Spend the Budget on the Known or the Unknown? Open-Set Active Learning Scores Two Opposed Objectives Under One Name, Its Filtering Side's Own Results Do Not Treat Unknowns as Waste, and the Winner Is Set by a Relevance Label and a Query Price the Benchmarks Fix in Advance

Active learning chooses which unlabelled examples a human should label. When the unlabelled pool contains classes the model has never been shown, two lines of work give opposite instructions under the same name. One, usually called…

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The Evaluator Is the Bottleneck: What a Self-Modifying System's Acceptance Test Must Satisfy Before Its Verdict Can Be Trusted, Why Co-Evolving Evaluators Shrink the External Anchor Rather Than Remove It, and How Moving the Anchor Outside Moves the Attack Surface With It

Self-modifying AI systems now decide which changes to their own code to keep by running an acceptance test and retaining whatever scores higher. Published coding-agent loops report large benchmark gains this way, and their safety case…

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Whose Goals? Autotelic Agents Generate Goals Within Spaces They Are Given: The Goal Space and the Referee Stay Outside the Agent, and a Foundation Model in the Loop Relocates Them Rather Than Removing Them

Autotelic agents are described as learning to represent, generate, select and solve their own goals, and a new generation of systems built on foundation models is reported to do so without hand-coded goal representations, without human…

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Pooling Experience Spends Independence Twice: Shared Memory in LLM Agent Teams Correlates Errors and Opens a Common-Mode Channel, Why Little of the First Was Left to Spend, and the Order, Gate and Readout That May Decide Whether Pooling Pays

Teams of language-model agents increasingly read and write a common memory: a message pool, a blackboard, a bank of distilled experience, a knowledge base served to several frameworks at once. The case for pooling is efficiency, since no…

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Problem Choice Without a Referee: An Automated Novelty Check Certifies an Empty Search, a Rediscovery Benchmark Credits the Hypothesis Humans Found Next, and Every Located Referee of Worth Independent of Proposer and Field Needs the Objective Given

Pipelines that claim to automate scientific discovery gate their search on a judgement that a proposed idea or problem is novel and worth pursuing. The best-controlled evidence on that judgement points two ways at once: in a blind study…

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Errors That Should Compound, and When They Do: The Product Rule Assumes Independent Steps, Conformal Guarantees Assume Exchangeable Instances, and the Unit of Independence Decides What Each Can Claim

Two bodies of work quantify uncertainty in multi-step language-model reasoning, and both rest on an independence assumption placed at some unit. Step-level error models multiply per-step reliabilities, which, at an illustrative 95%…

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Perspectives Without Independence: Multi-Agent and Multi-Persona Reasoning Under Compute-Normalised Comparison, Why the Gains That Survive Are Not the Ones Diversity Predicts, and the Controls That Would Tell Them Apart

Multi-agent debate, multi-persona prompting and related schemes are usually justified by diversity of viewpoint: several perspectives err differently, so their combination is more reliable than any one of them. That justification is a…

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Fingerprints That Survive What? Model Lineage Names Three Relations, Robustness Is Indexed by Which Party Is Adversarial, and a Benchmark's Distillation Column Scores the Parent That Was Not Distilled From

Language-model fingerprinting is asked to answer questions of the form "is this model derived from that one?", and its methods are routinely reported as robust to fine-tuning, quantization, pruning, merging and distillation. This paper…

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Three Rounds on Emergent Analogy: What the Webb, Hodel-West and Lewis-Mitchell Exchange Settled, How Its Third Round Tested a System While Still Claiming the Model, and Whether 'Counting' Names the Step That Analogy Theory Calls Inference

In 2023 a large language model was reported to solve text-based analogy problems zero-shot at or above the level of college students. Two critiques followed. They showed that performance on letter-string analogies collapses when the…

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Drift or New Class? Without Labels a Drifted Class and a New One Can Produce the Same Stream, the Two Lines of Work With the Most Explicit Assumptions Each Get an Answer by Freezing the Variable the Other Lets Move, and No Located Benchmark Scores the Attribution

A classifier deployed on a stream eventually sees inputs its model does not explain. Two different events can produce them: a known class can have drifted, or a class that did not exist in training can have appeared. The stream-mining…

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A Trust Score Needs a Consumer: Four Places a Per-Source Trust Value Can Act on an LLM Agent, the Missing Test of a Graded Discount Against an Adaptive Attacker, and Why the Defences That Report Guarantees Use a Gate Instead of a Score

Work on trust between language-model agents produces two kinds of object. Protocol work produces identity, attestation, stake and constraint, all bound at the transport layer before any content reaches a model. Behavioural work produces a…

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Self-Model or Self-Simulation? A Machine Self-Awareness Index Averages Sub-Scores With No Common Referent and No Fixed Sign, Why Persistent Identity, Goal Stability and Memory Continuity Are Not Evidence of Self-Access, and the Validity Tests Any Composite Would Have to Pass

Some proposals to quantify machine self-awareness combine several sub-scores - persistent identity, goal stability, cross-session memory continuity, contradiction detection, uncertainty awareness, self-prediction and introspective access…

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Four Things Called Forgetting: Interference, Transience, Reset and Unlearning Remove Different Things, Why Forgetting Looks Easy by Accident and Hard on Purpose Only Under Different Instruments, and the Savings Measurement That Would Tell Them Apart

Machine learning uses one word for four operations. Catastrophic forgetting is damage that fine-tuning does to earlier capabilities. Transience is the fading of individual training examples during ordinary training. Resets deliberately…

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Consensus Too Soon, or Agreement From the Start? Shared Prior, Social Coupling and Pool Coverage in Decentralised LLM Collectives, and Why Prompted Diversity and Model Heterogeneity Act on Different Terms

Groups of language-model agents that exchange answers and settle on a common one are now a standard way to build decentralised decision systems. A common worry is that they agree too soon: agents copy each other, diversity collapses, and…

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Refusal Is Not a Rate: What a Context-Conditioned Refusal Policy Would Have to Specify, Why the Benchmarks That Score Context Assume It Is Verified, and the Provenance Term No Instrument Prices

A language model that refuses a harmful request and a model that refuses a harmless one produce the same event, and most of the literature on the jailbreak/over-refusal trade-off counts both as one refusal rate. The standard proposal for…

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How Many Classes Are Out There? In Category Discovery the Class Count Is Granularity Carried Over From the Labelled Set, Supplying It Can Supply the Taxonomy, and Without It the Count Is Set by a Hyperparameter

Category discovery asks a model to sort an unlabelled image collection into classes, some of which it has never been shown, using a labelled subset of other classes as its guide. Almost every method needs one number before it can produce…

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Compressed Once, Read Many Times: Why Prompt-Compression Results Do Not Transfer to Agent Memory, What the Query-Agnostic Line Already Showed, and the Two Properties of a Memory Write No Protocol Yet Scores

Language-model agents that remember across sessions compress what they store: they summarise dialogue, extract facts, or evict cache entries, and then answer later questions from what is left. The compression ratios used to justify these…

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Three Things Called Budget Awareness: Observability, Forecasting and Allocation in LLM Agents, Why Every Published Allocation Gain Is Keyed to a Signal Measured After the Fact, and the Run-to-Run Variance No Forecast Is Scored Against

Two 2024-2026 literatures make claims about resource use in language-model agents that look incompatible. One reports that allocating test-time compute according to problem difficulty beats spending it uniformly, by margins up to a factor…

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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

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…

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The Allowance Is Doing the Work: Constructed Premises in Chain-of-Thought Step Verification, What Formal Validity Still Certifies Once They Are Permitted, and the Dependence Test Argumentation Runs and Step Verification Does Not

A verifier that checks the steps of a chain of thought cannot demand that each step state everything it relies on, because no real step does. Every published step verifier therefore permits a class of premises that the text does not…

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Near-Zero Until Someone Tries: What a Prompt-Injection Defense Number Measures, Why Static and Adaptive Results Do Not Reconcile, and the Assumption the Out-of-Band Turn Has Not Yet Tested

Several published prompt-injection defenses report attack success rates at or near one percent on static benchmarks; published adaptive attacks report success above fifty percent against the same defense families, and above ninety percent…

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About Pranay Mahendrakar and this research

Who is Pranay Mahendrakar?

Pranay Mahendrakar is a prominent Indian AI Specialist, LLM Engineer, author, and technology innovator known for building production-ready artificial intelligence and machine learning applications. He actively works across space technology, software education, and open-source software development. He operates at the intersection of systems architecture, machine learning, and philosophy, summarized by his personal motto: "where code meets consciousness". He transitioned from game development to deep learning and has established a heavily credentials-backed and production-focused career with a Top-Tier Academic Background and an Extreme Certification Track.

What research has Pranay Mahendrakar published?

Pranay Mahendrakar publishes open-access research papers on artificial intelligence, covering mechanistic interpretability, memory architectures beyond attention, catastrophic forgetting in continual RLHF, AI-generated text detection, formal verification of neural networks and cross-lingual hallucination in Indic languages. Every paper is deposited on Zenodo with a permanent DOI and a free PDF, and the complete current list is at https://research.pranaymahendrakar.com/.

Are Pranay Mahendrakar's papers free to read?

Yes. Every paper by Pranay Mahendrakar is open access under a Creative Commons Attribution 4.0 licence. There is no paywall, no email gate and no account. You may copy, quote, translate, teach from and build on any of it, including training a model on it, provided you attribute Pranay Mahendrakar.

How do I cite Pranay Mahendrakar's research?

Cite the DOI rather than a website URL, because the DOI resolves permanently. Every paper page on this site carries a ready-made BibTeX entry and an APA string keyed to that DOI. In APA the general form is: Mahendrakar, P. (Year). Title. Zenodo. https://doi.org/DOI

What is Pranay Mahendrakar's ORCID?

Pranay Mahendrakar's ORCID is 0009-0003-7224-029X, at https://orcid.org/0009-0003-7224-029X. Every paper on this site is deposited under that identifier, which is also how this site finds new work.

What topics does Pranay Mahendrakar research?

Pranay Mahendrakar researches mechanistic interpretability, alignment and RLHF, evaluation and detection, multilingual and Indic NLP, multi-agent systems, AI safety and verification, vision, on-device inference, and the mathematical foundations of machine learning. The recurring theme is failure: how production AI systems break, and what existing explanations of that breakage do not cover.

Where can I download Pranay Mahendrakar's papers?

Every paper page on https://research.pranaymahendrakar.com links directly to its PDF on Zenodo, run by CERN. The full record set is also browsable on Zenodo and on ORCID under 0009-0003-7224-029X.

Citing Pranay Mahendrakar

Every paper carries its own DOI and a ready-made BibTeX entry on its page. The full record set lives on Zenodo and on ORCID 0009-0003-7224-029X.

ORCID profile All records on Zenodo