Artificial intelligence / open access / permanent DOIs

Artificial Intelligence
Research by
Pranay Mahendrakar

Pranay Mahendrakar is an Indian AI specialist and LLM engineer based in Bengaluru, India. He is the Managing Director of SonyTech, Nodal Coordinator at IIRS-ISRO, and an instructor at Tutorials Point. His work covers large language models, natural language processing, computer vision and retrieval-augmented generation. He publishes open-access research papers and is the author of three books: Just AI With Pranay, Multiverse of AI and It's Me LLM. 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.

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

All research papers by Pranay Mahendrakar

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Which Way the Verdict Falls: Noise-Induced Flips in an LLM Bias Judge Run Toward the Label Its Prompt Makes Easiest to Reach, the Harm-Detection Literature Records the Opposite Sign, and Whether Noise Inflates a Bias Score Depends on the Score

Life of Research Language models are now used as judges that read a passage and decide whether it leans toward a stereotype, and the passages they read are often noisy. A 2026 study corrupted 3,822 stereotype-probing responses with typos…

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A Tally of Framings Is Not a Census of Causes: What the AI Risk Repository's Human-Versus-AI Split Measures, Why Its Gap Closed From Seventeen Points to Four as the Corpus Grew, and the Attribution Rule a Causal Claim Would Need

Life of Research The AI Risk Repository extracts every risk named in published AI risk frameworks and codes each one by the entity presented as causing it. Its journal version reports that "human decisions cause nearly as many AI risks…

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A Named Answer Is Not a Correction: The Moral Self-Correction Threshold Moved From 22B to 3.8B on Benchmarks a Context-Blind Policy Can Pass, and the Controls That Would Separate Steering From Correcting

Life of Research Instructing a language model to avoid stereotypes lowers its measured social bias, and a 2023 study reported that this capacity for "moral self-correction" emerges at 22 billion parameters. A 2024 study reported the same…

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A Hidden Shift Is Not a Hidden Bias: What a Representation-Level Counterfactual Gap Certifies About an LLM Recommender, Why the Encoded-Versus-Expressed Studies Disagree, and the Placebo, Mediation and Transport Tests That Would Settle It

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Fairness audits of language-model recommenders compare the lists a model returns when a single stated identity is swapped and the rest of the prompt is held fixed. A…

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Hard to Read Is Not Hard to Audit: Why Degrading a Text Image Raises Jailbreak Success in One 2026 Study and Lowers It in Another, Four Accounts That Fit the Same Curve, and the Ablations That Would Separate Them

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Rendering text as an image is now proposed as a way to compress long contexts for multimodal language models, and a 2026 study reports that the same move opens a…

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A Rewrite That Keeps the Label Has Already Read It: Why an LLM Paraphrase Can Raise a Sentiment Classifier's Accuracy Without the Classifier Getting Better, Four Accounts of the Gain, and the Re-Annotation and Neutral-Rate Controls That Would Separate Them

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. A 2026 study reports that sentiment classifiers score higher on reviews rewritten by small language models than on the human originals, by up to 5.8 accuracy points…

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Who Spoke Next Is Not Who Should Have: What Zero-Shot Failure on Multi-Party Turn-Taking Benchmarks Measures, Why the Human Reference Sits Beside the Models, and What Would Show That Knowing When to Speak Must Be Trained

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. An assistant that joins a group conversation has to decide, at every pause, whether to take the floor. Two 2026 benchmarks report that language models prompted…

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A Blind Score Is Not a Reliance Measure: What Answering Without the Audio Shows About Audio-Language Benchmarks, Why the Partial-Input Debate Already Settled Half of It, and the Counterfactual-Audio Test the Other Half Needs

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Audio-language models are now routinely tested with the audio taken away, and the results are read in opposite directions. One 2026 audit reports that eight models…

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Robust Because Coarse, or Robust Because Planned? A Research Agenda for Training-Data Provenance After the Aggregate-Disclosure Turn, in Which Granularity Buys Statistical Power, Prior Randomisation Buys Soundness, and Per-Example Value Has Neither

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Regulators have chosen coarse disclosure of training data: the template for the EU AI Act's public summary of training content asks for lists of large datasets, a…

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Which Signal Did the Synthesis Import? Information, Re-presentation and Selection in Synthetic Pretraining, Why 'the Verifier Is Doing the Work' Holds Only Where Correctness Is the Target, and the Controls That Would Assign the Credit

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. The expectation that language models will keep improving after the supply of human text runs out rests on a claim about synthetic data: that a model, or a family of…

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Lossless at What Length? Near-Lossless KV Cache Compression Is a Claim Indexed by Decode Exposure, Scoring Statistic and Budget Accounting, the Decode-Aware Line Has Answered the First Index Mostly on Final-Answer Accuracy, and Divergence From the Full Cache Is Not by Itself the Defect

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Key-value cache compression is routinely reported as near-lossless at aggressive budgets: eviction, quantisation and merging methods keep a fifth or a tenth of the…

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INT4 Is Not One Treatment: Why the Quantisation-Safety Literature Cannot Be Reconciled as Reported, How Six Design Axes Separate Its Studies Before Bit-Width Does, and Why Two 2026 Studies of the Same Model Names Reach Opposite Verdicts

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Post-training quantisation to 4 bits is widely treated as nearly free, a judgement resting on perplexity and multiple-choice accuracy. Whether it is also free for…

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Where the Transcript Runs Out: When Model Internals Beat Behavioural Monitors, Why the Strongest Generalisation Evidence Comes From Targets the Text Already Shows, and the Comparisons No Study Has Yet Run

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Linear probes and other readers of a language model's activations are now deployed as safety monitors, and two published literatures disagree about what they buy. One…

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What Counts as a Control? A Scaffold Effect Is a Contrast Conditional on What Was Held Fixed, the 2026 Controlled Studies Each Held Something Different, and Only a Declared Spend Interval Makes a Harness Claim Refutable

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. A language-model agent is a model wrapped in a harness that builds its context, exposes its tools, decides when to retry and when to stop. Several 2026 studies set out…

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Repair Without a Repairer: Five Mechanisms Called Self-Repair in Neural Networks, Why Much of the Measured Restoration Is Restored Scale, and What Would License the Claim That a Network Restores Its Own Performance

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. "Self-repair" is used in at least three research communities that do not cite one another. In transformer interpretability it names the Hydra effect: when one…

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Circuits Are Estimates, Not Mechanisms: A Recovered Subgraph Is One Draw From a Class Set by the Ablation, the Dataset and the Grain, the Results That Defend Circuits Hold at the Level of That Class, and What Ablation Evidence Licenses at Each Level

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. A circuit in a language model is usually presented as the mechanism by which the model performs a task: a sparse subgraph, found by ablation or patching, that…

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One Pass, Three Purchases: Deduplication's Efficiency, Generalisation and Privacy Arguments Each Hold in Their Own Regime, Rest on Different Properties of the Repetition Distribution, and Cannot All Be Bought With One Similarity Threshold

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Deduplication became a fixed stage of language-model pretraining on the strength of three claims made together: removing duplicates saves compute, improves what the…

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Deskilling Is Not One Endpoint: Five Quantities Behind Assisted Gains and Unassisted Losses in Clinical AI, Why Two Studies of the Same Design Disagree, and Why a Within-Clinician Trial's Control Arm Has Already Been Exposed

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Clinical AI tools are approved and bought on one endpoint, the performance of a clinician working with the tool, while a second literature asks what happens to the…

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Dictionaries Without Ontologies: What a Sparse Autoencoder Latent Is Evidence For, Why Decodability, Use and Reproducibility Do Not Add Up to Canonicity, and the Identifiability Premise No Real-Model Result Has Checked

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Sparse autoencoders are now the default instrument for decomposing language model activations, and their latents are routinely reported as the model's features: units…

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Three Hazards Under One Horizon: Constant-Hazard, Degrading-Step and Log-Logistic Accounts of Agent Failure Describe Different Levels of the Same Data, the 50 Percent Trend Barely Depends on Which Is Right, and Every High-Reliability Forecast Does

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Forecasts of autonomous AI capability increasingly rest on one number: the length of task, measured in human working time, that an agent completes half the time. That…

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A Contamination Flag Is Not an Inflation Estimate: What Benchmark Contamination Detectors Certify, What Decontamination Is Meant to Buy, and Why the Clean-Subset Comparison That Joins Them Is Biased From Both Sides

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Almost every large language model report now includes a contamination analysis: benchmark items that overlap the training corpus are flagged, the model is rescored on…

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No Stable Self: What Self-Referential Anomaly Defence Can Certify for a Continually Learning System, Why Every Located Guarantee Holds the Benign Distribution Still, and Why Immunology's Own Answer Is a Damage Signal From Outside

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Immune-inspired security proposes that a system can protect itself by learning what it normally is and treating departures from that as threats. Continual learning…

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Agreement Is Not a Criterion: What Matching Human Labels Certifies About an LLM Judge, Why a Shared Shortcut Passes the Test, and Why Every External Criterion Found So Far Validates a Use Rather Than a Judge

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. An automatic judge is usually accepted on one piece of evidence: it agrees with human preference labels about as often as humans agree with each other. That number is…

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A Stated Limit Is a Forecast: What an Agent Can Truthfully Say About Its Own Permissions, Why the Checkable Part Should Never Come From the Model, and How to Score the Part Only the Model Can Supply

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Agent interfaces, skill specifications and disclosure proposals increasingly ask a tool-using language-model agent to tell its user what it may and may not do. The…

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Age Is Not Evidence of Staleness: A Recency Decay Is a One-Rate Survival Model, the Rate Belongs to the Fact Rather Than the Clock, and the Silent Change Only a Rate Could Catch Is the Case No Agent-Memory Benchmark Scores

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Language agents that keep memories across sessions usually discount a record by how long ago it was written or last read. That discount was introduced to decide what…

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Did the Model Change, or Did Your Measurement? A Drift Claim Is a Test Against a Declared Null, the Careful Longitudinal Studies Mostly Caught Announced Version Moves, and a Capability Change Under a Pinned Identifier Is the Claim the Evidence Supports Least

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Two bodies of work describe the same deployed language models in opposite terms. One reports that models behind a fixed product name change over time and calls for…

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Detectors Say 'Different', Not 'What': Score, Deviation and Name Are Three Claims With Three Referees, Only the Name Needs a Referee Outside the Detector, and Published Results Show Novelty Detected Far More Reliably Than It Is Named

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. An anomaly or novelty detector reports that an input is unlike the data it was built from. Users want more: they want to know what the unusual thing is. Explainable…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Frontier safety frameworks decide when a model needs stronger safeguards by testing it against capability thresholds at a fixed cadence, and they place an…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. 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…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. 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…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. 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…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. 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…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. 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…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. 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…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Multi-agent debate, multi-persona prompting and related schemes are usually justified by diversity of viewpoint: several perspectives err differently, so their…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. 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…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. 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…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. 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…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Work on trust between language-model agents produces two kinds of object. Protocol work produces identity, attestation, stake and constraint, all bound at the…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Some proposals to quantify machine self-awareness combine several sub-scores - persistent identity, goal stability, cross-session memory continuity, contradiction…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Machine learning uses one word for four operations. Catastrophic forgetting is damage that fine-tuning does to earlier capabilities. Transience is the fading of…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. 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…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. 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…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Language-model agents that remember across sessions compress what they store: they summarise dialogue, extract facts, or evict cache entries, and then answer later…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. 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…

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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

Life of Research (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…

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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

Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. 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…

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About Pranay Mahendrakar and this research

Who is Pranay Mahendrakar?

Pranay Mahendrakar is an Indian AI specialist and LLM engineer based in Bengaluru, India. He is the Managing Director of SonyTech, Nodal Coordinator at IIRS-ISRO, and an instructor at Tutorials Point. His work covers large language models, natural language processing, computer vision and retrieval-augmented generation. He publishes open-access research papers and is the author of three books: Just AI With Pranay, Multiverse of AI and It's Me LLM.

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