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Researcher Evidence Record

Nikhil Muralidhar

This bounded record lists 20 Pith paper rows and 0 imported work rows attributed to this corpus identity. The enumerated, non-disputed paper rows include cs.LG, cs.CL, cs.CR work dated 2019 to 2026. The record describes sources and coverage; it makes no judgment about the person.

Compiled coverage vector

Measured lane counts only. Not a trust score or person verdict.

Enumerated paper scope: 5 fields (cs.LG, cs.CL, cs.CR, +2 more) · 2019-2026 sources: authors, author_identifiers · paper_authors · author_works · current_verdicts · cited_works

A sourced case file for attributed work. It is neither a profile score nor a verdict about this researcher.

Attributed works

A bounded ledger from the Pith paper and imported-work queries. Counts and source confidence stay with each work.

  1. 2026 Pith paper

    LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks

    cs.LG provisional current review present

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    4
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: a current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  2. 2026 Pith paper

    TRIE: An Evaluation Framework for Stochastic PDE Surrogates

    cs.LG provisional current review present

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    3
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: a current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  3. 2025 Pith paper

    Can AI Validate Science? Benchmarking LLMs for Accurate Scientific Claim $\rightarrow$ Evidence Reasoning

    cs.CL provisional current review present

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    5
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: a current Pith review exists.
    Citation counts
    • 1 pith inbound references from cited_work_pith_inbound_counts
  4. 2025 Pith paper

    The Prompt is Mightier than the Example

    cs.LG provisional current review present

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: a current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  5. 2025 Pith paper

    Model-Agnostic Knowledge Guided Correction for Improved Neural Surrogate Rollout

    cs.LG provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    5
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    • 1 pith inbound references from cited_work_pith_inbound_counts
  6. 2025 Pith paper

    DenoMAE2.0: Improving Denoising Masked Autoencoders by Classifying Local Patches

    cs.LG provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    6
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  7. 2025 Pith paper

    DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals

    cs.LG provisional current review present

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    6
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: a current Pith review exists.
    Citation counts
    • 1 pith inbound references from cited_work_pith_inbound_counts
  8. 2024 Pith paper

    NMformer: A Transformer for Noisy Modulation Classification in Wireless Communication

    eess.SP provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    5
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  9. 2024 Pith paper

    Why LLMs Are Bad at Synthetic Table Generation (and what to do about it)

    cs.LG provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    5
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    • 3 pith inbound references from cited_work_pith_inbound_counts
  10. 2024 Pith paper

    Information Guided Regularization for Fine-tuning Language Models

    cs.CL provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    • 1 pith inbound references from cited_work_pith_inbound_counts
  11. 2024 Pith paper

    Laying Anchors: Semantically Priming Numerals in Language Modeling

    cs.CL provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    4
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  12. 2024 Pith paper

    Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation

    cs.LG provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    5
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  13. 2024 Pith paper

    Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems

    cs.NI provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    4
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    • 1 pith inbound references from cited_work_pith_inbound_counts
  14. 2023 Pith paper

    Learning Non-linguistic Skills without Sacrificing Linguistic Proficiency

    cs.CL provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  15. 2022 Pith paper

    Overcoming Barriers to Skill Injection in Language Modeling: Case Study in Arithmetic

    cs.CL provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  16. 2022 Pith paper

    Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback

    cs.CR provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  17. 2022 Pith paper

    Contrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Cores

    cs.LG provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    1
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  18. 2021 Pith paper

    Using AntiPatterns to avoid MLOps Mistakes

    cs.LG provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    1
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    • 1 pith inbound references from cited_work_pith_inbound_counts
  19. 2020 Pith paper

    Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19

    cs.LG provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  20. 2019 Pith paper

    Physics-guided Design and Learning of Neural Networks for Predicting Drag Force on Particle Suspensions in Moving Fluids

    cs.LG provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Nikhil Muralidhar
    Author position
    1
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.

Evidence apparatus

The machinery behind this record. Every lane states whether Pith measured it, did not query it, could not reach it, or withheld it.

LaneStateObservedBoundary and source
identity Measured 2 Canonical identity row plus public typed identifiers.
source=authors, author_identifiers
papers Measured 20 of 20 bounded rows Rows attributed to this author UUID in the Pith corpus.
source=paper_authors
works Measured zero 0 of 0 bounded rows Imported works not duplicated by the paper ledger.
source=author_works
reviews Measured 5 of 20 bounded rows Coverage count only. No review outcome is projected onto the person.
source=current_verdicts
citations Measured 7 of 20 bounded rows Counts remain itemized by work and source.
source=cited_works
coauthors Measured 50 of 20 bounded rows Shared-work edges from admitted paper rows.
source=paper_authors
account Unavailable No public count of 1 bounded rows Account metadata is separate from corpus evidence.
source=users.author_id
Public identity sources
  • name variant
    Nikhil Muralidhar
    backfill
    confidence 0.6
Enumerated research scope

Fields and dates come only from enumerated, non-disputed Pith paper rows. They do not claim career completeness.

  • cs.LG12 rows
  • cs.CL5 rows
  • cs.CR1 rows
  • cs.NI1 rows
  • eess.SP1 rows
  • 20191 rows
  • 20201 rows
  • 20211 rows
  • 20223 rows
  • 20231 rows
  • 20246 rows
  • 20255 rows
  • 20262 rows
Shared-work index
Record scope

The work queries are bounded. Missing rows may mean measured zero, an unavailable source, a query that did not run, or private data that Pith withheld. The lane table keeps those cases separate.

Paper findings remain attached to papers. They do not become findings about this researcher.