--- title: "Shiv Shankar — author profile and papers — Elamur Research" description: "Shiv Shankar, University of Massachusetts Amherst: 3 papers on Elamur Research (2026), h-index 3 — affiliations, topics, co-authors and…" canonical: "https://research.elamur.ai/free/en/authors/shiv-shankar/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/shiv-shankar/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Shiv Shankar # Shiv Shankar Shiv Shankar has 3 papers in our library, in 2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/). 3 papers in our library · 2026 [OpenAlex](https://openalex.org/A5113150390) · [ORCID](https://orcid.org/0000-0003-1631-2570) · [Wikidata](https://www.wikidata.org/wiki/Q131125140) · [**h-index 3**](https://openalex.org/authors/A5113150390) · [45 works](https://openalex.org/works?filter=author.id:A5113150390) · [29 citations](https://openalex.org/works?filter=author.id:A5113150390&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/shiv-shankar/#papers) · 🔗 Cited 1 time in our library · [⭐ Paper Stars ranking: #11402](https://md.research.elamur.ai/free/en/paper-stars/page/115/#star-11402) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Shiv+Shankar) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Shiv+Shankar) · [dblp](https://dblp.org/search?q=Shiv+Shankar) · [ACL Anthology](https://aclanthology.org/search/?q=Shiv+Shankar) ## Affiliations [🎓**University of Massachusetts Amherst** Amherst Center · United States 2019–2026 · 14 papers*current*](https://md.research.elamur.ai/free/en/universities/university-of-massachusetts-amherst/) · [🎓**Vivekananda Global University** Jaipur · India 2024 · 25 papers](https://md.research.elamur.ai/free/en/universities/vivekananda-global-university/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 4 The field of science this work belongs to (OpenAlex fields).[Artificial Intelligence · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Language and Linguistics · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 2 Research subjects and languages the work is about.[Language models & pretraining · 2 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning · 2 works](https://md.research.elamur.ai/free/en/topics/reasoning/) ## Works by year ### 2020s [**2026** 3 papers](https://md.research.elamur.ai/free/en/authors/shiv-shankar/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[Energy Matching based Preference Learning for Diffusion Language Models](https://md.research.elamur.ai/free/en/papers/shankar-2026-energy-matching-based-preference-learning/)** *(2026 · 1 citation)* Policy-gradient RL methods such as GRPO depend on sequence likelihoods that factorize token by token. Diffusion language models (dLLMs) generate text non-autoregressively, so these likelihoods are intractable. ELBO-based approximations introduce bias, and off-policy importance weights become unstable over long… 2. **[Learning Shortcut Models for Efficient Recursive Reasoning](https://md.research.elamur.ai/free/en/papers/shankar-2026-learning-shortcut-models-efficient-recursive/)** *(2026)* Recursive latent reasoners such as HRM and TRM refine hidden states through many small sequential passes. Their inference cannot be parallelised, they receive weak supervision for early steps, and they cannot adjust their compute budget at test time. This paper views the iterative refinement steps of models in the… 3. **[Pseudo-Likelihood Training for Reasoning Diffusion Language Models](https://md.research.elamur.ai/free/en/papers/shankar-2026-pseudo-likelihood-training-reasoning-diffusion/)** *(2026)* Policy-gradient RL methods such as PPO and GRPO are the standard way to post-train LLMs for reasoning. They need token-level likelihoods of sampled outputs, which masked diffusion language models (dLLMs) cannot provide cheaply. Existing workarounds either use expensive Monte-Carlo ELBO estimates or a one-step… ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/) ×2: [EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/) ×2 - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/): [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) ## Cited by Papers of our library cite their works 1 time (of the 149,439 papers whose bibliographies we read). Updated 1 November 2026