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researcher

C. Singh

24 papers hereh-index 11438 citations26 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author20
  • last author1

Across the 22 of 24 papers where every author was matched, so the position is known.

fields
  • cs.CL15
  • cs.LG5
  • cs.AI1
  • cs.CV1
  • cs.GR1
  • q-bio.NC1
same name
  • C. Singh — 165 papers, h 50
  • C. Singh — 33 papers, h 16
  • C. Singh — 14 papers, h 15
  • C. Singh — 14 papers, h 63
  • C. Singh — 8 papers, h 5
  • C. Singh — 8 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedRethinking Interpretability in the Era of Large Language Models

43 citations · 52 across the 21 of their papers we have counts for

collaborators
Showing 2026 · cs.CLShow all

4 papers · 2 filters

cs.CL2026

TokenEconomizer: Experience-Driven Black-Box Cost Control for Coding Agents

Aaron J. Li, Weijia Xu, Chandan Singh +8

Advances in frontier coding agents have sharply increased the token costs of AI-assisted software engineering. Their token efficiency is typically evaluated on isolated tasks, wher…

cs.CL2026

Register Tokens for Bounded-State Reasoning in Diffusion Language Models

Albert Ge, Chandan Singh, Yufan Zhuang +3

Masked diffusion language models (dLLMs) generate text by iteratively denoising masked tokens with bidirectional attention. Extending reasoning across generation chunks normally re…

cs.CL2026

StudentSim: Training LLM-based Student Simulators

Ke Yang, Chenglong Wang, Michel Galley +4

AI tutors are most useful when they adapt to each student's strengths, weaknesses, and preferred guidance, but evidence about which guidance works for which student is sparse, slow…

cs.CL2026

Test-time Recursive Thinking: Self-Improvement without External Feedback

Yufan Zhuang, Chandan Singh, Liyuan Liu +5

Modern Large Language Models (LLMs) have shown rapid improvements in reasoning capabilities, driven largely by reinforcement learning (RL) with verifiable rewards. Here, we ask whe…

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