Publications

2026

1. ICLR 2026

   

   THE PATH OF LEAST RESISTANCE: GUIDING LLM REASONING TRAJECTORIES WITH PREFIX CONSENSUS

   Ishan Jindal, Sai Prashanth Akuthota, Jayant Taneja, and SACHIN DEV SHARMA

   In The Fourteenth International Conference on Learning Representations, 2026

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   Large language models achieve strong reasoning performance, but inference strategies such as Self-Consistency (SC) are computationally expensive, as they fully expand all reasoning traces. We introduce PoLR (Path of Least Resistance), the first inference-time method to leverage prefix self-consistency for compute-efficient reasoning. PoLR clusters short prefixes of reasoning traces, identifies the dominant cluster, and expands only a subset of promising paths, preserving the accuracy benefits of SC while substantially reducing token usage and latency. Our theoretical analysis, framed via mutual information and entropy, explains why early reasoning steps encode strong signals predictive of final correctness. Empirically, PoLR consistently matches or exceeds SC across GSM8K, Math500, AIME 2024/2025, and GPQA-Diamond, reducing token usage by up to 60% and wall-clock latency by up to 50%. Moreover, PoLR is fully complementary to adaptive inference methods (e.g., Adaptive Consistency, Early-Stopping SC) and can serve as a drop-in pre-filter, making SC substantially more efficient and scalable without requiring model fine-tuning.

2025

1. EMNLP 2025

   

   Offloaded Reasoning: Efficient Inference for Large Language Models via Modular Reasoning and Refinement

   Ishan Jindal, Jayant Taneja, Badrinath Chandana, Vikas Kapur, and Sachin Dev Sharma

   In Findings of the Association for Computational Linguistics: EMNLP 2025, Nov 2025

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   Large language models (LLMs) demonstrate strong reasoning capabilities but are expensive to run at inference time, limiting their practical deployment. We propose Offloaded Reasoning (OR), a modular strategy where a lightweight model generates intermediate reasoning traces that are then used by a larger model to produce the final answer. We further introduce Offloaded Reasoning with Refinement (ORR), where the large model first edits or improves the reasoning trace before answering. Unlike token-level acceleration methods, OR and ORR operate at the reasoning level and require no retraining of the large model. Experiments on GSM8K and Math500 show that OR achieves up to 8x faster inference than full large-model reasoning with minimal accuracy loss, while ORR recovers or exceeds full accuracy at substantially lower cost. Our results highlight the potential of modular, delegation-based reasoning for building more efficient and adaptable LLM systems.