Papers by Gaurav Srivastava
ThinkSLM: Towards Reasoning in Small Language Models (2025.emnlp-main)
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| Challenge: | Reasoning has long been viewed as an emergent property of large language models (LLMs), appearing at or above a certain scale (100B parameters). |
| Approach: | They propose a benchmark to evaluate the reasoning abilities of small language models (SLMs) using quantization, pruning, and distillation. |
| Outcome: | The proposed benchmark systematically evaluates reasoning abilities of small language models trained from scratch or derived from LLMs using quantization, pruning, and distillation. |
Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models (2026.findings-acl)
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| Challenge: | Large language models (LLMs) achieve impressive performance on complex benchmarks yet sometimes fail on basic math reasoning. |
| Approach: | They propose a benchmark to evaluate the efficiency of reasoning in large language models . they formalize the accuracy-verbosity tradeoff and introduce the overthinking score . |
| Outcome: | The proposed model performs well on complex benchmarks but fails on basic math reasoning . the proposed model generates 18 more tokens while achieving lower accuracy . |
MailEx: Email Event and Argument Extraction (2023.emnlp-main)
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| Challenge: | Existing work on email event extraction only covers one specific aspect of email information and cannot connect with other relevant tasks. |
| Approach: | They propose a new taxonomy for performing event extraction from conversational email threads. |
| Outcome: | The proposed taxonomy covers 10 event types and 76 arguments in the email domain. |
SoundBreak: A Systematic Study of Audio-Only Adversarial Attacks on Trimodal Models (2026.acl-long)
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| Challenge: | Recent advances in multimodal large language models have increased their vulnerability to adversarial manipulation. |
| Approach: | They propose to target audio-only adversarial attacks on multimodal audio–video–language models . they show that attacks can be successful at low perceptual distortions . |
| Outcome: | The proposed models achieve up to 96% success rate under realistic conditions . the proposed models are more robust to noise than to noise and distortion than to speech recognition systems . |
DEBATE, TRAIN, EVOLVE: Self‐Evolution of Language Model Reasoning (2025.emnlp-main)
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| Challenge: | Large language models have improved significantly in reasoning through extensive training on massive datasets. |
| Approach: | They propose a ground truth-free training framework that uses multi-agent debate traces to evolve a single language model. |
| Outcome: | The proposed framework achieves 8.92% accuracy gain on the GSM-PLUS dataset. |
Hindsight: Structured Agent Memory that Retains, Recalls, and Reflects (2026.acl-demo)
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Christopher Latimer, Nicolò Boschi, Andrew Neeser, Chris Bartholomew, Gaurav Srivastava, Xuan Wang, Naren Ramakrishnan
| Challenge: | Hindsight organizes long-term memory into four logical networks and exposes three core operations. |
| Approach: | Hindsight organizes long-term memory into four logical networks and exposes three core operations. |
| Outcome: | Hindsight is a working memory system for AI agents that separates facts from beliefs . the system outperforms existing models on LongMemEval and LoCoMo with 83.6% accuracy . |