4 citations · 4 across the 9 of their papers we have counts for
13 papers
Position, Role or Label? A Controlled Study of Instruction Conflicts in LLM Agents
Anish Laddha, Nitesh Pradhan, Gaurav Srivastava
LLM agents take instructions from the operator, repository configuration, user, tool results and saved memory, which often conflict. Instruction-hierarchy benchmarks ask whether th…
Joint, Decoupled, or Agent: When Each Multimodal Alignment Paradigm Wins
Yi Fang, Gaurav Srivastava, Sirui Ding +1
Jointly pretrained multimodal encoders are a common way to combine modalities, while alternatives integrate their information during downstream training or inference. When does eac…
Cache2LoRA: Understanding and Improving Context Internalization
Yi Fang, Gaurav Srivastava, Zhenyu Bi +2
Context-to-LoRA methods encode documents as low-rank parameter updates, allowing language models to answer without retaining the source text in their decoding context. How reliably…
SLMJury: Can Small Language Models Judge as Well as Large Ones?
Anish Laddha, Nitesh Pradhan, Gaurav Srivastava
Large language models (LLMs) are widely used as judges for evaluating model outputs, but their high cost, latency, and opacity limit scalability. We introduce SLMJury, a framework…
EffGen: Enabling Small Language Models as Capable Autonomous Agents
Gaurav Srivastava, Aafiya Hussain, Chi Wang +2
Most existing language model agentic systems today are built and optimized for large language models (e.g., GPT, Claude, Gemini) via API calls; while powerful, this approach faces…
SoundBreak: A Systematic Study of Audio-Only Adversarial Attacks on Trimodal Models
Aafiya Hussain, Gaurav Srivastava, Alvi Ishmam +2
Multimodal foundation models that integrate audio, vision, and language achieve strong performance on reasoning and generation tasks, yet their robustness to adversarial manipulati…
Hindsight is 20/20: Building Agent Memory that Retains, Recalls, and Reflects
Chris Latimer, Nicoló Boschi, Andrew Neeser +4
Agent memory has been touted as a dimension of growth for LLM-based applications, enabling agents that can accumulate experience, adapt across sessions, and move beyond single-shot…
Scaling Agentic Reinforcement Learning for Tool-Integrated Reasoning in VLMs
Meng Lu, Ran Xu, Yi Fang +14
While recent vision-language models (VLMs) demonstrate strong image understanding, their ability to "think with images", i.e., to reason through multi-step visual interactions, rem…
JudgeBoard: Benchmarking and Enhancing Small Language Models for Reasoning Evaluation
Zhenyu Bi, Gaurav Srivastava, Yang Li +4
While small language models (SLMs) have shown promise on various reasoning tasks, their ability to judge the correctness of answers remains unclear compared to large language model…
BeyondBench: Contamination-Resistant Evaluation of Reasoning in Language Models
Gaurav Srivastava, Aafiya Hussain, Zhenyu Bi +5
Evaluating language models fairly is increasingly difficult as static benchmarks risk contamination by training data, obscuring whether models truly reason or recall. We introduce…
Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models
Gaurav Srivastava, Aafiya Hussain, Sriram Srinivasan +1
Large language models (LLMs) achieve impressive performance on complex mathematical benchmarks yet sometimes fail on basic math reasoning while generating unnecessarily verbose res…
DEBATE, TRAIN, EVOLVE: Self Evolution of Language Model Reasoning
Gaurav Srivastava, Zhenyu Bi, Meng Lu +1
Large language models (LLMs) have improved significantly in their reasoning through extensive training on massive datasets. However, relying solely on additional data for improveme…
Towards Reasoning Ability of Small Language Models
Gaurav Srivastava, Shuxiang Cao, Xuan Wang
Reasoning has long been viewed as an emergent property of large language models (LLMs). However, recent studies challenge this assumption, showing that small language models (SLMs)…