1 citations · 2 across the 32 of their papers we have counts for
32 papers
Event Signature Transfer: Model-Agnostic Forecast Scenario Construction from Historical Events
Karthik Sridhar, Aaditya Jain, Murari Mandal +1
Forecasters often know an event is imminent but not the shape, size, or timing of its effect. We introduce Event Signature Transfer (EST), a training-free, model-agnostic operator…
The BatchNorm Illusion: Diagnosing Normalization Artifacts in Machine Unlearning Evaluation
Aaryaman Kalani, Murari Mandal, Dhruv Kumar +2
Approximate machine unlearning aims to remove the influence of specific training data from a trained model without retraining from scratch. We identify a previously undocumented co…
Semantics or Structure? Auditing Text Sensitivity in Multimodal Time-Series Forecasting
Karthik Sridhar, Atharva Gupta, Nishant Pradhan +3
Multimodal time-series forecasting has emerged as a promising paradigm in which natural-language context is expected to improve predictive performance. Recent multimodal foundation…
The Complexity Ceiling Benchmark: A Multi-Domain Evaluation of Sequential Reasoning Under Depth Scaling
Shubh Chapra, Dhruv Kumar, Murari Mandal +1
We introduce the Complexity Ceiling Benchmark (CCB), a controlled evaluation of how language-model reasoning decays as the number of required sequential steps grows. CCB fixes the…
FinBalance: A Multi-Document Accounting Reconciliation Benchmark
Sasank Tumpati, Devansh Agarwal, Ayush Kedia +6
Existing financial-NLP benchmarks mostly evaluate prepared artifacts such as filings, tables, or extracted values. Real accounting begins earlier: source documents must be reconcil…
Where Computation Lives Inside TabPFN: Causal Localisation of Attention Head Function
Atharva Gupta, Dhruv Kumar, Murari Mandal +1
We present the first causal mechanistic analysis of a tabular foundation model, investigating how TabPFN 2.5's feature wise attention heads distribute computation across layers. Us…
Mix, Don't Pick: Why Synthetic Corpus Composition Matters for Time Series Foundation Model Pretraining
Aaryan Nagpal, Debdeep Sanyal, Murari Mandal +2
Choosing the wrong synthetic generator for time-series foundation model pretraining is costly: under identical training budgets, the best and worst generators produce up to a $2\ti…
GITCO: Gated Inference-Time Context Optimization in TSFMs
Manya Pandey, Dhruv Kumar, Murari Mandal +1
Patch-based Time Series Foundation Models (TSFMs) suffer from context poisoning: structurally anomalous patches capture disproportionate attention and silently degrade zero-shot fo…
REGEN: Reference-Guided Synthetic Multivariate Time Series Generation for Forecasting
Moulik Gupta, Dhruv Kumar, Murari Mandal +1
Training robust multivariate time series forecasting models requires large, diverse corpora, yet many real-world domains provide only a handful of observed sequences. Existing gene…
"I Strongly Suspect This Website Is a Scam": Benchmarking PII Leakage and Detection without Defense in Autonomous Web Agents
Soham Roy, Sarthakbrata Halder, Arya Bharaty +5
Deceptive web content, widely instantiated across the internet and commonly known as \textit{social-engineering attacks}, manipulates autonomous web agents into submitting users' p…
Beyond Accuracy: Diagnosing Algebraic Reasoning Failures in LLMs Across Nine Complexity Dimensions
Parth Patil, Dhruv Kumar, Yash Sinha +1
Algebraic reasoning remains one of the most informative stress tests for large language models, yet current benchmarks provide no mechanism for attributing failure to a specific ca…
Measuring Representation Robustness in Large Language Models for Geometry
Vedant Jawandhia, Yash Sinha, Murari Mandal +2
Large language models (LLMs) are increasingly evaluated on mathematical reasoning, yet their robustness to equivalent problem representations remains poorly understood. In geometry…
TSQueryBench: LLM-as-a-Judge for Time Series Explanations
Preetham Sivalingam, Murari Mandal, Dhruv Kumar +1
Natural language explanations of time series data are increasingly produced by foundation models in high stakes domains, making factual correctness critical. Evaluating such explan…
Forgetting is Competition: Rethinking Unlearning as Representation Interference in Diffusion Models
Ashutosh Ranjan, Vivek Srivastava, Shirish Karande +1
Deployed text-to-image diffusion models increasingly require post-hoc concept unlearning for copyright claims, artist opt-outs, safety updates, and protected-content mitigation wit…
The Compliance Paradox: Semantic-Instruction Decoupling in Automated Academic Code Evaluation
Devanshu Sahoo, Manish Prasad, Vasudev Majhi +5
The rapid integration of Large Language Models (LLMs) into educational assessment rests on the unverified assumption that instruction following capability translates directly to ob…
When Reject Turns into Accept: Quantifying the Vulnerability of LLM-Based Scientific Reviewers to Indirect Prompt Injection
Devanshu Sahoo, Manish Prasad, Vasudev Majhi +5
Driven by surging submission volumes, scientific peer review has catalyzed two parallel trends: individual over-reliance on LLMs and institutional AI-powered assessment systems. Th…
NagaNLP: Bootstrapping NLP for Low-Resource Nagamese Creole with Human-in-the-Loop Synthetic Data
Agniva Maiti, Manya Pandey, Murari Mandal
The vast majority of the world's languages, particularly creoles like Nagamese, remain severely under-resourced in Natural Language Processing (NLP), creating a significant barrier…
How to Trick Your AI TA: A Systematic Study of Academic Jailbreaking in LLM Code Evaluation
Devanshu Sahoo, Vasudev Majhi, Arjun Neekhra +3
The use of Large Language Models (LLMs) as automatic judges for code evaluation is becoming increasingly prevalent in academic environments. But their reliability can be compromise…
The Realignment Problem: When Right becomes Wrong in LLMs
Aakash Sen Sharma, Debdeep Sanyal, Manodeep Ray +3
Post-training alignment of large language models (LLMs) relies on large-scale human annotations guided by policy specifications that change over time. Cultural shifts, value reinte…
Confidence is Not Competence
Debdeep Sanyal, Manya Pandey, Dhruv Kumar +2
Large language models (LLMs) often exhibit a puzzling disconnect between their asserted confidence and actual problem-solving competence. We offer a mechanistic account of this dec…
Policy Optimization Prefers The Path of Least Resistance
Debdeep Sanyal, Aakash Sen Sharma, Dhruv Kumar +2
Policy optimization (PO) algorithms are used to refine Large Language Models for complex, multi-step reasoning. Current state-of-the-art pipelines enforce a strict think-then-answe…
time2time: Causal Intervention in Hidden States to Simulate Rare Events in Time Series Foundation Models
Debdeep Sanyal, Aaryan Nagpal, Dhruv Kumar +2
While transformer-based foundation models excel at forecasting routine patterns, two questions remain: do they internalize semantic concepts such as market regimes, or merely fit c…
AntiDote: Bi-level Adversarial Training for Tamper-Resistant LLMs
Debdeep Sanyal, Manodeep Ray, Murari Mandal
The release of open-weight large language models (LLMs) creates a tension between advancing accessible research and preventing misuse, such as malicious fine-tuning to elicit harmf…
Step-by-Step Reasoning Attack: Revealing 'Erased' Knowledge in Large Language Models
Yash Sinha, Manit Baser, Murari Mandal +2
Knowledge erasure in large language models (LLMs) is important for ensuring compliance with data and AI regulations, safeguarding user privacy, mitigating bias, and misinformation.…