222 citations · 400 across the 85 of their papers we have counts for
87 papers
Swiss-Knife: A Framework for Reconfigurable Externalised Multi-Objective Alignment at Decode Time
Agnibh Karmakar, Mayur Parvatikar, Shreyash Dhoot +5
Decode-time alignment methods steer a frozen language model by scoring candidate continuations with an external reward and selecting the maximiser. We argue that this shared design…
Do World Models Make Better Robots? A Survey of Evaluation Benchmarks for Predictive Embodied Intelligence
Gaytri Jena, Kapil Wanaskar, Vinija Jain +3
Robot learning now advances along two tracks that rarely meet. On one side, direct Vision-Language-Action (VLA) policies map observations to actions and are scored by closed-loop t…
GuardPaint:SpeculativeSafetyDecodingforText-to-ImageGeneration
Shreyash Dhoot, Paras Dhiman, Arsh Abbas Naqvi +4
Text-to-image (T2I) diffusion models offer powerful visual generation, but their controllability creates a critical safety challenge: adversarial prompts can steer the denoising tr…
Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills
Gaytri Jena, Kapil Wanaskar, Vinija Jain +3
Robot learning is splitting into two bets: policies that bake competence into frozen weights (vision-language-action, or VLA, models), and agents that write and refine their own ex…
FactorJEPA: Factorizing Monolithic Futures into Layout-Agent-Interaction Channels for Crowded and Chaotic Global South Urban Worlds
Kapil Wanaskar, Gaytri Jena, Aman Chadha +3
World models have attracted significant attention for their ability to capture and predict the structure and dynamics of the physical world. In this emerging landscape, Joint Embed…
Pessimism's Paradox: Conservative Offline Training Amplifies Reward Hacking During Online Adaptation in Reasoning Models
Subramanyam Sahoo, Aman Chadha, Vinija Jain +1
Conservative offline training is widely advocated as a safe foundation for subsequent online adaptation: if a policy stays close to well-supported behaviour, the argument goes, it…
RECOM: A Validity Discrimination Tradeoff in Automatic Metrics for Open Ended Reddit Question Answering
Pushwitha Krishnappa, Amit Das, Vinija Jain +2
Automatic metrics are the default for evaluating LLM-generated text, yet a metric is quietly asked to do two jobs: tell genuine content alignment from surface coincidence (validity…
Moral Sensitivity in LLMs: A Tiered Evaluation of Contextual Bias via Behavioral Profiling and Mechanistic Interpretability
Yash Aggarwal, Atmika Gorti, Vinija Jain +3
Large language models (LLMs) are increasingly deployed in settings that require nuanced ethical reasoning, yet existing bias evaluations treat model outputs as simply "biased" or "…
MENTIS: What Belief Changes Under Alignment? Measuring Multi-Scale Latent Torsion in Language Models
Partha Pratim Saha, Samarth Raina, Mayur Parvatikar +4
Preference alignment has substantially improved the observable behavior of large language models, yet it remains unclear what alignment changes internally. Aligned systems still fa…
SPHERICAL KV: Angle-Domain Attention and Rate-Distortion Retention for Efficient Long-Context Inference
Anay Chauhan, Gurucharan Marthi Krishna Kumar, Arion Das +4
Long-context inference is increasingly constrained by the KV cache: resident memory grows with context length, and decoding becomes limited by repeated High Bandwidth Memory (HBM)…
SleepWalk: A Three-Tier Benchmark for Stress-Testing Instruction-Guided Vision-Language Navigation
Niyati Rawal, Sushant Ravva, Shah Alam Abir +5
Vision-Language Models (VLMs) have advanced rapidly in multimodal perception and language understanding, yet it remains unclear whether they can reliably ground language into spati…
Linear Probes Detect Task Format, Not Reasoning Mode in Language Model Hidden States
Subramanyam Sahoo, Vinija Jain, Aman Chadha +1
Linear probing of large language model (LLM) hidden states is widely used to claim that models learn distinct representations for different reasoning types. We test this by probing…
MAAT: Multi-phase Adapter-Aware Targeted Unlearning
Suryash Yagnik, Shubham Gaur, Saksham Thakur +3
Machine unlearning evaluation is structurally skewed: Why-type questions, which probe causal and relational knowledge, comprise less than 0.06% of CounterFact, 0.6% of ZSRE, and le…
Findings of the Counter Turing Test: AI-Generated Image Detection
Rajarshi Roy, Nasrin Imanpour, Ashhar Aziz +16
The rapid advancements in generative AI technologies, such as Stable Diffusion, DALL-E, and Midjourney, have significantly transformed the creation of synthetic visual content. Whi…
Findings of the Counter Turing Test: AI-Generated Text Detection
Rajarshi Roy, Gurpreet Singh, Ashhar Aziz +16
The growing capability of large language models to produce fluent, contextually coherent text has created mounting pressure on the systems and institutions responsible for ensuring…
PermaFrost-Attack: Stealth Pretraining Seeding(SPS) for planting Logic Landmines During LLM Training
Harsh Kumar, Rahul Maity, Tanmay Joshi +4
Aligned large language models (LLMs) remain vulnerable to adversarial manipulation, and their reliance on web-scale pretraining creates a subtle but consequential attack surface. W…
Personality Shapes Gender Bias in Persona-Conditioned LLM Narratives Across English and Hindi: An Empirical Investigation
Tanay Kumar, Shreya Gautam, Aman Chadha +2
Large Language Models (LLMs) are increasingly deployed in persona-driven applications such as education, customer service, and social platforms, where models are prompted to adopt…
CONSCIENTIA: Can LLM Agents Learn to Strategize? Emergent Deception and Trust in a Multi-Agent NYC Simulation
Aarush Sinha, Arion Das, Soumyadeep Nag +7
As large language models (LLMs) are increasingly deployed as autonomous agents, understanding how strategic behavior emerges in multi-agent environments has become an important ali…
Reasoning or Rhetoric? An Empirical Analysis of Moral Reasoning Explanations in Large Language Models
Aryan Kasat, Smriti Singh, Aman Chadha +1
Do large language models reason morally, or do they merely sound like they do? We investigate whether LLM responses to moral dilemmas exhibit genuine developmental progression thro…
The Reasoning Trap -- Logical Reasoning as a Mechanistic Pathway to Situational Awareness
Subramanyam Sahoo, Aman Chadha, Vinija Jain +1
Situational awareness, the capacity of an AI system to recognize its own nature, understand its training and deployment context, and reason strategically about its circumstances, i…
SAHOO: Safeguarded Alignment for High-Order Optimization Objectives in Recursive Self-Improvement
Subramanyam Sahoo, Aman Chadha, Vinija Jain +1
Recursive self-improvement is moving from theory to practice: modern systems can critique, revise, and evaluate their own outputs, yet iterative self-modification risks subtle alig…
When Shallow Wins: Silent Failures and the Depth-Accuracy Paradox in Latent Reasoning
Subramanyam Sahoo, Aman Chadha, Vinija Jain +1
Mathematical reasoning models are widely deployed in education, automated tutoring, and decision support systems despite exhibiting fundamental computational instabilities. We demo…
I Can't Believe It's Not Robust: Catastrophic Collapse of Safety Classifiers under Embedding Drift
Subramanyam Sahoo, Vinija Jain, Divya Chaudhary +1
Instruction tuned reasoning models are increasingly deployed with safety classifiers trained on frozen embeddings, assuming representation stability across model updates. We system…
Dial E for Ethical Enforcement: institutional VETO power as a governance primitive
Subramanyam Sahoo, Vinija Jain, Aman Chadha +1
The persistent militarization of large reasoning models stems not from technical necessity but from governance arrangements that strip researchers of meaningful authority to refuse…