1 citations · 1 across the 22 of their papers we have counts for
11 papers · 1 filter
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)…
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…
Towards Explainability of SLMs by investigating Token Level Activation
Sayantani Ghosh, Rajashik Datta, Amit Kumar Das +1
Transformer-based language models such as BERT having 110M+ parameters have revolutionized natural language understanding, yet their internal mechanisms remain largely opaque to re…
A New Technique for AI Explainability using Feature Association Map
Sayantani Ghosh, Amit Kumar Das, Amlan Chakrabarti
Lack of transparency in AI systems poses challenges in critical real-life applications. It is important to be able to explain the decisions of an AI system to ensure trust on the s…
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…
SPINAL -- Scaling-law and Preference Integration in Neural Alignment Layers
Arion Das, Partha Pratim Saha, Amit Dhanda +3
Direct Preference Optimization (DPO) is a principled, scalable alternative to RLHF for aligning large language models from pairwise preferences, but its internal geometric footprin…