Abhilash Shankarampeta

We are excited to release AMA-Bench 🎉 Our goal is to evaluate agent memory itself, not just dialogue. Many existing memory benchmarks are still centered on conversation or long context. But real agent memory happens over long horizon agent-environment trajectories, with machine generated observations, evolving states, and causal structure. AMA-Bench includes real world + synthetic settings across multiple domains, and we also introduce AMA-Agent. Paper: huggingface.co/papers/2602.22… Project: ama-bench.github.io Dataset: huggingface.co/datasets/AMA-b… #LLM #AgentMemory #Memory #Agent

Seedance-2 and Kling-3 signal that AI video generation is entering a “photorealistic” era, but realism does not guarantee reasoning and scientific correctness. In the classic breaking dry spaghetti experiment, real fractures arise from elastic energy and stress waves, often producing three pieces. Yet models frequently generate physically incorrect snaps. We introduce VideoScience-Bench to evaluate scientific reasoning in video generation. Most models look convincing but lack true scientific understanding, with only weak signals from Kling-3, Sora-2, and Veo-3. Learn details from our blog 👇🧐🧪 hao-ai-lab.github.io/blogs/videosci…

@varunyer @NYUDataScience @eunsolc @hhexiy @CILVRatNYU Congratulations Varun 🎉

⭐Soo happy to share that I'll be joining @NYUDataScience as a PhD student this Fall! Excited to learn from and work with @eunsolc, @hhexiy, and the amazing folks at @CILVRatNYU. Looking forward to better understanding and improving large ML models.

@ycombinator @ChonkieAI Congratulations @minhash 🎉

Chonkie (@ChonkieAI) is building the open source library for connecting your data to AI. Split unstructured data into optimized AI-ingestible chunks that boost your AI accuracy, improve app performance, and reduce token costs. ycombinator.com/launches/NUw-c… Congrats on the launch, @shreyash_nm and @minhash!

So excited to share this work #TRANSIENTTABLES 🎉 Addressing temporal reasoning gaps in LLMs is crucial. Honored to present our work on LLM temporal reasoning at @naaclmeeting. See you at #NAACL2025!

I am in Singapore for #EMNLP2023. Check out poster our “Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data“. We know SOTA LLMs struggle with numbers, but where exactly does the challenge lie? 🧵 arxiv.org/pdf/2311.02216…

🌟 News: I'm on the academic market! 🌟 🔍 Seeking: Tenure-Track Faculty positions in Computer Science. I work on Natural language processing systems. 💡 Expertise: Elevating reasoning and inference capabilities in semi-structured tabular data. #NLProc #AI #AcademicTwitter

Excited to share that our paper is accepted at #EMNLP2023 🎉. In this work we evaluated the language models across various numerical reasoning tasks on tabular data. It was pleasure working with @akhtarmubashara, @keviv9 , Arpit

📢 Happy to share two first-author papers accepted @emnlpmeeting. It was a pleasure working with @michael_sejr, @ZhijiangG, Oana Cocarascu, @esimperl, @vlachos_nlp, @areddys53 (co-first author), Arpit Pital, and @keviv9. #EMNLP2023 #EMNLP @kclinformatics

#AI researchers @areddys53 of @IITGuwahati / @iitgai, @UUtah's @keviv9 & @imsure318 will present their paper “Enhancing Tabular Reasoning with Pattern Exploiting Training” during Poster Session 1 at @aaclmeeting (12:20 PM CST) #aacl22 #NLProc

Check out our work "Enhancing Tabular Reasoning with Pattern Exploiting Training" at #AACL2022 @aaclmeeting. Link: infoadapet.github.io

Check "Enhancing Tabular Reasoning with Pattern Exploiting Training" at #AACL2022 @aaclmeeting. Joint work with @areddys53 (@IITGuwahati, @iitgai); @imsure318 (@TechAtBloomberg). We exploit prompt based learning, i.e, PET, to extract knowledge for tables @UUtah @UtahSoC @UtahNLP

(4/4) Congrats to @IITGuwahati's Abhilash Reddy Shankarampeta, @UUtah's @keviv9 & @imsure318 for having their paper “Enhancing Tabular Reasoning with Pattern Exploiting Training” accepted for #aacl22

4. Enhancing Tabular Reasoning with Pattern Exploiting Training @suki_2022 (Non-Archival), we use the PET (AdaPET) pre-training i.e. MLM objective jointly train with tabular inference task for entity style tabular data. Joint work with @areddys53. -8/n

@parthsh_ Datalore and Sagemaker Studio are also free but have their own cons.

Trying out gradient by Paperspace today, to reduce my dependence on Colab. Pros - No timeouts, A Jupyter lab environment that can be modified and is more responsive than Colab. Cons - 2 CPUs of 2 Gb only, the promised free GPU is also not always available. What do you guys use?

The Animal-AI Olympics is an AI competition with tests inspired by animal cognition. The Animal-AI environment is a new AI experimentation and evaluation platform that implements ideas from animal cognition in order to better train AI agents that possess cognitive skills.

#Fake_News_Detection Identifying fake news is one of the most challenging and open ended tasks of AI today! We have seen the devastating effects of Fake News especially nowadays in the case of the Covid-19 pandemic..

@HuaweiIndia when do android pie and EMUI 9 update will come in Huawei P20 lite

@9GAG The Flash

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