Pranav agarwal (@pranav_al) 's Twitter Profile
Pranav agarwal

@pranav_al

PhD Candidate @Mila_Quebec, @etsmtl and @vxsim
Previous Researcher @INRIA and @sutdsg
Reinforcement Learning | Deep Learning | Robotics

ID: 3724940594

linkhttps://pranaval.github.io/ calendar_today29-09-2015 10:49:13

593 Tweet

410 Followers

1,1K Following

Hua Shen✨ (@huashen218) 's Twitter Profile Photo

📢Is current “human-AI alignment” research clarified and comprehensive? 🤔 We systematically reviewed 400+ papers across HCI, NLP, and ML to develop a framework for 👫<>🤖"Bidirectional Human-AI Alignment", encompassing the dual paths of “Aligning AI to Human” and “Aligning Human

📢Is current “human-AI alignment” research clarified and comprehensive? 🤔 We systematically reviewed 400+ papers across HCI, NLP, and ML to develop a framework for 👫&lt;&gt;🤖"Bidirectional Human-AI Alignment", encompassing the dual paths of “Aligning AI to Human” and “Aligning Human
Hua Shen✨ (@huashen218) 's Twitter Profile Photo

2/ 💎【Bidirectional Human-AI Alignment Framework】 We introduce our “Bidirectional Human-AI Alignment” framework developed from the systematic review. 🔸A🔸 “Align AI to Human” focuses on mechanisms ensuring AI systems’ objectives match those of humans’. 🔸B🔸 “Align Humans to

2/ 💎【Bidirectional Human-AI Alignment Framework】
We introduce our “Bidirectional Human-AI Alignment” framework developed from the systematic review. 🔸A🔸 “Align AI to Human” focuses on mechanisms ensuring AI systems’ objectives match those of humans’. 🔸B🔸 “Align Humans to
Amanda Askell (@amandaaskell) 's Twitter Profile Photo

I had a lot of fun talking with Lex Fridman about a wide range of topics on his podcast, alongside Dario and Chris. Hope it's interesting to others!

Manling Li (@manlingli_) 's Twitter Profile Photo

Can VLMs build Spatial Mental Models like humans? Reasoning from limited views? Reasoning from partial observations? Reasoning about unseen objects behind furniture / beyond current view? Check out MindCube! 🌐mll-lab-nu.github.io/mind-cube/ 📰arxiv.org/pdf/2506.21458

Denny Zhou (@denny_zhou) 's Twitter Profile Photo

Slides for my lecture “LLM Reasoning” at Stanford CS 25: dennyzhou.github.io/LLM-Reasoning-… Key points: 1. Reasoning in LLMs simply means generating a sequence of intermediate tokens before producing the final answer. Whether this resembles human reasoning is irrelevant. The crucial

Archiki Prasad (@archikiprasad) 's Twitter Profile Photo

📢 Excited to share our new paper, where we introduce, ✨GrAInS✨, an inference-time steering approach for LLMs and VLMs via token attribution. Some highlights: ➡️GrAIns leverages contrastive, gradient-based attribution to identify the most influential textual or visual tokens

Pranav agarwal (@pranav_al) 's Twitter Profile Photo

Just got our Neurips reviews back, and the reviewers nailed every limitation and missing experiment we already knew about (and no, these weren't LLM-generated😅). Here's a thought: authors are often their own harshest critics. What if conferences required us to submit detailed

Sang Cho (@saaaang94) 's Twitter Profile Photo

We are hiring! Interested in optimizing/scaling RL framework for pretrain scale RL? DM me or apply here: job-boards.greenhouse.io/xai/jobs/47991…

Dr Singularity (@dr_singularity) 's Twitter Profile Photo

Insane AI news A new paper introduces ASI-ARCH, a fully automated AI research loop that can independently discover superior neural network architectures, outpacing human designed models. Unlike traditional methods limited by human trial and error, ASI-ARCH connects LLM based

Smoke-away (@smokeawayyy) 's Twitter Profile Photo

Theory: Everything Exists in Latent Space At a certain scale of base model intelligence, latent space contains nearly every possible idea, invention, and technology, along with every combination of those things. Therefore, AGI exists in latent space. AGI isn’t something we

Rohan Paul (@rohanpaul_ai) 's Twitter Profile Photo

Survery paper with lots of insights on Continual Reinforcement Learning Detailed review of existing works, organizing and analyzing their metrics, tasks, benchmarks, and scenario settings. 🧩 Why the field exists A classic RL agent hones one policy for one environment then

Survery paper with lots of insights on Continual Reinforcement Learning

Detailed review of existing works, organizing and analyzing their metrics, tasks, benchmarks, and scenario settings.

🧩 Why the field exists

A classic RL agent hones one policy for one environment then
Pranav agarwal (@pranav_al) 's Twitter Profile Photo

"Heartbreak at Headingley, heartbreak at Lord's, and yet a performance with so much heart in Manchester." As always, amazing commentary by Cricbuzz, and what a match! While it ended in a draw, it was probably the best fighting performance by Team India in recent times.

ACL 2025 (@aclmeeting) 's Twitter Profile Photo

📅 10-Year ToT Award (2015) Thang Luong, Hieu Pham & Christopher D. Manning: “Effective Approaches to Attention-based Neural Machine Translation” EMNLP 2015 🔗 aclanthology.org/D15-1166/ A milestone in neural MT and attention mechanisms. 🔁🧠