Joykirat
@joykiratsingh
CS PhD Student @unc_ai_group @UNC, advised by. @mohitban47 | ex RF @MSFTResearch
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🚨 Excited to announce TRAAC, an online difficulty-adaptive, attention-based method that handles the tradeoff of under & overthinking in reasoning models to improve both accuracy and efficiency. Underthinking ❌: Models terminate reasoning too early on harder problems, leading…
🚨 Excited to share new work on inferring symbolic world models from observations! OneLife can infer world models in stochastic, complex environments by proposing rules via LLM and reweighting code-based environment laws from observations collected in a single interaction…
🚨 Excited to share our new work ✨ OneLife ✨, which investigates how an agent can infer executable symbolic world models 🌐 from a single unguided trajectory in a stochastic environment. I’m especially excited about our planning + evaluation contributions: 1️⃣ We support…
How can an agent reverse engineer the underlying laws of an unknown, hostile & stochastic environment in “one life”, without millions of steps + human-provided goals / rewards? In our work, we: 1️⃣ infer an executable symbolic world model (a probabilistic program capturing…
🚨 New Paper Alert! Introducing SciVideoBench — a comprehensive benchmark for scientific video reasoning! 🔬SciVideoBench: 1. Spans Physics, Chemistry, Biology & Medicine with authentic experimental videos. 2. Features 1,000 challenging MCQs across three reasoning types:…
🚨 Thrilled to introduce Self-Improving Demonstrations (SID) for Goal-Oriented Vision-and-Language Navigation — a scalable paradigm where navigation agents learn to explore by teaching themselves. ➡️ Agents iteratively generate and learn from their own successful trajectories ➡️…
Landed in Montreal 🇨🇦 for #COLM2025 to present my first-author work on task-conditioned mixed-precision quantization: “Task-Circuit Quantization” (Thursday 11am, Poster Session 5). I'm applying to PhD programs this cycle and am excited to chat about this or other interests (LLM…
🚨 Check out our awesome students/postdocs' papers at #COLM2025 and say hi to them (several are on the job market or hiring) --> -- Archiki, David are on the post-PhD job market! -- Elias finished his postdoc & is now faculty at UT-Austin CS and looking to admit PhD students!…
I am attending #COLM2025 🇨🇦 this week to present our work on: Unit Test Generation: 📅 Oct 8th (Wed), 4:30 PM, #79 RAG with conflicting evidence: 📅 Oct 9th (Thu), 11 AM, #71 PS: I'm on the industry job market for RS roles, so you can reach me via DM or in-person to chat! 😄
🚨 Check out our awesome students/postdocs' papers at #COLM2025 and say hi to them (several are on the job market or hiring) --> -- Archiki, David are on the post-PhD job market! -- Elias finished his postdoc & is now faculty at UT-Austin CS and looking to admit PhD students!…
✈️ Arrived at #COLM2025 where I'll be helping to present the following 4 papers. I'm also recruiting multiple PhD students for my new lab at UT Austin -- happy to chat about research, PhD applications, or postdoc openings in my former postdoc lab at UNC! -- Learning to Generate…
🚨 Check out our awesome students/postdocs' papers at #COLM2025 and say hi to them (several are on the job market or hiring) --> -- Archiki, David are on the post-PhD job market! -- Elias finished his postdoc & is now faculty at UT-Austin CS and looking to admit PhD students!…
🚨 Check out our awesome students/postdocs' papers at #COLM2025 and say hi to them (several are on the job market or hiring) --> -- Archiki, David are on the post-PhD job market! -- Elias finished his postdoc & is now faculty at UT-Austin CS and looking to admit PhD students!…
🚨 "Think the right amount" for improving both reasoning accuracy and efficiency! --> Large reasoning models under-adapt = underthink on hard problems and overthink on easy ones --> ✨TRAAC✨ is an online RL, difficulty-adaptive, attention-based compression method that prunes…
🚨 Excited to announce TRAAC, an online difficulty-adaptive, attention-based method that handles the tradeoff of under & overthinking in reasoning models to improve both accuracy and efficiency. Underthinking ❌: Models terminate reasoning too early on harder problems, leading…
🚨 TRAAC uses an online difficulty-adaptive, attention-based compression method to address a core problem in long thinking: an inability to adapt to problem difficulty! Leads to underthinking on hard problems, overthinking on easy ones, reducing accuracy and efficiency. TRAAC…
🚨 Excited to announce TRAAC, an online difficulty-adaptive, attention-based method that handles the tradeoff of under & overthinking in reasoning models to improve both accuracy and efficiency. Underthinking ❌: Models terminate reasoning too early on harder problems, leading…
Models often think too much on easy problems and not enough on harder reasoning problems. Our new method ✨TRAAC✨ fixes this by teaching models to adaptively compress their "thinking budget" to the difficulty of the task during GRPO rollouts. Result? The model uses…
🚨 Excited to announce TRAAC, an online difficulty-adaptive, attention-based method that handles the tradeoff of under & overthinking in reasoning models to improve both accuracy and efficiency. Underthinking ❌: Models terminate reasoning too early on harder problems, leading…
Large reasoning models suffer from under-adaptiveness, which underthink on hard problems and overthink on easy ones. TRAAC addresses this by introducing ✨difficulty calibration and attention-based compression✨→ +8.4% accuracy & +36.8% efficiency! 1️⃣ TRAAC adaptively mitigates…
🚨 Excited to announce TRAAC, an online difficulty-adaptive, attention-based method that handles the tradeoff of under & overthinking in reasoning models to improve both accuracy and efficiency. Underthinking ❌: Models terminate reasoning too early on harder problems, leading…
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