6 papers · 1 filter
Fork-and-Flush: Escaping Idea Basins in Autoresearch Agents
Ziyang Cai, Christos Ziakas, Vasilis Kontonis +5
Autoresearch agents tackle open-ended problems by repeatedly proposing candidate solutions, evaluating them, and using feedback to guide subsequent experiments. We show that indepe…
Scaling Discovery through Test-Time Communication
Jongho Park, Vasilis Kontonis, Shivam Garg +2
Science advances not in isolation but through collaboration, yet existing agentic systems capture little of this. Whether communicating agents help remains an open question with mi…
SuperThoughts: Reasoning Tokens in Superposition
Zheyang Xiong, Shivam Garg, Max Yu +4
Long Chain-of-Thought (CoT) reasoning improves LLM problem-solving but is computationally expensive due to sequential token generation. While recent works explore reasoning in cont…
Endless Terminals: Scaling RL Environments for Terminal Agents
Kanishk Gandhi, Shivam Garg, Noah D. Goodman +1
Environments are the bottleneck for self-improving agents. Current terminal benchmarks were built for evaluation, not training; reinforcement learning requires a scalable pipeline,…
Wait, Wait, Wait... Why Do Reasoning Models Loop?
Charilaos Pipis, Shivam Garg, Vasilis Kontonis +3
Reasoning models (e.g., DeepSeek-R1) generate long chains of thought to solve harder problems, but they often loop, repeating the same text at low temperatures or with greedy decod…
Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead
Vidhisha Balachandran, Jingya Chen, Lingjiao Chen +8
Inference-time scaling can enhance the reasoning capabilities of large language models (LLMs) on complex problems that benefit from step-by-step problem solving. Although lengtheni…