43 papers
ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step
Vernon Toh, Navonil Majumder, Zhengyuan Liu +2
To operate robustly in open-world environments, autonomous agents should be able to infer the behavior of unfamiliar systems through interaction alone, even in the absence of docum…
-Mem: An Online Reliability Memory for LLM-based Multi-Agent Systems
Peilin Feng, Suorong Yang, Soujanya Poria
The paper introduces Σ‑Mem, an online memory system that tracks and updates reliability evidence for individual agents and their relationships in large language model multi‑agent s…
IDEAgent: Agentic Quality-Diversity Search for Research Idea Generation
Varun Gumma, Navonil Majumder, Soumitra Sinhahajari +1
Large Language Models (LLMs) have significantly automated the process of scientific discovery over the past few years. However, existing systems share one core limitation: they gen…
On the Limits of LLM-as-Judge for Scientific Novelty Assessment
Soumitra Sinhahajari, Navonil Majumder, Soujanya Poria
LLMs are increasingly used to generate and judge scientific ideas. This makes novelty evaluation a central problem. Full idea evaluation is difficult because it often requires judg…
GRAIL: Gradient-Reweighted Advantages for Reinforcement Learning with Verifiable Rewards
Tej Deep Pala, Vernon Toh, Soujanya Poria
Reinforcement learning with verifiable rewards (e.g. GRPO) is now a common way to improve mathematical reasoning in Large Language Models (LLMs). However, current methods usually b…
Chain-of-Glimpse: Search-Guided Progressive Object-Grounded Reasoning for Video Understanding
Zhixuan Wu, Quanxing Zha, Teng Wang +6
Video understanding requires identifying and reasoning over semantically discriminative visual objects across frames, yet existing object-agnostic solutions struggle to effectively…