1 citations · 1 across the 6 of their papers we have counts for
20 papers
Probing Dec-POMDP Reasoning in Cooperative MARL
Kale-ab Tessera, Leonard Hinckeldey, Riccardo Zamboni +2
Cooperative multi-agent reinforcement learning (MARL) is typically framed as a decentralised partially observable Markov decision process (Dec-POMDP), a setting whose hardness stem…
Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Tracking
Vaisakh Shaj, Cameron Barker, Aidan Scannell +3
State-space language models such as Mamba and gated linear attention (GLA) offer linear-complexity, parallelisable alternatives to transformers, but their linear state updates limi…
Multi-Turn Evaluation of Deep Research Agents Under Process-Level Feedback
Rishabh Sabharwal, Hongru Wang, Amos Storkey +1
Existing benchmarks for deep research agents (DRAs) assess only single-shot outputs, ignoring a key question: can DRAs improve their reports when guided by feedback? To investigate…
Benchmarking Open-Ended Multi-Agent Coordination in Language Agents
Kale-ab Abebe Tessera, Andras Szecsenyi, Cameron Barker +7
As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks. Yet existing evaluations rar…
Rationality Measurement and Theory for Reinforcement Learning Agents
Kejiang Qian, Amos Storkey, Fengxiang He
This paper proposes a suite of rationality measures and associated theory for reinforcement learning agents, a property increasingly critical yet rarely explored. We define an acti…
Position: Agent Should Invoke External Tools ONLY When Epistemically Necessary
Hongru Wang, Cheng Qian, Manling Li +6
As large language models evolve into tool-augmented agents, a central question remains unresolved: when is external tool use actually justified? Existing agent frameworks typically…