Author

Prajwal Raghunath

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#artificial intelligence Preprint Sep 2026

Environment Steering: Using Data Flow Control to Improve Agent Utility and Safety

LLM agents can make unsafe tool calls even when instructed to behave safely. Existing defenses constrain agents before execution, modify tool inputs/outputs, or rely on LLM judges; these approaches may depend on model behavior or block unsafe actions without helping the agent recover. We argue that the execution enviro...

Charlie Summers, Prajwal Raghunath, Aaditya Pai et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Receiver-Conditioned Latent Communication gives 94% CacheBack

CacheBack is a simple, robust, training-free instance of receiver conditioning based on the sender's attention weights that improves accuracy and reduces median task-completion latency on FanOutQA and shows comparable improvements across model families that span dense Transformers, Mamba-attention hybrids, and sliding-...

M. Rossi, Prajwal Raghunath, Hao Xuan et al. · 0 citations