Texas Tech University

Optimal control of tumor-immune dynamics is usually solved by a forward-backward sweep on the Pontryagin necessary conditions, but that iteration can cycle between two saturation patterns instead of converging when the control saturates. Representing the control as a neural network and the cost functional as the training loss makes every stationary point of the loss satisfy the continuous first-order necessary conditions to a discretisation-controlled accuracy. On a 4×4 grid of scenarios from a tumor-immune model, the standard sweep failed on 14 of 16 scenarios, while network training reached a stable optimum on every scenario with a final cost 2 to 10 times lower than the converged damped sweep.

Optimal control of tumor-immune dynamics is usually solved by a forward-backward sweep on the Pontryagin necessary conditions, but that iteration can cycle between two saturation patterns instead of converging when the control saturates. Representing the control as a neural network and the cost functional as the training loss makes every stationary point of the loss satisfy the continuous first-order necessary conditions to a discretisation-controlled accuracy. On a 4×4 grid of scenarios from a tumor-immune model, the standard sweep failed on 14 of 16 scenarios, while network training reached a stable optimum on every scenario with a final cost 2 to 10 times lower than the converged damped sweep.

Noncanonical amino acids push peptide design spaces to the 10⁵⁰ scale, where exhaustive screening is infeasible. PepOpt encodes each residue as a position-specific molecular fingerprint, predicts potency with linear regression, and uses dynamic programming under an additivity assumption to find the globally optimal sequence deterministically. Across 11 internal peptide series it matches or beats nonlinear models, and in 10²⁰ and 10⁵⁰ spaces it returns higher predicted scores than a genetic algorithm.

Noncanonical amino acids push peptide design spaces to the 10⁵⁰ scale, where exhaustive screening is infeasible. PepOpt encodes each residue as a position-specific molecular fingerprint, predicts potency with linear regression, and uses dynamic programming under an additivity assumption to find the globally optimal sequence deterministically. Across 11 internal peptide series it matches or beats nonlinear models, and in 10²⁰ and 10⁵⁰ spaces it returns higher predicted scores than a genetic algorithm.

When an AI agent calls an external tool, retrieval previously ranked candidates by semantic relevance alone and could not tell whether a tool would actually work at runtime. RATED pairs a frozen dual-encoder for semantic matching with a lightweight contextual bandit that re-ranks candidates from each execution's success or failure feedback. Under 30% failure injection, fault selection drops from 86.2% for the ToolLLM API retriever to 2.9%, at 0.172 ms added latency per step.

When an AI agent calls an external tool, retrieval previously ranked candidates by semantic relevance alone and could not tell whether a tool would actually work at runtime. RATED pairs a frozen dual-encoder for semantic matching with a lightweight contextual bandit that re-ranks candidates from each execution's success or failure feedback. Under 30% failure injection, fault selection drops from 86.2% for the ToolLLM API retriever to 2.9%, at 0.172 ms added latency per step.

The authors introduce Forward CIP (F-CIP), an RL-native formulation of the Controllable Information Production (CIP) objective that is defined by the system's dynamics alone and requires no selection of information variables; they prove F-CIP is compatible with RL and demonstrate its effectiveness with existing algorithms, where training yields unsupervised discovery of primitive behaviors such as balancing and maintaining controllability, and pairing it with a simple forward-velocity reward produces coordinated gaits such as hopping and running that otherwise require reward engineering.

The authors introduce Forward CIP (F-CIP), an RL-native formulation of the Controllable Information Production (CIP) objective that is defined by the system's dynamics alone and requires no selection of information variables; they prove F-CIP is compatible with RL and demonstrate its effectiveness with existing algorithms, where training yields unsupervised discovery of primitive behaviors such as balancing and maintaining controllability, and pairing it with a simple forward-velocity reward produces coordinated gaits such as hopping and running that otherwise require reward engineering.