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19,709 papers
This work introduces FINCOSMOS, an open-world evaluation paradigm for agents doing financial work, and shows how an agent with persistent memory learned all ten tasks with FINCOSMOS feedback.
Results show that how information is represented to an agent shapes both its cost and its success, making the environment worth optimizing in addition to the parameters and the harness, making the environment worth optimizing in addition to the parameters and the harness.
When a tropical cyclone (TC) threatens several ports, preparedness planning depends on anticipating which ports are likely to be affected and where disruptions may last longest. Limited historical observations at individual ports and uncertainty in landfall location make these relative impacts difficult to estimate. We...
A satisfaction floor, computable from two statistics of interaction logs, offers a way to monitor the power users retain in a deployed system, and simulations in random environments show the three quantities are correlated but far from interchangeable.
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This work introduces PromptOps, a closedloop framework for monitoring and iteratively recalibrating LLM-as-a-Judge (LLMaaJ) systems and establishes prompt optimization for deployed LLM judges as an inherently iterative maintenance problem and demonstrates that PromptOps provides a practical abstraction for studying and...
Behavior-Conditioned Symbolic Synthesis is introduced---an approach for automated search of interpretable, deterministic symbolic programs that verifiably reproduce behavior---and applied to model both human and machine behavior across hundreds of challenging reasoning problems, pointing to underlying mechanisms remini...
The results show that verification success alone is insufficient evidence of independent cross-agent agreement, and that model capability does not guarantee agreement when formalizing the same source independently.
EFXPLORER is proposed, a constrained exploration framework that maximizes exploration objectives subject to a constraint that preserves established task performance and incorporates a successor distance-based intrinsic reward that captures long-horizon dependencies.
Close-loop evaluation is proposed, an evaluation method for deployed agents that models evaluation as performance estimation under biased raters and censored outcomes, and it requires every level's conclusion to trace back to atomic data and to be computed by versioned rules.
A theoretical framework comparing Darwinian, Lamarckian, and agent-directed evolution following environmental change is developed, to preserve humanity's ability to guide its direction, limit its speed, and intervene when necessary.
The Trust Responsive Authorization Policy is empirically characterized, which maps an Authorization Readiness Score (ARS), a weighted combination of runtime trust, operational stability, and contextual risk, onto discrete authorization levels through an asymmetric rule: escalation requires consecutive observations abov...
Simulated collaboration reveals that, over the course of proof construction, AI agents are far more strongly drawn than humans to explicitly marked gaps; show a relative aversion to densely connected proof centers and to complex or surprising proof goals; and are relatively attracted to surprising proof bodies.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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