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Foundation models have vast potential to enable diverse AI applications. The powerful yet incomplete nature of these models has spurred a wide range of mechanisms to augment them with capabilities such as in-context learning, information retrieval, and code interpreting. We propose Vieira, a declarative framework that unifies these mechanisms in a general solution for programming with foundation models. Vieira follows a probabilistic relational paradigm and treats foundation models as stateless functions with relational inputs and outputs. It supports neuro-symbolic applications by enabling the seamless combination of such models with logic programs, as well as complex, multi-modal applications by streamlining the composition of diverse sub-models. We implement Vieira by extending the Scallop compiler with a foreign interface that supports foundation models as plugins. We implement plugins for 12 foundation models including GPT, CLIP, and SAM. We evaluate Vieira on 9 challenging tasks that span language, vision, and structured and vector databases. Our evaluation shows that programs in Vieira are concise, can incorporate modern foundation models, and have comparable or better accuracy than competitive baselines.

 

We provide a unifying framework for the design and analysis of multi-calibrated predictors. By placing the multi-calibration problem in the general setting of multi-objective learning---where learning guarantees must hold simultaneously over a set of distributions and loss functions---we exploit connections to game dynamics to achieve state-of-the-art guarantees for a diverse set of multi-calibration learning problems. In addition to shedding light on existing multi-calibration guarantees and greatly simplifying their analysis, our approach also yields improved guarantees, such as error tolerances that scale with the square-root of group size versus the constant tolerances guaranteed by prior works, and improving the complexity of k-class multi-calibration by an exponential factor of k versus Gopalan et al.. Beyond multi-calibration, we use these game dynamics to address emerging considerations in the study of group fairness and multi-distribution learning. 

Type systems typically only define the conditions under which an expression is well-typed, leaving ill-typed expressions formally meaningless. This approach is insufficient as the basis for language servers driving modern programming environments, which are expected to recover from simultaneously localized errors and continue to provide a variety of downstream semantic services. This paper addresses this problem, contributing the first comprehensive formal account of total type error localization and recovery: the marked lambda calculus. In particular, we define a gradual type system for expressions with marked errors, which operate as non-empty holes, together with a total procedure for marking arbitrary unmarked expressions. We mechanize the metatheory of the marked lambda calculus in Agda and implement it, scaled up, as the new basis for Hazel, a full-scale live functional programming environment with, uniquely, no meaningless editor states.

The marked lambda calculus is bidirectionally typed, so localization decisions are systematically predictable based on a local flow of typing information. Constraint-based type inference can bring more distant information to bear in discovering inconsistencies but this notoriously complicates error localization. We approach this problem by deploying constraint solving as a type-hole-filling layer atop this gradual bidirectionally typed core. Errors arising from inconsistent unification constraints are localized exclusively to type and expression holes, i.e. the system identifies unfillable holes using a system of traced provenances, rather than localized in an ad hoc manner to particular expressions. The user can then interactively shift these errors to particular downstream expressions by selecting from suggested partially consistent type hole fillings, which returns control back to the bidirectional system. We implement this type hole inference system in Hazel.

 

We report a new approach to monitor drug release from nanocarriers via a paclitaxel–methylene blue conjugate (PTX‐MB) with redox activity. This construct is in a photoacoustically silent reduced state inside poly(lactic‐co‐glycolic acid) (PLGA) nanoparticles (PTX‐MB@PLGA NPs). During release, PTX‐MB is spontaneously oxidized to produce a concentration‐dependent photoacoustic signal. An in vitro drug‐release study showed an initial burst release (25 %) between 0–24 h and a sustained release between 24–120 h with a cumulative release of 40.6 % and a 670‐fold increase in photoacoustic signal. An in vivo murine drug release showed a photoacoustic signal enhancement of up to 649 % after 10 hours. PTX‐MB@PLGA NPs showed an IC₅₀of 78 μg mL⁻¹and 44.7±4.8 % decrease of tumor burden in an orthotopic model of colon cancer via luciferase‐positive CT26 cells.

 

We report a new approach to monitor drug release from nanocarriers via a paclitaxel–methylene blue conjugate (PTX‐MB) with redox activity. This construct is in a photoacoustically silent reduced state inside poly(lactic‐co‐glycolic acid) (PLGA) nanoparticles (PTX‐MB@PLGA NPs). During release, PTX‐MB is spontaneously oxidized to produce a concentration‐dependent photoacoustic signal. An in vitro drug‐release study showed an initial burst release (25 %) between 0–24 h and a sustained release between 24–120 h with a cumulative release of 40.6 % and a 670‐fold increase in photoacoustic signal. An in vivo murine drug release showed a photoacoustic signal enhancement of up to 649 % after 10 hours. PTX‐MB@PLGA NPs showed an IC₅₀of 78 μg mL⁻¹and 44.7±4.8 % decrease of tumor burden in an orthotopic model of colon cancer via luciferase‐positive CT26 cells.